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Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation. Cistrome-wide association study (CWAS) is an approach for nominating variants that impact traits through effects on chromatin state. CWAS performed on 307 prostate cistromes identifies candidate loci for prostate cancer and androgen-related traits., This work is supported by grants from the PhRMA Foundation and the Kure It Cancer Research Foundation (S.C.B.). The androgen deprivation GWAS was supported in part by the National Cancer Institute of the National Institutes of Health under award numbers U10CA180820, U10CA180794 and UG1CA233180 (C.J.S.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We are grateful to the E3805: CHAARTED investigators; the patients who participated in the trial; the Prostate Cancer Foundation Mazzone Awards; and Sanofi for partial financial support and supplying docetaxel for early use (C.J.S.). In addition, we acknowledge Public Health Service grants CA180794, CA180820, CA23318, CA66636, CA21115, CA49883, CA16116, CA21076, CA27525, CA13650, CA14548, CA35421, CA32102, CA31946, CA04919, CA107868 and CA184734 (C.J.S.). We are grateful for the generous support of Rebecca and Nathan Milikowsky and Debbie and Bob First (S.C.B.).

Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss to follow-up as well as competing events such as death or disease progression. To that end, we propose a generative time-to-event model, SurvLatent ODE, which adopts an Ordinary Differential Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectively parameterize dynamics of latent states under irregularly sampled input data. Our model then utilizes the resulting latent embedding to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. We demonstrate competitive performance of our model on MIMIC-III, a freely-available longitudinal dataset collected from critical care units, on predicting hospital mortality as well as the data from the Dana-Farber Cancer Institute (DFCI) on predicting onset of Venous Thromboembolism (VTE), a life-threatening complication for patients with cancer, with death as a competing event. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying VTE risk groups, while providing clinically meaningful and interpretable latent representations., Comment: Proceedings of Machine Learning Research, Volume 182: Machine Learning for Healthcare Conference (MLHC) 2022

PurposeGenetic differences in immunity may contribute to toxicity and outcomes with immune checkpoint inhibitor (CPI) therapy, but these relationships are poorly understood. We examined the genetics of thyroid immune-related adverse events (irAE).Experimental designIn patients with non-small cell lung cancer (NSCLC) treated with CPIs at Memorial Sloan Kettering (MSK) and Vanderbilt University Medical Center (VUMC), we evaluated thyroid irAEs. We typed germline DNA using genome-wide single-nucleotide polymorphism (SNP) arrays and imputed genotypes. Germline SNP imputation was also performed in an independent Dana-Farber Cancer Institute (DFCI) cohort. We developed and validated polygenic risk scores (PRS) for hypothyroidism in noncancer patients using the UK and VUMC BioVU biobanks. These PRSs were applied to thyroid irAEs and CPI response in patients with NSCLC at MSK, VUMC, and DFCI.ResultsAmong 744 patients at MSK and VUMC, thyroid irAEs occurred in 13% and were associated with improved outcomes [progression-free survival adjusted HR (PFS aHR) = 0.68; 95% confidence interval (CI), 0.52-0.88]. The PRS for hypothyroidism developed from UK Biobank predicted hypothyroidism in the BioVU dataset in noncancer patients [OR per standard deviation (SD) = 1.33, 95% CI, 1.29-1.37; AUROC = 0.6]. The same PRS also predicted development of thyroid irAEs in both independent cohorts of patients treated with CPIs (HR per SD = 1.34; 95% CI, 1.08-1.66; AUROC = 0.6). The results were similar in the DFCI cohort. However, PRS for hypothyroidism did not predict CPI benefit.ConclusionsThyroid irAEs were associated with response to anti-PD-1 therapy. Genetic risk for hypothyroidism was associated with risk of developing thyroid irAEs. Additional studies are needed to determine whether other irAEs also have shared genetic risk with known autoimmune disorders and the association with treatment response.

Survival models are a popular tool for the analysis of time to event data with applications in medicine, engineering, economics, and many more. Advances like the Cox proportional hazard model have enabled researchers to better describe hazard rates for the occurrence of single fatal events, but are unable to accurately model competing events and transitions. Common phenomena are often better described through multiple states, for example: the progress of a disease modeled as healthy, sick and dead instead of healthy and dead, where the competing nature of death and disease has to be taken into account. Moreover, Cox models are limited by modeling assumptions, like proportionality of hazard rates and linear effects. Individual characteristics can vary significantly between observational units, like patients, resulting in idiosyncratic hazard rates and different disease trajectories. These considerations require flexible modeling assumptions. To overcome these issues, we propose the use of neural ordinary differential equations as a flexible and general method for estimating multi-state survival models by directly solving the Kolmogorov forward equations. To quantify the uncertainty in the resulting individual cause-specific hazard rates, we further introduce a variational latent variable model and show that this enables meaningful clustering with respect to multi-state outcomes as well as interpretability regarding covariate values. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting, Comment: 19 pages, 14 figures

Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patients. Recent data collections provide a comprehensive characterization of cancer cell data, including genetic data on copy number alterations (CNAs). We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach that encodes each cancer sample as a persistence diagram of topological features, i.e., high-dimensional voids represented in the data. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data and demonstrate the viability of some applications on finding substructures in cancer data as well as comparing similarity of cancer types.