bioRxivCancer BiologyPreprint
Pathway and network analysis of more than 2,500 whole cancer genomes
Matthew A. ReynaiD, David HaaniD, Marta Paczkowska, Lieven P.C. VerbekeiD, Miguel VazqueziD, Abdullah KahramaniD, Sergio Pulido-TamayoiD, Jonathan Barenboim, Lina WadiiD, Priyanka DhingraiD, Raunak ShresthaiD, Gad GetziD, Michael S. LawrenceiD, Jakob Skou PederseniD, Mark A. RubiniD, David A. WheeleriD, Søren BrunakiD, Jose MG IzarzugazaiD, Ekta KhuranaiD, Kathleen MarchaliD, Christian von MeringiD, S. Cenk SahinalpiD, Alfonso ValenciaiD, Jüri ReimandiD, Joshua M. StuartiD, , on behalf of the PCAWG Drivers and Functional Interpretation Group and the ICGC/TCGA Pan-Cancer Analysis of Whole Genome Network
Abstract
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed 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. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses 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.
- organism: human
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: statistical model
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