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

Yongsoo Kim; Tycho Bismeijer; Wilbert Zwart; Lodewyk F.A. Wessels; Daniel J. Vis

doi:10.1038/s41467-019-13027-2

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

Yongsoo Kim
, Tycho Bismeijer
, Wilbert Zwart (Corresponding author)
, Lodewyk F.A. Wessels (Corresponding author)
, Daniel J. Vis (Corresponding author)

Chemical Biology

Research output: Contribution to journal › Article › Academic › peer-review

11 Citations (Scopus)

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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.

Original language	English
Article number	5034
Number of pages	12
Journal	Nature Communications
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-13027-2
Publication status	Published - 6 Nov 2019

Funding

This research was supported by an Alpe d’HuZes/KWF Bas Mulder Award and a VIDI grant to WZ and Alpe d’HuZes/STD(12725)/ERC-synergy grant to LFAW. We express our gratitude to our colleagues from the Computational Cancer Biology group and the Zwart Lab for useful discussions and constructive criticism. Nanne Aben is thanked for sharing great insights. We acknowledge the RHPC facility for providing resources. All authors are part of the Oncode Institute.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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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.",

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AU - Vis, Daniel J.

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

Abstract

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UN SDGs

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