Abstract A003: An active learning platform for predictive oncology in rare cancers

Directly testing patient tissues ex vivo against panels of anti-cancer agents has been shown in multiple recent clinical trials to provide superior treatment guidance for patients with rare and high-risk cancers. All trials to date have focused on recommending a single agent, even though rationally designed combination therapies typically lead to better outcomes. The main bottleneck in these trials is the combinatorial explosion of exhaustively screening all combinations in a panel of drugs. We developed a new Bayesian active learning algorithm called BATCHIE that enables large-scale combination drug screens over huge libraries in cancer cell line experiments. Given a set of previous experiments, BATCHIE optimally designs the next batch of combination screens to maximize the utility of the batch. To bootstrap our predictive models, we collected and integrated more than 2M ex vivo drug screen results from two dozen published studies. The talk will conclude with initial results translating our platform into the clinic for patients with desmoplastic small round cell tumor, an ultra-rare cancer with no standard of care or targetable recurrent alterations.

Citation Format:

Christopher Tosh, Glorymar Ibanez Sanchez, Emily Stockfisch, Andrew Kung, Filemon Dela Cruz, Emily Slotkin, Wesley Tansey. An active learning platform for predictive oncology in rare cancers [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 A003.