TY - JOUR
T1 - Pathway and network analysis of more than 2500 whole cancer genomes
AU - PCAWG Drivers and Functional Interpretation Working Group
AU - PCAWG Consortium
AU - Reyna, Matthew A.
AU - Haan, David
AU - Paczkowska, Marta
AU - Verbeke, Lieven P.C.
AU - Vazquez, Miguel
AU - Kahraman, Abdullah
AU - Pulido-Tamayo, Sergio
AU - Barenboim, Jonathan
AU - Wadi, Lina
AU - Dhingra, Priyanka
AU - Shrestha, Raunak
AU - Getz, Gad
AU - Lawrence, Michael S.
AU - Pedersen, Jakob Skou
AU - Rubin, Mark A.
AU - Wheeler, David A.
AU - Brunak, Søren
AU - Izarzugaza, Jose M.G.
AU - Khurana, Ekta
AU - Marchal, Kathleen
AU - von Mering, Christian
AU - Sahinalp, S. Cenk
AU - Valencia, Alfonso
AU - Abascal, Federico
AU - Amin, Samirkumar B.
AU - Bader, Gary D.
AU - Bandopadhayay, Pratiti
AU - Beroukhim, Rameen
AU - Bertl, Johanna
AU - Boroevich, Keith A.
AU - Busanovich, John
AU - Campbell, Peter J.
AU - Carlevaro-Fita, Joana
AU - Chakravarty, Dimple
AU - Chan, Calvin Wing Yiu
AU - Chen, Ken
AU - Choi, Jung Kyoon
AU - Deu-Pons, Jordi
AU - Diamanti, Klev
AU - Feuerbach, Lars
AU - Fink, J. Lynn
AU - Fonseca, Nuno A.
AU - Frigola, Joan
AU - Gambacorti-Passerini, Carlo
AU - Garsed, Dale W.
AU - Gerstein, Mark
AU - Guo, Qianyun
AU - Gut, Ivo G.
AU - Hamilton, Mark P.
AU - Raphael, Benjamin J.
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 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 find 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 is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. 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.
AB - 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. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 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 find 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 is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. 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.
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U2 - 10.1038/s41467-020-14367-0
DO - 10.1038/s41467-020-14367-0
M3 - Article
C2 - 32024854
AN - SCOPUS:85079060258
SN - 2041-1723
VL - 11
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 729
ER -