Abstract 472: PACpAInt: A deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides

Abstract

Abstract Introduction: Pancreatic adenocarcinoma (PAC) is highly heterogeneous, resulting in overall ineffectiveness of most anti-tumor treatments. Two tumor subtypes (Classical and Basal) and two stromal subtypes (“active” and “inactive”) have been described. The Basal and the active stroma have worse prognosis. These subtypes could also be predictive of the response to different chemotherapies. To date, molecular subtype classification or phenotype quantification can only be defined by RNAseq, a complex technique sensitive to the quantity and quality of samples, requiring a timescale limiting its routine application. We propose a deep learning approach (PACpAInt) to predict molecular subtypes in PAC on routine histological slides. Patients and Methods: 424 digitalized HES slides of 202 resected PAC from 3 centers with clinical and transcriptomic data were used as training cohort. 3 validation cohorts were used (i) 250 resected PAC from a 4th center including 97 cases with an exact HES/RNAseq spatial match and all tumor slides digitalized (n = 891); (ii) 126 resected PAC from the TCGA (HES + RNAseq); (iii) 25 liver biopsies from metastatic PAC (HES + RNAseq). A multi-step deep learning model was developed to recognize tumor tissue, tumor from stroma cells, and then predicts their transcriptomic molecular subtypes, either at the level of an entire slide, or at the tile level (squares of 112 μm) allowing to study intratumor heterogeneity. Results: PACpAInt correctly predicted the tumor subtype at the whole slide level (AUC = 0.86 and 0.81 in 2 validation cohorts) and improved for samples with unambiguous molecular subtype (AUC = 0.91 and 0.88) confirming the limit of a binary approach. Similar results were obtained on liver biopsies (AUC = 0.85 and 0.92 on unambiguous cases). PACpAInt independently predicted progression-free and overall survival (PFS HR=1.37 [1.16 - 1.62] and OS HR=1.27 [1.08 - 1.49]). Analysis of all tumor slides from 77 Classical cases showed that 39% were heterogeneous with a Basal contingent. These cases had shorter PFS (15 vs. 47 months, p= 0.001) and OS (31 vs. 64 months, p= 5e-5).The analysis of intratumoral heterogeneity using PACpAInt predicted the molecular subtype of tumor and stroma cells of each tile within a slide (> 6 million tiles analyzed). 61% of cases had a main subtype either Classical (42%) or Basal (19%). 39% of the cases were ambiguous could be considered either hybrid (10%) with coexistence of Basal and Classical cells, or intermediate (29%) corresponding to homogeneous tumors but of intermediate differentiation. This classification had a strong prognostic impact (OS: 45.1 vs 33.0 vs 23.4 vs 13.6 months, resp. Classical, intermediate, hybrid, Basal; p <e-12). Conclusion: This study provides the first PAC subtyping tool widely usable in clinical practice, opening the possibility of molecular classification useful for routine care and clinical trials. Citation Format: Charlie Saillard, Flore Delecourt, Benoit Schmauch, Olivier Moindrot, Magali Svrcek, Armelle Bardier-Dupas, Jean Francois Emile, Mira Ayadi, Vinciane Rebours, Louis De Mestier, Pascal Hammel, Cindy Neuzillet, Jean Baptiste Bachet, Juan Iovanna, Nelson Dusetti, Yuna Blum, Magali Richard, Yasmina Kermezli, Valerie Paradis, Mikhail Zaslavskiy, Pierre Courtiol, Aurelie Kamoun, Remy Nicolle, Jerome Cros. PACpAInt: A deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 472.