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.