Patient-level prediction of oncology clinical trial outcomes using a calibrated pan-cancer foundation model for synthetic comparator arms and indirect treatment comparisons

We present a framework that calibrates patient-level SOC predictions from RWD to published clinical trial evidence. The framework has two complementary components: a pre-trained pan-cancer foundation model that enables transfer learning across biomarkers, indications, and treatments, enhancing the utility of small and sparse datasets; and a principled calibration procedure that anchors patient-level simulations to gold-standard aggregate trial evidence.