A histology-based artificial intelligence (AI) biomarker may help personalize adjuvant chemotherapy selection for patients with resected pancreatic ductal adenocarcinoma, according to the results of a study by Beaufils et al. The investigators developed and validated a deep learning model that analyzes routine histology slides to predict the relative benefit of adjuvant gemcitabine vs modified FOLFIRINOX (mFOLFIRINOX). The resulting biomarker, termed PANCprAId, identified patient subgroups with differential benefit from the two regimens in an external validation cohort from the randomized phase III PRODIGE-24/CCTG PA6 trial. The study is published in the Journal of Clinical Oncology.
Study Details
The investigators trained separate deep learning models to predict disease-free survival following adjuvant gemcitabine or mFOLFIRINOX using digitized whole-slide images from hematoxylin, eosin, and saffron–stained resection specimens. The development cohort comprised 231 patients who underwent curative-intent pancreatectomy and subsequently received either gemcitabine (n = 177) or mFOLFIRINOX (n = 54). The treatment-specific models were then integrated into the PANCprAId algorithm, which estimated the relative benefit of one regimen compared with the other for an individual patient.
External validation was performed in 313 assessable patients from the randomized PRODIGE-24/CCTG PA6 trial, including 137 patients who were treated with gemcitabine and 176 treated with mFOLFIRINOX. Survival analyses used stratified Cox proportional hazards models, Kaplan-Meier estimates, and interaction testing to determine whether the biomarker predicted differential treatment benefit.
Key Results
Among patients who received gemcitabine, those whose tumors were predicted to respond less favorably had significantly shorter disease-free survival than those whose tumors were predicted to respond more favorably, with a hazard ratio (HR) of 1.69 (95% confidence interval [CI] = 1.04–2.73, P = .03). Among patients treated with mFOLFIRINOX, those whose tumors were predicted to respond less favorably experienced significantly worse disease-free survival than those whose tumors were predicted to respond more favorably (HR = 2.02, 95% CI = 1.40–3.00, P < .001). Each model predicted outcomes only in patients receiving its corresponding treatment and showed no significant prognostic value in patients treated with the alternate regimen.
When the two models were combined into PANCprAId, the biomarker identified patients predicted to derive greater benefit from gemcitabine (favGEM; 15%) or mFOLFIRINOX (favFFX; 85%). In the favFFX subgroup, patients assigned to mFOLFIRINOX achieved longer median disease-free survival than those receiving gemcitabine (21.4 vs 11.1 months, HR = 2.00, 95% CI = 1.48–2.67, P < .001). In the favGEM subgroup, median disease-free survival was 33.5 months with gemcitabine vs 23.6 months with mFOLFIRINOX (HR = 0.48, 95% CI = 0.23–1.02, P = .09).
The investigators concluded: “Histology-based deep learning can derive a predictive biomarker of relative benefit from adjuvant [gemcitabine] vs mFOLFIRINOX in resected [pancreatic ductal adenocarcinoma]."
Remy Nicolle, PhD, of Centre de Recherche sur l'Inflammation (CRI), Université Paris Cité, Paris, is the corresponding author for the Journal of Clinical Oncology article.
DISCLOSURE: The study was supported by the Ligue Contre le Cancer and the Institute National du Cancer. For full disclosures of the study authors, visit ascopubs.org.
