Development and Validation of a Computational Histology Artificial Intelligence-Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer.
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Hendifar Andrew E, Krishna Viswesh, Krishna Vrishab, Zhang Haochen, Tarsode Asit, Nimgaonkar Vivek, Smith Katelyn, Abdilleh Kawther, Sonawane Snehal, Neema Akshay, Tiu Ekin, Larson Brent K, Kazarov Vladimir, Moshayedi Natalie, Hutchinson Shawn, Bevacqua Daniela, Doss Sudheer, Alvarez Alejandra, Watson Drew, Abuzeid Waleed M, Grünwald Barbara T, Noel Marcus, Samdani Rashmi, Keith Dove, Sears Rosalie C, Sohal Davendra, Fountzilas Christos, O'Kane Grainne M, Grant Robert C, Osipov Arsen, Collisson Eric A, Kiedrowski Lesli A, Royce Trevor J, Joshi Anirudh R, Singhi Aatur D, Knox Jennifer J
Аннотация
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.