Abstract 1936: MethylationToRegulation: A deep-learning approach to infer chromatin properties from DNA methylomes

Abstract

Abstract Gene regulation is critical for cell identity, and its dysregulation is a defining characteristic of common diseases including cancers. Although promoter activity is a strong predictor of gene expression, activities at other regulatory elements, including enhancers and super-enhancers (SE), are major contributors to gene regulation. For example, distinct transcription factor-regulating SEs define neuroblastoma (NB) subtypes with distinct clinical outcomes. However, technical limitations largely prevent ChIP-seq based enhancer/SE activity profiling in primary patient samples. We previously developed MethylationToActivity (M2A) and demonstrated that high-order DNA methylation (DNAm) features are strong predictors of promoter activity measured by ChIP-seq. This tool is important because genomewide DNAm assays are widely used in clinic to classify tumors and stratify patients on clinical trials. However, it is limited by its reliance on known gene position annotations. Here we present MethylationToRegulation (M2R), a method that utilizes a convolutional neural network (CNN)-based deep learning framework to infer intergenic enhancer/SE activities from DNA methylomes. We obtained paired WGBS and H3K27ac ChIP-seq data for 16 pediatric NB samples profiled in the Pediatric Cancer Genome Project. M2R was trained on 6 samples and tested on the remaining 10 samples. It achieved an average relative prediction accuracy of 84% on test samples when compared to the H3K27ac ChIP-seq replicate consistency from the ENCODE project. We adapted the Rank Ordering of Super-Enhancers algorithm to interpret H3K27ac signals inferred from DNA methylomes to identify SEs. M2R faithfully captured subtype-defining SEs with high specificity, including those associated with master NB transcription factors. Our results demonstrate that M2R accurately quantifies enhancer/SE activities and infers critical epigenetic marks from DNA methylomes. Application of M2R will enable the profiling of epigenetic dysregulation in patient tumor samples, seeking to improve clinical outcomes. Citation Format: Daniel K. Putnam, Brian J. Abraham, Xiang Chen. MethylationToRegulation: A deep-learning approach to infer chromatin properties from DNA methylomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1936.