Identifying pancreatic adenocarcinoma molecular subtypes from routine histology slides


Pancreatic adenocarcinoma (PAC) is a very heterogeneous tumor with a high trial failure rate. Currently, molecular subtypes are defined by RNA profiling whose limitations prevents its application in routine care.


We used a multiple instance learning model with a self-attention mechanism called PACpAInt.

This multistep approach used deep learning models to detect PAC tumors from histology slides and predict molecular subtypes.


Identified molecular subtypes in the three validation cohorts with independent prognostic value comparable to RNAseq.

Identified inter-slide heterogeneity in 39% of tumors that impacted survival. This helped us refine existing subgroups based on tumor heterogeneity.


Increased statistical power - Pharma can increase the statistical power of phase III trials by using this tool to select high-value subgroups with the greatest unmet need and that are most likely to benefit from the treatment.


"This study provides the first PAC subtyping tool usable worldwide in clinical practice, finally opening the possibility of patient molecular stratification in routine care and clinical trials."
Prof. Jérôme Cros
Hopital Beaujon, APHP