Format

Send to

Choose Destination

See 1 citation found by title matching your search:

Nat Commun. 2018 Oct 8;9(1):4159. doi: 10.1038/s41467-018-06464-y.

Typing tumors using pathways selected by somatic evolution.

Wang S1, Ma J2,3, Zhang W2,3, Shen JP2,3,4, Huang J2,3,5, Peng J6,7, Ideker T8,9,10,11.

Author information

1
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
2
Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
3
The Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, La Jolla, CA, 92093, USA.
4
Moores UCSD Cancer Center, La Jolla, CA, 92093, USA.
5
Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA.
6
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. jianpeng@illinois.edu.
7
Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. jianpeng@illinois.edu.
8
Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA. tideker@ucsd.edu.
9
The Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, La Jolla, CA, 92093, USA. tideker@ucsd.edu.
10
Moores UCSD Cancer Center, La Jolla, CA, 92093, USA. tideker@ucsd.edu.
11
Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA. tideker@ucsd.edu.

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.

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center