TY - JOUR
T1 - Network-Based Coverage of Mutational Profiles Reveals Cancer Genes
AU - Hristov, Borislav H.
AU - Singh, Mona
N1 - Funding Information:
This work is partly supported by the NIH ( CA208148 to M.S.) and the Forese Family Fund for Innovation. An early version of this paper was submitted to and peer reviewed at the 2017 Annual International Conference on Research in Computational Molecular Biology (RECOMB). The manuscript was revised and then independently further reviewed at Cell Systems.
Publisher Copyright:
© 2017 The Authors
PY - 2017/9/27
Y1 - 2017/9/27
N2 - A central goal in cancer genomics is to identify the somatic alterations that underpin tumor initiation and progression. While commonly mutated cancer genes are readily identifiable, those that are rarely mutated across samples are difficult to distinguish from the large numbers of other infrequently mutated genes. We introduce a method, nCOP, that considers per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are altered across (i.e., “cover”) a large fraction of individuals. By analyzing 6,038 samples across 24 different cancer types, we demonstrate that nCOP is highly effective in identifying cancer genes, including those with low mutation frequencies. Overall, our work demonstrates that combining per-individual mutational information with interaction networks is a powerful approach for tackling the mutational heterogeneity observed across cancers. Cancer-relevant genes, including those rarely mutated across samples, can be effectively identified by considering per-individual mutational profiles in the context of interaction networks and uncovering small connected subnetworks of genes, presumably participating in shared processes, that together are altered across (i.e., “cover”) a large fraction of individuals.
AB - A central goal in cancer genomics is to identify the somatic alterations that underpin tumor initiation and progression. While commonly mutated cancer genes are readily identifiable, those that are rarely mutated across samples are difficult to distinguish from the large numbers of other infrequently mutated genes. We introduce a method, nCOP, that considers per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are altered across (i.e., “cover”) a large fraction of individuals. By analyzing 6,038 samples across 24 different cancer types, we demonstrate that nCOP is highly effective in identifying cancer genes, including those with low mutation frequencies. Overall, our work demonstrates that combining per-individual mutational information with interaction networks is a powerful approach for tackling the mutational heterogeneity observed across cancers. Cancer-relevant genes, including those rarely mutated across samples, can be effectively identified by considering per-individual mutational profiles in the context of interaction networks and uncovering small connected subnetworks of genes, presumably participating in shared processes, that together are altered across (i.e., “cover”) a large fraction of individuals.
KW - TCGA
KW - cancer driver genes
KW - cancer genomes
KW - mutation frequency
KW - network-based analysis
KW - pathways
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U2 - 10.1016/j.cels.2017.09.003
DO - 10.1016/j.cels.2017.09.003
M3 - Article
C2 - 28957656
AN - SCOPUS:85031126233
SN - 2405-4712
VL - 5
SP - 221-229.e4
JO - Cell Systems
JF - Cell Systems
IS - 3
ER -