Machine learning analysis reveals tumor heterogeneity and stromal-immune niches in breast cancer

机器学习分析揭示乳腺癌的肿瘤异质性和基质免疫微环境

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Abstract

Breast cancer is a leading cause of cancer-related mortality, with tumor heterogeneity and drug resistance posing significant challenges to treatment. We integrated single-cell RNA sequencing, spatial transcriptomics, and bulk RNA-seq deconvolution to analyze BRCA samples. Our analysis identified 15 major cell clusters, including neoplastic epithelial, immune, stromal, and endothelial populations. Notably, low-grade tumors showed enriched subtypes, such as CXCR4+ fibroblasts, IGKC+ myeloid cells, and CLU+ endothelial cells, with distinct spatial localization and immune-modulatory functions. These subtypes were paradoxically linked to reduced immunotherapy responsiveness, despite their association with favorable clinical features. High-grade tumors exhibited reprogrammed intercellular communication, with expanded MDK and Galectin signaling. Bulk RNA-seq deconvolution further supported the prognostic significance of low-grade-enriched subtypes. Our findings highlight the heterogeneity of the tumor microenvironment and provide new insights into immune evasion and therapeutic resistance in breast cancer.

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