Abstract
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 genetic interactions and uses them to predict a patient’s response to a variety of therapies in multiple cancer types, without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: Personalized Oncology (PO), 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 performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient’s treatment response across multiple therapies and cancer types (obtaining an aggregate odds ratio of 2.59). Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable to that of supervised predictors developed for specific indications and drugs, without requiring specific response data for training. In combination with the IFN-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting patients’ response to immune checkpoint therapy. In the CTD scenario, we demonstrate that ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies (mAb) by excluding non-responders, while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. In sum, in this retrospective study, ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.
Competing Interest Statement
G.D., E.D.S, E.E., D.S.B.Z, O.T., E.M., E.E., B.T., P.V., T.B. and R.A. are employees of Pangea Biomed. I.M. is a paid consultant of Pangea Biomed. E.S. is the Chairman of the Board of Pangea Biomed.
Footnotes
We have considerably widened the comparison of ENLIGHT to SELECT and other biomarkers, including studying the results of combining ENLIGHT with other biomarkers, and have moved this analysis and corresponding figures to the main text. We now have a new figure devoted to comprehensive benchmarking (Figure 3, with 4 panels).