Spatial Analysis Dissects the Microenvironmental Architecture of Local Recurrence after Breast-Conserving Surgery

Ipsilateral breast tumor recurrence (IBTR) is a critical prognostic determinant in breast cancer patients undergoing breast-conserving surgery (BCS). The spatial arrangement of immune, stromal, and malignant cells can influence local recurrence progression and treatment efficacy in IBTR tumors. Here, we performed single-cell spatial transcriptomics on primary breast cancer (PBC) and matched IBTR specimens from 22 patients, comprising 18 true recurrence (TR) and 4 new primary (NP) cases, to generate a comprehensive atlas of over 600,000 cells. Cellular neighborhood (CN) analysis identified a specific tumor cell-stromal region significantly enriched in IBTR tumors. Tumor recurrence was driven by a highly proliferative epithelial cell subpopulation characterized by immune exclusion and invasive features. Spatial analysis uncovered a tumor-stromal region where CD24+ tumor cells spatially intersected with BGN+ cancer-associated fibroblasts (CAFs) alongside upregulation of MDK signaling. In parallel, the BGN+ CAFs displayed enriched CXCL12 pathways, suggesting a potential spatial axis that aligns with local immunosuppressive remodeling and recurrent niche formation. Critically, the spatial colocalization of CD24⁺ tumor cells and BGN⁺ CAFs was validated as an independent predictor of recurrence after BCS. This study delivers a high-resolution spatial landscape of IBTR, uncovering distinct microenvironmental landscapes between TR and NP subtypes, and establishes these spatially defined cellular networks as both mechanistic drivers and translatable prognostic biomarkers.

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Publication Details

Journal
Cancer Research
Published
2026-09-25
DOI
https://doi.org/10.1158/0008-5472.can-25-5613
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Spatial Analysis Dissects the Microenvironmental Architecture of Local Recurrence after Breast-Conserving Surgery

Zhi‐Ming Shao, Feilin Qu, Chao You, Yi Xiao et al.
Cancer Research
Single-cell and spatial transcriptomics
article

Spatial Analysis Dissects the Microenvironmental Architecture of Local Recurrence after Breast-Conserving Surgery

Zhi‐Ming Shao, Feilin Qu, Chao You, Yi Xiao, Ying Xu, Fenfang Chen, Xiao-Hui Zhu, Jin-Hui Li
article en

Abstract

Ipsilateral breast tumor recurrence (IBTR) is a critical prognostic determinant in breast cancer patients undergoing breast-conserving surgery (BCS). The spatial arrangement of immune, stromal, and malignant cells can influence local recurrence progression and treatment efficacy in IBTR tumors. Here, we performed single-cell spatial transcriptomics on primary breast cancer (PBC) and matched IBTR specimens from 22 patients, comprising 18 true recurrence (TR) and 4 new primary (NP) cases, to generate a comprehensive atlas of over 600,000 cells. Cellular neighborhood (CN) analysis identified a specific tumor cell-stromal region significantly enriched in IBTR tumors. Tumor recurrence was driven by a highly proliferative epithelial cell subpopulation characterized by immune exclusion and invasive features. Spatial analysis uncovered a tumor-stromal region where CD24+ tumor cells spatially intersected with BGN+ cancer-associated fibroblasts (CAFs) alongside upregulation of MDK signaling. In parallel, the BGN+ CAFs displayed enriched CXCL12 pathways, suggesting a potential spatial axis that aligns with local immunosuppressive remodeling and recurrent niche formation. Critically, the spatial colocalization of CD24⁺ tumor cells and BGN⁺ CAFs was validated as an independent predictor of recurrence after BCS. This study delivers a high-resolution spatial landscape of IBTR, uncovering distinct microenvironmental landscapes between TR and NP subtypes, and establishes these spatially defined cellular networks as both mechanistic drivers and translatable prognostic biomarkers.

Cancer Research
Fudan University Shanghai Cancer Center (CN)
Reduced inequalities
Openalex Percentile: Top 19%
Single-cell and spatial transcriptomics
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