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.
Highlights
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
Since we focus on cancer, we selected the largest cancer-specific gene-panel we could find, which was the Center for Personalized Cancer Treatment (CPCT, The Netherlands) mini cancer genome panel[15] comprising 1977 genes of which 1815 (92%) were available in the cell line panel[16]
Weighted Orthogonal Non-negative (WON)-parallel factor analysis (PARAFAC) decomposed the cube into three sparse matrices with 130 factors (Fig. 1c, S2, and S3; see Methods), where each factor has a gene-factor, a cell-factor, and a data type (DT)-factor
Summary
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. 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. The prime example of such resource is the Genomics of Drug Sensitivity in Cancer 1000 (GDSC1000) data[2], one of the largest and best-characterized cell lines screens publicly available It holds data on mutations, DNA copy number, and gene expression of 1000 cancer cell lines comprising 55 tumor types tested for 265 compounds. Genomics data is high dimensional (many features) and consist of multiple data types (mutation, DNA copy number alteration, and gene expression), which makes it difficult to construct interpretable prediction models[5]. WON-PARAFAC offers a new level of data integration that appreciates the links between samples and features and data types, and provides interpretable results while allowing for improved translation of in-vitro drug sensitivity to animal models of multiple cancer types
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