Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

npj Precision Oncology
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. npj precision oncology
  3. articles
  4. article
COL3A1high cancer-associated fibroblasts orchestrate metabolic and immune microenvironments to confer chemoresistance in breast cancer
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 23 February 2026

COL3A1high cancer-associated fibroblasts orchestrate metabolic and immune microenvironments to confer chemoresistance in breast cancer

  • Peicheng Jiang1 na1,
  • Xinyan Li2 na1,
  • Ziyi Wang3,
  • Su Li4,
  • Yonglian Huang5,
  • Ye-Xiong Li1,
  • Yuqiong Chen6 na1 &
  • …
  • Xiangyu Sun5 

npj Precision Oncology , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Cancer
  • Oncology

Abstract

Chemoresistance remains a critical challenge in breast cancer (BC) treatment. By integrating multi-omics (single-cell, spatial, and bulk transcriptomics) with clinical validation, we identified a specific COL3Ahigh CAF subset that drives BC chemoresistance. Mechanistically, these CAFs undergo lipid metabolic reprogramming, secreting excess oleic acid via SCD. This oleic acid binds to ENO1 on tumor cells, activating the PI3K/Akt pathway and inhibiting chemotherapy-induced apoptosis. Simultaneously, COL3Ahigh CAFs orchestrate an immunosuppressive niche by recruiting regulatory T cells and impairing cytotoxic CD8+ T cells. Our findings establish COL3Ahigh CAFs as key mediators of resistance through metabolic symbiosis and immune evasion. The strong correlation between COL3Ahigh CAF abundance and clinical poor response highlights their potential as both predictive biomarkers and therapeutic targets to overcome chemoresistance in BC patients.

Data availability

The sequencing data utilized in this study are publicly available from the databases referenced in the manuscript. Other data supporting the findings are available from the corresponding author upon reasonable request.

Code availability

Relevant code is accessible through the corresponding author(s) upon reasonable request.

References

  1. Santucci, C. et al. European cancer mortality predictions for the year 2025 with focus on breast cancer. Ann. Oncol. 36, 460–468 (2025).

    Google Scholar 

  2. Waks, A. G. & Winer, E. P. Breast cancer treatment: a review. Jama 321, 288–300 (2019).

    Google Scholar 

  3. Harbeck, N. et al. Breast cancer. Nat. Rev. Dis. Prim. 5, 66 (2019).

    Google Scholar 

  4. Gascard, P. & Tlsty, T. D. Carcinoma-associated fibroblasts: orchestrating the composition of malignancy. Genes Dev. 30, 1002–1019 (2016).

    Google Scholar 

  5. Liao, Z., Tan, Z. W., Zhu, P. & Tan, N. S. Cancer-associated fibroblasts in tumor microenvironment—accomplices in tumor malignancy. Cell Immunol. 343, 103729 (2019).

    Google Scholar 

  6. Chen, C. et al. Crosstalk between cancer-associated fibroblasts and regulated cell death in tumors: insights into apoptosis, autophagy, ferroptosis, and pyroptosis. Cell Death Discov. 10, 189 (2024).

    Google Scholar 

  7. Öhlund, D. et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J. Exp. Med. 214, 579–596 (2017).

    Google Scholar 

  8. Butti, R., Khaladkar, A., Bhardwaj, P. & Prakasam, G. Heterotypic signaling of cancer-associated fibroblasts in shaping the cancer cell drug resistance. Cancer Drug Resist. 6, 182–204 (2023).

    Google Scholar 

  9. Chen, X., Chen, S. & Yu, D. Metabolic reprogramming of chemoresistant cancer cells and the potential significance of metabolic regulation in the reversal of cancer chemoresistance. Metabolites 10, 289 (2020).

  10. Linares, J., Marín-Jiménez, J. A., Badia-Ramentol, J. & Calon, A. Determinants and functions of CAFs secretome during cancer progression and therapy. Front. Cell Dev. Biol. 8, 621070 (2020).

    Google Scholar 

  11. Maeda, A. et al. The interaction between cancer-associated fibroblasts and cancer cells enhances Bcl-xL and Mcl-1 in colorectal cancer. Anticancer Res. 42, 1277–1288 (2022).

    Google Scholar 

  12. Bian, L., Sun, X., Jin, K. & He, Y. Oral cancer-associated fibroblasts inhibit heat-induced apoptosis in Tca8113 cells through upregulated expression of Bcl-2 through the Mig/CXCR3 axis. Oncol. Rep. 28, 2063–2068 (2012).

    Google Scholar 

  13. Li, Y. et al. Cancer-Associated Fibroblasts Hinder Lung Squamous Cell Carcinoma Oxidative Stress-Induced Apoptosis via METTL3 Mediated m(6)A Methylation of COL10A1. Oxid. Med. Cell. Longev. 2022, 4320809 (2022).

    Google Scholar 

  14. Zhang, Q., An, Z. Y., Jiang, W., Jin, W. L. & He, X. Y. Collagen code in tumor microenvironment: functions, molecular mechanisms, and therapeutic implications. Biomed. Pharmacother. 166, 115390 (2023).

    Google Scholar 

  15. Xu, K. et al. Distinct fibroblast subpopulations associated with bone, brain or intrapulmonary metastasis in advanced non-small-cell lung cancer. Clin. Transl. Med. 14, e1605 (2024).

    Google Scholar 

  16. LaRue, M. M. et al. Metabolic reprogramming of tumor-associated macrophages by collagen turnover promotes fibrosis in pancreatic cancer. Proc. Natl. Acad. Sci. USA 119, e2119168119 (2022).

    Google Scholar 

  17. Chen, Y. et al. Oncogenic collagen I homotrimers from cancer cells bind to α3β1 integrin and impact tumor microbiome and immunity to promote pancreatic cancer. Cancer Cell 40, 818–834.e9 (2022).

    Google Scholar 

  18. Li, G. et al. Collagen-targeted tumor-specific transepithelial penetration enhancer mediated intravesical chemoimmunotherapy for non-muscle-invasive bladder cancer. Biomaterials 283, 121422 (2022).

    Google Scholar 

  19. Zhang, J. Y. et al. Cancer-associated fibroblasts promote oral squamous cell carcinoma progression through LOX-mediated matrix stiffness. J. Transl. Med. 19, 513 (2021).

    Google Scholar 

  20. Zhou, Y., Jiang, Z., Cao, L. & Yang, J. The role of various collagen types in tumor biology: a review. Front. Oncol. 15, 1549797 (2025).

    Google Scholar 

  21. Zhang, H. et al. Data mining-based study of collagen type III alpha 1 (COL3A1) prognostic value and immune exploration in pan-cancer. Bioengineered 12, 3634–3646 (2021).

    Google Scholar 

  22. Stewart, D. C. et al. Prognostic and therapeutic implications of tumor-restrictive type III collagen in the breast cancer microenvironment. NPJ Breast Cancer 10, 86 (2024).

    Google Scholar 

  23. Ren, J., Zhao, S. & Lai, J. Role and mechanism of COL3A1 in regulating the growth, metastasis, and drug sensitivity in cisplatin-resistant non-small cell lung cancer cells. Cancer Biol. Ther. 25, 2328382 (2024).

    Google Scholar 

  24. Sun, Y. et al. Integrative plasma and fecal metabolomics identify functional metabolites in adenoma-colorectal cancer progression and as early diagnostic biomarkers. Cancer Cell 42, 1386–400.e8 (2024).

    Google Scholar 

  25. Chen, Y., McAndrews, K. M. & Kalluri, R. Clinical and therapeutic relevance of cancer-associated fibroblasts. Nat. Rev. Clin. Oncol. 18, 792–804 (2021).

    Google Scholar 

  26. Biffi, G. & Tuveson, D. A. Diversity and biology of cancer-associated fibroblasts. Physiol. Rev. 101, 147–176 (2021).

    Google Scholar 

  27. Costa, A. et al. Fibroblast heterogeneity and immunosuppressive environment in human breast cancer. Cancer Cell 33, 463–79.e10 (2018).

    Google Scholar 

  28. Yan, Y. et al. Multi-omic profiling highlights factors associated with resistance to immuno-chemotherapy in non-small-cell lung cancer. Nat. Genet 57, 126–139 (2025).

    Google Scholar 

  29. Xiao, L. et al. Tumor endothelial cells with distinct patterns of TGFβ-driven endothelial-to-mesenchymal transition. Cancer Res. 75, 1244–1254 (2015).

    Google Scholar 

  30. Mousset, A. et al. Neutrophil extracellular traps formed during chemotherapy confer treatment resistance via TGF-β activation. Cancer Cell 41, 757–75.e10 (2023).

    Google Scholar 

  31. Singh, A. & Settleman, J. EMT, cancer stem cells and drug resistance: an emerging axis of evil in the war on cancer. Oncogene 29, 4741–4751 (2010).

    Google Scholar 

  32. Saikia, S. et al. Reprogramming of lipid metabolism in cancer: new insight into pathogenesis and therapeutic strategies. Curr. Pharm. Biotechnol. 24, 1847–1858 (2023).

    Google Scholar 

  33. Liu, S. et al. Metabolic reprogramming and therapeutic resistance in primary and metastatic breast cancer. Mol. Cancer 23, 261 (2024).

    Google Scholar 

  34. Yang, D. et al. Utilization of adipocyte-derived lipids and enhanced intracellular trafficking of fatty acids contribute to breast cancer progression. Cell Commun. Signal. 16, 32 (2018).

    Google Scholar 

  35. Yu, L. et al. Tumor-derived arachidonic acid reprograms neutrophils to promote immune suppression and therapy resistance in triple-negative breast cancer. Immunity 58, 909–925.e7 (2025).

    Google Scholar 

  36. Roongta, U. V. et al. Cancer cell dependence on unsaturated fatty acids implicates stearoyl-CoA desaturase as a target for cancer therapy. Mol. Cancer Res. 9, 1551–1561 (2011).

    Google Scholar 

  37. Griffiths, B. et al. Sterol regulatory element binding protein-dependent regulation of lipid synthesis supports cell survival and tumor growth. Cancer Metab. 1, 3 (2013).

    Google Scholar 

  38. Williams, K. J. et al. An essential requirement for the SCAP/SREBP signaling axis to protect cancer cells from lipotoxicity. Cancer Res. 73, 2850–2862 (2013).

    Google Scholar 

  39. Lai, J. I., Chao, T. C., Liu, C. Y., Huang, C. C. & Tseng, L. M. A systemic review of taxanes and their side effects in metastatic breast cancer. Front. Oncol. 12, 940239 (2022).

    Google Scholar 

  40. Gradishar, W. J. et al. Breast cancer, version 3.2024, NCCN clinical practice guidelines in oncology. J. Natl. Compr. Cancer Netw. 22, 331–357 (2024).

    Google Scholar 

  41. Huang, T. et al. Current perspectives and trends of CD39-CD73-eAdo/A2aR research in tumor microenvironment: a bibliometric analysis. Front. Immunol. 15, 1427380 (2024).

    Google Scholar 

  42. Luo, L. et al. Single-cell RNA sequencing identifies molecular biomarkers predicting late progression to CDK4/6 inhibition in patients with HR+/HER2- metastatic breast cancer. Mol. Cancer 24, 48 (2025).

    Google Scholar 

  43. Liu, Z. et al. THBS2-producing matrix CAFs promote colorectal cancer progression and link to poor prognosis via the CD47-MAPK axis. Cell Rep. 44, 115555 (2025).

    Google Scholar 

  44. Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    Google Scholar 

  45. Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Google Scholar 

  46. Tran, K. A. et al. Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures. Nat. Commun. 14, 5758 (2023).

    Google Scholar 

  47. Sun, D. et al. Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data. Nat. Biotechnol. 40, 527–538 (2022).

    Google Scholar 

  48. Chu, T., Wang, Z., Pe’er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat. Cancer 3, 505–517 (2022).

    Google Scholar 

  49. Fustero-Torre, C. et al. Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data. Genome Med. 13, 187 (2021).

    Google Scholar 

  50. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Google Scholar 

  51. Ru, B., Huang, J., Zhang, Y., Aldape, K. & Jiang, P. Estimation of cell lineages in tumors from spatial transcriptomics data. Nat. Commun. 14, 568 (2023).

    Google Scholar 

Download references

Acknowledgements

We would like to express our gratitude to all the patients who participated in this study. This work was supported by National Natural Science Foundation of China (82403430), the Technology Program Joint Fund of Liaoning Province (2023-BSBA-207), Oncology Project of Liaoning Cancer Hospital (2024-ZLKF-09), Tianjin Key Medical Discipline Construction Project (TJYXZDXK-3-003A) and Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0502200).

Author information

Author notes
  1. These authors contributed equally: Peicheng Jiang, Xinyan Li, Yuqiong Chen.

Authors and Affiliations

  1. State Key Laboratory of Molecular Oncology and Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Peicheng Jiang & Ye-Xiong Li

  2. Center for Precision Cancer Medicine & Translational Research, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, Tianjin, China

    Xinyan Li

  3. Department of Surgical Oncology and General Surgery, First Hospital of China Medical University, Shenyang, Liaoning Province, China

    Ziyi Wang

  4. Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Shanghai, China

    Su Li

  5. Department of Breast Surgery, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang, China

    Yonglian Huang & Xiangyu Sun

  6. Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China

    Yuqiong Chen

Authors
  1. Peicheng Jiang
    View author publications

    Search author on:PubMed Google Scholar

  2. Xinyan Li
    View author publications

    Search author on:PubMed Google Scholar

  3. Ziyi Wang
    View author publications

    Search author on:PubMed Google Scholar

  4. Su Li
    View author publications

    Search author on:PubMed Google Scholar

  5. Yonglian Huang
    View author publications

    Search author on:PubMed Google Scholar

  6. Ye-Xiong Li
    View author publications

    Search author on:PubMed Google Scholar

  7. Yuqiong Chen
    View author publications

    Search author on:PubMed Google Scholar

  8. Xiangyu Sun
    View author publications

    Search author on:PubMed Google Scholar

Contributions

P.J.: Conceptualization, methodology, formal analysis, investigation, data curation, visualization, writing—original draft. X.L.: Investigation, methodology, visualization, writing—original draft, funding acquisition. Z.W.: Investigation, methodology; S.L.: Investigation, writing—editing; Y.H.: Clinical samples collection; Y.L.: Funding acquisition, project administration, resources, supervision; Y.C.: Conceptualization, supervision, validation, writing—review. X.S.: Conceptualization, funding acquisition, project administration, supervision, methodology, investigation, writing—review & editing.

Corresponding authors

Correspondence to Ye-Xiong Li, Yuqiong Chen or Xiangyu Sun.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplemental Information

Supplementary Table 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, P., Li, X., Wang, Z. et al. COL3A1high cancer-associated fibroblasts orchestrate metabolic and immune microenvironments to confer chemoresistance in breast cancer. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-026-01338-9

Download citation

  • Received: 29 July 2025

  • Accepted: 12 February 2026

  • Published: 23 February 2026

  • DOI: https://doi.org/10.1038/s41698-026-01338-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Collections
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • Aims & Scope
  • Content types
  • Journal Information
  • Open Access
  • About the Editors
  • Contact
  • Calls for Papers
  • Editorial policies
  • Journal Metrics
  • About the Partner

Publish with us

  • For Authors and Referees
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

npj Precision Oncology (npj Precis. Onc.)

ISSN 2397-768X (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer