Integrated transcriptome study reveals a stress response state CD4 +T cells related to immune tolerance in breast cancer

Lymph node status is of great clinical importance in tumor staging and prognosis where antitumoral immunity is generated and preserved. However, the immune landscape of lymph nodes before and after metastasis in breast cancer is not clear. We analyze a total of 67 samples from breast cancers and lymph nodes to profile immune cells at single-cell resolution. Additionally, 13 patients with breast cancer were enrolled to confirm our findings using flow cytometry and immunofluorescence with primary breast tumors and axillary lymph nodes. The distribution of immune cells is similar in uninvolved lymph nodes, but heterogeneity of immune cells is observed in metastatic lymph nodes (metLNs) across different subtypes of breast cancer. In metLNs, a cluster of CD4 +T cells (CD4 +Tstr cells) is predominantly found in luminal-like breast cancer with a state of anergy, probably due to immunosuppression caused by myeloid and tumor cells. By comparing scRNA and single-cell T cell receptor seq data, these un-expanded CD4+Tstr cells seldom show shared T cell receptor sequence between primary tumors and paired metLNs, therefore do not migrate from paired metLNs. Moreover, CD4+Tstr cells are associated with poor survival and predicted tolerance to immunotherapy in triple-negative breast cancer. In summary, we identify a specific cluster of CD4 +T cells related to immune tolerance that serves as a new predictor for immunotherapy in breast cancer.

Authors

Institutions

Publication Details

Journal
Breast Cancer Research
Published
2025-12-24
DOI
https://doi.org/10.1186/s13058-025-02205-4
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrated transcriptome study reveals a stress response state CD4 +T cells related to immune tolerance in breast cancer

Jinyuan Gu, Xinyu Tang, S. Wang, Wang Ji et al.
Breast Cancer Research
Cancer Immunotherapy and Biomarkers
article

Integrated transcriptome study reveals a stress response state CD4 +T cells related to immune tolerance in breast cancer

Jinyuan Gu, Xinyu Tang, S. Wang, Wang Ji, Ke Zhang, Xiaoxiang Guan, Muxin Yu, Xinrui Mao, Cong Wang, Qi Qi, Hong Pan, Wei Li, Yue Sun, Wenbin Zhou, Rong Cong, Wen Qiu
article en

Abstract

Lymph node status is of great clinical importance in tumor staging and prognosis where antitumoral immunity is generated and preserved. However, the immune landscape of lymph nodes before and after metastasis in breast cancer is not clear. We analyze a total of 67 samples from breast cancers and lymph nodes to profile immune cells at single-cell resolution. Additionally, 13 patients with breast cancer were enrolled to confirm our findings using flow cytometry and immunofluorescence with primary breast tumors and axillary lymph nodes. The distribution of immune cells is similar in uninvolved lymph nodes, but heterogeneity of immune cells is observed in metastatic lymph nodes (metLNs) across different subtypes of breast cancer. In metLNs, a cluster of CD4 +T cells (CD4 +Tstr cells) is predominantly found in luminal-like breast cancer with a state of anergy, probably due to immunosuppression caused by myeloid and tumor cells. By comparing scRNA and single-cell T cell receptor seq data, these un-expanded CD4+Tstr cells seldom show shared T cell receptor sequence between primary tumors and paired metLNs, therefore do not migrate from paired metLNs. Moreover, CD4+Tstr cells are associated with poor survival and predicted tolerance to immunotherapy in triple-negative breast cancer. In summary, we identify a specific cluster of CD4 +T cells related to immune tolerance that serves as a new predictor for immunotherapy in breast cancer.

Breast Cancer ResearchVol. 28(1)
Jiangsu Cancer Hospital (CN), The First People's Hospital of Xiaoshan District, Hangzhou (CN), Nanjing Medical University (CN)
Good health and well-being
Openalex Percentile: Top 31%
Cancer Immunotherapy and Biomarkers
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.