Abstract IA004: Functional Precision Oncology in Rare Cancers: From Biobank to Drug Discovery
Rare cancers collectively account for ∼25% of all cancer diagnoses yet remain underserved due to limited biological understanding, a lack of preclinical models, and challenges associated with conducting clinical trials. Overcoming these barriers requires both the development of rare cancer research resources and new approaches for identifying therapeutic vulnerabilities from limited patient samples. This presentation will highlight how rare cancer biorepositories, patient-derived tumor models, large-scale polypharmacology datasets, and machine learning approaches can be integrated to accelerate therapeutic discovery and functional precision oncology. A rare cancer biorepository containing cryopreserved patient specimens, molecular profiling data, patient-derived microtumors, and xenograft models provides a foundation for studying tumor biology across diverse rare malignancies and generating clinically relevant experimental systems. To enable systematic therapeutic discovery, we developed KIRHub, a comprehensive functional atlas of FDA-approved kinase inhibitors and oncogenic kinase variants that supports characterization of kinase inhibitor polypharmacology, drug repurposing, and mechanism discovery. This approach identified Polo-like kinase 1 (PLK1) as a therapeutic vulnerability in fibrolamellar carcinoma, an ultra-rare liver cancer driven by the DNAJB1-PRKACA fusion, illustrating how functional and computational approaches can uncover actionable dependencies in rare cancers. Patient-derived microtumor models that preserve key features of the native tumor microenvironment further enable rapid ex vivo drug testing and identification of therapeutic vulnerabilities that may not be captured in conventional cancer cell lines. Building on these models, SmartMatch integrates microtumor screening with machine learning-based prediction of therapeutic response. By leveraging responses to a focused set of compounds, SmartMatch prioritizes treatment options across thousands of drugs while delivering clinically actionable results within seven days. Together, these examples demonstrate how experimental and computational modeling approaches can be combined to accelerate therapeutic discovery, identify actionable vulnerabilities, and expand treatment opportunities for patients with rare and treatment-refractory cancers. Taran Gujral. Functional Precision Oncology in Rare Cancers: From Biobank to Drug Discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr IA004.