Multimodal Machine Learning for Predicting Outcomes in the PASS-01 Trial of Systemic Therapy for Metastatic Pancreatic Cancer
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Purpose
Modified FOLFIRINOX (FFX) and gemcitabine plus nab-paclitaxel (GNP) are standard first-line treatments for metastatic pancreatic ductal adenocarcinoma (PDAC), but no validated biomarker guides treatment selection. We developed MULTIPL, a multimodal machine learning system, and established the PASS-01 Challenge to benchmark prognostic and predictive biomarkers.
Patients and Methods
MULTIPL was trained in the COMPASS study (N=268), integrating clinical, digitized histopathology, whole-genome, and RNA-seq data. MULTIPL, PurIST, hENT1 expression, and HRDetect were evaluated in the PASS-01 trial, a randomized phase II trial of FFX versus GNP (N=160), within the Challenge. The primary endpoint was differential treatment benefit measured by concordance-for-benefit for progression-free survival.
Results
MULTIPL had the highest concordance index for OS among individually evaluated biomarkers (0.595; 95% confidence interval [CI], 0.55–0.65) and separated high-versus low-risk patients (hazard ratio, 1.62; 95% CI, 1.13–2.33; P =0.009). Patients recommended for GNP by MULTIPL had significantly longer OS with GNP than with FFX (hazard ratio, 0.47; 95% CI, 0.28–0.82; P =0.007), whereas patients recommended for FFX had similar OS between treatments. Interpretability analysis of MULTIPL in COMPASS identified KDM6A alterations and SSTR1 expression as prognostic biomarkers, which were validated in PASS-01. However, none of the tested biomarkers significantly predicted differential treatment benefit in the PASS-01 Challenge.
Conclusion
MULTIPL demonstrated robust prognostic performance in external validation, identified a subgroup enriched for benefit from GNP, and enabled discovery and validation of prognostic biomarkers in metastatic PDAC. However, no biomarker met the primary endpoint for differential treatment benefit, underscoring the value of the PASS-01 Challenge.
Translational Relevance
Several biomarkers have been proposed to guide first-line treatment selection in metastatic pancreatic cancer, but none are validated from randomized data. We developed MULTIPL, a multimodal machine-learning model that integrates clinical, histopathologic, genomic, and transcriptomic data from the observational COMPASS study. In parallel, we launched the PASS-01 Challenge to evaluate biomarkers in a randomized trial of modified FOLFIRINOX versus gemcitabine plus nab-paclitaxel to evaluate predictive and prognostic biomarkers. Neither MULTIPL nor the published biomarkers PurIST, hENT1, and HRDetect met the prespecified endpoint for predicting differential treatment benefit measured using concordance for benefit. MULTIPL nevertheless demonstrated prognostic capabilities and identified a subgroup with longer survival on gemcitabine plus nab-paclitaxel. Model interpretation also identified KDM6A alterations and SSTR1 expression as prognostic biomarkers, which were validated in PASS-01. These findings demonstrate the potential of multimodal machine learning in pancreatic cancer and establish the PASS-01 Challenge as a randomized evaluation of biomarkers for treatment selection.
