Decoding pancreatic cancer subtypes from routine histology slides

Context

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.

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Methods

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.

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Results

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.

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Impact

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.