Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Here we present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genomic interactions and uses them to predict patients’ response to a variety of therapies in multiple cancer types without training on previous response data. We study ENLIGHT in two translationally oriented scenarios, Personalized Medicine (PM) , aimed at prioritizing treatments for a single patient, and Clinical Trial Design (CTD) , selecting the most likely responders in a patient cohort. Evaluating ENLIGHT’s PM performance on 21 blinded clinical trial datasets, we show that it can effectively predict treatment response across multiple therapies and cancer types (obtaining an odds ratio of 2.59), substantially improving upon SELECT, a previously published transcriptomics-based approach, and performing as well as supervised predictors developed for specific indications and drugs, but on a much broader array of therapies and indications. In the CTD scenario, ENLIGHT can markedly enhance the success of clinical trials by excluding non-responders while achieving more than 90% of the response rate attainable under an optimal exclusion strategy. In sum, ENLIGHT is one of the first approaches to demonstrably predict therapeutic response across multiple cancer types by leveraging the transcriptome. ### Competing Interest Statement G.D., E.S., E.E., D.B., O.T., E.M., E.E., T.B. and R.A. are employees of Pangea Biomed Ltd. E.S. is the chairman of Pangea Biomed Ltd. I.M. is a paid consultant of Pangea Biomed Ltd.