Quan, Wei, Henault, David, Zhang, Amy, Jang, Gun Ho, Light, Nicholas, Ji, Zongliang, Dodd, Anna, Wilson, Julie, Renouf, Daniel, Laheru, Daniel, Yu, Kenneth, Perez, Kimberly, Habowski, Amber, O'Kane, Grainne M, Gallinger, Steven, Tuveson, David, Jaffee, Elizabeth, Knox, Jennifer J, Krishnan, Rahul G, Fischer, Sandra, Haider, Masoom A, Notta, Faiyaz, Grant, Robert C (September 2025) External validation of a multimodal machine learning system to predict outcomes in advanced pancreatic cancer in the PASS-01 trial. In: AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions, 2025 Sep 28-Oct 1, Boston, MA.
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal disease with limited tools for predicting treatment response or survival. Our prior work, which applied machine learning to the COMPASS trial (NCT02750657), demonstrated that multimodal integration can enhance performance. Here, we evaluate model generalizability on an independent cohort from the PASS-01 clinical trial (NCT04469556). We developed predictive models using data from the COMPASS trial, incorporating clinical variables, histopathology image features, radiology-derived imaging features, RNA sequencing (RNA-seq), and whole-genome sequencing (WGS). Our updated pipeline applied TabPFN, a transformer-based model, using repeated 5-fold cross-validation. Fusion approaches included both early and late modality integration. We focused on predicting disease control rate (DCR). We compared model performance to the PurIST RNA-seq classifier, a strong baseline. The area under the receiver operating characteristic curve (AUC) was the primary metric. We externally validated the performance of models trained on COMPASS in the PASS-01 trial dataset. Among unimodal models, RNA-seq-based predictors achieved the highest AUC at 0.709 (95% CI: 0.595-0.820), significantly outperforming PurIST (p = 0.01). The performance of other unimodal models varied (clinical: 0.680; DNA: 0.527), with no significant difference compared to PurIST. The late fusion model, “MULTIPL”, integrated clinical, RNA, and DNA modalities and achieved the best overall performance at 0.733 (95% CI: 0.613-0.832), significantly outperforming PurIST (p = 0.002). The top 25th percentile of patients based on MULTIPL predicted DCR had significantly better prognosis (median overall survival 13.9 versus 8.6 months, hazard ratio 0.47 (95% CI: 0.28-0.78). The probability of DCR predicted by MULTIPL was correlated with the PurIST predictions of basal and classical transcriptomic subtypes (r = 0.63, p < 0.001), indicating a shared biology, which was further evidenced with SHapley Additive exPlanation interpretability analyses. Nonetheless, MULTIPL captured additional prognostic information, since PurIST was only modestly associated with DCR (AUC 0.55) and not significantly prognostic. Furthermore, the multimodal model was significantly associated with survival within the classical transcriptomic subtype. In conclusion, multimodal models trained on COMPASS data generalized to the PASS-01 trial in external validation. Late fusion of clinical, RNA, and DNA features achieved the best predictive performance for DCR and was also associated with survival outcomes, including within each transcriptomic subtype. In contrast to other models, which typically identify poor prognostic subgroups such as basal-like cancers, our multimodal model for DCR identifies a subset of patients with a more favourable prognosis. Together, these results demonstrate the potential of multimodal machine learning to improve prognostic modeling in advanced pancreatic cancer and guide personalized treatment strategies.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | diseases & disorders > cancer diseases & disorders diseases & disorders > cancer > cancer types > pancreatic cancer diseases & disorders > cancer > cancer types |
| CSHL Authors: | |
| Communities: | CSHL labs > Tuveson lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 28 September 2025 |
| Date Deposited: | 21 Oct 2025 12:39 |
| Last Modified: | 21 Oct 2025 12:39 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41985 |
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