Explainable multi-omics deep clustering model reveals an important role of DNA methylation in pancreatic ductal adenocarcinoma

Abstract

Patients with pancreatic ductal adenocarcinoma (PDAC) have the lowest survival rate among all cancer patients in Europe. Since western societies have the highest incidence of pancreatic cancer, it has been projected that PDAC will soon become the second leading cause of cancer-related deaths. The main challenge of PDAC treatment is that patients with similar somatic genotypes exhibit a wide range of disease phenotypes. Artificial Intelligence (AI) is currently transforming the field of healthcare and represents a promising technology for integrating various datasets and optimizing evidence-based decision making. However, the interpretability of most AI models is limited and it is challenging to understand how and why a decision is made. In this study, we developed a deep clustering model for PDAC patient stratification using integrated methylation and gene expression data. We placed a specific emphasis on model explainability, with the aim to understand the hidden patterns learned by the model. The results showed two subgroups of PDAC patients with different prognoses and biological factors. The multi-omics profile analysis revealed the important role of DNA methylation. We also showed how the model was able to learn underlying patterns using both single modalities and their combinations. We hope that this study will help to promote more explainable AI in real-world clinical applications, where the knowledge of decision factors is crucial. The code of this project is publicly available in GitHub (https://github.com/albertolzs/edc_mo_pdac).