Highlights
• AI-driven framework discovers predictive, rather than prognostic, biomarkers
• Framework outperforms existing approaches across real-world and clinical trial data
• Framework generates interpretable biomarkers to facilitate clinical actionability
• Retrospective improvement of patient selection for phase 3 immuno-oncology trials

Summary
Modern clinical trials can capture tens of thousands of clinicogenomic measurements per individual. Discovering predictive biomarkers, as opposed to prognostic markers, remains challenging. To address this, we present a neural network framework based on contrastive learning—the Predictive Biomarker Modeling Framework (PBMF)—that explores potential predictive biomarkers in an automated, systematic, and unbiased manner. Applied retrospectively to real clinicogenomic datasets, particularly for immuno-oncology (IO) trials, our algorithm identifies biomarkers of IO-treated individuals who survive longer than those treated with other therapies. We demonstrate how our framework retrospectively contributes to a phase 3 clinical trial by uncovering a predictive, interpretable biomarker based solely on early study data. Patients identified with this predictive biomarker show a 15% improvement in survival risk compared to those in the original trial. The PBMF offers a general-purpose, rapid, and robust approach to inform biomarker strategy, providing actionable outcomes for clinical decision-making.

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