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Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo

Author

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  • Yongsoo Kim

    (Oncode Institute, The Netherlands Cancer Institute
    Oncode Institute, The Netherlands Cancer Institute
    VU University Medical Center)

  • Tycho Bismeijer

    (Oncode Institute, The Netherlands Cancer Institute)

  • Wilbert Zwart

    (Oncode Institute, The Netherlands Cancer Institute
    Eindhoven University of Technology)

  • Lodewyk F. A. Wessels

    (Oncode Institute, The Netherlands Cancer Institute
    Delft University of Technology)

  • Daniel J. Vis

    (Oncode Institute, The Netherlands Cancer Institute)

Abstract

Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.

Suggested Citation

  • Yongsoo Kim & Tycho Bismeijer & Wilbert Zwart & Lodewyk F. A. Wessels & Daniel J. Vis, 2019. "Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13027-2
    DOI: 10.1038/s41467-019-13027-2
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