Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

Background: Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients' care. Tremendous progress has been made. Results: In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to similar to 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions: We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

Automated summary

The study established predictive models for 453 drugs based on gene expression and drug sensitivity data from cancer cell lines, identifying known drug-gene interactions and novel associations. The models were applied to predict drug sensitivity in normal and tumor tissues, with a website created for data visualization and download. The approach demonstrated the prediction of tumor-specific drugs with higher sensitivity in tumors compared to normal tissues and sensitivity differences across breast cancer subtypes. Potential implications include preclinical drug testing and phase I clinical trial design if validated.