Journal Article

Parsimonious Gene Correlation Network Analysis (PGCNA): a tool to define modular gene co-expression for refined molecular stratification in cancer

Care, Matthew A, Westhead, David R, Tooze, Reuben M

2019

Overview

Cancers converge onto shared patterns that arise from constraints placed by the biology of the originating cell lineage and microenvironment on programs driven by oncogenic events. Here we define consistent expression modules reflecting this structure in colon and breast cancer by exploiting expression data resources and a new computationally efficient approach that we validate against other comparable methods. This approach, Parsimonious Gene Correlation Network Analysis (PGCNA), allows comparison of network structures between these cancer types identifying shared modules of gene co-expression reflecting: cancer hallmarks, functional and structural gene batteries, copy number variation and biology of originating lineage. These networks along with the mapping of outcome data at gene and module level provide an interactive resource that generates context for relationships between genes within and between such modules. Assigning module expression values (MEVs) provides a tool to summarize network level gene expression in individual cases illustrating potential utility in classification and allowing analysis of linkage between module expression and mutational state. Exploiting TCGA data thus defines both recurrent patterns of association between module expression and mutation at data-set level, and exemplifies the polarization of mutation patterns with the leading edge of module expression at individual case level. We illustrate the scalable nature of the approach within immune response related modules, which in the context of breast cancer demonstrates the selective association of immune subsets, in particular mast cells, with the underlying mutational pattern. Together our analyses provide evidence for a generalizable framework to enhance molecular stratification in cancer.Gene expression networks: radical pruning enhances resolutionTo control cell behavior in physiology and disease, genes are expressed in co-ordinated networks, but the sheer number of connections between genes challenges interpretation of this structure. Care, Westhead and Tooze—University of Leeds, UK—show how retaining only the 3 nearest neighbors of any gene improves information content of expression networks. This new approach, PGCNA, outperforms alternatives and can be used to integrate multiple sets of data. With breast and colon cancer as examples the team demonstrate how such networks can be used to derive bar-codes that summarize cancer gene expression and link to underlying gene mutation states. This has potential application in precision medicine and establishes a scalable, efficient, approach to enhance network analysis.

• Publisher: Nature Publishing Group
• Subject: Breast cancer / Cell lineage / Colon / Colon cancer / Colorectal cancer / Copy number / Gene expression / Gene mapping / Immune response / Linkage analysis / Mast cells / Mutation / Precision medicine / Structure-function relationships