WO2006113529A2 - Diagnosis of sepsis - Google Patents
Diagnosis of sepsis Download PDFInfo
- Publication number
- WO2006113529A2 WO2006113529A2 PCT/US2006/014241 US2006014241W WO2006113529A2 WO 2006113529 A2 WO2006113529 A2 WO 2006113529A2 US 2006014241 W US2006014241 W US 2006014241W WO 2006113529 A2 WO2006113529 A2 WO 2006113529A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- biomarkers
- biomarker
- features
- sepsis
- biomarker profile
- Prior art date
Links
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
- G01N33/56911—Bacteria
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/112—Disease subtyping, staging or classification
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/118—Prognosis of disease development
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/158—Expression markers
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/16—Primer sets for multiplex assays
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
- G01N2333/52—Assays involving cytokines
- G01N2333/54—Interleukins [IL]
- G01N2333/5412—IL-6
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
- G01N2333/52—Assays involving cytokines
- G01N2333/54—Interleukins [IL]
- G01N2333/5421—IL-8
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/26—Infectious diseases, e.g. generalised sepsis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/50—Determining the risk of developing a disease
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Definitions
- the present invention relates to methods and compositions for diagnosing or predicting sepsis and/or its stages of progression in a subject.
- the present invention also relates to methods and compositions for diagnosing systemic inflammatory response syndrome in a subject.
- SIRS systemic inflammatory response syndrome
- SIRS systemic inflammatory response syndrome
- MOD multiple organ dysfunction
- Sepsis may also arise in an infected subject when the subject subsequently develops SIRS.
- Sepsis is commonly defined as the systemic host response to infection with SIRS plus a documented infection.
- severe sepsis is associated with MOD, hypotension, disseminated intravascular coagulation ("DIC”) or hypoperfusion abnormalities, including lactic acidosis, oliguria, and changes in mental status.
- DIC disseminated intravascular coagulation
- Septic shock is commonly defined as sepsis-induced hypotension that is resistant to fluid resuscitation with the additional presence of hypoperfusion abnormalities.
- Documenting the presence of the pathogenic microorganisms that are clinically significant to sepsis has proven difficult.
- Causative microorganisms typically are detected by culturing a subject's blood, sputum, urine, wound secretion, in-dwelling line catheter surfaces, etc.
- Causative microorganisms may reside only in certain body microenvironments such that the particular material that is cultured may not contain the contaminating microorganisms. Detection may be complicated further by low numbers of microorganisms at the site of infection.
- the present invention relates to methods and compositions for diagnosing sepsis, including the onset of sepsis, in a test subject.
- the present invention also relates to methods and compositions for predicting sepsis in a test subject.
- the present invention further relates to methods and compositions for diagnosing or predicting stages of sepsis progression in a test subject.
- the present invention still further relates to methods and compositions for diagnosing systemic inflammatory response syndrome (SIRS) in a test subject.
- SIRS systemic inflammatory response syndrome
- the present invention provides a method of predicting the development of sepsis in a test subject at risk for developing sepsis.
- This method comprises evaluating whether a plurality of features in a biomarker profile of the test subject satisfies a value set, wherein satisfying the value set means that the test subject will develop sepsis with a likelihood that is determined by the accuracy of the decision rule to which the plurality of features are applied in order to determine whether they satisfy the value set.
- the accuracy of the decision rule is at least 60%. Therefore, correspondingly, the likelihood that the test subject will develop sepsis when the plurality of features satisfies the value set is at least 60%.
- Yet another aspect of the invention comprises a method of diagnosing sepsis in a test subject.
- These methods comprise evaluating whether a plurality of features in a biomarker profile of the test subject satisfies a value set, wherein satisfying the value set predicts that the test subject has sepsis with a likelihood that is determined by the accuracy of the decision rule to which the plurality of features are applied in order to determine whether they satisfy the value set.
- the accuracy of the decision rule is at least 60%. Therefore, correspondingly, the likelihood that the test subject has sepsis when the plurality of features satisfies the value set is at least 60%.
- the biomarker profile comprises at least two features, each feature representing a feature of a corresponding biomarker listed in column four or five of Table 30.
- the biomarker profile comprises at least two different biomarkers listed in column four or five of Table 30.
- the biomarker profile can comprise a respective corresponding feature for the at least two biomarkers.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker in the at least two different biomarkers is listed in column four of Table 30, can be, for example, a transcript made by the listed gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein listed in column five of Table 30, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker.
- Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table 30).
- such an assay utilizes a nucleic acid microarray.
- the biomarker profile comprises at least two different biomarkers from column four or five of Table 32. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, or 50 different biomarkers from Table 30. [0013] In a particular embodiment, the biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in column 2 of Table 30, biomarkers that contain the complement of one of the probesets of Table 30, or biomarkers that contain an amino acid sequence encoded by a gene that either contains one of the probesets of Table 30 or the complement of one of the probesets of Table 30.
- biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 30, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from any one of Table 31, 32, 33, 34, or 36.
- the biomarker profile comprises at least two different biomarkers listed in column three of Table 31.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker can be, for example, a transcript made by gene listed in Table 31, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein encoded by a gened listed in column three of Table 31, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker.
- Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table 31).
- such an assay utilizes a nucleic acid microarray.
- the biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in column 2 of Table 31, biomarkers that contain the complement of one of the probesets of Table 31, or biomarkers that contain an amino acid sequence encoded by a gene that either contains one of the probesets of Table 31 or the complement of one of the probesets of Table 31.
- biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 31, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, or 50 different biomarkers from Table 31.
- the biomarker profile comprises at least three features, each feature representing a feature of a corresponding biomarker listed in column 3 or four of Table I.
- the biomarker profile comprises at least three different biomarkers listed in column three or four of Table I.
- the biomarker profile can comprise a respective corresponding feature for the at least three biomarkers.
- the at least three biomarkers are derived from at least three different genes listed in Table I.
- the biomarker in the at least three different biomarkers is listed in column three of Table I, can be, for example, a transcript made by the listed gene, a complement thereof, a splice variant thereof, a complement of a splice variant thereof, or a discriminating fragment or complement of any of the foregoing, a cDNA of any of the forgoing, a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein listed in column four of Table I, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above- identified transcript, cDNA, amplified nucleic acid, splice- variant thereof or protein.
- the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker.
- Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table I),
- such an assay utilizes a nucleic acid microarray.
- the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, or 50 different biomarkers from Table I.
- the biomarker profile comprises at least three features, each feature representing a feature of a corresponding biomarker listed in column 3 or four of Table J.
- the biomarker profile comprises at least three different biomarkers listed in column three or four of Table J.
- the biomarker profile can comprise a respective corresponding feature for the at least three biomarkers.
- the at least three biomarkers are derived from at least three different genes.
- the biomarker in the at least three different biomarkers is listed in column three of Table J, can be, for example, a transcript made by the listed gene, a complement thereof, a splice variant thereof, a complement of a splice variant thereof, or a discriminating fragment or complement of any of the foregoing, a cDNA of any of the forgoing, a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein listed in column four of Table J, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, splice- variant thereof or protein.
- the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker.
- Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table J).
- a gene of interest e.g., a gene disclosed in Table J.
- such an assay utilizes a nucleic acid microarray.
- the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40 different biomarkers from Table J.
- the biomarker profile comprises at least three features, each feature representing a feature of a corresponding biomarker listed in column 3 or four of Table K.
- the biomarker profile comprises at least three different biomarkers listed in column three or four of Table K.
- the biomarker profile can comprise a respective corresponding feature for the at least three biomarkers.
- the at least two or three biomarkers are derived from at least two or three different genes, respectively.
- the biomarker in the at least two or three different biomarkers is listed in column three of Table K, can be, for example, a transcript made by the listed gene, a complement thereof, a splice variant thereof, a complement of a splice variant thereof, or a discriminating fragment or complement of any of the foregoing, a cDNA of any of the forgoing, a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein listed in column four of Table K, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, splice-variant thereof or protein.
- the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker.
- Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table K).
- a gene of interest e.g., a gene disclosed in Table K.
- such an assay utilizes a nucleic acid microarray.
- the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10 different biomarkers from Table K.
- the methods of the present invention are particularly useful for detecting or predicting the onset of sepsis in SIRS subjects, one of skill in the art will understand that the present methods may be used for any subject: including, but not limited to, subjects suspected of having SIRS or of being at any stage of sepsis.
- a biological sample can be taken from a subject, and a profile of biomarkers in the sample can be evaluated in light of biomarker profiles obtained from several different types of training populations.
- Representative training populations variously include, for example, populations that include subjects who are SIRS-negative, populations that include subjects who are SIRS-positive, and/or populations that include subjects at a particular stage of sepsis.
- the invention also provides kits that are useful in diagnosing or predicting the development of sepsis or SIRS in a subject (see Section 5.3, infra).
- kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more biomarkers and/or reagents used to detect the presence or abundance of such biomarkers.
- each of these biomarkers is from Table 30.
- each of these biomarkers is from Table 31.
- each of these biomarkers is from Table 32.
- each of these biomarkers is from Table 33. In some embodiments, each of these biomarkers is from Table 36. In some embodiments, each of these biomarkers is from Figure 39, Figure 43, Figure 52, Figure 53, or Figure 56. In another embodiment, the kits of the present invention comprise at least two, but as many as several hundred or more biomarkers and/or reagents used to detect the presence or abundance of such biomarkers.
- kits of the present invention comprise at least
- kits can comprise nucleic acid molecules and/or antibody molecules that specifically bind to biomarkers of the present invention.
- biomarkers that are useful in the present invention are set forth in Section 5.6, Section 5.11, as well as Tables 30, 31, 32, 34 and 36 of Section 6.
- the biomarkers of the kit can be used to generate biomarker profiles according to the present invention.
- types of biomarkers and/or reagents within such kits include, but are not limited to, proteins and fragments thereof, peptides, polypeptides, antibodies, proteoglycans, glycoproteins, lipoproteins, carbohydrates, lipids, nucleic acids (mRNA, DNA, cDNA), organic and inorganic chemicals, and natural and synthetic polymers or a discriminating molecule or fragment thereof.
- kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more biomarkers.
- each of these biomarkers is from Table I.
- each of these biomarkers is from Table J.
- each of these biomarkers is from Table K.
- each of these biomarkers is found in Table I or Table 30.
- each of these biomarkers is found in Table I or Table 31.
- each of these biomarkers is from Figure 39, Figure 43, Figure 52, Figure 53, or Figure 56.
- the kits of the present invention comprise at least two, but as many as 50 or more biomarkers.
- the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more reagents that specifically bind the biomarkers of the present invention.
- biomarkers that are useful in the present invention are set forth in Section 5.6, Section 5.11, as well as Tables I, J, K, L, M, N, and O.
- the biomarkers of the kits can be used to generate biomarker profiles according to the present invention.
- classes of compounds of the kits include, but are not limited to, proteins and fragments thereof, peptides, polypeptides, proteoglycans, glycoproteins, lipoproteins, carbohydrates, lipids, nucleic acids (mRNA, DNA, cDNA), organic and inorganic chemicals, and natural and synthetic polymers or a discriminating molecule or fragment thereof.
- Still another aspect of the present invention comprises computers and computer readable media for evaluating whether a test subject is likely to develop sepsis or SIRS.
- one embodiment of the present invention provides a computer program product for use in conjunction with a computer system.
- the computer program product comprises a computer readable storage medium and a computer program mechanism embedded therein.
- the computer program mechanism comprises instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis.
- the features are measurable aspects of a plurality of biomarkers comprising at least three biomarkers listed in Table I.
- the computer program product further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfaction of the second value set predicts that the test subject is not likely to develop sepsis.
- the biomarker profile has between 3 and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listed in Table I, at least four biomarkers listed in Table I, or at least six biomarkers listed in Table I.
- Another computer embodiment of the present invention comprises a central processing unit and a memory coupled to the central processing unit.
- the memory stores instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis.
- the features are measurable aspects of a plurality of biomarkers. This plurality of biomarkers comprises at least three biomarkers from Table I.
- the memory further stores instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set, wherein satisfying the second value set predicts that the test subject is not likely to develop sepsis, hi some embodiments, the biomarker profile consists of between 3 and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listed in Table I, at least four biomarkers listed in Table L, or at least eight biomarkers listed in Table I.
- Another computer embodiment in accordance with the present invention comprises a computer system for determining whether a subject is likely to develop sepsis.
- the computer system comprises a central processing unit and a memory, coupled to the central processing unit.
- the memory stores instructions for obtaining a biomarker profile of a test subject.
- the biomarker profile comprises a plurality of features.
- the plurality of biomarkers comprises at least three biomarkers listed in Table I.
- the memory further comprises instructions for transmitting the biomarker profile to a remote computer.
- the remote computer includes instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis.
- the memory further comprises instructions for receiving a determination, from the remote computer, as to whether the plurality of features in the biomarker profile of the test subject satisfies the first value set.
- the memory also comprises instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the first value set.
- the remote computer further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfaction of the second value set predicts that the test subject is not likely to develop sepsis.
- the memory further comprises instructions for receiving a determination, from the remote computer, as to whether the plurality of features in the biomarker profile of the test subject satisfies the second set as well as instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the second value set.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I.
- Still another embodiment of the present invention comprises a digital signal embodied on a carrier wave comprising a respective value for each of a plurality of features in a biomarker profile.
- the features are measurable aspects of a plurality of biomarkers.
- the plurality of biomarkers comprises at least three biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- Still another aspect of the present invention provides a digital signal embodied on a carrier wave comprising a determination as to whether a plurality of features in a biomarker profile of a test subject satisfies a value set.
- the features are measurable aspects of a plurality of biomarkers.
- This plurality of biomarkers comprises at least three biomarkers listed in Table I. Satisfying the value set predicts that the test subject is likely to develop sepsis.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I.
- the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- Still another embodiment provides a digital signal embodied on a carrier wave comprising a determination as to whether a plurality of features in a biomarker profile of a test subject satisfies a value set.
- the features are measurable aspects of a plurality of biomarkers.
- the plurality of biomarkers comprises at least three biomarkers listed in Table I. Satisfaction of the value set predicts that the test subject is not likely to develop sepsis.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I.
- the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- Still another embodiment of the present invention provides a graphical user interface for determining whether a subject is likely to develop sepsis.
- the graphical user interface comprises a display field for a displaying a result encoded in a digital signal embodied on a carrier wave received from a remote computer.
- the features are measurable aspects of a plurality of biomarkers.
- the plurality of biomarkers comprises at least three biomarkers listed in Table I.
- the result has a first value when a plurality of features in a biomarker profile of a test subject satisfies a first value set.
- the result has a second value when a plurality of features in a biomarker profile of a test subject satisfies a second value set.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I.
- the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- the computer system comprises a central processing unit and a memory, coupled to the central processing unit.
- the memory stores instructions for obtaining a biomarker profile of a test subject.
- the biomarker profile comprises a plurality of features.
- the features are measurable aspects of a plurality of biomarkers.
- the plurality of biomarkers comprise at least three biomarkers listed in Table I.
- the memory further stores instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a first value set. Satisfying the first value set predicts that the test subject is likely to develop sepsis.
- the memory also stores instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the first value set.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- FIG. 1 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using T- 36 static data obtained from a training population in accordance with an embodiment of the present invention.
- FIG. 2 shows the distribution of feature values for five biomarkers used in the decision tree of FIG. 1 across T -36 static data obtained from a training population in accordance with an embodiment of the present invention.
- the biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
- FIG. 3 illustrates the overall accuracy, sensitivity, and specificity of 500 trees used to train a decision tree using the Random Forests method based upon T -36 static data obtained from a training population in accordance with an embodiment of the present invention.
- FIG. 4 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 3.
- FIG. 5 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using the biomarkers of the present invention across T -36 static data obtained from a training population.
- PAM microarrays
- FIG. 6 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T -36 static data obtained from a training population.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 7 illustrates CART, PAM, and random forests classification algorithm performance data, and associated 95% confidence intervals, for T -36 static data obtained from a training population.
- FIG. 8 illustrates the number of times that common biomarkers were found to be important across the decision rules developed using (i) CART, (ii) PAM, (iii) random forests, and (iv) the Wilcoxon (adjusted) test, for T -36 static data obtained from a training population.
- FIG. 9 illustrates an overall ranking of biomarkers for T -36 static data obtained from a training population.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 10 illustrates a classification and regression tree for discriminating ⁇ between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using data using T -12 static data obtained from a training population in accordance with an embodiment of the present invention.
- FIG. 11 shows the distribution of feature values for four biomarkers used in the decision tree of FIG. 10 using T -12 static data obtained from a training population in accordance with an embodiment of the present invention.
- the biomarkers are referenced by their corresponding Affymetrix U 133 plus 2.0 probeset names given in Table 30.
- FIG. 12 illustrates the overall accuracy, sensitivity, and specificity of 500 trees used to train a decision tree using the Random Forests method based upon T -12 static data obtained from a training population in accordance with an embodiment of the present invention.
- FIG. 13 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 12.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 14 illustrates a calculation of biomarker importance, summing to 100%, determined by a multiple additive regression tree (MART) approach using T -12 static data obtained from a training population.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 15 illustrates the distribution of feature values of the biomarkers selected by the MART approach illustrated in FIG. 14 between the Sepsis and SIRS groups using T -12 static data obtained from a training population.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 16 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using the biomarkers of the present invention using T -12 static data obtained from a training population.
- PAM microarrays
- FIG. 17 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T -12 static data obtained from a training population.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 18 provides a summary of the CART, MART, PAM 3 and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T -12 static data obtained from a training population.
- RF random forests
- FIG. 19 illustrates the number of times that common biomarkers were found to be important across the decision rules developed using (i) CART, (ii) MART, (iii) PAM, (iv) random forests, and (v) the Wilcoxon (adjusted) test using T -12 static data obtained from a training population.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 20 illustrates an overall ranking of biomarkers using T -12 static data obtained from a training population.
- FIG. 21 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using T -12 baseline data obtained from a training population in accordance with an embodiment of the present invention.
- FIG. 22 shows the distribution of the feature values of five biomarkers used in the decision tree of FIG. 21 using T -12 baseline data obtained from a training population in accordance with an embodiment of the present invention.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 23 illustrates the overall accuracy, sensitivity, and specificity of 500 trees used to train a decision tree using the Random Forests method using T -12 baseline data obtained from a training population in accordance with an embodiment of the present invention.
- FIG. 24 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 23.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 25 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using select biomarkers of the present invention and T -12 baseline data obtained from a training population.
- PAM microarrays
- FIG. 26 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T -12 baseline data obtained from a training population.
- the biomarkers are referenced by their corresponding Affymetrix Ul 33 plus 2.0 probeset names given in Table 30.
- FIG. 27 illustrates CART, PAM, and random forests classification algorithm
- FIG. 28 illustrates the number of times that common biomarkers were found to be important across the decision rules developed using (i) CART, (ii) PAM, (iii) random forests, and (iv) the Wilcoxon (adjusted) test using T -12 baseline data obtained from a training population.
- FIG. 29 illustrates an overall ranking of biomarkers for data obtained using
- T -12 baseline data obtained from a training population The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
- FIG. 30 illustrates the filters applied to identify biomarkers that discriminate between subjects that will get sepsis during a defined time period and subjects that will not get sepsis during the defined time period using data obtained from a training population, in accordance with an embodiment of the present invention.
- Other combinations of biomarkers are disclosed herein including, for example, in Section 5.3 and in Section 6.
- FIG. 31 shows the correlation between ILl 8Rl expression, as determined by
- RT-PCR and the intensity of the X206618_at probeset, as determined using Affymetrix Ul 33 plus 2.0 microarray measurements, across a training population.
- FIG. 32 shows the correlation between FCGRlA expression, as determined by RT-PCR, and the intensity of the X21451 l_x_at, X216950_s_at and X216951_at probesets, as determined using Affymetrix Ul 33 plus 2.0 microarray measurements, across a training population.
- FIG. 33 shows the correlation between MMP9 expression, as determined by
- RT-PCR and the intensity of the X203936_s_at probeset, as determined using Affymetrix U133 plus 2.0 microarray measurements, across a training population.
- FIG. 34 shows the correlation between CD86 expression, as determined by
- RT-PCR and the intensity of the X205685_at, X205686_s_at, and X210895_s_at probesets, as determined using Affymetrix Ul 33 plus 2.0 microarray measurements, across a training population.
- FIG. 35 shows a computer system in accordance with the present invention.
- FIG. 36 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using T -12 static data obtained from an RT-PCR discovery training population in accordance with an embodiment of the present invention.
- FIG. 37 shows the distribution of feature values for seven biomarkers used in the decision tree of FIG. 36 across T -12 static data obtained from an RT-PCR discovery training population in accordance with an embodiment of the present invention.
- FIG. 38 illustrates the overall accuracy, sensitivity, and specificity of 462 trees used to train a decision tree using the Random Forests method based upon T -12 static data obtained from an RT-PCR discovery training population in accordance with an embodiment of the present invention.
- FIG. 39 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 38.
- FIG. 40 illustrates a calculation of biomarker importance, summing to 100%, determined by a multiple additive regression tree (MART) approach using T -12 static data obtained from an RT-PCR discovery training population.
- FIG. 41 illustrates the distribution of feature values of the biomarkers selected by the MART approach illustrated in FIG. 40 between the Sepsis and SIRS groups using T -12 static data obtained from an RT-PCR discovery training population.
- FIG. 42 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using the biomarkers of the present invention using T -12 static data obtained from an RT-PCR discovery training population.
- PAM microarrays
- FIG. 43 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T -12 static data obtained from an RT-PCR discovery training population.
- FIG. 44 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T -12 static data obtained from an RT-PCR discovery training population.
- RF random forests
- FIG. 45 identified fifty selected biomarkers selected based on the decision rule performance summarized in FIG. 44.
- FIG. 46 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T -12 static data obtained from an Affymetrix gene chip discovery training population.
- RF random forests
- FIG. 47 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T -12 static data obtained from an RT-PCR confimatory training population.
- RF random forests
- FIG. 48 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T -12 static data obtained from a combined pool of a Affymetrix gene chip confirmatory training population and an RT-PCR confirmatory training population.
- RF random forests
- FIG. 49 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using T -12 static data obtained from a bead- based protein discovery training population in accordance with an embodiment of the present invention.
- FIG. 50 shows the distribution of feature values for ten biomarkers used in the decision tree of FIG. 49 across T -12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
- FIG. 51 illustrates the overall accuracy, sensitivity, and specificity of 64 trees used to train a decision tree using the Random Forests method based upon T -12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
- FIG. 52 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 51.
- FIG. 53 illustrates a calculation of biomarker importance, summing to 100%, determined by a multiple additive regression tree (MART) approach using T -12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
- MART multiple additive regression tree
- FIG. 54 illustrates the distribution of feature values of the biomarkers selected by the MART approach illustrated in FIG. 53 between the Sepsis and SIRS groups using T -12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
- FIG. 55 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using the biomarkers of the present invention using T -12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
- PAM microarrays
- FIG. 56 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T -12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
- FIG. 57 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T -12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
- RF random forests
- FIG. 58 illustrates the number of times that common biomarkers were found to be important across the decision rules developed using (i) CART, (ii) MART, (iii) PAM, (iv) random forests, and (v) the Wilcoxon (adjusted) test using T -12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
- FIG. 59 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T -12 static data obtained from a bead-based protein confirmation training population in accordance with an embodiment of the present invention.
- Figure 60 plots the sepsis predicting accuracy of each of 24 families of subcombinations from Table J, using T -12 nucleic acid data, in a bar graph fashion, in accordance with an embodiment of the present invention.
- Figure 61 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 24 families of subcombinations, for a total of 4800 subcombinations from Table J, using T -12 nucleic acid data, in accordance with an embodiment of the present invention.
- Figure 62 plots the sepsis predicting accuracy of each of 8 families of subcombinations from Table K, using T -12 protein data, in a bar graph fashion, in accordance with an embodiment of the present invention.
- Figure 63 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 8 families of subcombinations, for a total of 1600 subcombinations from Table K, using T -12 protein data, in accordance with an embodiment of the present invention.
- Figure 64 plots the sepsis predicting accuracy of each of 8 families of subcombinations from Table K, using T -36 protein data, in a bar graph fashion, in accordance with an embodiment of the present invention.
- Figure 65 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 8 families of subcombinations, for a total of 1600 subcombinations from Table K, using T -36 protein data, in accordance with an embodiment of the present invention.
- Figure 66 plots the sepsis predicting accuracy of each of 23 families of subcombinations from Table J, using T. 36 nucleic acid data, in a bar graph fashion, in accordance with an embodiment of the present invention.
- Figure 67 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 23 families of subcombinations, for a total of 4600 subcombinations from Table J, using T -36 nucleic acid data, in accordance with an embodiment of the present invention.
- Figure 68 plots the sepsis predicting accuracy of each of 23 families of subcombinations from Table I, using T -12 combined protein and nucleic acid data, in a bar graph fashion, in accordance with an embodiment of the present invention.
- Figure 69 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 23 families of subcombinations, for a total of 4600 subcombinations from Table I, using T- 12 combined protein and nucleic acid data, in accordance with an embodiment of the present invention.
- Figure 70 plots the sepsis predicting accuracy of each of 23 families of subcombinations from Table I, using T -36 combined protein and nucleic acid data, in a bar graph fashion, in accordance with an embodiment of the present invention.
- Figure 71 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 23 families of subcombinations, for a total of 4600 subcombinations from Table I, using T -36 combined protein and nucleic acid data, in accordance with an embodiment of the present invention.
- the present invention allows for the rapid and accurate diagnosis or prediction of sepsis by evaluating biomarker features in biomarker profiles.
- biomarker profiles can be constructed from one or more biological samples of subjects at a single time point ("snapshot"), or multiple such time points, during the course of time the subject is at risk for developing sepsis.
- spikehot single time point
- sepsis can be diagnosed or predicted prior to the onset of conventional clinical sepsis symptoms, thereby allowing for more effective therapeutic intervention.
- Systemic inflammatory response syndrome refers to a clinical response to a variety of severe clinical insults, as manifested by two or more of the following conditions within a 24-hour period:
- body temperature greater than 38°C (100.4 0 F) or less than 36 0 C (96.8 0 F);
- WBC white blood cell count
- SIRS International Health Organization
- present definition is used to clarify current clinical practice and does not represent a critical aspect of the invention (see, e.g., American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: Definitions for Sepsis and Organ Failure and Guidelines for the Use of innovative Therapies in Sepsis, 1992, Crit. Care. Med. 20, 864-874, the entire contents of which are herein incorporated by reference).
- a subject with SIRS has a clinical presentation that is classified as SIRS, as defined above, but is not clinically deemed to be septic.
- Methods for determining which subjects are at risk of developing sepsis are well known to those in the art. Such subjects include, for example, those in an ICU and those who have otherwise suffered from a physiological trauma, such as a burn, surgery or other insult.
- a hallmark of SIRS is the creation of a proinflammatory state that can be marked by tachycardia, tachypnea or hyperpnea, hypotension, hypoperfusion, oliguria, leukocytosis or leukopenia, pyrexia or hypothermia and the need for volume infusion.
- SIRS characteristically does not include a documented source of infection ⁇ e.g., bacteremia).
- Sepsis refers to a systemic host response to infection with SIRS plus a documented infection ⁇ e.g., a subsequent laboratory confirmation of a clinically significant infection such as a positive culture for an organism).
- a documented infection e.g., a documented infection
- sepsis refers to the systemic inflammatory response to a documented infection (see, e.g., American College of Chest Physicians Society of Critical Care Medicine, Chest, 1997, 101:1644-1655, the entire contents of which are herein incorporated by reference).
- sepsis includes all stages of sepsis including, but not limited to, the onset of sepsis, severe sepsis, septic shock and multiple organ dysfunction ("MOD”) associated with the end stages of sepsis.
- MOD multiple organ dysfunction
- the "onset of sepsis” refers to an early stage of sepsis, e.g. , prior to a stage when conventional clinical manifestations are sufficient to support a clinical suspicion of sepsis. Because the methods of the present invention are used to detect sepsis prior to a time that sepsis would be suspected using conventional techniques, the subject's disease status at early sepsis can only be confirmed retrospectively, when the manifestation of sepsis is more clinically obvious. The exact mechanism by which a subject becomes septic is not a critical aspect of the invention. The methods of the present invention can detect the onset of sepsis independent of the origin of the infectious process.
- Severe sepsis refers to sepsis associated with organ dysfunction, hypoperfusion abnormalities, or sepsis-induced hypotension. Hypoperfusion abnormalities include, but are not limited to, lactic acidosis, oliguria, or an acute alteration in mental status.
- Septic shock refers to sepsis-induced hypotension that is not responsive to adequate intravenous fluid challenge and with manifestations of peripheral hypoperfusion.
- a "converter” or “converter subject” refers to a SIRS-positive subject who progresses to clinical suspicion of sepsis during the period the subject is monitored, typically during an ICU stay.
- non-converter or “non-converter subject” refers to a SIRS-positive subject who does not progress to clinical suspicion of sepsis during the period the subject is monitored, typically during an ICU stay.
- a “biomarker” is virtually any detectable compound, such as a protein, a peptide, a proteoglycan, a glycoprotein, a lipoprotein, a carbohydrate, a lipid, a nucleic acid (e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA), an organic or inorganic chemical, a natural or synthetic polymer, a small molecule (e.g., a metabolite), or a discriminating molecule or discriminating fragment of any of the foregoing, that is present in or derived from a biological sample.
- a nucleic acid e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA
- an organic or inorganic chemical e.g., a natural or synthetic polymer, a small molecule (e.g., a metabolite), or a discriminating molecule or discriminating fragment of any of the foregoing, that is
- Detecting from refers to a compound that, when detected, is indicative of a particular molecule being present in the biological sample.
- detection of a particular cDNA can be indicative of the presence of a particular RNA transcript in the biological sample.
- detection of or binding to a particular antibody can be indicative of the presence of a particular antigen (e.g., protein) in the biological sample.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of an above-identified compound.
- a biomarker can, for example, be isolated from the biological sample, directly measured in the biological sample, or detected in or determined to be in the biological sample.
- a biomarker can, for example, be functional, partially functional, or non-functional.
- a biomarker is isolated and used, for example, to raise a specifically-binding antibody that can facilitate biomarker detection in a variety of diagnostic assays.
- Any immunoassay may use any antibodies, antibody fragment or derivative thereof capable of binding the biomarker molecules (e.g., Fab, F(ab') 2 , Fv, or scFv fragments). Such immunoassays are well-known in the art.
- the biomarker is a protein or fragment thereof, it can be sequenced and its encoding gene can be cloned using well-established techniques.
- a species of a biomarker refers to any discriminating portion or discriminating fragment of a biomarker described herein, such as a splice variant of a particular gene described herein (e.g., a gene listed in Table 30, or Table I, or Table J, or Table K, infra).
- a discriminating portion or discriminating fragment is a portion or fragment of a molecule that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the terms “protein”, “peptide”, and “polypeptide” are, unless otherwise indicated, interchangeable.
- a “biomarker profile” comprises a plurality of one or more types of biomarkers (e.g., an mRNA molecule, a cDNA molecule, a protein and/or a carbohydrate, etc.), or an indication thereof, together with a feature, such as a measurable aspect (e.g., abundance) of the biomarkers.
- a biomarker profile comprises at least two such biomarkers or indications thereof, where the biomarkers can be in the same or different classes, such as, for example, a nucleic acid and a carbohydrate.
- a biomarker profile may also comprise at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more biomarkers or indications thereof.
- a biomarker profile comprises hundreds, or even thousands, of biomarkers or indications thereof.
- a biomarker profile can further comprise one or more controls or internal standards.
- the biomarker profile comprises at least one biomarker, or indication thereof, that serves as an internal standard.
- a biomarker profile comprises an indication of one or more types of biomarkers.
- indication merely refers to a situation where the biomarker profile contains symbols, data, abbreviations or other similar indicia for a biomarker, rather than the biomarker molecular entity itself.
- the biomarker profile of the present invention comprises the Affymetrix (Santa Clara, California) U133 plus 2.0 205013_s_at and 209369_at probesets.
- Another exemplary biomarker profile of the present invention comprises the name of genes used to derive the Affymetrix (Santa Clara, California) U133 plus 2.0 205013_s_at and 209369_at probesets.
- the biomarker profile comprises a physical quantity of a transcript of a gene from which the 205013_s_at probeset was derived, and a physical quantity of a transcript of a gene from which the 209369_at probeset was derived.
- the biomarker profile comprises a nominal indication of the quantity of a transcript of a gene from which the 205013_s_at probeset was derived and a nominal indication of the quantity of transcript of a gene from which the 209369_at probeset was derived.
- Still another exemplary biomarker profile of the present invention comprises a microarray to which a physical quantity of a gene transcript from which the 205013_s_at probeset was derived is bound at a first probe spot on the microarray and an abundance of a gene transcript from which the 209369_at probeset was derived is bound to a second probe spot on the microarray.
- this last exemplary biomarker profile at least twenty percent, forty percent, or more than forty percent of the probes spots are based on sequences in the probesets given in Table 30.
- at least twenty percent, forty percent, or more than forty percent of the probes spots are based on sequences in the probesets given in Table 31.
- Each biomarker in a biomarker profile includes a corresponding "feature.”
- feature refers to a measurable aspect of a biomarker.
- a feature can include, for example, the presence or absence of biomarkers in the biological sample from the subject as illustrated in exemplary biomarker profile 1 :
- biomarker profile 1 Exemplary biomarker profile 1.
- A is "presence” and the feature value for the transcript of gene B is "absence.”
- a feature can include, for example, the abundance of a biomarker in the biological sample from a subject as illustrated in exemplary biomarker profile 2:
- A is 300 units and the feature value for the transcript of gene B is 400 units.
- a feature can also be a ratio of two or more measurable aspects of a biomarker as illustrated in exemplary biomarker profile 3: Exemplary biomarker profile 3.
- a feature may also be the difference between a measurable aspect of the corresponding biomarker that is taken from two samples, where the two samples are collected from a subject at two different time points.
- the biomarker is a transcript of a gene A and the "measurable aspect" is abundance of the transcript, in samples obtained from a test subject as determined by, e.g., RT-PCR or microarray analysis.
- the abundance of the transcript of gene A is measured in a first sample as well as a second sample. The first sample is taken from the test subject a number of hours before the second sample.
- a feature can also be an indication as to whether an abundance of a biomarker is increasing in biological samples obtained from a subject over time and/or an indication as to whether an abundance of a biomarker is decreasing in biological samples obtained from a subject over time.
- biomarker profile 1 there is a one-to-one correspondence between features and biomarkers in a biomarker profile as illustrated in exemplary biomarker profile 1, above.
- the relationship between features and biomarkers in a biomarker profile of the present invention is more complex, as illustrated in Exemplary biomarker profile 3, above.
- a feature can represent the average of an abundance of a biomarker across biological samples collected from a subject at two or more time points.
- a feature can be the difference or ratio of the abundance of two or more biomarkers from a biological sample obtained from a subject in a single time point.
- a biomarker profile may also comprise at least three, four, five, 10, 20, 30 or more features.
- a biomarker profile comprises hundreds, or even thousands, of features.
- features of biomarkers are measured using microarrays.
- a microarray comprises a plurality of probes. In some instances, each probe recognizes, e.g., binds to, a different biomarker. In some instances, two or more different probes on a microarray recognize, e.g. , bind to, the same biomarker.
- the relationship between probe spots on the microarray and a subject biomarker is a two to one correspondence, a three to one correspondence, or some other form of correspondence.
- a "phenotypic change” is a detectable change in a parameter associated with a given state of the subject.
- a phenotypic change can include an increase or decrease of a biomarker in a bodily fluid, where the change is associated with SIRS, sepsis, the onset of sepsis or with a particular stage in the progression of sepsis.
- a phenotypic change can further include a change in a detectable aspect of a given state of the subject that is not a change in a measurable aspect of a biomarker.
- a change in phenotype can include a detectable change in body temperature, respiration rate, pulse, blood pressure, or other physiological parameter.
- nucleic acid sequence e.g., a nucleotide sequence encoding a gene described herein
- complementary in the context of a nucleic acid sequence (e.g., a nucleotide sequence encoding a gene described herein), refers to the chemical affinity between specific nitrogenous bases as a result of their hydrogen bonding properties.
- guanine (G) forms a hydrogen bond with only cytosine (C)
- adenine forms a hydrogen bond only with thymine (T) in the case of DNA
- U uracil
- nucleic acid sequences may be complementary if their nitrogenous bases are able to form hydrogen bonds.
- Such sequences are referred to as "complements" of each other.
- complement sequences can be naturally occurring, or, they can be chemically synthesized by any method known to those skilled in the art, as for example, in the case of antisense nucleic acid molecules which are complementary to the sense strand of a DNA molecule or an RNA molecule (e.g., an mRNA transcript). See, e.g., Lewin, 2002, Genes VII. Oxford University Press Inc., New York, NY, which is hereby incorporated by reference.
- inventions in the context of diagnosing or predicting sepsis or SIRS are those techniques that classify a subject based on phenotypic changes without obtaining a biomarker profile according to the present invention.
- a "decision rule” is a method used to evaluate biomarker profiles. Such decision rules can take on one or more forms that are known in the art, as exemplified in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, which is hereby incorporated by reference in its entirety.
- a decision rule may be used to act on a data set of features to, inter alia, predict the onset of sepsis, to determine the progression of sepsis, or to diagnose sepsis.
- Exemplary decision rules that can be used in some embodiments of the present invention are described in further detail in Section 5.5, below.
- Predicting the development of sepsis is the determination as to whether a subject will develop sepsis. Any such prediction is limited by the accuracy of the means used to make this determination.
- the present invention provides a method, e.g. , by utilizing a decision rule(s), for making this determination with an accuracy that is 60% or greater.
- the terms "predicting the development of sepsis” and “predicting sepsis” are interchangeable.
- the act of predicting the development of sepsis is accomplished by evaluating one or more biomarker profiles from a subject using a decision rule that is indicative of the development of sepsis and, as a result of this evaluation, receiving a result from the decision rule that indicates that the subject will become septic.
- a decision rule that is indicative of the development of sepsis
- Such an evaluation of one or more biomarker profiles from a test subject using a decision rule uses some or all the features in the one or more biomarker profiles to obtain such a result.
- the term "specifically,” and analogous terms, in the context of an antibody refers to peptides, polypeptides, and antibodies or fragments thereof that specifically bind to an antigen or a fragment and do not specifically bind to other antigens or other fragments.
- a peptide or polypeptide that specifically binds to an antigen may bind to other peptides or polypeptides with lower affinity, as determined by standard experimental techniques, for example, by any immunoassay well-known to those skilled in the art.
- immunoassays include, but are not limited to, radioimmunoassays (RIAs) and enzyme-linked immunosorbent assays (ELISAs).
- Antibodies or fragments that specifically bind to an antigen may be cross-reactive with related antigens. Preferably, antibodies or fragments thereof that specifically bind to an antigen do not cross-react with other antigens. See, e.g., Paul, ed., 2003, Fundamental Immunology, 5th ed., Raven Press, New York at pages 69-105, which is incorporated by reference herein, for a discussion regarding antigen- antibody interactions, specificity and cross-reactivity, and methods for determining all of the above.
- a "subject” is an animal, preferably a mammal, more preferably a non-human primate, and most preferably a human.
- the terms “subject” “individual” and “patient” are used interchangeably herein.
- test subject typically, is any subject that is not in a training population used to construct a decision rule.
- a test subject can optionally be suspected of either having sepsis at risk of developing sepsis.
- tissue type is a type of tissue.
- a tissue is an association of cells of a multicellular organism, with a common embryoloical origin or pathway and similar structure and function. Often, cells of a tissue are contiguous at cell membranes but occasionally the tissue may be fluid (e.g. , blood). Cells of a tissue may be all of one type (a simple tissue, e.g., squamous epithelium, plant parentchyma) or of more than one type (a mixed tissue, e.g., connective tissue).
- a "training population” is a set of samples from a population of subjects used to construct a decision rule, using a data analysis algorithm, for evaluation of the biomarker profiles of subjects at risk for developing sepsis.
- a training population includes samples from subjects that are converters and subjects that are nonconverters.
- a "data analysis algorithm” is an algorithm used to construct a decision rule using biomarker profiles of subjects in a training population. Representative data analysis algorithms are described in Section 5.5.
- a "decision rule” is the final product of a data analysis algorithm, and is characterized by one or more value sets, where each of these value sets is indicative of an aspect of SIRS, the onset of sepsis, sepsis, or a prediction that a subject will acquire sepsis.
- a value set represents a prediction that a subject will develop sepsis.
- a value set represents a prediction that a subject will not develop sepsis.
- a "validation population” is a set of samples from a population of subjects used to determine the accuracy of a decision rule.
- a validation population includes samples from subjects that are converters and subjects that are nonconverters.
- a validation population does not include subjects that are part of the training population used to train the decision rule for which an accuracy measurement is sought.
- a "value set” is a combination of values, or ranges of values for features in a biomarker profile. The nature of this value set and the values therein is dependent upon the type of features present in the biomarker profile and the data analysis algorithm used to construct the decision rule that dictates the value set. To illustrate, reconsider exemplary biomarker profile 2:
- the biomarker profile of each member of a training population is obtained.
- Each such biomarker profile includes a measured feature, here abundance, for the transcript of gene A, and a measured feature, here abundance, for the transcript of gene B.
- These feature values, here abundance values are used by a data analysis algorithm to construct a decision rule.
- the data analysis algorithm is a decision tree, described in Section 5.5.1 and the final product of this data analysis algorithm, the decision rule, is a decision tree.
- An exemplary decision tree is illustrated in Figure. 1.
- the decision rule defines value sets. One such value set is predictive of the onset of sepsis. A subject whose biomarker feature values satisfy this value set is likely to become septic.
- An exemplary value set of this class is exemplary value set 1 :
- Another such value set is predictive of a septic-free state.
- a subject whose biomarker feature values satisfy this value set is not likely to become septic.
- An exemplary value set of this class is exemplary value set 2:
- one value set is those ranges of biomarker profile feature values that will cause the weighted neural network to indicate that onset of sepsis is likely.
- Another value set is those ranges of biomarker profile feature values that will cause the weighted neural network to indicate that onset of sepsis is not likely.
- the term "probe spot" in the context of a microarray refers to a single stranded DNA molecule (e.g., a single stranded cDNA molecule or synthetic DNA oligomer), referred to herein as a "probe,” that is used to determine the abundance of a particular nucleic acid in a sample.
- a probe spot can be used to determine the level of mRNA in a biological sample (e.g., a collection of cells) from a test subject.
- a typical microarray comprises multiple probe spots that are placed onto a glass slide (or other substrate) in known locations on a grid.
- the nucleic acid for each probe spot is a single stranded contiguous portion of the sequence of a gene or gene of interest (e.g., a 10-mer, 11-mer, 12-mer, 13-mer, 14-mer, 15-mer, 16-mer, 17-mer, 18-mer, 19-mer, 20-mer, 21-mer, 22-mer, 23-mer, 24-mer, 25-mer or larger) and is a probe for the mRNA encoded by the particular gene or gene of interest.
- Each probe spot is characterized by a single nucleic acid sequence, and is hybridized under conditions that cause it to hybridize only to its complementary DNA strand or mRNA molecule.
- probe spots on a substrate there can be many probe spots on a substrate, and each can represent a unique gene or sequence of interest.
- two or more probe spots can represent the same gene sequence.
- a labeled nucleic sample is hybridized to a probe spot, and the amount of labeled nucleic acid specifically hybridized to a probe spot can be quantified to determine the levels of that specific nucleic acid (e.g., mRNA transcript of a particular gene) in a particular biological sample.
- Probes, probe spots, and microarrays generally, are described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, Chapter, 2, which is hereby incorporated by reference in its entirety.
- annotation data refers to any type of data that describes a property of a biomarker.
- Annotation data includes, but is not limited to, biological pathway membership, enzymatic class (e.g., phosphodiesterase, kinase, metalloproteinase, etc.), protein domain information, enzymatic substrate information, enzymatic reaction information, protein interaction data, disease association, cellular localization, tissue type localization, and cell type localization.
- T -12 refers to the last time blood was obtained from a subject before the subject is clinically diagnosed with sepsis. Since, in the present invention, blood is collected from subjects each 24 hour period, T -12 references the average time period prior to the onset of sepsis for a pool of patients, with some patients turning septic prior to the 12 hour mark and some patients turning septic after the 12 hour mark. However, across a pool of subjects, the average time period for this last blood sample is the 12 hour mark, hence the name "T -12 .”
- the present invention allows for accurate, rapid prediction and/or diagnosis of sepsis through detection of two or more features of a biomarker profile of a test individual suspected of or at risk for developing sepsis in each of one or more biological samples from a test subject.
- only a single biological sample taken at a single point in time from the test subject is needed to construct a biomarker profile that is used to make this prediction or diagnosis of sepsis
- multiple biological samples taken at different points in time from the test subject are used to construct a biomarker profile that is used to make this prediction or diagnosis of sepsis.
- subjects at risk for developing sepsis or SIRS are screened using the methods of the present invention.
- a biological sample such as, for example, blood
- a biological sample is taken upon admission.
- a biological sample is blood, plasma, serum, saliva, sputum, urine, cerebral spinal fluid, cells, a cellular extract, a tissue specimen, a tissue biopsy, or a stool specimen.
- a biological sample is whole blood and this whole blood is used to obtain measurements for a biomarker profile.
- a biological sample is some component of whole blood.
- some portion of the mixture of proteins, nucleic acid, and/or other molecules (e.g., metabolites) within a cellular fraction or within a liquid (e.g., plasma or serum fraction) of the blood is resolved as a biomarker profile. This can be accomplished by measuring features of the biomarkers in the biomarker profile.
- the biological sample is whole blood but the biomarker profile is resolved from biomarkers in a specific cell type that is isolated from the whole blood.
- the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in monocytes that are isolated from the whole blood.
- the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in red blood cells that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in platelets that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in neutriphils that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in eosinophils that are isolated from the whole blood.
- the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in basophils that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in lymphocytes that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in monocytes that are isolated from the whole blood.
- the biological sample is whole blood but the biomarker profile is resolved from one, two, three, four, five, six, or seven cell types from the group of cells types consisting of red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, and monocytes.
- a biomarker profile comprises a plurality of one or more types of biomarkers
- a biomarker profile can comprise at least two such biomarkers or indications thereof, where the biomarkers can be in the same or different classes, such as, for example, a nucleic acid and a carbohydrate.
- a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more biomarkers or indications thereof.
- a biomarker profile comprises hundreds, or even thousands, of biomarkers or indications thereof.
- a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more biomarkers or indications thereof.
- a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more biomarkers selected from Table I of Section 5.11, or indications thereof.
- a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40 or more biomarkers selected from Table J of Section 5.11, or indications thereof.
- a biomarker profile comprises any 2, 3, 4, 5, 6, 7, 8, 9, or all ten biomarkers in Table K of Section 5.11, or indications thereof.
- each biomarker in the biomarker profile is represented by a feature.
- the correspondence between biomarkers and features is 1:1, meaning that for each biomarker there is a feature.
- the number of features corresponding to one biomarker in the biomarker profile is different than then number of features corresponding to another biomarker in the biomarker profile.
- a biomarker profile can include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more features, provided that there are at least 2, 3, 4, 5, 6, or 7 or more biomarkers in the biomarker profile.
- a biomarker profile can include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more features. Regardless of embodiment, these features can be determined through the use of any reproducible measurement technique or combination of measurement techniques. Such techniques include those that are well known in the art including any technique described herein or, for example, any technique disclosed in Section 5.4, infra. Typically, such techniques are used to measure feature values using a biological sample taken from a subject at a single point in time or multiple samples taken at multiple points in time.
- an exemplary technique to obtain a biomarker profile from a sample taken from a subject is a cDNA microarray (see, e.g., Section 5.4.1.2 and Section 6, infra).
- an exemplary technique to obtain a biomarker profile from a sample taken from a subject is a protein-based assay or other form of protein-based technique such as described in the BD Cytometric Bead Array (CBA) Human Inflammation Kit Instruction Manual (BD Biosciences) or the bead assay described in U.S. Pat. No. 5,981,180, each of which is incorporated herein by reference in their entirety, and in particular for their teachings of various methods of assay protein concentrations in biological samples.
- CBA Cytometric Bead Array
- U.S. Pat. No. 5,981,180 each of which is incorporated herein by reference in their entirety, and in particular for their teachings of various methods of assay protein concentrations in biological samples.
- the biomarker profile is mixed, meaning that it comprises some biomarkers that are nucleic acids, or indications thereof, and some biomarkers that are proteins, or indications thereof.
- both protein based and nucleic acid based techniques are used to obtain a biomarker profile from one or more samples taken from a subject.
- the feature values for the features associated with the biomarkers in the biomarker profile that are nucleic acids are obtained by nucleic acid based measurement techniques (e.g., a nucleic acid microarray) and the feature values for the features associated with the biomarkers in the biomarker profile that are proteins are obtained by protein based measurement techniques.
- biomarker profiles can be obtained using a kit, such as a kit described in Section 5.3 below.
- a subject is screened using the methods and compositions of the invention as frequently as necessary (e.g., during their stay in the ICU) to diagnose or predict sepsis or SIRS in a subject, hi a preferred embodiment, the subject is screened soon after they arrive in the ICU. In some embodiments, the subject is screened daily after they arrive in the ICU. In some embodiments, the subject is screened every 1 to 4 hours, 1 to 8 hours, 8 to 12 hours, 12 to 16 hours, or 16 to 24 hours after they arrive in the ICU.
- kits that are useful in diagnosing or predicting the development of sepsis or diagnosing SIRS in a subject.
- the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more biomarkers and/or reagents to detect the presence or abundance of such biomarkers.
- kits of the present invention comprise at least 2, but as many as several hundred or more biomarkers. In some embodiments, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more biomarkers selected from Table I of Section 5.11. In some embodiments, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40 or more biomarkers selected from Table J of Section 5.11. In some embodiments, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, or all 10 of the biomarkers in Table K of Section 5.11.
- a biomarker is in fact a discriminating molecule of, for example, a gene, mRNA, or protein rather than the gene, mRNA, or protein itself.
- a biomarker could be a molecule that indicates the presence or abundance of a particular gene or protein, or fragment thereof, identified in any one of Tables I, J, or K of Section 5.11 rather than the actual gene or protein itself.
- Such discriminating molecules are sometimes referred to in the art as "reagents.”
- the kits of the present invention comprise at least 2, but as many as several hundred or more biomarkers.
- the biomarkers of the kits of the present invention can be used to generate biomarker profiles according to the present invention.
- classes of compounds of the kit include, but are not limited to, proteins and fragments thereof, peptides, proteoglycans, glycoproteins, lipoproteins, carbohydrates, lipids, nucleic acids (e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA), organic or inorganic chemicals, natural or synthetic polymers, small molecules (e.g., metabolites), or discriminating molecules or discriminating fragments of any of the foregoing.
- nucleic acids e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA
- organic or inorganic chemicals e.g., natural or synthetic polymers
- small molecules e.g., metabolites
- a biomarker is of a particular size, (e.g., at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 1000, 2000, 3000, 5000, 10k, 20k, 100k Daltons or greater).
- the biomarker(s) may be part of an array, or the biomarker(s) may be packaged separately and/or individually.
- the kit may also comprise at least one internal standard to be used in generating the biomarker profiles of the present invention.
- kits comprising probes and/or primers that may or may not be immobilized at an addressable position on a substrate, such as found, for example, in a microarray. In a particular embodiment, the invention provides such a microarray.
- the invention provides a kit for predicting the development of sepsis in a test subject that comprises a plurality of antibodies that specifically bind the protein-based biomarkers listed in any one of Tables 30, 31, 32, 33, 34, 36, 1, J, or K.
- the antibodies themselves are biomarkers within the scope of the present invention.
- the kit may comprise a set of antibodies or functional fragments or derivatives thereof (e.g., Fab, F(ab') 2 , Fv, or scFv fragments) that specifically bind at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more of the protein-based biomarkers set forth in any one of Tables 30, 31, 32, 33, 34, 36, 1, J, or K.
- Fab fragment fragments or derivatives thereof
- the kit may include antibodies, fragments or derivatives thereof (e.g., Fab, F(ab') 2 , Fv, or scFv fragments) that are specific for the biomarkers of the present invention.
- the antibodies may be detectably labeled.
- the invention provides a kit for predicting the development of sepsis in a test subject comprises a plurality of antibodies that specifically bind a plurality of the protein-based biomarkers listed in Table I of Section 5.11.
- the kit may comprise a set of antibodies or functional fragments or derivatives thereof (e.g., Fab, F(ab') 2 , Fv, or scFv fragments) that specifically bind at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more of the biomarkers set forth in Table I.
- the kit may include antibodies, fragments or derivatives thereof (e.g., Fab, F(ab')2, Fv, or scFv fragments) that are specific for the biomarkers of the present invention.
- the antibodies may be detectably labeled.
- a kit may comprise a specific biomarker binding component, such as an aptamer. If the biomarkers comprise a nucleic acid, the kit may provide an oligonucleotide probe that is capable of forming a duplex with the biomarker or with a complementary strand of a biomarker. The oligonucleotide probe may be detectably labeled. In such embodiments, the probes are themselves biomarkers that fall within the scope of the present invention.
- kits of the present invention may also include additional compositions, such as buffers, that can be used in constructing the biomarker profile.
- additional compositions such as buffers
- Prevention of the action of microorganisms can be ensured by the inclusion of various antibacterial and antifungal agents, for example, paraben, chlorobutanol, phenol sorbic acid, and the like. It may also be desirable to include isotonic agents such as sugars, sodium chloride, and the like.
- kits of the present invention comprise a microarray.
- this microarray comprises a plurality of probe spots, wherein at least twenty percent of the probe spots in the plurality of probe spots correspond to biomarkers in any one of Tables 30, 31, 32, 33, 34, 36, 1, J, or K.
- at least twenty-five percent, at least thirty percent, at least thirty-five percent, at least forty percent, or at least sixty percent, or at least eighty percent of the probe spots in the plurality of probe spots correspond to biomarkers in any one of Tables 30, 31, 32, 33, 34, 36, 1, J, or K.
- Such probe spots are biomarkers within the scope of the present invention.
- the microarray consists of between about three and about one hundred probe spots on a substrate. In some embodiments, the microarray consists of between about three and about one hundred probe spots on a substrate. As used in this context, the term "about” means within five percent of the stated value, within ten percent of the stated value, or within twenty-five percent of the stated value. In some embodiments, such microarrays contain one or more probe spots for inter-microarray calibration or for calibration with other microarrays such as reference microarrays using techniques that are known to those of skill in the art. In some embodiments such microarrays are nucleic acid microarrays. In some embodiments, such microarrays are protein microarrays.
- kits of the invention may further comprise a computer program product for use in conjunction with a computer system, wherein the computer program product comprises a computer readable storage medium and a computer program mechanism embedded therein.
- the computer program mechanism comprises instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfying the first value set predicts that the test subject is likely to develop sepsis, hi one embodiment, the plurality of features corresponds to biomarkers listed in any one of Tables 30, 31, 32, 33, 34, 36, 1, J, or K.
- kits of the computer program product further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfying the second value set predicts that the test subject is not likely to develop sepsis.
- Some kits of the present invention comprise a computer having a central processing unit and a memory coupled to the central processing unit. The memory stores instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfying the first value set predicts that the test subject is likely to develop sepsis.
- the plurality of features corresponds to biomarkers listed in any one of Tables 30, 31, 32, 33, 34, 36, 1 5 J, or K.
- Fig. 35 details an exemplary system that supports the functionality described above.
- the system is preferably a computer system 10 having:
- a main non- volatile storage unit 14 for example, a hard disk drive, for storing software and data, the storage unit 14 controlled by storage controller 12;
- system memory 36 preferably high speed random-access memory (RAM), for storing system control programs, data, and application programs, comprising programs and data loaded from non-volatile storage unit 14; system memory 36 may also include read-only memory (ROM);
- RAM random-access memory
- ROM read-only memory
- a user interface 32 comprising one or more input devices (e.g., keyboard 28) and a display 26 or other output device;
- a network interface card 20 for connecting to any wired or wireless communication network 34 (e.g., a wide area network such as the Internet);
- Operation of computer 10 is controlled primarily by operating system 40, which is executed by central processing unit 22.
- Operating system 40 can be stored in system memory 36.
- system memory 36 includes:
- file system 42 for controlling access to the various files and data structures used by the present invention
- a biomarker profile evaluation module 60 for determining whether a plurality of features in a biomarker profile of a test subject satisfies a first value set or a second value set;
- test subject biomarker profile 62 comprising biomarkers 64 and, for each such biomarkers, features 66;
- a database 68 of select biomarkers of the present invention e.g., Table 30 and/or Table I and/or Table J and/or Table K, and/or Table L and/or Table M and/or Table N and/or Table O etc.
- select biomarkers of the present invention e.g., Table 30 and/or Table I and/or Table J and/or Table K, and/or Table L and/or Table M and/or Table N and/or Table O etc.
- Training data set 46 comprises data for a plurality of subjects 46. For each subject 46 there is a subject identifier 48 and a plurality of biomarkers 50. For each biomarker 50, there is at least one feature 52. Although not shown in Figure 35, for each feature 52, there is a feature value. For each decision rule 56 constructed using data analysis algorithms, there is at least one decision rule value set 58.
- computer 10 comprises software program modules and data structures. The data structures stored in computer 10 include training data set 44, decision rules 56, test subject biomarker profile 62, and biomarker database 68.
- Each of these data structures can comprise any form of data storage system including, but not limited to, a flat ASCII or binary file, an Excel spreadsheet, a relational database (SQL), or an on-line analytical processing (OLAP) database (MDX and/or variants thereof).
- data structures are each in the form of one or more databases that include hierarchical structure (e.g., a star schema).
- such data structures are each in the form of databases that do not have explicit hierarchy (e.g., dimension tables that are not hierarchically arranged).
- each of the data structures stored or accessible to system 10 are single data structures.
- such data structures in fact comprise a plurality of data structures (e.g., databases, files, archives) that may or may not all be hosted by the same computer 10.
- training data set 44 comprises a plurality of Excel spreadsheets that are stored either on computer 10 and/or on computers that are addressable by computer 10 across wide area network 34.
- traim ' ng data set 44 comprises a database that is either stored on computer 10 or is distributed across one or more computers that are addressable by computer 10 across wide area network 34.
- biomarker profile evaluation module 60 and/or other modules can reside on a client computer that is in communication with computer 10 via network 34.
- biomarker profile evaluation module 60 can be an interactive web page.
- training data set 44, decision rules 56, and/or biomarker database 68 illustrated in Figure 35 are on a single computer (computer 10) and in other embodiments one or more of such data structures and modules are hosted by one or more remote computers (not shown). Any arrangement of the data structures and software modules illustrated in Figure 35 on one or more computers is within the scope of the present invention so long as these data structures and software modules are addressable with respect to each other across network 34 or by other electronic means. Thus, the present invention fully encompasses a broad array of computer systems.
- Still another kit of the present invention comprises computers and computer readable media for evaluating whether a test subject is likely to develop sepsis or SIRS.
- one embodiment of the present invention provides a computer program product for use in conjunction with a computer system.
- the computer program product comprises a computer readable storage medium and a computer program mechanism embedded therein.
- the computer program mechanism comprises instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis.
- the plurality of features are measurable aspects of a plurality of biomarkers, the plurality of biomarkers comprising at least three biomarkers listed in Table I.
- the plurality of biomarkers comprises at least six biomarkers listed in Table I, wherein the plurality of biomarkers comprises both IL-6 and IL-8.
- the computer program product further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfaction of the second value set predicts that the test subject is not likely to develop sepsis.
- the biomarker profile has between 3 and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listed in Table I, at least four biomarkers listed in Table I, or at least eight biomarkers listed in Table I.
- Another kit of the present invention comprises a central processing unit and a memory coupled to the central processing unit.
- the memory stores instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis.
- the plurality of features are measurable aspects of a plurality of biomarkers. This plurality of biomarkers comprises at least three biomarkers from Table I.
- the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL- 8.
- the memory further stores instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set, wherein satisfying the second value set predicts that the test subject is not likely to develop sepsis.
- the biomarker profile consists of between 3 and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listed in Table I, at least four biomarkers listed in Table I., or at least eight biomarkers listed in Table I.
- kits in accordance with the present invention comprises a computer system for determining whether a subject is likely to develop sepsis.
- the computer system comprises a central processing unit and a memory, coupled to the central processing unit.
- the memory stores instructions for obtaining a biomarker profile of a test subject.
- the biomarker profile comprises a plurality of features. Each feature in the plurality of features is a measurable aspect of a corresponding biomarker in a plurality of biomarkers.
- the plurality of biomarkers comprises at least three biomarkers listed in Table I.
- the memory further comprises instructions for transmitting the biomarker profile to a remote computer.
- the remote computer includes instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a first value set.
- the memory further comprises instructions for receiving a determination, from the remote computer, as to whether the plurality of features in the biomarker profile of the test subject satisfies the first value set.
- the memory also comprises instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the first value set.
- the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL- 8.
- the remote computer further comprises instructions for evaluating whether the plurality of ' features in the biomarker profile of the test subject satisfies a second value set.
- the memory further comprises instructions for receiving a determination, from the remote computer, as to whether the plurality of features in the biomarker profile of the test subject satisfies the second set as well as instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the second value set.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I.
- Still another aspect of the present invention comprises a digital signal embodied on a carrier wave comprising a respective value for each of a plurality of features in a biomarker profile.
- the plurality of features are measurable aspects of a plurality of biomarkers.
- the plurality of biomarkers comprises at least three biomarkers listed in Table I.
- the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I.
- the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- Still another aspect of the present invention provides a digital signal embodied on a carrier wave comprising a determination as to whether a plurality of features in a biomarker profile of a test subject satisfies a value set.
- the plurality of features are measurable aspects of a plurality of biomarkers.
- This plurality of biomarkers comprises at least three biomarkers listed in Table I. Satisfying the value set predicts that the test subject is likely to develop sepsis.
- the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I.
- the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- Still another embodiment provides a digital signal embodied on a carrier wave comprising a determination as to whether a plurality of features in a biomarker profile of a test subject satisfies a value set.
- the plurality of features are measurable aspects of a plurality of biomarkers.
- the plurality of biomarkers comprise at least three biomarkers listed in Table I. Satisfaction of the value set predicts that the test subject is not likely to develop sepsis.
- the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8.
- the plurality of biomarkers comprises at least four biomarkers listed in Table I.
- the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- Still another embodiment of the present invention provides a graphical user interface for determining whether a subject is likely to develop sepsis.
- the graphical user interface comprises a display field for a displaying a result encoded in a digital signal embodied on a carrier wave received from a remote computer.
- the plurality of features are measurable aspects of a plurality of biomarkers.
- the plurality of biomarkers comprise at least three biomarkers listed in Table I.
- the result has a first value when a plurality of features in a biomarker profile of a test subject satisfies a first value set.
- the result has a second value when a plurality of features in a biomarker profile of a test subject satisfies a second value set.
- the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises IL-6 and IL- 8. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- kits of the present invention provides a computer system for determining whether a subject is likely to develop sepsis.
- the computer system comprises a central processing unit and a memory, coupled to the central processing unit.
- the memory stores instructions for obtaining a biomarker profile of a test subject.
- the biomarker profile comprises a plurality of features.
- the plurality of features are measurable aspects of a plurality of biomarkers.
- the plurality of biomarkers comprise at least three biomarkers listed in Table I.
- the memory further stores instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a first value set. Satisfying the first value set predicts that the test subject is likely to develop sepsis.
- the memory also stores instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the first value set.
- the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL- 8. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
- the methods of the present invention comprise generating a biomarker profile from a biological sample taken from a subject.
- the biological sample may be, for example, whole blood, plasma, serum, red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, monocytes, saliva, sputum, urine, cerebral spinal fluid, cells, a cellular extract, a tissue sample, a tissue biopsy, a stool sample or any sample that may be obtained from a subject using techniques well known to those of skill in the art.
- a biomarker profile is determined using samples collected from a subject at one or more separate time points.
- a biomarker profile is generated using samples obtained from a subject at separate time points. In one example, these samples are obtained from the subject either once or, alternatively, on a daily basis, or more frequently, e.g., every 4, 6, 8 or 12 hours. In a specific embodiment, a biomarker profile is determined using samples collected from a single tissue type. In another specific embodiment, a biomarker profile is determined using samples collected from at least two different tissue types.
- biomarkers in a biomarker profile are nucleic acids.
- Such biomarkers and corresponding features of the biomarker profile may be generated, for example, by detecting the expression product (e.g., a polynucleotide or polypeptide) of one or more genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K.).
- the biomarkers and corresponding features in a biomarker profile are obtained by detecting and/or analyzing one or more nucleic acids expressed from a gene disclosed herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K) using any method well known to those skilled in the art including, but by no means limited to, hybridization, microarray analysis, RT-PCR, nuclease protection assays and Northern blot analysis.
- a gene disclosed herein e.g., a gene listed in Table 30, Table I, Table J, or Table K
- any method well known to those skilled in the art including, but by no means limited to, hybridization, microarray analysis, RT-PCR, nuclease protection assays and Northern blot analysis.
- nucleic acids detected and/or analyzed by the methods and compositions of the invention include RNA molecules such as, for example, expressed RNA molecules which include messenger RNA (mRNA) molecules, mRNA spliced variants as well as regulatory RNA, cRNA molecules (e.g., RNA molecules prepared from cDNA molecules that are transcribed in vitro) and discriminating fragments thereof.
- RNA molecules such as, for example, expressed RNA molecules which include messenger RNA (mRNA) molecules, mRNA spliced variants as well as regulatory RNA, cRNA molecules (e.g., RNA molecules prepared from cDNA molecules that are transcribed in vitro) and discriminating fragments thereof.
- Nucleic acids detected and/or analyzed by the methods and compositions of the present invention can also include, for example, DNA molecules such as genomic DNA molecules, cDNA molecules, and discriminating fragments thereof (e.g., oligonucleotides, ESTs, STSs, etc.).
- the nucleic acid molecules detected and/or analyzed by the methods and compositions of the invention may be naturally occurring nucleic acid molecules such as genomic or extragenomic DNA molecules isolated from a sample, or RNA molecules, such as mRNA molecules, present in, isolated from or derived from a biological sample.
- the sample of nucleic acids detected and/or analyzed by the methods and compositions of the invention comprise, e.g., molecules of DNA, RNA, or copolymers of DNA and RNA.
- these nucleic acids correspond to particular genes or alleles of genes, or to particular gene transcripts (e.g., to particular mRNA sequences expressed in specific cell types or to particular cDNA sequences derived from such mRNA sequences).
- the nucleic acids detected and/or analyzed by the methods and compositions of the invention may correspond to different exons of the same gene, e.g., so that different splice variants of that gene may be detected and/or analyzed.
- the nucleic acids are prepared in vitro from nucleic acids present in, or isolated or partially isolated from biological a sample.
- RNA is extracted from a sample (e.g., total cellular RNA, poly(A) + messenger RNA, fraction thereof) and messenger RNA is purified from the total extracted
- RNA is extracted from a sample using guanidinium thiocyanate lysis followed by CsCl centrifugation and an oligo dT purification (Chirgwin et al, 1979, Biochemistry 18:5294- 5299).
- RNA is extracted from a sample using guanidinium thiocyanate lysis followed by purification on RNeasy columns (Qiagen, Valencia, California).
- cDNA is then synthesized from the purified mRNA using, e.g. , oligo-dT or random primers.
- the target nucleic acids are cRNA prepared from purified messenger RNA extracted from a sample.
- cRNA is defined here as RNA complementary to the source RNA.
- the extracted RNAs are amplified using a process in which doubled-stranded cDNAs are synthesized from the RNAs using a primer linked to an RNA polymerase promoter in a direction capable of directing transcription of anti-sense RNA.
- Anti-sense RNAs or cRNAs are then transcribed from the second strand of the double-stranded cDNAs using an RNA polymerase (see, e.g., U.S. Patent Nos. 5,891,636, 5,716,785; 5,545,522 and 6,132,997, which are hereby incorporated by reference). Both oligo-dT primers (U.S. Patent Nos.
- the target nucleic acids are short and/or fragmented nucleic acid molecules which are representative of the original nucleic acid population of the sample.
- nucleic acids of the invention can be detectably labeled.
- cDNA can be labeled directly, e.g., with nucleotide analogs, or indirectly, e.g., by making a second, labeled cDNA strand using the first strand as a template.
- the double-stranded cDNA can be transcribed into cRNA and labeled.
- the detectable label is a fluorescent label, e.g., by incorporation of nucleotide analogs.
- radioactive isotopes include P, S, C, N and I.
- Fluorescent molecules suitable for the present invention include, but are not limited to, fluorescein and its derivatives, rhodamine and its derivatives, Texas red, 5'carboxy-fluorescein (“FMA”), 6- carboxy-4',5'-dichloro-2',7'-dimethoxyfluorescein, succinimidyl ester (“JOE”), 6- carboxytetramethylrhodamine (“TAMRA”), 6Ncarboxy-X-rhodamine (“ROX”), HEX, TET, IRD40, and IRD41.
- FMA 5'carboxy-fluorescein
- TAMRA 6- carboxytetramethylrhodamine
- ROX 6Ncarboxy-X-rhodamine
- Fluorescent molecules that are suitable for the invention further include, but are not limited to: cyamine dyes, including by not limited to Cy3, Cy3.5 and Cy5; BODIPY dyes including but not limited to BODIPY-FL, BODIPY-TR, BODIPY-TMR, BODIPY-630/650, BODIPY-650/670; and ALEXA dyes, including but not limited to ALEXA-488, ALEXA-532, ALEXA-546, ALEXA-568, and ALEXA-594; as well as other fluorescent dyes which will be known to those who are skilled in the art.
- Electron-rich indicator molecules suitable for the present invention include, but are not limited to, ferritin, hemocyanin, and colloidal gold.
- the target nucleic acids may be labeled by specifically complexing a first group to the nucleic acid.
- a second group covalently linked to an indicator molecules and which has an affinity for the first group, can be used to indirectly detect the target nucleic acid.
- compounds suitable for use as a first group include, but are not limited to, biotin and iminobiotin.
- Compounds suitable for use as a second group include, but are not limited to, avidin and streptavidin.
- nucleic acid arrays are employed to generate features of biomarkers in a biomarker profile by detecting the expression of any one or more of the genes described herein (e.g., a gene listed in Table 30, Table I, Table J or Table K).
- a microarray such as a cDNA microarray, is used to determine feature values of biomarkers in a biomarker profile.
- the diagnostic use of cDNA arrays is well known in the art. (See, e.g., Zou et.
- the feature values for biomarkers in a biomarker profile are obtained by hybridizing to the array detectably labeled nucleic acids representing or corresponding to the nucleic acid sequences in mRNA transcripts present in a biological sample (e.g., fluorescently labeled cDNA synthesized from the sample) to a microarray comprising one or more probe spots.
- a biological sample e.g., fluorescently labeled cDNA synthesized from the sample
- Nucleic acid arrays for example, microarrays
- the arrays are reproducible, allowing multiple copies of a given array to be produced and results from said microarrays compared with each other.
- the arrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. Those skilled in the art will know of suitable supports, substrates or carriers for hybridizing test probes to probe spots on an array, or will be able to ascertain the same by use of routine experimentation.
- Arrays, for example, microarrays, used can include one or more test probes.
- each such test probe comprises a nucleic acid sequence that is complementary to a subsequence of RNA or DNA to be detected.
- Each probe typically has a different nucleic acid sequence, and the position of each probe on the solid surface of the array is usually known or can be determined.
- Arrays useful in accordance with the invention can include, for example, oligonucleotide microarrays, cDNA based arrays, SNP arrays, spliced variant arrays and any other array able to provide a qualitative, quantitative or semi-quantitative measurement of expression of a gene described herein (e.g., a gene listed in Table 30, Table I, Table J or Table K).
- Some types of microarrays are addressable arrays.
- microarrays are positionally addressable arrays.
- each probe of the array is located at a known, predetermined position on the solid support so that the identity (e.g., the sequence) of each probe can be determined from its position on the array (e.g., on the support or surface).
- the arrays are ordered arrays. Microarrays are generally described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, which is hereby incorporated herein by reference in its entirety.
- an expressed transcript (e.g. , a transcript of a gene described herein) is represented in the nucleic acid arrays.
- a set of binding sites can include probes with different nucleic acids that are complementary to different sequence segments of the expressed transcript.
- Exemplary nucleic acids that fall within this class can be of length of 15 to 200 bases, 20 to 100 bases, 25 to 50 bases, 40 to 60 bases or some other range of bases.
- Each probe sequence can also comprise one or more linker sequences in addition to the sequence that is complementary to its target sequence.
- a linker sequence is a sequence between the sequence that is complementary to its target sequence and the surface of support.
- the nucleic acid arrays of the invention can comprise one probe specific to each target gene or exon.
- the nucleic acid arrays can contain at least 2, 5, 10, 100, or 1000 or more probes specific to some expressed transcript (e.g., a transcript of a gene described herein, e.g., in Table 30, Table I, Table J, or Table K).
- the array may contain probes tiled across the sequence of the longest mRNA isoform of a gene.
- RNA complementary to the RNA of a cell for example, a cell in a biological sample
- the level of hybridization to the site in the array corresponding to a gene described herein e.g., a gene listed in Table 30, Table I, Table J, or Table K
- a gene described herein e.g., a gene listed in Table 30, Table I, Table J, or Table K
- detectably labeled (e.g., with a fluorophore) cDNA complementary to the total cellular mRNA can be hybridized to a microarray, and the site on the array corresponding to an exon of the gene that is not transcribed or is removed during RNA splicing in the cell will have little or no signal (e.g., fluorescent signal), and a site corresponding to an exon of a gene for which the encoded mRNA expressing the exon is prevalent will have a relatively strong signal.
- the relative abundance of different mRNAs produced from the same gene by alternative splicing is then determined by the signal strength pattern across the whole set of exons monitored for the gene.
- hybridization levels at different hybridization times are measured separately on different, identical microarrays.
- the microarray is washed briefly, preferably in room temperature in an aqueous solution of high to moderate salt concentration (e.g., 0.5 to 3 M salt concentration) under conditions which retain all bound or hybridized nucleic acids while removing all unbound nucleic acids.
- the detectable label on the remaining, hybridized nucleic acid molecules on each probe is then measured by a method which is appropriate to the particular labeling method used.
- the resulting hybridization levels are then combined to form a hybridization curve.
- hybridization levels are measured in real time using a single microarray.
- the microarray is allowed to hybridize to the sample without interruption and the microarray is interrogated at each hybridization time in a non-invasive manner.
- one can use one array hybridize for a short time, wash and measure the hybridization level, put back to the same sample, hybridize for another period of time, wash and measure again to get the hybridization time curve.
- nucleic acid hybridization and wash conditions are chosen so that the nucleic acid biomarkers to be analyzed specifically bind or specifically hybridize to the complementary nucleic acid sequences of the array, typically to a specific array site, where its complementary DNA is located.
- Arrays containing double-stranded probe DNA situated thereon can be subjected to denaturing conditions to render the DNA single-stranded prior to contacting with the target nucleic acid molecules.
- Arrays containing single-stranded probe DNA may need to be denatured prior to contacting with the target nucleic acid molecules, e.g., to remove hairpins or dimers which form due to self complementary sequences.
- Optimal hybridization conditions will depend on the length (e.g. , oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids.
- length e.g. , oligomer versus polynucleotide greater than 200 bases
- type e.g., RNA, or DNA
- Specific hybridization conditions for nucleic acids are described in Sambrook et al, (supra), and in Ausubel et al, 1988, Current Protocols in Molecular Biology, Greene Publishing and Wiley-Interscience, New York.
- a microarray can be used to sort out RT-PCR products that have been generated by the methods described, for example, below in Section
- the level of expression of one or more of the genes described herein is measured by amplifying RNA from a sample using reverse transcription (RT) in combination with the polymerase chain reaction (PCR).
- RT reverse transcription
- PCR polymerase chain reaction
- the reverse transcription may be quantitative or semi-quantitative.
- the RT-PCR methods taught herein may be used in conjunction with the microarray methods described above, for example, in Section 5.4.1.1. For example, a bulk PCR reaction may be performed, the PCR products may be resolved and used as probe spots on a microarray. See also Section 6.10, infra.
- RNA, or niRNA from a sample is used as a template and a primer specific to the transcribed portion of the gene(s) is used to initiate reverse transcription.
- Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 2001, supra.
- Primer design can be accomplished based on known nucleotide sequences that have been published or available from any publicly available sequence database such as GenBank.
- primers may be designed for any of the genes described herein (see, e.g., in Table 30, Table I, Table J, or Table K). Further, primer design may be accomplished by utilizing commercially available software (e.g., Primer Designer 1.0, Scientific Software etc.). The product of the reverse transcription is subsequently used as a template for PCR.
- PCR provides a method for rapidly amplifying a particular nucleic acid sequence by using multiple cycles of DNA replication catalyzed by a thermostable, DNA-dependent DNA polymerase to amplify the target sequence of interest.
- PCR requires the presence of a nucleic acid to be amplified, two single-stranded oligonucleotide primers flanking the sequence to be amplified, a DNA polymerase, deoxyribonucleoside triphosphates, a buffer and salts.
- the method of PCR is well known in the art. PCR, is performed, for example, as described in Mullis and Faloona, 1987, Methods Enzymol. 155:335, which is hereby incorporated herein by reference in its entirety.
- PCR can be performed using template DNA or cDNA (at least lfg; more usefully, 1-1000 ng) and at least 25 pmol of oligonucleotide primers.
- a typical reaction mixture includes: 2 ⁇ l of DNA, 25 pmol of oligonucleotide primer, 2.5 ⁇ l of 10 M PCR buffer 1 (Perkin-Elmer, Foster City, CA), 0.4 ⁇ l of 1.25 M dNTP, 0.15 ⁇ l (or 2.5 units) of Taq DNA polymerase (Perkin Elmer, Foster City, CA) and deionized water to a total volume of 25 ⁇ l.
- Mineral oil is overlaid and the PCR is performed using a programmable thermal cycler.
- QRT-PCR Quantitative RT-PCR
- RNA-specific antisense probe is performed with a transcript-specific antisense probe.
- This probe is specific for the PCR product (e.g. a nucleic acid fragment derived from a gene) and is prepared with a quencher and fluorescent reporter probe complexed to the 5' end of the oligonucleotide.
- Different fluorescent markers are attached to different reporters, allowing for measurement of two products in one reaction.
- Taq DNA polymerase When Taq DNA polymerase is activated, it cleaves off the fluorescent reporters of the probe bound to the template by virtue of its 5'-to-3' exonuclease activity.
- the reporters now fluoresce.
- the color change in the reporters is proportional to the amount of each specific product and is measured by a fluorometer; therefore, the amount of each color is measured and the PCR product is quantified.
- the PCR reactions are performed in 96-well plates so that samples derived from many individuals are processed and measured simultaneously.
- the Taqman system has the additional advantage of not requiring gel electrophoresis and allows for quantification when used with a standard curve.
- a second technique useful for detecting PCR products quantitatively is to use an intercolating dye such as the commercially available QuantiTect SYBR Green PCR (Qiagen, Valencia California).
- RT-PCR is performed using SYBR green as a fluorescent label which is incorporated into the PCR product during the PCR stage and produces a flourescense proportional to the amount of PCR product.
- Both Taqman and QuantiTect SYBR systems can be used subsequent to reverse transcription of RNA. Reverse transcription can either be performed in the same reaction mixture as the PCR step (one-step protocol) or reverse transcription can be performed first prior to amplification utilizing PCR (two-step protocol).
- Reverse transcription can either be performed in the same reaction mixture as the PCR step (one-step protocol) or reverse transcription can be performed first prior to amplification utilizing PCR (two-step protocol).
- other systems to quantitatively measure rnRNA expression products are known including Molecular Beacons ® which uses a probe having a fluorescent molecule and a quencher molecule, the probe capable of forming a hairpin structure such that when in the hairpin form, the fluorescence molecule is quenched, and when hybridized the fluorescence increases giving a quantitative measurement of gene expression.
- RNA expression includes, but are not limited to, polymerase chain reaction, ligase chain reaction, Qbeta replicase (see, e.g., International Application No. PCT/US87/00880, which is hereby incorporated by reference), isothermal amplification method (see, e.g., Walker et al.,1992, PNAS 89:382- 396, which is hereby incorporated herein by reference), strand displacement amplification (SDA), repair chain reaction, Asymmetric Quantitative PCR (see, e.g., U.S. Publication No. US 2003/30134307A1, herein incorporated by reference) and the multiplex microsphere bead assay described in Fuja et ah, 2004, Journal of Biotechnology 108:193-205, herein incorporated by reference.
- polymerase chain reaction see, e.g., ligase chain reaction, Qbeta replicase (see, e.g., International Application No. PCT/US87/008
- the level of expression of one or more of the genes described herein can, for example, be measured by amplifying RNA from a sample using amplification (NASBA).
- NASBA amplification
- the nucleic acids may be prepared for amplification using conventional methods, e.g., phenol/chloroform extraction, heat denaturation, treatment with lysis buffer and minispin columns for isolation of DNA and RNA or guanidinium chloride extraction of RNA.
- amplification techniques involve annealing a primer that has target specific sequences.
- DNA/RNA hybrids are digested with RNase H while double stranded DNA molecules are heat denatured again. In either case the single stranded DNA is made fully double stranded by addition of second target specific primer, followed by polymerization.
- the double-stranded DNA molecules are then multiply transcribed by a polymerase such as T7 or SP6.
- RNA' s are reverse transcribed into double stranded DNA, and transcribed once with a polymerase such as T7 or SP6.
- the resulting products whether truncated or complete, indicate target specific sequences.
- amplification products may be separated by agarose, agarose-acrylamide or polyacrylamide gel electrophoresis using conventional methods. See Sambrook et ah, 2001.
- chromatographic techniques may be employed to effect separation.
- chromatography There are many kinds of chromatography which may be used in the present invention: adsorption, partition, ion-exchange and molecular sieve, HPLC, and many specialized techniques for using them including column, paper, thin-layer and gas chromatography (Freifelder, Physical Biochemistry Applications to Biochemistry and Molecular Biology, 2nd ed., Wm. Freeman and Co., New York, N. Y., 1982, which is hereby incorporated by reference).
- Another example of a separation methodology is to covalently label the oligonucleotide primers used in a PCR reaction with various types of small molecule ligands.
- a different ligand is present on each oligonucleotide.
- a molecule, perhaps an antibody or avidin if the ligand is biotin, that specifically binds to one of the ligands is used to coat the surface of a plate such as a 96 well ELISA plate.
- the PCR products are bound with specificity to the surface.
- a solution containing a second molecule that binds to the first ligand is added.
- This second molecule is linked to some kind of reporter system.
- the second molecule only binds to the plate if a PCR product has been produced whereby both oligonucleotide primers are incorporated into the final PCR products.
- the amount of the PCR product is then detected and quantified in a commercial plate reader much as ELISA reactions are detected and quantified.
- An ELISA-like system such as the one described here has been developed by Raggio Italgene (under the C-Track tradename.
- Amplification products should be visualized in order to confirm amplification of the nucleic acid sequences of interest, i.e., nucleic acid sequences of one or more of the genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K).
- One typical visualization method involves staining of a gel with ethidium bromide and visualization under UV light.
- the amplification products may then be exposed to x-ray film or visualized under the appropriate stimulating spectra, following separation.
- a labeled, nucleic acid probe is brought into contact with the amplified nucleic acid sequence of interest, i.e., nucleic acid sequences of one or more of the genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K).
- the probe preferably is conjugated to a chromophore but may be radiolabeled.
- the probe is conjugated to a binding partner, such as an antibody or biotin, where the other member of the binding pair carries a detectable moiety.
- detection is by Southern blotting and hybridization with a labeled probe.
- nuclease protection assays including both ribonuclease protection assays and Sl nuclease assays
- mRNAs e.g., niRNAs of a gene described in Table 30, Table I 5 Table J, or Table K.
- an antisense probe hybridizes in solution to an RNA sample.
- RNA Following hybridization, single-stranded, unhybridized probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments.
- solution hybridization is more efficient than membrane- based hybridization,- and it can accommodate up to 100 ⁇ g of sample RNA, compared with the 20-30 ⁇ g maximum of blot hybridizations.
- RNA probes Oligonucleotides and other single-stranded DNA probes can only be used in assays containing Sl nuclease.
- the single- stranded, antisense probe must typically be completely homologous to target RNA to prevent cleavage of the probe:target hybrid by nuclease.
- RNAs of a gene described in Table 30, Table I, Table J, or Table K can be detected by Northern blot analysis.
- a standard Northern blot assay can be used to ascertain an RNA transcript size, identify alternatively spliced RNA transcripts, and the relative amounts of one or more genes described herein (in particular, mRNA) in a sample, in accordance with conventional Northern hybridization techniques known to those persons of ordinary skill in the art.
- RNA samples are first separated by size via electrophoresis in an agarose gel under denaturing conditions. The RNA is then transferred to a membrane, crosslinked and hybridized with a labeled probe.
- Nonisotopic or high specific activity radiolabeled probes can be used including random- primed, nick-translated, or PCR-generated DNA probes, in vitro transcribed RNA probes, and oligonucleotides. Additionally, sequences with only partial homology (e.g., cDNA from a different species or genomic DNA fragments that might contain an exon) may be used as probes.
- the labeled probe e.g., a radiolabeled cDNA, either containing the full-length, single stranded DNA or a fragment of that DNA sequence may be at least 20, at least 30, at least 50, or at least 100 consecutive nucleotides in length.
- the probe can be labeled by any of the many different methods known to those skilled in this art.
- the labels most commonly employed for these studies are radioactive elements, enzymes, chemicals that fluoresce when exposed to ultraviolet light, and others.
- a number of fluorescent materials are known and can be utilized as labels. These include, but are not limited to, fluorescein, rhodamine, auramine, Texas Red, AMCA blue and Lucifer Yellow.
- the radioactive label can be detected by any of the currently available counting procedures.
- Non-limiting examples of isotopes include 3 H, 14 C, 32 P, 35 S, 36 Cl, 51 Cr, 57 Co, 58 Co, 59 Fe 5 90 Y, 125 1, 131 I, and 186 Re.
- Enzyme labels are likewise useful, and can be detected by any of the presently utilized colorimetric, spectrophotometric, fluorospectrophotometric, amperometric or gasometric techniques.
- the enzyme is conjugated to the selected particle by reaction with bridging molecules such as carbodiimides, diisocyanates, glutaraldehyde and the like. Any enzymes known to one of skill in the art can be utilized.
- enzymes include, but are not limited to, peroxidase, beta-D-galactosidase, urease, glucose oxidase plus peroxidase and alkaline phosphatase.
- U.S. Patent Nos. 3,654,090, 3,850,752, and 4,016,043 are referred to by way of example for their disclosure of alternate labeling material and methods.
- feature values of biomarkers in a biomarker profile can be obtained by detecting proteins, for example, by detecting the expression product (e.g., a nucleic acid or protein) of one or more genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K), or post-translationally modified, or otherwise modified, or processed forms of such proteins.
- the expression product e.g., a nucleic acid or protein
- a biomarker profile is generated by detecting and/or analyzing one or more proteins and/or discriminating fragments thereof expressed from a gene disclosed herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K) using any method known to those skilled in the art for detecting proteins including, but not limited to protein microarray analysis, immunohistochemistry and mass spectrometry.
- Standard techniques may be utilized for determining the amount of the protein or proteins of interest (e.g., proteins expressed from genes listed in Table 30, Table I, Table J, or Table K) present in a sample.
- standard techniques can be employed using, e.g., immunoassays such as, for example Western blot, immunoprecipitation followed by sodium dodecyl sulfate polyacrylamide gel electrophoresis, (SDS-PAGE), immunocytochemistry, and the like to determine the amount of protein or proteins of interest present in a sample.
- One exemplary agent for detecting a protein of interest is an antibody capable of specifically binding to a protein of interest, preferably an antibody detectably labeled, either directly or indirectly.
- a protein from the sample to be analyzed can easily be isolated using techniques which are well known to those of skill in the art. Protein isolation methods can, for example, be such as those described in Harlow and Lane, 1988, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press (Cold Spring Harbor, New York), which is incorporated by reference herein in its entirety.
- methods of detection of the protein or proteins of interest involve their detection via interaction with a protein-specific antibody.
- antibodies directed to a protein of interest e.g., a protein expressed from a gene described herein, e.g., a protein listed in Table 30, Table I, Table J, or Table K).
- Antibodies can be generated utilizing standard techniques well known to those of skill in the art.
- antibodies can be polyclonal, or more preferably, monoclonal.
- An intact antibody, or an antibody fragment ⁇ e.g., scFv, Fab or F(ab') 2 ) can, for example, be used.
- antibodies, or fragments of antibodies, specific for a protein of interest can be used to quantitatively or qualitatively detect the presence of a protein. This can be accomplished, for example, by immunofluorescence techniques. Antibodies (or fragments thereof) can, additionally, be employed histologically, as in immunofluorescence or immunoelectron microscopy, for in situ detection of a protein of interest. In situ detection can be accomplished by removing a biological sample ⁇ e.g., a biopsy specimen) from a patient, and applying thereto a labeled antibody that is directed to a protein of interest ⁇ e.g., a protein expressed from a gene in Table 30, Table I, Table J, or Table K).
- the antibody (or fragment) is preferably applied by overlaying the antibody (or fragment ) onto a biological sample.
- a biological sample Through the use of such a procedure, it is possible to determine not only the presence of the protein of interest, but also its distribution, in a particular sample.
- histological methods such as staining procedures
- Immunoassays for a protein of interest typically comprise incubating a biological sample of a detectably labeled antibody capable of identifying a protein of interest, and detecting the bound antibody by any of a number of techniques well-known in the art.
- labeled can refer to direct labeling of the antibody via, e.g., coupling ⁇ i.e., physically linking) a detectable substance to the antibody, and can also refer to indirect labeling of the antibody by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of a primary antibody using a fluorescently labeled secondary antibody.
- the biological sample can be brought in contact with and immobilized onto a solid phase support or carrier such as nitrocellulose, or other solid support which is capable of immobilizing cells, cell particles or soluble proteins.
- a solid phase support or carrier such as nitrocellulose, or other solid support which is capable of immobilizing cells, cell particles or soluble proteins.
- the support can then be washed with suitable buffers followed by treatment with the detectably labeled fingerprint gene-specific antibody.
- the solid phase support can then be washed with the buffer a second time to remove unbound antibody.
- the amount of bound label on solid support can then be detected by conventional methods.
- solid phase support or carrier any support capable of binding an antigen or an antibody.
- supports or carriers include glass, polystyrene, polypropylene, polyethylene, dextran, nylon, amylases, natural and modified celluloses, polyacrylamides and magnetite.
- the nature of the carrier can be either soluble to some extent or insoluble for the purposes of the present invention.
- the support material can have virtually any possible structural configuration so long as the coupled molecule is capable of binding to an antigen or antibody.
- the support configuration can be spherical, as in a bead, or cylindrical, as in the inside surface of a test tube, or the external surface of a rod.
- the surface can be flat such as a sheet, test strip, etc.
- Preferred supports include polystyrene beads. Those skilled in the art will know many other suitable carriers for binding antibody or antigen, or will be able to ascertain the same by use of routine experimentation.
- EIA enzyme immunoassay
- the enzyme which is bound to the antibody will react with an appropriate substrate, preferably a chromogenic substrate, in such a manner as to produce a chemical moiety which can be detected, for example, by spectrophotometric, fluorimetric or by visual means.
- Enzymes which can be used to detectably label the antibody include, but are not limited to, malate dehydrogenase, staphylococcal nuclease, delta-5-steroid isomerase, yeast alcohol dehydrogenase, alpha-glycerophosphate, dehydrogenase, triose phosphate isomerase, horseradish peroxidase, alkaline phosphatase, asparaginase, glucose oxidase, beta-galactosidase, ribonuclease, urease, catalase, glucose-6-phosphate dehydrogenase, glucoamylase and acetylcholinesterase.
- the detection can be accomplished by colorimetric methods which employ a chromogenic substrate for the enzyme. Detection can also be accomplished by visual comparison of the extent of enzymatic reaction of a substrate in comparison with similarly prepared standards.
- Detection can also be accomplished using any of a variety of other immunoassays.
- a radioimmunoassay RIA
- the radioactive isotope ⁇ e.g., 125 1, 131 1, 35 S or 3 H
- a gamma counter or a scintillation counter can be detected by such means as the use of a gamma counter or a scintillation counter or by autoradiography.
- fluorescent labeling compounds fluorescein isothiocyanate, rhodamine, phycoerythrin, phycocyanin, allophycocyanin, o-phthaldehyde and fluorescamine.
- the antibody can also be detectably labeled using fluorescence emitting metals such as 152 Eu, or others of the lanthanide series. These metals can be attached to the antibody using such metal chelating groups as diethylenetriarninepentacetic acid (DTPA) or ethylenediaminetetraacetic acid (EDTA).
- DTPA diethylenetriarninepentacetic acid
- EDTA ethylenediaminetetraacetic acid
- the antibody also can be detectably labeled by coupling it to a chemiluminescent compound.
- the presence of the chemiluminescent-tagged antibody is then determined by detecting the presence of luminescence that arises during the course of a chemical reaction.
- chemiluminescent labeling compounds are luminol, isoluminol, theromatic acridinium ester, imidazole, acridinium salt and oxalate ester.
- Bioluminescence is a type of chemiluminescence found in biological systems in, which a catalytic protein increases the efficiency of the chemiluminescent reaction. The presence of a bioluminescent protein is determined by detecting the presence of luminescence. Important bioluminescent compounds for purposes of labeling are luciferin, luciferase and aequorin.
- biomarker profile may comprise a measurable aspect of an infectious agent (e.g., lipopolysaccharides or viral proteins) or a component thereof.
- an infectious agent e.g., lipopolysaccharides or viral proteins
- a protein chip assay e.g. , The ProteinChip ®
- Biomarker System Ciphergen, Fremont, California
- Ciphergen Ciphergen, Fremont, California
- a bead assay is used to measure feature values for the biomarkers in the biomarker profile.
- One such bead assay is the Becton Dickinson Cytometric Bead Array (CBA).
- CBA employs a series of particles with discrete fluorescence intensities to simultaneously detect multiple soluble analytes.
- CBA is combined with flow cytometry to create a multiplexed assay.
- the Becton Dickinson CBA system as embodied for example in the Becton Dickinson Human Inflammation Kit, uses the sensitivity of amplified fluorescence detection by flow cytometry to measure soluble analytes in a particle-based immunoassay.
- Each bead in a CBA provides a capture surface for a specific protein and is analogous to an individually coated well in an ELISA plate.
- the BD CBA capture bead mixture is in suspension to allow for the detection of multiple analytes in a small volume sample.
- the multiplex analysis method described in U.S. Pat. No. 5,981,180 (“the '180 patent”), herein incorporated by reference in its entirety, and in particular for its teachings of the general methodology, bead technology, system hardware and antibody detection, is used to measure feature values for the biomarkers in a biomarker profile.
- a matrix of microparticles is synthesized, where the matrix consists of different sets of microparticles.
- Each set of microparticles can have thousands of molecules of a distinct antibody capture reagent immobilized on the microparticle surface and can be color-coded by incorporation of varying amounts of two fluorescent dyes.
- the ratio of the two fluorescent dyes provides a distinct emission spectrum for each set of microparticles, allowing the identification of a microparticle a set following the pooling of the various sets of microparticles.
- U.S. Pat. Nos. 6,268,222 and 6,599,331 also are incorporated herein by reference in their entirety, and in particular for their teachings of various methods of labeling microparticles for multiplex analysis.
- a separation method may be used determine feature values for biomarkers in a biomarker profile, such that only a subset of biomarkers within the sample is analyzed.
- the biomarkers that are analyzed in a sample may be mRNA species from a cellular extract which has been fractionated to obtain only the nucleic acid biomarkers within the sample, or the biomarkers may be from a fraction of the total complement of proteins within the sample, which have been fractionated by chromatographic techniques.
- Feature values for biomarkers in a biomarker profile can also, for example, be generated by the use of one or more of the following methods described below.
- methods may include nuclear magnetic resonance (NMR) spectroscopy, a mass spectrometry method, such as electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS) n (n is an integer greater than zero), matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(NMR) spect
- mass spectrometry methods may include, inter alia, quadrupole, Fourier transform mass spectrometry (FTMS) and ion trap.
- suitable methods may include chemical extraction partitioning, column chromatography, ion exchange chromatography, hydrophobic (reverse phase) liquid chromatography, isoelectric focusing, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-P AGE) or other chromatography, such as thin-layer, gas or liquid chromatography, or any combination thereof.
- the biological sample may be fractionated prior to application of the separation method.
- laser desorption/ionization time-of-flight mass spectrometry is used to create determine feature values in a biomarker profile where the biomarkers are proteins or protein fragments that have been ionized and vaporized off an immobilizing support by incident laser radiation and the feature values are the presence or absence of peaks representing these fragments in the mass spectra profile.
- a variety of laser desorption/ionization techniques are known in the art (see, e.g., Guttman et ah, 2001, Anal. Chem. 73:1252-62 and Wei et ah, 1999, Nature 399:243-246, each of which is hereby incorporated by herein be reference in its entirety).
- Laser desorption/ionization time-of-flight mass spectrometry allows the generation of large amounts of information in a relatively short period of time.
- a biological sample is applied to one of several varieties of a support that binds all of the biomarkers, or a subset thereof, in the sample.
- Cell lysates or samples are directly applied to these surfaces in volumes as small as 0.5 ⁇ L, with or without prior purification or fractionation.
- the lysates or sample can be concentrated or diluted prior to application onto the support surface.
- Laser desorption/ionization is then used to generate mass spectra of the sample, or samples, in as little as three hours.
- Biomarkers whose corresponding feature values are capable of discriminating between converters and nonconverters are identified in the present invention.
- the identity of these biomarkers and their corresponding features can be used to develop a decision rule, or plurality of decision rules, that discriminate between converters and nonconverters.
- Section 6 below illustrates how data analysis algorithms can be used to construct a number of such decision rules.
- Each of the data analysis algorithms described in Section 6 use features (e.g., expression values) of a subset of the biomarkers identified in the present invention across a training population that includes converters and nonconverters.
- a SIRS subject is considered a nonconverter when the subject does not develop sepsis in a defined time period (e.g., observation period).
- This defined time period can be, for example, twelve hours, twenty four hours, forty-eight hours, a day, a week, a month, or longer.
- Specific data analysis algorithms for building a decision rule, or plurality of decision rules, that discriminate between subjects that develop sepsis and subjects that do not develop sepsis during a defined period will be described in the subsections below.
- the decision rule can be used to classify a test subject into one of the two or more phenotypic classes (e.g., a converter or a nonconverter). This is accomplished by applying the decision rule to a biomarker profile obtained from the test subject.
- phenotypic classes e.g., a converter or a nonconverter.
- the present invention provides, in one aspect, for the evaluation of a biomarker profile from a test subject to biomarker profiles obtained from a training population.
- each biomarker profile obtained from subjects in the training population, as well as the test subject comprises a feature for each of a plurality of different biomarkers.
- this comparison is accomplished by (i) developing a decision rule using the biomarker profiles from the training population and (ii) applying the decision rule to the biomarker profile from the test subject.
- the decision rules applied in some embodiments of the present invention are used to determine whether a test subject having SIRS will or will not likely acquire sepsis.
- the subject when the results of the application of a decision rule indicate that the subject will likely acquire sepsis, the subject is diagnosed as a "sepsis" subject. If the results of an application of a decision rule indicate that the subject will not acquire sepsis, the subject is diagnosed as a "SIRS" subject.
- the result in the above-described binary decision situation has four possible outcomes:
- TP could have been defined as instances where the decision rule indicates that the subject will not acquire sepsis and the subject, in fact, does not acquire sepsis during the definite time period. While all such alternative definitions are within the scope of the present invention, for ease of understanding the present invention, the definitions for TP, FP, TN, and FN given by definitions (i) through (iv) above will be used herein, unless otherwise stated.
- a number of quantitative criteria can be used to communicate the performance of the comparisons made between a test biomarker profile and reference biomarker profiles (e.g., the application of a decision rule to the biomarker profile from a test subject). These include positive predicted value (PPV), negative predicted value (NPV), specificity, sensitivity, accuracy, and certainty. In addition, other constructs such a receiver operator curves (ROC) can be used to evaluate decision rule performance.
- PPV positive predicted value
- NPV negative predicted value
- ROC receiver operator curves
- NPV TN+FN
- N is the number of samples compared (e.g., the number of test samples for which a determination of sepsis or SIRS is sought). For example, consider the case in which there are ten subjects for which SIRS/sepsis classification is sought. Biomarker profiles are constructed for each of the ten test subjects. Then, each of the biomarker profiles is evaluated by applying a decision rule, where the decision rule was developed based upon biomarker profiles obtained from a training population. In this example, N, from the above equations, is equal to 10. Typically, N is a number of samples, where each sample was collected from a different member of a population. This population can, in fact, be of two different types.
- the population comprises subjects whose samples and phenotypic data (e.g., feature values of biomarkers and an indication of whether or not the subject acquired sepsis) was used to construct or refine a decision rule.
- a population is referred to herein as a training population.
- the population comprises subjects that were not used to construct the decision rule.
- Such a population is referred to herein as a validation population.
- the population represented by N is either exclusively a training population or exclusively a validation population, as opposed to a mixture of the two population types. It will be appreciated that scores such as accuracy will be higher (closer to unity) when they are based on a training population as opposed to a validation population.
- all criteria used to assess the performance of a decision rule or other forms of evaluation of a biomarker profile from a test subject
- certainty refer to criteria that were measured by applying the decision rule corresponding to the criteria to either a training population or a validation population.
- the definitions for PPV, NPV, specificity, sensitivity, and accuracy defined above can also be found in Draghici, Data Analysis Tools for DNA Microanalysis, 2003, CRC Press LLC, Boca Raton, Florida, pp. 342-343, which is hereby incorporated herein by reference.
- the training population comprises nonconverters and converters.
- biomarker profiles are constructed from this population using biological samples collected from the training population at some time period prior to the onset of sepsis by the converters of the population.
- a biological sample can be collected two week before, one week before, four days before, three days before, one day before, or any other time period before the converters became septic.
- collections are obtained by collecting a biological sample at regular time intervals after admittance into the hospital with a SIRS diagnosis. For example, in one approach, subjects who have been diagnosed with SIRS in a hospital are used as a training population.
- the biological samples are collected from the subjects at selected times (e.g., hourly, every eight hours, every twelve hours, daily, etc.). A portion of the subjects acquire sepsis and a portion of the subjects do not acquire sepsis.
- the biological sample taken from the subjects just prior to the onset of sepsis are termed the T -12 biological samples. All other biological samples from the subjects are retroactively indexed relative to these biological samples. For instance, when a biological sample has been taken from a subject on a daily basis, the biological sample taken the day before the T. 12 sample is referred to as the T -36 biological sample.
- Time points for biological samples for a nonconverter in the training population are identified by "time-matching" the nonconverter subject with a converter subject.
- T -36 is day four of the study
- the T -36 biological sample is the biological sample that was obtained on day four of the study.
- T -36 for the matched nonconverter subject is deemed to be day four of the study on this paired nonconverter subject.
- N is more than one, more than five, more than ten, more than twenty, between ten and 100, more than 100, or less than 1000 subjects.
- a decision rule (or other forms of comparison) can have at least about 99% certainty, or even more, in some embodiments, against a training population or a validation population. In other embodiments, the certainty is at least about 97%, at least about 95%, at least about 90%, at least about 85%, at least about 80%, at least about 75%, at least about 70%, at least about 65%, or at least about 60% against a training population or a validation population (and therefore against a single subject that is not part of a training population such as a clinical patient).
- the useful degree of certainty may vary, depending on the particular method of the present invention.
- "certainty" means “accuracy.”
- the sensitivity and/or specificity is at is at least about 97%, at least about 95%, at least about 90%, at least about 85%, at least about 80%, at least about 75%, or at least about 70% against a training population or a validation population.
- such decision rules are used to predict the development of sepsis with the stated accuracy.
- such decision rules are used to diagnoses sepsis with the stated accuracy.
- such decision rules are used to determine a stage of sepsis with the stated accuracy.
- the number of features that may be used by a decision rule to classify a test subject with adequate certainty is two or more. In some embodiments, it is three or more, four or more, ten or more, or between 10 and 200. Depending on the degree of certainty sought, however, the number of features used in a decision rule can be more or less, but in all cases is at least two. In one embodiment, the number of features that may be used by a decision rule to classify a test subject is optimized to allow a classification of a test subject with high certainty.
- microarray data abundance data was collected for a plurality of biomarkers in each subject. That is, for each biomarker in a biomarker profile, a feature, microarray abundance data for the biomarker, was measured.
- Decision rules are developed from such biomarker profiles from a training population using data analysis algorithms in order to predict sample phenotypes based on observed gene expression patterns. While new and microarray specific classification tools are constantly being developed, the existing body of pattern recognition and prediction algorithms provide effective data analysis algorithms for constructing decision rules. See, for example, National Research Council; Panel on Discriminant Analysis Classification and Clustering, Discriminant Analysis and Clustering, Washington, D. C: National Academy Press, which is hereby incorporated by reference.
- Relevant data analysis algorithms for developing a decision rule include, but are not limited to, discriminant analysis including linear, logistic, and more flexible discrimination techniques (see, e.g., Gnanadesikan, 1977, Methods for Statistical Data Analysis of Multivariate Observations, New York: Wiley 1977, which is hereby incorporated by reference herein in its entirety); tree-based algorithms such as classification and regression trees (CART) and variants (see, e.g., Breiman, 1984, Classification and Regression Trees, Belmont, California: Wadsworth International Group, which is hereby incorporated by reference herein in its entirety, as well as Section 5.1.3, below); generalized additive models (see, e.g., Tibshirani , 1990, Generalized Additive Models, London: Chapman and Hall, which is hereby incorporated by reference herein in its entirety); and neural networks (see, e.g., Neal, 1996, Bayesian Learning for Neural Networks, New York: Springer-Verlag;
- comparison of a test subject's biomarker profile to a biomarker profiles obtained from a training population is performed, and comprises applying a decision rule.
- the decision rule is constructed using a data analysis algorithm, such as a computer pattern recognition algorithm.
- Other suitable data analysis algorithms for constructing decision rules include, but are not limited to, logistic regression (see Section 5.5.10, below) or a nonparametric algorithm that detects differences in the distribution of feature values (e.g., a Wilcoxon Signed Rank Test (unadjusted and adjusted)).
- the decision rule can be based upon two, three, four, five, 10, 20 or more features, corresponding to measured observables from one, two, three, four, five, 10, 20 or more biomarkers.
- the decision rule is based on hundreds of features or more. Decision rules may also be built using a classification tree algorithm. For example, each biomarker profile from a training population can comprise at least three features, where the features are predictors in a classification tree algorithm (see Section 5.5.1, below).
- the decision rule predicts membership within a population (or class) with an accuracy of at least about at least about 70%, of at least about 75%, of at least about 80%, of at least about 85%, of at least about 90%, of at least about 95%, of at least about 97%, of at least about 98%, of at least about 99%, or about 100%.
- a data analysis algorithm of the invention comprises Classification and Regression Tree (CART; Section 5.5.1, below), Multiple Additive Regression Tree (MART; Section 5.5.4, below), Prediction Analysis for Microarrays (PAM; Section 5.5.2, below) or Random Forest analysis (Section 5.5.1, below).
- CART Classification and Regression Tree
- MART Multiple Additive Regression Tree
- PAM Prediction Analysis for Microarrays
- Random Forest analysis Section 5.5.1, below.
- a data analysis algorithm of the invention comprises ANOVA and nonparametric equivalents, linear discriminant analysis (Section 5.5.10, below), logistic regression analysis (Section 5.5.10, below), nearest neighbor classifier analysis (Section 5.5.9, below), neural networks (Section 5.5.6, below), principal component analysis (Section 5.5.8, below), quadratic discriminant analysis (Section 5.5.11, below), regression classifiers (Section 5.5.5, below) and support vector machines (Section 5.5.12, below).
- Decision rules can be used to evaluate biomarker profiles, regardless of the method that was used to generate the biomarker profile.
- suitable decision rules that can be used to evaluate biomarker profiles generated using gas chromatography, as discussed in Harper, "Pyrolysis and GC in Polymer Analysis,” Dekker, New York (1985). Further, Wagner et al, 2002, Anal. Chem.
- One type of decision rule that can be constructed using the feature values of the biomarkers identified in the present invention is a decision tree.
- the "data analysis algorithm” is any technique that can build the decision tree
- the final “decision tree” is the decision rule.
- a decision tree is constructed using a training population and specific data analysis algorithms. Decision trees are described generally by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 395-396, which is hereby incorporated by reference. Tree-based methods partition the feature space into a set of rectangles, and then fit a model (like a constant) in each one.
- the training population data includes the features ⁇ e.g., expression values, or some other observable) for the biomarkers of the present invention across a training set population.
- One specific algorithm that can be used to construct a decision tree is a classification and regression tree (CART).
- Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and Random Forests. CART, ID3, and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 396-408 and pp. 411-412, which is hereby incorporated by reference.
- decision trees are used to classify subjects using features for combinations of biomarkers of the present invention.
- Decision tree algorithms belong to the class of supervised learning algorithms.
- the aim of a decision tree is to induce a classifier (a tree) from real- world example data. This tree can be used to classify unseen examples that have not been used to derive the decision tree.
- a decision tree is derived from training data.
- Exemplary training data contains data for a plurality of subjects (the training population). For each respective subject there is a plurality of features the class of the respective subject ⁇ e.g., sepsis / SIRS).
- the training data is expression data for a combination of biomarkers across the training population.
- Tree(Examples,Class,Features) Create a root node
- the I- value shows how much information we need in order to be able to describe the outcome of a classification for the specific dataset used. Supposing that the dataset contains p positive (e.g. will develop sepsis) and n negative (e.g. will not develop sepsis) examples (e.g. subjects), the information contained in a correct answer is:
- v is the number of unique attribute values for feature A in a certain dataset
- i is a certain attribute value
- pi is the number of examples for feature A where the classification is positive (e.g. will develop sepsis)
- n is the number of examples for feature A where the classification is negative (e.g. will not develop sepsis).
- the information gain of a specific feature A is calculated as the difference between the information content for the classes and the remainder of feature A:
- the information gain is used to evaluate how important the different features are for the classification (how well they split up the examples), and the feature with the highest information.
- decision tree algorithms In general there are a number of different decision tree algorithms, many of which are described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc. Decision tree algorithms often require consideration of feature processing, impurity measure, stopping criterion, and pruning. Specific decision tree algorithms include, but are not limited to classification and regression trees (CART), multivariate decision trees, ID3, and C4.5.
- the gene expression data for a select combination of genes described in the present invention across a training population is standardized to have mean zero and unit variance.
- the members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set.
- the expression values for a select combination of biomarkers described in the present invention is used to construct the decision tree. Then, the ability for the decision tree to correctly classify members in the test set is determined. In some embodiments, this computation is performed several times for a given combination of biomarkers. In each computational iteration, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of biomarkers is taken as the average of each such iteration of the decision tree computation.
- multivariate decision trees can be implemented as a decision rule.
- some or all of the decisions actually comprise a linear combination of feature values for a plurality of biomarkers of the present invention.
- Such a linear combination can be trained using known techniques such as gradient descent on a classification or by the use of a sum-squared-error criterion. To illustrate such a decision tree, consider the expression:
- X 1 and x 2 refer to two different features for two different biomarkers from among the biomarkers of the present invention.
- the values of features X 1 and X 2 are obtained from the measurements obtained from the unclassified subject. These values are then inserted into the equation. If a value of less than 500 is computed, then a first branch in the decision tree is taken. Otherwise, a second branch in the decision tree is taken. Multivariate decision trees are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 408-409, which is hereby incorporated by reference.
- MARS multivariate adaptive regression splines
- MARS is an adaptive procedure for regression, and is well suited for the high-dimensional problems addressed by the present invention.
- MARS can be viewed as a generalization of stepwise linear regression or a modification of the CART method to improve the performance of CART in the regression setting.
- MARS is described in Hastie et ah, 2001, The Elements of Statistical Learning, Springer-Verlag, New York, pp. 283-295, which is hereby incorporated by reference in its entirety.
- PAM Predictive analysis of microarrays
- One approach to developing a decision rule using feature values of biomarkers of the present invention is the nearest centroid classifier.
- Such a technique computes, for each class (sepsis and SIRS), a centroid given by the average feature levels of the biomarkers in the class, and then assigns new samples to the class whose centroid is nearest.
- This approach is similar to k-means clustering except clusters are replaced by known classes. This algorithm can be sensitive to noise when a large number of biomarkers are used.
- One enhancement to the technique uses shrinkage: for each biomarker, differences between class centroids are set to zero if they are deemed likely to be due to chance.
- Bagging, boosting, the random subspace method, and additive trees are data analysis algorithms known as combining techniques that can be used to improve weak decision rules. These techniques are designed for, and usually applied to, decision trees, such as the decision trees described in Section 5.5.1, above. In addition, such techniques can also be useful in decision rules developed using other types of data analysis algorithms such as linear discriminant analysis.
- phenotype 1 ⁇ e.g., acquiring sepsis during a defined time periond
- phenotype 2 e.g., SIRS only, meaning that the subject does acquire sepsis within a defined time period.
- a decision rule G(X) produces a prediction taking one of the type values in the two value set: ⁇ phenotype 1, phenotype 2 ⁇ .
- the error rate on the training sample is
- N is the number of subjects in the training set (the sum total of the subjects that have either phenotype 1 or phenotype 2). For example, if there are 49 organisms that acquire sepsis and 72 organisms that remain in the SIRS state, N is 121.
- ⁇ ls ⁇ 2 , ..., ⁇ M are computed by the boosting algorithm and their purpose is to weigh the contribution of each respective decision rule Gm(x). Their effect is to give higher influence to the more accurate decision rules in the sequence.
- the data modifications at each boosting step consist of applying weights W 1 ,
- each object is, in fact, a factor.
- the current decision rule G m (x) is induced on the weighted observations at line 2a.
- the resulting weighted error rate is computed at line 2b.
- Line 2c calculates the weight ⁇ m given to G m (x) in producing the final classifier G(x) (line 3).
- the individual weights of each of the observations are updated for the next iteration at line 2d.
- Observations misclassified by G m (x) have their weights scaled by a factor exp( ⁇ m ), increasing their relative influence for inducing the next classifier G m +l(x) in the sequence.
- modifications of the Freund and Schapire, 1997, Journal of Computer and System Sciences 55, pp. 119-139, boosting methods are used. See, for example, Hasti et ah, The Elements of Statistical Learning, 2001, Springer, New York, Chapter 10, which is hereby incorporated by reference in its entirety.
- feature preselection is performed using a technique such as the nonparametric scoring methods of Park et ah, 2002, Pac. Symp. Biocomput. 6, 52-63, which is hereby incorporated by reference in its entirety.
- Feature preselection is a form of dimensionality reduction in which the genes that discriminate between classifications the best are selected for use in the classifier.
- the LogitBoost procedure introduced by Friedman et ah, 2000, Ann Stat 28, 337-407 is used rather than the boosting procedure of Freund and Schapire.
- the boosting and other classification methods of Ben-Dor et ah, 2000, Journal of Computational Biology 7, 559-583, hereby incorporated by reference in its entirety are used in the present invention.
- the boosting and other classification methods of Freund and Schapire, 1997, Journal of Computer and System Sciences 55, 119-139, hereby incorporated by reference in its entirety are used.
- decision rules are constructed in random subspaces of the data feature space. These decision rules are usually combined by simple majority voting in the final decision rule.
- MART Multiple additive regression trees
- the first line of the algorithm initializes to the optimal constant model, which is just a single terminal node tree.
- the components of the negative gradient computed in line 2(a) are referred to as generalized pseudo residuals, r.
- Gradients for commonly used loss functions are summarized in Table 10.2, of Hastie et ah, 2001, The Elements of Statistical Learning, Springer- Verlag, New York, p. 321, which is hereby incorporated by reference.
- the algorithm for classification is similar and is described in Hastie et al, Chapter 10, which is hereby incorporated by reference in its entirety.
- a decision rule used to classify subjects is built using regression.
- the decision rule can be characterized as a regression classifier, preferably a logistic regression classifier.
- a regression classifier includes a coefficient for each of the biomarkers (e.g., a feature for each such biomarker) used to construct the classifier.
- the coefficients for the regression classifier are computed using, for example, a maximum likelihood approach. In such a computation, the features for the biomarkers (e.g., RT-PCR, microarray data) is used.
- molecular marker data from only two trait subgroups is used (e.g., trait subgroup a: will acquire sepsis in a defined time period and trait subgroup b: will not acquire sepsis in a defined time period) and the dependent variable is absence or presence of a particular trait in the subjects for which biomarker data is available.
- the training population comprises a plurality of trait subgroups (e.g., three or more trait subgroups, four or more specific trait subgroups, etc.). These multiple trait subgroups can correspond to discrete stages in the phenotypic progression from healthy, to SIRS, to sepsis, to more advanced stages of sepsis in a training population.
- a generalization of the logistic regression model that handles multicategory responses can be used to develop a decision that discriminates between the various trait subgroups found in the training population.
- measured data for selected molecular markers can be applied to any of the multi-category logit models described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, Inc., New York, Chapter 8, hereby incorporated by reference in its entirety, in order to develop a classifier capable of discriminating between any of a plurality of trait subgroups represented in a training population.
- the feature data measured for select biomarkers of the present invention can be used to train a neural network.
- a neural network is a two-stage regression or classification decision rule.
- a neural network has a layered structure that includes a layer of input units (and the bias) connected by a layer of weights to a layer of output units. For regression, the layer of output units typically includes just one output unit.
- neural networks can handle multiple quantitative responses in a seamless fashion.
- Neural networks are also described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC; and Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, each of which is hereby incorporated by reference in its entirety. What is disclosed below is some exemplary forms of neural networks.
- the basic approach to the use of neural networks is to start with an untrained network, present a training pattern to the input layer, and to pass signals through the net and determine the output at the output layer. These outputs are then compared to the target values; any difference corresponds to an error.
- This error or criterion function is some scalar function of the weights and is minimized when the network outputs match the desired outputs. Thus, the weights are adjusted to reduce this measure of error.
- this error can be sum-of-squared errors.
- this error can be either squared error or cross-entropy (deviation). See, e.g., Hastie et al, 2001, The Elements of Statistical Learning, Springer- Verlag, New York, which is hereby incorporated by reference in its entirety.
- weights are near zero, then the operative part of the sigmoid commonly used in the hidden layer of a neural network (see, e.g., Hastie et al, 2001, The Elements of Statistical Learning, Springer- Verlag, New York, hereby incorporated by reference) is roughly linear, and hence the neural network collapses into an approximately linear classifier.
- starting values for weights are chosen to be random values near zero. Hence the classifier starts out nearly linear, and becomes nonlinear as the weights increase. Individual units localize to directions and introduce nonlinearities where needed. Use of exact zero weights leads to zero derivatives and perfect symmetry, and the algorithm never moves. Alternatively, starting with large weights often leads to poor solutions.
- a recurrent problem in the use of three-layer networks is the optimal number of hidden units to use in the network.
- the number of inputs and outputs of a three-layer network are determined by the problem to be solved.
- the number of inputs for a given neural network will equal the number of biomarkers selected from the training population.
- the number of output for the neural network will typically be just one. However, in some embodiments more than one output is used so that more than just two states can be defined by the network.
- a multi-output neural network can be used to discriminate between, healthy phenotypes, various stages of SIRS, and/or various stages of sepsis.
- the network will have too many degrees of freedom and is trained too long, there is a danger that the network will overfit the data. If there are too few hidden units, the training set cannot be learned. Generally speaking, however, it is better to have too many hidden units than too few. With too few hidden units, the classifier might not have enough flexibility to capture the nonlinearities in the date; with too many hidden units, the extra weight can be shrunk towards zero if appropriate regularization or pruning, as described below, is used. In typical embodiments, the number of hidden units is somewhere in the range of 5 to 100, with the number increasing with the number of inputs and number of training cases.
- One general approach to determining the number of hidden units to use is to apply a regularization approach.
- a new criterion function is constructed that depends not only on the classical training error, but also on classifier complexity. Specifically, the new criterion function penalizes highly complex classifiers; searching for the minimum in this criterion is to balance error on the training set with error on the training set plus a regularization term, which expresses constraints or desirable properties of solutions:
- the parameter ⁇ is adjusted to impose the regularization more or less strongly. In other words, larger values for ⁇ will tend to shrink weights towards zero: typically cross- validation with a validation set is used to estimate ⁇ . This validation set can be obtained by setting aside a random subset of the training population. Other forms of penalty have been proposed, for example the weight elimination penalty (see, e.g., Hastie et ah, 2001, The Elements of Statistical Learning, Springer- Verlag, New York, hereby incorporated by reference).
- Another approach to determine the number of hidden units to use is to eliminate - prune - weights that are least needed.
- the weights with the smallest magnitude are eliminated (set to zero).
- Such magnitude-based pruning can work, but is nonoptimal; sometimes weights with small magnitudes are important for learning and training data.
- WaId statistics are computed. The fundamental idea in WaId Statistics is that they can be used to estimate the importance of a hidden unit (weight) in a classifier. Then, hidden units having the least importance are eliminated (by setting their input and output weights to zero).
- Optimal Brain Damage and the Optimal Brain Surgeon (OBS) algorithms that use second-order approximation to predict how the training error depends upon a weight, and eliminate the weight that leads to the smallest increase in training error.
- OBD Optimal Brain Damage
- OBS Optimal Brain Surgeon
- Optimal Brain Damage and Optimal Brain Surgeon share the same basic approach of training a network to local minimum error at weight w, and then pruning a weight that leads to the smallest increase in the training error.
- the predicted functional increase in the error for a change in full weight vector ⁇ w is:
- u q is the unit vector along the qth direction in weight space and L q is approximation to the saliency of the weight q - the increase in training error if weight q is pruned and the other weights updated ⁇ w.
- H 0 ⁇ I, where ⁇ is a small parameter - effectively a weight constant.
- the matrix is updated with each pattern according to H -l ⁇ Y 7" ⁇ -1 m A »+l ⁇ «+l n «
- the Optimal Brain Surgeon method begins initialize YI H , W, ⁇ train a reasonably large network to minimum error do compute H "1 by Eqn. 1 q* ⁇ - arg mm (saliency L q )
- the Optimal Brain Damage method is computationally simpler because the calculation of the inverse Hessian matrix in line 3 is particularly simple for a diagonal matrix.
- the above algorithm terminates when the error is greater than a criterion initialized to be ⁇ .
- Another approach is to change line 6 to terminate when the change in J(w) due to elimination of a weight is greater than some criterion value.
- the back-propagation neural network See, for example Abdi, 1994, "A neural network primer," J. Biol System. 2, 247-283, hereby incorporated by reference in its entirety.
- features for select biomarkers of the present invention are used to cluster a training set. For example, consider the case in which ten features (corresponding to ten biomarkers) described in the present invention is used. Each member m of the training population will have feature values (e.g. expression values) for each of the ten biomarkers. Such values from a member m in the training population define the vector:
- Xj n is the expression level of the i th biomarker in organism m. If there are m organisms in the training set, selection of i biomarkers will define m vectors. Note that the methods of the present invention do not require that each the expression value of every single biomarker used in the vectors be represented in every single vector m. In other words, data from a subject in which one of the i* biomarkers is not found can still be used for clustering. In such instances, the missing expression value is assigned either a "zero" or some other normalized value. In some embodiments, prior to clustering, the feature values are normalized to have a mean value of zero and unit variance.
- a particular combination of genes of the present invention is considered to be a good classifier in this aspect of the invention when the vectors cluster into the trait groups found in the training population. For instance, if the training population includes class a: subjects that do not develop sepsis, and class b: subjects that develop sepsis, an ideal clustering classifier will cluster the population into two groups, with one cluster group uniquely representing class a and the other cluster group uniquely representing class b.
- Duda 1973 Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York, (hereinafter "Duda 1973") which is hereby incorporated by reference in its entirety.
- the clustering problem is described as one of finding natural groupings in a dataset.
- This metric similarity measure
- Particular exemplary clustering techniques that can be used in the present invention include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering.
- PCA Principal component analysis
- Principal component analysis is a classical technique to reduce the dimensionality of a data set by transforming the data to a new set of variable (principal components) that summarize the features of the data. See, for example, Jolliffe, 1986, Principal Component Analysis, Springer, New York, which is hereby incorporated by reference. Principal component analysis is also described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, which is hereby incorporated by reference. What follows is non-limiting examples of principal components analysis.
- Principal components are uncorrelated and are ordered such that the k th
- PC has the Mi largest variance among PCs.
- the & th PC can be interpreted as the direction that maximizes the variation of the projections of the data points such that it is orthogonal to the first k - ⁇ PCs.
- the first few PCs capture most of the variation in the data set.
- the last few PCs are often assumed to capture only the residual 'noise' in the data.
- PCA can also be used to create a classifier in accordance with the present invention. In such an approach, vectors for the select biomarkers of the present invention can be constructed in the same manner described for clustering above.
- the set of vectors where each vector represents the feature values (e.g., abundance values) for the select genes from a particular member of the training population, can be viewed as a matrix.
- this matrix is represented in a Free- Wilson method of qualitative binary description of monomers (Kubinyi, 1990, 3D QSAR in drug design theory methods and applications, Pergamon Press, Oxford, pp 589-638), and distributed in a maximally compressed space using PCA so that the first principal component (PC) captures the largest amount of variance information possible, the second principal component (PC) captures the second largest amount of all variance information, and so forth until all variance information in the matrix has been considered.
- PC Principal component
- each of the vectors (where each vector represents a member of the training population) is plotted.
- Many different types of plots are possible.
- a one-dimensional plot is made.
- the value for the first principal component from each of the members of the training population is plotted.
- the expectation is that members of a first subgroup (e.g. those subjects that do not develop sepsis in a determined time period) will cluster in one range of first principal component values and members of a second subgroup (e.g., those subjects that develop sepsis in a determined time period) will cluster in a second range of first principal component values.
- the training population comprises two subgroups:
- the first principal component is computed using the molecular marker expression values for the select biomarkers of the present invention across the entire training population data set. Then, each member of the training set is plotted as a function of the value for the first principal component. In this ideal example, those members of the training population in which the first principal component is positive are the “responders” and those members of the training population in which the first principal component is negative are “subjects with sepsis.”
- the members of the training population are plotted against more than one principal component.
- the members of the training population are plotted on a two-dimensional plot in which the first dimension is the first principal component and the second dimension is the second principal component.
- the expectation is that members of each subgroup represented in the training population will cluster into discrete groups. For example, a first cluster of members in the two-dimensional plot will represent subjects that develop sepsis in a given time period and a second cluster of members in the two- dimensional plot will represent subjects that do not develop sepsis in a given time period.
- Nearest neighbor classifiers are memory-based and require no classifier to be fit. Given a query point xo, the k training points X (r) , r, ..., k closest in distance to X 0 are identified and then the point X 0 is classified using the k nearest neighbors. Ties can be broken at random. In some embodiments, Euclidean distance in feature space is used to determine distance as:
- the expression data used to compute the linear discriminant is standardized to have mean zero and variance 1.
- the members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set.
- a select combination of biomarkers of the present invention represents the feature space into which members of the test set are plotted. Next, the ability of the training set to correctly characterize the members of the test set is computed.
- nearest neighbor computation is performed several times for a given combination of biomarkers of the present invention, hi each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of biomarkers is taken as the average of each such iteration of the nearest neighbor computation.
- the nearest neighbor rule can be refined to deal with issues of unequal class priors, differential misclassification costs, and feature selection. Many of these refinements involve some form of weighted voting for the neighbors.
- Linear discriminant analysis attempts to classify a subject into one of two categories based on certain object properties. In other words, LDA tests whether object attributes measured in an experiment predict categorization of the objects. LDA typically requires continuous independent variables and a dichotomous categorical dependent variable.
- the feature values for the select combinations of biomarkers of the present invention across a subset of the training population serve as the requisite continuous independent variables.
- the trait subgroup classification of each of the members of the training population serves as the dichotomous categorical dependent variable.
- LDA seeks the linear combination of variables that maximizes the ratio of between-group variance and within-group variance by using the grouping information. Implicitly, the linear weights used by LDA depend on how the feature values of a molecular marker across the training set separates in the two groups (e.g., a group a that develops sepsis during a defined time period and a group b that does not develop sepsis during a defined time period) and how these feature values correlate with the feature values of other biomarkers.
- LDA is applied to the data matrix of the N members in the training sample by K biomarkers in a combination of biomarkers described in the present invention. Then, the linear discriminant of each member of the training population is plotted.
- those members of the training population representing a first subgroup e.g. those subjects that develop sepsis in a defined time period
- those member of the training population representing a second subgroup e.g. those subjects that will not develop sepsis in a defined time period
- a second range of linear discriminant values e.g., positive
- the LDA is considered more successful when the separation between the clusters of discriminant values is larger.
- Quadratic discriminant analysis takes the same input parameters and returns the same results as LDA.
- QDA uses quadratic equations, rather than linear equations, to produce results.
- LDA and QDA are interchangeable, and which to use is a matter of preference and/or availability of software to support the analysis.
- Logistic regression takes the same input parameters and returns the same results as LDA and QDA.
- Support vector machines [00319] In some embodiments of the present invention, support vector machines
- SVMs are used to classify subjects using feature values of the genes described in the present invention.
- SVMs are a relatively new type of learning algorithm. See, for example, Cristianini and Shawe-Taylor, 2000, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge; Boser et ah, 1992, "A training algorithm for optimal margin classifiers," in Proceedings of the 5 th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, PA, pp.
- SVMs can work in combination with the technique of 'kernels', which automatically realizes a non-linear mapping to a feature space.
- the hyper- plane found by the SVM in feature space corresponds to a non-linear decision boundary in the input space.
- the feature data is standardized to have mean zero and unit variance and the members of a training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set.
- the expression values for a combination of genes described in the present invention is used to train the SVM. Then the ability for the trained SVM to correctly classify members in the test set is determined. In some embodiments, this computation is performed several times for a given combination of molecular markers. In each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of biomarkers is taken as the average of each such iteration of the SVM computation.
- the present invention provides biomarkers that are useful in diagnosing or predicting sepsis and/or its stages of progression in a subject. While the methods of the present invention may use an unbiased approach to identifying predictive biomarkers, it will be clear to the artisan that specific groups of biomarkers associated with physiological responses or with various signaling pathways may be the subject of particular attention. This is particularly the case where biomarkers from a biological sample are contacted with an array that can be used to measure the amount of various biomarkers through direct and specific interaction with the biomarkers (e.g., an antibody array or a nucleic acid array).
- an array that can be used to measure the amount of various biomarkers through direct and specific interaction with the biomarkers (e.g., an antibody array or a nucleic acid array).
- the choice of the components of the array may be based on a suggestion that a particular pathway is relevant to the determination of the status of sepsis or SIRS in a subject.
- the indication that a particular biomarker has a feature that is predictive or diagnostic of sepsis or SIRS may give rise to an expectation that other biomarkers that are physiologically regulated in a concerted fashion likewise may provide a predictive or diagnostic feature.
- the artisan will appreciate, however, that such an expectation may not be realized because of the complexity of biological systems.
- mRNA expression level of a biomarker may be affected by multiple converging pathways that may or may not be involved in a physiological response to sepsis.
- Biomarkers can be obtained from any biological sample, which can be, by way of example and not of limitation, whole blood, plasma, saliva, serum, red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, monocytes, urine, cerebral spinal fluid, sputum, stool, cells and cellular extracts, or other biological fluid sample, tissue sample or tissue biopsy from a host or subject.
- biological sample which can be, by way of example and not of limitation, whole blood, plasma, saliva, serum, red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, monocytes, urine, cerebral spinal fluid, sputum, stool, cells and cellular extracts, or other biological fluid sample, tissue sample or tissue biopsy from a host or subject.
- the precise biological sample that is taken from the subject may vary, but the sampling preferably is minimally invasive and is easily performed by conventional techniques.
- Measurement of a phenotypic change may be carried out by any conventional technique. Measurement of body temperature, respiration rate, pulse, blood pressure, or other physiological parameters can be achieved via clinical observation and measurement. Measurements of biomarker molecules may include, for example, measurements that indicate the presence, concentration, expression level, or any other value associated with a biomarker molecule. The form of detection of biomarker molecules typically depends on the method used to form a profile of these biomarkers from a biological sample. See Section 5.4, above, and Tables 30, 1, J, K, L, and M below. [00326] In a particular embodiment, the biomarker profile comprises at least two different biomarkers listed in column four or five of Table 30.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers.
- biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker in the at least two different biomarkers is listed in column four of Table 30, can be, for example, a transcript made by the listed gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein listed in column five of Table 30, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker.
- Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table 30).
- such an assay utilizes a nucleic acid microarray.
- the biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in column 2 of Table 30, biomarkers that contain the complement of one of the probesets of Table 30, or biomarkers that contain an amino acid sequence encoded by a gene that either contains one of the probesets of Table 30 or the complement of one of the probesets of Table 30.
- biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 30, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profile has between 2 and 626 biomarkers listed in Table 30. In some embodiments, the biomarker profile has between 3 and 50 biomarkers listed in Table 30. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table 30. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 30. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 30. In some embodiments, the biomarker profile has at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, or 100 biomarkers listed in Table 30. In some embodiments, each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein.
- biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins.
- the biomarker profile has between 2 and 130 biomarkers listed in Table 31.
- the biomarker profile has between 3 and 50 biomarkers listed in Table 31.
- the biomarker profile has between 4 and 25 biomarkers listed in Table 31.
- the biomarker profile has at least 3 biomarkers listed in Table 31.
- the biomarker profile has at least 4 biomarkers listed in Table 30.
- the biomarker profile has at least 6, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, or 100 biomarkers listed in Table 31.
- the biomarker profile has between 2 and 10 biomarkers listed in Table 33. In some embodiments, the biomarker profile has between 3 and 10 biomarkers listed in Table 32. In some embodiments, the biomarker profile has between 4 and 10 biomarkers listed in Table 32. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 32. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 32. In some embodiments, the biomarker profile has at least 6, 7, 8, 9, or 10 biomarkers listed in Table 32. In some embodiments, each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein.
- biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins.
- the biomarker profile has between 2 and 10 biomarkers listed in Table 33. In some embodiments, the biomarker profile has between 3 and 10 biomarkers listed in Table 33. In some embodiments, the biomarker profile has between 4 and 10 biomarkers listed in Table 33. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 33. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 33. In some embodiments, the biomarker profile has at least 6, 7, 8, 9, or 10 biomarkers listed in Table 33.
- each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein. In some embodiments, some of the biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins. [00332] In some embodiments the biomarker profile has between 2 and 130 biomarkers listed in Table 34. In some embodiments, the biomarker profile has between 3 and 40 biomarkers listed in Table 34. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table 34. In some embodiments, the biomarker profile has at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, or 40 biomarkers listed in Table 34.
- each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein. In some embodiments, some of the biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins.
- the biomarker profile has between 2 and 7 biomarkers listed in Table 36. In some embodiments, the biomarker profile has between 3 and 6 biomarkers listed in Table 36. In some embodiments, the biomarker profile has between 4 and 7 biomarkers listed in Table 36. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 36. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 36. In some embodiments, the biomarker profile has at least 6, 7, 8, 9, or 10 biomarkers listed in Table 36. In some embodiments, each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein.
- biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins.
- the biomarker profile has between 2 and 53 biomarkers listed in Table I. In some embodiments, the biomarker profile has between 3 and 50 biomarkers listed in Table I. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table I. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table I. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table I.
- the biomarker profile has at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, or 53 biomarkers listed in Table I.
- each of the biomarkers in the biomarker profile is a nucleic acid in Table I.
- each of the biomarkers in the biomarker profile is a protein in Table I.
- some of the biomarkers in a biomarker profile are proteins in Table I and some of the biomarkers in the same biomarker profile are nucleic acids in Table I.
- the biomarker profile has between 2 and 44 biomarkers listed in Table J. In some embodiments, the biomarker profile has between 3 and 44 biomarkers listed in Table J. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table J. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table J. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table J. In some embodiments, the biomarker profile has at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, or 43 biomarkers listed in Table J.
- each of the biomarkers in the biomarker profile is a nucleic acid in Table J. In some embodiments, each of the biomarkers in the biomarker profile is a protein in Table J. In some embodiments, some of the biomarkers in a biomarker profile are proteins in Table J and some of the biomarkers in the same biomarker profile are nucleic acids in Table J.
- the biomarker profile has between 2 and 10 biomarkers listed in Table K. In some embodiments, the biomarker profile has between 3 and 10 biomarkers listed in Table K. In some embodiments, the biomarker profile has between 4 and 10 biomarkers listed in Table K. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table K. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table K. In some embodiments, the biomarker profile has at least 5, 6, 7, 8, or 9 biomarkers listed in Table K. In some embodiments, each of the biomarkers in the biomarker profile is a nucleic acid in Table K.
- each of the biomarkers in the biomarker profile is a protein in Table K. In some embodiments, some of the biomarkers in a biomarker profile are proteins in Table K and some of the biomarkers in the same biomarker profile are nucleic acids in Table K.
- the biomarkers of the present invention may, for example, be used to raise antibodies that bind the biomarker if it is a protein (using methods described in Section 5.4.2, supra, or any method well known to those of skill in the art), or they may be used to develop a specific oligonucleotide probe, if it is a nucleic acid, for example, using a method described in Section 5.4.1, supra, or any method well known to those of skill in the art.
- the skilled artisan will readily appreciate that useful features can be further characterized to determine the molecular structure of the biomarker.
- Methods for characterizing biomarkers in this fashion include X-ray crystallography, high-resolution mass spectrometry, infravod spectrometry, ultraviolet spectrometry and nuclear magnetic resonance.
- Methods for determining the nucleotide sequence of nucleic acid biomarkers, the amino acid sequence of polypeptide biomarkers, and the composition and sequence of carbohydrate biomarkers also are well-known in the art.
- the presently described methods are used to screen SIRS subjects who are at risk for developing sepsis.
- a biological sample is taken from a SIRS- positive subject and used to construct a biomarker profile.
- the biomarker profile is then evaluated to determine whether the feature values of the biomarker profile satisfy a first value set associated with a particular decision rule. This evaluation classifies the subject as a converter or a nonconverter.
- a treatment regimen may then be initiated to forestall or prevent the progression of sepsis when the subject is classified as a converter.
- the presently described methods are used to screen subjects who are particularly at risk for developing a certain stage of sepsis.
- a biological sample is taken from a subject and used to construct a biomarker profile.
- the biomarker profile is then evaluated to determine whether the feature values of the biomarker profile satisfy a first value set associated with a particular decision rule. This evaluation classifies the subject as having or not having a particular stage of sepsis.
- a treatment regimen may then be initiated to treat the specific stage of sepsis.
- the stage of sepsis is for example, onset of sepsis, severe sepsis, septic shock, or multiple organ dysfunction.
- a biomarker profile is obtained using a biological sample from a test subject, particularly a subject at risk of developing sepsis, having sepsis, or suspected of having sepsis.
- the biomarker profile in such embodiments is evaluated. This evaluation can be made, for example, by applying a decision rule to the test subject.
- the decision rule can, for example, be or have been constructed based upon the biomarker profiles obtained from subjects in the training population.
- the training population in one embodiment, includes (a) subjects that had SIRS and were then diagnosed as septic during an observation time period as well as (b) subjects that had SIRS and were not diagnosed as septic during an observation time period.
- the test subject is diagnosed as having a more likely chance of becoming septic, as being afflicted with sepsis or as being at the particular stage in the progression of sepsis.
- Various populations of subjects including those who are suffering from SIRS (e.g., SIRS- positive subjects) or those who are suffering from an infection but who are not suffering from SIRS (e.g., SIRS-negative subjects) can serve as training populations.
- the present invention allows the clinician to distinguish, inter alia, between those subjects who do not have SIRS, those who have SIRS but are not likely to develop sepsis within a given time frame, those who have SIRS and who are at risk of eventually becoming septic, and those who are suffering from a particular stage in the progression of sepsis.
- suitable training populations and suitable data collected from such populations see Section 5.5, above.
- data analysis algorithms identify a large set of biomarkers whose features discriminate between converters and nonconverters. For example, in some embodiments, application of a data analysis algorithm to a training population results in the selection of more than 500 biomarkers, more than 1000 biomarkers, or more than 10,000 biomarkers. In some embodiments, further reduction in the number of biomarkers that are deemed to be discriminating is desired. Accordingly, in some embodiments, filtering rules that are complementary to data analysis algorithms (e.g., the data analysis algorithms of Section 5.5) are used to further reduce the list of discriminating biomarkers identified by the data analysis algorithms.
- the list of biomarkers identified by application of one or more data analysis algorithms to the biomarker profile data measured in a training population is further refined by application of annotation data based filtering rules to the list.
- those biomarkers in the set of biomarkers identified by the one or more data analysis algorithms that satisfy the one or more applied annotation data based filtering rules remain in the set of discriminating biomarkers.
- those biomarkers in the set of biomarkers identified by the one or more data analysis algorithms that do not satisfy the one or more applied annotation data based filtering rules are removed from the set.
- Annotation data based filtering rules are rules based upon annotation data.
- Annotation data refers to any type of data that describes a property of a biomarker.
- An example of annotation data is the identification of biological pathways to which a given biomarker belongs.
- Another example of annotation data is enzymatic class (e.g., phosphodiesterases, kinases, metalloproteinases, etc.).
- Still other examples of annotation data include, but are not limited to, protein domain information, enzymatic substrate information, enzymatic reaction information, and protein interaction data.
- Yet another example of annotation data is disease association, in other words, which disease process a given biomarker has been linked to or otherwise affects.
- Another form of annotation data is any type of data that associates biomarker expression, other forms of biomarker abundance, and/or biomarker activity, with cellular localization, tissue type localization, and/or cell type localization.
- annotation data is used to construct an annotation data based filtering rule.
- An example of an annotation data based filtering rule is:
- Annotation rule 1 remove all transcription factors from the training set.
- Another type of annotation data based filtering rule is:
- Annotation rule 2 keep all biomarkers that are enriched for annotation X in a biomarker list. Application of this filtering rule will only keep those biomarkers in a given list that are enriched (overrepresented) for annotation X in the list.
- This filtering rule consider an exemplary biomarker set that has been identified by application of a data analysis algorithm (Section 5.5) to biomarker profiles measured using training population data measured in accordance with a technique disclosed in Section 5.4.
- This exemplary biomarker set has 500 biomarkers. Assume, for in this illustrative example, that the full set of biomarkers in a human consists of 25,000 biomarkers. Here, the 25,000 biomarkers is a population and the 500 biomarker set is the sample.
- the term "population” consists of all possible observable biomarkers.
- the term "sample” is the data that is actually considered.
- X kinases.
- this two-way contingency table can be analyzed by treating each row as an independent bionomial variable.
- N 1 and N 2 are the samples sizes for the population and the sample selected by data analysis algorithm, respectively.
- the standard error decreases, and hence the estimate OfTr 1 -7t 2 improves, as the sample sizes increase.
- a large-sample (100(1 - ⁇ ))% confidence interval for 7C 1 — ⁇ 2 is
- the estimated error is
- a 95% confidence interval for the true difference ⁇ i - ⁇ 2 is -0.068 ⁇ 1.96(0.013), or - 0.068 ⁇ 0.025. Since the 95% confidence interval contains only negative values, the conclusion can be reached that kinases are enriched in the sample (the biomarker set produced by the data analysis algorithm) relative to the population of 25,000 biomarkers.
- the two-way contingency table in the example above can be analysed using methods known in the art other than the one disclosed above. For example, the chi-square test for independence and/or Fisher's exact test can be used to test the null hypothesis that the row and column classification variables of the two-way contingency table are independent.
- annotation rule 2 can be any form of annotation data.
- "X" is any biological pathway.
- annotation data based filtering rule has the following form.
- the number of biomarkers in a particular biological pathway in the sample is compared with the number of biomarkers that are in the particular biological pathway in the population using, for example, the two- way contingency table analysis described above, or other techniques known in the art. If the biological pathway is enriched in the sample, then all biomarkers in the sample that are also in the biological pathway are retained for further analysis, in accordance with the annotation data based filtering rule.
- biomarkers having a given annotation are considered enriched in the sample relative to the population when the proportion of biomarkers having the annotation in the sample is greater than the proportion of biomarkers having the annotation in the population across its entire 95% degree confidence interval as determined by two-way contingency table analysis, hi another embodiment, biomarkers having a given annotation are considered enriched in the sample relative to the population if ap value as determined by the Fisher exact test, Chi-square test, or relative algorithms is 0.05 or less, 0.005 or less or 0.0005 or less.
- Another form of annotation data based filtering rule has the following form:
- a set of biomarkers is determined using a data analysis algorithm.
- Exemplary data analysis algorithms are disclosed in Section 5.5.
- Section 6 describes certain tests that can also serve as data analysis algorithms. These tests include, but are not limited to a Wilcoxon test and the like with a statistically significant ⁇ value (e.g., 0.05 or less, 0.04, or less, etc.), and/or a requirement that a biomarker exhibit a mean differential abundance between biological samples obtained from converters and biological samples obtained from nonconverters in a training population.
- a set of biomarkers that discriminates between converters and nonconverters is determined.
- annotation rule 4 is applied to the set of discriminating biomarkers in order to further reduce the set of biomarkers.
- annotation rule 4 can be applied first and then certain data analysis algorithms can be applied, or vice versa.
- biomarkers ultimately deemed as discriminating between converters and nonconverters satisfy each of the following criteria: (i) ap value of 0.05 or less (p ⁇ 0.05) as determined from a Wilcoxon adjusted test using static (single time point) data; (ii) a mean-fold change of 1.2 or greater between converters and nonconverters across the training set using static (single time point data), and (iii) present in a specific biological pathway. See also, Section 6.7, infra, for a detailed example. In this example, there is no requirement that members of the pathway are enriched in the set of biomarkers identified by the data analysis algorithms. Furthermore, it is noted that criteria (i) and (ii) are forms of data analysis algorithms and criterion (iii) is a annotation data based filtering rule.
- the biomarkers can then be used to determine the identity of the particular biological pathways from which the discriminating biomarkers are implicated.
- annotation data-based filtering rules are applied to the list of discriminating biomarkers identified by the methods of the present invention ⁇ e.g., the methods described in Sections 5.4, 5.5 and 6). Such annotation data-based filtering rules identify the particular biological pathway or pathways that are enriched in the discriminating list of biomarkers identified by the data analysis algorithms.
- DAVID 2.0 software available at appsl.niaid.nih.gov/david/, is used to identify and apply such annotation data-based filtering rules to the set of biomarkers identified by the data analysis algorithms in order to identify pathways that are enriched in the set.
- those biomarkers that are in an enriched biological pathway are selected for use as discriminating biomarkers in the kits of the present invention.
- biomarkers that are in biological pathways that are enriched in the biomarker set determined by application of a data analysis algorithm to a training population that includes converters and nonconverters can be used as filtering step to reduce the number of biomarkers in the set.
- a nucleic acid array such as a cDNA microarray
- a nucleic acid array may be employed to generate features of biomarkers in a biomarker profile by detecting the expression of any one or more of the genes known to be or suspected to be involved in the selected biological pathways.
- Data derived from the cDNA microarray analysis may then be analyzed using any one or more of the analysis algorithms described in Section 5.5, supra, to identify biomarkers whose features discriminate between converters and nonconverters.
- Biomarkers whose corresponding feature values are capable of discriminating, for example, between converters ⁇ i.e., SIRS patients who subsequently develop sepsis) and non-converters (i.e., SIRS patients who do not subsequently develop sepsis) can thus be identified and classified as discriminating biomarkers.
- Biomarkers that are in enriched biological pathways can be selected from this set by applying Annotation rule 3, from above. Representative biological pathways that could be found include, for example, genes involved in the Thl/Th2 cell differentiation pathway).
- biomarkers ultimately deemed as discriminating between converters and nonconverters satisfy each of the following criteria: (i) ap value of 0.05 or less (p ⁇ 0.05) as determined from a Wilcoxon adjusted test; (ii) a mean-fold change of 1.2 or greater between converters and nonconverters across the training set, and (iii) present in a biological pathway that is enriched in the set of biomarkers derived by application of criteria (i) and (ii). [00354]
- annotation data based filtering rules are used to identify biological pathways that are enriched in a given biomarker set.
- This biomarker set can be, for example, a set of biomarkers that is identified by application of a data analysis algorithm to training data comprising converters and nonconverters. Then, biomarkers in these enriched biological pathways are analyzed using any of the data analysis algorithms disclosed herein in order to identify biomarkers that discriminate between converters and nonconverters. In some instances, some of the biomarkers analyzed in the enriched biological pathways were not among the biomarkers in the original given biomarker set. In some instances, some of the biomarkers in the enriched biological pathways are among the biomarkers in the original given biomarker set.
- a secondary assay is used to collect feature data for biomarkers that are in enriched pathways and it is this data that is used to determine whether the biomarkers in the enriched biological pathways discriminate between converters and nonconverters.
- biomarkers in biological pathways of interest are identified.
- genes involved in the Thl/Th2 cell differentiation pathway are identified. Then, these biomarkers are evaluated using the data analysis algorithms disclosed herein to determine whether they discriminate between converters and nonconverters.
- Sections 6.11 through 6.13 identify a number of biomarkers that are of interest in one embodiment in accordance with the present invention.
- one embodiment of the present invention comprises the 10 biomarkers identified in Table 48 of Section 6.11.1 , the 34 biomarkers listed in Table 59 of Section 6.11.2, and the 10 biomarkers listed in Table 93 of Section 6.13.1, below.
- Table 48 and Table 93 each identify MMP9 as a discriminating biomarker.
- the total number of biomarkers in Table I is one less than the sum of the biomarkers identified in Tables 48, Table 59, and Table 93, (34 + 10 + 10 -1) or 53. These biomarkers are reproduced in Table I 5 below.
- Section 5.11.1 provides details on each of the individual biomarkers.
- Section 5.11.2, below, provides more details on select combinations of the biomarkers listed in Tables I 5 J, and K.
- Each of the biomarkers listed in Table I were selected based on the experimental results summarized in Sections 6.11 through 6.13.
- the identified biomarkers were proteins or fragments thereof. Such protein biomarkers, which discriminate between sepsis and SIRS, are listed in Table I with a "P" designation in column 5.
- the identified biomarkers were nucleic acids or fragment thereof. Such nucleic acid biomarkers, which discriminate between sepsis and SIRS, are listed in Table I with an "N" designation in column 5.
- biomarker MMP9 was identified both as a protein and as a nucleic acid biomarker.
- Table J lists the biomarkers in accordance with one embodiment of the present invention in which the biomarkers were discovered using nucleic acid based assays described in Section 6, such as RT-PCR.
- Table K lists the biomarkers in accordance with one embodiment of the present invention in which the biomarkers were discovered using protein based assays, described in Section 6, such as bead assays.
- One embodiment of the invention comprises at least 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from any one of Tables 48, 59, or 93.
- J and K are not limited by their physical form in the experiments summarized in Sections 6.11 through 6.13.
- the discriminatory nature of a biomarker may have been discovered by the abundance of the biomarker, in nucleic acid form, in a nucleic acid assay such as RT-PCR and accordingly listed in Table I on this basis with an "N" designation in column 5 of Table I 5
- the physical manifestation of the biomarker in the methods, kits, and biomarker profiles of the present invention is not limited to nucleic acids. Rather, any physical manifestation of the biomarker as defined for the term "biomarker" in Section 5.1 is encompassed in the present invention.
- a biomarker has the designation DOWN, in column 6, that means that the biomarker, in the form indicated in column 5, was, on average, less abundant in subjects that will convert to sepsis (sepsis subjects), relative to subjects that will not convert to sepsis (SIRS subjects).
- Table I Biomarkers in accordance with an embodiment of the present invention.
- AFP identified by accession no. NM_001134
- the nucleotide sequence of AFP is disclosed in, e.g., Beattie et al, 1982, "Structure and evolution of human alpha-fetoprotein deduced from partial sequence of cloned cDNA" Gene 20 (3): 415-422, Harper, M.E. et al, 1983, "Linkage of the evolutionarily-related serum albumin and alpha-fetoprotein genes within ql 1-22 of human chromosome 4," Am. J. Hum. Genet. 35 (4):565-572, Morinaga, T. et al, 1983, "Primary structures of human alpha-fetoprotein and its mRNA," Proc.
- NM_144590 is disclosed in, e.g., Strausberg, 2002, "Homo sapiens ankyrin repeat domain
- mRNA (cDNA clone MGC:22805 IMAGE:3682099)," unpublished, and the amino acid sequence of ANKRD22 (identified by accession no. NP_653191) is disclosed in, e.g., Strausberg, 2002, "Homo sapiens ankyrin repeat domain 22, mRNA (cDNA clone MGC:22805 IMAGE:3682099),” unpublished, each of which is incorporated by reference herein in its entirety.
- NM_005139 is disclosed in, e.g., Pepinsky, R.B. et al, 1988," Five distinct calcium and phospholipid binding proteins share homology with lipocortin I,” J. Biol. Chem. 263 (22): 10799-10811, Tait, J.F. et al, 1988, "Placental anticoagulant proteins: isolation and comparative characterization four members of the lipocortin family," Biochemistry 27 (17):6268-6276, Ross, T.S.
- ARG2 identified by accession no. NM_001172
- the nucleotide sequence of ARG2 is disclosed in, e.g., Gotoh et al,1996 "Molecular cloning of cDNA for nonhepatic mitochondrial arginase(arginase II) and comparison of its induction with nitric oxide synthase in a murine macrophage-like cell line," FEBS Lett.
- CAG38787 is disclosed in, e.g., Halleck et al, 2004, Direct submission, RZPD Deutsches Sell scholar fuer Genomaba GmbH, Im Neuenheimer FeId 580, D-69120 Heidelberg, Germany, Halleck et al, unpublished, "Cloning of human full open reading frames in Gateway(TM) system entry vector (pDONR201),” each of which is incorporated by reference herein in its entirety.
- B2M The nucleotide sequence of B2M (identified by accession no. NM_004048) is disclosed in, e.g., Kxangel, M.S. et al, 1979, "Assembly and maturation of HLA-A and HLA-B antigens in vivo," Cell 18 (4):979-991, Suggs, S.V. et al, 1981, "Use of synthetic oligonucleotides as hybridization probes: isolation of cloned cDNA sequences for human beta 2-microglobulin," Proc. Natl. Acad. Sci. U.S.A. 78 (11):6613-6617, Rosa, F.
- NM_004049 is disclosed in, e.g., Lin, E. Y. et ⁇ /.,1993, "Characterization of Al, a novel hemopoietic-specific early-response gene with sequence similarity to bcl-2," J. Immunol. 151 (4):1979-1988, Savitsky, K. et al, "The complete sequence of the coding region of the ATM gene reveals similarity to cell cycle regulators in different species," Hum. MoI. Genet. 4 (11):2025-2032, Choi, S.S.
- NM_175862 is disclosed in, e.g., Azuma, M. et al, 1993, "B70 antigen is a second ligand for CTLA-4 and CD28," Nature 366 (6450):76-79, Freeman, GJ. et al, 1993, "Cloning of B7-2: a CTLA-4 counter-receptor that costimulates human T cell proliferation," Science 262 (5135):909-911, Chen, C. et al, 1994, "Molecular cloning and expression of early T cell costimulatory molecule- 1 and its characterization as B7-2 molecule," J. Immunol. 152 (10):4929-4936, and the amino acid sequence of CD86 (identified by accession nos.
- NP_008820, NP_787058 is disclosed in, e.g., Azuma, M. et al, 1993, "B70 antigen is a second ligand for CTLA-4 and CD28," Nature 366 (6450):76-79, Azuma, M. et al, 1993, “B70 antigen is a second ligand for CTLA-4 and CD28," Nature 366 (6450):76-79, Freeman, GJ. et al, 1993, "Cloning of B7-2: a CTLA-4 counter-receptor that costimulates human T cell proliferation," Science 262 (5135):909-911, Chen, C.
- NM_001712 is disclosed in, e.g., Svenberg, T. et al, 1979, "Immunofluorescence studies on the occurrence and localization of the CEA-related biliary glycoprotein I (BGP I) in normal human gastrointestinal tissues," Clin. Exp. Immunol. 36 (3) :436-441, Hinoda, Y. et al, 1988, "Molecular cloning of a cDNA coding biliary glycoprotein I: primary structure of a glycoprotein immunologically crossreactive with carcinoembryonic antigen," Proc. Natl. Acad. Sci. U.S.A. 85 (18):6959-6963, Barnett, T.R.
- C Reactive Protein (identified by accession no. NM_000567) is disclosed in, e.g., Song et al, 2006, "C-reactive protein contributes to the hypercoagulable state in coronary artery disease," J. Thromb. Haemost.
- CAA39671 is described in a direct submissiong by Tenchini et al, 1990, Tenchini M.L., Dipartimento di Biologia e Genetica per Ie Scienze mediche, via Viotti 3, 20133 Milano, Italy, each of which is incorporated by reference herein in its entirety.
- NM_006371 is disclosed in, e.g., Castagnola, P. et al, 1997, "Cartilage associated protein (CASP) is a novel developmentally regulated chick embryo protein," J. Cell. Sci. 110 (PT 12):1351-1359; Tonachini, L. et al, 1999, “cDNA cloning, characterization and chromosome mapping of the gene encoding human cartilage associated protein (CRTAP)," Cytogenet. Cell Genet. 87:(3-4); Morello, R. et al, 1999, “cDNA cloning, characterization and chromosome mapping of Crtap encoding the mouse cartilage associated protein," Matrix Biol.
- CRTAP human cartilage associated protein
- CRTAP is a novel developmentally regulated chick embryo protein
- J. Cell. Sci. 110 (PT 12):1351-1359 Tonachini, L. et al, 1999, "cDNA cloning, characterization and chromosome mapping of the gene encoding human cartilage associated protein (CRTAP) 5 " Cytogenet. Cell Genet. 87:(3-4), Morello, R.
- NM_005211 is disclosed in, e.g., Verbeek, J.S. et al, 1985, "Human c-fms proto- oncogene: comparative analysis with an abnormal allele," MoL Cell. Biol. 5 (2):422-426; Xu, D. Q. et al, 1985, "Restriction fragment length polymorphism of the human c-fms gene,” Proc. Natl. Acad. Sci. U.S.A. 82 (9):2862-2865; Sherr, CJ.
- NM_022763 is disclosed in, e.g., Clark, H.F. et al, 2003, "The secreted protein discovery initiative (SPDI), a large-scale effort to identify novel human secreted and transmembrane proteins : a bioinformatics assessment," Genome Res. 13 (10):2265-2270, Tominaga, K. et al, 2004, "The novel gene fadlO4, containing a fibronectin type III domain, has a significant role in adipogenesis," FEBS Lett. 577 (l-2):49-54, and the amino acid sequence of FAD104 (identified by accession no. NP_073600) is disclosed in, e.g., Clark, H.F.
- FCGRlA The secreted protein discovery initiative (SPDI), a large-scale effort to identify novel human secreted and transmembrane proteins: a bioinformatics assessment," Genome Res. 13 (10):2265-2270, Tominaga, K. et al, 2004, "The novel gene fadlO4, containing a fibronectin type III domain, has a significant role in adipogenesis," FEBS Lett. 577 (1- 2):49-54, each of which is incorporated by reference herein in its entirety. [00374] The nucleotide sequence of FCGRlA (identified by accession no.
- NM_000566 is disclosed in, e.g., Eizuru, Y. et al, 1988, "Induction of Fc (IgG) receptor(s) by simian cytomegaloviruses in human embryonic lung fibroblasts," Intervirology 29 (6):339-345, Allen, J.M. et al, 1988, "Nucleotide sequence of three cDNAs for the human high affinity Fc receptor (FcRI),” Nucleic Acids Res. 16 (24): 11824, van de Winkel, J.G. et al, 1991, “Gene organization of the human high affinity receptor for IgG, Fc gamma RI (CD64). Characterization and evidence for a second gene," J.
- FCGRl A amino acid sequence of FCGRl A (identified by accession no. NP_000557) is disclosed in, e.g., Eizuru, Y. et al, 1988, "Induction of Fc (IgG) receptor(s) by simian cytomegaloviruses in human embryonic lung fibroblasts," Intervirology 29 (6):339-345, Allen, J.M. et al, 1988, "Nucleotide sequence of three cDNAs for the human high affinity Fc receptor (FcRI),” Nucleic Acids Res. 16 (24):11824, van de Winkel, J.G.
- NM_001924 is disclosed in, e.g., Papathanasiou, M. A. et al, 1991, "Induction by ionizing radiation of the gadd45 gene in cultured human cells: lack of mediation by protein kinase C," MoI. Cell. Biol. 11 (2):1009-1016, Hollander, M.C. et al, 1993, "Analysis of the mammalian gadd45 gene and its response to DNA damage," J. Biol. Chem. 268 (32):24385- 24393, Smith, M.L.
- GADD45A amino acid sequence of GADD45A (identified by accession no. NP_001915) is disclosed in, e.g., Papathanasiou, M. A. et al, 1991, "Induction by ionizing radiation of the gadd45 gene in cultured human cells: lack of mediation by protein kinase C," MoI. Cell. Biol. 11 (2):1009- 1016, Hollander, M.C. et al, 1993, "Analysis of the mammalian gadd45 gene and its response to DNA damage," J. Biol. Chem.
- NM_015675 is disclosed in, e.g., Abdollahi, A. et al, 1991, "Sequence and expression of a cDNA encoding MyDl 18: a novel myeloid differentiation primary response gene induced by multiple cytokines," Oncogene 6 (1): 165-167, Vairapandi, M. et al, 1996, "The differentiation primary response gene MyDl 18, related to GADD45, encodes for a nuclear protein which interacts with PCNA and p2 IWAF 1/CIPl 5 " Oncogene 12 (12):2579-2594, Koonin, E.
- GADD45B identified by accession no. NP_056490
- GADD45B amino acid sequence of GADD45B (identified by accession no. NP_056490) is disclosed in, e.g., Abdollahi, A. et al, 1991, "Sequence and expression of a cDNA encoding MyDl 18: a novel myeloid differentiation primary response gene induced by multiple cytokines," Oncogene 6 (1): 165-167, , Vairapandi, M.
- NM_002123 is disclosed in, e.g., Larhammar, D. et al, 1981, Evolutionary relationship between HLA-DR antigen beta-chains, HLA-A, B, C antigen subunits and immunoglobulin chains," Scand. J. Immunol. 14 (6):617-622, Wiman, K. et al, 1982, "Isolation and identification of a cDNA clone corresponding to an HLA-DR antigen beta chain," Proc. Natl. Acad. Sci. U.S.A. 79 (6): 1703-1707, Larhammar, D.
- HLA-DR antigen-like beta chain as predicted from the nucleotide sequence: similarities with immunoglobulins and HLA-A, -B, and -C antigens
- Proc. Natl. Acad. Sci. U.S.A. 79 (12):3687-3691, and the amino acid sequence of HLA-DRA (identified by accession no. NP_002114) is disclosed in, e.g., Larhammar, D. et al, 1981, Evolutionary relationship between HLA-DR antigen beta-chains, HLA-A, B, C antigen subunits and immunoglobulin chains," Scand. J. Immunol.
- NM_000416 is disclosed in, e.g., Novick, D. et at, 1987, "The human interferon-gamma receptor. Purification, characterization, and preparation of antibodies, each of which is incorporated by reference herein in its entirety," J. Biol. Chem. 262 (18): 8483-8487, Aguet, M. et at, 1988, "Molecular cloning and expression of the human interferon-gamma receptor," Cell 55 (2): 273-280, Le Coniat, M. et at, 1989, "Human interferon gamma receptor 1 (IFNGRl) gene maps to chromosome region 6q23-6q24," Hum. Genet.
- IFNGRl Human interferon gamma receptor 1
- IFNGRl amino acid sequence of IFNGRl (identified by accession no. NP_000407) is disclosed in, e.g., Novick, D. et at, 1987, "The human interferon-gamma receptor. Purification, characterization, and preparation of antibodies," J. Biol. Chem. 262 (18):8483-8487, Aguet, M. et at, 1988, "Molecular cloning and expression of the human interferon-gamma receptor," Cell 55 (2): 273-280, Le Coniat, M. et at, 1989, "Human interferon gamma receptor 1 (IFNGRl) gene maps to chromosome region 6q23-6q24," Hum. Genet. 84 (l):92-94, each of which is incorporated by reference herein in its entirety. [00379] The nucleotide sequence of ILlRN (identified by accession nos.
- NM_000577, NM_173841, NM_173842, NMJ73843 is disclosed in, e.g., Eisenberg, S.P. et at, 1990, "Primary structure and functional expression from complementary DNA of a human interleukin-1 receptor antagonist," Nature 343 (6256):341-346, Carter, D.B. et at, 1990, “Purification, cloning, expression and biological characterization of an interleukin-1 receptor antagonist protein," Nature 344 (6267):633-638, Seckinger, P. et at, 1990, “Natural and recombinant human IL-I receptor antagonists block the effects of IL-I on bone resorption and prostaglandin production," J. Immunol.
- ILlRN amino acid sequence of ILlRN (identified by accession no. AAN87150) is disclosed in, e.g., Rieder, MJ. etat, 2002, Direct submission, Genome Sciences, University of Washington, 1705 NE Pacific, Seattle, WA 98195, USA, each of which is incorporated by reference herein in its entirety.
- nucleotide sequence of IL-6 (identified by accession no. NM_000600) is disclosed in, e.g., Haegeman, G. et at, 1986, "Structural analysis of the sequence coding for an inducible 26-kDa protein in human fibroblasts," Eur. J. Biochem. 159 (3):625-632, Zilberstein, A. et at, 1986, "Structure and expression of cDNA and genes for human interferon-beta-2, a distinct species inducible by growth-stimulatory cytokines," EMBO J. 5 (10):2529-2537, Hirano, T.
- M28130 and the amino acid sequence of IL-8 (identified by accession no.AAA59158) are each disclosed in, e.g., Mukaida et al, 1989, "Genomic structure of the human monocyte-derived neutrophil chemotactic factor IL-8," J. Immunol. 143, 1366-1371 which is incorporated by reference herein in its entirety.
- IL- 10 The nucleotide sequence of IL- 10 (identified by accession no. NM_000572) is disclosed in, e.g., Ghosh, S. et al, 1975, "Anaerobic acidogenesis of wastewater sludge," Breast Cancer Res. Treat. 47 (l):30-45, Hsu, D.H. et al, 1990, "Expression of interleukin- 10 activity by Epstein-Barr virus protein BCRFl," Science 250 (4982):830-832, Vieira, P. et al, 1991, "Isolation and expression of human cytokine synthesis inhibitory factor cDNA clones: homology to Epstein-Barr virus open reading frame BCRFI," Proc. Natl.
- NM_001558 is disclosed in, e.g., Tan, J.C. et al, 1993, "Characterization of interleukin- 10 receptors on human and mouse cells," J. Biol. Chem. 268 (28):21053-21059, Ho, A.S. et al, 1993, "A receptor for interleukin 10 is related to interferon receptors," Proc. Natl. Acad. Sci. U.S.A. 90 (23):11267-11271, Liu, Y. et al, 1994, “Expression cloning and characterization of a human IL-10 receptor," J. Immunol.
- ILlORA amino acid sequence of ILlORA (identified by accession no. NP_001549) is disclosed in, e.g., Tan, J.C. et al, 1993, "Characterization of interleukin- 10 receptors on human and mouse cells," J. Biol. Chem. 268 (28):21053-21059, Ho, A.S. et al, 1993, "A receptor for interleukin 10 is related to interferon receptors," Proc. Natl. Acad. Sci. U.S.A. 90 (23):11267-11271, Liu, Y. et al, 1994, “Expression cloning and characterization of a human IL-10 receptor," J. Immunol. 152 (4): 1821-1829, each of which is incorporated by reference herein in its entirety.
- IL-lRrp is a novel receptor-like molecule similar to the type I interleukin-1 receptor and its homologues T1/ST2 and IL-IR AcP
- J. Biol. Chem. 271 (8):3967-3970 Lovenberg, T.W. et al, 1996
- Cloning of a cDNA encoding a novel interleukin-1 receptor related protein (IL lR-rp2) J. Neuroimmunol. 70 (2): 113-122, Torigoe, K. et al, 1997, “Purification and characterization of the human interleukin-18 receptor," J.
- IL18R1 identified by accession no. NP_003846
- IL-lRrp is a novel receptor-like molecule similar to the type I interleukin-1 receptor and its homologues T1/ST2 and IL-IR AcP
- J. Biol. Chem. 271 (8):3967-3970 Lovenberg, T.W. et al, 1996
- Cloning of a cDNA encoding a novel interleukin-1 receptor related protein IL lR-rp2
- the nucleotide sequence of INSL3 (identified by accession no. NM_005543) is disclosed in, e.g., Adham, LM. et al, 1993, "Cloning of a cDNA for a novel insulin-like peptide of the testicular Leydig cells," J. Biol. Chem. 268 (35):26668-26672, Burkhardt, E. et al, 1994, "Structural organization of the porcine and human genes coding for a Leydig cell-specific insulin-like peptide (LEY I-L) and chromosomal localization of the human gene (INSL3),” Genomics 20 (1):13-19, Burkhardt, E.
- NM_001570 is disclosed in, e.g., Muzio, M. et al, 1997, "IRAK (Pelle) family member IRAK-2 and MyD88 as proximal mediators of IL-I signaling," Science 278 (5343):1612- 1615, Auron, P.E., 1998, "The interleukin 1 receptor: ligand interactions and signal transduction," Cytokine Growth Factor Rev. 9 (3-4):221-237, Maschera, B. et al, 1999, “Overexpression of an enzymically inactive interleukin- 1 -receptor-associated kinase activates nuclear factor-kappaB," Biochem. J.
- IRAK2 (identified by accession no. NP_001561)
- IRAK (Pelle) family member IRAK-2 and MyD88 as proximal mediators of IL-I signaling
- Science 278 (5343):1612-1615 Auron, P.E., 1998
- the interleukin 1 receptor: ligand interactions and signal transduction Cytokine Growth Factor Rev. 9 (3- 4):221-237, Maschera, B.
- NM_016123 is disclosed in, e.g. , Siu, G. et al , 1986, "Analysis of a human V beta gene subfamily," J. Exp. Med. 164 (5):1600-1614, Scanlan, M.J. et al, 1999, "Antigens recognized by autologous antibody in patients with renal-cell carcinoma,” Int. J. Cancer 83 (4):456-464, Li, S. et al, 2002, "IRAK-4: a novel member of the IRAK family with the properties of an IRAK-kinase," Proc. Natl. Acad. Sci. U.S.A.
- NM_000632 is disclosed in, e.g., Micklem, KJ. et al, 1985, "Isolation of complement- fragment-iC3b-binding proteins by affinity chromatography. The identification of pi 50,95 as an iC3b-binding protein," Biochem. J. 231 (l):233-236, Pierce, M.W. et al, 1986, "N- terminal sequence of human leukocyte glycoprotein MoI conservation across species and homology to platelet Ilb/IIIa,” Biochim. Biophys. Acta 874 (3):368-371, Arnaout, M.A.
- JAK2 The nucleotide sequence of JAK2 (identified by accession no . NM_004972) is disclosed in, e.g., Wilks, A.F. et al, 1991, "Two novel protein-tyrosine kinases, each with a second phosphotransferase-related catalytic domain, define a new class of protein kinase," MoI. Cell. Biol. 11 (4):2057-2065, Pritchard, M.A. et al, 1992, "Two members of the JAK family of protein tyrosine kinases map to chromosomes Ip31.3 and 9p24," Mamm.
- LDLR LDLR
- accession no. NM_0005257 The nucleotide sequence of LDLR (identified by accession no. NM_000527) is disclosed in, e.g., Brown, M.S. et al, 1979, "Receptor-mediated endocytosis: insights from the lipoprotein receptor system," Proc. Natl. Acad. Sci. U.S.A. 76 (7):3330-3337, Goldstein, J.L. et al, 1982, "Receptor-mediated endocytosis and the cellular uptake of low density lipoprotein," Ciba Found. Symp. 92, 77-95, Tolleshaug H.
- nucleotide sequence of LY96 (identified by accession no. NM_015364) is disclosed in, e.g., Shimazu, R. et al, 1999, "MD-2, a molecule that confers lipopolysaccharide responsiveness on Toll-like receptor 4," J. Exp. Med. 189 (11):1777- 1782, Kato, K. et al, 2000, "ESOP-I, a secreted protein expressed in the hematopoietic, nervous, and reproductive systems of embryonic and adult mice," Blood 96 (l):362-364, Dziarski, R.
- NM_002758, NM_031988) is disclosed in, e.g., Han, J. et al, 1996, "Characterization of the structure and function of a novel MAP kinase kinase (MKK6), J. Biol. Chem. 271 (6):2886-2891, Raingeaud, J. et al, 1996, "MKK3- and MKK6-regulated gene expression is mediated by the p38 mitogen-activated protein kinase signal transduction pathway," MoI. Cell. Biol. 16 (3), 1247-1255, Stein, B.
- NM_001315, NMJ39012, NM_139013, NMJ39014) is disclosed in, e.g., Zhukov- Verezhnikov, N.N. et al, 1976, "Study of the heterogenetic antigens in vaccinal preparations of V. cholerae," Biochem. Biophys. Res. Commun. 82 (8):961-962, Schultz, SJ. et al, 1993, Identification of 21 novel human protein kinases, including 3 members of a family related to the cell cycle regulator nimA of Aspergillus nidulans," Cell Growth Differ. 4 (10):821-830, Lee, J.C.
- accession nos. AF493698 and AF493697 is disclosed in, e.g., Shanmugasundaram et al, 2002, Virology II, National Institute of Immunology, Aruna Asag AIi Marg, J.N.U. Campus, New Delhi 110 067, India, and the amino acid sequence of MCPl (identified by accession no. AAQ75526) is disclosed in, e.g., Nyquist et al, 2003, direct submission, Medicine, Inova Fairfax, 3300 Gallows Road, Falls Church, Virginia 22402-3300, each of which is incorporated by reference herein in its entirety. [00395] The nucleotide sequence of MKNKl (identified by accession nos.
- NM_003684, NM_198973 is disclosed in, e.g., Fukunaga et al, 1997, "MNKl, a new MAP kinase-activated protein kinase, isolated by a novel expression screening method for identifying protein kinase substrates, EMBO J. 16: 1921-1933; Pyronnet et al , 1999, "Human eukaryotic translation initiation factor 4G (eIF4G) recruits mnkl to phosphorylate eIF4E," EMBO J.
- eIF4G Human eukaryotic translation initiation factor 4G
- MKNKl a new MAP kinase-activated protein kinase, isolated by a novel expression screening method for identifying protein kinase substrates.
- MMP9 The nucleotide sequence of MMP9 (identified by accession no. NM_004994) is disclosed in, e.g., Wilhelm et al, 1989, "SV40-transformed human lung fibroblasts secrete a 92-kDa type IV collagenase which is identical to that secreted by normal human macrophages," J. Biol. Chem.
- NP_004985 is disclosed in, e.g., Wilhelm et al, 1989, "SV40-transformed human lung fibroblasts secrete a 92-kDa type IV collagenase which is identical to that secreted by normal human macrophages," J. Biol. Chem.
- NM_004829 is disclosed in, e.g., Sivori et al, 1997, "p46, a novel natural killer cell-specific surface molecule that mediates cell activation," J. Exp. Med. 186:1129-1136, Vitale,M. et al, NKp44, 1998, "NKp44, a novel triggering surface molecule specifically expressed by activated natural killer cells, is involved in non-major histocompatibility complex-restricted tumor cell lysis," J. Exp. Med. 187: 2065-2072, Pessino et al, 1998, "Molecular cloning of NKp46: a novel member of the immunoglobulin superfamily involved in triggering of natural cytotoxicity," J. Exp.
- NCRl a novel natural killer cell-specific surface molecule that mediates cell activation
- J. Exp. Med. 186:1129-1136 Vitale et ⁇ /., NKp44, 1998
- NKp44 a novel triggering surface molecule specifically expressed by activated natural killer cells, is involved in non-major histocompatibility complex-restricted tumor cell lysis
- NKp46 a novel member of the immunoglobulin superfamily involved in triggering of natural cytotoxicity
- OSM otide sequence of OSM (identified by accession no. NM_020530) is disclosed in, e.g., Zarling et al, 1986, "Oncostatin M: a growth regulator produced by differentiated histiocytic lymphoma cells," Proc. Natl. Acad. Sci. U.S.A. 83 (24): 9739-9743, Malik et al, 1989, "Molecular cloning, sequence analysis, and functional expression of a novel growth regulator, oncostatin M," MoI. Cell. Biol. 9 (7):2847-2853, Linsley, P. S.
- NM_004566 is disclosed in, e.g., Sakai, A. et al, 1996, "Cloning of cDNA encoding for a novel isozyme of fructose 6-phosphate, 2-kinase/fructose 2,6-bisphosphatase from human placenta," J. Biochem. 119 (3):506-511, Hamilton, J.A. et al, 1997, "Identification of PRGl, a novel progestin-responsive gene with sequence homology to 6-phosphofructo-2- kinase/fructose-2,6-bisphosphatase,"
- nucleotide sequence of PRVl (identified by accession no. NM_020406) is disclosed in, e.g., Lalezari, P. et al, 1971, "NBl, a new neutrophil-specific antigen involved in the pathogenesis of neonatal neutropenia," J. Clin. Invest. 50 (5):1108-1115, Goldschmeding, R. et al, 1992, "Further characterization of the NB 1 antigen as a variably expressed 56-62 kD GPI-linked glycoprotein of plasma membranes and specific granules of neutrophils," Br. J. Haematol. 81 (3):336-345, Stroncek, D.F.
- NM_024430 is disclosed in, e.g., Hillier, L.D. et al, 1996, “Generation and analysis of 280,000 human expressed sequence tags," Genome Res. 6 (9):807-828, Wu 5 Y. et al, 1998, "PSTPIP 2, a second tyrosine phosphorylated, cytoskeletal-associated protein that binds a PEST-type protein-tyrosine phosphatase," J. Biol. Chem. 273 (46):30487-30496, Yeung, Y.G. et al, 1998, "A novel macrophage actin-associated protein (MAYP) is tyrosine- phosphorylated following colony stimulating factor- 1 stimulation," J. Biol.
- MAYP novel macrophage actin-associated protein
- NM_003955 is disclosed in, e.g., Minamoto, S. et al, 1997, "Cloning and functional analysis of new members of STAT induced STAT inhibitor (SSI) family: SSI-2 and SSI-3," Biochem. Biophys. Res. Commun. 237 (l):79-83, Masuhara, M. et al, 1997, “Cloning and characterization of novel CIS family genes,” Biochem. Biophys. Res. Commun. 239 (2):439-446, Zhang, J.G.
- SSI STAT induced STAT inhibitor
- NM_153046 is disclosed in, e.g., Isogai et al, 2003, "Homo sapiens cDNA FLJ43990 fis, clone TESTI4019566, weakly similar to Dosage compensation regulator," unpublished, and the amino acid sequence of TDRD9 (identified by accession no. NP_694591) is disclosed in, e.g., Isogai et al, 2003, “Homo sapiens cDNA FLJ43990 fis, clone TESTI4019566, weakly similar to Dosage compensation regulator," unpublished, each of which is incorporated by reference herein in its entirety.
- NM_000358 is disclosed in, e.g., Skonier et al, 1992, "cDNA cloning and sequence analysis of beta ig-h3, a novel gene induced in a human adenocarcinoma cell line after treatment with transforming growth factor-beta," DNA Cell Biol. 11 (7):511-522, Stone et al, 1994, "Three autosomal dominant corneal dystrophies map to chromosome 5q," Nat. Genet.
- beta ig-h3 a transforming growth factor-beta- responsive gene encoding a secreted protein that inhibits cell attachment in vitro and suppresses the growth of CHO cells in nude mice
- DNA Cell Biol. 13 (6): 571-584 and the amino acid sequence of TGFBI (identified by accession no. NP_000349) is disclosed in, e.g., Skonier et al, 1992, "cDNA cloning and sequence analysis of beta ig-h3, a novel gene induced in a human adenocarcinoma cell line after treatment with transforming growth factor-beta," DNA Cell Biol.
- TIFA The nucleotide sequence of TIFA (identified by accession no. NM_052864) is disclosed in, e.g., Kanamori, M. et al, 2002, "T2BP, a novel TRAF2 binding protein, can activate NF-kappaB and AP-I without TNF stimulation," Biochem. Biophys. Res. Commun. 290 (3):1108-1113, Takatsuna, H. et al, 2003, "Identification of TIFA as an adapter protein that links tumor necrosis factor receptor-associated factor 6 (TRAF6) to interleukin-1 (IL-I) receptor-associated kinase-1 (IRAK-I) in IL-I receptor signaling," J. Biol.
- T2BP tumor necrosis factor receptor-associated factor 6
- IRAK-I interleukin-1 receptor-associated kinase-1
- TIFA tumor necrosis factor receptor-associated factor 6
- IL-I interleukin-1 receptor-associated kinase- 1
- IRAK-I interleukin-1 receptor-associated kinase- 1
- Tissue Inhibitor of Metalloproteinase 1 (TIMPl)
- TIMPl identified by accession no. AAA75558
- the amino acid sequence of TIMPl is disclosed in, e.g., Hardcastle et al, 1997, "Genomic organization of the human TIMP-I gene. Investigation of a causative role in the pathogenesis of X-linked retinitis pigmentosa," Invest. Ophthalmol. Vis. Sci. 38, 1893-1896, which is incorporated by reference herein in its entirety.
- the nucleotide sequence of TLR4 (identified by accession no. AH009665) is disclosed in, e.g., Arbour, N.C.
- NM_152877 is disclosed in, e.g., Oehm, A. et al, 1992, "Purification and molecular cloning of the APO-I cell surface antigen, a member of the tumor necrosis factor/nerve growth factor receptor superfamily. Sequence identity with the Fas antigen," J. Biol. Chem. 267 (15):10709-10715, Inazawa, J. et al, 1992, "Assignment of the human Fas antigen gene (Fas) to 10q24.1," Genomics 14 (3):821-822, Cheng, J.
- Fas human Fas antigen gene
- Genomics 14 (3):821-822 Cheng, J. etal, 1994, "Protection from Fas-mediated apoptosis by a soluble form of the Fas molecule," Science 263 (5154): 1759-1762, each of which is incorporated by reference herein in its entirety.
- NM_003810 is disclosed in, e.g., Wiley, S.R. et al, 1995, "Identification and characterization of a new member of the TNF family that induces apoptosis," Immunity 3 (6):673-682, Pitti, R.M. et al, 1996, "Induction of apoptosis by Apo-2 ligand, a new member of the tumor necrosis factor cytokine family," J. Biol. Chem. 271 (22): 12687- 12690, Pan, G.
- TNFSFlO The receptor for the cytotoxic ligand TRAIL
- Science 276 (5309): 111-113 and the amino acid sequence of TNFSFlO (identified by accession no. NP_003801) is disclosed in, e.g., Wiley, S.R. et al, 1995, "Identification and characterization of a new member of the TNF family that induces apoptosis," Immunity 3 (6):673-682, Pitti, R.M. et al, 1996, "Induction of apoptosis by Apo-2 ligand, a new member of the tumor necrosis factor cytokine family," J. Biol. Chem.
- NM_006573 is disclosed in, e.g., Shu, H.B. et al, 1999, "TALL-I is a novel member of the TNF family that is down-regulated by mitogens," J. Leukoc. Biol. 65 (5): 680-683, Mukhopadhyay, A. et al, 1999, "Identification and characterization of a novel cytokine, THANK, a TNF homologue that activates apoptosis, nuclear factor-kappaB, and c-Jun NH2-terminal kinase," J. Biol. Chem. 274 (23):15978-15981, Schneider, P.
- TNFSF13B a novel ligand of the tumor necrosis factor family, stimulates B cell growth
- NP_006564 the amino acid sequence of TNFSF13B (identified by accession no. NP_006564) is disclosed in, e.g., Shu, H.B. et al, 1999, "TALL-I is a novel member of the TNF family that is down-regulated by mitogens," J. Leukoc. Biol. 65 (5): 680-683, Mukhopadhyay, A.
- VNNl The nucleotide sequence of VNNl (identified by accession no. NM_004666) is disclosed in, e.g., Aurrand-Lions, M. et al, 1996, "Vanin-1, a novel GPI-linked perivascular molecule involved in thymus homing," Immunity 5 (5):391-405, Galland, F. et al, 1998, "Two human genes related to murine vanin-1 are located on the long arm of human chromosome 6," Genomics 53 (2):203-213, Maras, B. et al, 1999, "Is pantetheinase the actual identity of mouse and human vanin-1 proteins?,” FEBS Lett.
- VNNl a novel GPI-linked perivascular molecule involved in thymus homing
- Immunity 5 (5):391-405 Galland, F. et al, 1998
- Tewo human genes related to murine vanin-1 are located on the long arm of human chromosome 6
- Genomics 53 (2):203-213 Maras, B. et al, 1999, "Is pantetheinase the actual identity of mouse and human vanin-1 proteins?”
- FEBS Lett. 461 (3): 149-152 each of which is incorporated by reference herein in its entirety.
- the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more biomarkers selected from Table I regardless of whether each such biomarker has an "N" designation or a "P" designation in Table I. In some nonlimiting exemplary embodiments, between 2 and 53, between 3 and 40, between 4 and 30, or between 5 and 20 such biomarkers are used. [00415] Nucleic acid based kits and methods.
- each biomarker is a nucleic acid (e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA), or a discriminating molecule or discriminating fragment of a nucleic acid.
- a nucleic acid e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA
- RNA such as mRNA
- discriminating molecule or discriminating fragment of a nucleic acid between 2 and 44, between 3 and 35, between 4 and 25, or between 5 and 20 such biomarkers are used.
- the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 of the biomarkers selected from Table K.
- biomarkers are peptide-based (e.g., a peptide, a full length protein, etc.), or a discriminating molecule or discriminating fragment of the foregoing.
- the biomarkers in the kit are specific antibodies to two or more of the biomarkers listed in Table K. In some nonlimiting exemplary embodiments, between 2 and 10, between 3 and 10, between 4 and 10, or between 5 and 10 such biomarkers are used.
- Homogenous kits and methods are used.
- each of the biomarkers in the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two or more biomarkers selected from Table I where each biomarker used in such methods or kits is in the same physical form.
- each biomarker in a method or kit in accordance Section 5.2 and Section 5.3, respectively is a biomarker selected from Table I and is a nucleic acid or a discriminating molecule of a nucleic acid in the method or kit.
- each biomarker in a method or kit in accordance Section 5.2 and Section 5.3, respectively is a biomarker selected from Table I and is peptide-based (e.g., a peptide, a full length protein, etc.) or a discriminating molecule of the forgoing.
- biomarkers are selected without regard as to whether they are designated "P" or "N” in Table I.
- a kit in accordance with these embodiments can include a biomarker in nucleic acid form, even when the biomarker is designated "P" on Table I.
- kits in accordance with this embodiment can include a biomarker in peptidic form, even when the biomarker is designated "N" on Table I.
- each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two or more biomarkers selected from Table I where each such biomarker is in the same physical form that the biomarker was in when identified in Sections 6.11 through 6.13 below.
- a nucleic acid form of the biomarker is used in the methods and kits respectively described or referenced in Section 5.2 and 5.3 in accordance with this embodiment of the invention.
- biomarker has a "P" designation in Table I
- a peptidic form of the biomarker is used in the methods and kits respectively described or referenced in Section 5.2 and 5.3 in accordance with this embodiment of the invention.
- biomarkers having an N designation in Table I are nucleic acids and biomarkers having a P designation in Table I are peptide-based or protein-based.
- a non-limiting exemplary kit in accordance with such mixed embodiments use two biomarkers from among the biomarkers listed in Table J, in nucleic acid form, and three biomarkers from among the biomarkers listed in Table K, in peptidic-based form.
- the non-limiting methods and kits respectively described or referenced in Sections 5.2 and 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, or more biomarkers from Table J, in nucleic acid form, and 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from Table K in peptide-based or protein-based form.
- each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least one biomarkers selected from Table I and at least one different biomarker from Table 31. In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers selected from Table I and at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from Table 31.
- each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least one biomarker in, nucleic acid form, selected from Table J and at least one different biomarker from Table 31. In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers selected from Table I, each in nucleic acid form, and at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from Table 31.
- each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least one biomarker in, protein form, selected from Table K and at least one different biomarker from Table 31. In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers selected from Table I, each in protein form, and at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from Table 31.
- each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least one biomarker from among the biomarkers listed in Table J, in nucleic acid form, and at least one biomarkers from among the biomarkers listed in Table K, in protein form, hi some embodiments, the non-limiting methods and kits respectively described or referenced in Sections 5.2 and 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, or more biomarkers from Table J, in nucleic acid form, and 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from Table K in protein form.
- any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6, IL-8, MMP9, B2M, HLA-DRA, and MCPl biomarkers are not used in such methods or kits.
- the IL-6, IL-8, MMP9, B2M, HLA-DRA, and MCPl biomarkers are not used in such methods or kits.
- the IL-6, IL-8, MMP9, B2M, HLA-DRA, and MCPl biomarkers are not used in such methods or kits.
- the IL-6, IL-8, MMP9, B2M, HLA-DRA, and MCPl biomarkers are not used in such methods or kits.
- the IL-6, IL-8, MMP9, B2M, HLA-DRA, and MCPl biomarkers are not used in such methods or kits.
- any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6, IL-8, IL-10, and CRP nucleic acid biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6 and MAPK biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6, IL-8, and IL-10 biomarkers are not used in such methods or kits.
- any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the CD86, IL-6, IL-8, IL-10, and CRP biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6 and IL-10 biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6 and CRP biomarkers are not used in such methods or kits.
- any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the CRP biomarker is not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-8 biomarker is not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the B2M biomarker is not used in such methods or kits.
- the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use any one biomarker set listed in Table L.
- the biomarker sets listed in Table L were identified in the computational experiments described in Section 6.14.1, below, in which 4600 random subcombinations of the biomarkers listed in Table J were tested.
- Table L lists some of the biomarker sets that provided high accuracy scores against the validation population described in Section 6.14.1. Each row of Table L lists a single biomarker set that can be used in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- each row of Table L describes a biomarker set that can be used to discriminate between sepsis and SIRS subjects (e.g., to determine whether a subject is likely to acquire sepsis).
- nucleic acid forms of the biomarkers listed in a biomarker set in Table L are used in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- protein forms of the biomarkers listed in a biomarker set in Table L are used in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- biomarkers in a biomarker set listed in Table L are in protein form and some of the biomarkers in the same biomarker set from Table L are in nucleic acid form in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- a given biomarker set listed in Table L is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers listed in Table I that are not within the given set of biomarkers from Table L.
- a given biomarker set listed in Table L is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from any one of Tables I, 30, 31, 32, 33, 34, or 36 that are not within the given biomarker set from Table L.
- accuracy, specificity, and senstitivity are described with reference to T -12 time point data described in Section 6.14.1, below.
- Table L Exemplary sets of biomarkers used in the methods or kits referenced in Sections 5.2 and 5.3
- the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use any one of the biomarker sets listed in Table M.
- the biomarker sets listed in Table M were identified in the computational experiments described in Section 6.14.2, below, in which 1600 random subcombinations of the biomarkers listed in Table K were tested.
- Table M lists some of the biomarker sets that provided high accuracy scores against the validation population described in Section 6.14.2. Each row of Table M lists a single biomarker set that can be used in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- each row of Table M describes a biomarker set that can be used to discriminate between sepsis and SIRS subjects (e.g., to determine whether a subject is likely to acquire SEPSIS).
- nucleic acid forms of the biomarkers listed in Table M are used in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- protein forms of the biomarkers listed in Table M are used.
- some of the biomarkers in a biomarker set from Table M are in protein form and some of the biomarkers in the same biomarker set from Table M are in nucleic acid form in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- a given biomarker set listed in Table M is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from Table I that are not within the given set of biomarkers from Table M.
- a given set of biomarkers from Table M is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from any one of Table I, 30, 31, 32, 33, 34, or 36 that are not within the given biomarker set from Table M.
- accuracy, specificity, and senstitivity are described with reference to T -12 time point data described in Section 6.14.2, below.
- Table M Exemplary sets of biomarkers used in the methods or kits referenced in Sections 5.2 and 5.3
- the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use any one of the subsets of biomarkers listed in Table N.
- the subsets of biomarkers listed in Table N were identified in the computational experiments described in Section 6.14.5, below, in which 4600 random subcombinations of the biomarkers listed in Table I were tested.
- Table N lists some of the biomarker sets that provided high accuracy scores against the validation population described in Section 6.14.5.
- Each row of Table N lists a single set of biomarkers that can be used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In other words, each row of Table N describes a set of biomarkers that can be used to discriminate between sepsis and SIRS subjects.
- nucleic acid forms of the biomarkers listed in Table N are used in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- protein forms of the biomarkers listed in Table N are used.
- some of the biomarkers in a biomarker set from Table N are in protein form and some of the biomarkers in the same biomarker set from Table N are in nucleic acid form in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- a given set of biomarkers from Table N is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from from any one of Table 30, 31, 32, 33, 34, or 36 that are not within the given set of biomarkers from Table N.
- accuracy, specificity, and senstitivity are described with reference to T -12 time point data described in Section 6.14.5, below. Start here
- Table N Exemplary sets of biomarkers used in the methods or kits referenced in Sections 5.2 and 5.3
- the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use any one of the subsets of biomarkers listed in Table O.
- the subsets of biomarkers listed in Table O were identified in the computational experiments described in Section 6.14.5, below, in which 4600 random subcombinations of the biomarkers listed in Table I were tested.
- Table O lists some of the biomarker sets that provided high accuracy scores against the validation population described in Section 6.14.5.
- Each row of Table O lists a single set of biomarkers that can be used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In other words, each row of Table O describes a set of biomarkers that can be used to discriminate between sepsis and SIRS subjects.
- nucleic acid forms of the biomarkers listed in Table O are used in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- protein forms of the biomarkers listed in Table O are used.
- some of the biomarkers in a biomarker set from Table O are in protein form and some of the biomarkers in the same biomarker set from Table O are in nucleic acid form in the methods and kits respectively referenced in Sections 5.2 and 5.3.
- a given set of biomarkers from Table O is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from from any one of Table 30, 31, 32, 33, 34, or 36 that are not within the given set of biomarkers from Table O.
- accuracy, specificity, and senstitivity are described with reference to T -36 time point data described in Section 6.14.6, below.
- Table O Exemplary sets of biomarkers used in the methods or kits referenced in Sections 5.2 and 5.3
- a biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in any one of Figures 6, 14, 17, or 26, biomarkers that each contain the complement of one of the probesets of any one of Figures 6, 14, 17, or 26, or biomarkers that each contain an amino acid sequence encoded by a gene that either contains one of the probesets of any one of Figures 6, 14, 17, or 26, or the complement of one of the probesets of any one of Figures 6, 14, 17, or 26.
- biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example, amplified nucleic acid, or proteins.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in any one of Figures 6, 14, 17, or 26, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers that each contains a probeset listed in any one of Figures 6, 14, 17, or 26.
- the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two different biomarkers listed in any one of Figures 39, 43, 52, 53, or 56.
- the biomarker profile comprises at least two different biomarkers listed in any one of Figures 39, 43, 52, 53, or 56.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker in the at least two different biomarkers is listed in any one of Figures 39, 43, 52, 53, or 56, the biomarker can be, for example, a transcript made by the listed gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein encoded by a gene listed in any one of Figures 39, 43, 52, 53, or 56, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker.
- Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table 30).
- such an assay utilizes a nucleic acid microarray.
- the biomarker profile comprises at least two different biomarkers from any one of Figures 39, 43, 52, 53, or 56.
- the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 different biomarkers from any one of Figures 39, 43, 52, 53, or 56.
- a biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in any one of the probeset collections of Table P, biomarkers that each contain the complement of one of the probesets from any one of the probeset collections of Table P, or biomarkers that each contain an amino acid sequence encoded by a gene that either contains one of the probesets from any one of the probeset collections of Table P, or the complement of one of the probesets of any one of the probeset collections of Table P.
- biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example, amplified nucleic acid, or proteins.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence from any one of the probeset collections listed in Table P, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers that each contains a probeset from any one of probeset collections listed in Table P.
- the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two different biomarkers listed in any one of the biomarker sets in Table Q.
- the biomarker profile comprises at least two different biomarkers listed in any one of the biomarker sets in Table Q.
- the biomarker profile further comprises a respective corresponding feature for the at least two biomarkers listed in any of the biomarker sets in Table Q.
- the at least two biomarkers are derived from at least two different genes.
- the biomarker in the at least two different biomarkers is listed in any one of biomarker sets of Table Q, can be, for example, a transcript made by the listed gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above.
- the biomarker can be, for example, a protein encoded by a gene listed in any one of the biomarker sets in Table Q, or a discriminating fragment of the protein, or an indication of any of the above.
- a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
- the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker.
- Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in any on of the biomarker sets of Table Q).
- a gene of interest e.g., a gene disclosed in any on of the biomarker sets of Table Q.
- such an assay utilizes a nucleic acid microarray.
- a biomarker profile comprising at least 2 or 3 different biomarkers from any one of the biomarker sets of Table Q is used.
- microarray biomarker abundance data from the two subjects that was collected at a particular single time point is evaluated in order to identify those biomarkers that have different abundance levels in the two subjects, as determined by a Ul 33 plus 2.0 microarray experiment.
- a whole population of subjects of type A and type B are evaluated and parametric and/or nonparametric statistical techniques are used to identify those biomarkers whose abundance levels discriminate between subjects that develop sepsis at some point during the observation period and subjects that do not develop sepsis during the observation period.
- an observation period refers to a time period that was a matter of hours, days, or weeks.
- biomarkers whose corresponding features (e.g. abundance value) at a single time point discriminate between sepsis subjects (subjects that develop sepsis at some point during the observation time period) and subjects that do not develop sepsis during the observed time frame biomarkers whose change in abundance value across two or more time points discriminates between the two populations types were identified. For example, again consider subject A, who develops sepsis shortly after time period T -12 , and subject B 3 who does not develop sepsis in any of the observed time points. In the basesline analysis, what were needed are biomarker abundance values for each subject from two different time points (e.g., time point 1 and time point 2).
- the difference in the abundance of the biomarker at the two different time points was computed.
- These differential abundances from each of the subjects is then used to determine which corresponding biomarkers, expressed as a differential between two different time points, discriminate between subjects that develop sepsis during the observation period and subjects that do not develop sepsis during the observation time period.
- Subjects were eighteen years of age or older and gave informed consent to comply with the study protocol. Subjects were excluded from the study if they were (i) pregnant, (ii) taking antibiotics to treat a suspected infection, ( ⁇ i) were taking systemic corticosteroids (total dosage greater than 100 mg hydrocortisone or equivalent in the past 48 hours prior to study entry), (iv) had a spinal cord injury or other illness requiring high-dose corticosteroid therapy, (v) pharmacologically immunosuppressed (e.g., azathioprine, methotrexate, cyclosporin, tacrolimus, cyclophosphamide, etanercept, anakinra, infliximab, leuflonamide, mycophenolic acid, OKT3, pentoxyphylin, etc.), (vi) were an organ transplant recipient, (vii) had active or metastatic cancer, (viii) had received chemotherapy or radiation therapy within 8 weeks prior to enrollment, and/or (ix)
- APACHE II is a system for rating the severity of medical illness.
- APACHE stands for "Acute Physiology And Chronic Health Evaluation," and is most frequently used to predict in-hospital death for patients in an intensive care unit. See, for example, Gupta et ah, 2004, Indian Journal of Medical Research 119, 273-282, which is hereby incorporated herein by reference in its entirety.
- SOFA is a test to measure the severity of sepsis. See, for example, Vincent et ah, 1996, Intensive Care Med. 22, 707-710, which is hereby incorporated herein by reference in its entirety. Patients were monitored daily for up to two weeks for clinical suspicion of sepsis including, but not limited to, any of the following signs and symptoms: [00442] • pneumonia: temperature > 38.3 0 C or ⁇ 36°C + white blood cell count
- urinary tract infection temperature > 38.3 0 C or WBC > 12,000/mm 3 or ⁇
- Blood was drawn daily for a minimum of four consecutive days beginning within 24 hours following study entry. Patients were followed and blood samples were drawn daily for a maximum of fourteen consecutive days unless clinical suspicion of infection occurred. The maximum volume of blood drawn from any one subject did not exceed 210 mL over the course of a 14 day study maximum. Blood draws for the study were discontinued if the loss of blood posed risk to the patient as defined by physician's judgment. Each patient had two Paxgene (RNA) tubes drawn on each day. One tube was used for the microarray analysis described in Section 6.2. The other tube was used for the RT-PCR analysis described in Section 6.10.
- RNA Paxgene
- Affymetrix Santa Clara, California
- Ul 33 plus 2.0 human genome chips containing 54,675 probesets To enhance detection sensitivity of the microarray, globin mRNA molecules were removed from the total RNA extracted from the blood samples using the methods described in, for example, U.S. Patent Publication 20050221310, filed August 9, 2004, and 10/948,635, filed September 24, 2004, both entitled “Methods of Enhancing Gene Expression Analysis,” each of which is incorporated by reference herein in its entirety.
- the U133 plus 2.0 has 62 probesets designed for special functions, such as measuring supplementally added transcripts. This leaves 54,613 probesets designed specifically for the detection of human genes.
- the Affymetrix human genome Ul 33 (HG- U133) set consisting of two microarrays, contains almost 45,000 probesets representing more than 39,000 transcripts derived from approximately 33,000 human genes. This set design uses sequences selected from GenBank, dbEST, and RefSeq. As used herein, the abundance value measured for each of the biomarkers that bind to these probesets is referred to as a feature. The examples below discuss abundance values of biomarkers that bind to particular probesets in the Ul 33 plus 2.0 human genome chip.
- T -36 static analysis was performed.
- biomarkers features are determined using a specific blood sample, designated the T -36 blood sample, from each subject in a training population.
- the identity of this specific blood sample from each respective subject in the training population is dependent upon whether the subject was a SIRS subject (did not develop sepsis during the observation period) or was a sepsis subject (did develop sepsis during the observation period).
- the T -36 sample is defined as the second to last blood sample taken from the subject before the subject acquired sepsis.
- T -36 samples in the SIRS subjects in the training population was more discretionary than for the sepsis counterpart subjects because there was no significant event in which the SIRS subjects became septic. Because of this, the identity of the T -36 samples for the sepsis subjects in the training population was used to identify the T -36 samples in the SIRS subjects in the training population. Specifically, T -36 time points (blood samples) for SIRS subjects in the training population were identified by "time-matching" a septic subject and a SIRS subject. For example, consider the case in which a subject that entered the study became clinically- defined as septic on their sixth day of enrollment.
- T -36 is day four of the study, and the T -36 blood sample is the blood sample that was obtained on day four of the study.
- T -36 for the SIRS subject that was matched to this sepsis subject is deemed to be day four of the study on this paired SIRS subject.
- the training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance.
- 35 were Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 29 were SIRS, meaning that they did not develop sepsis during the observation time period.
- Table 1 provides distributions of the race, gender and age for these samples.
- Table 1 Distributions of the race, gender, and age for the training data
- Each sample in the training data was randomly assigned to one often groups used for cross-validation.
- the number of training samples in these groups ranged from 6 to 7.
- the samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds.
- several different methods were used to judge whether select biomarkers, which bind to particular probesets in the microarray, discriminate between the Sepsis and SIRS groups.
- Wilcoxon and Q-value tests The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted).
- the Wilcoxon test is a distribution-free test is resistant to extreme values.
- the Wilcoxon test is described in Agresti, 1996, An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc, New York, Chapter 2, which is hereby incorporated by reference in its entirety.
- the Wilcoxon test produces a/> value. The abundance value for a given biomarker from all samples in the training data is subjected to the Wilcoxon test.
- the Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute ap value for the given O O O O biomarker.
- the p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state.
- a specific confidence level such as 0.05
- Q values Q-values are described in Storey, 2002, J.R. Statist. Soc. B 64, Part 3, pp. 479- 498, which is hereby incorporated by reference in its entirety.
- the biomarkers are ordered by their q-values and if a biomarker has a q-value of X, then this biomarker and all others more significant have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were 2431 significant markers using this method (see Table 3).
- Table 3 Cumulative number of significant calls for the three methods.
- CART classification and regression tree
- decision 102 makes a decision based on the abundance of the biomarker that binds to X204319_s_at. If the biomarker that binds to X204319_s_at has an abundance that is greater than 2.331 units in a biological sample from a subject to be diagnosed (test biological sample), then control passes to decision 104. If, on the other hand, the biomarker that binds to probeset X204319_s_at has abundance that is less than 2.331 units in the test biological sample, decision control passes to decision 106. Decisions are made in this manner until a terminal leaf of the decision tree is reached, at which point diagnoses of sepsis or SIRS is made.
- the decision tree in Figure 1 makes use of the biomarkers that bind to the following five probesets: X204319_s_at, X1562290_at, X1552501_a_at, X1552283_s_at, and Xl 17_at.
- Figure 2 shows the distribution of the biomarkers that bind to the five probesets used in the decision tree between the sepsis and SIRS groups in the training data set.
- the top of each box denotes the 75 th percentile of the data across the training set and the bottom of each box denotes the 25 th percentile, and the median value for each biomarker across the training set is drawn as a line within each box.
- the confusion matrix for the training data where the predicted classifications were made from the cross- validated model is given in Table 4. From this confusion matrix, the overall accuracy was estimated to be 70.3% with a 95% confidence interval of 57.6% to 81.1%. The estimated sensitivity was 60% and the estimated specificity was 82.8%.
- Table 4 Confusion matrix for training samples using the cross-validated CART algorithm of Figure 1.
- Table 5 Confusion matrix for validation samples using the cross-validated CART algorithm of Figure 1.
- Random Forests Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree.
- Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees ⁇ e.g. bootstrap iterations) is specified. No more than 500 were used in this example, but at least 50 are needed for a burn-in period. The number of trees was chosen based on the accuracy of the training data. For this data, 500 trees were used to train the algorithm (see Figure 3).
- curve 302 is a smoothed estimate of overall accuracy as a function of tree number.
- Curve 304 is a smoothed curve of tree sensitivity as a function of tree number.
- Curve 306 is a smoothed curve of tree specificity as a function of tree number.
- the random forest method uses a number of different decision trees.
- a biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis.
- a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
- Table 6 Confusion matrix for training samples against the decision tree developed using the Random Forest method.
- Table 7 Confusion matrix for the 20 validation samples against the decision tree developed using the Random Forest method.
- PAM predictive analysis of microarrays
- PAM microarrays
- a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross- validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 2.07, corresponding to 258 biomarkers.
- Figure 5 shows the accuracy across different thresholds. In Figure 5, curve 502 is the overall accuracy (with 95% confidence interval bars).
- Curve 504 shows decision rule sensitivity as a function of threshold value.
- Curve 506 shows decision rule specificity as a function of threshold value. Using the threshold of 2.07, the overall accuracy for the training samples was estimated to be 73.4% with 95% a confidence interval of 61.4% to 82.8%. The estimated sensitivity was 79.3% and the estimated specificity was 68.6%.
- Table 8 Confusion matrix for training samples using cross-validated PAM algorithm
- Figure 6 shows the selected biomarkers, ranked by their relative discriminatory power, and their relative importance in the model.
- Figure 6 only shows the fifty most important biomarkers found using the PAM analysis. However, 258 important biomarkers were found.
- the biomarkers in Figure 6 are labeled based upon the Ul 33 plus 2.0 probeset to which they bind.
- Figure 7 provides a summary of the CART, PAM, and random forests classification algorithm (decision rule) performance and associated 95% confidence intervals. Fifty distinct biomarkers were selected from across all the algorithms illustrated in Figure 7.
- Figure 8 illustrates the number of times that common biomarkers were selected across the techniques of Wilcoxon (adjusted), CART, PAM, and RF.
- Figure 9 illustrates an overall ranking of biomarkers for the T-36 data set. For the selected biomarkers, the x-axis depicts the percentage of times that it was selected. Within the percentage of times that biomarkers were selected, the biomarkers are ranked. The biomarkers in Figure 7 are labeled based upon the probeset (oligonucleotide identity) to which they bind.
- T -12 static analysis was performed.
- biomarkers features were measured using a specific blood sample, designated the T -12 blood sample, obtained from each subject in the training population.
- the identity of this specific blood sample from a given subject in the training population was dependent upon whether the subject was a SIRS subject (did not develop sepsis during the observation period) or a sepsis subject (did develop sepsis during the observation period).
- the T -12 sample was defined as the last blood sample taken from the subject before the subject acquired sepsis.
- T -12 samples in the SIRS subjects in the training population was more discretionary than for the sepsis counterpart subjects because there was no significant event in which the SIRS subjects became septic. Because of this, the identity of the T -12 samples for the sepsis subjects in the training population was used to identify the T -12 samples in the SIRS subjects in the training population. Specifically, T -12 time points (blood samples) for SIRS subjects in the training population were identified by "time-matching" a septic subject and a SIRS subject. For example, consider the case in which a subject that entered the study became clinically-defined as septic on their sixth day of enrollment.
- T -12 was day five of the study (1-24 hours prior to sepsis), and the T -12 blood sample was the blood sample that was obtained on day five of the study.
- T -12 for the SIRS subject that was matched to this sepsis subject was deemed to be day five of study on this paired SIRS subject. While time matching between arbitrary pairs of SIRS and sepsis subjects was done to identify T -12 blood samples for as many of the SIRS subjects in the training population as possible, in some instances, T -12 samples from SIRS subjects had to be selected from the time points based on sample availability. [00472]
- T -12 static analysis there were 54,613 biomarkers measured on 90 samples for a total of 90 corresponding microarray experiments from 90 different subjects.
- each sample was collected from a different member the population.
- 31,047 were transformed by log transformations.
- 2518 were transformed by a square root transformation.
- the remaining 21,048 probesets in each microarray experiment were not transformed.
- the training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance.
- 34 were labeled Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 35 were SIRS, meaning that they did not develop sepsis during the observation time period.
- Table 10 provides distributions of the race, gender and age for these samples.
- Table 10 Distributions of the race, gender, and age for the training data
- Table 11 provides distributions of the race, gender and age for these samples.
- Table 11 Distributions of the race, gender, and age for the validation data
- Each sample in the training data was randomly assigned to one often groups used for cross— validation.
- the number of training samples in these groups ranged from 6 to 8.
- the samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers discriminate between the Sepsis and SIRS groups.
- the biomarkers are ordered by their q-values and if a respective biomarker has a q-value of X, then respective biomarker and all others more significant have a combined false discovery rate of X.
- the false discovery rate for any one biomarker may be much larger. There were 11851 significant biomarkers using this method (see Table 12).
- Table 12 Cumulative number of significant calls for the three methods. Note that all 90 samples (training and validation) were used to compare Sepsis and SIRS groups. Missing biomarker feature values were not included in the analyses.
- CART classification and regression tree
- decision 1002 makes a decision based on the abundance of the biomarker that binds to probeset X214681_at. If biomarker X214681_at has an abundance that is greater than 7.862 units in a biological sample from a subject to be diagnosed (test biological sample), than control passes to decision 1004. If 5 on the other hand, if the biomarker that binds to probeset (U133 plus 2.0 oligonucleotide) X214681_at has an abundance that is less than 7.862 units in the test biological sample, decision control passes to decision 1006. Decisions are made in this manner until a terminal leaf of the decision tree is reached, at which point diagnoses of sepsis or SIRS is made. The decision tree in Figure 10 makes use of the biomarkers that bind to the following four probesets: X214681_at, X1560432_at, X230281_at, and X1007_s_at.
- Figure 11 shows the distribution of the four biomarkers used in the decision tree between the sepsis and SIRS groups in the training data set.
- the top of each box denotes the 75 th percentile of the data across the training set and the bottom of each box denotes the 25 th percentile, and the median value for each biomarker across the training set is drawn as a line within each box.
- the biomarkers are labeled in Figure 11 based on the identity of the Ul 33 plus 2.0 probes to which they bind).
- the confusion matrix for the training data where the predicted classifications were made from the cross- validated model is given in Table 13. From this confusion matrix, the overall accuracy was estimated to be 65.2% with a 95% confidence interval of 52.8% to 76.3%. The estimated sensitivity was 61.8% and the estimated specificity was 68.6%.
- Table 14 Confusion matrix for validation samples using the cross-validated CART algorithm of Figure 10 True Diagnosis
- Random Forests Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree.
- Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees ⁇ e.g. bootstrap iterations) is specified. No more than 500 were used in this example, but at least 50 are needed for a burn-in period. The number of trees was chosen based on the accuracy of the training data. For this data, 439 trees were used to train the algorithm (see Figure 12).
- curve 1202 is a smoothed estimate of overall accuracy as a function of tree number.
- Curve 1204 is a smoothed curve of tree sensitivity as a function of tree number.
- Curve 1206 is a smoothed curve of tree specificity as a function of tree number.
- a biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis.
- a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
- the predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 15. From this confusion matrix, the overall accuracy was estimated to be 75.4% (confidence intervals cannot be computed when using the bootstrap accuracy estimate). The estimated sensitivity was 73.5% and the estimated specificity was 77.1%.
- Table 15 Confusion matrix for training samples against the decision tree developed using the Random Forest method.
- Table 16 Confusion matrix for the 20 validation samples against the decision tree developed using the Random Forest method.
- MART Multiple Additive Regression Trees
- step size commonly referred to as “shrinkage”
- degree of interaction related to the number of splits for each tree. More information on MART is described in Section 5.5.4 above. The degree of interaction was set to 1 to enforce an additive model (e.g. each tree has one split and only uses one biomarker).
- Estimating interactions may require more data to function well.
- the step size was set to 0.05 so that the model complexity was dictated by the number of trees.
- the optimal number of trees was estimated by leaving out a random subset of cases at each fitting iteration, then assessing quality of prediction on that subset. After fitting more trees than were warranted, the point at which prediction performance stopped improving was estimated as the optimal point.
- MART algorithm also provides a calculation of biomarker importance (summing to 100%), which are given in Figure 14. Biomarkers with zero importance were excluded. In Figure 14, biomarkers are labeled by the Ul 33 plus 2.0 oligonucleotide to which they bind.
- Figure 15 shows the distribution of the selected biomarkers between the Sepsis and SIRS groups. In Figure 15, biomarkers are labeled by the U133 plus 2.0 oligonucleotide to which they bind.
- Cross-validation was carried out, with the optimal number of trees estimated independently in each of the 10 iterations.
- the confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 17. From this confusion matrix, the overall accuracy was estimated to be 76.8% with a 95% confidence interval of 65.1% to 86.1%. The estimated sensitivity was 76.5% and the estimated specificity was 77.1%.
- Table 17 Confusion matrix for the training samples using the cross-validated MART algorithm.
- Table 18 Confusion matrix for the validation samples using the MART algorithm.
- PAM predictive analysis of microarrays
- PAM microarrays
- a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 2.1, corresponding to 820 biomarkers.
- Figure 16 shows the accuracy across different thresholds. In Figure 16, curve 1602 is the overall accuracy (with 95% confidence interval bars). Curve 1604 shows decision rule sensitivity as a function of threshold value. Curve 1606 shows decision rule specificity as a function of threshold value.
- the overall accuracy for the training samples was estimated to be 80.9% with a 95% confidence interval of 73.4% to 86.7%.
- the estimated sensitivity was 85.7% and the estimated specificity was 76.5%.
- Table 19 shows the confusion matrix for the training data where the predicted classifications were made from the cross-validated models.
- the two time points for each respective subject in a training population were (i) the T -12 time point and (ii) the first measurement, Tf lrst , taken of the respective subject.
- T first could differ across the training population.
- T f i rst was two days before T -12
- T first was three days before T. 12; and so forth.
- the training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance.
- 33 were Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 35 were SIRS, meaning that they did not develop sepsis during the observation time period.
- Table 21 provides distributions of the race, gender and age for these samples.
- Table 21 Distributions of the race, gender, and age for the training data
Abstract
Description
Claims
Priority Applications (7)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
BRPI0609302-7A BRPI0609302A2 (en) | 2005-04-15 | 2006-04-14 | methods for predicting the development of sepsis and for diagnosing sepsis in an individual to be tested, microarray, kit for predicting the development of sepsis in an individual to be tested, computer program product, computer, computer system for determining if an individual is likely to develop sepsis, digital signal embedded in a carrier wave, and, graphical user interface to determine if an individual is likely to develop sepsis |
JP2008506786A JP2008538007A (en) | 2005-04-15 | 2006-04-14 | Diagnosis of sepsis |
AU2006236588A AU2006236588A1 (en) | 2005-04-15 | 2006-04-14 | Diagnosis of sepsis |
CA002605143A CA2605143A1 (en) | 2005-04-15 | 2006-04-14 | Diagnosis of sepsis |
EP06740954A EP1869463A4 (en) | 2005-04-15 | 2006-04-14 | Diagnosis of sepsis |
IL186649A IL186649A0 (en) | 2005-04-15 | 2007-10-14 | Diagnosis of sepsis |
NO20075906A NO20075906L (en) | 2005-04-15 | 2007-11-15 | Sepsisdiagnoser |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US67162005P | 2005-04-15 | 2005-04-15 | |
US60/671,620 | 2005-04-15 | ||
US67404605P | 2005-04-22 | 2005-04-22 | |
US60/674,046 | 2005-04-22 |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2006113529A2 true WO2006113529A2 (en) | 2006-10-26 |
WO2006113529A3 WO2006113529A3 (en) | 2007-07-19 |
Family
ID=37115774
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2006/014241 WO2006113529A2 (en) | 2005-04-15 | 2006-04-14 | Diagnosis of sepsis |
Country Status (10)
Country | Link |
---|---|
US (6) | US7767395B2 (en) |
EP (1) | EP1869463A4 (en) |
JP (1) | JP2008538007A (en) |
KR (1) | KR20080006617A (en) |
AU (1) | AU2006236588A1 (en) |
BR (1) | BRPI0609302A2 (en) |
CA (1) | CA2605143A1 (en) |
IL (1) | IL186649A0 (en) |
NO (1) | NO20075906L (en) |
WO (1) | WO2006113529A2 (en) |
Cited By (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2009033282A1 (en) * | 2007-09-10 | 2009-03-19 | The University Of British Columbia | Mitogen-activated protein kinase kinase kinase 14 (map3k14) polymorphisms as indicators of subject outcome in critically 111 subjects |
WO2010041232A2 (en) * | 2008-10-09 | 2010-04-15 | The Provost, Fellows And Scholars Of The College Of The Holy And Undivided Trinity Of Queen Elizabeth, Near Dublin | A method of estimating sepsis risk in an individual with infection |
JP2011500079A (en) * | 2007-10-29 | 2011-01-06 | バイオカルテイス・エス・エイ | Automatic detection of infectious diseases |
DE102008000715B4 (en) * | 2008-03-17 | 2012-02-02 | Sirs-Lab Gmbh | Method for in vitro detection and differentiation of pathophysiological conditions |
US8114615B2 (en) | 2006-05-17 | 2012-02-14 | Cernostics, Inc. | Method for automated tissue analysis |
WO2012120026A1 (en) * | 2011-03-08 | 2012-09-13 | Sirs-Lab Gmbh | Method for identifying a subset of polynucleotides from an initial set of polynucleotides corresponding to the human genome for the in vitro determination of the severity of the host response of a patient |
WO2015077781A1 (en) | 2013-11-25 | 2015-05-28 | Children's Hospital Medical Center | Temporal pediatric sepsis biomarker risk model |
GB2527164A (en) * | 2014-02-11 | 2015-12-16 | Secr Defence | Apparatus, kits and methods for the prediction of onset of sepsis |
US10018631B2 (en) | 2011-03-17 | 2018-07-10 | Cernostics, Inc. | Systems and compositions for diagnosing Barrett's esophagus and methods of using the same |
US10443099B2 (en) | 2005-04-15 | 2019-10-15 | Becton, Dickinson And Company | Diagnosis of sepsis |
EP3882922A1 (en) * | 2020-03-21 | 2021-09-22 | Tata Consultancy Services Limited | Discriminating features based sepsis prediction |
WO2021185660A1 (en) * | 2020-03-17 | 2021-09-23 | Koninklijke Philips N.V. | Prognostic pathways for high risk sepsis patients |
WO2022064164A1 (en) * | 2020-09-22 | 2022-03-31 | The Secretary Of State For Defence Dstl | Apparatus, kits and methods for predicting the development of sepsis |
WO2022064162A1 (en) * | 2020-09-22 | 2022-03-31 | The Secretary Of State For Defence Dstl | Apparatus, kits and methods for predicting the development of sepsis |
WO2022064163A1 (en) * | 2020-09-22 | 2022-03-31 | The Secretary Of State For Defence | Apparatus, kits and methods for predicting the development of sepsis |
WO2022189530A1 (en) * | 2021-03-11 | 2022-09-15 | Koninklijke Philips N.V. | Prognostic pathways for high risk sepsis patients |
Families Citing this family (111)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8932227B2 (en) | 2000-07-28 | 2015-01-13 | Lawrence A. Lynn | System and method for CO2 and oximetry integration |
US9042952B2 (en) | 1997-01-27 | 2015-05-26 | Lawrence A. Lynn | System and method for automatic detection of a plurality of SPO2 time series pattern types |
US20060161071A1 (en) | 1997-01-27 | 2006-07-20 | Lynn Lawrence A | Time series objectification system and method |
US9521971B2 (en) | 1997-07-14 | 2016-12-20 | Lawrence A. Lynn | System and method for automatic detection of a plurality of SPO2 time series pattern types |
US20070191697A1 (en) | 2006-02-10 | 2007-08-16 | Lynn Lawrence A | System and method for SPO2 instability detection and quantification |
US9053222B2 (en) | 2002-05-17 | 2015-06-09 | Lawrence A. Lynn | Patient safety processor |
US20060195041A1 (en) | 2002-05-17 | 2006-08-31 | Lynn Lawrence A | Centralized hospital monitoring system for automatically detecting upper airway instability and for preventing and aborting adverse drug reactions |
US20090281838A1 (en) | 2008-05-07 | 2009-11-12 | Lawrence A. Lynn | Medical failure pattern search engine |
BR0316197A (en) * | 2002-11-12 | 2005-09-27 | Becton Dickinson Co | Kit, biomarker profile and method of isolating a host response biomarker |
TW200418992A (en) * | 2002-11-12 | 2004-10-01 | Becton Dickinson Co | Diagnosis of sepsis or sirs using biomarker profiles |
US7191068B2 (en) * | 2003-03-25 | 2007-03-13 | Proteogenix, Inc. | Proteomic analysis of biological fluids |
US8068990B2 (en) * | 2003-03-25 | 2011-11-29 | Hologic, Inc. | Diagnosis of intra-uterine infection by proteomic analysis of cervical-vaginal fluids |
EP1619257A1 (en) * | 2004-07-23 | 2006-01-25 | One Way Liver Genomics, S.L. | Method for the in vitro diagnosis of non-alcoholic steatohepatitis |
JP2008538238A (en) * | 2005-03-31 | 2008-10-16 | ザ・ボード・オブ・トラスティーズ・オブ・ザ・レランド・スタンフォード・ジュニア・ユニバーシティ | Compositions and methods for diagnosing and treating neuropsychiatric disorders |
US20100150885A1 (en) | 2005-06-01 | 2010-06-17 | Joslin Diabetes Center, Inc. | Methods and compositions for inducing brown adipogenesis |
FR2888252A1 (en) * | 2005-07-07 | 2007-01-12 | Biomerieux Sa | METHOD FOR THE DIAGNOSIS OF BREAST CANCER |
US7668579B2 (en) | 2006-02-10 | 2010-02-23 | Lynn Lawrence A | System and method for the detection of physiologic response to stimulation |
WO2007100913A2 (en) * | 2006-02-28 | 2007-09-07 | The Regents Of The University Of California | Genes differentially expressed in bipolar disorder and/or schizophrenia |
WO2007118073A2 (en) * | 2006-04-03 | 2007-10-18 | Joslin Diabetes Center, Inc. | Obesity and body fat distribution |
US9114398B2 (en) | 2006-11-29 | 2015-08-25 | Canon U.S. Life Sciences, Inc. | Device and method for digital multiplex PCR assays |
US20090104605A1 (en) * | 2006-12-14 | 2009-04-23 | Gary Siuzdak | Diagnosis of sepsis |
DE102006062398A1 (en) * | 2006-12-20 | 2008-06-26 | Edi (Experimentelle & Diagnostische Immunologie) Gmbh | Methods for detecting and / or characterizing cellular activity patterns, use of toll-like receptor ligands (TLR ligands) and a kit |
US8050870B2 (en) * | 2007-01-12 | 2011-11-01 | Microsoft Corporation | Identifying associations using graphical models |
US8224670B2 (en) * | 2007-01-25 | 2012-07-17 | Cerner Innovation, Inc. | Graphical user interface for visualizing person centric infection risk |
US20090047694A1 (en) * | 2007-08-17 | 2009-02-19 | Shuber Anthony P | Clinical Intervention Directed Diagnostic Methods |
WO2008137586A1 (en) * | 2007-05-01 | 2008-11-13 | University Of Miami | Transcriptomic biomarkers for individual risk assessment in new onset heart failure |
US20090029372A1 (en) * | 2007-05-14 | 2009-01-29 | Kobenhavns Universitet | Adam12 as a biomarker for bladder cancer |
US8126690B2 (en) * | 2007-05-18 | 2012-02-28 | The Regents Of The University Of Michigan | Algorithms to predict clinical response, adherence, and shunting with thiopurines |
GB0711327D0 (en) | 2007-06-12 | 2007-07-25 | Hansa Medical Ab | Diagnostic method |
US8431367B2 (en) | 2007-09-14 | 2013-04-30 | Predictive Biosciences Corporation | Detection of nucleic acids and proteins |
US20090075266A1 (en) * | 2007-09-14 | 2009-03-19 | Predictive Biosciences Corporation | Multiple analyte diagnostic readout |
US7955822B2 (en) * | 2007-09-14 | 2011-06-07 | Predictive Biosciences Corp. | Detection of nucleic acids and proteins |
US20100267041A1 (en) * | 2007-09-14 | 2010-10-21 | Predictive Biosciences, Inc. | Serial analysis of biomarkers for disease diagnosis |
GB0722582D0 (en) * | 2007-11-16 | 2007-12-27 | Secr Defence | Early detection of sepsis |
EP2229453A4 (en) * | 2007-12-04 | 2011-04-13 | Univ Miami | Molecular targets for modulating intraocular pressure and differentiation of steroid responders versus non-responders |
US8669113B2 (en) | 2008-04-03 | 2014-03-11 | Becton, Dickinson And Company | Advanced detection of sepsis |
CA2725208A1 (en) * | 2008-05-06 | 2009-11-12 | Joslin Diabetes Center, Inc. | Methods and compositions for inducing brown adipogenesis |
CA2729763C (en) | 2008-07-01 | 2020-09-29 | Mylene W.M. Yao | Methods and systems for assessment of clinical infertility |
GB0816867D0 (en) * | 2008-09-15 | 2008-10-22 | Glaxosmithkline Biolog Sa | Method |
WO2010090834A2 (en) * | 2009-01-20 | 2010-08-12 | The Penn State Research Foundation | Netrin-1 as a biomarker of injury and disease |
WO2010124269A1 (en) * | 2009-04-24 | 2010-10-28 | National Center For Genome Resources | Methods for diagnosis of sepsis and risk of death |
EP2249161B1 (en) * | 2009-05-05 | 2020-05-13 | InfanDx AG | Method of diagnosing asphyxia |
WO2010141546A1 (en) | 2009-06-02 | 2010-12-09 | University Of Miami | Diagnostic transcriptomic biomakers in inflammatory cardiomyopathies |
DE102009044085A1 (en) | 2009-09-23 | 2011-11-17 | Sirs-Lab Gmbh | Method for in vitro detection and differentiation of pathophysiological conditions |
EP2985352A1 (en) | 2009-09-23 | 2016-02-17 | Analytik Jena AG | Method for in vitro recording and differentiation of pathophysiological states |
EP2308999A1 (en) * | 2009-09-24 | 2011-04-13 | Ludwig-Maximilians-Universität München | A combination of markers for predicting the mortality risk in a polytraumatized patient |
WO2011066527A1 (en) * | 2009-11-30 | 2011-06-03 | The Ohio State University | Zinc status biomarker materials and related methods |
US20130203044A1 (en) * | 2010-04-30 | 2013-08-08 | University of Virginia Patent Foundation, d/b/a Universiy of Virginia Licensing & Ventures Group | System, method and computer program product for the organism-specific diagnosis of septicemia in infants |
US20110312521A1 (en) * | 2010-06-17 | 2011-12-22 | Baylor Research Institute | Genomic Transcriptional Analysis as a Tool for Identification of Pathogenic Diseases |
US8904511B1 (en) * | 2010-08-23 | 2014-12-02 | Amazon Technologies, Inc. | Virtual firewalls for multi-tenant distributed services |
GB201016161D0 (en) | 2010-09-24 | 2010-11-10 | Hansa Medical Ab | Diagnostic method |
EP2643483A4 (en) * | 2010-11-26 | 2014-04-30 | Immunexpress Pty Ltd | Diagnostic and/or screening agents and uses therefor |
EP2656257A2 (en) * | 2010-12-20 | 2013-10-30 | Koninklijke Philips N.V. | Methods and systems for identifying patients with mild cognitive impairment at risk of converting to alzheimer's |
EP2668287B1 (en) * | 2011-01-25 | 2017-09-06 | TC LAND Expression | Genes and genes combinations based on gene mknk1 predictive of early response or non response of subjects suffering from rheumatoid arthritis to tnf-alpha blocking monoclonal antibody |
GB201102108D0 (en) | 2011-02-07 | 2011-03-23 | Hansa Medical Ab | Diagnostic method |
EP2520662A1 (en) * | 2011-05-04 | 2012-11-07 | Stichting Sanquin Bloedvoorziening | Means and methods to determine a risk of multiple organ failure |
WO2013083754A1 (en) * | 2011-12-09 | 2013-06-13 | Sietze Sietzema | Method for detection of bacteria in milk |
WO2013107826A2 (en) * | 2012-01-17 | 2013-07-25 | Institut Pasteur | Use of cellular biomarkers expression to diagnose sepsis among intensive care patients |
JP6169619B2 (en) | 2012-02-09 | 2017-07-26 | メメド ダイアグノスティクス リミテッド | Kit for detecting bacterial infections or bacterial and viral co-infections |
EP2648133A1 (en) * | 2012-04-04 | 2013-10-09 | Biomerieux | Identification of microorganisms by structured classification and spectrometry |
US9275334B2 (en) * | 2012-04-06 | 2016-03-01 | Applied Materials, Inc. | Increasing signal to noise ratio for creation of generalized and robust prediction models |
DE102012209059A1 (en) | 2012-05-30 | 2013-12-05 | Siemens Aktiengesellschaft | Method and device for determining a temporal change of a biomarker in a study area |
WO2013183001A1 (en) | 2012-06-05 | 2013-12-12 | Cheetah Medical, Inc. | Method and system for assessing likelihood of sepsis |
FR2991777B1 (en) | 2012-06-07 | 2015-08-21 | Biomerieux Sa | METHOD FOR DETECTING AND / OR ASSAYING APPENDIX A3 OF A MAMMAL IN THE BLOOD OR AT LEAST ONE OF ITS DERIVATIVES |
US9336302B1 (en) | 2012-07-20 | 2016-05-10 | Zuci Realty Llc | Insight and algorithmic clustering for automated synthesis |
US10354429B2 (en) | 2012-11-14 | 2019-07-16 | Lawrence A. Lynn | Patient storm tracker and visualization processor |
US9953453B2 (en) | 2012-11-14 | 2018-04-24 | Lawrence A. Lynn | System for converting biologic particle density data into dynamic images |
US9274971B2 (en) | 2012-11-27 | 2016-03-01 | International Business Machines Corporation | Low latency data exchange |
CN104021264B (en) | 2013-02-28 | 2017-06-20 | 华为技术有限公司 | A kind of failure prediction method and device |
EP2961313A4 (en) | 2013-02-28 | 2016-11-09 | Lawrence A Lynn | System for analysis and imaging using perturbation feature quanta |
DE102013206291A1 (en) * | 2013-04-10 | 2014-10-16 | Robert Bosch Gmbh | Method and apparatus for creating a non-parametric, data-based function model |
KR101515700B1 (en) * | 2013-05-15 | 2015-04-27 | 경북대학교 산학협력단 | Diagnostic composition for sepsis using TGFBI and pharmaceutical composition for preventing or treating of sepsis using the same and screening method thereof |
US10190169B2 (en) | 2013-06-20 | 2019-01-29 | Immunexpress Pty Ltd | Biomarker identification |
CN105473743A (en) * | 2013-06-28 | 2016-04-06 | 睿智研究实验室私人有限公司 | Sepsis biomarkers and uses thereof |
CN103455616B (en) * | 2013-09-10 | 2017-06-13 | 广东金鉴检测科技有限公司 | A kind of method and system that SIMS data are read out and analyzed |
KR101520931B1 (en) * | 2013-09-16 | 2015-05-15 | 주식회사 옵티팜 | Composition for diagnosing sepsis and method thereof |
GB201322034D0 (en) | 2013-12-12 | 2014-01-29 | Almac Diagnostics Ltd | Prostate cancer classification |
US10260111B1 (en) * | 2014-01-20 | 2019-04-16 | Brett Eric Etchebarne | Method of detecting sepsis-related microorganisms and detecting antibiotic-resistant sepsis-related microorganisms in a fluid sample |
CA2930925A1 (en) | 2014-02-06 | 2015-08-13 | Immunexpress Pty Ltd | Biomarker signature method, and apparatus and kits therefor |
DK3117030T3 (en) | 2014-03-14 | 2022-06-27 | Robert E W Hancock | DIAGNOSIS OF SEPSIS |
GB201406259D0 (en) * | 2014-04-07 | 2014-05-21 | Univ Edinburgh | Molecular predictors of neonatal sepsis |
CN111624346A (en) | 2014-08-14 | 2020-09-04 | 米密德诊断学有限公司 | Kit for predicting and/or determining prognosis |
WO2016057945A1 (en) | 2014-10-09 | 2016-04-14 | Texas Tech University System | Micro-device to detect infection |
WO2016059636A1 (en) | 2014-10-14 | 2016-04-21 | Memed Diagnostics Ltd. | Signatures and determinants for diagnosing infections in non-human subjects and methods of use thereof |
WO2016094720A1 (en) * | 2014-12-10 | 2016-06-16 | Neogenomics Laboratories, Inc. | Automated flow cytometry analysis method and system |
US10592637B2 (en) | 2014-12-24 | 2020-03-17 | Luminare Incorporated | System, apparatus, method, and graphical user interface for screening |
CA2977422A1 (en) * | 2015-03-12 | 2016-09-15 | The Board Of Trustees Of The Leland Stanford Junor University | Methods for diagnosis of sepsis |
US20180107783A1 (en) * | 2015-05-28 | 2018-04-19 | Immunexpress Pty Ltd | Validating biomarker measurement |
EP3423590A4 (en) | 2016-03-03 | 2020-02-26 | Memed Diagnostics Ltd. | Rna determinants for distinguishing between bacterial and viral infections |
WO2017165693A1 (en) * | 2016-03-23 | 2017-09-28 | Peach Intellihealth, Inc. | Use of clinical parameters for the prediction of sirs |
US11340223B2 (en) | 2016-07-10 | 2022-05-24 | Memed Diagnostics Ltd. | Early diagnosis of infections |
EP4141448A1 (en) | 2016-07-10 | 2023-03-01 | MeMed Diagnostics Ltd. | Protein signatures for distinguishing between bacterial and viral infections |
WO2018060998A1 (en) | 2016-09-29 | 2018-04-05 | Memed Diagnostics Ltd. | Methods of prognosis and treatment |
WO2018060999A1 (en) | 2016-09-29 | 2018-04-05 | Memed Diagnostics Ltd. | Methods of risk assessment and disease classification |
GB201616557D0 (en) * | 2016-09-29 | 2016-11-16 | Secretary Of State For Health The | Assay for distinguishing between sepsis and systemic inflammatory response syndrome |
US11205103B2 (en) | 2016-12-09 | 2021-12-21 | The Research Foundation for the State University | Semisupervised autoencoder for sentiment analysis |
US11286525B2 (en) | 2017-01-17 | 2022-03-29 | Duke University | Gene expression signatures useful to predict or diagnose sepsis and methods of using the same |
US9820157B1 (en) * | 2017-03-06 | 2017-11-14 | Wipro Limited | Method and system for localizing spatially separated wireless transmitters |
US10209260B2 (en) | 2017-07-05 | 2019-02-19 | Memed Diagnostics Ltd. | Signatures and determinants for diagnosing infections and methods of use thereof |
KR102633621B1 (en) | 2017-09-01 | 2024-02-05 | 벤 바이오사이언시스 코포레이션 | Identification and use of glycopeptides as biomarkers for diagnosis and therapeutic monitoring |
US11852640B2 (en) | 2017-10-27 | 2023-12-26 | Beckman Coulter, Inc. | Hematology analyzers and methods of operation |
CN107862319B (en) * | 2017-11-19 | 2021-11-16 | 桂林理工大学 | Heterogeneous high-light optical image matching error eliminating method based on neighborhood voting |
US11644464B2 (en) * | 2018-04-20 | 2023-05-09 | Beckman Coulter, Inc. | Sepsis infection determination systems and methods |
US11521706B2 (en) | 2018-04-20 | 2022-12-06 | Beckman Coulter, Inc. | Testing and representing suspicion of sepsis |
CN110095603B (en) * | 2019-04-15 | 2022-05-17 | 中国人民解放军陆军特色医学中心 | Application of VNN1 in blood plasma or blood serum in marker for early warning of sepsis |
WO2021011356A1 (en) | 2019-07-12 | 2021-01-21 | Beckman Coulter, Inc. | Systems and methods for evaluating immune response to infection |
CN110954701B (en) * | 2019-12-18 | 2023-07-21 | 重庆医科大学 | Diagnostic kit for hepatic fibrosis or cirrhosis |
GB2596142B (en) * | 2020-06-19 | 2023-11-29 | Secr Defence | Methods and associated uses, kits and systems for assessing sepsis |
FR3112207A1 (en) * | 2020-07-06 | 2022-01-07 | bioMérieux | Method for determining the risk of occurrence of an infection associated with care in a patient |
FR3112211A1 (en) * | 2020-07-06 | 2022-01-07 | bioMérieux | Method for determining the risk of occurrence of an infection associated with care in a patient |
CN117597583A (en) * | 2021-03-18 | 2024-02-23 | 康博国际有限公司 | Method and system for detecting and quantifying a plurality of molecular biomarkers in a bodily fluid sample |
Family Cites Families (145)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
SE7609263L (en) | 1976-08-20 | 1978-02-21 | Wadsworth Charlies | PROCEDURE FOR DETERMINING THE QUANTITY OF CERTAIN IN A BIOLOGICAL LIQUID SOLUBLE COMPONENT, AND APPARATUS FOR EXERCISING THE PROCEDURE |
US4329152A (en) | 1980-10-06 | 1982-05-11 | International Lead Zinc Research Organization, Inc. | Determination of β2 -microglobulin in human urine and serum by latex immunoassay |
WO1987001597A1 (en) | 1985-09-19 | 1987-04-26 | Toray Industries, Inc. | beta2-MICROGLOBULIN-REMOVING COLUMN |
US5175113A (en) | 1986-01-16 | 1992-12-29 | Novo Nordisk A/S | Modified β2 -microglobulin |
DK160944C (en) | 1986-01-16 | 1991-10-21 | Novo Industri As | MODIFIED BETA2 MICROGLOBULINES AND PHARMACEUTICAL PREPARATIONS CONTAINING THESE |
EP0247592B1 (en) | 1986-05-30 | 1992-03-04 | Kanegafuchi Kagaku Kogyo Kabushiki Kaisha | Adsorbent for beta2-microglobulin and immunoglobulin l-chain |
SU1504597A1 (en) | 1986-06-25 | 1989-08-30 | Актюбинский государственный медицинский институт | Method of differential diagnosis of sepsis and localized purulent infection in newborn infants |
JPH0653170B2 (en) | 1986-07-07 | 1994-07-20 | 旭光学工業株式会社 | β2 microglobulin adsorbent |
US5093271A (en) | 1986-11-28 | 1992-03-03 | Shimadzu Corporation | Method for the quantitative determination of antigens and antibodies by ratio of absorbances at different wavelengths |
EP0319144A1 (en) | 1987-11-06 | 1989-06-07 | Asahi Kasei Kogyo Kabushiki Kaisha | Adsorbent of beta 2-microglobulin |
ES2048191T3 (en) | 1987-12-11 | 1994-03-16 | Akzo Nv | CELLULOSE BASED BIOCOMPATIBLE DIALYSIS MEMBRANE WITH INCREASED ADSORPTION OF BETA-2-MICROGLOBULIN. |
US5500345A (en) | 1989-04-25 | 1996-03-19 | Iatron Laboratories, Inc. | Hybridomas producing monoclonal antibodies specific for C-reactive protein and methods for detection of C-reactive protein |
US5272258A (en) | 1989-06-29 | 1993-12-21 | Rush-Presbyterian-St. Luke's Medical Center | Monoclonal antibodies to C-reactive protein |
EP0571442A4 (en) | 1991-01-14 | 1995-05-03 | Univ New York | Cytokine-induced protein, tsg-14, dna coding therefor and uses thereof. |
AU2158292A (en) | 1991-05-31 | 1993-01-08 | New England Deaconess Hospital Corporation | Cea-binding proteins and methods for their isolation and use |
DE4227454C1 (en) | 1992-08-19 | 1994-02-03 | Henning Berlin Gmbh | Process for early detection, for the detection of the severity as well as for the therapy-accompanying assessment of the course of sepsis as well as means for carrying out the process |
US5358852A (en) | 1992-12-21 | 1994-10-25 | Eastman Kodak Company | Use of calcium in immunoassay for measurement of C-reactive protein |
ATE242485T1 (en) | 1993-05-28 | 2003-06-15 | Baylor College Medicine | METHOD AND MASS SPECTROMETER FOR THE DESORPTION AND IONIZATION OF ANALYTES |
RU2072103C1 (en) | 1993-06-10 | 1997-01-20 | Ростовский научно-исследовательский институт акушерства и педиатрии | Method of diagnosis of bacterial infections in newborns |
US5484705A (en) | 1994-01-24 | 1996-01-16 | Xoma Corporation | Method for quantifying lipopolysaccharide binding protein |
US5882872A (en) | 1994-04-28 | 1999-03-16 | Kudsk; Kenneth A. | Use of an IL-6 assay for predicting the development of post-trauma complications |
US5646005A (en) | 1994-04-28 | 1997-07-08 | Kudsk; Kenneth A. | Use of an IL-6 assay for predicting the development of post-trauma complications |
US6190872B1 (en) | 1994-05-06 | 2001-02-20 | Gus J. Slotman | Method for identifying and monitoring patients at risk for systemic inflammatory conditions and apparatus for use in this method |
US5804370A (en) | 1994-06-08 | 1998-09-08 | Critichem Medical Products Limited | Early diagnosis of sepsis utilizing antigen-antibody interactions amplified by whole blood chemiluminescence |
US6306614B1 (en) | 1994-06-08 | 2001-10-23 | Sepsis, Inc. | Measurement of analytes in whole blood |
US5932536A (en) | 1994-06-14 | 1999-08-03 | The Rockefeller University | Compositions for neutralization of lipopolysaccharides |
US7625697B2 (en) * | 1994-06-17 | 2009-12-01 | The Board Of Trustees Of The Leland Stanford Junior University | Methods for constructing subarrays and subarrays made thereby |
US5780237A (en) | 1994-10-12 | 1998-07-14 | Cell Therapeutics, Inc. | Sepsis, adult respiratory distress syndrome, and systemic inflammatory response syndrome diagnostic |
US7597886B2 (en) | 1994-11-07 | 2009-10-06 | Human Genome Sciences, Inc. | Tumor necrosis factor-gamma |
US5708591A (en) | 1995-02-14 | 1998-01-13 | Akzo Nobel N.V. | Method and apparatus for predicting the presence of congenital and acquired imbalances and therapeutic conditions |
US6429017B1 (en) | 1999-02-04 | 2002-08-06 | Biomerieux | Method for predicting the presence of haemostatic dysfunction in a patient sample |
JP3436444B2 (en) | 1995-07-21 | 2003-08-11 | ヤマサ醤油株式会社 | Measurement method and kit for oxidized HDL |
US5981180A (en) | 1995-10-11 | 1999-11-09 | Luminex Corporation | Multiplexed analysis of clinical specimens apparatus and methods |
US5830679A (en) | 1996-03-01 | 1998-11-03 | New England Medical Center Hospitals, Inc. | Diagnostic blood test to identify infants at risk for sepsis |
US5780286A (en) | 1996-03-14 | 1998-07-14 | Smithkline Beecham Corporation | Arginase II |
US6077665A (en) | 1996-05-07 | 2000-06-20 | The Board Of Trustees Of The Leland Stanford Junior University | Rapid assay for infection in neonates |
AU3878697A (en) | 1996-06-20 | 1998-02-02 | Cornell Research Foundation Inc. | Identification of abnormalities in the expression of t and cell antigen receptors as indicators of disease diagnosis, prognosis and therapeutic predictors |
US6172220B1 (en) | 1997-01-21 | 2001-01-09 | Board Of Regents Of University Of Nebraska | Isolated algal lipopolysaccharides and use of same to inhibit endotoxin-initiated sepsis |
US6420526B1 (en) | 1997-03-07 | 2002-07-16 | Human Genome Sciences, Inc. | 186 human secreted proteins |
NZ516848A (en) | 1997-06-20 | 2004-03-26 | Ciphergen Biosystems Inc | Retentate chromatography apparatus with applications in biology and medicine |
US6416487B1 (en) | 1997-07-30 | 2002-07-09 | Renal Tech International Llc | Method of removing beta-2 microglobulin from blood |
US5904663A (en) | 1997-07-30 | 1999-05-18 | Braverman; Andrew | Method of removing beta-2 microglobulin from blood |
CA2306501C (en) | 1997-10-14 | 2011-03-29 | Luminex Corporation | Precision fluorescently dyed particles and methods of making and using same |
DK1650219T3 (en) | 1997-10-17 | 2008-05-05 | Genentech Inc | Human TOLL homologues |
US6159683A (en) | 1997-12-16 | 2000-12-12 | Spectral Diagnostics, Inc. | Method of determining stage of sepsis |
AU2464299A (en) | 1998-01-22 | 1999-08-09 | Luminex Corporation | Microparticles with multiple fluorescent signals |
CA2331897C (en) | 1998-05-14 | 2008-11-18 | Luminex Corporation | Multi-analyte diagnostic system and computer implemented process for same |
US6682741B1 (en) | 1998-06-10 | 2004-01-27 | The United States Of America As Represented By The Department Of Health And Human Services | β2 microglobulin fusion proteins and high affinity variants |
US6406862B1 (en) | 1998-10-06 | 2002-06-18 | The United States Of America As Represented By The Secretary Of The Army | Dip-stick assay for C-reactive protein |
DE19847690A1 (en) | 1998-10-15 | 2000-04-20 | Brahms Diagnostica Gmbh | Diagnosing sepsis and severe infections, useful for assessing severity and progress of treatment, by measuring content of peptide prohormone or their derived fragments |
US6248553B1 (en) | 1998-10-22 | 2001-06-19 | Atairgin Technologies, Inc. | Enzyme method for detecting lysophospholipids and phospholipids and for detecting and correlating conditions associated with altered levels of lysophospholipids |
US6251598B1 (en) | 1998-10-30 | 2001-06-26 | Interleukin Genetics, Inc. | Methods for diagnosing sepsis |
US6200766B1 (en) | 1998-12-03 | 2001-03-13 | Becton Dickinson And Company | Methods and reagents for quantitation of HLA-DR expression on peripheral blood cells |
US6423505B1 (en) | 1998-12-03 | 2002-07-23 | Becton Dickinson And Company | Methods and reagents for quantitation of HLA-DR and CD11b expression on peripheral blood cells |
AU2965500A (en) | 1999-01-15 | 2000-08-01 | Gene Logic, Inc. | Immobilized nucleic acid hybridization reagent and method |
WO2000046603A1 (en) | 1999-02-04 | 2000-08-10 | Akzo Nobel N.V. | A method and apparatus for predicting the presence of haemostatic dysfunction in a patient sample |
EP2022862B1 (en) | 1999-02-05 | 2015-04-15 | WRAIR (Walter Reed Army Institute of Research) | Method of diagnosing of exposure to toxic agents by measuring distinct pattern in the levels of expression of specific genes |
US6303321B1 (en) | 1999-02-11 | 2001-10-16 | North Shore-Long Island Jewish Research Institute | Methods for diagnosing sepsis |
AU3862200A (en) | 1999-03-01 | 2000-09-21 | Board Of Trustees Of The Leland Stanford Junior University | Rapid assay for infection in small children |
US6692916B2 (en) | 1999-06-28 | 2004-02-17 | Source Precision Medicine, Inc. | Systems and methods for characterizing a biological condition or agent using precision gene expression profiles |
US20040225449A1 (en) | 1999-06-28 | 2004-11-11 | Bevilacqua Michael P. | Systems and methods for characterizing a biological condition or agent using selected gene expression profiles |
US20050060101A1 (en) | 1999-06-28 | 2005-03-17 | Bevilacqua Michael P. | Systems and methods for characterizing a biological condition or agent using precision gene expression profiles |
US6960439B2 (en) | 1999-06-28 | 2005-11-01 | Source Precision Medicine, Inc. | Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles |
DE10027113A1 (en) | 1999-12-23 | 2001-09-27 | Andreas Hoeft | Method for determining microbial DNA / RNA, kit therefor and use of the method |
US6660482B1 (en) | 2000-02-28 | 2003-12-09 | Rhode Island Hospital | Inter-alpha-trypsin inhibitor as a marker for sepsis |
AU5610801A (en) | 2000-03-03 | 2001-09-12 | Gerd Schmitz | Method for the analysis of exogenic and endogenic cell activation based on measuring the aggregation of receptors |
US6838250B2 (en) | 2000-03-31 | 2005-01-04 | Ortho-Clinical Diagnostics, Inc. | Immunoassay for C-reactive protein |
US7363165B2 (en) | 2000-05-04 | 2008-04-22 | The Board Of Trustees Of The Leland Stanford Junior University | Significance analysis of microarrays |
JP5051960B2 (en) | 2000-06-09 | 2012-10-17 | ビオメリュー・インコーポレイテッド | Methods for detecting complexes of lipoproteins and acute phase proteins to predict increased risk of system failure or lethality |
CA2407910C (en) | 2000-06-16 | 2013-03-12 | Steven M. Ruben | Antibodies that immunospecifically bind to blys |
US7220840B2 (en) | 2000-06-16 | 2007-05-22 | Human Genome Sciences, Inc. | Antibodies that immunospecifically bind to B lymphocyte stimulator protein |
JP2002017938A (en) | 2000-07-07 | 2002-01-22 | Toyo Kogyo Kk | Leg guard |
CA2415775A1 (en) | 2000-07-18 | 2002-01-24 | Correlogic Systems, Inc. | A process for discriminating between biological states based on hidden patterns from biological data |
WO2002016411A2 (en) | 2000-08-18 | 2002-02-28 | Human Genome Sciences, Inc. | Binding polypeptides and methods based thereon |
WO2003031031A1 (en) | 2000-11-16 | 2003-04-17 | Ciphergen Biosystems, Inc. | Method for analyzing mass spectra |
JP2004522742A (en) | 2000-12-08 | 2004-07-29 | ベイラー カレッジ オブ メディシン | TREM-1 splice variants for use in modifying an immune response |
WO2002103059A2 (en) | 2001-02-15 | 2002-12-27 | Bayer Corporation | Innate immunity markers for rapid diagnosis of infectious diseases |
US7713705B2 (en) | 2002-12-24 | 2010-05-11 | Biosite, Inc. | Markers for differential diagnosis and methods of use thereof |
US20040121350A1 (en) | 2002-12-24 | 2004-06-24 | Biosite Incorporated | System and method for identifying a panel of indicators |
US20040126767A1 (en) | 2002-12-27 | 2004-07-01 | Biosite Incorporated | Method and system for disease detection using marker combinations |
US20040253637A1 (en) | 2001-04-13 | 2004-12-16 | Biosite Incorporated | Markers for differential diagnosis and methods of use thereof |
US20020160420A1 (en) | 2001-04-30 | 2002-10-31 | George Jackowski | Process for diagnosis of physiological conditions by characterization of proteomic materials |
US6627607B2 (en) | 2001-04-30 | 2003-09-30 | Syn X Pharma, Inc. | Biopolymer marker indicative of disease state having a molecular weight of 1845 daltons |
EP1270740A1 (en) | 2001-06-29 | 2003-01-02 | SIRS-Lab GmbH | Biochip and its use for determining inflammation |
US6872541B2 (en) | 2001-07-25 | 2005-03-29 | Coulter International Corp. | Method and compositions for analysis of pentraxin receptors as indicators of disease |
US6548646B1 (en) | 2001-08-23 | 2003-04-15 | Bio-Rad Laboratories, Inc. | Reference control for high-sensitivity C-reactive protein testing |
CA2458667A1 (en) | 2001-08-30 | 2003-03-13 | The University Of Pittsburgh Of The Commonwealth System Of Higher Educat Ion Of Pennsylvania | Method for predicting the outcome of an infection |
WO2003023839A1 (en) | 2001-09-12 | 2003-03-20 | Mass Consortium Corporation | High throughput chemical analysis by improved desorption/ionization on silicon mass spectrometry |
US6939716B2 (en) | 2001-09-19 | 2005-09-06 | Washington University | Method for detecting conditions indicative of sepsis |
DE10155600B4 (en) | 2001-11-09 | 2009-08-27 | Oligene Gmbh | Nucleic acid array |
EP1451340B1 (en) | 2001-11-09 | 2014-01-08 | Life Technologies Corporation | Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles |
EP1318403B1 (en) | 2001-12-04 | 2004-07-28 | B.R.A.H.M.S Aktiengesellschaft | Diagnosis of sepsis determining S100B |
ATE293794T1 (en) | 2001-12-04 | 2005-05-15 | Brahms Ag | METHOD FOR DIAGNOSIS OF SEPSIS DETERMINING CA 125 |
DE50104561D1 (en) | 2001-12-04 | 2004-12-23 | Brahms Ag | Method for the diagnosis of sepsis with determination of soluble cytokeratin fragments |
DE50103032D1 (en) | 2001-12-04 | 2004-09-02 | Brahms Ag | Procedure for the diagnosis of sepsis with determination of CA 19-9 |
EP1318407B1 (en) | 2001-12-07 | 2004-09-29 | B.R.A.H.M.S Aktiengesellschaft | Use of aldose-1-epimerase (mutarotase) for the diagnosis of infections and sepsis |
US20040072237A1 (en) | 2001-12-26 | 2004-04-15 | Barry Schweitzer | Use of cytokines secreted by dendritic cells |
US20040038201A1 (en) | 2002-01-22 | 2004-02-26 | Whitehead Institute For Biomedical Research | Diagnostic and therapeutic applications for biomarkers of infection |
EP1476752A4 (en) | 2002-02-27 | 2008-02-13 | Bio Merieux Inc | Method for diagnosing and monitoring hemostatic dysfunction, severe infection and systematic inflammatory response syndrome |
US20030235816A1 (en) | 2002-03-14 | 2003-12-25 | Baylor College Of Medicine (By Slawin And Shariat) | Method to determine outcome for patients with prostatic disease |
US7465555B2 (en) | 2002-04-02 | 2008-12-16 | Becton, Dickinson And Company | Early detection of sepsis |
US6930085B2 (en) | 2002-04-05 | 2005-08-16 | The Regents Of The University Of California | G-type peptides to ameliorate atherosclerosis |
EP1355158A1 (en) | 2002-04-19 | 2003-10-22 | B.R.A.H.M.S Aktiengesellschaft | Method for diagnosing inflammatory and infectious diseases by detecting the phosphoprotein LASP-1 as inflammation marker |
EP1355159A1 (en) | 2002-04-19 | 2003-10-22 | B.R.A.H.M.S Aktiengesellschaft | Use of fragments of carbamyl phosphate synthetase I (CPS 1) for the diagnosis of inflammatory diseases and sepsis |
US7774143B2 (en) * | 2002-04-25 | 2010-08-10 | The United States Of America As Represented By The Secretary, Department Of Health And Human Services | Methods for analyzing high dimensional data for classifying, diagnosing, prognosticating, and/or predicting diseases and other biological states |
EP1369693A1 (en) | 2002-06-04 | 2003-12-10 | B.R.A.H.M.S Aktiengesellschaft | Method for the diagnosis of sepsis and the control of donor blood with the help of anti-asialo ganglioside antibodies |
US7015022B2 (en) | 2002-06-07 | 2006-03-21 | University Of Medicine & Dentistry Of New Jersey | Mammalian catalase-dependent oxidation processes and methods for stimulating oxidative activities |
US20040009503A1 (en) | 2002-07-03 | 2004-01-15 | Molecular Staging, Inc. | Immune modulatory activity of human ribonucleases |
CN1665940A (en) | 2002-07-05 | 2005-09-07 | 英属哥伦比亚大学 | Diagnosis of sepsis using mitochondrial nucleic acid assays |
EP1521624A2 (en) | 2002-07-11 | 2005-04-13 | Upfront Chromatography A/S | An extracorporeal stabilised expanded bed adsorption method for the treatment of sepsis |
EP2386864A1 (en) | 2002-10-09 | 2011-11-16 | DMI Biosciences, Inc. | Diagnosis and monitoring of Ischemia |
WO2004043223A2 (en) | 2002-11-05 | 2004-05-27 | The Regents Of The University Of Michigan | Compositions and methods for the diagnosis and treatment of sepsis |
BR0316197A (en) * | 2002-11-12 | 2005-09-27 | Becton Dickinson Co | Kit, biomarker profile and method of isolating a host response biomarker |
TW200418992A (en) | 2002-11-12 | 2004-10-01 | Becton Dickinson Co | Diagnosis of sepsis or sirs using biomarker profiles |
BR0316232A (en) | 2002-11-12 | 2005-10-04 | Becton Dickinson Co | Methods for determining sepsis status, predicting the onset of sepsis, and diagnosing systemic inflammatory response syndrome in an individual |
EP1426447A1 (en) | 2002-12-06 | 2004-06-09 | Roche Diagnostics GmbH | Method for the detection of pathogenic gram positive bacteria selected from the genera Staphylococcus, Enterococcus and Streptococcus |
ATE474933T1 (en) | 2002-12-06 | 2010-08-15 | Hoffmann La Roche | METHOD FOR DETECTING PATHOGENIC ORGANISMS |
MXPA05006804A (en) | 2002-12-19 | 2006-03-30 | Source Precision Medicine Inc | Identification, monitoring and treatment of infectious disease and characterization of inflammatory conditions related to infectious disease using gene expression profiles. |
JP2006526140A (en) | 2002-12-24 | 2006-11-16 | バイオサイト インコーポレイテッド | Marker for differential diagnosis and method of using the same |
US7191068B2 (en) | 2003-03-25 | 2007-03-13 | Proteogenix, Inc. | Proteomic analysis of biological fluids |
WO2004086043A1 (en) | 2003-03-26 | 2004-10-07 | Johnson & Johnson Medical Limited | Prediction and detection of wound infection |
WO2004087949A2 (en) | 2003-04-02 | 2004-10-14 | Sirs-Lab Gmbh | Method for recognising acute generalised inflammatory conditions (sirs), sepsis, sepsis-like conditions and systemic infections |
FR2855832B1 (en) | 2003-06-03 | 2007-09-14 | Biomerieux Sa | DIAGNOSTIC AND / OR PROGNOSTIC METHOD OF SEPTIC SYNDROME |
US20050009074A1 (en) | 2003-07-07 | 2005-01-13 | Medtronic Vascular, Inc. | Implantable monitor of vulnerable plaque and other disease states |
US20050181386A1 (en) | 2003-09-23 | 2005-08-18 | Cornelius Diamond | Diagnostic markers of cardiovascular illness and methods of use thereof |
US20050069958A1 (en) | 2003-09-26 | 2005-03-31 | Mills Rhonda A. | Method for simultaneous evaluation of a sample containing a cellular target and a soluble analyte |
US20050148029A1 (en) | 2003-09-29 | 2005-07-07 | Biosite, Inc. | Methods and compositions for determining treatment regimens in systemic inflammatory response syndromes |
EP1673465A4 (en) | 2003-09-29 | 2008-04-30 | Biosite Inc | Methods and compositions for the diagnosis of sepsis |
WO2005034952A2 (en) | 2003-10-07 | 2005-04-21 | The Feinstein Institute For Medical Research | Isoxazole and isothiazole compounds useful in the treatment of inflammation |
US20070083333A1 (en) | 2003-11-17 | 2007-04-12 | Vitiello Maria A | Modeling of systemic inflammatory response to infection |
CA2548990C (en) | 2003-12-10 | 2014-09-23 | Novimmune Sa | Toll-like receptor 4 (tlr4)-neutralizing antibodies |
ATE458201T1 (en) | 2003-12-23 | 2010-03-15 | Hoffmann La Roche | METHOD FOR ASSESSING RHEUMATOID ARTHRITIS BY DETERMINING ANTI-CCP AND INTERLEUKIN 6 |
US20050196817A1 (en) | 2004-01-20 | 2005-09-08 | Molecular Staging Inc. | Biomarkers for sepsis |
DE502004000540D1 (en) | 2004-02-13 | 2006-06-14 | Brahms Ag | Method for determining the formation of endothelins for medical diagnostic purposes, and antibodies and kits for carrying out such a method |
US20060211752A1 (en) | 2004-03-16 | 2006-09-21 | Kohn Leonard D | Use of phenylmethimazoles, methimazole derivatives, and tautomeric cyclic thiones for the treatment of autoimmune/inflammatory diseases associated with toll-like receptor overexpression |
CA2614507A1 (en) | 2004-07-09 | 2006-08-17 | Amaox, Ltd. | Immune cell biosensors and methods of using same |
ES2300681T3 (en) | 2004-07-22 | 2008-06-16 | Brahms Aktiengesellschaft | PROCEDURE FOR THE DIAGNOSIS AND TREATMENT OF CRITICAL PATIENTS WITH ENDOTHELINE, ENDOTHELINE AGONISTS AND ADRENOMEDULIN ANTAGONISTS. |
US20060024744A1 (en) | 2004-07-28 | 2006-02-02 | Mills Rhonda A | Methods for substantially simultaneous evaluation of a sample containing a cellular target and a soluble analyte |
GB0426982D0 (en) | 2004-12-09 | 2005-01-12 | Secr Defence | Early detection of sepsis |
DE602005021694D1 (en) | 2004-12-10 | 2010-07-15 | Novimmune Sa | COMBINATION THERAPIES AGAINST MULTIPLE FABRIC LIKE RECEPTORS AND THEIR USE |
WO2006102408A2 (en) | 2005-03-22 | 2006-09-28 | Children's Hospital Medical Center | Methods and compositions for the modulation of immune responses and autoimmune diseases |
US20060241040A1 (en) | 2005-04-06 | 2006-10-26 | Alberto Visintin | Methods of treating disorders associated with toll-like receptor 4 (TLR4) signalling |
BRPI0609302A2 (en) | 2005-04-15 | 2011-10-11 | Becton Dickinson Co | methods for predicting the development of sepsis and for diagnosing sepsis in an individual to be tested, microarray, kit for predicting the development of sepsis in an individual to be tested, computer program product, computer, computer system for determining if an individual is likely to develop sepsis, digital signal embedded in a carrier wave, and, graphical user interface to determine if an individual is likely to develop sepsis |
AU2006294481B2 (en) | 2005-09-28 | 2012-12-20 | Becton, Dickinson And Company | Detection of lysophosphatidylcholine for prognosis or diagnosis of a systemic inflammatory condition |
US8669113B2 (en) | 2008-04-03 | 2014-03-11 | Becton, Dickinson And Company | Advanced detection of sepsis |
-
2006
- 2006-04-14 BR BRPI0609302-7A patent/BRPI0609302A2/en not_active Application Discontinuation
- 2006-04-14 CA CA002605143A patent/CA2605143A1/en not_active Abandoned
- 2006-04-14 US US11/404,744 patent/US7767395B2/en active Active
- 2006-04-14 EP EP06740954A patent/EP1869463A4/en not_active Withdrawn
- 2006-04-14 WO PCT/US2006/014241 patent/WO2006113529A2/en active Application Filing
- 2006-04-14 JP JP2008506786A patent/JP2008538007A/en active Pending
- 2006-04-14 AU AU2006236588A patent/AU2006236588A1/en not_active Abandoned
- 2006-04-14 KR KR1020077026526A patent/KR20080006617A/en not_active Application Discontinuation
-
2007
- 2007-10-14 IL IL186649A patent/IL186649A0/en unknown
- 2007-11-15 NO NO20075906A patent/NO20075906L/en not_active Application Discontinuation
-
2010
- 2010-05-07 US US12/776,245 patent/US20110105350A1/en not_active Abandoned
-
2013
- 2013-08-12 US US13/965,038 patent/US20140141435A1/en not_active Abandoned
-
2015
- 2015-09-10 US US14/850,933 patent/US10443099B2/en active Active
-
2019
- 2019-10-08 US US16/596,481 patent/US11578367B2/en active Active
-
2023
- 2023-02-13 US US18/168,517 patent/US20240060132A1/en active Pending
Non-Patent Citations (1)
Title |
---|
See references of EP1869463A4 * |
Cited By (28)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11578367B2 (en) | 2005-04-15 | 2023-02-14 | Becton, Dickinson And Company | Diagnosis of sepsis |
US10443099B2 (en) | 2005-04-15 | 2019-10-15 | Becton, Dickinson And Company | Diagnosis of sepsis |
US8114615B2 (en) | 2006-05-17 | 2012-02-14 | Cernostics, Inc. | Method for automated tissue analysis |
US8597899B2 (en) | 2006-05-17 | 2013-12-03 | Cernostics, Inc. | Method for automated tissue analysis |
WO2009033282A1 (en) * | 2007-09-10 | 2009-03-19 | The University Of British Columbia | Mitogen-activated protein kinase kinase kinase 14 (map3k14) polymorphisms as indicators of subject outcome in critically 111 subjects |
JP2011500079A (en) * | 2007-10-29 | 2011-01-06 | バイオカルテイス・エス・エイ | Automatic detection of infectious diseases |
DE102008000715B4 (en) * | 2008-03-17 | 2012-02-02 | Sirs-Lab Gmbh | Method for in vitro detection and differentiation of pathophysiological conditions |
DE102008000715B9 (en) * | 2008-03-17 | 2013-01-17 | Sirs-Lab Gmbh | Method for in vitro detection and differentiation of pathophysiological conditions |
US8765371B2 (en) | 2008-03-17 | 2014-07-01 | Analytik Jena Ag | Method for the in vitro detection and differentiation of pathophysiological conditions |
WO2010041232A2 (en) * | 2008-10-09 | 2010-04-15 | The Provost, Fellows And Scholars Of The College Of The Holy And Undivided Trinity Of Queen Elizabeth, Near Dublin | A method of estimating sepsis risk in an individual with infection |
WO2010041232A3 (en) * | 2008-10-09 | 2010-06-03 | The Provost, Fellows And Scholars Of The College Of The Holy And Undivided Trinity Of Queen Elizabeth, Near Dublin | A method of estimating sepsis risk in an individual with infection |
WO2012120026A1 (en) * | 2011-03-08 | 2012-09-13 | Sirs-Lab Gmbh | Method for identifying a subset of polynucleotides from an initial set of polynucleotides corresponding to the human genome for the in vitro determination of the severity of the host response of a patient |
GB2502759A (en) * | 2011-03-08 | 2013-12-04 | Analytik Jena Ag | Method for identifying a subset of polynucleotides from an initial set of polynucleotides corresponding to the human genome for the in vitro determination |
US10018631B2 (en) | 2011-03-17 | 2018-07-10 | Cernostics, Inc. | Systems and compositions for diagnosing Barrett's esophagus and methods of using the same |
EP3074536A4 (en) * | 2013-11-25 | 2017-05-10 | Children's Hospital Medical Center | Temporal pediatric sepsis biomarker risk model |
WO2015077781A1 (en) | 2013-11-25 | 2015-05-28 | Children's Hospital Medical Center | Temporal pediatric sepsis biomarker risk model |
US10815526B2 (en) | 2013-11-25 | 2020-10-27 | Children's Hospital Medical Center | Temporal pediatric sepsis biomarker risk model |
GB2559070B (en) * | 2014-02-11 | 2018-12-05 | Secr Defence | Uses, Apparatus and Kits for the Prediction of Onset of Sepsis |
GB2527164A (en) * | 2014-02-11 | 2015-12-16 | Secr Defence | Apparatus, kits and methods for the prediction of onset of sepsis |
US10597720B2 (en) | 2014-02-11 | 2020-03-24 | The Secretary Of State For Defence | Apparatus, kits and methods for the prediction of onset of sepsis |
GB2527164B (en) * | 2014-02-11 | 2018-08-15 | Secr Defence | Apparatus, kits and methods for the prediction of onset of sepsis |
GB2559070A (en) * | 2014-02-11 | 2018-07-25 | Secr Defence | Apparatus, kits and methods for the prediction of onset of sepsis |
WO2021185660A1 (en) * | 2020-03-17 | 2021-09-23 | Koninklijke Philips N.V. | Prognostic pathways for high risk sepsis patients |
EP3882922A1 (en) * | 2020-03-21 | 2021-09-22 | Tata Consultancy Services Limited | Discriminating features based sepsis prediction |
WO2022064164A1 (en) * | 2020-09-22 | 2022-03-31 | The Secretary Of State For Defence Dstl | Apparatus, kits and methods for predicting the development of sepsis |
WO2022064162A1 (en) * | 2020-09-22 | 2022-03-31 | The Secretary Of State For Defence Dstl | Apparatus, kits and methods for predicting the development of sepsis |
WO2022064163A1 (en) * | 2020-09-22 | 2022-03-31 | The Secretary Of State For Defence | Apparatus, kits and methods for predicting the development of sepsis |
WO2022189530A1 (en) * | 2021-03-11 | 2022-09-15 | Koninklijke Philips N.V. | Prognostic pathways for high risk sepsis patients |
Also Published As
Publication number | Publication date |
---|---|
IL186649A0 (en) | 2008-01-20 |
WO2006113529A3 (en) | 2007-07-19 |
JP2008538007A (en) | 2008-10-02 |
CA2605143A1 (en) | 2006-10-26 |
EP1869463A2 (en) | 2007-12-26 |
US7767395B2 (en) | 2010-08-03 |
BRPI0609302A2 (en) | 2011-10-11 |
US10443099B2 (en) | 2019-10-15 |
US20060246495A1 (en) | 2006-11-02 |
US20110105350A1 (en) | 2011-05-05 |
KR20080006617A (en) | 2008-01-16 |
US20240060132A1 (en) | 2024-02-22 |
EP1869463A4 (en) | 2010-05-05 |
US20210087632A1 (en) | 2021-03-25 |
US20160168638A1 (en) | 2016-06-16 |
US20140141435A1 (en) | 2014-05-22 |
US11578367B2 (en) | 2023-02-14 |
NO20075906L (en) | 2008-01-08 |
AU2006236588A1 (en) | 2006-10-26 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11578367B2 (en) | Diagnosis of sepsis | |
EP2715348B1 (en) | Molecular diagnostic test for cancer | |
US11091809B2 (en) | Molecular diagnostic test for cancer | |
CA2897828C (en) | Methods for identifying, diagnosing, and predicting survival of lymphomas | |
CN101208602A (en) | Diagnosis of sepsis | |
AU2012261820A1 (en) | Molecular diagnostic test for cancer | |
JP2013066474A (en) | Gene expression signature in blood leukocyte permits differential diagnosis of acute infection | |
MXPA05005072A (en) | Diagnosis of sepsis or sirs using biomarker profiles. | |
WO2003083140A2 (en) | Classification and prognosis prediction of acute lymphoblasstic leukemia by gene expression profiling | |
EP2283155A2 (en) | Preterm delivery diagnostic assay | |
US20100304987A1 (en) | Methods and kits for diagnosis and/or prognosis of the tolerant state in liver transplantation | |
WO2014165753A1 (en) | Methods and compositions for diagnosis of glioblastoma or a subtype thereof | |
WO2006048262A2 (en) | Classification of acute myeloid leukemia |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
WWE | Wipo information: entry into national phase |
Ref document number: 200680021670.5 Country of ref document: CN |
|
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
ENP | Entry into the national phase |
Ref document number: 2008506786 Country of ref document: JP Kind code of ref document: A |
|
WWE | Wipo information: entry into national phase |
Ref document number: 186649 Country of ref document: IL |
|
ENP | Entry into the national phase |
Ref document number: 2605143 Country of ref document: CA |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2006740954 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 562846 Country of ref document: NZ |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2006236588 Country of ref document: AU |
|
WWE | Wipo information: entry into national phase |
Ref document number: 1020077026526 Country of ref document: KR |
|
NENP | Non-entry into the national phase |
Ref country code: RU |
|
WWE | Wipo information: entry into national phase |
Ref document number: 5152/CHENP/2007 Country of ref document: IN |
|
ENP | Entry into the national phase |
Ref document number: 2006236588 Country of ref document: AU Date of ref document: 20060414 Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: PI0609302 Country of ref document: BR Kind code of ref document: A2 Effective date: 20071015 |