Single-Cell Multiomics Integration Reveals Cell Trajectories and Regulatory Networks in Breast Cancer Progression.

BACKGROUND

Breast cancer is a molecularly heterogeneous disease encompassing distinct subtypes with divergent prognoses and therapeutic responses. Despite extensive bulk genomic characterization, the single-cell transcriptional trajectories, chromatin regulatory landscapes, and intercellular communication networks that drive breast cancer progression from luminal to mesenchymal states remain incompletely understood. Emerging evidence implicates gut microbiome dysbiosis as a systemic modulator of breast cancer risk, immune regulation, and treatment response; however, its coordinated characterization alongside high-resolution single-cell profiling has not been undertaken. Integrating single-cell RNA sequencing (scRNA-seq) with chromatin accessibility profiling (scATAC-seq), cell-cell communication inference, and gut microbiome 16S rRNA analysis offers a comprehensive multilayered approach to dissecting both the intratumoral regulatory logic and the systemic microbial landscape of breast cancer progression.

METHODS

We performed integrated multiomics analysis of 42,037 high-quality single cells from breast cancer patient samples obtained from the GEO database (GSE161529), encompassing tumor and adjacent normal tissue. scRNA-seq data were processed using Seurat v4.3.0 and Scanpy v1.9.1. Dimensionality reduction, Leiden clustering, differential expression analysis, pseudotime trajectory inference (Monocle3, scVelo, Slingshot), SCENIC regulon analysis, chromatin accessibility profiling, cell-cell communication inference (CellChat), and functional enrichment analysis were applied. Key marker genes were validated by quantitative RT-PCR in MCF-7 and MDA-MB-231 cell lines. In parallel, gut microbiome 16S rRNA amplicon sequencing data from 599 matched breast cancer-healthy control pairs (266 genera, four BioProjects) were analyzed for alpha/beta diversity, differential genus abundance (Mann-Whitney U with Benjamini-Hochberg correction), genus co-occurrence networks, and random forest breast cancer classification.

RESULTS

Unbiased clustering identified 12 observed cell populations spanning luminal (Luminal A, Luminal B), basal, HER2+, cancer-associated fibroblast (CAF), tumor-associated macrophage (TAM), CD8 T cell, regulatory T cell (Treg), NK cell, B cell, endothelial, and pericyte compartments. Quality control confirmed robust data quality with median 2400-3100 genes per cell and mitochondrial content below 20%. Pseudotime trajectory analysis revealed a continuous epithelial-to-mesenchymal transition (EMT) spectrum with three divergent lineages: EMT, senescence, and immune activation branches. SCENIC identified SNAI1(+), ZEB1(+), and FOXM1(+) as the most differentially activated regulons in basal/mesenchymal cells. ATAC-seq chromatin accessibility profiling confirmed EMT transcription factor access through distal enhancers. CellChat identified TGF-β1-TGFBR1 (CAF → tumor) and PD-L1-PD-1 (tumor → CD8 T cell) as dominant intercellular communication axes. Multiomics integration enhanced cell-type classification AUC from 0.76 (scRNA-seq alone) to 0.91 (full multiomics). EMT-high tumor status was associated with significantly inferior overall survival (HR = 2.14, p = 0.001). qRT-PCR validation confirmed ERBB2 enrichment in HER2+ lines and KRT5/VIM enrichment in basal/TNBC lines (all p < 0.001). Gut microbiome analysis revealed significant dysbiosis in breast cancer patients versus healthy controls, with reduced Shannon diversity (1.94 vs. 2.06, p = 6.1 × 10-4), significant community-level separation (Bray-Curtis PERMANOVA F = 30.2, p = 0.001), and nine differentially abundant genera including enriched Faecalitalea (log2 F C = 1.44), Parasutterella (log2 F C = 1.21), and Bacteroides (log2 F C = 1.19). A random forest microbiome classifier achieved a cross-validated AUC of 0.79 (95% CI: 0.74-0.82), supporting the gut-breast axis as a systemic codeterminant of cancer state.

CONCLUSIONS

This integrated single-cell multiomics and gut microbiome atlas of breast cancer progression characterizes the transcriptional regulatory architecture driving cellular trajectory diversification, highlights SNAI1-ZEB1-FOXM1 coregulatory networks as candidate therapeutic targets, suggests that multiomics integration can enhance molecular stratification of breast cancer subtypes with clinical prognostic relevance, and identifies gut microbiome dysbiosis as a potential systemic dimension-characterized by loss of microbial diversity and enrichment of specific genera-as a systemic dimension of breast cancer pathobiology that warrants integration with intratumoral molecular profiling.