Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer

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.

Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer

“…In the upcoming years, integrating different data sources will be key in identifying definitive TNBC subtypes that will help guide clinicians toward specific treatment recommendations for their patients. Integrative analyses comparing TNBC and non-TNBC patients from the TCGA cohort have been performed combining gene expression, DNAm, and somatic mutations, revealing differential signatures between these two types of BC ( 58 ). Thus, a similar approach combining even more layers of information may identify consensus TNBC subtypes.…”

PARP Inhibitors: A Major Therapeutic Option in Endocrine-Receptor Positive Breast Cancers

“…The frequency of somatic BRCA1 and BRCA2 PV may be between 1 and 3% of patients in unselected breast cancer populations [19,22,[39][40][41][42][43][44], including ER+/HER2-breast cancer patients [19,40,42] and even higher in patients with ER+/HER2-mBC [12,21,45]. In addition, silencing methylation of BRCA1 and BRCA2 promoters was reported in 2-4% of patients [39,43,46].…”

“…Germline PV of other genes of the HR, especially PALB2, CHEK2, and ATM, were found in 0.5-1.5% of patients with unselected breast cancers [12,[18][19][20][21][39][40][41][42][43][47][48][49] (Table 1), and were obviously more frequent among patients with family breast cancer history [18,[50][51][52][53]. In addition, rearrangements of other genes that are not directly involved in DNA repair could also confer PARPi sensitivity, such as CDK12 deficiency [54,55], or the recently described EWS-FLI1 fusion [56].…”

“…It is now estimated that 10-25% of breast carcinomas are associated with HRD [11][12][13], due to BRCA1/2 PV [14][15][16][17], but also of other genes such as PALB2, ATM, ATR, BARD1, RAD51, BRIP1, or FANC [18][19][20][21]. Because of the higher frequency of BRCA1 PV (~10%) in patients with triple negative breast cancers (TNBC) [22,23], and the limited number of effective treatment options, PARPi have recently been adopted as a valuable asset in this subgroup [24].…”

A molecular generative model with genetic algorithm and tree search for cancer samples

“…Among the cancer types, TNBC is notorious for its clinical difficulty [53]. Because of the genetic variance of different subsets of TNBC, drug treatment based on genetic profiles is an essential therapeutic strategy for TNBC [54,55].…”