WO2018160548A1 - Marqueurs d'une maladie coronarienne et utilisations de ces marqueurs - Google Patents

Marqueurs d'une maladie coronarienne et utilisations de ces marqueurs Download PDF

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WO2018160548A1
WO2018160548A1 PCT/US2018/019910 US2018019910W WO2018160548A1 WO 2018160548 A1 WO2018160548 A1 WO 2018160548A1 US 2018019910 W US2018019910 W US 2018019910W WO 2018160548 A1 WO2018160548 A1 WO 2018160548A1
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subject
data representing
cad
score
protein
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PCT/US2018/019910
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English (en)
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Philip Beineke
James A. Wingrove
Karen FITCH
Steven Rosenberg
Andrea M. Johnson
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Cardiodx, Inc.
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Publication of WO2018160548A1 publication Critical patent/WO2018160548A1/fr

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    • 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
    • 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
    • G01N33/6887—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids from muscle, cartilage or connective tissue
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/32—Cardiovascular disorders
    • G01N2800/324—Coronary artery diseases, e.g. angina pectoris, myocardial infarction
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • obstructive CAD obstructive coronary artery disease
  • ICA invasive coronary angiography
  • peripheral blood gene expression profiling can be used to determine the likelihood of obstructive CAD in symptomatic patients (e.g., Corus; see related, co-owned patents including USPNs 9,122,777 and 8,914,240, each of which is herein incorporated by reference, in its entirety, for all purposes).
  • Peripheral blood gene expression is typically limited at present to interrogating the changes in gene expression within circulating cells of the immune system due to the interaction of the cells with the diseased tissue.
  • gene expression-based assays can be expensive to utilize and can be difficult to implement in a clinical lab setting, which can limit the placement of such assays in those settings.
  • Figure 1A shows an assessment of the correlation between top Phase 1 markers; overall, pairwise correlation was low (r ⁇ 0.7). The color key begins dark for 0 and ends light for 1 (left to right).
  • Figure IB shows distribution of percent stenosis across genders and age groups.
  • Figure 2 shows marginal distributions of protein markers (log transformed, centered and scaled values).
  • Figure 3 shows rank correlations among pairs of predictor variables.
  • Figure 4 shows a cluster diagram of the Spearman's non-parametric measure of correlation among the quantitative variables in the models.
  • Figure 5 shows the median AIC and corrected AIC values for main models Ml through Mi l.
  • Figure 6 shows estimated odds ratios for all markers using Model 7.
  • Figure 7 shows estimates of AUC (area under the curve) values for all main models.
  • Figure 8 shows plots of the ROC curves for the best proteomics model and the Corus scores on the CADP2 patients (left; AUC for Model 7 is 0.811 and AUC for Corus is 0.770) and on the same set, after excluding Corus Alg. Dev. Subject (right; AUC for Model 7 is 0.832 and AUC for Corus is 0.768). Model 7 is sold line and Corus is dashed line.
  • Figure 9 shows relative diagnostic performance measures for Model 7 on CADP2 patients for two cutoffs, compared to performance of Corus on the same patients (Corus. cl5), and the published values for Corus in the COMPASS and PREDICT studies.
  • Figure 10 shows ROC plots comparing the predictive performance of Model 7 and Corus within different subsets of subjects. Model 7 is sold line and Corus is dashed line. For each graph the AUC for Model 7 is on the top and the AUC for Corus is on the bottom.
  • Figure 11 shows odds ratio estimates for the terms in the exploratory models in Exp 1.1 to Exp 1.13.
  • the odds ratio for gender is not shown for platting purposes, due to its large size, relative to the other OR (odds ratio).
  • Figure 12 shows odds ratio (OR) estimates for the terms in the exploratory models Exp2.1 to Exp2.3. Note that this data set excludes the Alg Dev subjects.
  • Figure 13 shows a comparison of predicted values from Model 7 to the percent stenosis of the same patient. Points are colored by agreement of model 7 to reference status.
  • Figure 14 shows comparison of predicted values from Model 7 to the percent stenosis of the same patient. Points are colored by agreement of model 7 to reference status.
  • Figure 15 shows comparison of predicted values from Model 7 to predicted values from the Corus score run on the same sample. Points are colored by true reference status. The dashed lines indicate the cutoff of 20% for Model 7 and the cutoff of 15 for Corus.
  • Figure 16 shows the ability of the model to explain the variation in the data (corrected AIC) compared to the ability of the model to correctly classify the patients for obstructive CAD (AUC) for the number of markers in the model (moving sequentially from 15 to 1 markers).
  • Figure 17 shows AIC and AICc Values for the Models Tested in Example 4.
  • Figure 18 shows AUC for the Models Tested in Example 4.
  • Described herein is a method for determining coronary artery disease risk in a subject, comprising: performing or having performed at least one protein detection assay on a sample from the subject to generate a dataset comprising data representing protein expression levels corresponding to cardiac troponin I (cTnl) and at least one marker comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S 100A12, or TNFAIP6; and generating or having generated, by a computer processor, a score indicative of coronary artery disease (CAD) risk by mathematically combining the data representing the protein expression levels, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels as measured using Quantitative Coronary Angiography (QCA) indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in
  • the at least one protein detection assay is at least one enzyme-linked immunosorbent assay (ELISA), wherein the dataset comprises data representing expression levels corresponding to cTnl and at least two, three, four, or five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S 100A12, or TNFAIP6, and wherein the score is more predictive of CAD than a score produced using Corus with the sample as measured using AIC or AUC.
  • ELISA enzyme-linked immunosorbent assay
  • the dataset comprises data representing expression levels corresponding to cTnl and at least three markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S 100A12, or TNFAIP6.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least five markers comprising APOB, HSP70, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S100A12, or TNFAIP6.
  • the method further comprises classifying the sample according to the score. In some aspects, the method further comprises rating CAD risk using the score.
  • the sample comprises protein extracted from the blood of the subject.
  • the mathematical combination is based on a predictive model, optionally wherein the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • CAD is obstructive CAD.
  • the method performance is characterized by an area under the curve (AUC) ranging from 0.52 to 0.81, 0.50 to 0.99, 0.55 to 0.65, 0.50 to 0.70, 0.70 to 0.79, 0.80 to 0.89, or 0.90 to 0.99. In some aspects, the method performance is characterized by an area under the curve (AUC) ranging of at least 0.5, 0.52, 0.6, 0.7, 0.8, or 0.81.
  • the method further comprises obtaining data representing at least one clinical factor associated with the subject, optionally wherein the clinical factor comprises age of the subject and/or gender of the subject, and optionally mathematically combining the data representing the at least one clinical factor with the data representing the protein expression levels to generate the score.
  • the method further comprises obtaining data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises at least one of age and gender.
  • the method further comprises obtaining data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises age and gender.
  • the method comprises mathematically combining the data representing the at least one clinical factor with the data representing the protein expression levels to generate the score.
  • the subject is human.
  • the at least one protein detection assay is an immunoassay, a protein- binding assay, an antibody-based assay, an antigen-binding protein-based assay, a protein- based array, an enzyme-linked immunosorbent assay (ELISA), flow cytometry, a protein array, a blot, a Western blot, nephelometry, turbidimetry, chromatography, mass spectrometry, enzymatic activity, and an immunoassays selected from RIA,
  • the method further comprises taking at least one action based on the score, optionally wherein the at least one action comprises treating the subject, advising lifestyle changes to the subject, performing a procedure on the subject, performing further diagnostics on the subject, assessing the subject's health further, optimizing medical therapy, investigating non-cardiac etiologies of symptoms, or performing angiography on the subject.
  • Also described herein is a method for determining coronary artery disease risk in a subject, comprising: obtaining or having obtained a dataset associated with a sample from the subject comprising data representing protein expression levels to cTnl and at least one marker comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S 100A12, or TNFAIP6; generating or having generated, by a computer processor, a score indicative of coronary artery disease (CAD) risk by mathematically combining the data representing the protein expression levels, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels as measured using Quantitative Coronary Angiography (QCA) indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel as measured using QCA indicates a decreased likelihood
  • the dataset comprises data representing expression levels corresponding to cTnl and at least five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S100A12, or TNFAIP6, and wherein the score is more predictive of CAD than a score produced using Corus with the sample as measured using AIC or AUC.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least two or three markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S100A12, or TNFAIP6.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least four markers comprising APOB, HSP70, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S100A12, or TNFAIP6.
  • the method further comprises classifying the sample according to the score. In some aspects, the method further comprises rating CAD risk using the score.
  • the mathematical combination is based on a predictive model, optionally wherein the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • CAD is obstructive CAD.
  • the method performance is characterized by an area under the curve (AUC) ranging from 0.52 to 0.81, 0.50 to 0.99, 0.55 to 0.65, 0.50 to 0.70, 0.70 to 0.79, 0.80 to 0.89, or 0.90 to 0.99.
  • the method performance is characterized by an area under the curve (AUC) ranging of at least 0.5, 0.52, 0.6, 0.7, 0.8, or 0.81.
  • the method further comprises obtaining data representing at least one clinical factor associated with the subject, optionally wherein the clinical factor comprises age of the subject and/or gender of the subject, and optionally mathematically combining the data representing the at least one clinical factor with the data representing the protein expression levels to generate the score.
  • the method further comprises obtaining data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises at least one of age and gender. In some aspects, the method further comprises obtaining data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises age and gender.
  • the method comprises mathematically combining the data representing the at least one clinical factor with the data representing the protein expression levels to generate the score.
  • the subject is human.
  • the method further comprises taking at least one action based on the score, optionally wherein the at least one action comprises treating the subject, advising lifestyle changes to the subject, performing a procedure on the subject, performing further diagnostics on the subject, assessing the subject's health further, optimizing medical therapy, investigating non-cardiac etiologies of symptoms, or performing angiography on the subject.
  • the sample comprises protein extracted from the blood of the subject.
  • obtaining the dataset comprises obtaining the sample and processing the sample to experimentally determine the dataset.
  • obtaining the dataset comprises performing at least one protein detection assay, optionally wherein the at least one protein detection assay is an immunoassay, a protein-binding assay, an antibody-based assay, an antigen-binding protein-based assay, a protein-based array, ELISA, flow cytometry, a blot, or mass spectrometry.
  • the at least one protein detection assay is an immunoassay, a protein-binding assay, an antibody-based assay, an antigen-binding protein-based assay, a protein-based array, ELISA, flow cytometry, a blot, or mass spectrometry.
  • the at least one protein detection assay is an immunoassay, a protein- binding assay, an antibody-based assay, an antigen-binding protein-based assay, a protein- based array, an enzyme-linked immunosorbent assay (ELISA), flow cytometry, a protein array, a blot, a Western blot, nephelometry, turbidimetry, chromatography, mass
  • spectrometry enzymatic activity, and an immunoassays selected from RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, immunoelectrophoretic, a competitive immunoassay, amd immunoprecipitation.
  • obtaining the dataset comprises receiving the dataset from a third party that has processed the sample to experimentally determine the dataset.
  • Also described herein is a method for generating a dataset comprising data representing protein expression levels for a subject that has CAD or is suspected of having CAD, comprising: obtaining or having obtained a sample from the subject, wherein the subject has CAD or is suspected of having CAD; performing or having performed at least one protein detection assay on the sample to generate a dataset comprising data representing protein expression levels corresponding to cTnl and at least one marker comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S100A12, or TNFAIP6.
  • the method further comprises generating, by a computer processor, a score indicative of coronary artery disease (CAD) risk by mathematically combining the data representing the protein expression levels, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels as measured using Quantitative Coronary Angiography (QCA) indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel as measured using QCA indicates a decreased likelihood that the subject has CAD.
  • CAD coronary artery disease
  • the at least one protein detection assay is at least one enzyme-linked immunosorbent assay (ELISA), and wherein the dataset comprises data representing expression levels corresponding to cTnl and at least five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S 100A12, or TNFAIP6.
  • ELISA enzyme-linked immunosorbent assay
  • the dataset comprises data representing expression levels corresponding to cTnl and at least two, three, four, or five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S 100A12, or TNFAIP6.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least three, four, or five markers comprising APOB, HSP70, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S 100A12, or TNFAIP6.
  • the method further comprises classifying the sample.
  • the method further comprises rating CAD risk.
  • the sample comprises protein extracted from the blood of the subject.
  • CAD is obstructive CAD.
  • the subject is human.
  • the at least one protein detection assay is an immunoassay, a protein- binding assay, an antibody-based assay, an antigen-binding protein-based assay, a protein- based array, an enzyme-linked immunosorbent assay (ELISA), flow cytometry, a protein array, a blot, a Western blot, nephelometry, turbidimetry, chromatography, mass
  • the method further comprises taking at least one action with the subject, optionally wherein the at least one action comprises treating the subject, advising lifestyle changes to the subject, performing a procedure on the subject, performing further diagnostics on the subject, assessing the subject's health further, optimizing medical therapy, investigating non-cardiac etiologies of symptoms, or performing angiography on the subject.
  • a system for determining coronary artery disease risk in a subject comprising: a storage memory for storing a dataset associated with a sample from the subject comprising data representing protein expression levels corresponding to cTnl and at least one marker comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S 100A12, or TNFAIP6; and a processor
  • a score indicative of CAD risk by mathematically combining the data representing the protein expression levels, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels as measured using Quantitative Coronary Angiography (QCA) indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel as measured using QCA indicates a decreased likelihood that the subject has CAD.
  • QCA Quantitative Coronary Angiography
  • the dataset comprises data representing expression levels corresponding to cTnl and at least five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S100A12, or TNFAIP6, and wherein the score is more predictive of CAD than a score produced using Corns with the sample as measured using AIC or AUC.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least two, three, four, or five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOAl, S 100A8, MPO, S 100A12, or TNFAIP6.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least three, four, or five markers comprising APOB, HSP70, SERPINA12, NTproBNP, PIGF, adiponectin, APOAl, S100A8, MPO, S100A12, or TNFAIP6.
  • system further comprises code for classifying the sample according to the score.
  • system further comprises code for rating CAD risk using the score.
  • the sample comprises protein extracted from the blood of the subject.
  • the mathematical combination is based on a predictive model, optionally wherein the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • CAD is obstructive CAD.
  • the performance of the mathematical combination is characterized by an area under the curve (AUC) ranging from 0.52 to 0.81, 0.50 to 0.99, 0.55 to 0.65, 0.50 to 0.70, 0.70 to 0.79, 0.80 to 0.89, or 0.90 to 0.99. In some aspects, the performance of the mathematical combination is characterized by an area under the curve (AUC) ranging of at least 0.5, 0.52, 0.6, 0.7, 0.8, or 0.81.
  • system further comprises a storage memory comprising data representing at least one clinical factor associated with the subject, optionally wherein the clinical factor comprises age of the subject and/or gender of the subject.
  • system further comprises a storage memory comprising data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises at least one of age and gender.
  • system further comprises a storage memory comprising data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises age and gender.
  • system further comprises a processor communicatively coupled to the storage memory for mathematically combining the data representing the at least one clinical factor with the data representing the protein expression levels to generate the score.
  • the subject is human.
  • the system further comprises an apparatus for providing a readout that provides instructions for taking at least one action based on the score, optionally wherein the at least one action comprises treating the subject, advising lifestyle changes to the subject, performing a procedure on the subject, performing further diagnostics on the subject, assessing the subject's health further, optimizing medical therapy, investigating non-cardiac etiologies of symptoms, or performing angiography on the subject.
  • a computer- readable storage medium storing computer- executable program code for determining coronary artery disease risk in a subject, comprising: program code for storing a dataset associated with a sample from the subject comprising data representing protein expression levels corresponding to cTnl and at least one marker comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S 100A12, or TNFAIP6; and program code for generating a score indicative of CAD risk by mathematically combining the data representing the protein expression levels, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels as measured using Quantitative Coronary Angiography (QCA) indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel as measured using QCA indicates
  • the dataset comprises data representing expression levels corresponding to cTnl and at least five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S100A12, or TNFAIP6, and wherein the score is more predictive of CAD than a score produced using Corus with the sample as measured using AIC or AUC.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least two, three, four, or five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S 100A12, or TNFAIP6.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least three, four, or five markers comprising APOB, HSP70, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S 100A12, or TNFAIP6.
  • the medium further comprises program code for classifying the sample according to the score. In some aspects, the medium further comprises program code for rating CAD risk using the score.
  • the sample comprises protein extracted from the blood of the subject.
  • the mathematical combination is based on a predictive model, optionally wherein the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • CAD is obstructive CAD.
  • the performance of the mathematical combination is characterized by an area under the curve (AUC) ranging from 0.52 to 0.81, 0.50 to 0.99, 0.55 to 0.65, 0.50 to 0.70, 0.70 to 0.79, 0.80 to 0.89, or 0.90 to 0.99. In some aspects, the performance of the mathematical combination is characterized by an area under the curve (AUC) ranging of at least 0.5, 0.52, 0.6, 0.7, 0.8, or 0.81.
  • the medium further comprises program code for storing data representing at least one clinical factor associated with the subject, optionally wherein the clinical factor comprises age of the subject and/or gender of the subject.
  • the medium further comprises program code for storing data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises at least one of age and gender.
  • the medium further comprises program code for storing data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises age and gender.
  • the medium further comprises program code for storing for mathematically combining the data representing the at least one clinical factor with the data representing the protein expression levels to generate the score.
  • the subject is human.
  • the medium further comprises program code for storing instructions for taking at least one action based on the score, optionally wherein the at least one action comprises treating the subject, advising lifestyle changes to the subject, performing a procedure on the subject, performing further diagnostics on the subject, assessing the subject's health further, optimizing medical therapy, investigating non-cardiac etiologies of symptoms, or performing angiography on the subject.
  • kits for determining coronary artery disease risk in a subject comprising: a set of reagents for generating a dataset via at least one protein detection assay that is associated with a sample from the subject comprising data representing protein expression levels corresponding to cTnl and at least one marker comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S 100A12, or TNFAIP6; and instructions for generating a score indicative of CAD risk by mathematically combining the data representing the protein expression levels, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels as measured using Quantitative Coronary Angiography (QCA) indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel as measured using QCA
  • QCA Quantitative Coronary Ang
  • the at least one protein detection assay is at least one enzyme-linked immunosorbent assay (ELISA), wherein the dataset comprises data representing expression levels corresponding to cTnl and at least five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S 100A12, or TNFAIP6, and wherein the score is more predictive of CAD than a score produced using Corns with the sample as measured using AIC or AUC.
  • ELISA enzyme-linked immunosorbent assay
  • the dataset comprises data representing expression levels corresponding to cTnl and at least two, three, four, or five markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S100A8, MPO, S 100A12, or TNFAIP6.
  • the dataset comprises data representing expression levels corresponding to cTnl and at least three, four, or five markers comprising APOB, HSP70, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S 100A12, or TNFAIP6.
  • the kit further comprises instructions for classifying the sample according to the score. In some aspects, the kit further comprises instructions for rating CAD risk using the score. In some aspects, the sample comprises protein extracted from the blood of the subject.
  • the mathematical combination is based on a predictive model, optionally wherein the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • the predictive model is a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, or a tree-based recursive partitioning model.
  • CAD is obstructive CAD.
  • the performance of the instructions for generating the score is characterized by an area under the curve (AUC) ranging from 0.52 to 0.81, 0.50 to 0.99, 0.55 to 0.65, 0.50 to 0.70, 0.70 to 0.79, 0.80 to 0.89, or 0.90 to 0.99.
  • the performance of the instructions for generating the score is characterized by an area under the curve (AUC) ranging of at least 0.5, 0.52, 0.6, 0.7, 0.8, or 0.81.
  • the kit further comprises instructions for obtaining data representing at least one clinical factor associated with the subject, optionally wherein the clinical factor comprises age of the subject and/or gender of the subject, and optionally comprising instructions for mathematically combining the data representing the at least one clinical factor with the data representing the protein expression levels to generate the score.
  • the kit further comprises instructions for obtaining data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises at least one of age and gender.
  • the kit further comprises instructions for obtaining data representing at least one clinical factor associated with the subject, wherein the at least one clinical factor comprises age and gender.
  • the kit further comprises instructions for mathematically combining the data representing the at least one clinical factor with the data representing the protein expression levels to generate the score.
  • the subject is human.
  • the at least one protein detection assay is an immunoassay, a protein-binding assay, an antibody-based assay, an antigen-binding protein-based assay, a protein-based array, an enzyme-linked immunosorbent assay (ELISA), flow cytometry, a protein array, a blot, a Western blot, nephelometry, turbidimetry, chromatography, mass spectrometry, enzymatic activity, and an immunoassays selected from RIA,
  • the reagents comprise one or more antibodies that bind to one or more of the markers, optionally wherein the antibodies are monoclonal antibodies or polyclonal antibodies.
  • the kit further comprises instructions for taking at least one action based on the score, optionally wherein the at least one action comprises treating the subject, advising lifestyle changes to the subject, performing a procedure on the subject, performing further diagnostics on the subject, assessing the subject's health further, optimizing medical therapy, investigating non-cardiac etiologies of symptoms, or performing angiography on the subject.
  • Circulating proteins are well-established as biomarkers of disease. 137 protein biomarkers were interrogated for association with coronary artery disease, and subsequently a multi-analyte predictive model utilizing a subset of markers was created. The identification of biomarkers associated with the likelihood of coronary artery disease and creation of a predictive model could lead, e.g., to better patient stratification for further cardiovascular workup and intervention. Models to assist in determining the likelihood of coronary artery disease in a subject based on proteins markers were developed and tested. These models have been demonstrated to have greater predictive value for the likelihood of coronary artery disease relative to earlier coronary artery disease tests, including Corns.
  • a "subject" in the context of the present teachings is generally a mammal, e.g., a human.
  • the subject can be a human patient, e.g., a human heart failure patient.
  • mammal as used herein includes but is not limited to a human, non-human primate, dog, cat, mouse, rat, cow, horse, and pig. Mammals other than humans can be advantageously used as subjects that represent animal models of, e.g., heart failure.
  • a subject can be male or female.
  • a subject can be one who has been previously diagnosed or identified as having coronary artery disease.
  • a subject can be one who has already undergone, or is undergoing, a therapeutic intervention for coronary artery disease.
  • a subject can also be one who has not been previously diagnosed as having coronary artery disease; e.g., a subject can be one who exhibits one or more symptoms or risk factors for coronary artery disease, or a subject who does not exhibit symptoms or risk factors for coronary artery disease, or a subject who is asymptomatic for coronary artery disease.
  • sample in the context of the present teachings refers to any biological sample that is isolated from a subject.
  • a sample can include, without limitation, a single cell or multiple cells, fragments of cells, an aliquot of body fluid, whole blood, platelets, serum, plasma, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsies, synovial fluid, lymphatic fluid, ascites fluid, and interstitial or extracellular fluid.
  • sample also encompasses the fluid in spaces between cells, including gingival crevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, semen, sweat, urine, or any other bodily fluids.
  • CSF cerebrospinal fluid
  • Blood sample can refer to whole blood or any fraction thereof, including blood cells, red blood cells, white blood cells or leucocytes, platelets, serum and plasma. Samples can be obtained from a subject by means including but not limited to venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage, scraping, surgical incision, or intervention or other means known in the art. In one embodiment the sample is a whole blood sample.
  • a sample can include protein extracted from blood of a subject.
  • Marker all refer to a sequence characteristic of a particular variant allele (i.e., polymorphic site) or wild-type allele.
  • a marker can include any allele, including wild-types alleles, SNPs, microsatellites, insertions, deletions, duplications, and translocations.
  • a marker can also include a peptide encoded by an allele comprising nucleic acids.
  • a marker in the context of the present teachings encompasses, without limitation, cytokines, chemokines, growth factors, proteins, peptides, nucleic acids, oligonucleotides, and metabolites, together with their related metabolites, mutations, variants, polymorphisms, modifications, fragments, subunits, degradation products, elements, and other analytes or sample-derived measures. Markers can also include mutated proteins, mutated nucleic acids, variations in copy numbers and/or transcript variants. Markers also encompass non-blood borne factors and non-analyte physiological markers of health status, and/or other factors or markers not measured from samples (e.g., biological samples such as bodily fluids), such as clinical parameters and traditional factors for clinical assessments.
  • Markers can also include any indices that are calculated and/or created mathematically. Markers can also include combinations of any one or more of the foregoing measurements, including temporal trends and differences. As used herein, markers typically refer to sequence characteristics of the D-loop mtDNA, e.g., Tm and/or single or multiple SNPS and/or number of polymorphisms.
  • To “analyze” includes measurement and/or detection of data associated with a marker (such as, e.g., presence or absence of a SNP, allele, melting temperature (Tm) or constituent expression levels) in the sample (or, e.g., by obtaining a dataset reporting such measurements, as described below).
  • a marker such as, e.g., presence or absence of a SNP, allele, melting temperature (Tm) or constituent expression levels
  • an analysis can include comparing the measurement and/or detection against a measurement and/or detection in a sample or set of samples from the same subject or other control subject(s).
  • the markers of the present teachings can be analyzed by any of various conventional methods known in the art.
  • a “dataset” is a set of data (e.g., numerical values) resulting from evaluation of a sample (or population of samples) under a desired condition.
  • the values of the dataset can be obtained, for example, by experimentally obtaining measures from a sample and constructing a dataset from these measurements; or alternatively, by obtaining a dataset from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored.
  • the term "obtaining a dataset associated with a sample” encompasses obtaining a set of data determined from at least one sample.
  • Obtaining a dataset encompasses obtaining a sample, and processing the sample to experimentally determine the data, e.g., via measuring, sequencing, PCR, RT-PCR, microarray, contacting with one or more primers, contacting with one or more probes, antibody binding, or ELISA.
  • the phrase also encompasses receiving a set of data, e.g., from a third party that has processed the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from at least one database or at least one publication or a combination of databases and publications.
  • Measurement refers to determining the presence, absence, quantity, amount, or effective amount of a substance in a clinical or subject-derived sample, including the presence, absence, or concentration levels of such substances, and/or evaluating the values or categorization of a subject's clinical parameters based on a control.
  • CAD coronary artery disease
  • FDR means to false discovery rate. FDR can be estimated by analyzing randomly-permuted datasets and tabulating the average number of genes at a given p-value threshold.
  • highly correlated gene expression or “highly correlated marker expression” refer to gene or marker expression values that have a sufficient degree of correlation to allow their interchangeable use in a predictive model of coronary artery disease.
  • highly correlated marker or “highly correlated substitute marker” refer to markers that can be substituted into and/or added to a predictive model based on, e.g., the above criteria.
  • a highly correlated marker can be used in at least two ways: (1) by substitution of the highly correlated marker(s) for the original marker(s) and generation of a new model for predicting CAD risk; or (2) by substitution of the highly correlated marker(s) for the original marker(s) in the existing model for predicting CAD risk.
  • myocardial infarction refers to an ischemic myocardial necrosis. This is usually the result of abrupt reduction in coronary blood flow to a segment of the myocardium, the muscular tissue of the heart. Myocardial infarction can be classified into ST-elevation and non-ST elevation MI (also referred to as unstable angina). Myocardial necrosis results in either classification. Myocardial infarction, of either ST-elevation or non- ST elevation classification, is an unstable form of atherosclerotic cardiovascular disease.
  • the term "obtaining a dataset associated with a sample” encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample, and processing the sample to experimentally determine the data. The phrase also encompasses receiving a set of data, e.g., from a third party that has processed the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from at least one database or at least one publication or a combination of databases and publications. A dataset can be obtained by one of skill in the art via a variety of known ways including stored on a storage memory.
  • Corus refers to a commercially available test offered by CardioDx. This test is described in USPNs 9,122,777 and 8,914,240, each of which is herein incorporated by reference, in its entirety, for all purposes.
  • Corus is a test where RNA is extracted from a sample of peripheral blood cells of a subject, converted to cDNA, and then assessed for the expression level of 23 distinct genes using RT- qPCR, followed by the transformation of the expression level data plus age and gender functions by an algorithm into a score that is predictive of the likelihood of CAD in the subject.
  • Genes included in the Corus test are: S100A12, CLEC4E, S100A8, CASP5, IL18RAP, TNFAIP6, AQP9, NCF4, CD3D, TMC8, CD79B, SPIB, HNRPF, TFCP2, RPL28, AF161365, AF289562, SLAMF7, KLRC4, IL8RB, TNFRSF10C, KCNE3, and TLR4.
  • the algorithm for producing the score is as shown below:
  • NK up ( 5*SLAMF7 + 5*KLRC4)
  • T cell ( 5*CD3D + 5*TMC8)
  • N up (1/3 * CASP5 + 1/3*IL18RAP + 1/3*TNFAIP6)
  • Ndown ( 25*IL8RB + 25*TNFRSF10C + 25*TLR4 + 25*KCNE3)
  • Score INTERCEPT - 0.755 *( N up - N down ) - 0.406*( NK up - T cell ) - 0.308 *SEX*( SCAi- Norm 1 )- 0.137* ( Been- T cell )- 0.548 *(1-SEX)*( SCAi- Neut)- 0.482 *SEX*(TSPAN)- 0.246 *( AF 2 - Norm 2 )
  • Such methods can include obtaining a dataset associated with a sample from a subject comprising data representing protein expression levels for one or more markers; and combining the data in the dataset to produce a score that is indicative of CAD risk associated with the sample.
  • Such methods can include obtaining a dataset associated with a sample from a subject comprising data representing one or more clinical factors and data representing protein expression levels for markers; and combining the data in the dataset to produce a score that is indicative of CAD risk associated with the sample.
  • Such methods can be computer-implemented, performed as physical assays, or a combination thereof.
  • Such methods can be useful in informing later actions to be taken by the subject on whom the method is performed or by a physician that is assisting the subject. For example, a score that suggests a subject is at increased risk of CAD can be used by a physician to inform an action that is likely to reduce that risk, such as administering aspirin.
  • Other actions that can be taken can include treating the subject, advising lifestyle changes to the subject, performing a procedure on the subject, performing further diagnostics on the subject, assessing the subject's health further, optimizing medical therapy, investigating non-cardiac etiologies of symptoms, or performing angiography on the subject.
  • Marker all refer to a sequence characteristic of a particular variant allele (i.e., polymorphic site) or wild-type allele.
  • a marker can include any allele, including wild-types alleles, SNPs, microsatellites, insertions, deletions, duplications, and translocations.
  • a marker can also include a peptide encoded by an allele comprising nucleic acids.
  • a marker in the context of the present teachings encompasses, without limitation, cytokines, chemokines, growth factors, proteins, peptides, nucleic acids, oligonucleotides, and metabolites, together with their related metabolites, mutations, variants, polymorphisms, modifications, fragments, subunits, degradation products, elements, and other analytes or sample-derived measures. Markers can also include mutated proteins, mutated nucleic acids, variations in copy numbers and/or transcript variants. Markers also encompass non-blood borne factors and non-analyte physiological markers of health status, and/or other factors or markers not measured from samples (e.g., biological samples such as bodily fluids), such as clinical parameters and traditional factors for clinical assessments. Markers can also include any indices that are calculated and/or created mathematically. Markers can also include combinations of any one or more of the foregoing measurements, including temporal trends and differences.
  • a marker can include at least one of cTnl, Adiponectin, APOAl, NT-proBNP, PIGF, and S 100A8-MPO.
  • a marker can include one or more of cTnl, corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOAl, S100A8, MPO, S100A12, or TNFAIP6.
  • a marker can include one or more of: cTnl, APOB, HSP70, SERPINA12, NTproBNP, PIGF, adiponectin, APOAl, S100A8, MPO, S100A12, or TNFAIP6.
  • a marker can include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 of: cTnl, corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOAl, S100A8, MPO, S100A12, or TNFAIP6.
  • polypeptide polypeptide
  • peptide protein
  • protein protein
  • the terms apply to naturally occurring amino acid polymers as well as amino acid polymers in which one or more amino acid residues is a non-naturally encoded amino acid.
  • the terms encompass amino acid chains of any length, including full length proteins, wherein the amino acid residues are linked by covalent peptide bonds.
  • amino acid refers to naturally occurring and non-naturally occurring amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to the naturally occurring amino acids.
  • Naturally encoded amino acids are the 20 common amino acids (alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, praline, serine, threonine, tryptophan, tyrosine, and valine) and pyrrolysine and
  • Amino acid analogs refers to compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., an a carbon that is bound to a hydrogen, a carboxyl group, an amino group, and an R group, such as, homoserine, norleucine, methionine sulfoxide, methionine methyl sulfonium.
  • Such analogs have modified R groups (such as, norleucine) or modified peptide backbones, but retain the same basic chemical structure as a naturally occurring amino acid.
  • Reference to an amino acid includes, for example, naturally occurring proteogenic L-amino acids; D-amino acids, chemically modified amino acids such as amino acid variants and derivatives; naturally occurring non- proteogenic amino acids such as ⁇ -alanine, ornithine, etc.; and chemically synthesized compounds having properties known in the art to be characteristic of amino acids.
  • non-naturally occurring amino acids include, but are not limited to, oc-methyl amino acids (e.g., oc-methyl alanine), D-amino acids, histidine-like amino acids (e.g., 2-amino-histidine, ⁇ -hydroxy-histidine, homohistidine), amino acids having an extra methylene in the side chain (“homo" amino acids), and amino acids in which a carboxylic acid functional group in the side chain is replaced with a sulfonic acid group (e.g., cysteic acid).
  • oc-methyl amino acids e.g., oc-methyl alanine
  • D-amino acids e.g., D-amino acids
  • histidine-like amino acids e.g., 2-amino-histidine, ⁇ -hydroxy-histidine, homohistidine
  • amino acids having an extra methylene in the side chain (“homo" amino acids)
  • D-amino acid-containing peptides, etc. exhibit increased stability in vitro or in vivo compared to L-amino acid-containing counterparts.
  • the construction of peptides, etc., incorporating D-amino acids can be particularly useful when greater intracellular stability is desired or required. More specifically, D-peptides, etc., are resistant to endogenous peptidases and proteases, thereby providing improved bioavailability of the molecule, and prolonged lifetimes in vivo when such properties are desirable.
  • D-peptides, etc. cannot be processed efficiently for major histocompatibility complex class Il-restricted presentation to T helper cells, and are therefore, less likely to induce humoral immune responses in the whole organism.
  • Amino acids may be referred to herein by either their commonly known three letter symbols or by the one-letter symbols recommended by the IUPAC-IUB Biochemical Nomenclature Commission. Nucleotides, likewise, may be referred to by their commonly accepted single-letter codes.
  • a derivative, or a variant of a polypeptide is said to share "homology" or be
  • the derivative or variant is at least 75% the same as that of either the peptide or a fragment of the peptide having the same number of amino acid residues as the derivative. . In certain embodiments, the derivative or variant is at least 85% the same as that of either the peptide or a fragment of the peptide having the same number of amino acid residues as the derivative. In certain embodiments, the amino acid sequence of the derivative is at least 90% the same as the peptide or a fragment of the peptide having the same number of amino acid residues as the derivative.
  • the amino acid sequence of the derivative is at least 95% the same as the peptide or a fragment of the peptide having the same number of amino acid residues as the derivative. In certain embodiments, the derivative or variant is at least 99% the same as that of either the peptide or a fragment of the peptide having the same number of amino acid residues as the derivative.
  • modified refers to any changes made to a given polypeptide, such as changes to the length of the polypeptide, the amino acid sequence, chemical structure, co-translational modification, or post-translational modification of a polypeptide.
  • the form "(modified)” term means that the polypeptides being discussed are optionally modified, that is, the polypeptides under discussion can be modified or unmodified.
  • a marker comprises an amino acid sequence that is at least 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% identical to a relevant amino acid sequence or fragment thereof set forth in the Table(s) or accession number(s) disclosed herein.
  • a marker comprises an amino acid sequence encoded by a polynucleotide that is at least 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% identical to a relevant nucleotide sequence or fragment thereof set forth in Table(s) or accession number(s) disclosed herein. Accession numbers of certain markers are shown in Table 9.1.
  • the invention includes a method of generating a prediction model for likelihood of CAD in subjects. Also disclosed herein are methods of using the predictive model to determine the likelihood of CAD in a subject.
  • a predictive model can include, for example, a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, and a tree-based recursive partitioning model.
  • a predictive model can also include Support Vector Machines, quadratic discriminant analysis, or a LASSO regression model. See Elements of Statistical Learning, Springer 2003, Hastie, Tibshirani, Friedman; which is herein incorporated by reference in its entirety for all purposes.
  • Predictive model performance can be characterized by an area under the curve (AUC).
  • AUC area under the curve
  • predictive model performance is characterized by an AUC ranging from 0.68 to 0.70.
  • predictive model performance is characterized by an AUC ranging from 0.70 to 0.79.
  • predictive model performance is characterized by an AUC ranging from 0.80 to 0.89.
  • predictive model performance is characterized by an AUC ranging from 0.90 to 0.99.
  • AUC can range from 0.52 to 0.81, 0.50 to 0.99, 0.55 to 0.65, 0.50 to 0.70, 0.70 to 0.79, 0.80 to 0.89, or 0.90 to 0.99.
  • AUC can be at least 0.5, 0.52, 0.6, 0.7, 0.8, or 0.81.
  • significance associated with one or more markers is measured by a relative risk. In a further embodiment, the significance is measured by a percentage. In one embodiment, a significant decreased risk is measured as a relative risk of at least about 1.2, including but not limited to: 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8 and 1.9. In a further embodiment, a relative risk of at least 1.2 is significant. In a further embodiment, a relative risk of at least about 1.5 is significant. In a further embodiment, a significant increase in risk is at least about 1.7 is significant.
  • a significant increase in risk is at least about 20%, including but not limited to about 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% and 98%. In a further embodiment, a significant increase in risk is at least about 50%.
  • Risk of CAD can be calculated by combining data representing expression levels of multiple protein markers, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more protein markers.
  • Risk of CAD can be calculated by combining data representing expression levels of multiple protein markers, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more protein markers with data representing one or more clinical factors (e.g., age and/or gender). Such data combination will typically result in a score. Oftentimes such a score will be indicative of CAD risk. For example, a higher score for a given subject relative to a control subject having less than 50% stenosis in all major vessels as measured using Quantitative Coronary Angiography (QCA) can indicate an increased likelihood that the subject has CAD. Alternatively or in addition to, a lower score for a given subject relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel as measured using QCA can indicate a decreased likelihood that the subject has CAD.
  • QCA Quantitative Coronary Angiography
  • a score produced via a combination of data can be useful in classifying, sorting, or rating a sample from which the score was generated. For example, a score can be used to classify a sample. A score can also be used to rate CAD risk for a given sample.
  • assays for one or more markers include DNA assays, microarrays, polymerase chain reaction (PCR), RT-PCR, Southern blots, Northern blots, antibody-binding assays, enzyme-linked immunosorbent assays (ELISAs), flow cytometry, protein assays, Western blots, nephelometry, turbidimetry, chromatography, mass spectrometry, immunoassays, including, by way of example, but not limitation, RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, or competitive
  • the information from the assay can be quantitative and sent to a computer system of the invention.
  • the information can also be qualitative, such as observing patterns or fluorescence, which can be translated into a quantitative measure by a user or automatically by a reader or computer system.
  • the subject can also provide information other than assay information to a computer system, such as race, height, weight, age, gender, eye color, hair color, family medical history and any other information that may be useful to a user, such as a clinical factor described above.
  • Protein detection assays are assays used to detect the expression level of a given protein from a sample. Protein detection assays are generally known in the art and can include an immunoassay, a protein-binding assay, an antibody-based assay, an antigen- binding protein-based assay, a protein-based array, an enzyme-linked immunosorbent assay
  • ELISA flow cytometry
  • a protein array a blot, a Western blot, nephelometry, turbidimetry, chromatography, mass spectrometry, enzymatic activity, and an immunoassays selected from RIA, immunofluorescence, immunochemiluminescence,
  • Protein based analysis using an antibody as described above that specifically binds to a polypeptide encoded by an altered nucleic acid or an antibody that specifically binds to a polypeptide encoded by a non-altered nucleic acid, or an antibody that specifically binds to a particular splicing variant encoded by a nucleic acid, can be used to identify the presence in a test sample of a particular splicing variant or of a polypeptide encoded by a polymorphic or altered nucleic acid, or the absence in a test sample of a particular splicing variant or of a polypeptide encoded by a non-polymorphic or non-altered nucleic acid.
  • polypeptide encoded by a polymorphic or altered nucleic acid is diagnostic for a susceptibility to coronary artery disease.
  • the level or amount of polypeptide encoded by a nucleic acid in a test sample is compared with the level or amount of the polypeptide encoded by the nucleic acid in a control sample.
  • a level or amount of the polypeptide in the test sample that is higher or lower than the level or amount of the polypeptide in the control sample, such that the difference is statistically significant, is indicative of an alteration in the expression of the polypeptide encoded by the nucleic acid, and is diagnostic.
  • the composition of the polypeptide encoded by a nucleic acid in a test sample is compared with the composition of the polypeptide encoded by the nucleic acid in a control sample (e.g., the presence of different splicing variants).
  • a difference in the composition of the polypeptide in the test sample, as compared with the composition of the polypeptide in the control sample, is diagnostic.
  • both the level or amount and the composition of the polypeptide can be assessed in the test sample and in the control sample.
  • a difference in the amount or level of the polypeptide in the test sample, compared to the control sample; a difference in composition in the test sample, compared to the control sample; or both a difference in the amount or level, and a difference in the composition is indicative of a likelihood of CAD, either increased or decreased.
  • the above described methods can also generally be used to detect markers that do not include a polymorphism.
  • one or more clinical factors in an subject e.g., a heart failure patient
  • assessment of one or more clinical factors in a subject can be combined with a marker analysis in the subject to identify likelihood of CAD in the subject.
  • Clinical factor refers to a measure of a condition of a subject, e.g., disease activity or severity.
  • “Clinical factor” encompasses all markers of a subject's health status, including non-sample markers, and/or other characteristics of a subject, such as, without limitation, age and gender.
  • a clinical factor can be a score, a value, or a set of values that can be obtained from evaluation of a sample (or population of samples) from a subject or a subject under a determined condition.
  • a clinical factor can also be predicted by markers and/or other parameters such as gene expression surrogates.
  • a clinical factor can include age of a subject.
  • a clinical factor can include gender of a subject.
  • a clinical factor can include age and gender of a subject.
  • clinical factors known to one of ordinary skill in the art to be associated with sudden cardiac events can include age, gender, race, implant indication, prior pacing status, ICD presence, cardiac re synchronization therapy defibrillator (CRT-D) presence, total number of devices, device type, defibrillation thresholds performed, number of programming zones, heart failure (HF) etiology, HF onset, left ventricular ejection fraction (LVEF) at implant, New York Heart Association (NYHA) class, months from most recent myocardial infarction (MI) at implant, prior arrhythmia event in setting of MI or arthroscopic chondral osseous autograft transplantation (Cor procedure), diabetes status, Blood Urea Nitrogen (BUN), Cr, renal disease history, rhythm parameters to determine sinus v.
  • CTR-D cardiac re synchronization therapy defibrillator
  • a condition can include one clinical factor or a plurality of clinical factors.
  • a clinical factor can be included within a dataset.
  • a dataset can include one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, or thirty or more overlapping or distinct clinical factor(s).
  • a clinical factor can be, for example, the condition of a subject in the presence of a disease or in the absence of a disease.
  • a clinical factor can be the health status of a subject.
  • a clinical factor can be age, gender, chest pain type, neutrophil count, ethnicity, disease duration, diastolic blood pressure, systolic blood pressure, a family history parameter, a medical history parameter, a medical symptom parameter, height, weight, a body-mass index, resting heart rate, and smoker/non-smoker status.
  • Clinical factors can include whether the subject has stable chest pain, whether the subject has typical angina, whether the subject has atypical angina, whether the subject has an anginal equivalent, whether the subject has been previously diagnosed with MI, whether the subject has had a revascularization procedure, whether the subject has diabetes, whether the subject has an inflammatory condition, whether the subject has an infectious condition, whether the subject is taking a steroid, whether the subject is taking an immunosuppressive agent, and/or whether the subject is taking a chemotherapeutic agent.
  • the methods of the invention including the methods of generating a prediction model and the methods of for determining the likelihood of CAD in a subject, are, in some embodiments, performed on a computer.
  • a computer comprises at least one processor coupled to a chipset. Also coupled to the chipset are a memory, a storage device, a keyboard, a graphics adapter, a pointing device, and a network adapter. A display is coupled to the graphics adapter. In one embodiment, the functionality of the chipset is provided by a memory controller hub and an I/O controller hub. In another embodiment, the memory is coupled directly to the processor instead of the chipset.
  • the storage device is any device capable of holding data, like a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory holds instructions and data used by the processor.
  • the pointing device may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard to input data into the computer system.
  • the graphics adapter displays images and other information on the display.
  • the network adapter couples the computer system to a local or wide area network.
  • a computer can have different and/or other components than those described previously.
  • the computer can lack certain components.
  • the storage device can be local and/or remote from the computer (such as embodied within a storage area network (SAN)).
  • SAN storage area network
  • module refers to computer program logic utilized to provide the specified functionality.
  • a module can be implemented in hardware, firmware, and/or software.
  • program modules are stored on the storage device, loaded into the memory, and executed by the processor.
  • Embodiments of the entities described herein can include other and/or different modules than the ones described here.
  • the functionality attributed to the modules can be performed by other or different modules in other embodiments.
  • this description occasionally omits the term "module" for purposes of clarity and convenience.
  • the methods disclosed can be employed together with the treatment of subjects, e.g., through use of, e.g., diagnostic methods disclosed herein.
  • a subject has stable chest pain.
  • a subject has typical angina or atypical angina or an anginal equivalent.
  • a subject has no previous diagnosis of myocardial infarction (MI).
  • MI myocardial infarction
  • a subject has not had a revascularization procedure.
  • a subject does not have diabetes.
  • a subject does not have a systemic autoimmune or infectious condition.
  • a subject is not currently taking a steroid, an immunosuppressive agent, or a chemotherapeutic agent.
  • methods can be employed for the treatment of other diseases or conditions associated with CAD.
  • a therapeutic agent can be used both in methods of treatment of CAD, as well as in methods of treatment of other diseases or conditions associated with CAD.
  • the methods of treatment can also utilize a therapeutic agent.
  • the therapeutic agent(s) are administered in a therapeutically effective amount (i.e., an amount that is sufficient for "treatment," as described above).
  • the amount which will be therapeutically effective in the treatment of a particular individual's disorder or condition will depend on the symptoms and severity of the disease, and can be determined by standard clinical techniques.
  • in vitro or in vivo assays may optionally be employed to help identify optimal dosage ranges.
  • the precise dose to be employed in the formulation will also depend on the route of administration, and the seriousness of the disease or disorder, and should be decided according to the judgment of a practitioner and each patient's circumstances. Effective doses may be extrapolated from dose-response curves derived from in vitro or animal model test systems.
  • Therapies for a subject with CAD or a subject with an increased risk of CAD can include lifestyle changes, administration of therapeutics such as drugs, and undertaking one or more procedures.
  • Lifestyle changes can include quitting smoking, avoiding secondhand smoke, eating a heart-healthy diet, regular exercise, achieving and/or maintaining a healthy weight, weight management, enrollment in a cardiac rehabilitation program, reducing blood pressure, reducing cholesterol, managing diabetes (if present), and keeping a healthy mental attitude.
  • Therapeutics can include aspirin, antiplatelets, ACE inhibitors, beta-blockers, statins, PCSK9 targeting therapeutics (e.g., PCSK9 inhibitors such as monoclonal antibodies such as evolocumab, bococizumab, and alirocumab), and agina medicines such as nitroglycerin. Procedures include angioplasty (with or without stenting) and bypass surgery.
  • PCSK9 targeting therapeutics e.g., PCSK9 inhibitors such as monoclonal antibodies such as evolocumab, bococizumab, and alirocumab
  • agina medicines such as nitroglycerin. Procedures include angioplasty (with or without stenting) and bypass surgery.
  • kits for assessing CAD can include reagents for detecting expression levels of one or markers and instructions for calculating a score based on the expression levels.
  • a kit can comprise a set of reagents for generating a dataset via at least one protein detection assay that is associated with a sample from the subject comprising data representing protein expression levels corresponding to at least two markers comprising corin, APOB, HSP70, RBP4, SERPINA12, NTproBNP, PIGF, adiponectin, APOA1, S 100A8, MPO, S100A12, or TNFAIP6; and instructions for generating a score indicative of CAD risk by mathematically combining the data representing the protein expression levels, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels as measured using Quantitative Coronary Angiography (QCA) indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel as measured using QCA indicates a decreased likelihood that the subject has CAD.
  • QCA Quantitative Coronary Angiography
  • the reagents can be selected from Table 9.2.
  • the reagents comprise one or more antibodies that bind to one or more of the markers, optionally wherein the antibodies are monoclonal antibodies or polyclonal antibodies.
  • the reagents can include reagents for performing ELISA including buffers and detection agents.
  • a kit can further include software for performing instructions included with the kit, optionally wherein the software and instructions are provided together.
  • a kit can include software for generating a score indicative of CAD risk by mathematically combining data generated using the set of reagents.
  • a kit can include instructions for classifying a sample according to a score.
  • a kit can include instructions for rating CAD risk using a score.
  • a kit can include instructions for obtaining data representing at least one clinical factor associated with a subject, wherein the at least one clinical factor comprises at least one of age and gender.
  • a kit can include instructions for mathematically combining the data representing at least one clinical factor with data representing protein expression levels to generate a score.
  • a kit can include instructions for use of a set of reagents.
  • a kit can include instructions for performing at least one protein detection assay such as an immunoassay, a protein-binding assay, an antibody-based assay, an antigen-binding protein- based assay, a protein-based array, an enzyme-linked immunosorbent assay (ELISA), flow cytometry, a protein array, a blot, a Western blot, nephelometry, turbidimetry,
  • protein detection assay such as an immunoassay, a protein-binding assay, an antibody-based assay, an antigen-binding protein- based assay, a protein-based array, an enzyme-linked immunosorbent assay (ELISA), flow cytometry, a protein array, a blot, a Western blot, nephelometry, turbidimetry,
  • a kit can include instructions for taking at least one action based on a score for a subject, e.g., treating the subject, advising lifestyle changes to the subject, performing a procedure on the subject, performing further diagnostics on the subject, assessing the subject's health further, optimizing medical therapy, investigating non-cardiac etiologies of symptoms, or performing angiography on the subject.
  • Example 1 Identification and testing of protein markers of CAD risk.
  • NCT00500617 herein incorporated by reference
  • PREDICT enrolled subjects who were symptomatic or high risk asymptomatic patients referred for invasive coronary angiography with no known previous history of myocardial infarction or cardiac intervention.
  • Phase I evaluated 126 assays that had been previously characterized by MesoScale Discovery and were commercially available; Phase 2 evaluated 9 additional assays that were developed for CardioDx by MesoScale Discovery. Summaries of Phase 1 and 2 assays are provided below.
  • S 100A8/MPO S 100A8/2 plus MPO/2).
  • Phas2 all assays measured protein levels at sufficient levels, well above the LLOD. • For both Phase 1 and 2 assays, three outcome variables were assessed:
  • ° QCAMaxStenosis Maximum % lesion in a subject's coronary bed as determined by QCA used as a continuous variable.
  • Table 5.1 Individual p values and directionality for all CAD models using Phase 1 markers. Significant p values are shown in bold.
  • Table 6.1a gives individual p values and directionality for all CAD models using
  • RISKSCORE2 0.033643483 + 0.288633218 * NT-proBNP - -0.259370805 * APOA1 - -0.09760706 * Adiponectin + 0.067488037 * P1GF + 0.106117284 * S100A8-MPO
  • Table 6.1b and 7.1 summarize the markers and coefficients for the two models side by side, including the model weights
  • Model performance was estimated via 2500 iteration of cross validation on random holdout sets of 14 patients; Area-Under-the-Curve (AUC) estimates are given in Table 8.1.
  • accession numbers for markers are shown in Table 9.1.
  • Reagents used to detect each marker via ELISA are shown in Table 9.2.
  • Clinical Covariates Clinical Covariates (Clinical Factors)
  • age and gender are important predictors of oCAD, so they were included in all main models. Some of the exploratory models do not include these predictors. Earlier work indicated some non-linearity in the relation- ship between oCAD, age and gender. To explore this, various splines for these predictors were put into different models. The main spline used was to include 3 knots for age, at 20, 60, and 80 years, based on the previous results.
  • Smoking ⁇ 1
  • Patient is female, ⁇ 65 years old and a current smoker
  • Dyslipidemia ⁇ 1
  • Patient is female, ⁇ 65 years old and diagnosed with Dyslipidemia
  • Chest pain ⁇ 1
  • the Custom Set 2 data was generated by splitting the patients into 6 patient sets. For each protein to be assayed, duplicate plates were produced for each patient set. For the APOB assay, 3 of the patient sets were diluted to one level, while the second third of the patient sets were diluted to another, less-dilute level. There were noticeable and consistent shifts in the APOB values from the first 3 sets, relative to the second, even after the standard curve adjustment. Some of the other markers also showed evidence of systematic plate effects, although not as dramatic as the APOB shift. Additional normalization beyond the standard curve application was therefore performed by first log2 transforming the concentration values, then subtracting off the deviations of individual plate medians from the overall median of each assay (centering the concentration values within each assay). Missing values were then imputed, and the mean of the two replicate values per sample, per assay was calculated. This was the original value used for analysis.
  • Dyslipidemia the 16 males subjects with missing data were imputed to have a 0 value for this, the seven females who were younger than 65 years were imputed to be 1 with a probability of 0.38, which is the frequency of Dyslipidemia in the entire patient set, and the remaining female was imputed to be 0.
  • Table 1 Summary of categorical clinical covariates by case and control group
  • Figure IB shows distribution of percent stenosis across genders and age groups.
  • Figure 2 shows marginal distributions of protein markers (log transformed, centered and scaled values).
  • Table 2 Summary statistics for continuous clinical covariates by case and control groups. 'NA' columns are counts of missing data for that variable of the original 472 patients. 'DF.p' is Diamond-Forrester probability, while 'Fram' is Framingham probability.
  • the first priority was to model complexity of the relationship of Age with oCAD.
  • NTproBNP, HSP70, APOA1, RBP4, Adiponectin, and corin were the rank order of the previous effect estimate strengths.
  • Age and NTproBNP non-linearity were explored in these models, due to sample size. Without being bound by theory, it is thought that further model optimization could be pursued during algorithm development based on the results observed here. [00215] Based on these calculations, a pre-specified set of 11 main models to address these primary questions of interest in modelling (summarization, complexity, and some limited marker selection), was compiled.
  • Table 5 Pairs with correlations above the cut points of the similarity statistics.
  • Figure 4 shows a cluster diagram of the Spearman's non-parametric measure of correlation among the quantitative variables in the models. The measure is expressed as the square of this statistic to deal with negative correlation values. This measure should reflect monotonic, non-linear relationships.
  • Table 6 The form of the various Catalog Models. The response is a binary variable indicating Case or Control status. 'ns(NTproBNP,3)' indicates a spline with 3 evenly spaced knots fitted to the NTproBNP marker values. 'AdipAl ' indicates the mean of Adiponectin and APOA1. A '*" indicates an interaction term. In this case, the individual terms plus an interaction term are actually fitted in the model. A '- indicates that Batch is to be used as an intercept term. Model G has a 3-knot spline fitted to each gender separately.
  • AIC Values and Determining the Best Model The ability of each model to explain the variation in the data was compared using two statistics, AIC, which is the deviance of the model plus two times the number of parameters estimated by the model, and AICc, which is more severely penalized than the original AIC for the number of parameters in the model, relative to the number of subjects in the data set.
  • AIC C can be calculated as
  • FIG. 5 shows the median AIC and corrected AIC values for main models Ml through Ml 1. Medians were calculated from the AICs obtained from all bootstrap iterations. Lower values indicate better fits of the model to the data.
  • Models 6 and 9 look the most promising, but with the AIC measure, Model 7 is superior.
  • Models 6 and 9 are similar to each other, both with non-linear age and gender splines, the summarization of Adiponectin and APOA1 into a single term, and a 3-knot spline fitted to NTproBNP.
  • Model 9 additionally has the clinical covariates.
  • Model 7 is a relatively simple linear, additive model, differing only from Model 1 in the combined Adiponectin - APOA1 term.
  • Model 7 Since Model 7 has adequate AIC, while Models 6 and 9 look less appealing by AICc, because of the high variability observed in the coefficients fitted to the spline terms among the bootstrap models, and due to the reduced complexity of Model 7, which could be of benefit in diagnostic development, Model 7 was selected as the model to use as a reference point for the performance of the current proteomic marker set after Discovery efforts.
  • Table 8 The form of the Main Models (cont ). The response is a binary variable indicating Case or Control status.
  • CatalogX indicates the X ⁇ values from the corresponding Catalog model.
  • a * indicates an interaction term. In this case, the individual terms plus an interaction term are actually fitted in the model.
  • Table 9 Prior odds ratios estimated from 193 CADP1A patients from an individual marker models, adjusted for age and gender. Ten of the 15 markers in this model were not selected to continue to this stage of discovery. Note that the prevalence of disease in CADP1A is 0.45, while it is 0.33 in the CADP2 group. Additionally, CADP1A were matched for age and gender.
  • Model 7 was selected based on AIC C and not on the basis of its AUC, it does have a superior value in the main model set (see Table 21 and Figures 7 and 8), and outperforms Corus, when compared on the full CADP2 data set. This is in the face of a possible upwards bias for Corus.
  • Figure 7 shows estimates of AUC (area under the curve) values for all main models. All estimates given are adjusted for optimism by bootstrap, except for the Corus model, which was not fitted in this analysis. However, it is worth noting that some of the subjects in this data set were sued for Algorithm Development (model fitting) of the Corus model.
  • Figure 8 shows plots of the ROC curves for the best proteomics model and the Corus scores on the CADP2 patients (left) and on the same set, after excluding Corus Alg. Dev. Subject (right). Note that while the reported estimates elsewhere in this document for AUC are adjusted for optimism in the proteomics models, these plots are necessarily made of unadjusted values.
  • Figure 9 shows relative diagnostic performance measures for Model 7 on CADP2 patients for two cutoffs, compared to performance of Corus on the same patients (Corns. cl5), and the published values for Corus in the COMPASS and PREDICT studies.
  • Figure 10 shows ROC plots comparing the predictive performance of Model 7 and Corus within different subsets of subjects.
  • the third set of models looked at the performance of Corus, and the bestproteomics model.
  • the fourth set examined the model 7 predictor terms in a proportional odds regression model, performed on the full set of 470 subjects.
  • Figure 11 shows odds ratio estimates for the terms in the exploratory models in Exp 1.1 to Exp 1.13.
  • the odds ratio for gender is not shown for platting purposes, due to its large size, relative to the other OR.
  • Figure 12 shows odds ratio estimates for the terms in the exploratory models Exp2.1 to Exp2.3. Note that this data set excludes the Alg Dev subjects.
  • Table 12 The forms of the Exploratory Set 1 models. The response is a binary variable indicating Case or Control status. 'Corus Genes Only' is the Corns score with the male or female intercept subtracted out, depending on the gender of the subject. 'Corus' is the raw Corus algorithm score. Note that an additional parameter for the Corus term is estimated in this model from this specific data set. Further note, for comparability purposes, Model 7 was re-estimated on this data set.
  • Table 13 The forms of the Exploratory Set 2 models. The response is a binary variable indicating Case or Control status. 'Corus' is the raw Corus algorithm score. Note that an additional parameter for the Corns term is estimated in this model from this specific data set. Further note, for comparability purposes, Model 7 was re-estimated data set.
  • Table 14 The forms of the Exploratory Set 3 models. The response is a binary variable indicating Case or Control status. 'Corus Genes Only' is the Corns score with the male or female intercept subtracted out, depending on the sex of the subject. Note that an additional parameter for the Corus term is estimated in this model from this specific data set.
  • Table 15 The form of the Exploratory Set 4 model.
  • Table 16 Unadjusted estimates of diagnostic performance of the exploratory models. The results given use the cutoff to the predicted probability of being a case being greater than 20%. Note that Model 7 and Corus in the full sense both contain Age and
  • Figure 13 shows a comparison of predicted values from Model 7 to the percent stenosis of the same patient. Points are colored by agreement of model 7 to reference status.
  • Figure 14 shows comparison of predicted values from Model 7 to the percent stenosis of the same patient. Points are colored by agreement of model 7 to reference status.
  • Figure 15 shows comparison of predicted values from Model 7 to predicted values from the Corus score run on the same sample.
  • Points are colored by true reference status.
  • the dashed lines indicate the cutoff of 20% for Model 7 and the cutoff of 15 for Corus. Both models predict all males 65 and over to be cases. However, there are two controls in this group that are borderline near the 20% cutoff used for Model 7, with predicted values of 0.223 and 0.202, respectively. No female under the age of 65 has a higher predicted value than 48%. Model 7 is better at discriminating Young Female cases than Corus, in this data set, while Corus does slightly better with Young Male cases in this analysis.
  • Table 17 Summary of incorrect calls using the Model 7 predicted values with a 20% cutoff. Also given is the interquartile range of observed percent stenosis for those subjects called incorrectly.
  • Table 18 Pairwise marker rank correlations, as shown in the heatmap, expressed as a percentage.
  • Table 19 Model fit measurements in the form of AIC and corrected AIC for the main models. Values are the median value across all bootstra iterations.
  • Table 21 Optimism corrected estimates of AUC values for all main models. These values come from the final model fitted on the full CADP2 data set. Note that Corus was not fitted on this data (for this analysis), and is not optimism adjusted so there may be some slight inflation in its performance, due to the presence of some Alg Dev subjects in this data set.
  • AdipAl mean of Adiponectin + APOAl
  • A8MPO mean of S 100A8 and MPO
  • A12T N F (mean of S 100A12 and TNFAIP6).
  • logitf Prfobstructive CAD Intercept + AdipAl + NTproBNP + PIGF + A8MPO + A12TNF,
  • AdipAl is the mean of Adiponectin and APOAl
  • A8MPO is the mean of S100A8 andMPO
  • A12TNF is the mean ofS100A12 and TNFAIP6.
  • AUC area under the curve.
  • AUC is the area under the ROC curve, which was calculated in the standard way, but is generally a rank ordered statistic, which is the probability for all possible (case, control) pairs that the model correctly orders the case as a higher risk of disease than the control.
  • AICc and the AUC were calculated after the models were fit, where the coefficient values were determined. They were calculated in the same way for all models. As such, they are generally, relatively comparable across all models, despite the differences in the specific terms used in each model as part of the subtractive analysis.
  • the individual models and values of AICc and AUC for each model are given in Table 26A-B of
  • Figure 16 shows the ability of a given model to explain the variation in the data (AIC) compared to the ability of the model to correctly classify the patients for obstructive CAD (AUC) for the number of markers in the given model (moving sequentially from 15 to 1 markers).
  • NCT00500617 served as the starting population for this study.
  • PREDICT enrolled subjects who were symptomatic or high risk asymptomatic patients referred for invasive coronary angiography with no known previous history of myocardial infarction or cardiac intervention.
  • non-diabetic subjects from PREDICT were utilized to screen a set of protein biomarkers for association with coronary artery disease, ultimately resulting the derivation of a classifier model for CAD including the patients age, sex and the expression levels of 13 protein markers; the model is shown below.
  • AdipAl mean of Adiponectin + APOA1
  • A8MPO mean of S100A8 and MPO
  • A12T N F mean of S 100A12 and TNFAIP6.
  • cTnl Cardiac Troponin I
  • the assay used for this biomarker has high sensitivity and is capable of detecting cTnl at picogram/liter levels in healthy individuals.
  • cTnl has accession number: NM_000363(mRNA); NP_000354 (protein); which is herein incorporated by reference for all purposes, as available on the NCBI website on Feb. 28, 2017.
  • Model 5 represents the original model previously disclosed.
  • Model 5 representing the previously disclosed model, had an AICc of 496.71 and an AUC of 0.784 (Table 29).
  • the addition of cTnl to this model resulted in stronger model performance as evidenced by both AICc and AUC (Model 7; 483.72 and 0.799 respectively).

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Abstract

L'invention concerne des marqueurs et des procédés utiles pour évaluer une maladie coronarienne chez un sujet, ainsi que des kits, des systèmes et des supports associés. L'invention concerne également des modèles prédictifs, reposant sur ces marqueurs, ainsi que des systèmes informatiques et des modes de réalisation sous forme de logiciels de ces modèles pour établir un score et optionnellement classer des échantillons.
PCT/US2018/019910 2017-02-28 2018-02-27 Marqueurs d'une maladie coronarienne et utilisations de ces marqueurs WO2018160548A1 (fr)

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US10704021B2 (en) 2012-03-15 2020-07-07 Flodesign Sonics, Inc. Acoustic perfusion devices
US10975368B2 (en) 2014-01-08 2021-04-13 Flodesign Sonics, Inc. Acoustophoresis device with dual acoustophoretic chamber
US11708572B2 (en) 2015-04-29 2023-07-25 Flodesign Sonics, Inc. Acoustic cell separation techniques and processes
US11214789B2 (en) 2016-05-03 2022-01-04 Flodesign Sonics, Inc. Concentration and washing of particles with acoustics
US11377651B2 (en) 2016-10-19 2022-07-05 Flodesign Sonics, Inc. Cell therapy processes utilizing acoustophoresis
US10785574B2 (en) 2017-12-14 2020-09-22 Flodesign Sonics, Inc. Acoustic transducer driver and controller
CN109709338B (zh) * 2018-12-28 2022-03-15 河北省科学院生物研究所 检测牛或羊骨骼肌肌钙蛋白i的酶联免疫试剂盒及其制备方法与应用
CN109709338A (zh) * 2018-12-28 2019-05-03 河北省科学院生物研究所 检测牛或羊骨骼肌肌钙蛋白i的酶联免疫试剂盒及其制备方法与应用
WO2020229691A3 (fr) * 2019-05-16 2020-12-24 Fundació Hospital Universitari Vall D'hebron - Institut De Recerca Procédé de sélection d'un patient pour une thérapie de reperfusion
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WO2021029998A1 (fr) * 2019-08-14 2021-02-18 Optum Technology, Inc. Analyse prédictive de données fondée sur des cohortes
CN113345525A (zh) * 2021-06-03 2021-09-03 谱天（天津）生物科技有限公司 一种用于高通量检测中减少协变量对检测结果影响的分析方法
CN115171916A (zh) * 2022-07-20 2022-10-11 广州蓝勃生物科技有限公司 试剂开发的实验方法、装置、计算机设备、存储介质
CN115171916B (zh) * 2022-07-20 2023-04-07 广州蓝勃生物科技有限公司 试剂开发的实验方法、装置、计算机设备、存储介质
CN115831306A (zh) * 2023-02-23 2023-03-21 北京康博众联电子科技有限公司 一种数据分析装置、方法及计算机存储介质

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