Faculty Bibliography

Platelets are key mediators of atherothrombosis, yet, limited tools exist to identify individuals with a hyperreactive platelet phenotype. In this study, we investigate the association of platelet hyperreactivity and cardiovascular events, and introduce a tool, the Platelet Reactivity ExpreSsion Score (PRESS), which integrates platelet aggregation responses and RNA sequencing. Among patients with peripheral artery disease (PAD), those with a hyperreactive platelet response (>60% aggregation) to 0.4 µM epinephrine had a higher incidence of the 30 day primary cardiovascular endpoint (37.2% vs. 15.3% in those without hyperreactivity, adjusted HR 2.76, 95% CI 1.5-5.1, p = 0.002). PRESS performs well in identifying a hyperreactive phenotype in patients with PAD (AUC [cross-validation] 0.81, 95% CI 0.68 -0.94, n = 84) and in an independent cohort of healthy participants (AUC [validation] 0.77, 95% CI 0.75 -0.79, n = 35). Following multivariable adjustment, PAD individuals with a PRESS score above the median are at higher risk for a future cardiovascular event (adjusted HR 1.90, CI 1.07-3.36; p = 0.027, n = 129, NCT02106429). This study derives and validates the ability of PRESS to discriminate platelet hyperreactivity and identify those at increased cardiovascular risk. Future studies in a larger independent cohort are warranted for further validation. The development of a platelet reactivity expression score opens the possibility for a personalized approach to antithrombotic therapy for cardiovascular risk reduction.

Cardiometabolic comorbidities, diabetes (DM), hypertension (HTN), and obesity, contribute to cardiovascular disease (CVD). Circulating biomarkers facilitate prognostication for patients with CVD. We explored the relationship between cardiometabolic comorbidity burden in patients with chronic coronary disease (CCD) and biomarkers of myocardial stretch, injury, inflammation, and platelet activity. We analyzed participants from the ISCHEMIA Trials biorepository with plasma biomarkers (NT-proBNP, hs-cTnT, hs-CRP, IL-6, sCD40L, and GDF-15) and clinical risk factors [hemoglobin A1c (HbA1c), systolic blood pressure (SBP), and body mass index (BMI)] at baseline. We defined cardiometabolic comorbidities as DM, HTN, and obesity at baseline. Comorbidity burden characterized by number and severity of comorbidities. Controlled comorbidities were defined as HbA1c <7% for those with DM, SBP <130 mmHg for those with HTN and BMI <30 kg/m². Severely uncontrolled was defined as HbA1c ≥8%, SBP ≥160 mmHg, and BMI ≥35 kg/m². We performed linear regression analyses to examine the association between comorbidity burden and log-transformed biomarker levels adjusting for age, sex, eGFR controlled for hemodialysis, and left ventricular ejection fraction. A total of 752 individuals (mean age 66, 19% female, 84% white) were included in this analysis. Self-reported Black race, current smokers, history of MI and HF had greater cardiometabolic comorbidity burden. The presence of ≥ 1 severely uncontrolled comorbidity was associated with significantly higher baseline levels of hs-cTnT, hs-CRP, IL-6, and GDF-15 compared to participants with no comorbidities. In conclusion, increasing cardiometabolic comorbidity burden in patients with CCD is associated with higher levels of circulating biomarkers of myocardial injury and inflammation.

INTRODUCTION/UNASSIGNED:Hyperoxaluria is a risk factor for kidney stone formation and chronic kidney disease progression. The microbiome is an important protective factor against oxalate accumulation through the activity of its oxalate-degrading enzymes (ODEs). In this cross-sectional study, we leverage multiomics to characterize the microbial community of participants with primary and enteric hyperoxaluria, as well as idiopathic calcium oxalate kidney stone (CKS) formers, focusing on the relationship between oxalate degrading functions of the microbiome. METHODS/UNASSIGNED:Patients diagnosed with type 1 primary hyperoxaluria (PH), enteric hyperoxaluria (EH), and CKS were screened for inclusion in the study. Participants completed a food frequency questionnaire recording their dietary oxalate content while fecal oxalate levels were ascertained. DNA and RNA were extracted from stool samples and sequenced. Metagenomic (MTG) and metatranscriptomic (MTT) data were processed through our bioinformatics pipelines, and microbiome diversity, differential abundance, and networks were subject to statistical analysis in relationship with oxalate levels. RESULTS/UNASSIGNED:A total of 38 subjects were recruited, including 13 healthy participants, 12 patients with recurrent CKS, 8 with PH, and 5 with EH. Urinary and fecal oxalate were significantly higher in the PH and the EH population compared to healthy controls. At the community level, alpha-diversity and beta-diversity indices were similar across all populations. The respective contributions of single bacterial species to the total oxalate degradative potential were similar in healthy and PH subjects. MTT-based network analysis identified the most interactive bacterial network in patients with PH. Patients with EH had a decreased abundance of multiple major oxalate degraders. CONCLUSION/UNASSIGNED:The composition and inferred activity of oxalate-degrading microbiota were differentially associated with host clinical conditions. Identifying these changes improves our understanding of the relationships between dietary constituents, microbiota, and oxalate homeostasis, and suggests new therapeutic approaches protecting against hyperoxaluria.

Lineage plasticity is a recognized hallmark of cancer progression that can shape therapy outcomes. The underlying cellular and molecular mechanisms mediating lineage plasticity remain poorly understood. Here, we describe a versatile in vivo platform to identify and interrogate the molecular determinants of neuroendocrine lineage transformation at different stages of prostate cancer progression. Adenocarcinomas reliably develop following orthotopic transplantation of primary mouse prostate organoids acutely engineered with human-relevant driver alterations (e.g., Rb1

BACKGROUND/UNASSIGNED:Identifying patients with the optimal risk:benefit for ticagrelor is challenging. The aim was to identify ticagrelor-responsive platelet transcripts as biomarkers of platelet function and cardiovascular risk. METHODS/UNASSIGNED:Healthy volunteers (n=58, discovery; n=49, validation) were exposed to 4 weeks of ticagrelor with platelet RNA data, platelet function, and self-reported bleeding measured pre-/post-ticagrelor. RNA sequencing was used to discover platelet genes affected by ticagrelor, and a subset of the most informative was summarized into a composite score and tested for validation. This score was further analyzed (1) in CD34+ megakaryocytes exposed to an P2Y12 inhibitor in vitro, (2) with baseline platelet function in healthy controls, (3) in peripheral artery disease patients (n=139) versus patient controls (n=30) without atherosclerosis, and (4) in patients with peripheral artery disease for correlation with atherosclerosis severity and risk of incident major adverse cardiovascular and limb events. RESULTS/UNASSIGNED:Ticagrelor exposure differentially expressed 3409 platelet transcripts. Of these, 111 were prioritized to calculate a Ticagrelor Exposure Signature score, which ticagrelor reproducibly increased in discovery and validation cohorts. Ticagrelor's effects on platelets transcripts positively correlated with effects of P2Y12 inhibition in primary megakaryocytes. In healthy controls, higher baseline scores correlated with lower baseline platelet function and with minor bleeding while receiving ticagrelor. In patients, lower scores independently associated with both the presence and extent of atherosclerosis and incident ischemic events. CONCLUSIONS/UNASSIGNED:Ticagrelor-responsive platelet transcripts are a biomarker for platelet function and cardiovascular risk and may have clinical utility for selecting patients with optimal risk:benefit for ticagrelor use.

BACKGROUND:Blood homeostasis requires the daily production of millions of terminally differentiated effector cells that all originate from hematopoietic stem cells (HSCs). HSCs are rare and exhibit unique self-renewal and multipotent properties, which depend on their ability to maintain quiescence through ill-defined processes. Defective control of cell cycle progression can eventually lead to bone marrow failure or malignancy. In particular, the molecular mechanisms tying cell cycle re-entry to cell fate commitment in HSCs remain elusive. Previous studies have identified chromatin coordination as a key regulator of differentiation in embryonic stem cells. RESULTS:phase of the cell cycle, which correlates with the engagement of specific signaling pathways, including aberrant expression of cell adhesion molecules and the interferon signaling program in LT-HSCs. In addition, we uncover the Sin3B-dependent accessibility of genomic elements controlling HSC differentiation, which points to cell cycle progression possibly dictating the priming of HSCs for differentiation. CONCLUSIONS:Our findings provide new insights into controlled cell cycle progression as a potential regulator of HSC lineage commitment through the modulation of chromatin features.

SARS-CoV-2 infection and vaccination elicit potent immune responses. Our study presents a comprehensive multimodal single-cell analysis of blood from COVID-19 patients and healthy volunteers receiving the SARS-CoV-2 vaccine and booster. We profiled immune responses via transcriptional analysis and lymphocyte repertoire reconstruction. COVID-19 patients displayed an enhanced interferon signature and cytotoxic gene upregulation, absent in vaccine recipients. B and T cell repertoire analysis revealed clonal expansion among effector cells in COVID-19 patients and memory cells in vaccine recipients. Furthermore, while clonal αβ T cell responses were observed in both COVID-19 patients and vaccine recipients, expansion of clonal γδ T cells was found only in infected individuals. Our dataset enables side-by-side comparison of immune responses to infection versus vaccination, including clonal B and T cell responses. Our comparative analysis shows that vaccination induces a robust, durable clonal B and T cell responses, without the severe inflammation associated with infection.

IMPORTANCE:Biomarkers may improve prediction of cardiovascular events for patients with stable coronary artery disease (CAD), but their importance in addition to clinical tests of inducible ischemia and CAD severity is unknown. OBJECTIVES:To evaluate the prognostic value of multiple biomarkers in stable outpatients with obstructive CAD and moderate or severe inducible ischemia. DESIGN AND SETTING:The ISCHEMIA and ISCHEMIA CKD trials randomized 5,956 participants with CAD to invasive or conservative management from July 2012 to January 2018; 1,064 participated in the biorepository. MAIN OUTCOME MEASURES:Primary outcome was cardiovascular death, myocardial infarction (MI), or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. Secondary outcome was cardiovascular death or MI. Improvements in prediction were assessed by cause-specific hazard ratios (HR) and area under the receiver operating characteristics curve (AUC) for an interquartile increase in each biomarker, controlling for other biomarkers, in a base clinical model of risk factors, left ventricular ejection fraction (LVEF) and ischemia severity. Secondary analyses were performed among patients in whom core-lab confirmed severity of CAD was ascertained by computed cardiac tomographic angiography (CCTA). EXPOSURES:Baseline levels of interleukin-6 (IL-6), high sensitivity troponin T (hsTnT), growth differentiation factor 15 (GDF-15), N-terminal pro-B-type natriuretic peptide (NT-proBNP), lipoprotein a (Lp[a]), high sensitivity C-reactive protein (hsCRP), Cystatin C, soluble CD 40 ligand (sCD40L), myeloperoxidase (MPO), and matrix metalloproteinase 3 (MMP3). RESULTS:Among 757 biorepository participants, median (IQR) follow-up was 3 (2-5) years, age was 67 (61-72) years, and 144 (19%) were female; 508 had severity of CAD by CCTA available. In an adjusted multimarker model with hsTnT, GDF-15, NT-proBNP and sCD40L, the adjusted HR for the primary outcome per interquartile increase in each biomarker was 1.58 (95% CI 1.22, 2.205), 1.60 (95% CI 1.16, 2.20), 1.61 (95% 1.22, 2.14), and 1.46 (95% 1.12, 1.90), respectively. The adjusted multimarker model also improved prediction compared with the clinical model, increasing the AUC from 0.710 to 0.792 (P < .01) and 0.714 to 0.783 (P < .01) for the primary and secondary outcomes, respectively. Similar findings were observed after adjusting for core-lab confirmed atherosclerosis severity. CONCLUSIONS AND RELEVANCE:Among ISCHEMIA biorepository participants, biomarkers of myocyte injury/distension, inflammation, and platelet activity improved cardiovascular event prediction in addition to risk factors, LVEF, and assessments of ischemia and atherosclerosis severity. These biomarkers may improve risk stratification for patients with stable CAD.

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.