Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

• Type of Material: Article
• Authors: Kevin Chappell, University of Arkansas; Kanishka Manna, University of Arkansas; Charity L Washam, University of Arkansas; Stefan Graw, Emory University; Duah Alkam, University of Arkansas; Matthew D Thompson, University of Arkansas; Maroof Khan Zafar, University of Arkansas; Lindsey Hazeslip, University of Arkansas; Christopher Randolph, Arkansas Children's Research Institute; Allen Gies, University of Arkansas; Jordan T Bird, University of Arkansas; Alicia K Byrd, University of Arkansas; Sayem Miah, University of Arkansas; Stephanie D Byrum, University of Arkansas
• Language: English
• Date: 2021-10-01
• Publisher: The Royal Society of Chemistry
• Publication Version: Final Published Version
• Copyright Statement: This journal is © The Royal Society of Chemistry
• License: Creative Commons Attribution-Noncommercial 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1039/d1mo00117e
• Title of Journal or Parent Work: Molecular Omics
• Volume: 17
• Issue: 5
• Start Page: 677
• End Page: 691
• Keywords: Proteomics; Cell Line, Tumor; Humans; Signal Transduction; Triple Negative Breast Neoplasms
• Research Categories: Chemistry, Biochemistry

Abstract

Triple negative breast cancer (TNBC) is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-Type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with non-Tumorigenic epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-Translational modification. Data integration methods identified regulatory features from each omics method that had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFβ signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling. We observed overexpression of PTEN, which antagonizes the PI3K/AKT/mTOR pathway, and MYC, which downregulates the same pathway in the HCC1937 cells relative to the MDA-MB-231 cells. The PI3K/AKT/mTOR and Wnt/beta-catenin pathways are both downregulated in HCC1937 cells relative to MDA-MB-231 cells, which likely explains the divergent sensitivities of these cell lines to inhibitors of downstream signaling pathways. The DNA methylation and RNAseq data is freely available via GEO GSE171958 and the proteomics data is available via the ProteomeXchange PXD025238.