Integration of chromosome conformation and gene expression networks reveals regulatory mechanisms in triple negative breast cancer.

Abstract

Introduction: Triple-negative breast cancer (TNBC) accounts for twelve percent of all breast cancer cases, with a survival rate around ten percent lower than ER+/PR+ positive breast cancers. There are limited therapeutic options as these tumors do not respond to hormonal therapy or HER2-targeted treatments. We hypothesized that new insights into pathogenic mechanisms in TNBC can be obtained from studying epigenetic alterations through Hi-C (genome-wide chromosome conformation capture) data analysis.

Methods: We developed a computational strategy that captured key properties of chromatin conformation while incorporating statistical measures of interaction significance. This model addresses limitations in Hi-C data analysis without relying on predefined features like TADs and compartments. We applied this model to Hi-C and RNA-seq data from TNBC patients, representing the data as multilayer networks to identify genome-wide properties of the TNBC 3D genome.

Results: Our network-based analysis revealed distinct chromatin interaction patterns in TNBC compared to healthy contralateral controls. Hi-C data can distinguish interaction patterns related to diseased phenotypes or interaction patterns with potential to exert regulatory effects instead of incidental contacts, but some apparently random interactions may also support important genome regulatory activities.

Discussion: Our findings demonstrate that network-based Hi-C analysis can capture the genome-wide complexity of chromatin interactions in TNBC. This integrative approach provides new insights into the epigenetic mechanisms underlying TNBC pathogenesis and contributes to the advancement of analysis methods for future investigations into novel therapeutic targets.