Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations across Pathways and Protein Complexes

By Mark D. M. Leiserson, Fabio Vandin, Hsin-Ta Wu, J. Dobson, Jonathan V. Eldridge, Jacob Thomas, A. Papoutsaki, Younhun Kim, Beifang Niu, M. McLellan and 9 more. Nature Genetics, 2014

867 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/364b33bc3dcb960198f5fabc662cdd60bf47407d

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Citations in the corpus

Pan-cancer genome and transcriptome analyses of 1,699 pediatric leukemias and solid tumors (2018, methodology): “edu/EVS/); (2) dbSNP (build 132); (3) St.”
Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “Similarly, TieDIE 55 and HotNet2, 56 an extension of HotNet, apply network diffusion to analyze multiple types of genomic alterations, and NetPathID 57 applies network diffusion to analyze CNVs in 16 types of cancers.”
Network propagation: a universal amplifier of genetic associations (2017, background): “However, ranking genes by mutation frequency alone performs poorly because driver and passenger genes can be mutated at similar frequencies, particularly in cohorts of a modest size 30 .”