AI-based predictive biomarker discovery via contrastive learning retrospectively improves clinical trial outcome

Modern clinical trials can capture tens of thousands of clinicogenomic measurements per individual. Employing manual approaches to discover predictive biomarkers, as differentiated from prognostic markers, is a challenging task. To address this challenge, we present an automated neural network framework based on contrastive learning, which we have named the predictive biomarker modeling framework (PBMF). This general-purpose framework explores potential predictive biomarkers in a systematic and unbiased manner, as demonstrated in simulated “ground truth” synthetic scenarios resembling clinical trials, and clinical studies for breast and lung cancer. Applied retrospectively to real clinicogenomic data sets, particularly in the complex field of immunooncology (IO) predictive biomarker discovery, our algorithm successfully found biomarkers that identify IO-treated individuals who survive longer than those treated with chemotherapy. In a retrospective analysis, we demonstrated how our framework could have contributed to a phase 3 clinical trial (NCT02008227) by uncovering a predictive biomarker based solely on early study data. Patients identified with this predictive biomarker had a 15% improvement in survival risk, as compared to those of the original trial. This improvement was achieved with a simple, interpretable decision tree generated via PBMF knowledge distillation. Our framework offers a rapid and robust approach to inform biomarker strategy, providing actionable outcomes for clinical decision-making.