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

Author

Listed:
  • Su-In Lee

    (University of Washington
    University of Washington
    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
    University of Washington)

  • Elihu H. Estey

    (Fred Hutchinson Cancer Research Center
    University of Washington)

  • 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
    University of Washington)

  • Pamela S. Becker

    (University of Washington
    Fred Hutchinson Cancer Research Center
    University of Washington)

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.

Suggested Citation

  • 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 Bl, 2018. "A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02465-5
    DOI: 10.1038/s41467-017-02465-5
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    Cited by:

    1. Zamir G Merali & Christopher D Witiw & Jetan H Badhiwala & Jefferson R Wilson & Michael G Fehlings, 2019. "Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-12, April.
    2. David Wang & Mathieu Quesnel-Vallieres & San Jewell & Moein Elzubeir & Kristen Lynch & Andrei Thomas-Tikhonenko & Yoseph Barash, 2023. "A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. Markus Eyting, 2020. "A Random Forest a Day Keeps the Doctor Away," Working Papers 2026, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    4. Asma Chaibi & Imed Zaiem, 2022. "Doctor Resistance of Artificial Intelligence in Healthcare," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(1), pages 1-13, January.

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