Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes

Benjamin Raphael and colleagues report an analysis of altered subnetworks of somatic aberrations in TCGA pan-cancer data sets, including 3,281 samples from 12 cancer types, using a newly developed HotNet2 algorithm. They identify 16 significantly mutated subnetworks and provide a more comprehensive view into altered pathways, including those with known roles in cancer development. Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.