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Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer

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Abstract

Background

Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated.

Methods

We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out.

Results

According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells.

Conclusions

Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity.

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Data availability

The authors declare that there are no primary datasets or computer codes associated with this study. All the data and materials are available to the researchers once published.

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Acknowledgements

We thank the breast cancer patients from Yunnan Cancer Hospital who participated in this study and acknowledge the contributions to this study of the entire research team.

Funding

The present study was supported by the National Natural Science Foundation of China (grant no. 81960542, 81960517, 82260575, 82360345, and 82360614), the Science and Technology Project of Yunnan Provincial Science and Technology Department (grant no. 202001AU070053, 202001AU070093,202201AY070001-169, and 202401AT070005), the Yunnan Health Training Project of High Level Talents (grant no. H-2019075), the Natural Science Foundation of Yunnan Province (subtitle: major basic research project) (grant no. 202001BC070001, 202102AA100053), the Beijing Science and Technology Innovation Medical Development Foundation (grant no. KC2021-JK-0044–6), the Scientific Research Foundation of the Education Department of Yunnan Province (grant no. 2024J0261), Kunming Medical Joint Special Project - Major Project (grant no. 202401AY070001-042), and Kunming Medical Joint Special Project - General Project (grant no. 202401AY070001-268), and the Yunnan Provincial Department of Education Scientific Research Fund Project (grant no.2024Y237). Wu Jieping Medical Foundation Tumor Targeted Research Special Fund, Grand Number: 320.6750.2021-10-73.

Author information

Author notes

  1. Shicong Tang, Qing Wang, Ke Sun and Ying Song contributed equally to this work.

Authors and Affiliations

  1. Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People’s Republic of China

    Shicong Tang, Qing Wang, Ying Song, Rui Liu, Xin Tan, Huimeng Li, Yafeng Lv, Fuying Yang, Jiawen Zhao, Sijia Li, Pingping Bi, Jiali Yang, Kai Zheng & Rong Guo

  2. State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People’s Republic of China

    Ke Sun & Kai Chen

  3. Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan, 650500, People’s Republic of China

    Ke Sun & Kai Chen

  4. State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People’s Republic of China

    Zhengna Zhu

  5. Department of Ultrasound, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People’s Republic of China

    Dong Chen, Zhirui Chuan & Xiaomao Luo

  6. Department of Pathology, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People’s Republic of China

    Zaoxiu Hu & Ying Liu

  7. Department of Radiology, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People’s Republic of China

    Zhenhui Li & Tengfei Ke

  8. Key Laboratory of Animal Models and Human, Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China

    Dewei Jiang

  9. Kunming College of Life Sciences, University of Chinese Academy Sciences, Kunming, Yunnan, China

    Dewei Jiang

  10. Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of China

    Rirong Yang

  11. Department of Immunology, School of Basic Medical Sciences, Guangxi Medical University, Guangxi, 530021, People’s Republic of China

    Rirong Yang

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  1. Shicong Tang
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  2. Qing Wang
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  3. Ke Sun
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  4. Ying Song
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  5. Rui Liu
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  6. Xin Tan
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Contributions

SCT, RRY, CK and RG conceived the project and revised the manuscript. QW, KS, RL, XT, HML, YFL, FYY, SJL, PPB, JLY and YS performed the experiments. QW, ZNZ,JWZ and DWJ analyzed the data. DC, ZRC and XML assessed the tumor blood supply before sample collection. ZXH, LY, ZHL and TFK diagnosed BC and performed the experiments. RL and KZ collected patient data.

Corresponding authors

Correspondence to Shicong Tang, Rirong Yang, Kai Chen or Rong Guo.

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Ethics approval and consent to participate

This study involved human participants and was approved by the Ethics Committee of Yunnan Cancer Hospital (No. KY201944). Prior to participation, subjects provided informed consent to participate in the investigation.

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All the authors declare no competing interests.

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Supplementary Information

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Supplementary

Figure S1 Functional status analysis of BC malignant epithelial cells. (A) Heatmap depicting the genes highly expressed in the BC malignant epithelial cell subclusters. (B) GO enrichment pathway analysis of malignant epithelial cell subsets. (C) KEGG enrichment pathway analysis of malignant epithelial cell subsets. (D) Overall survival Kaplan‒Meier curves for BC patients with high and low CD74, FDCSP and KRT81 expression levels. (TIF 11211 kb)

Supplementary Figure S2

Effect of metabolic heterogeneity on chemotherapy response. (A) IHC analysis of key enzymes involved in the TCA cycle, glycolysis, and fatty acid synthesis in patients exhibiting resistance to neoadjuvant chemotherapy. (B) IHC analysis of key enzymes involved in the TCA cycle, glycolysis, and fatty acid synthesis in patients with a favorable response to neoadjuvant chemotherapy. (TIF 23146 kb)

Supplementary Figure S3

Outgoing and incoming communication patterns between tumor cells and immune cells. (TIFF 4834 kb)

Supplementary Figure S4

Functional status analysis of CAFs. (A) tSNE diagram showing the distribution of CAFs in various types of BC tissues and normal tissues. (B) Proportions of CAF subsets in the classification of BC and normal tissues. (C) Hallmark enrichment pathway analysis of CAF subsets. (TIF 3684 kb)

Supplementary Figure S5

Functional status analysis of T cells. (A) t-SNE diagram showing the distribution of T cells in various types of BC and normal tissues. (B) Proportions of T-cell subsets in the classification of BC tissues and normal tissues. (C) Dot plot showing the immune checkpoint genes of the T-cell subclusters. (D) GO enrichment pathway analysis of T-cell subsets. (E) KEGG enrichment pathway analysis of T-cell subsets. (F) Violin plot displaying the expression levels of various marker genes in ten T-cell clusters. (TIF 9643 kb)

Supplementary Figure S6

Trajectory analysis of T cells. (A) Development path of T cells associated with monocle2. (B) Heatmap depicting the relative expression of T-cell markers along the estimated route. (C) Trajectory analysis of 10 subclusters of T cells. (TIF 4485 kb)

Supplementary Figure S7

Expression levels of different BC subtypes. (A) Circos maps depicting the functional enrichment results of tumor epithelial cells in TNBC patients. (B) Circos maps depicting the functional enrichment results of tumor epithelial cells in HER2+ BC patients. (C) Circos maps depicting the functional enrichment results of tumor epithelial cells in luminal BC patients. (D) Circos maps depicting the functional enrichment results of tumor epithelial cells in TNBC patients in the BC cohort (GSE176078). (E) Circos maps depicting the functional enrichment results of tumor epithelial cells in HER2+ BC patients in the BC cohort (GSE176078). (F) Circos maps depicting the functional enrichment results of tumor epithelial cells in luminal BC patients in the BC cohort (GSE176078). (G) Heatmap of average metabolic gene expression levels in the TCA cycle, glycolysis and gluconeogenesis, and fatty acid biosynthesis in different types of BC. (TIF 46649 kb)

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Tang, S., Wang, Q., Sun, K. et al. Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer. Apoptosis (2024). https://doi.org/10.1007/s10495-024-01952-7

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