Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 67,545 bioRxiv papers from 297,698 authors.
Pharmacogenomic databases of drug response and genomics data in tumor cell lines allow the development of pan-cancer (i.e. tissue-agnostic) predictions of drug response. However, it is unclear whether between-tissue differences in both drug response and molecular characteristics bias pan-cancer drug response predictions. Using two datasets containing 346 and 504 cell lines with MEK inhibitor (MEKi) response data and RNA, SNP, and CNV data, we show between-tissue differences produced confounding effects that increase apparent performance in pan-cancer MEKi response predictions. We estimate that 45-58% of the variance in pan-cancer prediction scores was driven by accurate between-tissue predictions, rather than inter-individual predictions within a tissue. Nevertheless, MEKi response is predicted as well or better by pan-cancer approaches compared to tissue-specific approaches. Our results demonstrate strong between-tissue effects in tissue-agnostic drug response prediction models, but also highlight how drug response may invoke shared regulatory mechanisms although different cancer types arise in tissue-specific routes.
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