IP Library Granted Patent US 11,300,575
Granted Patent B2
US 11,300,575 · App. 14/832,427 · Granted Apr 12, 2022

Biomarkers and methods for measuring and monitoring inflammatory disease activity

Inventors: Guy L. Cavet (Burlingame, CA); Yijing Shen (San Mateo, CA); Nicholas Knowlton (Choctaw, OK); Michael Centola (Oklahoma City, OK)
Assignees: Laboratory Corporation of America Holdings; Oklahoma Medical Research Foundation
G01N33/6893C12Q1/6883G01N33/53G01N33/564G16B40/00G16B40/20C12Q2600/118C12Q2600/158G01N2333/475G01N2333/4709G01N2333/4737G01N2333/485G01N2333/5412G01N2333/70503G01N2333/70578G01N2333/72G01N2333/96494G01N2800/102G01N2800/60
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Quick Facts
Patent No.
US 11,300,575
App. No.
14/832,427
Granted
Apr 12, 2022
Kind
B2
Abstract

Biomarkers useful for diagnosing and assessing inflammatory disease are provided, along with kits for measuring their expression. The invention also provides predictive models, based on the biomarkers, as well as computer systems, and software embodiments of the models for scoring and optionally classifying samples. The biomarkers include at least two biomarkers selected from the DAIMRK group and the score is a disease activity index (DAI).

Claims (38)

1. A method for generating protein level data for a first subject comprising:

performing at least one immunoassay on a first blood sample from the first subject to generate a first dataset comprising protein level data for at least four protein markers, wherein the at least four protein markers comprise at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or, vascular endothelial growth factor A (VEGFA);

calculating a disease activity index score for the sample by combining the protein level data, wherein the disease activity index score tracks a clinical disease activity score determined from clinical data of a reference population of confirmed RA patients, wherein the clinical data supplies a clinical assessment comprising at least one of DAS, DAS28, DAS28-ESR, DAS28-CRP, HAQ, mHAQ, MDHAQ, physician global assessment VAS, patient global assessment VAS, pain VAS, fatigue VAS, overall VAS, sleep VAS, SDAI, CDAI, RAPID3, RAPID4, RAPID5, ACR20, ACR50, ACR70, SF-36, RAMRIS, TSS, modified TSS, Larsen, TJC, SJC, and GHA,

wherein the disease activity index score=((0.56*sqrt(PTJC)+0.28*sqrt(PSJC)+0.36*log(CRP/106+1)+(0.14*PPGHA)+0.96)*10.53)+1;

wherein the tracking of the clinical disease activity score is determined by one or more of analysis of variants (ANOVA), Bayesian networks, boosting and Ada-boosting, bootstrap aggregating or bagging, Classification and Regression Trees (CART), boosted CART, Random Forest (RF), Recursive Partitioning Trees (RPART), Curds and Whey (CW), Curds and Whey-Lasso, principal component analysis (PCA), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Discriminant Function Analysis (DFA), factor rotation, Hidden Markov Models, kernel density estimation, kernel partial least squares, kernel matching pursuit, kernel Fisher's discriminate analysis, kernel principal components analysis, linear regression, Forward Linear Stepwise Regression, LASSO shrinkage and selection, Elastic Net regularization and selection, glmnet (Lasso and Electric Net-regularized generalized linear model), Logistic Regression (LogReg), meta-learner, Kth-nearest neighbor (KNN), non-linear regression, neural networks, partial least square, shrunken centroids (SC), sliced inverse regression, Standard for the Exchange of Product model data, super principal component (SPC) regression, Support Vector Machines (SVM), and Recursive Support Vector Machines (RSVM);

diagnosing or prognosing the subject as needing treatment for rheumatoid arthritis (RA) based on the protein level disease activity index score exceeding a reference value of the clinical disease activity score, wherein the diagnosis or prognosis is the same for subjects with and without comorbidities; and

administering a therapy to the subjected diagnosed or prognosed as needing treatment, the therapy comprising one or more of

administering a therapeutic compound selected from DMARDs, biologic DMARDs, non-steroidal anti-inflammatory drugs (NSAID's), and corticosteroids; and

administering bariatric surgical intervention.

2. The method of claim 1 , wherein performance of the at least one immunoassay comprises: obtaining the first blood sample, wherein the first blood sample comprises the protein markers; contacting the first blood sample with a plurality of distinct reagents; generating a plurality of distinct complexes between the reagents and markers; and detecting the complexes to generate the data.

3. The method of claim 1 , wherein the at least one immunoassay comprises a multiplex assay.

4. The method of claim 1 , wherein the at least four protein markers comprise at least five markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

5. The method of claim 1 , wherein the at least four protein markers comprise at least six markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

6. The method of claim 1 , wherein the at least four protein markers comprise at least seven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

7. The method of claim 1 , wherein the at least four protein markers comprise at least eight markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

8. The method of claim 1 , wherein the at least four protein markers comprise at least nine markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

9. The method of claim 1 , wherein the at least four protein markers comprise at least ten markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

10. The method of claim 1 , wherein the at least four protein markers comprise at least eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

11. The method of claim 1 , wherein the CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2, wherein the CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2, wherein the EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2, wherein the IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1, wherein the LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1, wherein the MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1, wherein the MMP3 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002413.1, wherein the RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1, wherein the SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2, wherein the TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1, wherein the VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1, and wherein the VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2.

12. A method for generating a protein level score comprising:

performing at least one immunoassay on a first blood sample from a first subject previously diagnosed with rheumatoid arthritis (RA), or suspected of having RA, to generate protein level data for each protein marker of a plurality of protein markers, wherein the plurality of protein markers comprises a plurality of test markers comprising at least four markers selected from chitinase 3-like 1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal growth factor (beta-urogastrone) (EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin (RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); or, vascular endothelial growth factor A (VEGFA);

calculating a disease activity index score for the sample by combining the protein level data, wherein the disease activity index score tracks a clinical disease activity score determined from clinical data of a reference population of confirmed RA patients, wherein the clinical data supplies a clinical assessment comprising one or more of DAS, DAS28, DAS28-ESR, DAS28-CRP, TJC, SJC, and PGA,

wherein the disease activity index score=((0.56 sqrt(PTJC)+0.28*sqrt(PSJC)+0.36*log(CRP/106+1)+(0.14*PPGHA)+0.96)*10.53)+1;

wherein the tracking of the clinical disease activity score is determined by one or more of analysis of variants (ANOVA), Bayesian networks, boosting and Ada-boosting, bootstrap aggregating or bagging, Classification and Regression Trees (CART), boosted CART, Random Forest (RF), Recursive Partitioning Trees (RPART), Curds and Whey (CW), Curds and Whey-Lasso, principal component analysis (PCA), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Discriminant Function Analysis (DFA), factor rotation, Hidden Markov Models, kernel density estimation, kernel partial least squares, kernel matching pursuit, kernel Fisher's discriminate analysis, kernel principal components analysis, linear regression, Forward Linear Stepwise Regression, LASSO shrinkage and selection, Elastic Net regularization and selection, glmnet (Lasso and Electric Net-regularized generalized linear model), Logistic Regression (LogReg), meta-learner, Kth-nearest neighbor (KNN), non-linear regression, neural networks, partial least square, shrunken centroids (SC), sliced inverse regression, Standard for the Exchange of Product model data, super principal component (SPC) regression, Support Vector Machines (SVM), and Recursive Support Vector Machines (RSVM);

diagnosing or prognosing the subject as needing treatment for rheumatoid arthritis (RA) based on the protein level disease activity index score exceeding a reference value of the clinical disease activity score, wherein the diagnosis or prognosis is the same for subjects with and without comorbidities; and

administering a therapy to the subjected diagnosed or prognosed as needing treatment, the therapy comprising one or more of

administering a therapeutic compound selected from DMARDs, biologic DMARDs, non-steroidal anti-inflammatory drugs (NSAID's), and corticosteroids; and administering bariatric surgical intervention.

13. The method of claim 12 , wherein performance of the at least one immunoassay comprises: obtaining the first blood sample, wherein the first blood sample comprises the protein markers; contacting the first blood sample with a plurality of distinct reagents; generating a plurality of distinct complexes between the reagents and markers; and detecting the complexes to generate the data.

14. The method of claim 12 , wherein the at least one immunoassay comprises a multiplex assay.

15. The method of claim 12 , wherein the at least four protein markers comprise at least five markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

16. The method of claim 12 , wherein the at least four protein markers comprise at least six markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

17. The method of claim 12 , wherein the at least four protein markers comprise at least seven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

18. The method of claim 12 , wherein the at least four protein markers comprise at least eight markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

19. The method of claim 12 , wherein the at least four protein markers comprise at least nine markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

20. The method of claim 12 , wherein the at least four protein markers comprise at least ten markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

21. The method of claim 12 , wherein the at least four protein markers comprise at least eleven markers selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, or VEGFA.

22. The method of claim 12 , wherein the plurality of test markers comprises CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, and VEGFA.

23. The method of claim 12 , wherein the CHI3L1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001267.2, wherein the CRP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000558.2, wherein the EGF is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001954.2, wherein the IL6 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000591.1, wherein the LEP is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000221.1, wherein the MMP1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002412.1, wherein the MMP3 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_002413.1, wherein the RETN is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_065148.1, wherein the SAA1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_000322.2, wherein the TNFRSF1A is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001056.1, wherein the VCAM1 is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001069.1, and wherein the VEGFA is at least 90% identical to the amino acid sequence of NCBI RefSeq NP_001020539.2.

Assignments (6)
RELEASE OF SECURITY INTEREST Recorded Jul 10, 2023
From: JPMORGAN CHASE BANK, N.A.
To: MYRIAD GENETICS, INC.; CRESCENDO BIOSCENCE, INC.; MYRIAD RBM, INC.; MYRIAD WOMEN'S HEALTH, INC.
Reel/Frame 064239/0091 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 6, 2021
From: CRESCENDO BIOSCIENCE, INC.
To: LABORATORY CORPORATION OF AMERICA HOLDINGS
Reel/Frame 057709/0825 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 30, 2021
From: CAVET, GUY; SHEN, YIJING
To: CRESCENDO BIOSCIENCE, INC.
Reel/Frame 057036/0758 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 24, 2020
From: CAVET, GUY L.; SHEN, YIJING
To: CRESCENDO BIOSCIENCE
Reel/Frame 054455/0072 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 24, 2020
From: KNOWLTON, NICHOLAS; CENTOLA, MICHAEL
To: OKLAHOMA MEDICAL RESEARCH FOUNDATION
Reel/Frame 054455/0182 →
SECURITY INTEREST Recorded Dec 27, 2016
From: CRESCENDO BIOSCIENCE, INC.
To: JPMORGAN CHASE BANK, N.A., AS COLLATERAL AGENT
Reel/Frame 040776/0702 →
Continuity (5)
Continuation 12905984 · Oct 15, 2010
Provisional Application 61355087 · Jun 15, 2010
Provisional Application 61304317 · Feb 12, 2010
Provisional Application 61252110 · Oct 15, 2009
Related Publication 20150377909A1 · Dec 31, 2015