Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% -- 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes

Integrated genomic characterization of adrenocortical carcinoma.

International audienceAdrenocortical carcinomas (ACCs) are aggressive cancers originating in the cortex of the adrenal gland. Despite overall poor prognosis, ACC outcome is heterogeneous. We performed exome sequencing and SNP array analysis of 45 ACCs and identified recurrent alterations in known driver genes (CTNNB1, TP53, CDKN2A, RB1 and MEN1) and in genes not previously reported in ACC (ZNRF3, DAXX, TERT and MED12), which we validated in an independent cohort of 77 ACCs. ZNRF3, encoding a cell surface E3 ubiquitin ligase, was the most frequently altered gene (21%) and is a potential new tumor suppressor gene related to the β-catenin pathway. Our integrated genomic analyses further identified two distinct molecular subgroups with opposite outcome. The C1A group of ACCs with poor outcome displayed numerous mutations and DNA methylation alterations, whereas the C1B group of ACCs with good prognosis displayed specific deregulation of two microRNA clusters. Thus, aggressive and indolent ACCs correspond to two distinct molecular entities driven by different oncogenic alterations