The abstract and poster entitled “Identification of pancreatic adenocarcinoma molecular subtypes on histology slides using deep learning models” demonstrates the first AI-based tool for predicting genomic subtypes of pancreatic cancer (PDAC) developed from machine learning applied to histology slides.
The tool, a trained and validated AI model, is usable in clinical practice worldwide and opens the possibility of patient molecular stratification in routine care and for clinical trials.
Gilles Wainrib, Chief Scientific Officer and Co-Founder of Owkin said “Our research shows AI can help connect information at the genomic, cellular and tissue levels, and how doing so can bring immediate value to make precision medicine a reality for patients. This study further underscores the value of using machine learning for identifying histo-genomic signals for cancer research and clinical development.”
Pancreatic adenocarcinoma is a complex and heterogeneous disease. Improvement of prognosis has stalled while pancreatic cancer is predicted to become the second most lethal cancer by the year 2030.
Heterogeneity and tumour plasticity are likely major factors in the failure of many clinical trials. Multiomics studies have revealed two main tumour transcriptomic subtypes, Basal-like and Classical that have been proposed to be predictive of patient response to first line chemotherapy.
The determination of these subtypes has been possible so far by RNA sequencing, a costly and complex technique that is not yet feasible in a clinical routine setting.
Taken together, these factors make it compelling to use advanced AI methods with common histological slides, trained alongside crucial context from expert researchers, to address the unmet needs of patients.
Prof. Jérôme Cros, Pathologist at Beaujon Hospital – Université de Paris said “This tool was developed using the unique histological and molecular resources from four AP-HP hospitals (Amboise Paré-Beaujon-Pitié Salpétrière-Saint Antoine) though a unique collaboration between pathologists from AP-HP, bioinformaticians from the group Carte d’Identité des Tumeurs de la Ligue Contre le Cancer and data scientists from Owkin. It can remotely subtype tumour in minutes paving the way for many applications from basic science (study of intra-tumour heterogeneity) to clinical practice (tumour subtyping in clinical trials).”
This research is born out of a successful and ongoing collaboration between Owkin’s multidisciplinary teams and those of the AP-HP Greater Paris University Hospitals.
Source: Owkin, Inc.
