MetaPaCS: A Novel Meta‐Learning Framework for Pancreatic Cancer Subtype Identification

Pancreatic cancer (PaC), the third leading cause of cancer related deaths in the United States, is a highly heterogenous malignancy with a multitude of potential subtypes. Each PaC subtype has unique molecular pathways and therapeutic characteristics, which can be used in downstream patient risk stratification and tailored treatment design. Conventional wet‐lab approaches for PaC subtyping (microdissection, histopathological studies, etc.) are laborious, costly, and time‐consuming. To address these concerns, we present MetaPaCS, a novel meta‐learning framework to accurately identify PaC subtypes based on transcriptomics data. Specifically, after preprocessing, the transcriptome‐based feature vectors are classified by multiple base machine learning classifiers, whose diverse prediction outputs are combined with the initial preprocessed feature vectors to constitute a new set of ensemble feature vectors for a meta‐learning classifier. Leave‐one‐out cross‐validation demonstrates that MetaPaCS performs remarkably better optimized individual base classifiers. We believe that MetaPaCS is a promising tool for downstream risk stratification and personalized treatment design.