PACpAInt: A model to define the molecular subtypes of pancreatic cancer

Pancreatic cancer is one of the world’s deadliest cancers. It is expected to be the second cause of death by cancer in 2030.

Specific tumor molecular subtypes respond differently to first-line chemotherapy. Most PDAC clinical trials fail due to disease heterogeneity. Other challenges consist of long and costly trial recruitment timeframes due to expensive and time-consuming RNAseq to define PDAC subtypes.

Owkin has developed a deep learning model to identify the main molecular subtypes of PDAC (i.e.; basal-like or classical), from digitized H&E slides, called PACpAInt. The project, developed in collaboration with the University of Paris Hospital (AP-HP) and Prof. Jérôme Cros, aims to help clinicians better subtype patients to specify the targeted population and demonstrate efficacy value based on the subtype targeted. The PACpAInT model can deliver examination results within minutes thanks to significantly more rapid and cost-effective digitized histology slide analysis. 

Our research lays the path for patient molecular stratification in standard treatment and clinical trials wherever H&E slides are routinely used. Discover how we add to the evidence of intra-tumor heterogeneity significance in PDAC and its effect on patient outcomes.

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If you find PACpAInT interesting, you may be interested in some of our other biomarker discovery and AI diagnostic solutions. 

To learn more check out our product and service offerings at owkin.com or contact us at owkin.com/contact.