What question did this study set out to answer?

To characterize the cellular diversity and tumor microenvironment in adenoid cystic carcinoma of the breast using single-cell RNA sequencing.

March 1, 2026Open Access

Single-cell RNA sequencing of adenoid cystic carcinoma of the breast reveals cellular heterogeneity and tumor microenvironment features

Key Points

To characterize the cellular diversity and tumor microenvironment in adenoid cystic carcinoma of the breast using single-cell RNA sequencing.
Utilized single-cell RNA sequencing to analyze a sample from a patient with adenoid cystic carcinoma of the breast.
Integrated data with publicly available datasets from related cancers.
Performed high-resolution gene expression profiling and cell clustering analysis to identify subpopulations.
Conducted differential expression and cell-cell communication analysis to understand the regulatory networks.
Identified seven major cell types, including malignant H19 + myoepithelial cells and cancer-associated fibroblasts.
H19 + myoepithelial cells showed high expression of oncogenic pathways and were prevalent in ACCB compared to other types.
Tumor microenvironment exhibited immunosuppression, with fewer CD4 +, CD8 + T cells and B cells.
Cell-cell communication analysis indicated H19 + myoepithelial cells coordinated signaling networks facilitating tumor progression.

Abstract

Adenoid cystic carcinoma of the breast (ACCB) is a rare malignant tumor with distinct pathological features and a relatively favorable prognosis compared to other breast cancer subtypes, yet its molecular mechanisms remain poorly understood due to challenges in sample acquisition and targeted research. In this study, we employed single-cell RNA sequencing (scRNA-seq) to systematically characterize the cellular heterogeneity and tumor microenvironment in ACCB. We obtained scRNAseq data from a single patient with ACCB and integrated it with publicly available datasets from head and neck adenoid cystic carcinoma (HNACC) and triple-negative breast cancer (TNBC). Through high-resolution gene expression profiling, cell clustering, differential expression analysis, and cell-cell communication analysis, we aimed to identify key malignant subpopulations and regulatory networks in ACCB. Unsupervised clustering revealed seven major cell types in the analyzed ACCB sample, including epithelial cells, fibroblasts, and immune cells. In-depth analysis of epithelial cells identified H19 + myoepithelial cells (H19 + myoEpC) as the dominant malignant subpopulation, enriched in ACCB compared to other cancers and characterized by high expression of oncogenic pathways and ligands such as LAMB1 and WNT6. Tumor-associated fibroblasts exhibited significant heterogeneity, with cancer-associated fibroblasts (CAFs) expressing EMT-related genes and interacting closely with H19 + myoEpC. The tumor microenvironment was immunosuppressive, with reduced infiltration of CD4 + and CD8 + T cells and B cells, and lacked classical Treg/Tex populations. Comprehensive cell-cell communication analysis showed that H19 + myoEpC orchestrated a signaling network with CAFs, immune cells, and endothelial cells, driving tumor progression and immune evasion. Our study provides the single-cell resolution map of ACCB from a representative case, highlighting the unique role of H19 + myoEpC in tumor progression and immune suppression. These findings offer new insights into the molecular classification of ACCB and potential therapeutic targets.

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