Each month Levy Library showcases the achievements of Mount Sinai faculty and researchers by highlighting an article and its altmetrics. Altmetrics are alternative measures of impact that capture non-traditional data like abstract views, article downloads, and social media activity.
This month we highlight Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. This study was written by a team of researchers including Mount Sinai’s John A. Martignetti (Genetics and Genomic Sciences), Robert Korst (Thoracic Surgery) and Peter Dottino (Gynecologic Oncology).
Citation: The Cancer Genome Atlas Research Network (2018). Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Reports, 23(1), 172-180.e3. DOI: 10.1016/j.celrep.2018.03.046
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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John A Martignetti MD, PhD
Robert Korst MD
Peter Dottino MD