As precision oncology drives development toward narrower biomarker-defined populations, patient-level outcome prediction models can improve trial planning and decision-making. Methods that integrate both real world data (RWD) and recent trial evidence can produce better-calibrated predictions and enable clinical applications such as synthetic control arms, comparative effectiveness, and trial design optimization.
We developed a calibrated pan-cancer foundation model integrating patient-level RWD with summary-level clinical trial outcomes. The model uses a transformer-based architecture to capture joint distributions across thousands of clinical and genomic features. It was trained on over 300,000 tumor biopsy records with linked clinical data, primarily from RWD sources. The model generates synthetic patient-level cohorts conditional on user-specified I/E criteria and predicts outcomes under specified treatments. An information-geometric calibration procedure aligns these predictions with published trial baseline characteristics and outcome landmarks.
We validated the model across three Phase III settings: (1) To evaluate subgroup-level prediction from population-level calibration, we generated a synthetic cohort matching mNSCLC trial POSEIDON baseline characteristics and calibrated to its published control-arm OS. We then predicted OS across PD-L1 strata, histology, and KEAP1/STK11/KRAS mutation status and recapitulated published results: 90.9% (40/44) of median and 2 to 5-year OS estimates fell within 95% CIs, with median absolute deviation 2.7%. (2) To assess out-of-sample prediction in a genetic subgroup, we simulated a BRAF V600E mCRC cohort matching BREAKWATER baseline characteristics. The model was calibrated on prior unselected mCRC trials (XELOX, TRIBE) and applied without calibration to BREAKWATER or any BRAF-selected trial. Despite only five training-data patients meeting BREAKWATER I/E criteria, model-predicted OS matched observed values at 6, 12, and 18 months. (3) To demonstrate indirect head-to-head comparison without a randomized trial, we compared nab-paclitaxel plus gemcitabine (NG) and FOLFIRINOX in mPDAC. These regimens were evaluated in MPACT and PRODIGE4 respectively, with differing populations. We simulated the MPACT arm, then used entropy balancing to conform baseline characteristics to PRODIGE4, estimating NG outcomes in a healthier PRODIGE4-like population. This decomposed the published 80-day median OS gap: ∼14% was attributable to baseline demographics, with a residual 73-day FOLFIRINOX advantage.
We present a framework that calibrates patient-level OS predictions to published clinical trial evidence. Pan-cancer pretraining enables transfer learning across data sources, improving prediction in narrow populations with sparse data. The model can be further fine-tuned on data to support tailored predictions across biomarkers, indications, and treatments. Together, these capabilities provide a data-efficient approach to generating patient-level evidence for clinical applications.
Daniele Bertolini, Franklin Fuller, Jason Christopher, Jonathan Walsh, Samantha I . Liang, Aaron Smith. Patient-level prediction of trial outcomes with a calibrated pan-cancer foundation model [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr PR006.
D. Bertolini, F. Fuller, J. Christopher et al.· Clinical Cancer Research· 0 citations
As precision oncology drives development toward narrower biomarker-defined populations, patient-level outcome prediction models can improve trial planning and decision-making. Methods that integrate both real world data (RWD) and recent trial evidence can produce better-calibrated predictions and enable clinical applications such as synthetic control arms, comparative effectiveness, and trial design optimization.
We developed a calibrated pan-cancer foundation model integrating patient-level RWD with summary-level clinical trial outcomes. The model uses a transformer-based architecture to capture joint distributions across thousands of clinical and genomic features. It was trained on over 300,000 tumor biopsy records with linked clinical data, primarily from RWD sources. The model generates synthetic patient-level cohorts conditional on user-specified I/E criteria and predicts outcomes under specified treatments. An information-geometric calibration procedure aligns these predictions with published trial baseline characteristics and outcome landmarks.
We validated the model across three Phase III settings: (1) To evaluate subgroup-level prediction from population-level calibration, we generated a synthetic cohort matching mNSCLC trial POSEIDON baseline characteristics and calibrated to its published control-arm OS. We then predicted OS across PD-L1 strata, histology, and KEAP1/STK11/KRAS mutation status and recapitulated published results: 90.9% (40/44) of median and 2 to 5-year OS estimates fell within 95% CIs, with median absolute deviation 2.7%. (2) To assess out-of-sample prediction in a genetic subgroup, we simulated a BRAF V600E mCRC cohort matching BREAKWATER baseline characteristics. The model was calibrated on prior unselected mCRC trials (XELOX, TRIBE) and applied without calibration to BREAKWATER or any BRAF-selected trial. Despite only five training-data patients meeting BREAKWATER I/E criteria, model-predicted OS matched observed values at 6, 12, and 18 months. (3) To demonstrate indirect head-to-head comparison without a randomized trial, we compared nab-paclitaxel plus gemcitabine (NG) and FOLFIRINOX in mPDAC. These regimens were evaluated in MPACT and PRODIGE4 respectively, with differing populations. We simulated the MPACT arm, then used entropy balancing to conform baseline characteristics to PRODIGE4, estimating NG outcomes in a healthier PRODIGE4-like population. This decomposed the published 80-day median OS gap: ∼14% was attributable to baseline demographics, with a residual 73-day FOLFIRINOX advantage.
We present a framework that calibrates patient-level OS predictions to published clinical trial evidence. Pan-cancer pretraining enables transfer learning across data sources, improving prediction in narrow populations with sparse data. The model can be further fine-tuned on data to support tailored predictions across biomarkers, indications, and treatments. Together, these capabilities provide a data-efficient approach to generating patient-level evidence for clinical applications.
Daniele Bertolini, Franklin Fuller, Jason Christopher, Jonathan Walsh, Samantha I . Liang, Aaron Smith. Patient-level prediction of trial outcomes with a calibrated pan-cancer foundation model [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A030.
D. Bertolini, F. Fuller, J. Christopher et al.· Clinical Cancer Research· 0 citations