Typing tumors using pathways selected by somatic evolution

Sheng Wang, Jianzhu Ma, Wei Zhang, John Paul Shen, Justin Huang, Jian Peng () and Trey Ideker ()
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Sheng Wang: University of Illinois at Urbana-Champaign
Jianzhu Ma: University of California San Diego
Wei Zhang: University of California San Diego
John Paul Shen: University of California San Diego
Justin Huang: University of California San Diego
Jian Peng: University of Illinois at Urbana-Champaign
Trey Ideker: University of California San Diego

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: Abstract Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient’s tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data.

Date: 2018
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DOI: 10.1038/s41467-018-06464-y

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