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. 2026 Feb 11:JCO2502199.
doi: 10.1200/JCO-25-02199. Online ahead of print.

Development and Validation of a Computational Histology Artificial Intelligence-Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer

Andrew E Hendifar  1 , Viswesh Krishna  2 , Vrishab Krishna  2 , Haochen Zhang  2 , Asit Tarsode  2 , Vivek Nimgaonkar  2   3 , Katelyn Smith  4 , Kawther Abdilleh  5 , Snehal Sonawane  2   6 , Akshay Neema  2 , Ekin Tiu  2 , Brent K Larson  1 , Vladimir Kazarov  1 , Natalie Moshayedi  1 , Shawn Hutchinson  7 , Daniela Bevacqua  7 , Sudheer Doss  5 , Alejandra Alvarez  5 , Drew Watson  2 , Waleed M Abuzeid  2 , Barbara T Grünwald  7   8   9 , Marcus Noel  10 , Rashmi Samdani  10 , Dove Keith  11 , Rosalie C Sears  11 , Davendra Sohal  12 , Christos Fountzilas  13 , Grainne M O'Kane  14 , Robert C Grant  7 , Arsen Osipov  1 , Eric A Collisson  15 , Lesli A Kiedrowski  2 , Trevor J Royce  2   16 , Anirudh R Joshi  2 , Aatur D Singhi  4 , Jennifer J Knox  7
Affiliations

Development and Validation of a Computational Histology Artificial Intelligence-Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer

Andrew E Hendifar et al. J Clin Oncol. .

Abstract

Purpose: Predictive biomarkers to guide selection of first-line chemotherapy for advanced pancreatic ductal adenocarcinoma (PDAC) are an unmet clinical need. This study used the Computational Histology Artificial Intelligence (CHAI) platform to develop and validate a histomorphology-based G-chemo versus F-chemo (GvF) biomarker that predicts benefit from first-line fluoropyrimidine-based (F-chemo) versus gemcitabine-based (G-chemo) regimens.

Methods: The CHAI platform extracted quantitative histomorphologic features from whole-slide images of hematoxylin and eosin-stained diagnostic biopsies. In a multi-institutional development cohort, features associated with differential outcomes as measured by time to next treatment or death (TNTD) between F-chemo-treated and G-chemo-treated patients produced continuous biomarker scores, which were dichotomized into G-pref or F-pref results. The biomarker and threshold were locked. An independent validation cohort from the prospective COMPASS and Know Your Tumor studies assessed differential treatment outcomes by TNTD and overall survival (OS).

Results: There were 477 patients (development: 178; validation: 299). In validation, among 173 F-pref patients, those treated with F-chemo had significantly better outcomes than G-chemo for both TNTD (P = .035; median TNTD: F-chemo 8.6 months; G-chemo 7.5 months) and OS (P = .003; median OS: F-chemo 14.4 months; G-chemo 11.7 months). Among 126 G-pref patients, G-chemo had significantly superior TNTD (P = .038; median TNTD: F-chemo 7.2 months; G-chemo 9.6 months), but no difference in OS (P = .5; median OS: F-chemo 12.4 months; G-chemo 14.3 months). In propensity score-weighted analysis, the biomarker predicted treatment effect (biomarker-treatment interaction TNTD P < .001; OS P = .005). RNA subtypes were associated with TNTD and OS but did not predict differential treatment effects (P = .3).

Conclusion: The histomorphology-based GvF biomarker predicted differential treatment benefit of first-line GvF. This biomarker can guide optimal treatment selection for first-line therapy in advanced PDAC.

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