Abstract B034: Radical collaboration in rare oncology: Accelerating therapeutic validation through federated learning and remote precision master protocols

Abstract

Rare malignancies, defined by an incidence of <6 cases per 100,000 individuals per year, collectively constitute 25% of the global cancer burden. However, progress is hindered by the "diagnostic odyssey" and structural barriers that concentrate clinical trials in urban academic centers, excluding 32 million rural Americans and leading to premature termination of up to 40% of rare cancer trials. This study evaluates the integration of federated learning architecture and decentralized master protocols to overcome these systemic challenges. We present technical benchmarks from the Cancer AI Alliance (CAIA), which utilizes the NVIDIA FLARE™ ecosystem to train predictive models on de-identified data from over one million patients across multiple institutions. This decentralized approach circumvents privacy constraints, allowing for the identification of rare genomic signatures and treatment resistance patterns with a tenfold increase in speed. Furthermore, we analyze data from the TCF-001 TRACK trial, a fully remote precision genomics study. Results demonstrate that utilizing multidisciplinary Virtual Molecular Tumor Boards (VMTBs) to interpret comprehensive genomic profiling (CGP) achieves high therapeutic matching scores (≥ 50%) for 41% of participants, significantly correlating with improved progression-free and overall survival in refractory cohorts. Disease-specific analysis highlights the impact of targeted PKC inhibition with darovasertib in primary uveal melanoma, achieving a 95% eye preservation rate among responders and establishing a vision-sparing neoadjuvant paradigm. In chordoma, the structural elucidation of the driver Brachyury (TBXT) in early 2025 has transitioned the field from "undruggable" hypotheses to fragment-based ligand optimization and the opening of first-in-human TCR T-cell trials in 2026. Complementary advancements in ultrasensitive liquid biopsy (superRCA) allow for the detection of minimal residual disease at tumor fractions below 0.01%, facilitating early interceptive treatment of relapse. Together, these technological and methodological shifts signal a transition toward "Precision Medicine 2.0", where spatial multi-omics and AI-driven stratification ensure diagnostic equity and therapeutic innovation for all patients, regardless of cancer rarity. Methods: This research synthesized evidence from the TCF-001 TRACK remote clinical trial, longitudinal data from the OptimUM-09 uveal melanoma program, and the technical implementation benchmarks of the CAIA federated learning platform. Generative AI was used in the development of this abstract to synthesize complex multi-institutional data and ensure stylistic alignment with 2026 AACR submission guidelines. Debanjan Gangopadhyay, Debanjan Gangopadhyay. Radical collaboration in rare oncology: Accelerating therapeutic validation through federated learning and remote precision master protocols [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 B034.