Newman, Jonathan D

Introduction: Circulating biomarkers may improve prediction of cardiovascular (CV) events in chronic coronary disease (CCD). The incremental value of biomarkers in CCD after adjustment for clinical factors, inducible ischemia and coronary artery disease (CAD) severity is uncertain. Methods: 756 ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) participants with site-determined moderate-severe ischemia and 9 assayed biomarkers (Figure) were included. Among participants with interpretable stress tests for ischemia and CAD by computed coronary tomography angiography (CCTA) (N=507), associations of biomarkers with the primary (CV death, myocardial infarction (MI), or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest) or secondary endpoints (CV death or MI) were evaluated using sequentially adjusted multivariable Cox proportional hazards models (Model 1=unadjusted, Model 2=clinical features, Model 3=Model 2 + multivessel CAD by CCTA, and Model 4=Model 3 + severe ischemia). Results: Median (IQR) participant age was 64 (57-70) years, 19% were female, 81% white, and 5% Hispanic. Hypertension (85%), diabetes (46%) and obesity (45%) were common, 28% had an estimated glomerular filtration rate <60 ml/min/1.73m 2 . Severe ischemia was present in 44%, and 39% had multivessel CAD (≥70% stenosis) by CCTA. Over 3.5 (2.4-4.7) years of follow-up there were 146 (19%) primary and 128 (17%) secondary endpoints. Soluble CD40L, GDF-15, NT-proBNP, hsTnT were associated with the primary and secondary endpoints in univariable and multivariable (adjusting for clinical features, multivessel CAD and severe ischemia) models. Conclusion: Biomarkers of inflammation (GDF-15), thrombosis (sCD40L), myocardial stretch (NT-proBNP) and injury (hs-troponin) were associated with CV endpoints in the ISCHEMIA trial independent of clinical features, multivessel CAD and severe ischemia.

Introduction: Chronic coronary disease (CCD) is a leading cause of death worldwide. Whole blood (WB) transcriptomic (RNA-seq) and methylation ‘omics’ data can discern molecular underpinnings of CCD and create molecular subtypes to aid risk prediction. Methods: Of 1,064 participants with moderate-severe ischemia (Age: 67.1±6.2, 19.4% Female, 17% non-White) from the ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) biorepository, 646 participants had available WB RNA-seq and 737 had WB methylation data ( Fig 1A ). Unsupervised clustering and differential analyses identified omics-derived molecular subtypes. We identified co-regulated gene modules of each subtype, performed subtype-level enrichment of clinical variables, and performed Kaplan-Meier analyses for time to cardiovascular (CV) death or myocardial infarction (MI). Results: Multi-omic analysis revealed 4 RNA-seq-derived subtypes (RS) and 3 methylation-derived subtypes (MS). RNA-seq Subtype 1 (RS1) was enriched for molecular drivers indicative of an adaptive immune response, had clinical enrichment of female sex, statin use, and greater ischemia, and had decreased risk of CV death or MI ( Fig 1B ). RNA-seq Subtype 2 (RS2) was enriched for innate immune response pathway, and patients who were older and White, and was associated with increased risk of CV death or MI. Similarly, methylation subtypes also yielded a high-risk subtype (MS3) and a low-risk subtype (MS2) ( Fig 1C ). Integration of transcriptomic and methylomic subtypes led to further distinction of resilient and vulnerable subgroups. Conclusions: WB RNA-seq and methylation data identified molecularly-defined subtypes that informed the underlying disease pathology of CCD patients in ISCHEMIA. These subgroups were differentially enriched for clinical characteristics and event outcomes, demonstrating the potential use for blood-based omics tests to aid in the prognosis of CCD.

Abstract CCGA [NCT02889978] is the largest study of cfDNA-based early cancer detection; the first CCGA learnings from multiple cfDNA assays are reported here. This prospective, multi-center, observational study has enrolled 10,012 of 15,000 demographically-balanced participants at 141 sites. Blood was collected from participants with newly diagnosed therapy-naive cancer (C, case) and participants without a diagnosis of cancer (noncancer [NC], control) as defined at enrollment. This preplanned substudy included 878 cases, 580 controls, and 169 assay controls (n=1627) across 20 tumor types and all clinical stages. All samples were analyzed by: 1) Paired cfDNA and white blood cell (WBC)-targeted sequencing (60,000X, 507 gene panel); a joint caller removed WBC-derived somatic variants and residual technical noise; 2) Paired cfDNA and WBC whole-genome sequencing (WGS; 35X); a novel machine learning algorithm generated cancer-related signal scores; joint analysis identified shared events; and 3) cfDNA whole-genome bisulfite sequencing (WGBS; 34X); normalized scores were generated using abnormally methylated fragments. In the targeted assay, non-tumor WBC-matched cfDNA somatic variants (SNVs/indels) accounted for 76% of all variants in NC and 65% in C. Consistent with somatic mosaicism (i.e., clonal hematopoiesis), WBC-matched variants increased with age; several were non-canonical loss-of-function mutations not previously reported. After WBC variant removal, canonical driver somatic variants were highly specific to C (e.g., in EGFR and PIK3CA, 0 NC had variants vs 11 and 30, respectively, of C). Similarly, of 8 NC with somatic copy number alterations (SCNAs) detected with WGS, 4 were derived from WBCs. WGBS data revealed informative hyper- and hypo-fragment level CpGs (1:2 ratio); a subset was used to calculate methylation scores. A consistent “cancer-like” signal was observed in &lt;1% of NC participants across all assays (representing potential undiagnosed cancers). An increasing trend was observed in NC vs stages I-III vs stage IV (nonsyn. SNVs/indels per Mb [Mean±SD] NC: 1.01±0.86, stages I-III: 2.43±3.98; stage IV: 6.45±6.79; WGS score NC: 0.00±0.08, I-III: 0.27±0.98; IV: 1.95± 2.33; methylation score NC: 0±0.50; I-III: 1.02±1.77; IV: 3.94±1.70). These data demonstrate the feasibility of achieving &gt;99% specificity for invasive cancer, and support the promise of cfDNA assay for early cancer detection. Additional data will be presented on detected plasma:tissue variant concordance and on multi-assay modeling. Citation Format: Alexander A. Aravanis, Geoffrey R. Oxnard, Tara Maddala, Earl Hubbell, Oliver Venn, Arash Jamshidi, Ling Shen, Hamed Amini, John A. Beausang, Craig Betts, Daniel Civello, Konstantin Davydov, Saniya Fazullina, Darya Filippova, Sante Gnerre, Samuel Gross, Chenlu Hou, Roger Jiang, Byoungsok Jung, Kathryn Kurtzman, Collin Melton, Shivani Nautiyal, Jonathan Newman, Joshua Newman, Cosmos Nicolaou, Richard Rava, Onur Sakarya, Ravi Vijaya Satya, Seyedmehdi Shojaee, Kristan Steffen, Anton Valouev, Hui Xu, Jeanne Yue, Nan Zhang, Jose Baselga, Rosanna Lapham, Daron G. Davis, David Smith, Donald Richards, Michael V. Seiden, Charles Swanton, Timothy J. Yeatman, Robert Tibshirani, Christina Curtis, Sylvia K. Plevritis, Richard Williams, Eric Klein, Anne-Renee Hartman, Minetta C. Liu. Development of plasma cell-free DNA (cfDNA) assays for early cancer detection: first insights from the Circulating Cell-Free Genome Atlas Study (CCGA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-343.