William Meyerson

TL;DR: The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

TL;DR: The authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery that discovers significantly enriched pathways across multiple datasets using statistical data fusion.

TL;DR: The comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes project is leveraged to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers, including undetected weak drivers.

TL;DR: A custom annotation within ENCODE for cancer is presented, highlighting a workflow that can help prioritise key elements in oncogenesis and targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the E NCODE resource.

TL;DR: A custom annotation for cancer-associated cell types is developed by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types to prioritize key elements and variants, in addition to regulators.