Identification of cancer omics commonality and difference via community fusion

TL;DR

This paper introduces Community Fusion (CoFu), a novel method for analyzing multiple cancer omics datasets that identifies shared and distinct markers by leveraging network community structures, demonstrated to outperform existing methods.

Contribution

The paper develops a new penalization technique within CoFu that effectively captures network community structures for multi-cancer omics analysis, revealing commonalities and differences.

Findings

CoFu outperforms competing methods in simulations.

Identifies shared and unique omics markers across cancer types.

Provides new insights into lung cancer and melanoma omics data.

Abstract

The analysis of cancer omics data is a "classic" problem, however, still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings