Abstract
Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.
Highlights
Despite remarkable progress in screening and patient management, breast cancer remains the most commonly diagnosed non-skin cancer and the second leading cause of cancer related death among women in the US [1]
We hypothesized that the molecular perturbation and aberrant DNA methylation in tumors from patients diagnosed with triple negative breast cancer (TNBC) or non-TNBC and control samples could lead to measurable changes distinguishing patients diagnosed with TNBC tumors from control samples, and patients diagnosed with non-TNBC tumors from control samples
We argue that integrating large-scale, high-dimensional somatic mutation, DNA methylation and gene expression data holds promise, for unraveling the genomic-epigenomic interaction landscape of TNBC and non-TNBC, and provides the avenue for uncovering the molecular mechanisms characterizing the biological differences and the drivers of the two types of breast cancer
Summary
Despite remarkable progress in screening and patient management, breast cancer remains the most commonly diagnosed non-skin cancer and the second leading cause of cancer related death among women in the US [1]. A majority of breast cancers are non-triple negative breast cancers (non-TNBC) These types of breast cancer are characterized by less aggressive clinical behavior, good prognosis, low recurrence and higher survival rates [2,3]. Somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC
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