Abstract 4640: Identifying novel drivers of drug sensitivity using an AI-enabled composite biomarker - osimertinib sensitivity beyond EGFR

Abstract Background: Precision medicine (PM) has transformed cancer treatment but remains effective for only a subset of patients. Most tumors profiled with comprehensive genomic testing lack actionable biomarkers, and even when present, many patients fail to benefit from corresponding therapies. Some without recognized biomarkers respond in off-label contexts, highlighting the need for better biomarkers to capture drug response biology and deliver on the promise of PM. Osimertinib, a third-generation EGFR inhibitor, is used in conjunction with the single gene biomarker, EGFR. Response rates vary by EGFR variant and off-target activity has been reported. We hypothesized that integrating multi-modal data with machine learning could enhance osimertinib response predictions beyond the single-gene EGFR biomarker. Methods: We developed an AI/ML-enabled drug response prediction model that integrates clinical and molecular features with drug structure and functional data to generate drug response predictions. The model produces an AI-enabled composite biomarker (AI-CB) based on a small amount of clinical and molecular patient input data. Each prediction is paired with a proprietary Vulnerability Network™ (VN), providing insights into biological dependencies underlying response predictions. Osimertinib response predictions were evaluated retrospectively across multiple real-world (rw) NSCLC cohorts, including clinicogenomic data from liquid biopsy. The AI-CB was also validated in EGFR-negative NSCLC patient-derived xenografts (PDX). Results: Across multiple osimertinib-treated EGFR+ rw-NSCLC cohorts, the AI-CB accurately stratified patients by sensitivity to osimertinib, despite the presence of EGFR alterations in all patients. EGFR+ patients predicted to be insensitive showed a significant reduction in rw-overall survival compared to those predicted to be sensitive, indicating that osimertinib sensitivity may be impacted by more than just EGFR alterations. Compared to EGFR+ osimertinib insensitive patients, post-hoc and VN analysis across these two cohorts linked sensitivity to upregulation of pathways downstream of EGFR including NOTCH and TP63 and downregulation of RAS/RAF/MEK activity. The AI-CB was successfully validated in human PDX models without activating EGFR alterations. AI-CB application to AACR Project GENIE cohorts suggest that sensitivity to osimertinib could extend to a novel group of patients with NSCLC and beyond, e.g., colorectal cancer. Conclusion: ML enables the integration of diverse data types relevant to drug response, overcoming limitations of single-dimensional approaches. Incorporating AI-driven patient stratification models into drug development and clinical practice could greatly expand the utility of targeted therapies and create effective stratification strategies for drugs lacking adequate biomarkers. Citation Format: Maayan Baron, Felicia Kuperwaser, Colin Tang, Sunil Kumar, Dillon Tracy, Zong Miao, Olena Marchenko, Sepideh Foroutan, Amy Sheide, Nathaniel Tann, Samantha Pindak, Jacob Kaffey, Jordan Wolinsky, Sean Klei, Andrey Chursov, Brandon Funkhouser, Nick Lee, Patrick Bohan, Fahad Khan, Ripple Khera, Jean Michel Rouly, George Komatsoulis, Jeff Sherman, Emily Vucic, Rachael Brake. Identifying novel drivers of drug sensitivity using an AI-enabled composite biomarker - osimertinib sensitivity beyond EGFR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4640.