Machine learning detects pan-cancer Ras pathway activation in The Cancer Genome Atlas Journal Article


Authors: Way, G. P.; Sanchez-Vega, F.; La, K.; Armenia, J.; Chatila, W. K.; Luna, A.; Sander, C.; Cherniack, A. D.; Mina, M.; Ciriello, G.; Schultz, N.; The Cancer Genome Atlas Research Network; Sanchez, Y.; Greene, C. S.
Article Title: Machine learning detects pan-cancer Ras pathway activation in The Cancer Genome Atlas
Abstract: Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these “hidden responders” may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines. © 2018 The Author(s)
Keywords: gene expression; kras; drug sensitivity; ras; nf1; hras; nras; tcga; machine learning; pan-cancer
Journal Title: Cell Reports
Volume: 23
Issue: 1
ISSN: 2211-1247
Publisher: Cell Press  
Date Published: 2018-04-03
Start Page: 172
End Page: 180.e3
Language: English
DOI: 10.1016/j.celrep.2018.03.046
PROVIDER: scopus
PMCID: PMC5918694
PUBMED: 29617658
DOI/URL:
Notes: Article -- Export Date: 1 June 2018 -- Source: Scopus
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MSK Authors
  1. Nikolaus D Schultz
    196 Schultz
  2. Joshua   Armenia
    22 Armenia
  3. Konnor C La
    7 La