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 () and Daniel J. Vis ()
Additional contact information
Yongsoo Kim: Oncode Institute, The Netherlands Cancer Institute
Tycho Bismeijer: Oncode Institute, The Netherlands Cancer Institute
Wilbert Zwart: Oncode Institute, The Netherlands Cancer Institute
Lodewyk F. A. Wessels: Oncode Institute, The Netherlands Cancer Institute
Daniel J. Vis: Oncode Institute, The Netherlands Cancer Institute

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-019-13027-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13027-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-13027-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().