Pcawg Drivers

TL;DR: ActivePathways as mentioned in this paper is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes, which improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.

TL;DR: In this article, the authors performed multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types.

TL;DR: These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types.

TL;DR: Multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-Coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

TL;DR: An additive effects model derived from complex trait studies is adapted to show that aggregating the impact of putative passenger variants provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance).