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The heterogeneity of tumour immune microenvironment revealing the CRABP2/CD69 signature discriminates distinct clinical outcomes in breast cancer

Abstract

Background

It has been acknowledged that the tumour immune microenvironment (TIME) plays a critical role in determining therapeutic responses and clinical outcomes in breast cancer (BrCa). Thus, the identification of the TIME features is essential for guiding therapy and prognostic assessment for BrCa.

Methods

The heterogeneous cellular composition of the TIME in BrCa by single-cell RNA sequencing (scRNA-seq). Two subtype-special genes upregulated in the tumour-rich subtype and the immune-infiltrating subtype were extracted, respectively. The CRABP2/CD69 signature was established based on CRABP2 and CD69 expression, and its predictive values for the clinical outcome and the neoadjuvant chemotherapy (NAT) responses were validated in multiple cohorts. Moreover, the oncogenic role of CRABP2 was explored in BrCa cells.

Results

Based on the heterogeneous cellular composition of the TIME in BrCa, the BrCa samples could be divided into the tumour-rich subtype and the immune-infiltrating subtype, which exhibited distinct prognosis and chemotherapeutic responses. Next, we extracted CRABP2 as the biomarker for the tumour-rich subtype and CD69 as the biomarker for the immune-infiltrating subtype. Based on the CRABP2/CD69 signature, BrCa samples were re-divided into three subtypes, and the CRABP2highCD69low subtype exhibited the worst prognosis and the lowest chemotherapeutic response, while the CRABP2lowCD69high subtype showed the opposite results. Furthermore, CARBP2 functioned as a novel oncogene in BrCa, which promoted tumour cell proliferation, migration, and invasion, and CRABP2 inhibition triggered the activation of cytotoxic T lymphocytes (CTLs).

Conclusion

The CRABP2/CD69 signature is significantly associated with the TIME features and could effectively predict the clinical outcome. Also, CRABP2 is determined to be a novel oncogene, which could be a therapeutic target in BrCa.

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Fig. 1: Integrated scRNA-seq analysis of tumour samples from BrCa patients.
Fig. 2: Recognition of molecular subtypes of BrCa patients at the single-cell levels.
Fig. 3: Recognition of molecular subtypes of BrCa patients in the RNA-seq datasets.
Fig. 4: Establishment of the CRABP2/CD69 signature.
Fig. 5: Validation of the CRABP2/CD69 signature.
Fig. 6: The cellular role of CRABP2 in BrCa.

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Data availability

Available of public BrCa datasets are described in “Methods”. Data can be provided upon reasonable request to the corresponding author.

Materials availability

Materials can be provided upon reasonable request to the corresponding author.

Code availability

The bioinformatics analysis used in this work was conducted using R 4.0.4, and the R packages that were utilised were thoroughly described in the materials and methods section. The authors are willing to provide any codes upon reasonable request.

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72:7–33.

    PubMed  Google Scholar 

  2. Byrd DR, Brierley JD, Baker TP, Sullivan DC, Gress DM. Current and future cancer staging after neoadjuvant treatment for solid tumors. CA Cancer J Clin. 2021;71:140–8.

    PubMed  Google Scholar 

  3. Kwa M, Makris A, Esteva FJ. Clinical utility of gene-expression signatures in early stage breast cancer. Nat Rev Clin Oncol. 2017;14:595–610.

    CAS  PubMed  Google Scholar 

  4. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24:541–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Ge R, Wang Z, Cheng L. Tumor microenvironment heterogeneity an important mediator of prostate cancer progression and therapeutic resistance. NPJ Precis Oncol. 2022;6:31.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Giraldo NA, Sanchez-Salas R, Peske JD, Vano Y, Becht E, Petitprez F, et al. The clinical role of the TME in solid cancer. Br J Cancer. 2019;120:45–53.

    PubMed  Google Scholar 

  7. Mao X, Xu J, Wang W, Liang C, Hua J, Liu J, et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer. 2021;20:131.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Gajewski TF. The next hurdle in cancer immunotherapy: overcoming the non-T-cell-inflamed tumor microenvironment. Semin Oncol. 2015;42:663–71.

    PubMed  PubMed Central  Google Scholar 

  9. Mao W, Cai Y, Chen D, Jiang G, Xu Y, Chen R, et al. Statin shapes inflamed tumor microenvironment and enhances immune checkpoint blockade in non-small cell lung cancer. JCI Insight. 2022;7:e161940.

  10. Ladoire S, Arnould L, Apetoh L, Coudert B, Martin F, Chauffert B, et al. Pathologic complete response to neoadjuvant chemotherapy of breast carcinoma is associated with the disappearance of tumor-infiltrating foxp3+ regulatory T cells. Clin Cancer Res. 2008;14:2413–20.

    CAS  PubMed  Google Scholar 

  11. Ueno T, Kitano S, Masuda N, Ikarashi D, Yamashita M, Chiba T, et al. Immune microenvironment, homologous recombination deficiency, and therapeutic response to neoadjuvant chemotherapy in triple-negative breast cancer: Japan Breast Cancer Research Group (JBCRG)22 TR. BMC Med. 2022;20:136.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Shepherd JH, Ballman K, Polley MC, Campbell JD, Fan C, Selitsky S, et al. CALGB 40603 (alliance): long-term outcomes and genomic correlates of response and survival after neoadjuvant chemotherapy with or without carboplatin and bevacizumab in triple-negative breast cancer. J Clin Oncol. 2022;40:1323–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2:401–4.

    PubMed  Google Scholar 

  14. de Ronde JJ, Lips EH, Mulder L, Vincent AD, Wesseling J, Nieuwland M, et al. SERPINA6, BEX1, AGTR1, SLC26A3, and LAPTM4B are markers of resistance to neoadjuvant chemotherapy in HER2-negative breast cancer. Breast Cancer Res Treat. 2013;137:213–23.

    CAS  PubMed  Google Scholar 

  15. Chen J, Hao L, Qian X, Lin L, Pan Y, Han X. Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients. Front Immunol. 2022;13:948601.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16:1289–96.

    CAS  PubMed Central  Google Scholar 

  18. Chu T, Wang Z, Pe’er D, Danko CG. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer. 2022;3:505–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26:1572–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Seiler M, Huang CC, Szalma S, Bhanot G. ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data. OMICS. 2010;14:109–13.

    CAS  PubMed  Google Scholar 

  21. Lovmar L, Ahlford A, Jonsson M, Syvanen AC. Silhouette scores for assessment of SNP genotype clusters. BMC Genomics. 2005;6:35.

    PubMed  PubMed Central  Google Scholar 

  22. Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020;15:1484–506.

    CAS  PubMed  Google Scholar 

  23. Cai Y, Ji W, Sun C, Xu R, Chen X, Deng Y, et al. Interferon-induced transmembrane protein 3 shapes an inflamed tumor microenvironment and identifies immuno-hot tumors. Front Immunol. 2021;12:704965.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.

    PubMed  Google Scholar 

  25. Wang Y, Deng J, Wang L, Zhou T, Yang J, Tian Z, et al. Expression and clinical significance of PD-L1, B7-H3, B7-H4 and VISTA in craniopharyngioma. J Immunother Cancer. 2019;7:1–9.

  26. Chen L, Dong J, Li Z, Chen Y, Zhang Y. The B7H4-PDL1 classifier stratifies immuno-phenotype in cervical cancer. Cancer cell Int. 2022;22:3.

    PubMed  PubMed Central  Google Scholar 

  27. Mei J, Liu Y, Yu X, Hao L, Ma T, Zhan Q, et al. YWHAZ interacts with DAAM1 to promote cell migration in breast cancer. Cell Death Discov. 2021;7:221.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Salgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol. 2015;26:259–71.

    CAS  PubMed  Google Scholar 

  29. Mei J, Cai Y, Wang H, Xu R, Zhou J, Lu J, et al. Formin protein DIAPH1 positively regulates PD-L1 expression and predicts the therapeutic response to anti-PD-1/PD-L1 immunotherapy. Clin Immunol. 2022;246:109204.

  30. McCracken MN, Cha AC, Weissman IL. Molecular pathways: activating T cells after cancer cell phagocytosis from blockade of CD47 “don’t eat me” signals. Clin Cancer Res. 2015;21:3597–601.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Muraoka RS, Dumont N, Ritter CA, Dugger TC, Brantley DM, Chen J, et al. Blockade of TGF-beta inhibits mammary tumor cell viability, migration, and metastases. J Clin Investig. 2002;109:1551–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Mir H, Kapur N, Gales DN, Sharma PK, Oprea-Ilies G, Johnson AT, et al. CXCR6-CXCL16 axis promotes breast cancer by inducing oncogenic signaling. Cancers. 2021;13:3568.

  33. Xiao G, Wang X, Wang J, Zu L, Cheng G, Hao M, et al. CXCL16/CXCR6 chemokine signaling mediates breast cancer progression by pERK1/2-dependent mechanisms. Oncotarget. 2015;6:14165–78.

    PubMed  PubMed Central  Google Scholar 

  34. Pitt JM, Marabelle A, Eggermont A, Soria JC, Kroemer G, Zitvogel L. Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy. Ann Oncol. 2016;27:1482–92.

    CAS  PubMed  Google Scholar 

  35. Zhang X, Zhao L, Zhang H, Zhang Y, Ju H, Wang X, et al. The immunosuppressive microenvironment and immunotherapy in human glioblastoma. Front Immunol. 2022;13:1003651.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Ruffin AT, Li H, Vujanovic L, Zandberg DP, Ferris RL, Bruno TC. Improving head and neck cancer therapies by immunomodulation of the tumour microenvironment. Nat Rev Cancer. 2022;23:173–88.

  37. Ding S, Chen X, Shen K. Single-cell RNA sequencing in breast cancer: understanding tumor heterogeneity and paving roads to individualized therapy. Cancer Commun. 2020;40:329–44.

    Google Scholar 

  38. Wang S, Xiong Y, Zhang Q, Su D, Yu C, Cao Y, et al. Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer. Brief Bioinform. 2021;22:bbaa311.

  39. Bagaev A, Kotlov N, Nomie K, Svekolkin V, Gafurov A, Isaeva O, et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell. 2021;39:845–65 e7.

    CAS  PubMed  Google Scholar 

  40. Berger AC, Korkut A, Kanchi RS, Hegde AM, Lenoir W, Liu W, et al. A comprehensive Pan-cancer molecular study of gynecologic and breast cancers. Cancer Cell. 2018;33:690–705 e9.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Cui K, Yao S, Liu B, Sun S, Gong L, Li Q, et al. A novel high-risk subpopulation identified by CTSL and ZBTB7B in gastric cancer. Br J Cancer. 2022;127:1450–60.

  42. Cui K, Yao S, Zhang H, Zhou M, Liu B, Cao Y, et al. Identification of an immune overdrive high-risk subpopulation with aberrant expression of FOXP3 and CTLA4 in colorectal cancer. Oncogene. 2021;40:2130–45.

    CAS  PubMed  Google Scholar 

  43. Gonzalez-Amaro R, Cortes JR, Sanchez-Madrid F, Martin P. Is CD69 an effective brake to control inflammatory diseases? Trends Mol Med. 2013;19:625–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Cibrian D, Sanchez-Madrid F. CD69: from activation marker to metabolic gatekeeper. Eur J Immunol. 2017;47:946–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Gorabi AM, Hajighasemi S, Kiaie N, Gheibi Hayat SM, Jamialahmadi T, Johnston TP, et al. The pivotal role of CD69 in autoimmunity. J Autoimmun. 2020;111:102453.

    CAS  PubMed  Google Scholar 

  46. Hu ZW, Sun W, Wen YH, Ma RQ, Chen L, Chen WQ, et al. CD69 and SBK1 as potential predictors of responses to PD-1/PD-L1 blockade cancer immunotherapy in lung cancer and melanoma. Front Immunol. 2022;13:952059.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Kim HD, Jeong S, Park S, Lee YJ, Ju YS, Kim D, et al. Implication of CD69(+) CD103(+) tissue-resident-like CD8(+) T cells as a potential immunotherapeutic target for cholangiocarcinoma. Liver Int. 2021;41:764–76.

    CAS  PubMed  Google Scholar 

  48. Salazar J, Guardiola M, Ferre R, Coll B, Alonso-Villaverde C, Winklhofer-Roob BM, et al. Association of a polymorphism in the promoter of the cellular retinoic acid-binding protein II gene (CRABP2) with increased circulating low-density lipoprotein cholesterol. Clin Chem Lab Med. 2007;45:615–20.

    CAS  PubMed  Google Scholar 

  49. Tang X, Liang Y, Sun G, He Q, Hou Z, Jiang X, et al. Upregulation of CRABP2 by TET1-mediated DNA hydroxymethylation attenuates mitochondrial apoptosis and promotes oxaliplatin resistance in gastric cancer. Cell Death Dis. 2022;13:848.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Liu CL, Hsu YC, Kuo CY, Jhuang JY, Li YS, Cheng SP. CRABP2 is associated with thyroid cancer recurrence and promotes invasion via the integrin/FAK/AKT pathway. Endocrinology. 2022;163:bqac171.

  51. Xie T, Tan M, Gao Y, Yang H. CRABP2 accelerates epithelial mesenchymal transition in serous ovarian cancer cells by promoting TRIM16 methylation via upregulating EZH2 expression. Environ Toxicol. 2022;37:1957–67.

    CAS  PubMed  Google Scholar 

  52. Wu JI, Lin YP, Tseng CW, Chen HJ, Wang LH. Crabp2 promotes metastasis of lung cancer cells via HuR and integrin beta1/FAK/ERK signaling. Sci Rep. 2019;9:845.

    PubMed  PubMed Central  Google Scholar 

  53. Zhao Y, Sun H, Zheng J, Shao C, Zhang D. Identification of predictors based on drug targets highlights accurate treatment of goserelin in breast and prostate cancer. Cell Biosci. 2021;11:5.

    CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by the Precision Medicine Project of Wuxi Municipal Health Commission (J202106), the Maternal and Child Health Research Project of Jiangsu Province (F202034), the Major project of Wuxi Science and Technology Bureau (N20201006), the 333 Project of Province (BRA2020380), the Wujieping Project (320.6750.2022-19-38).

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Authors and Affiliations

Authors

Contributions

YZ, XP and TX designed and performed the experiments. JM, YC, LC, YW and JL conducted the data analysis of single-cell sequencing. JM, ZQ, YJ and PZ performed the statistics and analysis of clinical data. JL and YJ helped the research methods. YC handled the processing of single-cell RNA sequencing. JM, YC, LC and YW organised the data and wrote the manuscript. YZ, XP and TX supervised the study. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Tiansong Xia, Xiang Pan or Yan Zhang.

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The authors declare no competing interests.

Ethics approval and consent to participate

The collection of the cohort 1 and the cohort 6 was approved by the institutional review board at Wuxi Maternal and Child Health Hospital (2021-01-0927-28), and the collection of the cohort 8 was approved by the Clinical Research Ethics Committee in Outdo Biotech (YB-M-05-02). The cohort 2, the cohort 3, the cohort 4, the cohort 5, and the cohort 7 were public cohorts and no ethical approval was needed. All experiments were performed in accordance with the Declaration of Helsinki, and informed consent was obtained from all subjects.

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Mei, J., Cai, Y., Chen, L. et al. The heterogeneity of tumour immune microenvironment revealing the CRABP2/CD69 signature discriminates distinct clinical outcomes in breast cancer. Br J Cancer 129, 1645–1657 (2023). https://doi.org/10.1038/s41416-023-02432-6

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