NEW YORK – A team led by investigators at the University of North Carolina at Chapel Hill has characterized regulatory features found in breast cancer cells, providing clues to processes and cell types at play during the transition to a range of breast cancer subtypes.
"This work highlights the importance of noncoding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multiomics to define the regulatory logic of cancer cells," senior and corresponding author Hector Franco, a genetics, bioinformatics, and computational biology researcher with the UNC Chapel Hill's Lineberger Comprehensive Cancer Center and the University of Puerto Rico Comprehensive Cancer Center, and his colleagues wrote in Cell Genomics on Wednesday.
Using a combination of single-cell RNA sequencing on nearly 111,900 individual cells and single-cell ATAC-seq profiling on more than 91,000 cells, the researchers assessed transcriptome profiles and chromatin accessibility, respectively, in a dozen primary breast cancer samples collected from 11 yet-to-be-treated individuals with breast cancer or in cancer-free mammary tissue samples collected from four individuals receiving breast reduction surgery.
"[O]ur work elucidates transcriptional and regulatory features that distinguish [breast cancer] cells from their nearest normal precursor cell types by identifying putative enhancers that regulate clinically relevant oncogenic expression programs in a cancer-specific manner," the authors reported, noting that the data "enabled us to study transcriptional and regulatory differences between [breast cancer] cells in vitro and in vivo."
The team's analyses of chromatin accessibility peaks and gene expression patterns in breast cancer and unaffected control cells pointed to the presence of cancer-specific enhancer sequences, for example, along with apparent changes from gene silencing to gene enhancer activity in the mammary tissues affected by cancer.
When they teased out the genes impacted by breast cancer-related regulatory shifts and gene expression, the researchers saw gene set enrichment and copy number variant profiles linked to survival in a subsequent analysis of RNA sequence data from the dbGaP database and the National Institutes of Health's Gene Expression Omnibus.
"Through these analyses, we identify context-specific mechanisms of gene regulation in [breast cancer] cells," the authors wrote, "and unveil clinically relevant noncoding mechanisms for [breast cancer] pathogenesis at single-cell resolution."
By comparing regulatory features found in the clusters of cells within and around breast tumors, meanwhile, the investigators got insights into the potential cell type origins and regulatory patterns present in specific breast cancer subtypes assessed in vivo or in vitro.
"Our differential peak-to-gene association analysis allowed us to classify peak-to-gene associations based on changes in direction of effect size between conditions," the authors explained. "For both basal-like and luminal [breast cancer] subtype analyses, this revealed thousands of cancer-specific associations with positive effect sizes indicative of context-specific putative enhancer activity and evidence to suggest the potential for regulatory switching events that were previously hidden using current peak-to-gene association methods."