Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

A subset of lung cancer cases shows robust signs of homologous recombination deficiency associated genomic mutational signatures.

PARP inhibitors are approved for the treatment of solid tumor types that frequently harbor alterations in the key homologous recombination (HR) genes, BRCA1/2. Other tumor types, such as lung cancer, may also be HR deficient, but the frequency of such cases is less well characterized. Specific DNA aberration profiles (mutational signatures) are induced by homologous recombination deficiency (HRD) and their presence can be used to assess the presence or absence of HR deficiency in a given tumor biopsy even in the absence of an observed alteration of an HR gene. We derived various HRD-associated mutational signatures from whole-genome and whole-exome sequencing data in the lung adenocarcinoma and lung squamous carcinoma cases from TCGA, and in a patient of ours with stage IVA lung cancer with exceptionally good response to platinum-based therapy, and in lung cancer cell lines. We found that a subset of the investigated cases, both with and without biallelic loss of BRCA1 or BRCA2, showed robust signs of HR deficiency. The extreme platinum responder case also showed a robust HRD-associated genomic mutational profile. HRD-associated mutational signatures were also associated with PARP inhibitor sensitivity in lung cancer cell lines. Consequently, lung cancer cases with HRD, as identified by diagnostic mutational signatures, may benefit from PARP inhibitor therapy.

Strand Orientation Bias Detector to determine the probability of FFPE sequencing artifacts.

Formalin-fixed paraffin-embedded tissue, the most common tissue specimen stored in clinical practice, presents challenges in the analysis due to formalin-induced artifacts. Here, we present Strand Orientation Bias Detector (SOBDetector), a flexible computational platform compatible with all the common somatic SNV-calling pipelines, designed to assess the probability whether a given detected mutation is an artifact. The underlying predictor mechanism is based on the posterior distribution of a Bayesian logistic regression model trained on The Cancer Genome Atlas whole exomes. SOBDetector is a freely available cross-platform program, implemented in Java 1.8.

Human endogenous retroviruses form a reservoir of T cell targets in hematological cancers.

Human endogenous retroviruses (HERV) form a substantial part of the human genome, but mostly remain transcriptionally silent under strict epigenetic regulation, yet can potentially be reactivated by malignant transformation or epigenetic therapies. Here, we evaluate the potential for T cell recognition of HERV elements in myeloid malignancies by mapping transcribed HERV genes and generating a library of 1169 potential antigenic HERV-derived peptides predicted for presentation by 4 HLA class I molecules. Using DNA barcode-labeled MHC-I multimers, we find CD8+ T cell populations recognizing 29 HERV-derived peptides representing 18 different HERV loci, of which HERVH-5, HERVW-1, and HERVE-3 have more profound responses; such HERV-specific T cells are present in 17 of the 34 patients, but less frequently in healthy donors. Transcriptomic analyses reveal enhanced transcription of the HERVs in patients; meanwhile DNA-demethylating therapy causes a small and heterogeneous enhancement in HERV transcription without altering T cell recognition. Our study thus uncovers T cell recognition of HERVs in myeloid malignancies, thereby implicating HERVs as potential targets for immunotherapeutic therapies.

Characterization of the enhancer and promoter landscape of inflammatory bowel disease from human colon biopsies.

Inflammatory bowel disease (IBD) is a chronic intestinal disorder, with two main types: Crohn's disease (CD) and ulcerative colitis (UC), whose molecular pathology is not well understood. The majority of IBD-associated SNPs are located in non-coding regions and are hard to characterize since regulatory regions in IBD are not known. Here we profile transcription start sites (TSSs) and enhancers in the descending colon of 94 IBD patients and controls. IBD-upregulated promoters and enhancers are highly enriched for IBD-associated SNPs and are bound by the same transcription factors. IBD-specific TSSs are associated to genes with roles in both inflammatory cascades and gut epithelia while TSSs distinguishing UC and CD are associated to gut epithelia functions. We find that as few as 35 TSSs can distinguish active CD, UC, and controls with 85% accuracy in an independent cohort. Our data constitute a foundation for understanding the molecular pathology, gene regulation, and genetics of IBD.