TY - JOUR T1 - The cancer cell proteome and transcriptome predicts sensitivity to targeted and cytotoxic drugs JF - Life Science Alliance JO - Life Sci. Alliance DO - 10.26508/lsa.201900445 VL - 2 IS - 4 SP - e201900445 AU - Mattias Rydenfelt AU - Matthew Wongchenko AU - Bertram Klinger AU - Yibing Yan AU - Nils Blüthgen Y1 - 2019/08/01 UR - https://www.life-science-alliance.org/content/2/4/e201900445.abstract N2 - Tumors of different molecular subtypes can show strongly deviating responses to drug treatment, making stratification of patients based on molecular markers an important part of cancer therapy. Pharmacogenomic studies have led to the discovery of selected genomic markers (e.g., BRAFV600E), whereas transcriptomic and proteomic markers so far have been largely absent in clinical use, thus constituting a potentially valuable resource for further substratification of patients. To systematically assess the explanatory power of different -omics data types, we assembled a panel of 49 melanoma cell lines, including genomic, transcriptomic, proteomic, and pharmacological data, showing that drug sensitivity models trained on transcriptomic or proteomic data outperform genomic-based models for most drugs. These results were confirmed in eight additional tumor types using published datasets. Furthermore, we show that drug sensitivity models can be transferred between tumor types, although after correcting for training sample size, transferred models perform worse than within-tumor–type predictions. Our results suggest that transcriptomic/proteomic signals may be alternative biomarker candidates for the stratification of patients without known genomic markers. ER -