Methods of assessing hypertrophic cardiomyopathy

Abstract

The present disclosure includes biomarkers, methods, reagents, systems, and kits for predicting an individual's likelihood of having HCM. In one aspect, the disclosure provides biomarkers that can be used alone or in various combinations to predict an individual's likelihood of having HCM. In another aspect, methods are provided for predicting an individual's likelihood of having HCM, where the methods include detecting, in a biological sample from an individual, at least one biomarker value corresponding to at least one biomarker selected from the group of biomarkers provided in Table 1.

Description

Attorney Docket No. 01137-0099-00PCTMETHODS OF ASSESSING HYPERTROPHIC CARDIOMYOPATHYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority of US Provisional Application No. 63 / 699,492, filed September 26, 2024, which is incorporated by reference herein in its entirety for any purpose.FIELD

[0002] The present application relates generally to the detection of biomarkers and methods of assessing hypertrophic cardiomyopathy (HCM) in an individual and, more specifically, to one or more biomarkers, methods, reagents, systems, and kits used to determine the likelihood of an individual having HCM.BACKGROUND

[0003] The following description provides a summary of information that may be relevant to the present application and is not an admission that any of the information provided or publications referenced herein is prior art to the present application.

[0004] Hypertrophic cardiomyopathy (HCM) is a common genetic form of heart disease that is estimated to affect -700,000 adults in the US. Although symptomatic hypertrophy (based on claims data) has only been shown to impact about 1 in 3,000 adults in the US, it is estimated that only -15% of adults with HCM are properly diagnosed and the prevalence of HCM (including asymptomatic individuals) in the US ranges from 1 in 200 to 1 in 500 adults. HCM often goes undiagnosed because clinical presentation can be highly variable, many patients are asymptomatic patients, or have non-specific symptoms (e.g., shortness of breath in HCM patients is often misdiagnosed as asthma or exercise induced asthma). HCM can also be difficult to diagnose because of overlapping etiology with other cardiovascular conditions, limitations of genetic testing (including difficulty getting genetic testing done unless a family history has already been established), and difficulties in early-stage diagnosis (i.e., early-stage patients may not meet key diagnostic criteria; LV wall thickness > 15mm). There is a need for a non- invasive, accurate screening test that can be used to identify patients from the general population (including asymptomatic, mildly symptomatic and individuals with a family history) who may be at increased risk for HCM or who have a likelihood of HCM. Individuals identified who may be at increased risk or who have a likelihood of HCM can be referred for further diagnostic work up (e.g., clinical assessment, 12-lead electrocardiogram (ECG), transthoracic echocardiogram (TTE), cardiovascular magnetic resonance (CMR), and cardiac computed tomography (CCT)).Attorney Docket No. 01137-0099-00PCT

[0005] The development of a proteomic model for predicting an individual’s likelihood of having HCM would be highly desirable. A need exists for biomarkers, methods, reagents, systems, and kits that enable the prediction of the likelihood of an individual having HCM.SUMMARY

[0006] The present application includes biomarkers, methods, reagents, systems, and kits for assessing HCM status. In some embodiments, methods of predicting the likelihood of HCM in individuals are provided. In some embodiments, methods of detecting levels of N biomarker proteins in a sample are provided.Embodiment 1. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising forming a biomarker panel comprising N biomarker proteins, and detecting a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or at least 12, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or 12 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 2. A method of detecting levels of N biomarker proteins in a sample, comprising forming a biomarker panel comprising N biomarker proteins, and detecting the level of each of the N biomarker proteins in the sample from a subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or at least 12, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or 12 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 3. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of IDS and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 4. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of CRLD2 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, atAttorney Docket No. 01137-0099-00PCT least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 5. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of PSME2 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 6. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of RAB31 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 7. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of DPEP1 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, KAAG1, and BNP.Embodiment 8. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of RPIA and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, DPEP1, RAB31, NRP1, KAAG1, and BNP.Embodiment 9. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of NRP1 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, atAttorney Docket No. 01137-0099-00PCT least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, KAAG1, and BNP.Embodiment 10. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of KAAG1 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, and BNP.Embodiment 11. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of BNP and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, and KAAG1.Embodiment 12. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of ANP and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, KAAG1, and BNP.Embodiment 13. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of N-terminal pro-BPN and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, KAAG1, and BNP.Embodiment 14. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of PolyUbiquitin K63 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, atAttorney Docket No. 01137-0099-00PCT least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PSME2, RAB31, RPIA, NRP1, KAAG1, and BNP.Embodiment 15. The method according to any one of the preceding embodiments, wherein N is 2 to 12, or N is 3 to 12, N is 4 to 12, N is 5 to 12, N is 6 to 12, N is 7 to 12, N is 8 to 12, N is 9 to 12, N is 10 to 12, or N is 11 to 12.Embodiment 16. The method according to any one of the preceding embodiments, wherein N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, or N is 12.Embodiment 17. The method according to any one of the preceding embodiments, wherein at least one of the N biomarker proteins is IDS, or at least one of the N biomarker proteins is CRLD2, or at least one of the N biomarker proteins is ANP, or at least one of N biomarker proteins is N-terminal pro-BPN, or at least one of the N biomarker proteins is PolyUbiquitin K63, or at least one of the N biomarker proteins is PSME2, or at least one of the N biomarker proteins is RAB31, or at least one of the N biomarker proteins is DPEP1, or at least one of the N biomarker proteins is RPIA, or at least one of the N biomarker proteins is NRP1, or at least one of the N biomarker proteins is KAAG1, or at least one of the N biomarker proteins is BNP.Embodiment 18. The method according to any one of the preceding embodiments, wherein each of the N biomarker proteins is selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 19. The method according to any one of the preceding embodiments, wherein at least 2, at least 3, at least 4, or at least 5 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 20. The method according to any one of embodiments 1-4 and 12-17, wherein at least 2, at least 3, at least 4, or at least 5 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, and PolyUbiquitin K63.Embodiment 21. The method according to any one of embodiments 1-3, wherein 2 of the N biomarker proteins are IDS and CRDL2, or 2 of the N biomarker proteins are IDS and ANP, or 2 of the N biomarker proteins are IDS and N-terminal pro-BPN, or 2 of the N biomarker proteins are IDS and PolyUbiquitin K63, or 2 of the N biomarker proteins are IDS and PSME2, or 2 of the N biomarker proteins are IDS and RAB31, or 2 of the N biomarker proteins are IDS and DPEP1, or 2 of the N biomarker proteins are IDS and RPIA, or 2 of the N biomarker proteins are IDS and NRP1, or 2 of the N biomarker proteins are IDS and KAAG1, or 2 of the N biomarker proteins are IDS and BNP.Attorney Docket No. 01137-0099-00PCTEmbodiment 22. The method according to any one of embodiments 1, 2 or 4, wherein 2 of the N biomarker proteins are CRLD2 and ANP, or 2 of the N biomarker proteins are CRLD2 and N-terminal pro-BPN, or 2 of the N biomarker proteins are CRLD2 and PolyUbiquitin K63, or 2 of the N biomarker proteins are CRLD2 and PSME2, or 2 of the N biomarker proteins are CRLD2 and RAB31, or 2 of the N biomarker proteins are CRLD2 and DPEP1, or 2 of the N biomarker proteins are CRLD2 and RPIA, or 2 of the N biomarker proteins are CRLD2 and NRP1, or 2 of the N biomarker proteins are CRLD2 and KAAG1, or 2 of the N biomarker proteins are CRLD2 and BNP.Embodiment 23. The method according to any one of embodiments 1, 2 or 5, wherein 2 of the N biomarker proteins are PSME2 and ANP, or 2 of the N biomarker proteins are PSME2 and N-terminal pro-BPN, or 2 of the N biomarker proteins are PSME2 and PolyUbiquitin K63, or 2 of the N biomarker proteins are PSME2 and RAB31, or 2 of the N biomarker proteins are PSME2 and DPEP1, or 2 of the N biomarker proteins are PSME2 and RPIA, or 2 of the N biomarker proteins are PSME2 and NRP1, or 2 of the N biomarker proteins are PSME2 and KAAG1, or 2 of the N biomarker proteins are PSME2 and BNP.Embodiment 24. The method according to any one of embodiments 1, 2 or 6, wherein 2 of the N biomarker proteins are RAB31 and ANP, or 2 of the N biomarker proteins are RAB31 and N-terminal pro-BPN, or 2 of the N biomarker proteins are RAB31 and PolyUbiquitin K63, or 2 of the N biomarker proteins are RAB31 and DPEP1, or 2 of the N biomarker proteins are RAB3 1 and RPIA, or 2 of the N biomarker proteins are RAB31 and NRP1, or 2 of the N biomarker proteins are RAB31 and KAAG1, or 2 of the N biomarker proteins are RAB31 and BNP.Embodiment 25. The method according to any one of embodiments 1, 2 or 7, wherein 2 of the N biomarker proteins are DPEP1 and ANP, or 2 of the N biomarker proteins are DPEP1 and N-terminal pro-BPN, or 2 of the N biomarker proteins are DPEP1 and PolyUbiquitin K63, or 2 of the N biomarker proteins are DPEP1 and RPIA, or 2 of the N biomarker proteins are DPEP1 and NRP1, or 2 of the N biomarker proteins are DPEP1 and KAAG1, or 2 of the N biomarker proteins are DPEP1 and BNP.Embodiment 26. The method according to any one of embodiments 1, 2 or 8, wherein 2 of the N biomarker proteins are RPIA and ANP, or 2 of the N biomarker proteins are RPIA and N- terminal pro-BPN, or 2 of the N biomarker proteins are RPIA and PolyUbiquitin K63, or 2 of the N biomarker proteins are RPIA and NRP1, or 2 of the N biomarker proteins are RPIA and KAAG1, or 2 of the N biomarker proteins are RPIA and BNP.Embodiment 27. The method according to any one of embodiments 1, 2 or 9, wherein 2 of the N biomarker proteins are NRP1 and ANP, or 2 of the N biomarker proteins are NRP1 and N-Attorney Docket No. 01137-0099-00PCT terminal pro-BPN, or 2 of the N biomarker proteins are NRP1 and PolyUbiquitin K63, or 2 of the N biomarker proteins are NRP1 and KAAG1, or 2 of the N biomarker proteins are NRP1 and BNP.Embodiment 28. The method according to any one of embodiments 1, 2, 10, and 11 wherein 2 of the N biomarker proteins are KAAG1 and ANP, or 2 of the N biomarker proteins are KAAG1 and N-terminal pro-BPN, or 2 of the N biomarker proteins are KAAG1 and PolyUbiquitin K63, or 2 of the N biomarker proteins are KAAG1 and BNP, or 2 of the N biomarker proteins are BNP and ANP, or 2 of the N biomarker proteins are BNP and N-terminal pro-BPN, or 2 of the N biomarker proteins are BNP and PolyUbiquitin K63.Embodiment 29. The method according to any one of embodiments 1, 2, 12, 13, and 14, wherein 2 of the N biomarker proteins are ANP and N-terminal pro-BPN, or 2 of the N biomarker proteins are ANP and PolyUbiquitin K63, or two of the N biomarker proteins are N- terminal pro-BNP and PolyUbiquitinK63.Embodiment 30. The method according to any one of the preceding embodiments, wherein the sample is a blood sample, a plasma sample, a serum sample, or a urine sample.Embodiment 31. The method according to any one of the preceding embodiments, wherein detecting is performed using mass spectrometry, an aptamer based assay and / or an antibody based assay.Embodiment 32. The method according to any one of the preceding embodiments, wherein the method comprises contacting biomarker proteins of the sample or samples with a set of biomarker capture reagents, wherein each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a different biomarker protein being detected.Embodiment 33. The method according to embodiment 32, wherein each biomarker capture reagent is an antibody or an aptamer.Embodiment 34. The method according to embodiment 33, wherein each biomarker capture reagent is an aptamer.Embodiment 35. The method according to embodiment 34, wherein at least one aptamer is a slow off-rate aptamer.Embodiment 36. The method according to embodiment 35, wherein at least one slow off- rate aptamer comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 nucleotides with modifications.Embodiment 37. The method according to embodiment 35 or embodiment 36, wherein each slow off-rate aptamer binds to its target protein with an off rate (tU) of > 20 minutes, > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.Attorney Docket No. 01137-0099-00PCTEmbodiment 38. The method according to any one of embodiments 31-37, wherein the level of each biomarker protein measured is determined from a relative florescence unit (RFU) or a protein concentration.Embodiment 39. The method according to any one of the preceding embodiments, wherein predicting the likelihood of HCM in the subject is based on input of the levels of the N biomarker proteins measured in a statistical model.Embodiment 40. The method according to embodiment 39, wherein the determining comprises analyzing the levels of the N biomarker protein using an elastic net logistic regression model.Embodiment 41. The method according to embodiment 39 or 40, wherein the model has an area under the curve (AUC) selected from at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.7, at least 0.75, at least 0.8, at least 0.85, at least 0.9, or at least 0.95.Embodiment 42. The method according to any one of the preceding embodiments, wherein the method comprises predicting a likelihood of HCM in a subject for the purpose of determining a medical insurance premium or life insurance premium.Embodiment 43. The method according to embodiment 42, wherein the method further comprises determining coverage for medical insurance or life insurance.Embodiment 44. The method according to any one of embodiments 1-43, wherein the method further comprises using information resulting from the method to predict and / or manage the utilization of medical resources.Embodiment 45. A kit comprising N biomarker protein capture reagents, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or at least 12 and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or 12 of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from IDS, CRLD2, ANP, N- terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 46. The kit according to embodiment 45, wherein N is at least two and at least one of the two N biomarker protein capture reagents specifically binds to the biomarker protein selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 47. The kit according to embodiment 45 or 46, wherein N is 2 to 12, or N is 3 to 12, or N is 4 to 12, or N is 5 to 12, or N is 6 to 12, or N is 7 to 12, or N is 8 to 12, or N is 9 to 12, or N is 10 to 12, or N is 11 to 23.Attorney Docket No. 01137-0099-00PCTEmbodiment 48. The kit according to any one of embodiments 45-47, wherein N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, or N is 12.Embodiment 49. The kit according to any one of embodiments 45-48, wherein each of the N biomarker protein capture reagents specifically binds to a different biomarker protein.Embodiment 50. The kit according to any one of embodiments 45-48, wherein each of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 51. The kit according to any one of embodiments 45-49, wherein at least 1 of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 52. The kit according to any one of embodiments 45-49, wherein at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or 12 of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.Embodiment 53. The kit according to any one of embodiments 45-49, wherein 2 of the N biomarker protein capture reagents specifically bind IDS and CRLD2, or 2 of the N biomarker protein capture reagents specifically bind IDS and ANP, or 2 of the N biomarker protein capture reagents specifically bind IDS and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind IDS and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind IDS and PSME2, or 2 of the N biomarker protein capture reagents specifically bind IDS and RAB31, or 2 of the N biomarker protein capture reagents specifically bind IDS and DPEP1, or 2 of the N biomarker protein capture reagents specifically bind IDS and RPIA, or 2 of the N biomarker protein capture reagents specifically bind IDS and NRP1, or 2 of the N biomarker protein capture reagents specifically bind IDS and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind IDS and BNP, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and ANP, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and PSME2, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and RAB31, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and DPEP1, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and RPIA, or 2 of the N biomarkerAttorney Docket No. 01137-0099-00PCT protein capture reagents specifically bind CRLD2 and NRP1, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and BNP, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and ANP, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and RAB31, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and DPEP1, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and RPIA, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and NRP1, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and BNP, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and ANP, or 2 of the N biomarker protein capture reagents specifically bind RAB3 1 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and DPEP1, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and RPIA, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and NRP1, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and BNP, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and ANP, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and RPIA, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and NRP1, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and BNP, or 2 of the N biomarker protein capture reagents specifically bind RPIA and ANP, or 2 of the N biomarker protein capture reagents specifically bind RPIA and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind RPIA and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind RPIA and NRP1, or 2 of the N biomarker protein capture reagents specifically bind RPIA and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind RPIA and BNP, or 2 of the N biomarker protein capture reagents specifically bind NRP1 and ANP, or 2 of the N biomarker protein capture reagents specifically bind NRP1 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind NRP1 and PolyUbiquitin K63, or 2 of the N biomarker proteinAttorney Docket No. 01137-0099-00PCT capture reagents specifically bind NRP1 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind NRP1 and BNP, or 2 of the N biomarker protein capture reagents specifically bind KAAG1 and ANP, or 2 of the N biomarker protein capture reagents specifically bind KAAG1 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind KAAG1 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind KAAG1 and BNP, or 2 of the N biomarker protein capture reagents specifically bind BNP and ANP, or 2 of the N biomarker protein capture reagents specifically bind BNP and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind BNP and PolyUbiquitin K63 or 2 of the N biomarker proteins are ANP and N-terminal pro-BPN, or 2 of the N biomarker proteins are ANP and PolyUbiquitin K63, or two of the N biomarker proteins are N-terminal pro-BNP and PolyUbiquitinK63. Embodiment 54. A kit comprising N biomarker protein capture reagents, wherein the kit comprises biomarker protein capture reagents for carrying out the method of any one of embodiments 1-44.Embodiment 55. The kit according to any one of embodiments 45-54, wherein each of the N biomarker protein capture reagents is an antibody or an aptamer.Embodiment 56. The kit according to embodiment 55, wherein each biomarker protein capture reagent is an aptamer.Embodiment 57. The kit according to embodiment 56, wherein at least one aptamer is a slow off-rate aptamer.Embodiment 58. The kit according to embodiment 57, wherein at least one slow off-rate aptamer comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 nucleotides with modifications.Embodiment 59. The kit according to embodiment 57 or embodiment 58, wherein each slow off-rate aptamer binds to its target protein with an off rate (tU) of > 20 minutes, > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.Embodiment 60. The kit according to any one of embodiments 45-59, for use in detecting the N biomarker proteins in a sample from a subject.Embodiment 61. The kit according to embodiment 60, for use in predicting an individual’s likelihood of having HCM.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 illustrates certain exemplary 5-position modified uridines and cytidines that may be incorporated into aptamers.Attorney Docket No. 01137-0099-00PCT

[0008] FIG. 2 illustrates certain exemplary modifications that may be present at the 5- position of uridine. The chemical structure of the C-5 modification includes the exemplary amide linkage that links the modification to the 5-position of uridine. The 5-position moieties shown include two phenyl groups covalently attached to one another. The 5-position moieties shown include a phenylbenzyl moiety (e.g., BPE, PBnd, DBM), a 4-phenoxybenzyl moiety (e.g., POP), a diphenylpropyl moiety (e.g., DPP), a benzhydryl moiety (e.g., BH).

[0009] FIG. 3 illustrates certain exemplary modifications that may be present at the 5- position of cytidine. The chemical structure of the C-5 modification includes the exemplary amide linkage that links the modification to the 5-position of cytidine. The 5-position moieties shown include two phenyl groups covalently attached to one another. The 5-position moieties shown include a phenylbenzyl moiety (e.g., BPE, PBnd, DBM), a 4-phenoxybenzyl moiety (e.g., POP), a diphenylpropyl moiety (e.g., DPP), a benzhydryl moiety (e.g., BH).

[0010] FIG. 4 illustrates certain exemplary modifications that may be present at the 5- position of uridine. The chemical structure of the C-5 modification includes the exemplary amide linkage that links the modification to the 5-position of the uridine. The 5-position moieties shown include a benzyl moiety (e.g., Bn, PE and a PP), a naphthyl moiety (e.g., Nap, 2Nap, NE), a butyl moiety (e.g, iBu), a fluorobenzyl moiety (e.g., FBn), a tyrosyl moiety (e.g., a Tyr), a 3,4-methylenedioxy benzyl (e.g., MBn), a morpholino moiety (e.g., MOE), a benzofuranyl moiety (e.g., BF), an indole moiety (e.g, Trp) and a hydroxypropyl moiety (e.g., Thr).

[0011] FIG. 5 illustrates certain exemplary modifications that may be present at the 5- position of cytidine. The chemical structure of the C-5 modification includes the exemplary amide linkage that links the modification to the 5-position of the cytidine. The 5-position moieties shown include a benzyl moiety (e.g., Bn, PE and a PP), a naphthyl moiety (e.g., Nap, 2Nap, NE, and 2NE) and a tyrosyl moiety (e.g., a Tyr).

[0012] FIG. 6 illustrates an exemplary computer system for use with various computer- implemented methods described herein.

[0013] FIG. 7 is a flowchart for a method of evaluating the likelihood of an individual having HCM in accordance with one or more embodiments.

[0014] FIG. 8 is a volcano plot illustrating proteomic differences in the training dataset between the control cohort (n=5627) and HCM cases (n=521).

[0015] FIG. 9 illustrates sensitivity, specificity, and Youden’s J for the final model across all possible cut-off points. The curved solid lines represent model sensitivity (red), specificity (green), and Youden’s J (blue). The horizontal dashed lines represent performance criteria for sensitivity (red) and specificity (green). The vertical dashed blue line represents the selected cut-off point of 0.603.Attorney Docket No. 01137-0099-00PCT

[0016] FIG. 10 illustrates a box plot of NYHA class predictions of HCM cases from the final model, on the training (red) and verification (blue) data sets.

[0017] FIG. 11 illustrates a Box plot of final model training HCM cases (red), training controls (green), and HFpEF patients (blue) predicted probabilities.

[0018] FIG. 12 illustrates absolute and relative risks of HCM diagnosis compared to baseline risk, split by quartiles. The absolute baseline risk is 0.085; the relative baseline risk is 1. Each training data quartile has N = 880. Verification data quartile Ns = 189 (Q1-Q3) or 188 (Q4).DETAILED DESCRIPTION

[0019] Reference will now be made in detail to representative embodiments of the invention. While the invention will be described in conjunction with the enumerated embodiments, it will be understood that the invention is not intended to be limited to those embodiments. On the contrary, the invention is intended to cover all alternatives, modifications, and equivalents that may be included within the scope of the present invention as defined by the claims.

[0020] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in and are within the scope of the practice of the present invention. The present invention is in no way limited to the methods and materials described.

[0021] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, certain methods, devices and materials are now described.

[0022] Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure, suitable methods and materials are described below. All publications, published patent documents, and patent applications cited in this application are indicative of the level of skill in the art(s) to which the application pertains. All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.

[0023] As used in this application, including the appended claims, the singular forms “a,” “an,” and “the” include plural references, unless the content clearly dictates otherwise, andAttorney Docket No. 01137-0099-00PCT are used interchangeably with “at least one” and “one or more.” Thus, reference to “a SOMAmer” includes mixtures of SOMAmers, reference to “a probe” includes mixtures of probes, and the like. It is further to be understood that all base sizes or amino acid sizes, and all molecular weight or molecular mass values, given for nucleic acids or polypeptides are approximate, and are provided for description.

[0024] Further, ranges provided herein are understood to be shorthand for all of the values within the range. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or sub-range from the group consisting 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 (as well as fractions thereof unless the context clearly dictates otherwise). Any concentration range, percentage range, ratio range, or integer range is to be understood to include the value of any integer within the recited range and, when appropriate, fractions thereof (such as one tenth and one hundredth of an integer), unless otherwise indicated. Also, any number range recited herein relating to any physical feature are to be understood to include any integer within the recited range, unless otherwise indicated.

[0025] As used herein, the term “about” represents an insignificant modification or variation of the numerical value such that the basic function of the item to which the numerical value relates is unchanged.

[0026] As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “contains,” “containing,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, product-by-process, or composition of matter.

[0027] The present application includes biomarkers, methods, reagents, systems, and kits for the determining the likelihood of HCM in an individual.

[0028] “Biological sample,” “sample,” and “test sample” are used interchangeably herein to refer to any material, biological fluid, tissue, or cell obtained or otherwise derived from an individual. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, and serum), dried blood spots, sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, bronchial brushing, synovial fluidjoint aspirate, organ secretions, cells, a cellular extract, and cerebrospinal fluid. This also includes experimentally separated fractions of all of the preceding. For example, a blood sample can be fractionated intoAttorney Docket No. 01137-0099-00PCT serum, plasma or into fractions containing particular types of blood cells, such as red blood cells or white blood cells (leukocytes). If desired, a sample can be a combination of samples from an individual, such as a combination of a tissue and fluid sample. The term “biological sample” also includes materials containing homogenized solid material, such as from a stool sample, a tissue sample, or a tissue biopsy, for example. The term “biological sample” also includes materials derived from a tissue culture or a cell culture. Any suitable methods for obtaining a biological sample can be employed; exemplary methods include, e.g., phlebotomy, swab (e.g., buccal swab), and a fine needle aspirate biopsy procedure. Exemplary tissues susceptible to fine needle aspiration include lymph node, lung, lung washes, BAL (bronchoalveolar lavage), thyroid, breast, pancreas, and liver. Samples can also be collected, e.g., by micro dissection (e.g., laser capture micro dissection (LCM) or laser micro dissection (LMD)), bladder wash, smear (e.g., a PAP smear), or ductal lavage. A “biological sample” obtained or derived from an individual includes any such sample that has been processed in any suitable manner after being obtained from the individual.

[0029] For purposes of this specification, the phrase “data attributed to a biological sample from an individual” is intended to mean that the data in some form derived from, or were generated using, the biological sample of the individual. The data may have been reformatted, revised, or mathematically altered to some degree after having been generated, such as by conversion from units in one measurement system to units in another measurement system; but, the data are understood to have been derived from, or were generated using, the biological sample.

[0030] “Target,” “target molecule,” and “analyte” are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample. A “molecule of interest” includes any minor variation of a particular molecule, such as, in the case of a protein, for example, minor variations in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component, which does not substantially alter the identity of the molecule. A “target molecule,” “target,” or “analyte” is a set of copies of one type or species of molecule or multi -molecular structure. “Target molecules,” “targets,” and “analytes” refer to more than one such set of molecules. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affybodies, antibody mimics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the foregoing. In someAttorney Docket No. 01137-0099-00PCT embodiments, a target molecule is a protein, in which case the target molecule may be referred to as a “target protein.”

[0031] As used herein, a “capture agent’ or “capture reagent” refers to a molecule that is capable of binding specifically to a biomarker. A “target protein capture reagent” refers to a molecule that is capable of binding specifically to a target protein. Nonlimiting exemplary capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligandbinding receptors, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments of any of the aforementioned capture reagents. In some embodiments, a capture reagent is selected from an aptamer and an antibody.

[0032] As used herein, “polypeptide,” “peptide,” and “protein” are used interchangeably herein to refer to polymers of amino acids of any length. The polymer may be linear or branched, it may comprise modified amino acids, and it may be interrupted by non-amino acids. The terms also encompass an amino acid polymer that has been modified naturally or by intervention; for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component. Also included within the definition are, for example, polypeptides containing one or more analogs of an amino acid (including, for example, unnatural amino acids, etc.), as well as other modifications known in the art. Polypeptides can be single chains or associated chains. Also included within the definition are preproteins and intact mature proteins; peptides or polypeptides derived from a mature protein; fragments of a protein; splice variants; recombinant forms of a protein; protein variants with amino acid modifications, deletions, or substitutions; digests; and post-translational modifications, such as glycosylation, acetylation, phosphorylation, and the like.

[0033] The term “antibody” refers to full-length antibodies of any species and fragments and derivatives of such antibodies, including Fab fragments, F(ab')2 fragments, single chain antibodies, Fv fragments, and single chain Fv fragments. The term “antibody” also refers to synthetically-derived antibodies, such as phage display-derived antibodies and fragments, affybodies, nanobodies, etc.

[0034] As used herein, “marker” and “biomarker” and “feature” are used interchangeably to refer to a target molecule that indicates or is a sign of a normal or abnormal process in an individual or of a disease or other condition in an individual. More specifically, a “marker” or “biomarker” or “feature” is an anatomic, physiologic, biochemical, or molecular parameter associated with the presence of a specific physiological state or process, whetherAttorney Docket No. 01137-0099-00PCT normal or abnormal, and, if abnormal, whether chronic or acute. Biomarkers are detectable and measurable by a variety of methods including laboratory assays and medical imaging. When a biomarker is a protein, it is also possible to use the expression of the corresponding gene as a surrogate measure of the amount or presence or absence of the corresponding protein biomarker in a biological sample or methylation state of the gene encoding the biomarker or proteins that control expression of the biomarker. In certain aspects, a feature is an analyte / SOMAmer reagent of other predictors in a statistical model.

[0035] As used herein, “biomarker value,” “value,” “biomarker level,” “feature level,” and “level” are used interchangeably to refer to a measurement that is made using any analytical method for detecting the biomarker in a biological sample and that indicates the presence, absence, absolute amount or concentration, relative amount or concentration, titer, a level, an expression level, a ratio of measured levels, or the like, of, for, or corresponding to the biomarker in the biological sample. The exact nature of the “value” or “level” depends on the specific design and components of the particular analytical method employed to detect the biomarker.

[0036] When a biomarker indicates or is a sign of an abnormal process or a disease or other condition in an individual, that biomarker is generally described as being either overexpressed or under-expressed as compared to an expression level or value of the biomarker that indicates or is a sign of a normal process or an absence of a disease or other condition in an individual. “Up-regulation,” “up-regulated,” “over-expression,” “over-expressed,” and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that is typically detected in similar biological samples from healthy or normal individuals. The terms may also refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that may be detected at a different stage of a particular disease.

[0037] “Down-regulation,” “down-regulated,” “under-expression,” “under-expressed,” and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that is typically detected in similar biological samples from healthy or normal individuals. The terms may also refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that may be detected at a different stage of a particular disease.

[0038] Further, a biomarker that is either over-expressed or under-expressed can also be referred to as being “differentially expressed” or as having a “differential level” or “differentialAttorney Docket No. 01137-0099-00PCT value” as compared to a “normal” expression level or value of the biomarker that indicates or is a sign of a normal process or an absence of a disease or other condition in an individual. Thus, “differential expression” of a biomarker can also be referred to as a variation from a “normal” expression level of the biomarker.

[0039] The term “differential gene expression” and “differential expression” are used interchangeably to refer to a gene (or its corresponding protein expression product) whose expression is activated to a higher or lower level in a subject suffering from a specific disease or condition, relative to its expression in a normal or control subject. The terms also include genes (or the corresponding protein expression products) whose expression is activated to a higher or lower level at different stages of the same disease or condition. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product. Such differences may be evidenced by a variety of changes including mRNA levels, surface expression, secretion or other partitioning of a polypeptide. Differential gene expression may include a comparison of expression between two or more genes or their gene products; or a comparison of the ratios of the expression between two or more genes or their gene products; or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disease; or between various stages of the same disease. Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products among, for example, normal and diseased cells, or among cells which have undergone different disease events or disease stages.

[0040] A “control level” of a target molecule refers to the level of the target molecule in a properly handled sample of the same sample type. Control level may refer to the average level of the target molecule in properly handled samples from a population of individuals.

[0041] As used herein, “individual” refers to a test subject or patient. The individual can be a mammal or a non-mammal. In various embodiments, the individual is a mammal. A mammalian individual can be a human or non-human. In various embodiments, the individual is a human.

[0042] “Diagnose,” “diagnosing,” “diagnosis,” and variations thereof refer to the detection, determination, or recognition of a health status or condition of an individual on the basis of one or more signs, symptoms, data, or other information pertaining to that individual. The health status of an individual can be diagnosed as healthy / normal (i.e., a diagnosis of the absence of a disease or condition) or diagnosed as ill / abnormal (i.e., a diagnosis of the presence, or an assessment of the characteristics, of a disease or condition). The termsAttorney Docket No. 01137-0099-00PCT“diagnose,” “diagnosing,” “diagnosis,” etc., encompass, with respect to a particular disease or condition, the initial detection of the disease; the characterization or classification of the disease; the detection of the progression, remission, or recurrence of the disease; and the detection of disease response after the administration of a treatment or therapy to the individual.

[0043] “Prognose,” “prognosing,” “prognosis,” and variations thereof refer to the prediction of a future course of a disease or condition in an individual who has the disease or condition (e.g., predicting patient survival), and such terms encompass the evaluation of disease or condition response after the administration of a treatment or therapy to the individual.

[0044] “Evaluate,” “evaluating,” “evaluation,” and variations thereof encompass both “diagnose” and “prognose” and also encompass determinations or predictions about the future course of a disease or condition in an individual who does not have the disease as well as determinations or predictions regarding the risk that a disease or condition will recur in an individual who apparently has been cured of the disease or has had the condition resolved. The term “evaluate” also encompasses assessing an individual’s response to a therapy, such as, for example, predicting whether an individual is likely to respond favorably to a therapeutic agent or is unlikely to respond to a therapeutic agent (or will experience toxic or other undesirable side effects, for example), selecting a therapeutic agent for administration to an individual, or monitoring or determining an individual’s response to a therapy that has been administered to the individual.

[0045] As used herein, “additional biomedical information” refers to one or more evaluations of an individual, other than using any of the biomarkers described herein, that are associated with HCM. “Additional biomedical information” includes any of the following: physical descriptors of an individual, including the height and / or weight of an individual; the age of an individual; the gender of an individual; change in weight; the ethnicity of an individual; occupational history; family history of HCM; the presence of a genetic marker(s); clinical symptoms such as abdominal pain, weight gain or loss gene expression values; physical descriptors of an individual, including physical descriptors observed by radiologic imaging; tobacco use status; alcohol use history; occupational history; dietary habits - salt, saturated fat and cholesterol intake; caffeine consumption; and imaging information. Additional biomedical information can be obtained from an individual using routine techniques known in the art, such as from the individual themselves by use of a routine patient questionnaire or health history questionnaire, etc., or from a medical practitioner, etc.

[0046] As used herein, “detecting” or “determining” with respect to a biomarker value includes the use of both the instrument required to observe and record a signal corresponding to a biomarker value and the material / s required to generate that signal. In various embodiments,Attorney Docket No. 01137-0099-00PCT the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0047] Solid support” refers herein to any substrate having a surface to which molecules may be attached, directly or indirectly, through either covalent or non-covalent bonds. A “solid support” can have a variety of physical formats, which can include, for example, a membrane; a chip (e.g., a protein chip); a slide (e.g., a glass slide or coverslip); a column; a hollow, solid, semi-solid, pore- or cavity- containing particle, such as, for example, a bead; a gel; a fiber, including a fiber optic material; a matrix; and a sample receptacle. Exemplary sample receptacles include sample wells, tubes, capillaries, vials, and any other vessel, groove or indentation capable of holding a sample. A sample receptacle can be contained on a multisample platform, such as a microtiter plate, slide, microfluidics device, and the like. A support can be composed of a natural or synthetic material, an organic or inorganic material. The composition of the solid support on which capture reagents are attached generally depends on the method of attachment (e.g., covalent attachment). Other exemplary receptacles include microdroplets and microfluidic controlled or bulk oil / aqueous emulsions within which assays and related manipulations can occur. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glasses, membranes, nylon, natural fibers (such as, for example, silk, wool and cotton), polymers, and the like. The material composing the solid support can include reactive groups such as, for example, carboxy, amino, or hydroxyl groups, which are used for attachment of the capture reagents. Polymeric solid supports can include, e.g., polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidenefluoride, polycarbonate, and polymethylpentene. Suitable solid support particles that can be used include, e.g., encoded particles, such as Luminex®-type encoded particles, magnetic particles, and glass particles.

[0048] As used herein, “analyte” is the protein target of a capture reagent. In certain aspects, the capture reagent is an aptamer. In certain further aspects, the capture reagent is a SOMAmer.

[0049] As used herein, “nucleic acid ligand,” “aptamer,” “SOMAmer,” “modified aptamer,” and “clone” are used interchangeably to refer to a non-naturally occurring nucleic acidAttorney Docket No. 01137-0099-00PCT that has a desirable action on a target molecule. A desirable action includes, but is not limited to, binding of the target, catalytically changing the target, reacting with the target in a way that modifies or alters the target or the functional activity of the target, covalently attaching to the target (as in a suicide inhibitor), and facilitating the reaction between the target and another molecule. In one embodiment, the action is specific binding affinity for a target molecule, such target molecule being a three dimensional chemical structure other than a polynucleotide that binds to the aptamer through a mechanism which is independent of Watson / Crick base pairing or triple helix formation, wherein the aptamer is not a nucleic acid having the known physiological function of being bound by the target molecule. Aptamers to a given target include nucleic acids that are identified from a candidate mixture of nucleic acids, where the aptamer is a ligand of the target, by a method comprising: (a) contacting the candidate mixture with the target, wherein nucleic acids having an increased affinity to the target relative to other nucleic acids in the candidate mixture can be partitioned from the remainder of the candidate mixture; (b) partitioning the increased affinity nucleic acids from the remainder of the candidate mixture; and (c) amplifying the increased affinity nucleic acids to yield a ligand-enriched mixture of nucleic acids, whereby aptamers of the target molecule are identified. It is recognized that affinity interactions are a matter of degree; however, in this context, the “specific binding affinity” of an aptamer for its target means that the aptamer binds to its target generally with a much higher degree of affinity than it binds to other, non-target, components in a mixture or sample. An “aptamer,” “SOMAmer,” or “nucleic acid ligand” is a set of copies of one type or species of nucleic acid molecule that has a particular nucleotide sequence. An aptamer can include any suitable number of nucleotides. “Aptamers” refer to more than one such set of molecules. Different aptamers can have either the same or different numbers of nucleotides. Aptamers may be DNA or RNA and may be single stranded, double stranded, or contain double stranded or triple stranded regions. In some embodiments, the aptamers are prepared using a SELEX process as described herein, or known in the art.

[0050] As used herein, “study,” refers to a set of samples and clinical data that are analyzed to derive the test.

[0051] As used herein, “training dataset,” refers to a subset of data from a study used to fit a model.

[0052] As used herein, “validation dataset,” refers to a final subset of data used to assess the performance of a selected model developed on a verification dataset.

[0053] As used herein, “verification dataset,” means a separate subset of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model parameters.Attorney Docket No. 01137-0099-00PCT

[0054] As used herein, “elastic net logistic regression” refers to a machine learning method that utilizes penalized regression techniques to select the features that best predict the endpoint while allowing correlated features to be grouped together.

[0055] As used herein, “feature” refers to an analyte / SOMAmer reagent or other predictors in a statistical model.

[0056] As used herein, “forward selection” refers to a method for feature selection and reduction. Forward selection is a form of stepwise regression that starts with zero features included in the model. In an iterative process, features are considered for addition using t-tests as the selection criterion.

[0057] As used herein, the term “need” or “needed” refers to a judgement made by a health care provider regarding treatment of a patient which is considered by the health care provider to be beneficial to the health status of the patient.

[0058] Likelihood of HCM Assessment

[0059] In some embodiments, the number of biomarkers useful for a biomarker subset or panel is based on the sensitivity and specificity value for the particular combination of biomarker levels. The terms “sensitivity” and “specificity” are used herein with respect to the ability to correctly classify an individual, based on one or more biomarker levels detected in their biological sample, as a likelihood of having HCM. “Sensitivity” indicates the performance of the biomarker(s) with respect to correctly classifying individuals that have a likelihood of having HCM or are healthy. “Specificity” indicates the performance of the biomarker(s) with respect to correctly classifying individuals who have a likelihood of having HCM.

[0060] In some embodiments, overall performance of a panel of one or more biomarkers is represented by the area-under-the-curve (AUC) value. The AUC value is derived from a receiver operating characteristic (ROC) curve. The ROC curve is the plot of the true positive rate (sensitivity) of a test against the false positive rate (1 -specificity) of the test. The term “area under the curve” or “AUC” refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., individuals having a likelihood of HCM or healthy individuals). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and / or any item of additional biomedical information) in distinguishing between two populations. Typically, the feature data across the entire population are sorted in ascending order based on the value of a single feature. Then, for each value for that feature, the true positive and false positive rates for the data are calculated. The true positive rate is determined by countingAttorney Docket No. 01137-0099-00PCT the number of cases above the value for that feature and then dividing by the total number of cases. The false positive rate is determined by counting the number of controls above the value for that feature and then dividing by the total number of controls. Although this definition refers to scenarios in which a feature is elevated in cases compared to controls, this definition also applies to scenarios in which a feature is lower in cases compared to the controls (in such a scenario, samples below the value for that feature would be counted). ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and this single sum value can be plotted in a ROC curve. Additionally, any combination of multiple features, in which the combination derives a single output value, can be plotted in a ROC curve.Exemplary Uses of Biomarkers

[0061] In various exemplary embodiments, methods are provided for predicting an individual’s likelihood of having HCM by detecting one or more biomarker values corresponding to one or more biomarkers that are present in the circulation of an individual, such as in blood, serum or plasma, by any number of analytical methods, including any of the analytical methods described herein. These biomarkers are, for example, differentially expressed in individuals who have a likelihood of having HCM as compared to an individual who does not. Detection of the differential expression of a biomarker in an individual can be used, for example, to predict an individual’s likelihood of having HCM.

[0062] In addition to testing biomarker levels as a stand-alone diagnostic test, biomarker levels can also be done in conjunction with determination of SNPs or other genetic lesions or variability that are indicative of increased risk of susceptibility of disease or condition. (See, e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).

[0063] Any of the described biomarkers may also be used in imaging tests. For example, an imaging agent can be coupled to any of the described biomarkers, which can be used to aid in predicting an individual’s likelihood of having HCM, to monitor response to therapeutic interventions, to select for target populations in a clinical trial among other uses.Detection and Determination of Biomarkers and Biomarker Levels

[0064] A biomarker level for the biomarkers described herein can be detected using any of a variety of known analytical methods. In one embodiment, a biomarker level is detected using a capture reagent. As used herein, a “capture agent” or “capture reagent” refers to a molecule that is capable of binding specifically to a biomarker. In various embodiments, the capture reagent can be exposed to the biomarker in solution or can be exposed to the biomarkerAttorney Docket No. 01137-0099-00PCT while the capture reagent is immobilized on a solid support. In other embodiments, the capture reagent contains a feature that is reactive with a secondary feature on a solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution, and then the feature on the capture reagent can be used in conjunction with the secondary feature on the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be conducted. Capture reagents include but are not limited to SOMAmers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, a F(ab’)2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, imprinted polymers, avimers, peptidomimetics, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments of these.

[0065] In some embodiments, a biomarker level is detected using a biomarker / capture reagent complex.

[0066] In other embodiments, the biomarker level is derived from the biomarker / capture reagent complex and is detected indirectly, such as, for example, as a result of a reaction that is subsequent to the biomarker / capture reagent interaction, but is dependent on the formation of the biomarker / capture reagent complex.

[0067] In some embodiments, the biomarker level is detected directly from the biomarker in a biological sample.

[0068] In one embodiment, the biomarkers are detected using a multiplexed format that allows for the simultaneous detection of two or more biomarkers in a biological sample. In one embodiment of the multiplexed format, capture reagents are immobilized, directly or indirectly, covalently or non-covalently, in discrete locations on a solid support. In another embodiment, a multiplexed format uses discrete solid supports where each solid support has a unique capture reagent associated with that solid support, such as, for example quantum dots. In another embodiment, an individual device is used for the detection of each one of multiple biomarkers to be detected in a biological sample. Individual devices can be configured to permit each biomarker in the biological sample to be processed simultaneously. For example, a microtiter plate can be used such that each well in the plate is used to uniquely analyze one of multiple biomarkers to be detected in a biological sample.

[0069] In one or more of the foregoing embodiments, a fluorescent tag can be used to label a component of the biomarker / capture complex to enable the detection of the biomarker value. In various embodiments, the fluorescent label can be conjugated to a capture reagent specific to any of the biomarkers described herein using known techniques, and the fluorescent label can then be used to detect the corresponding biomarker value. Suitable fluorescent labelsAttorney Docket No. 01137-0099-00PCT include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other such compounds.

[0070] In one embodiment, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule includes at least one substituted indolium ring system in which the substituent on the 3-carbon of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule includes an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In other embodiments, the dye molecule includes a first type and a second type of dye molecule, such as, e.g., two different AlexaFluor molecules. In other embodiments, the dye molecule includes a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.

[0071] Fluorescence can be measured with a variety of instrumentation compatible with a wide range of assay formats. For example, spectrofluorimeters have been designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy, by J.R. Lakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0072] In one or more of the foregoing embodiments, a chemiluminescence tag can optionally be used to label a component of the biomarker / capture complex to enable the detection of a biomarker value. Suitable chemiluminescent materials include any of oxalyl chloride, Rodamin 6G, Ru(bipy)32+ , TMAE (tetrakis(dimethylamino)ethylene), Pyrogallol (1,2,3-trihydroxibenzene), Lucigenin, peroxyoxalates, Aryl oxalates, Acridinium esters, dioxetanes, and others.

[0073] In yet other embodiments, the detection method includes an enzyme / substrate combination that generates a detectable signal that corresponds to the biomarker value. Generally, the enzyme catalyzes a chemical alteration of the chromogenic substrate which can be measured using various techniques, including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferases, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, betagalactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6- phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and the like.

[0074] In yet other embodiments, the detection method can be a combination of fluorescence, chemiluminescence, radionuclide or enzyme / substrate combinations that generateAttorney Docket No. 01137-0099-00PCT a measurable signal. Multimodal signaling could have unique and advantageous characteristics in biomarker assay formats.

[0075] More specifically, the biomarker levels for the biomarkers described herein can be detected using known analytical methods including, singleplex SOMAmer assays, multiplexed SOMAmer assays, singleplex or multiplexed immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometric analysis, histological / cytological methods, etc. as detailed below.Determination of Biomarker Levels using Aptamer-Based Assays

[0076] Assays directed to the detection and quantification of physiologically significant molecules in biological samples and other samples are important tools in scientific research and in the health care field. One class of such assays involves the use of a microarray that includes one or more aptamers immobilized on a solid support. The aptamers are each capable of binding to a target molecule in a highly specific manner and with very high affinity. See, e.g., U.S. Patent No. 5,475,096 entitled “Nucleic Acid Ligands”; see also, e.g., U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543, and U.S. Patent No. 6,503,715, each of which is entitled “Nucleic Acid Ligand Diagnostic Biochip”. Once the microarray is contacted with a sample, the aptamers bind to their respective target molecules present in the sample and thereby enable a determination of a biomarker value corresponding to a biomarker.

[0077] As used herein, an “aptamer” refers to a nucleic acid that has a specific binding affinity for a target molecule. It is recognized that affinity interactions are a matter of degree; however, in this context, the “specific binding affinity” of an aptamer for its target means that the aptamer binds to its target generally with a much higher degree of affinity than it binds to other components in a test sample. An “aptamer” is a set of copies of one type or species of nucleic acid molecule that has a particular nucleotide sequence. An aptamer can include any suitable number of nucleotides, including any number of chemically modified nucleotides. “Aptamers” refers to more than one such set of molecules. Different aptamers can have either the same or different numbers of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single stranded, double stranded, or contain double stranded regions, and can include higher ordered structures. An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein can include the use of two or more aptamers that specifically bind the same target molecule. As further described below, an aptamer may include a tag. If an aptamer includes a tag, all copies of the aptamer need not have the same tag. Moreover, if differentAttorney Docket No. 01137-0099-00PCT aptamers each include a tag, these different aptamers can have either the same tag or a different tag.

[0078] An aptamer can be identified using any known method, including the SELEX process. Once identified, an aptamer can be prepared or synthesized in accordance with any known method, including chemical synthetic methods and enzymatic synthetic methods.

[0079] As used herein, a “SOMAmer” or Slow Off-Rate Modified Aptamer refers to an aptamer having improved off-rate characteristics. SOMAmers can be generated using the improved SELEX methods described in U.S. Patent No. 7,947,447, entitled “Method for Generating Aptamers with Improved Off-Rates.” In some embodiments, a slow off-rate aptamer (including an aptamers comprising at least one nucleotide with a hydrophobic modification) has an off-rate (tU) of > 20 minutes > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

[0080] The terms “SELEX” and “SELEX process” are used interchangeably herein to refer generally to a combination of (1) the selection of aptamers that interact with a target molecule in a desirable manner, for example binding with high affinity to a protein, with (2) the amplification of those selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity to a specific target or biomarker.

[0081] SELEX generally includes preparing a candidate mixture of nucleic acids, binding of the candidate mixture to the desired target molecule to form an affinity complex, separating the affinity complexes from the unbound candidate nucleic acids, separating and isolating the nucleic acid from the affinity complex, purifying the nucleic acid, and identifying a specific aptamer sequence. The process may include multiple rounds to further refine the affinity of the selected aptamer. The process can include amplification steps at one or more points in the process. See, e.g., U.S. Patent No. 5,475,096, entitled “Nucleic Acid Ligands”. The SELEX process can be used to generate an aptamer that covalently binds its target as well as an aptamer that non-covalently binds its target. See, e.g., U.S. Patent No. 5,705,337 entitled “Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX .”

[0082] The SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved characteristics on the aptamer, such as, for example, improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at the ribose and / or phosphate and / or base positions. SELEX process-identified aptamers containing modified nucleotides are described in U.S. Patent No. 5,660,985, entitled “High Affinity Nucleic Acid Ligands Containing Modified Nucleotides”, which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5’- and 2’-positions of pyrimidines. U.S. Patent No. 5,580,737, see supra, describes highlyAttorney Docket No. 01137-0099-00PCT specific aptamers containing one or more nucleotides modified with 2’-amino (2’-NH2), 2’- fluoro (2’-F), and / or 2’-O-methyl (2’-0Me). See also, U.S. Patent Application Publication 20090098549, entitled “SELEX and PHOTOSELEX”, which describes nucleic acid libraries having expanded physical and chemical properties and their use in SELEX and photoSELEX.

[0083] SELEX can also be used to identify aptamers that have desirable off-rate characteristics. See U.S. Patent Application Publication 2009 / 0004667, entitled “Method for Generating Aptamers with Improved Off-Rates”, which describes improved SELEX methods for generating aptamers that can bind to target molecules. As mentioned above, these slow off-rate aptamers are known as “SOMAmers.” Methods for producing aptamers or SOMAmers and photoaptamers or SOMAmers having slower rates of dissociation from their respective target molecules are described. The methods involve contacting the candidate mixture with the target molecule, allowing the formation of nucleic acid-target complexes to occur, and performing a slow off-rate enrichment process wherein nucleic acid-target complexes with fast dissociation rates will dissociate and not reform, while complexes with slow dissociation rates will remain intact. Additionally, the methods include the use of modified nucleotides in the production of candidate nucleic acid mixtures to generate aptamers or SOMAmers with improved off-rate performance. Nonlimiting exemplary modified nucleotides include, for example, the modified pyrimidines shown in FIGS. 1-5.

[0084] A variation of this assay employs aptamers that include photoreactive functional groups that enable the aptamers to covalently bind or “photocrosslink” their target molecules. See, e.g., U.S. Patent No. 6,544,776 entitled “Nucleic Acid Ligand Diagnostic Biochip”. These photoreactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Patent No. 5,763,177, U.S. Patent No. 6,001,577, and U.S. Patent No. 6,291,184, each of which is entitled “Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX”; see also, e.g., U.S. Patent No. 6,458,539, entitled “Photoselection of Nucleic Acid Ligands”. After the microarray is contacted with the sample and the photoaptamers have had an opportunity to bind to their target molecules, the photoaptamers are photoactivated, and the solid support is washed to remove any non- specifically bound molecules. Harsh wash conditions may be used, since target molecules that are bound to the photoaptamers are generally not removed, due to the covalent bonds created by the photoactivated functional group(s) on the photoaptamers. In this manner, the assay enables the detection of a biomarker value corresponding to a biomarker in the test sample.

[0085] In both of these assay formats, the aptamers or SOMAmers are immobilized on the solid support prior to being contacted with the sample. Under certain circumstances, however, immobilization of the aptamers or SOMAmers prior to contact with the sample mayAttorney Docket No. 01137-0099-00PCT not provide an optimal assay. For example, pre-immobilization of the aptamers or SOMAmers may result in inefficient mixing of the aptamers or SOMAmers with the target molecules on the surface of the solid support, perhaps leading to lengthy reaction times and, therefore, extended incubation periods to permit efficient binding of the aptamers or SOMAmers to their target molecules. Further, when photoaptamers or photoSOMAmers are employed in the assay and depending upon the material utilized as a solid support, the solid support may tend to scatter or absorb the light used to effect the formation of covalent bonds between the photoaptamers or photoSOMAmers and their target molecules. Moreover, depending upon the method employed, detection of target molecules bound to their aptamers or photoSOMAmers can be subject to imprecision, since the surface of the solid support may also be exposed to and affected by any labeling agents that are used. Finally, immobilization of the aptamers or SOMAmers on the solid support generally involves an aptamer or SOMAmer-preparation step (i.e., the immobilization) prior to exposure of the aptamers or SOMAmers to the sample, and this preparation step may affect the activity or functionality of the aptamers or SOMAmers.

[0086] SOMAmer assays that permit a SOMAmer to capture its target in solution and then employ separation steps that are designed to remove specific components of the SOMAmer-target mixture prior to detection have also been described (see U.S. Patent Application Publication 20090042206, entitled “Multiplexed Analyses of Test Samples”). The described SOMAmer assay methods enable the detection and quantification of a non-nucleic acid target (e.g., a protein target) in a test sample by detecting and quantifying a nucleic acid (i.e., a SOMAmer). The described methods create a nucleic acid surrogate (i.e, the SOMAmer) for detecting and quantifying a non-nucleic acid target, thus allowing the wide variety of nucleic acid technologies, including amplification, to be applied to a broader range of desired targets, including protein targets.

[0087] SOMAmers can be constructed to facilitate the separation of the assay components from a SOMAmer biomarker complex (or photoSOMAmer biomarker covalent complex) and permit isolation of the SOMAmer for detection and / or quantification. In some embodiments, these constructs can include a cleavable or releasable element within the SOMAmer sequence. In other embodiments, additional functionality can be introduced into the SOMAmer, for example, a labeled or detectable component, a spacer component, or a specific binding tag or immobilization element. For example, the SOMAmer can include a tag connected to the SOMAmer via a cleavable moiety, a label, a spacer component separating the label, and the cleavable moiety. In one embodiment, a cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and a spacer section, can include anAttorney Docket No. 01137-0099-00PCTNHS group for derivatization of amines, and can be used to introduce a biotin group to an aptamer, thereby allowing for the release of the aptamer later in an assay method.

[0088] Homogenous assays, done with all assay components in solution, do not require separation of sample and reagents prior to the detection of signal. These methods are rapid and easy to use. These methods generate signal based on a molecular capture or binding reagent that reacts with its specific target. For predicting an individual’s likelihood of having HCM, the molecular capture reagents would be an aptamer or an antibody or the like and the specific target would be an HCM biomarker as in Table 1.

[0089] In some embodiments, a method for signal generation takes advantage of anisotropy signal change due to the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture reagent reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore attached to the complex to become much slower changing the anisotropy value. By monitoring the anisotropy change, binding events may be used to quantitatively measure the biomarkers in solutions. Other methods include fluorescence polarization assays, molecular beacon methods, time resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like.

[0090] An exemplary solution-based aptamer assay that can be used to detect a biomarker value corresponding to a biomarker in a biological sample includes the following: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has a specific affinity for the biomarker, wherein an aptamer affinity complex is formed when the biomarker is present in the sample; (b) exposing the mixture to a first solid support including a first capture element, and allowing the first tag to associate with the first capture element; (c) removing any components of the mixture not associated with the first solid support; (d) attaching a second tag to the biomarker component of the aptamer affinity complex; (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support that includes a second capture element and allowing the second tag to associate with the second capture element; (g) removing any noncomplexed aptamer from the mixture by partitioning the non-complexed aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

[0091] Any means known in the art can be used to detect a biomarker value by detecting the aptamer component of an aptamer affinity complex. A number of different detection methods can be used to detect the aptamer component of an affinity complex, such as, for example, hybridization assays, mass spectroscopy, or QPCR. In some embodiments, nucleicAttorney Docket No. 01137-0099-00PCT acid sequencing methods can be used to detect the aptamer component of an aptamer affinity complex and thereby detect a biomarker value. Briefly, a test sample can be subjected to any kind of nucleic acid sequencing method to identify and quantify the sequence or sequences of one or more aptamers present in the test sample. In some embodiments, the sequence includes the entire aptamer molecule or any portion of the molecule that may be used to uniquely identify the molecule. In other embodiments, the identifying sequencing is a specific sequence added to the aptamer; such sequences are often referred to as “tags,” “barcodes,” or “zipcodes.” In some embodiments, the sequencing method includes enzymatic steps to amplify the aptamer sequence or to convert any kind of nucleic acid, including RNA and DNA that contain chemical modifications to any position, to any other kind of nucleic acid appropriate for sequencing.

[0092] In some embodiments, the sequencing method includes one or more cloning steps. In other embodiments the sequencing method includes a direct sequencing method without cloning.

[0093] In some embodiments, the sequencing method includes a directed approach with specific primers that target one or more aptamer in the test sample. In other embodiments, the sequencing method includes a shotgun approach that targets all aptamer in the test sample.

[0094] In some embodiments, the sequencing method includes enzymatic steps to amplify the molecule targeted for sequencing. In other embodiments, the sequencing method directly sequences single molecules. An exemplary nucleic acid sequencing-based method that can be used to detect a biomarker value corresponding to a biomarker in a biological sample includes the following: (a) converting a mixture of aptamers that contain chemically modified nucleotides to unmodified nucleic acids with an enzymatic step; (b) shotgun sequencing the resulting unmodified nucleic acids with a massively parallel sequencing platform such as, for example, the 454 Sequencing System (454 Life Sciences / Roche), the Illumina Sequencing System (Illumina), the ABI SOLiD Sequencing System (Applied Biosystems), the Heli Scope Single Molecule Sequencer (Helicos Biosciences), or the Pacific Biosciences Real Time SingleMolecule Sequencing System (Pacific BioSciences) or the Polonator G Sequencing System (Dover Systems); and (c) identifying and quantifying the SOMAmers present in the mixture by specific sequence and sequence count.Determination of Biomarker Values using Immunoassays

[0095] Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format. To improve specificity and sensitivity of an assay method based on immunoreactivity, monoclonal antibodies are often used because of their specific epitope recognition.Attorney Docket No. 01137-0099-00PCTPolyclonal antibodies have also been successfully used in various immunoassays because of their increased affinity for the target as compared to monoclonal antibodies. Immunoassays have been designed for use with a wide range of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results.

[0096] Quantitative results are generated through the use of a standard curve created with known concentrations of the specific analyte to be detected. The response or signal from an unknown sample is plotted onto the standard curve, and a quantity or value corresponding to the target in the unknown sample is established.

[0097] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of an analyte. This method relies on attachment of a label to either the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme. ELISA tests may be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels such as, for example, radioisotopes (1125) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0098] Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time resolved-FRET (TR-FRET) immunoassays. Examples of procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.

[0099] Methods of detecting and / or quantifying a detectable label or signal generating material depend on the nature of the label. The products of reactions catalyzed by appropriate enzymes (where the detectable label is an enzyme; see above) can be, without limitation, fluorescent, luminescent, or radioactive or they may absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, without limitation, x-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.

[0100] Any of the methods for detection can be performed in any format that allows for any suitable preparation, processing, and analysis of the reactions. This can be, for example, in multi-well assay plates (e.g., 96 wells or 384 wells) or using any suitable array or microarray. Stock solutions for various agents can be made manually or robotically, and all subsequent pipetting, diluting, mixing, distribution, washing, incubating, sample readout, data collection andAttorney Docket No. 01137-0099-00PCT analysis can be done robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting a detectable label.Determination of Biomarker Values using Gene Expression Profiling

[0101] Measuring mRNA in a biological sample may be used as a surrogate for detection of the level of the corresponding protein in the biological sample. Thus, any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA.

[0102] mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR). RT-PCR is used to create a cDNA from the mRNA. The cDNA may be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0103] miRNA molecules are small RNAs that are non-coding but may regulate gene expression. Any of the methods suited to the measurement of mRNA expression levels can also be used for the corresponding miRNA. Recently many laboratories have investigated the use of miRNAs as biomarkers for disease. Many diseases involve wide-spread transcriptional regulation, and it is not surprising that miRNAs might find a role as biomarkers. The connection between miRNA concentrations and disease is often even less clear than the connections between protein levels and disease, yet the value of miRNA biomarkers might be substantial. Of course, as with any RNA expressed differentially during disease, the problems facing the development of an in vitro diagnostic product will include the requirement that the miRNAs survive in the diseased cell and are easily extracted for analysis, or that the miRNAs are released into blood or other matrices where they must survive long enough to be measured. Protein biomarkers have similar requirements, although many potential protein biomarkers are secreted intentionally at the site of pathology and function, during disease, in a paracrine fashion. Many potential protein biomarkers are designed to function outside the cells within which those proteins are synthesized.Detection of Biomarkers Using In Vivo Molecular Imaging Technologies

[0104] Any of the described biomarkers (see, e.g., Table 1) may also be used in molecular imaging tests. For example, an imaging agent can be coupled to any of the described biomarkers, which can be used to aid in assessing likelihood of having HCM, to monitor response to therapeutic interventions, to select a population for clinical trials among other uses.Attorney Docket No. 01137-0099-00PCT

[0105] In vivo imaging technologies provide non-invasive methods for determining the state of a particular disease or condition in the body of an individual. For example, entire portions of the body, or even the entire body, may be viewed as a three dimensional image, thereby providing valuable information concerning morphology and structures in the body. Such technologies may be combined with the detection of the biomarkers described herein to provide information concerning predicting an individual’s likelihood of HCM.

[0106] The use of in vivo molecular imaging technologies is expanding due to various advances in technology. These advances include the development of new contrast agents or labels, such as radiolabels and / or fluorescent labels, which can provide strong signals within the body; and the development of powerful new imaging technology, which can detect and analyze these signals from outside the body, with sufficient sensitivity and accuracy to provide useful information. The contrast agent can be visualized in an appropriate imaging system, thereby providing an image of the portion or portions of the body in which the contrast agent is located. The contrast agent may be bound to or associated with a capture reagent, such as an aptamer or an antibody, for example, and / or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression), or a complex containing any of these with one or more macromolecules and / or other particulate forms.

[0107] The contrast agent may also feature a radioactive atom that is useful in imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphic studies. Other readily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI) such as, for example, iodine-123 again, iodine-131, indium-i l l, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese or iron. Such labels are well known in the art and could easily be selected by one of ordinary skill in the art.

[0108] Standard imaging techniques include but are not limited to magnetic resonance imaging, computed tomography scanning (coronary calcium score), positron emission tomography (PET), single photon emission computed tomography (SPECT), computed tomography angiography, and the like. For diagnostic in vivo imaging, the type of detection instrument available is a major factor in selecting a given contrast agent, such as a given radionuclide and the particular biomarker that it is used to target (protein, mRNA, and the like). The radionuclide chosen typically has a type of decay that is detectable by a given type of instrument. Also, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to enable detection at the time of maximum uptake by the target tissue but short enough that deleterious radiation of the host is minimized.

[0109] Exemplary imaging techniques include but are not limited to PET and SPECT, which are imaging techniques in which a radionuclide is synthetically or locally administered toAttorney Docket No. 01137-0099-00PCT an individual. The subsequent uptake of the radiotracer is measured over time and used to obtain information about the targeted tissue and the biomarker. Because of the high-energy (gammaray) emissions of the specific isotopes employed and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity may be inferred from outside of the body.

[0110] Commonly used positron-emitting nuclides in PET include, for example, carbon- 11, nitrogen-13, oxygen-15, and fluorine-18. Isotopes that decay by electron capture and / or gamma-emission are used in SPECT and include, for example iodine-123 and technetium-99m. An exemplary method for labeling amino acids with technetium-99m is the reduction of pertechnetate ion in the presence of a chelating precursor to form the labile technetium-99m- precursor complex, which, in turn, reacts with the metal binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0111] Antibodies are frequently used for such in vivo imaging diagnostic methods. The preparation and use of antibodies for in vivo diagnosis is well known in the art. Labeled antibodies which specifically bind any of the biomarkers in Table 1 can be injected into an individual, detectable according to the particular biomarker used, for the purpose of diagnosing or evaluating the disease status or condition of the individual. The label used will be selected in accordance with the imaging modality to be used, as previously described. Localization of the label permits determination of the tissue damage or other indications related to an individual’s likelihood of having HCM. The amount of label within an organ or tissue also allows determination of the involvement of the biomarkers predicting an individual’s likelihood of having HCM.

[0112] Similarly, aptamers may be used for such in vivo imaging diagnostic methods. For example, an aptamer that was used to identify a particular biomarker described in Table 1 (and therefore binds specifically to that particular biomarker) may be appropriately labeled and injected into an individual being evaluated for determination of likelihood of HCM, detectable according to the particular biomarker, for the purpose of diagnosing or evaluating the levels of tissue damage, components of inflammatory response, and other factors associated with the likelihood of HCM in the individual. The label used will be selected in accordance with the imaging modality to be used, as previously described. Localization of the label permits determination of the site of the processes leading to increased risk. The amount of label within an organ or tissue also allows determination of the infiltration of the pathological process in that organ or tissue. Aptamer-directed imaging agents could have unique and advantageous characteristics relating to tissue penetration, tissue distribution, kinetics, elimination, potency, and selectivity as compared to other imaging agents.Attorney Docket No. 01137-0099-00PCT

[0113] Such techniques may also optionally be performed with labeled oligonucleotides, for example, for detection of gene expression through imaging with antisense oligonucleotides. These methods are used for in situ hybridization, for example, with fluorescent molecules or radionuclides as the label. Other methods for detection of gene expression include, for example, detection of the activity of a reporter gene.

[0114] Another general type of imaging technology is optical imaging, in which fluorescent signals within the subject are detected by an optical device that is external to the subject. These signals may be due to actual fluorescence and / or to bioluminescence. Improvements in the sensitivity of optical detection devices have increased the usefulness of optical imaging for in vivo diagnostic assays.

[0115] The use of in vivo molecular biomarker imaging is increasing, including for clinical trials, for example, to more rapidly measure clinical efficacy in trials for new disease or condition therapies and / or to avoid prolonged treatment with a placebo for those diseases, such as multiple sclerosis, in which such prolonged treatment may be considered to be ethically questionable.

[0116] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.Determination of Biomarker Values using Mass Spectrometry Methods

[0117] A variety of configurations of mass spectrometers can be used to detect biomarker values. Several types of mass spectrometers are available or can be produced with various configurations. In general, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrumentcontrol system, and a data system. Difference in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, an inlet can be a capillary-column liquid chromatography source or can be a direct probe or stage such as used in matrix-assisted laser desorption. Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-assisted laser desorption. Common mass analyzers include a quadrupole mass filter, ion trap mass analyzer and time-of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)).

[0118] Protein biomarkers and biomarker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI- MS / (MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight massAttorney Docket No. 01137-0099-00PCT spectrometry (SELDI-TOF-MS), desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology, called ultraflex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS)N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0119] Sample preparation strategies are used to label and enrich samples before mass spectroscopic characterization of protein biomarkers and determination biomarker values. Labeling methods include but are not limited to isobaric tag for relative and absolute quantitation (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples for candidate biomarker proteins prior to mass spectroscopic analysis include but are not limited to aptamers, antibodies, nucleic acid probes, chimeras, small molecules, an F(ab’)2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g. diabodies etc) imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acid, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments of these.Determination of Biomarker Values using a Proximity Ligation Assay

[0120] A proximity ligation assay can be used to determine biomarker values. Briefly, a test sample is contacted with a pair of affinity probes that may be a pair of antibodies or a pair of aptamers, with each member of the pair extended with an oligonucleotide. The targets for the pair of affinity probes may be two distinct determinates on one protein or one determinate on each of two different proteins, which may exist as homo- or hetero-multimeric complexes. When probes bind to the target determinates, the free ends of the oligonucleotide extensions are brought into sufficiently close proximity to hybridize together. The hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide which serves to bridge together the oligonucleotide extensions when they are positioned in sufficient proximity. Once the oligonucleotide extensions of the probes are hybridized, the ends of the extensions are joined together by enzymatic DNA ligation.

[0121] Each oligonucleotide extension comprises a primer site for PCR amplification. Once the oligonucleotide extensions are ligated together, the oligonucleotides form a continuous DNA sequence which, through PCR amplification, reveals information regarding the identityAttorney Docket No. 01137-0099-00PCT and amount of the target protein, as well as information regarding protein-protein interactions where the target determinates are on two different proteins. Proximity ligation can provide a highly sensitive and specific assay for real-time protein concentration and interaction information through use of real-time PCR. Probes that do not bind the determinates of interest do not have the corresponding oligonucleotide extensions brought into proximity and no ligation or PCR amplification can proceed, resulting in no signal being produced.

[0122] The foregoing assays enable the detection of biomarker levels that are useful in methods for determining the likelihood of an individual having HCM, where the methods comprise detecting, in a biological sample from an individual, biomarker levels that each correspond to a biomarker selected from the group consisting of the biomarkers provided in Table 1, wherein a classification, as described in detail below, using the biomarker levels indicates whether the individual has a likelihood of HCM. While certain of the described biomarkers are useful alone for determining a likelihood of HCM in an individual, methods are also described herein for the grouping of multiple subsets of the biomarkers that are each useful as a panel of two or more biomarkers. In accordance with any of the methods described herein, biomarker levels can be detected and classified individually or they can be detected and classified collectively, as for example in a multiplex assay format.Classification of Biomarkers and Calculation of Disease Scores

[0123] In some embodiments, biomarker “signature” for a given diagnostic or predictive test contains a set of markers, each marker having different levels in the populations of interest. Different levels, in this context, may refer to different means of the marker levels for the individuals in two or more groups, or different variances in the two or more groups, or a combination of both. For the simplest form of a diagnostic test, these markers can be used to assign an unknown sample from an individual into one of two groups, such as having or not having likelihood of having HCM. The assignment of a sample into one of two or more groups is known as classification, and the procedure used to accomplish this assignment is known as a classifier or a classification method. Classification methods may also be referred to as scoring methods. There are many classification methods that can be used to construct a diagnostic classifier from a set of biomarker values. In general, classification methods are most easily performed using supervised learning techniques where a data set is collected using samples obtained from individuals within two (or more, for multiple classification states) distinct groups one wishes to distinguish. Since the class (group or population) to which each sample belongs is known in advance for each sample, the classification method can be trained to give the desiredAttorney Docket No. 01137-0099-00PCT classification response. It is also possible to use unsupervised learning techniques to produce a diagnostic classifier.

[0124] Common approaches for developing diagnostic classifiers include decision trees; bagging, boosting, forests and random forests; rule inference based learning; Parzen Windows; linear models; logistic; neural network methods; unsupervised clustering; K-means; hierarchical ascending / descending; semi-supervised learning; prototype methods; nearest neighbor; kernel density estimation; support vector machines; hidden Markov models; Boltzmann Learning; and classifiers may be combined either simply or in ways which minimize particular objective functions. For a review, see, e.g., Pattern Classification, R.O. Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; see also, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009; each of which is incorporated by reference in its entirety.

[0125] To produce a classifier using supervised learning techniques, a set of samples called training data are obtained. In the context of diagnostic tests, training data includes samples from the distinct groups (classes) to which unknown samples will later be assigned. For example, samples collected from individuals in a control population and individuals in a particular disease, condition or event population, such as individuals having a likelihood of HCM, can constitute training data to develop a classifier that can classify unknown samples (or, more particularly, the individuals from whom the samples were obtained) as either having a likelihood of having HCM or healthy. The development of the classifier from the training data is known as training the classifier. Specific details on classifier training depend on the nature of the supervised learning technique (see, e.g., Pattern Classification, R.O. Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; see also, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009).

[0126] Since typically there are many more potential biomarker values than samples in a training set, care must be used to avoid over-fitting. Over-fitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Over-fitting can be avoided in a variety of ways, including, for example, by limiting the number of markers used in developing the classifier, by assuming that the marker responses are independent of one another, by limiting the complexity of the underlying statistical model employed, and by ensuring that the underlying statistical model conforms to the data.

[0127] In order to identify a set of biomarkers associated with occurrence of events, the combined set of control and early event samples were analyzed using Principal Component Analysis (PCA). PCA displays the samples with respect to the axes defined by the strongestAttorney Docket No. 01137-0099-00PCT variations between all the samples, without regard to the case or control outcome, thus mitigating the risk of overfitting the distinction between case and control. Since the occurrence of serious thrombotic events has a strong component of chance involved, requiring unstable plaque to rupture in vital vessels to be reported, one would not expect to see a clear separation between the control and event sample sets. While the observed separation between case and control is not large, it occurs on the second principal component, corresponding to around 10% of the total variation in this set of samples, which indicates that the underlying biological variation is relatively simple to quantify.

[0128] In the next set of analyses, biomarkers can be analyzed for those components of difference between samples which were specific to the separation between the control samples and early event samples. One method that may be employed is the use of DSGA (Bair,E. and Tibshirani,R. (2004) Semi-supervised methods to predict patient survival from gene expression data. PLOS Biol., 2, 511-522) to remove (deflate) the first three principal component directions of variation between the samples in the control set. Although the dimensionality reduction is performed on the control set to discover, both the samples in the control and the samples from the early event samples are run through the PC A. Separation of cases from early events can be observed along the horizontal axis.Cross Validated Selection of Proteins Relevant to the Likelihood of HCM

[0129] In order to avoid over-fitting of protein predictive power to idiosyncratic features of a particular selection of samples, a cross-validation and dimensional reduction approach can be taken. Cross-validation involves the multiple selection of sets of samples to determine the association of risk by protein combined with the use of the unselected samples to monitor the ability of the method to apply to samples which were not used in producing the model of risk (The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009). We applied the supervised PCA method of Tibshirani et al (Bair,E. and Tibshirani,R. (2004) Semi-supervised methods to predict patient survival from gene expression data. PLOS Biol., 2, 511-522.) which is applicable to high dimensional datasets in the modeling of the likelihood of an individual having HCM. The supervised PCA (SPCA) method involves the univariate selection of a set of proteins statistically associated with the observed event hazard in the data and the determination of the correlated component which combines information from all of these proteins. This determination of the correlated component is a dimensionality reduction step which not only combines information across proteins, but also mitigates the likelihood of overfitting by reducing the number of independent variables from the full protein menu of over 1000 proteinsAttorney Docket No. 01137-0099-00PCT down to a few principal components (in this work, we only examined the first principal component).Kits

[0130] Any combination of the biomarkers of Table 1 can be detected using a suitable kit, such as for use in performing the methods disclosed herein. Furthermore, any kit can contain one or more detectable labels as described herein, such as a fluorescent moiety, etc.

[0131] In one embodiment, a kit includes (a) one or more capture reagents (such as, for example, at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, wherein the biomarkers include any of the biomarkers set forth in Table 1 and optionally (b) one or more software or computer program products for classifying the individual from whom the biological sample was obtained as either having or not having a likelihood of HCM, as further described herein. Alternatively, rather than one or more computer program products, one or more instructions for manually performing the above steps by a human can be provided.

[0132] The combination of a solid support with a corresponding capture reagent having a signal generating material is referred to herein as a “detection device” or “kit”. The kit can also include instructions for using the devices and reagents, handling the sample, and analyzing the data. Further the kit may be used with a computer system or software to analyze and report the result of the analysis of the biological sample.

[0133] The kits can also contain one or more reagents (e.g., solubilization buffers, detergents, washes, or buffers) for processing a biological sample. Any of the kits described herein can also include, e.g., buffers, blocking agents, mass spectrometry matrix materials, antibody capture agents, positive control samples, negative control samples, software and information such as protocols, guidance and reference data.

[0134] In one aspect, the invention provides kits for the assessment of likelihood of HCM. The kits include PCR primers for one or more aptamers specific to biomarkers selected from Table 1. The kit may further include instructions for use and correlation of the biomarkers with prediction of likelihood of an individual having HCM. The kit may also include a DNA array containing the complement of one or more of the aptamers specific for the biomarkers selected from Table 1, reagents, and / or enzymes for amplifying or isolating sample DNA. The kits may include reagents for real-time PCR, for example, TaqMan probes and / or primers, and enzymes.

[0135] For example, a kit can comprise (a) reagents comprising at least capture reagent for quantifying one or more biomarkers in a test sample, wherein said biomarkers comprise theAttorney Docket No. 01137-0099-00PCT biomarkers set forth in Table 1, or any other biomarkers or biomarkers panels described herein or elsewhere, and optionally (b) one or more algorithms or computer programs for performing the steps of comparing the amount of each biomarker quantified in the test sample to one or more predetermined cutoffs and assigning a score for each biomarker quantified based on said comparison, combining the assigned scores for each biomarker quantified to obtain a total score, comparing the total score with a predetermined score, and using said comparison to determine the probability of an individual having HCM. Alternatively, rather than one or more algorithms or computer programs, one or more instructions for manually performing the above steps by a human can be provided.Computer Methods and Software

[0136] Once a biomarker or biomarker panel is selected, a method for diagnosing an individual can comprise the following: 1) collect or otherwise obtain a biological sample; 2) perform an analytical method to detect and measure the biomarker or biomarkers in the panel in the biological sample; 3) perform any data normalization or standardization required for the method used to collect biomarker levels; 4) calculate the marker score; 5) combine the marker scores to obtain a total diagnostic or predictive score; and 6) report the individual’s diagnostic or predictive score. In this approach, the diagnostic or predictive score may be a single number determined from the sum of all the marker calculations that is compared to a preset threshold value that is an indication of the presence or absence of disease or likely to have HCM. Or the diagnostic or predictive score may be a series of bars that each represent a biomarker level and the pattern of the responses may be compared to a pre-set pattern for determination of the presence or absence of disease, condition or the increased risk (or not) of an event.

[0137] At least some embodiments of the methods described herein can be implemented with the use of a computer. An example of a computer system 100 is shown in FIG. 6. With reference to FIG. 6, system 100 is shown comprised of hardware elements that are electrically coupled via bus 108, including a processor 101, input device 102, output device 103, storage device 104, computer-readable storage media reader 105a, communications system 106, processing acceleration (e.g., DSP or special -purpose processors) 107 and memory 109. Computer-readable storage media reader 105a is further coupled to computer-readable storage media 105b, the combination comprehensively representing remote, local, fixed and / or removable storage devices plus storage media, memory, etc. for temporarily and / or more permanently containing computer-readable information, which can include storage device 104, memory 109 and / or any other such accessible system 100 resource. System 100 also comprisesAttorney Docket No. 01137-0099-00PCT software elements (shown as being currently located within working memory 191) including an operating system 192 and other code 193, such as programs, data and the like.

[0138] With respect to FIG. 6, system 100 has extensive flexibility and configurability. Thus, for example, a single architecture might be utilized to implement one or more servers that can be further configured in accordance with currently desirable protocols, protocol variations, extensions, etc. However, it will be apparent to those skilled in the art that embodiments may well be utilized in accordance with more specific application requirements. For example, one or more system elements might be implemented as sub-elements within a system 100 component (e.g., within communications system 106). Customized hardware might also be utilized and / or particular elements might be implemented in hardware, software or both. Further, while connection to other computing devices such as network input / output devices (not shown) may be employed, it is to be understood that wired, wireless, modem, and / or other connection or connections to other computing devices might also be utilized.

[0139] In one aspect, the system can comprise a database containing features of biomarkers characteristic of prediction of the probability of an individual having HCM. The biomarker data (or biomarker information) can be utilized as an input to the computer for use as part of a computer implemented method. The biomarker data can include the data as described herein.

[0140] In one aspect, the system further comprises one or more devices for providing input data to the one or more processors.

[0141] The system further comprises a memory for storing a data set of ranked data elements.

[0142] In another aspect, the device for providing input data comprises a detector for detecting the characteristic of the data element, e.g., such as a mass spectrometer or gene chip reader.

[0143] The system additionally may comprise a database management system. User requests or queries can be formatted in an appropriate language understood by the database management system that processes the query to extract the relevant information from the database of training sets.

[0144] The system may be connectable to a network to which a network server and one or more clients are connected. The network may be a local area network (LAN) or a wide area network (WAN), as is known in the art. Preferably, the server includes the hardware necessary for running computer program products (e.g., software) to access database data for processing user requests.Attorney Docket No. 01137-0099-00PCT

[0145] The system may include an operating system (e.g., UNIX or Linux) for executing instructions from a database management system. In one aspect, the operating system can operate on a global communications network, such as the internet, and utilize a global communications network server to connect to such a network.

[0146] The system may include one or more devices that comprise a graphical display interface comprising interface elements such as buttons, pull down menus, scroll bars, fields for entering text, and the like as are routinely found in graphical user interfaces known in the art. Requests entered on a user interface can be transmitted to an application program in the system for formatting to search for relevant information in one or more of the system databases. Requests or queries entered by a user may be constructed in any suitable database language.

[0147] The graphical user interface may be generated by a graphical user interface code as part of the operating system and can be used to input data and / or to display inputted data. The result of processed data can be displayed in the interface, printed on a printer in communication with the system, saved in a memory device, and / or transmitted over the network or can be provided in the form of the computer readable medium.

[0148] The system can be in communication with an input device for providing data regarding data elements to the system (e.g., expression values). In one aspect, the input device can include a gene expression profiling system including, e.g., a mass spectrometer, gene chip or array reader, and the like.

[0149] The methods and apparatus for analyzing the likelihood of HCM biomarker information according to various embodiments may be implemented in any suitable manner, for example, using a computer program operating on a computer system. A conventional computer system comprising a processor and a random access memory, such as a remotely-accessible application server, network server, personal computer or workstation may be used. Additional computer system components may include memory devices or information storage systems, such as a mass storage system and a user interface, for example a conventional monitor, keyboard and tracking device. The computer system may be a stand-alone system or part of a network of computers including a server and one or more databases.

[0150] The HCM likelihood assessment biomarker analysis system can provide functions and operations to complete data analysis, such as data gathering, processing, analysis, reporting and / or diagnosis. For example, in one embodiment, the computer system can execute the computer program that may receive, store, search, analyze, and report information relating to the HCM likelihood assessment prediction biomarkers. The computer program may comprise multiple modules performing various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing rawAttorney Docket No. 01137-0099-00PCT data and supplemental data to generate a HCM likelihood prediction status. Determination of the probability of an individual having HCM may optionally comprise generating or collecting any other information, including additional biomedical information, regarding the condition of the individual relative to the disease, condition or event, identifying whether further tests may be desirable, or otherwise evaluating the health status of the individual.

[0151] Referring now to FIG. 7, an example of a method of utilizing a computer in accordance with principles of a disclosed embodiment can be seen. In FIG. 7, a flowchart 3000 is shown. In block 3004, biomarker information can be retrieved for an individual. The biomarker information can be retrieved from a computer database, for example, after testing of the individual’s biological sample is performed. The biomarker information can comprise biomarker levels that each correspond to one or more of the biomarkers of Table 1. In block 3008, a computer can be utilized to classify each of the biomarker levels. And, in block 3012, a determination can be made as to the probability that an individual has HCM based upon a plurality of classifications. The indication can be output to a display or other indicating device so that it is viewable by a person. Thus, for example, it can be displayed on a display screen of a computer or other output device.

[0152] Some embodiments described herein can be implemented so as to include a computer program product. A computer program product may include a computer readable medium having computer readable program code embodied in the medium for causing an application program to execute on a computer with a database.

[0153] As used herein, a “computer program product” refers to an organized set of instructions in the form of natural or programming language statements that are contained on a physical media of any nature (e.g., written, electronic, magnetic, optical or otherwise) and that may be used with a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to act in accordance with the particular content of the statements. Computer program products include without limitation: programs in source and object code and / or test or data libraries embedded in a computer readable medium. Furthermore, the computer program product that enables a computer system or data processing equipment device to act in pre-selected ways may be provided in a number of forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing and any and all equivalents.

[0154] In one aspect, a computer program product is provided for assessment of HCM likelihood. The computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program codeAttorney Docket No. 01137-0099-00PCT comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises biomarker values that each correspond to one or more of the biomarkers of Table 1; and code that executes a classification method that indicates a probability of the individual having HCM as a function of the biomarker values.

[0155] While various embodiments have been described as methods or apparatuses, it should be understood that embodiments can be implemented through code coupled with a computer, e.g., code resident on a computer or accessible by the computer. For example, software and databases could be utilized to implement many of the methods discussed above. Thus, in addition to embodiments accomplished by hardware, it is also noted that these embodiments can be accomplished through the use of an article of manufacture comprised of a computer usable medium having a computer readable program code embodied therein, which causes the enablement of the functions disclosed in this description. Therefore, it is desired that embodiments also be considered protected by this patent in their program code means as well. Furthermore, the embodiments may be embodied as code stored in a computer-readable memory of virtually any kind including, without limitation, RAM, ROM, magnetic media, optical media, or magneto-optical media. Even more generally, the embodiments could be implemented in software, or in hardware, or any combination thereof including, but not limited to, software running on a general purpose processor, microcode, PLAs, or ASICs.

[0156] It is also envisioned that embodiments could be accomplished as computer signals embodied in a carrier wave, as well as signals (e.g., electrical and optical) propagated through a transmission medium. Thus, the various types of information discussed above could be formatted in a structure, such as a data structure, and transmitted as an electrical signal through a transmission medium or stored on a computer readable medium.

[0157] It is also noted that many of the structures, materials, and acts recited herein can be recited as means for performing a function or step for performing a function. Therefore, it should be understood that such language is entitled to cover all such structures, materials, or acts disclosed within this specification and their equivalents, including the matter incorporated by reference.

[0158] The biomarker identification process, the utilization of the biomarkers disclosed herein, and the various methods for determining biomarker values are described in detail above with respect to evaluation of a probability of an having HCM. However, the application of the process, the use of identified biomarkers, and the methods for determining biomarker values are fully applicable to other specific types of diseases or medical conditions, or to the identification of individuals who may or may not be benefited by an ancillary medical treatment.Other MethodsAttorney Docket No. 01137-0099-00PCT

[0159] In some embodiments, the biomarkers and methods described herein are used to determine a medical insurance premium or coverage decision and / or a life insurance premium or coverage decision. In some embodiments, the results of the methods described herein are used to determine a medical insurance premium and / or a life insurance premium. In some such instances, an organization that provides medical insurance or life insurance requests or otherwise obtains information concerning an individual’s likelihood of HCM and uses that information to determine an appropriate medical insurance or life insurance premium for the subject. In some embodiments, the test is requested by, and paid for by, the organization that provides medical insurance or life insurance. In some embodiments, the test is used by the potential acquirer of a practice or health system or company to predict future liabilities or costs should the acquisition go ahead.

[0160] In some embodiments, the biomarkers and methods described herein are used to predict and / or manage the utilization of medical resources. In some such embodiments, the methods are not carried out for the purpose of such prediction, but the information obtained from the method is used in such a prediction and / or management of the utilization of medical resources. For example, a testing facility or hospital may assemble information from the present methods for many subjects in order to predict and / or manage the utilization of medical resources at a particular facility or in a particular geographic area.EXAMPLES

[0161] The following examples are provided for illustrative purposes only and are not intended to limit the scope of the application as defined by the appended claims. All examples described herein were carried out using standard techniques, which are well known and routine to those of skill in the art. Routine molecular biology techniques described in the following examples can be carried out as described in standard laboratory manuals, such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd. ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., (2001).Example 1. Multiplex Aptamer Assay and Statistical Approaches for Biomarker Identification

[0162] A multiplex aptamer assay was used to analyze test samples and control samples to identify biomarkers predictive of a probability of an individual having HCM. The multiplexed analysis used in this experiment included aptamers to detect approximately 5,000 proteins in blood from small sample volumes (~65 pl of serum or plasma), with low limits of detection (1 pM median), ~7 logs of dynamic range, and ~5% median coefficient of variation. The multiplex aptamer assay is described, generally, e.g., in Gold et al. (2010) Aptamer-BasedAttorney Docket No. 01137-0099-00PCTMultiplexed Proteomic Technology for Biomarker Discovery. PLoS ONE 5(12): el 5004; and U.S. Publication Nos: 2012 / 0101002 and 2012 / 0077695.Example 2. Model Specification

[0163] Endpoint Description: For this analysis, the outcome measure was a classification endpoint denoting HCM diagnosis (based on LV wall thickness > 15 mm with appropriate ICD diagnosis code or confirmation that diagnosis met ACCF / AHA guidelines) at the time of blood draw. The HCM diagnosis endpoint is a classification endpoint that denotes if an individual has been diagnosed with HCM. The two classes are “Case” if there is a HCM diagnosis, and “Control” if there is no diagnosis. The diagnosis endpoint was modeled as a binary classification measure. Eight cohorts with a total of 764 HCM cases and 4,266 controls were used for development and validation.

[0164] The selected model is a 12-feature, protein-only (see Table 1) elastic net logistic regression model.

[0165] Table 1. Analytes included in the selected HCM prediction model

[0166] The selected HCM screening model from refinement was a 12-feature elastic net logistic regression model. This model had a sensitivity of 0.826 and specificity of 0.925 on the training data, sensitivity of 0.835 and specificity of 0.914 on the verification data, and sensitivity of 0.878 and specificity of 0.923 on the validation data. This model passes refinement and validation. Performance is summarized in Table 2.

[0167] Table 2. Performance metrics of the selected model for HCM with 95% confidence intervals calculated via bootstrapping.Attorney Docket No. 01137-0099-00PCT

[0168] Development and Validation Cohort(s): The model was developed using data from eight studies, assayed using SomaScan™ assays. HCM cases came from BMS EXPLORER, BMS MAVERICK (v4.1), BMS VALOR, CLI1002F021, and CLI8007F159. Control samples came from CLI1001F034, CLI1001F025, and Covance.

[0169] CLI1002F021 is a multi-center case-control study of patients with an HCM diagnosis (based on LV wall thickness >15 mm and ICD code confirmation of diagnosis and excluding any HCM phenocopies) and controls with hypertensive heart disease. Control samples with hypertensive heart disease were excluded from these analyses.

[0170] CLI8007F159 is an extension of the CLI1002F021 study consisting only of individuals with an HCM diagnosis (following the same adjudication as CLI1002F021).

[0171] CLI1001F025 is a diagnostic and prognostic cohort study in patients presenting with non-acute symptoms for suspected exercise-induced myocardial ischemia referred for rest / ergometry myocardial perfusion SPECT. Participants with a known history of HCM or hypertrophic heart disease (composite variable), or heart failure were excluded from these analyses.

[0172] CLI1001F034 is an observational cohort designed to study the correlates, predictors, and progression of subclinical cardiovascular disease in asymptomatic individuals without prior cardiovascular disease.

[0173] Covance is a study designed by SomaLogic, Inc. to measure baseline information from healthy individuals based on clinical labs, lifestyle factors, and health history. Participants in Covance with a history of cardiovascular disease (composite variable that included hypertrophic heart disease and heart failure) were excluded from these analyses.

[0174] For this model development, the data were split into training, verification, and validation sets (70% / l 5% / l 5% of the total sample population), allowing for the identification of a robust model while mitigating overfitting issues.

[0175] Model development data: There were 8,784 samples available for analysis for this endpoint in total (6,148 in the training data set and 1,318 each in the verification andAttorney Docket No. 01137-0099-00PCT validation data sets). See Tables 3, 4, and 5 for demographic information. The data were stratified based on diagnosis group (i.e., HCM vs. no HCM) to ensure equal proportions of the outcome variable in each of the training, verification, and validation data sets. The three subsets of the data all had very similar average age and BMI, and similar proportions of subgroups for gender, ethnicity, and site ID. Clinical covariates were not included in the model because none of them has a statistically significant association (FDR < 0.1) with diagnosis group. Note that this full sample set was used for Proof-of-Concept (POC), but the selected model used a subset of these data, where the large CLI1001F034 study was subset as analyses found that the very unequal sample sizes between control studies was affecting the robustness of the model. More details on the choice for the selected model are provided below along with the corresponding demographics tables.

[0176] Table 3. Demographic information for the POC model development (training) dataset.Attorney Docket No. 01137-0099-00PCT

[0177] Table 4. Demographic information for the POC verification dataset.

[0178] Table 5. Demographic information for the POC validation dataset. Note: the information provided for this “validation” dataset summarizes demographic attributes for the remaining 15% of the entire POC dataset. Samples from these individuals were not used for POC analysis.Attorney Docket No. 01137-0099-00PCT

[0179] Data Quality Control and Pre-analytics Approach and Results: A total of 8,953 samples were available for analysis. Data QC showed that 150 (1.675%) samples were flagged, meaning at least one of the hybridization or three median scale factors were outside the 0.4 to 2.5 range, indicating technical issues (e.g., clogs) with that sample that would not be fixed by running it again. The typical rate for flagged samples is approximately 5%, so this rate is lower than normal and thus was not considered an issue. Data QC also showed that there were 19 (0.212%) outlier samples, defined as >5% of analytes exceed 6 median absolute deviations from the median. Only features that passed target confirmation specificity testing were used for this analysis, so 363 were removed (e.g., SOMAmers found to bind to mouse proteins, etc.). There was no noticeable association between clinical covariates (gender, age, and ethnicity) and the normalization scale factors (gender |mean normalization scale factor difference! < 0.03; age RA2< 0.02; ethnicity T|A2 < 0.02).

[0180] After removal of samples and analytes as listed above, there were 8,784 samples and 4,921 analytes for analysis. Table 6 below summarizes the data QC steps and the number of samples removed from the dataset prior to analysis.

[0181] Table 6. Summary of data QC and sample removal for analysis data using assay data.Attorney Docket No. 01137-0099-00PCT

[0182] POC Approach and Results: The POC preliminary models explored were logistic regression with elastic net regularization using 5 repeats of 10-fold cross-validation. Downsampling was utilized to handle the class imbalance as only 9.8% of the participants were cases. The top 200 significant analytes from univariate analysis were used to fit models during POC.

[0183] Univariate results: The univariate results showed that the majority of analytes were statistically significant at the FDR < 0.10 significance threshold. Those numbers and percentages are listed in Table 7 for univariate t-tests, which test the average difference between mean log-10 RFU values between the HCM and control groups. The volcano plot (Figure 8) highlights the proteomic features that have both a significant univariate t-test result and a large effect size (fold-change). Proteomic features that are significantly different are colored purple, with the y-axis denoting the unadjusted p-value for each t-test. The magnitude of the fold change is displayed on the x-axis and proteomic features with a fold-change greater than one are colored gold. The cutoffs are based on the corrected p < 0.05 / n (or l. le-05) and fold change > 1.Proteins with a positive fold change (right side of 0 on the x-axis) are increased in the control cohort and proteins with a negative fold change (left side of 0 on the x-axis) are decreased in the control cohort vs. HCM cases.

[0184] Table 7 Number and percentage of analytes significant at different significance levels for the univariate t- tests.

[0185] Preliminary machine learning model results: The model that performed the best during POC was an elastic net logistic regression model with 166 features. The observed specificity and sensitivity exceeded the target criteria of sensitivity > 0.75 and specificity > 0.85 (Table 8).

[0186] Table 8 Performance metrics of best performing POC model for HCM as shown by cross-validation mean and (min, max).

[0187] Preliminary model assessments: Performance metrics for different model sizes were also assessed to understand how the number of features impacts average modelAttorney Docket No. 01137-0099-00PCT performance. Model performance metrics plateau as the number of analyte features increases starting from 2 and increasing to 164, suggesting that reducing the number of analyte features below 166 in the refinement stage does not significantly lower model performance.

[0188] Table 9 shows the percentage of samples within each clinical covariate and case / control group which were classified correctly with the POC model (i.e., what percentile had a residual of 0.5 or less).

[0189] Table 9. Percentage of samples correctly classified for each covariate group in POC. A value of 100% means all samples in the group were correctly classified. N / A means there were no samples in that particular group.Attorney Docket No. 01137-0099-00PCT

[0190] Refinement Approach and Results: Models developed in refinement used the same data sets as POC (Tables 3-4), although some models used different random subsamples from the CLI1001F034 study due to it being much larger than all other studies (see each model for specific details of the samples used, as subsampling methods varied between models). Models were fit using the training data or subsets thereof and were evaluated on the verification data. Only elastic net logistic regression models were explored during refinement based on POC performance, and the final model was set to have a sensitivity of > 0.75 and specificity of > 0.85 for both the training and verification data sets.

[0191] Selected Model Description: In the selected model, CLI1001F034 was randomly subset such that the number of samples from CLI1001F034 would match the sample size of CLI1001F025 and Covance combined. This reduced the total sample size from N = 8,784 to N = 5,030 for the final model. The data retained a 70% / l 5% / l 5% split for the training, verification, and validation data sets (N = 3,520 / 755 / 755). Tables 10-12 present the demographic information of the data used to train, refine, and validate the selected model.

[0192] Table 10. Demographic information for the selected model development (training) dataset.Attorney Docket No. 01137-0099-00PCT

[0193] Table 11. Demographic information for the verification dataset for the selected model.Attorney Docket No. 01137-0099-00PCT

[0194] Table 12. Demographic information for the validation dataset for the selected model.

[0195] The selected model is a 12-analyte logistic regression model with elastic net regularization tuned using 5 repeats of 10-fold cross-validation optimized with AUC. Elastic net regression optimizes a mixture of LASSO and ridge penalties, which are referred to as alpha and lambda parameters, where lambda is the penalty factor and alpha is the mixing factor between LASSO and ridge penalties.Attorney Docket No. 01137-0099-00PCT

[0196] Two well-performing potential models (Leave-One-Out consensus nested cross- validation (CNCV) and Sample Size-Controlled CNCV) provided a shortlist of 23 analytes to select from, with Atrial natriuretic factor and N- terminal pro-BNP being common to both models. These two models used consensus nested cross-validation for feature selection and filtered analytes based on sensitivity to sample handling conditions, which was done to account for between-study differences. Sample handling is unique to every sample (both within and between studies), and sensitivity to sample handling differences varies between analytes. As such, filtering analytes based on their sensitivity to sample handling results in a more robust model, particularly in this case where the data comes from multiple independent datasets.

[0197] Feature selection for the selected model used the 23 analytes from the two initial models as a candidate feature list. The final feature set was selected using forward selection starting with the two proteins in common between the models, N-terminal pro-BNP and Atrial natriuretic factor. Then analytes were added one at a time, based on ranking of absolute value of the t statistic from univariate t-tests. Our functions only return p-values> 2.2 xlOA-16, so using the t-statistic allows analytes to be ranked when the p-values are < 2.2 xlOA-16, which, in the case of this model, they all were. Each univariate t-test had the same degrees of freedom.

[0198] With each additional feature added, models were assessed for performance and robustness. The model increased in size to 13 analytes, at which point one analyte was pruned due to being much more sensitive to sample handling conditions than all the other analytes in the model. The final 12-analyte model selected (Table 1) was the model which exceeded the sensitivity and specificity performance criteria and also passed each robustness assessment (described below).

[0199] Prior to fitting model coefficients, all analytes were log- 10 transformed, then centered and scaled using the means and standard deviations of each analyte in the training data. The selected HCM diagnosis model is an elastic net logistic regression with hyperparameters a = 1 and X = 0.000357487 with a cut-off of 0.603, with individuals equal to or above that value labeled as “HCM” and below as “Not HCM.”

[0200] Selected Model Performance: The HCM diagnosis model had an AUC of 0.951 (95% CI = (0.942, 0.960)) on the training data and of 0.950 (95% CI = (0.931, 0.970)) on the verification data. The criteria for this model were sensitivity > 0.75 and specificity > 0.85. With the default “HCM” vs “no HCM” decision cut-off of 0.5, the model had a sensitivity and specificity of 0.865 and 0.895, respectively, on the training data. Two alternative cut- offs were considered:Attorney Docket No. 01137-0099-00PCT

[0201] cut-off = 0.441, the cut-off which maximizes Youden’s J (maximized when “sensitivity + specificity” is maximized), which resulted in sensitivity and specificity of 0.890 and 0.875, respectively, and

[0202] cut-off = 0.603, the cut-off such that the distances from the performance criteria to the actual training data model performances are equal (i.e., the cut-off such that sensitivity - 0.75 = specificity - 0.85) which resulted in sensitivity and specificity of 0.826 and 0.925, respectively.

[0203] Figure 9 shows how sensitivity and specificity change with cut-off point. To reflect the priority of high specificity while maintaining high sensitivity the cut-off point was set to 0.603, where distance from the performance criteria to actual model performance was equal. Table 14 below the probability bin ranges and associated labels. The probability reported by this model reflects the probability of a patient having HCM within the training cohort.

[0204] Table 14. Predicted classes with associated predicted probability ranges.

[0205] Table 15 shows the model performance on training and verification data with the decision cut-off set to 0.603.

[0206] Table 15. Performance metrics of the selected model for HCM on training and verification data as shown by cross-validation mean and 95% confidence intervals with 0.603 as the cut-off point.

[0207] Table 16 shows the percentage of samples within each clinical covariate and case / control group which were classified correctly with the final HCM screening model (i.e., what percentile for controls with residuals < 0.603 and for cases with residuals < 0.397). As shown, the large majority of the data were classified correctly.

[0208] Table 16. Percentages of samples correctly classified for each covariate group in the final model. A value of 100% means all samples in the group were correctly classified. N / A means there were no samples in that particular group.Attorney Docket No. 01137-0099-00PCT

[0209] Example 3: Secondary Aims

[0210] There were two secondary aims for this model:

[0211] 1) assess the ability of the HCM screening model to discriminate between NewYork Heart Association (NYHA) functional classes, where ideally participants with more advanced NYHA class have higher predicted probability scores compared to those at class I, and2) assess how the HCM screening model classifies participants with heart failure with preserved ejection fraction (HFpEF).Attorney Docket No. 01137-0099-00PCT

[0212] For discriminating between NYHA class for HCM patients, NYHA functional classes III and IV were merged into class III / IV, due to very low numbers of cases in class IV (n = 1). Table 17 shows the median prediction probabilities for each NYHA class for the final HCM screening model, and Figure 10 shows the box plot of the predicted probabilities from the model, demonstrating good discrimination between classes I and II.

[0213] Table 17. Median prediction probabilities of NYHA class for the selected HCM model, on training and verification data.

[0214] Participants with a history of heart failure were excluded from the control cohorts due to the overlap between HCM and HFpEF (many participants with HCM go on to develop HFpEF). This HFpEF analysis utilized a new dataset with HFpEF participants (specifically, the Penn Heart Failure study) to determine the degree of overlap between normal vs. HFpEF vs. HCM cases.

[0215] Table 18 shows the median predicted probability for the final model for the HFpEF patients, as well as the median for the model training cases and controls, for reference. Figure 11 shows the box plot of the HCM screening model predicted probabilities grouped by training data cases, training data controls, and HFpEF patients

[0216] Table 18. Median prediction probabilities of training HCM cases, training controls, and HFpEF patients for the selected model.

[0217] Example 4: Relative Risk Analysis

[0218] Relative risk is a continuous value that allows for more clinically relevant interpretation of the model predictions. The relative risk (RR) is calculated as follows:where p* is the probability that an individual has HCM, generated by the model, and q is the baseline risk. The baseline risk was defined as 0.086. This value was derived using the following formula:Baseline Risk = expit(prevalence * mean(linear predictions of cases) + (1 - prevalence) * mean(linear predictions of controls)),Attorney Docket No. 01137-0099-00PCT whereand prevalence was estimated to be 0.002 (i.e., 1 in 500). Linear predictions of cases / controls were calculated as the logit of the model prediction probabilities.

[0219] RR greater than 1 indicates risk greater than baseline, and RR less than 1 indicates reduced risk compared to baseline. RR can range from 0 (0 / q) to 11.6 (1 / q).

[0220] Example Calculations: For example, an individual with a predicted probability of HCM of 0.163 would have a RR ofRR = 0.163 / 0.086 = 1.90, meaning this individual is 1.90 times as likely to have HCM as compared to the average person.

[0221] An individual with a predicted probability of 0.046 would have a RR ofRR = 0.046 / 0.086 = 0.534, meaning this individual is about half as likely to have HCM compared to the average person.

[0222] Relative Risk Results: Figure 12 shows absolute risk and RR for four quartiles of results for training and verification data. The median values for each quartile are presented in Table 19. Based on median prediction probabilities of 0.946 and 0.080 for cases and controls, respectively, the median RR for cases is 11.000 and the median RR for controls is 0.930.

[0223] Table 19. Median absolute and relative risks of quartiles in training and verification data.

[0224] Example 5: Model Robustness

[0225] During refinement, candidate models were applied to independent datasets to assess robustness in the presence of technical variation, biological variation, and differing sample handling conditions.

[0226] QC Samples: Model predictions were made on -9000 quality control (QC) replicates, assayed over the course of many months, to ensure that the model is robust across replicate samples subject to technical variation of the assay. The distribution of modelAttorney Docket No. 01137-0099-00PCT predictions should be very narrow and with most values close to zero as the QC samples are likely from people without HCM. The selected HCM diagnostic model passed this check, as the QC replicate distribution was narrow and centered close to zero.

[0227] Concordance of Matched Samples: Each sample in the training data was randomly perturbed (“jittered”) to simulate a second assay run of the dataset. The level of perturbation was based on the known precision for each of the analytes in the model. Lin’s Concordance Correlation Coefficient (CCC) was calculated between the matched samples in the original and jittered training data. A CCC > 0.95 is considered acceptably high. A low CCC would indicate lack of confidence model predictions as a sample run twice could have substantially different predictions. With a CCC of 0.971, this model demonstrates stability in predictions across the intended use population.

[0228] Longitudinal Stability: The HCM diagnostic model was also assessed for stability in the presence of biological variability using a longitudinal dataset. This dataset contained samples from 45 individuals across 3 timepoints: baseline, 3-month, and 6-month follow-up. Individuals were not known to have HCM and were generally healthy. If the model was robust to biological variability, then model predictions for each individual should not fluctuate much across the three timepoints. The model predictions were generally consistent over time. The limited number of larger fluctuations were within acceptable limits for model development. Specifically, only a few subjects had class switches and the variability of predictions was low and consistent with previously developed SomaSignal Tests.

[0229] Replicate Sample Variability Assessment and Tolerance Bounds: The replicate sample variability assessment dataset was comprised of samples from 10 subjects, each with 9 replicates. Predictions were made for each replicate, grouped by subject, and compared to the tolerance bounds. The tolerance bounds were estimated using the variance of the QC sample predictions. Tolerance intervals were specified by coverage rate and confidence level. The 99.99% coverage and 99% confidence tolerance interval can be interpreted as the interval such that “99% of the time 99.99% of future data should fall within the interval.” Specifying a 99% confidence level for each of the tolerance bounds, all replicate sample predictions should fall within the 99.99% coverage tolerance interval, and no more than 1 sample should fall outside the 99% coverage tolerance interval. Failure of this test implies insufficient model robustness. In the case of the selected HCM model, only one sample fell outside the 99% tolerance interval, meeting the robustness metric.

[0230] Example 6: Validation Results

[0231] Validation was assessed on the 15% hold out portion of the combined dataset (that was not used in model training or refinement). The selected proteomic model (a 12-featureAttorney Docket No. 01137-0099-00PCT elastic net regression model) was used to generate predicted probabilities for an HCM diagnosis on the validation datasets to determine whether the model meets the selected criteria for success, sensitivity > 0.75 and specificity > 0.85. Predicted probabilities of HCM were compared to the clinical truth standard to determine the selected HCM model performance.

[0232] Sensitivity and specificity were higher than the selected criteria (sensitivity > 0.75; specificity > 0.85) when the selected HCM model was run on the validation data; hence, the model passes validation (Table 20).

[0233] Table 20. Performance metrics of the selected model for HCM on training, verification, and validation data as shown by cross-validation mean and 95% confidence intervals.

[0234] Imputation Method for Out -of-Range RFU Values

[0235] In this section, winsorization bounds were calculated for each aptamer in a candidate model using the training data. These bounds signify the maximum and minimum values that define the acceptable range of aptamer RFUs. Table 21 specifies the concordance between predictions on the original test dataset and the test dataset with imputations applied to it, using winsorized values and replacing out-of-range aptamer measurements with 0. Accuracy is reported for classification models.

[0236] Table 21. Results of imputation methods.Example 7: Analysis of HCM Model Biomarker Panels

[0237] Model biomarker panels comprising various combinations of the biomarkers listed in Table 1 were analyzed to determine the Area Under the Curve (AUC) value for the various combinations. The model biomarker panels may be based on a panel of N biomarker proteins having an AUC value of at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.7, at least 0.75, at least 0.8, at least 0.85, at least 0.9, or at least 0.95, where N is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and / or 12 of the biomarker proteins listed in Table 1.Atorney Docket No. 01137-0099-00PCTThe Table below shows exemplary model results when various combinations comprising 1 to 12 biomarker proteins were measured.Attorney Docket No. 01137-0099-00PCTTable 22: HCM Biomarker PanelsAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCTAttorney Docket No. 01137-0099-00PCT

Claims

Attorney Docket No. 01137-0099-00PCTWhat is claimed is:

1. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising forming a biomarker panel comprising N biomarker proteins, and detecting a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or at least 12, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or 12 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

2. A method of detecting levels of N biomarker proteins in a sample, comprising forming a biomarker panel comprising N biomarker proteins, and detecting the level of each of the N biomarker proteins in the sample from a subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or at least 12, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or 12 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

3. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of IDS and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

4. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of CRLD2 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the NAttorney Docket No. 01137-0099-00PCT biomarker proteins are selected from IDS, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

5. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of PSME2 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

6. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of RAB31 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, DPEP1, RPIA, NRP1, KAAG1, and BNP.

7. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of DPEP1 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, KAAG1, and BNP.

8. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of RPIA and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, DPEP1, RAB31, NRP1, KAAG1, and BNP.Attorney Docket No. 01137-0099-00PCT9. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of NRP1 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, KAAG1, and BNP.

10. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of KAAG1 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, and BNP.

11. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of BNP and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, and KAAG1.

12. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of ANP and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, KAAG1, and BNP.

13. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of N-terminal pro-BPN and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2,Attorney Docket No. 01137-0099-00PCT at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, PolyUbiquitin K63, PSME2, RAB31, RPIA, NRP1, KAAG1, and BNP.

14. A method of predicting a likelihood of hypertrophic cardiomyopathy (HCM) in a subject, comprising detecting a level of PolyUbiquitin K63 and a level of each of N biomarker proteins in a sample from the subject, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 11, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PSME2, RAB31, RPIA, NRP1, KAAG1, and BNP.

15. The method according to any one of the preceding claims, wherein N is 2 to 12, or N is 3 to 12, N is 4 to 12, N is 5 to 12, N is 6 to 12, N is 7 to 12, N is 8 to 12, N is 9 to 12, N is 10 to 12, or N is 11 to 12.

16. The method according to any one of the preceding claims, wherein N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, or N is 12.

17. The method according to any one of the preceding claims, wherein at least one of the N biomarker proteins is IDS, or at least one of the N biomarker proteins is CRLD2, or at least one of the N biomarker proteins is ANP, or at least one of N biomarker proteins is N-terminal pro- BPN, or at least one of the N biomarker proteins is PolyUbiquitin K63, or at least one of the N biomarker proteins is PSME2, or at least one of the N biomarker proteins is RAB31, or at least one of the N biomarker proteins is DPEP1, or at least one of the N biomarker proteins is RPIA, or at least one of the N biomarker proteins is NRP1, or at least one of the N biomarker proteins is KAAG1, or at least one of the N biomarker proteins is BNP.

18. The method according to any one of the preceding claims, wherein each of the N biomarker proteins is selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

19. The method according to any one of the preceding claims, wherein at least 2, at least 3, at least 4, or at least 5 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N-Attomey Docket No. 01137-0099-00PCT terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

20. The method according to any one of claims 1-4 and 12-17, wherein at least 2, at least 3, at least 4, or at least 5 of the N biomarker proteins are selected from IDS, CRLD2, ANP, N- terminal pro-BPN, and PolyUbiquitin K63.

21. The method according to any one of claims 1-3, wherein 2 of the N biomarker proteins are IDS and CRDL2, or 2 of the N biomarker proteins are IDS and ANP, or 2 of the N biomarker proteins are IDS and N-terminal pro-BPN, or 2 of the N biomarker proteins are IDS and PolyUbiquitin K63, or 2 of the N biomarker proteins are IDS and PSME2, or 2 of the N biomarker proteins are IDS and RAB31, or 2 of the N biomarker proteins are IDS and DPEP1, or 2 of the N biomarker proteins are IDS and RPIA, or 2 of the N biomarker proteins are IDS and NRP1, or 2 of the N biomarker proteins are IDS and KAAG1, or 2 of the N biomarker proteins are IDS and BNP.

22. The method according to any one of claims 1, 2 or 4, wherein 2 of the N biomarker proteins are CRLD2 and ANP, or 2 of the N biomarker proteins are CRLD2 and N-terminal pro- BPN, or 2 of the N biomarker proteins are CRLD2 and PolyUbiquitin K63, or 2 of the N biomarker proteins are CRLD2 and PSME2, or 2 of the N biomarker proteins are CRLD2 and RAB3 1, or 2 of the N biomarker proteins are CRLD2 and DPEP1, or 2 of the N biomarker proteins are CRLD2 and RPIA, or 2 of the N biomarker proteins are CRLD2 and NRP1, or 2 of the N biomarker proteins are CRLD2 and KAAG1, or 2 of the N biomarker proteins are CRLD2 and BNP.

23. The method according to any one of claims 1, 2 or 5, wherein 2 of the N biomarker proteins are PSME2 and ANP, or 2 of the N biomarker proteins are PSME2 and N-terminal pro- BPN, or 2 of the N biomarker proteins are PSME2 and PolyUbiquitin K63, or 2 of the N biomarker proteins are PSME2 and RAB31, or 2 of the N biomarker proteins are PSME2 and DPEP1, or 2 of the N biomarker proteins are PSME2 and RPIA, or 2 of the N biomarker proteins are PSME2 and NRP1, or 2 of the N biomarker proteins are PSME2 and KAAG1, or 2 of the N biomarker proteins are PSME2 and BNP.

24. The method according to any one of claims 1, 2 or 6, wherein 2 of the N biomarker proteins are RAB31 and ANP, or 2 of the N biomarker proteins are RAB31 and N-terminal pro-Attorney Docket No. 01137-0099-00PCTBPN, or 2 of the N biomarker proteins are RAB31 and PolyUbiquitin K63, or 2 of the N biomarker proteins are RAB31 and DPEP1, or 2 of the N biomarker proteins are RAB31 and RPIA, or 2 of the N biomarker proteins are RAB31 and NRP1, or 2 of the N biomarker proteins are RAB31 and KAAG1, or 2 of the N biomarker proteins are RAB31 and BNP.

25. The method according to any one of claims 1, 2 or 7, wherein 2 of the N biomarker proteins are DPEP1 and ANP, or 2 of the N biomarker proteins are DPEP1 and N-terminal pro- BPN, or 2 of the N biomarker proteins are DPEP1 and PolyUbiquitin K63, or 2 of the N biomarker proteins are DPEP1 and RPIA, or 2 of the N biomarker proteins are DPEP1 and NRP1, or 2 of the N biomarker proteins are DPEP1 and KAAG1, or 2 of the N biomarker proteins are DPEP1 and BNP.

26. The method according to any one of claims 1, 2 or 8, wherein 2 of the N biomarker proteins are RPIA and ANP, or 2 of the N biomarker proteins are RPIA and N-terminal pro- BPN, or 2 of the N biomarker proteins are RPIA and PolyUbiquitin K63, or 2 of the N biomarker proteins are RPIA and NRP1, or 2 of the N biomarker proteins are RPIA and KAAG1, or 2 of the N biomarker proteins are RPIA and BNP.

27. The method according to any one of claims 1, 2 or 9, wherein 2 of the N biomarker proteins are NRP1 and ANP, or 2 of the N biomarker proteins are NRP1 and N-terminal pro- BPN, or 2 of the N biomarker proteins are NRP1 and PolyUbiquitin K63, or 2 of the N biomarker proteins are NRP1 and KAAG1, or 2 of the N biomarker proteins are NRP1 and BNP.

28. The method according to any one of claims 1, 2, 10, and 11 wherein 2 of the N biomarker proteins are KAAG1 and ANP, or 2 of the N biomarker proteins are KAAG1 and N- terminal pro-BPN, or 2 of the N biomarker proteins are KAAG1 and PolyUbiquitin K63, or 2 of the N biomarker proteins are KAAG1 and BNP, or 2 of the N biomarker proteins are BNP and ANP, or 2 of the N biomarker proteins are BNP and N-terminal pro-BPN, or 2 of the N biomarker proteins are BNP and PolyUbiquitin K63.

29. The method according to any one of claims 1, 2, 12, 13, and 14, wherein 2 of the N biomarker proteins are ANP and N-terminal pro-BPN, or 2 of the N biomarker proteins are ANP and PolyUbiquitin K63, or two of the N biomarker proteins are N-terminal pro-BNP and PolyUbiquitinK63.Attorney Docket No. 01137-0099-00PCT30. The method according to any one of the preceding claims, wherein the sample is a blood sample, a plasma sample, a serum sample, or a urine sample.

31. The method according to any one of the preceding claims, wherein detecting is performed using mass spectrometry, an aptamer based assay and / or an antibody based assay.

32. The method according to any one of the preceding claims, wherein the method comprises contacting biomarker proteins of the sample or samples with a set of biomarker capture reagents, wherein each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a different biomarker protein being detected.

33. The method according to claim 32, wherein each biomarker capture reagent is an antibody or an aptamer.

34. The method according to claim 33, wherein each biomarker capture reagent is an aptamer.

35. The method according to claim 34, wherein at least one aptamer is a slow off-rate aptamer.

36. The method according to claim 35, wherein at least one slow off-rate aptamer comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 nucleotides with modifications.

37. The method according to claim 35 or claim 36, wherein each slow off-rate aptamer binds to its target protein with an off rate (t’ ) of > 20 minutes, > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

38. The method according to any one of claims 31-37, wherein the level of each biomarker protein measured is determined from a relative florescence unit (RFU) or a protein concentration.

39. The method according to any one of the preceding claims, wherein predicting the likelihood of HCM in the subject is based on input of the levels of the N biomarker proteins measured in a statistical model.Attorney Docket No. 01137-0099-00PCT40. The method according to claim 39, wherein the determining comprises analyzing the levels of the N biomarker protein using an elastic net logistic regression model.

41. The method according to claim 39 or 40, wherein the model has an area under the curve (AUC) selected from at least 0.64, at least 0.65, at least 0.66, at least 0.67, at least 0.68, at least 0.69, at least 0.7, at least 0.75, at least 0.8, at least 0.85, at least 0.9, or at least 0.95.

42. The method according to any one of the preceding claims, wherein the method comprises predicting a likelihood of HCM in a subject for the purpose of determining a medical insurance premium or life insurance premium.

43. The method according to claim 42, wherein the method further comprises determining coverage for medical insurance or life insurance.

44. The method according to any one of claims 1-43, wherein the method further comprises using information resulting from the method to predict and / or manage the utilization of medical resources.

45. A kit comprising N biomarker protein capture reagents, wherein N is at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or at least 12 and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or 12 of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from IDS, CRLD2, ANP, N-terminal pro- BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

46. The kit according to claim 45, wherein N is at least two and at least one of the two N biomarker protein capture reagents specifically binds to the biomarker protein selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

47. The kit according to claim 45 or 46, wherein N is 2 to 12, or N is 3 to 12, or N is 4 to 12, or N is 5 to 12, or N is 6 to 12, or N is 7 to 12, or N is 8 to 12, or N is 9 to 12, or N is 10 to 12, or N is 11 to 23.

48. The kit according to any one of claims 45-47, wherein N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, or N is 12.Attorney Docket No. 01137-0099-00PCT49. The kit according to any one of claims 45-48, wherein each of the N biomarker protein capture reagents specifically binds to a different biomarker protein.

50. The kit according to any one of claims 45-48, wherein each of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from IDS, CRLD2, ANP, N- terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

51. The kit according to any one of claims 45-49, wherein at least 1 of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

52. The kit according to any one of claims 45-49, wherein at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or 12 of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from IDS, CRLD2, ANP, N-terminal pro-BPN, PolyUbiquitin K63, PSME2, RAB31, DPEP1, RPIA, NRP1, KAAG1, and BNP.

53. The kit according to any one of claims 45-49, wherein 2 of the N biomarker protein capture reagents specifically bind IDS and CRLD2, or 2 of the N biomarker protein capture reagents specifically bind IDS and ANP, or 2 of the N biomarker protein capture reagents specifically bind IDS and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind IDS and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind IDS and PSME2, or 2 of the N biomarker protein capture reagents specifically bind IDS and RAB31, or 2 of the N biomarker protein capture reagents specifically bind IDS and DPEP1, or 2 of the N biomarker protein capture reagents specifically bind IDS and RPIA, or 2 of the N biomarker protein capture reagents specifically bind IDS and NRP1, or 2 of the N biomarker protein capture reagents specifically bind IDS and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind IDS and BNP, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and ANP, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and PSME2, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and RAB31, or 2 of the N biomarker protein capture reagentsAttorney Docket No. 01137-0099-00PCT specifically bind CRLD2 and DPEP1, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and RPIA, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and NRP1, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind CRLD2 and BNP, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and ANP, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and RAB31, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and DPEP1, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and RPIA, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and NRP1, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind PSME2 and BNP, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and ANP, or 2 of the N biomarker protein capture reagents specifically bind RAB3 1 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and DPEP1, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and RPIA, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and NRP1, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind RAB31 and BNP, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and ANP, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and RPIA, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and NRP1, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind DPEP1 and BNP, or 2 of the N biomarker protein capture reagents specifically bind RPIA and ANP, or 2 of the N biomarker protein capture reagents specifically bind RPIA and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind RPIA and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind RPIA and NRP1, or 2 of the N biomarker protein capture reagents specifically bind RPIA and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind RPIA and BNP, or 2 of the N biomarker protein capture reagents specifically bind NRP1 and ANP, or 2 of the N biomarker protein captureAttorney Docket No. 01137-0099-00PCT reagents specifically bind NRP1 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind NRP1 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind NRP1 and KAAG1, or 2 of the N biomarker protein capture reagents specifically bind NRP1 and BNP, or 2 of the N biomarker protein capture reagents specifically bind KAAG1 and ANP, or 2 of the N biomarker protein capture reagents specifically bind KAAG1 and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind KAAG1 and PolyUbiquitin K63, or 2 of the N biomarker protein capture reagents specifically bind KAAG1 and BNP, or 2 of the N biomarker protein capture reagents specifically bind BNP and ANP, or 2 of the N biomarker protein capture reagents specifically bind BNP and N-terminal pro-BPN, or 2 of the N biomarker protein capture reagents specifically bind BNP and PolyUbiquitin K63 or 2 of the N biomarker proteins are ANP and N-terminal pro-BPN, or 2 of the N biomarker proteins are ANP and PolyUbiquitin K63, or two of the N biomarker proteins are N-terminal pro-BNP and PolyUbiquitinK63.

54. A kit comprising N biomarker protein capture reagents, wherein the kit comprises biomarker protein capture reagents for carrying out the method of any one of claims 1-44.

55. The kit according to any one of claims 45-54, wherein each of the N biomarker protein capture reagents is an antibody or an aptamer.

56. The kit according to claim 55, wherein each biomarker protein capture reagent is an aptamer.

57. The kit according to claim 56, wherein at least one aptamer is a slow off-rate aptamer.

58. The kit according to claim 57, wherein at least one slow off-rate aptamer comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 nucleotides with modifications.

59. The kit according to claim 57 or claim 58, wherein each slow off-rate aptamer binds to its target protein with an off rate (t%) of > 20 minutes, > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

60. The kit according to any one of claims 45-59, for use in detecting the N biomarker proteins in a sample from a subject.Attorney Docket No. 01137-0099-00PCT61. The kit according to claim 60, for use in predicting an individual’s likelihood of having HCM.

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