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.
The current international cancer genomic sequencing landscape
BACKGROUND:
While next generation sequencing has enhanced our understanding of the biological basis of malignancy, current knowledge on global practices for sequencing cancer samples is limited. To address this deficiency, we developed a survey to provide a snapshot of current sequencing activities globally, identify barriers to data sharing and use this information to develop sustainable solutions for the cancer research community.
Continue reading The current international cancer genomic sequencing landscape
A Landscape of Pharmacogenomic Interactions in Cancer
Research published in Cell on July 7th has shown that patient-derived cancer cell lines harbor most of the same genetic changes found in patients’ tumors, and could be used to learn how tumors are likely to respond to new drugs, increasing the success rate for developing new personalized cancer treatments.
Scientists from the Netherlands Cancer Institute (NKI), the Wellcome Trust Sanger Institute (WTSI), and the European Bioinformatics Institute (EMBL-EBI) discovered a strong link between many of the mutations commonly found in patient cancer samples and sensitivity to specific anti-cancer drugs.
In this systematic, large-scale study, the researchers combined molecular data from tumors and cancer cell lines with drug sensitivity measurements. First, they identified molecular alterations strongly associated with cancer in more than 11,000 tumors across 29 different tumor types. Then they mapped these cancer relevant alterations onto 1000 cancer cell lines and determined whether these alterations explain the response of the cell lines to 265 anti-cancer drugs.
‘These data will help advance personalized cancer medicine by guiding researchers towards cancer specific molecular alterations that clearly associate with drug sensitivity. After further validation, this will allow the creation of clinical trials specifically tailored to well-defined subgroups of patients. Ultimately, this will inform the scientific community about potential anti-cancer drugs given a molecular profile of a tumor’, says joint first author of the study Dr Daniel J. Vis, research fellow at the NKI.
Continue reading A Landscape of Pharmacogenomic Interactions in Cancer
Estimating IC50 values
Recently I published a paper on the estimation of IC50 values. The central idea is that it is better to estimate the sensitivity of all items (here, cell lines / compounds) simultaneously compared to one-by-one. This allows us to borrow strength across all the observations and thereby improve stability and accuracy of the estimates. This page intends to give a high-level overview of the proposed method, the peer reviewed publication can be found here (PMID 27180993, Pharmacogenomics, May 2016, Vol. 17, No. 7, Pages 691-700). The associated script/computer code can be found here.
Stelvio for Life
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