DLSOM: A Deep learning-based strategy for liver cancer subtyping

TL;DR

This paper presents DLSOM, a deep learning framework that classifies liver cancer into five subtypes based on somatic mutation data, aiding personalized diagnosis and treatment strategies.

Contribution

DLSOM is the first deep learning approach to analyze the complete mutation landscape for liver cancer subtyping, revealing biologically distinct groups.

Findings

Identified five liver cancer subtypes with unique mutational profiles.

Linked mutational signatures to biological mechanisms like hypermutation.

Demonstrated potential for subtype-specific diagnostics and therapies.

Abstract

Liver cancer is a leading cause of cancer-related mortality worldwide, with its high genetic heterogeneity complicating diagnosis and treatment. This study introduces DLSOM, a deep learning framework utilizing stacked autoencoders to analyze the complete somatic mutation landscape of 1,139 liver cancer samples, covering 20,356 protein-coding genes. By transforming high-dimensional mutation data into three low-dimensional features, DLSOM enables robust clustering and identifies five distinct liver cancer subtypes with unique mutational, functional, and biological profiles. Subtypes SC1 and SC2 exhibit higher mutational loads, while SC3 has the lowest, reflecting mutational heterogeneity. Novel and COSMIC-associated mutational signatures reveal subtype-specific molecular mechanisms, including links to hypermutation and chemotherapy resistance. Functional analyses further highlight the biological relevance of each subtype. This comprehensive framework advances precision medicine in liver cancer by enabling the development of subtype-specific diagnostics, biomarkers, and therapies, showcasing the potential of deep learning in addressing cancer complexity.