A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

Su-In Lee (), Safiye Celik, Benjamin A. Logsdon, Scott M. Lundberg, Timothy J. Martins, Vivian G. Oehler, Elihu H. Estey, Chris P. Miller, Sylvia Chien, Jin Dai, Akanksha Saxena, C. Anthony Blau and Pamela S. Becker
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Su-In Lee: University of Washington
Safiye Celik: University of Washington
Benjamin A. Logsdon: Sage Bionetworks
Scott M. Lundberg: University of Washington
Timothy J. Martins: University of Washington
Vivian G. Oehler: Fred Hutchinson Cancer Research Center
Elihu H. Estey: Fred Hutchinson Cancer Research Center
Chris P. Miller: University of Washington
Sylvia Chien: University of Washington
Jin Dai: University of Washington
Akanksha Saxena: University of Washington
C. Anthony Blau: University of Washington
Pamela S. Becker: University of Washington

Nature Communications, 2018, vol. 9, issue 1, 1-13

Abstract: Abstract Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene’s potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.

Date: 2018
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DOI: 10.1038/s41467-017-02465-5

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