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

Publication ,  Journal Article
Way, GP; Sanchez-Vega, F; La, K; Armenia, J; Chatila, WK; Luna, A; Sander, C; Cherniack, AD; Mina, M; Ciriello, G; Schultz, N; Sanchez, Y ...
Published in: Cell Rep
April 3, 2018

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.

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Published In

Cell Rep

DOI

EISSN

2211-1247

Publication Date

April 3, 2018

Volume

23

Issue

1

Start / End Page

172 / 180.e3

Location

United States

Related Subject Headings

  • ras Proteins
  • Signal Transduction
  • Neoplasms
  • Machine Learning
  • Humans
  • Genome, Human
  • Gene Expression Regulation, Neoplastic
  • Cell Line, Tumor
  • 31 Biological sciences
  • 1116 Medical Physiology
 

Citation

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Way, G. P., Sanchez-Vega, F., La, K., Armenia, J., Chatila, W. K., Luna, A., … Greene, C. S. (2018). Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep, 23(1), 172-180.e3. https://doi.org/10.1016/j.celrep.2018.03.046
Way, Gregory P., Francisco Sanchez-Vega, Konnor La, Joshua Armenia, Walid K. Chatila, Augustin Luna, Chris Sander, et al. “Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas.Cell Rep 23, no. 1 (April 3, 2018): 172-180.e3. https://doi.org/10.1016/j.celrep.2018.03.046.
Way GP, Sanchez-Vega F, La K, Armenia J, Chatila WK, Luna A, et al. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep. 2018 Apr 3;23(1):172-180.e3.
Way, Gregory P., et al. “Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas.Cell Rep, vol. 23, no. 1, Apr. 2018, pp. 172-180.e3. Pubmed, doi:10.1016/j.celrep.2018.03.046.
Way GP, Sanchez-Vega F, La K, Armenia J, Chatila WK, Luna A, Sander C, Cherniack AD, Mina M, Ciriello G, Schultz N, Cancer Genome Atlas Research Network, Sanchez Y, Greene CS. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep. 2018 Apr 3;23(1):172-180.e3.
Journal cover image

Published In

Cell Rep

DOI

EISSN

2211-1247

Publication Date

April 3, 2018

Volume

23

Issue

1

Start / End Page

172 / 180.e3

Location

United States

Related Subject Headings

  • ras Proteins
  • Signal Transduction
  • Neoplasms
  • Machine Learning
  • Humans
  • Genome, Human
  • Gene Expression Regulation, Neoplastic
  • Cell Line, Tumor
  • 31 Biological sciences
  • 1116 Medical Physiology