Drug sensitivity prediction across cancer cell lines with a focus on sarcoma

Screening of drugs on cancer cell lines is a first step in the search for compounds leading to cancer cell death, providing the basis for follow-up investigations. Cell line-based drug sensitivity data can be used for predictive modeling, for example, in the context of drug repurposing. In this work, we report a machine learning framework for the systematic prediction of drug sensitivity based on transcriptomic data from cancer cell line screening. Three different categories of complementary classification models are introduced to predict cell line sensitivities to known drugs based on encoded gene expression profiles, predict activity of new drugs for given cell lines based on compound fingerprints, and predict responses of new cell lines to new drugs based on combined molecular representations and gene expression profiles. The models are found to have adequate predictive performance for cell-based data and are shown to be applicable to predict drug sensitivity for sarcoma cell lines, a rare form of cancer, for which data are limited. For the subset of sarcoma cell lines, tested drugs were also ranked based on cell line sensitivity rates. Taken together, our results suggest that the complementary machine learning models have potential for practical applications to search for new compounds for the treatment of different types of cancers including sarcoma.