Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

G. P. Way, F. Sanchez-Vega, K. La, J. Armenia, W. K. Chatila, A. Luna, C. Sander, A. D. Cherniack, M. Mina, G. Ciriello, N. Schultz, Cancer Genome Atlas Research Network, Y. Sanchez, C. S. Greene, M. (come contributors) Marino

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)172-180.e3
JournalCell Reports
Volume23
Issue number1
DOIs
Publication statusPublished - Apr 3 2018
Externally publishedYes

Keywords

  • Gene expression
  • HRAS
  • KRAS
  • NF1
  • NRAS
  • Ras
  • TCGA
  • drug sensitivity
  • machine learning
  • pan-cancer

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