Abstract A014: Machine Learning-Guided Discovery of Drug Combinations Targeting PI3K and mTOR Pathways Using Cell Line and PDX Data

Abstract Combinational cancer therapies can overcome drug resistance, minimize dosage and toxicity, and enhance therapeutic efficacy. However, identifying effective drug combinations remains a significant challenge. Although machine learning (ML) models trained on high-throughput screening data offer a promising strategy, their clinical translation is often limited by discrepancies between in vitro cell line data and patient outcomes. In this study, we present a novel approach that improves the predictive performance of ML models by incorporating pharmacological data from patient-derived xenograft (PDX) models, which more accurately reflect clinical responses. Leveraging gene expression profiles, drug structure information, and ML models based on the high throughput datasets, we predicted effective drug combinations on proprietary PDX models developed at Certic Oncology. Drug combinations targeting the PI3K and mTOR pathways consistently ranked among top candidates across multiple KRAS G12 mutant models, including those derived from non-small cell lung cancer (NSCLC) and gastric cancer. We experimentally evaluated six PI3K inhibitors and four mTOR inhibitors across 13 KRAS G12 mutant PDX models spanning lung, gastric, pancreatic, and colorectal cancers. These results enabled the selection of seven gene biomarkers for further ML model refinement. Retraining the models with PDX-derived data and the expression profiles of the biomarkers significantly improved the predictive accuracy, achieving a Pearson’s correlation coefficient of 0.63 in forecasting the efficacy of PI3K–mTOR combinations on novel KRAS G12 PDX models. Notably, the combination of everolimus and alpelisib—prioritized by our model—has been independently validated in literature and is currently in clinical trials for solid tumors. Citation Format: Yuan-Hung Chien, Raffaella Pippa, Warren Andrews, Long Do. Machine Learning-Guided Discovery of Drug Combinations Targeting PI3K and mTOR Pathways Using Cell Line and PDX Data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A014.

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