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A novel immune checkpoint-related signature for prognosis and immune analysis in breast cancer

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Abstract

Breast cancer is one of the most prevailing forms of cancer globally. Immunotherapy has demonstrated efficacy in improving the overall survival of breast cancer. The aim of us was to formulate a novel signature predicated on immune checkpoint-related genes (ICGs) that could anticipate the prognosis and further analyze the immune status of patients with breast cancer. After acquiring data, we pinpointed the definitive ICGs for constructing the prognostic model of breast cancer. We constructed a novel prognostic model and created a fresh risk score called Immune Checkpoint-related Risk Score in breast cancer (ICRSBC). The nomogram was constructed to evaluate the accuracy of the model, and the new web-based tool was created to be more intuitive for predicting prognosis. We also investigated immunotherapy responsiveness and analyzed the tumor mutational burden (TMB) in ICRSBC subgroups. The ICRSBC was found to have significant correlations with the immune environment, immunotherapy responsiveness, and TMB. The expression levels of the 9 ICGs that construct the prognostic model and their promoter methylation levels are significantly different between breast cancer and normal tissues. Furthermore, the mutation profiles, the copy number alterations, and the levels of protein expression also exhibit marked disparities among the 9 ICGs. We have identified and validated a novel signature related to ICGs that is strongly associated with breast cancer progression. This signature enables us to create a risk score for prognosticating the survival and assessing the immune status of individuals affected by breast cancer.

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Data availability

The raw data utilized in this study are obtainable through the Gene Expression Omnibus (GEO) under accession number GSE42568 (https://www.ncbi.nlm.nih.gov/geo/). Additional data and materials are available from the University of California, Santa Cruz (UCSC), Xena browser (https://xenabrowser.net/). Access to the data is subject to any restrictions required by the relevant ethics committees, and requests will be assessed on an individual basis. Code used for analysis is available upon request from the corresponding author.

Abbreviations

ICGs:

Immune checkpoint-related genes

TCGA:

The Cancer Genome Atlas

GEO:

Gene Expression Omnibus

ICRSBC:

Immune checkpoint-related risk score in breast cancer

IPS:

Immune phenotype score

TMB:

Tumor mutational burden

TME:

Tumor microenvironment

IARC:

International Agency for Research on Cancer

DCA:

Decision curve analysis

PFS:

Progression-free survival

OS:

Overall survival

PD-1:

Programmed death 1

PD-L1:

Programmed death-ligand 1

CTLA-4:

Cytotoxic T lymphocyte-associated antigen 4

LASSO:

Least absolute shrinkage selection operator

KM:

Kaplan–Meier

RECIST:

Response evaluation criteria in solid tumors

irRC:

Immune-related response criteria

irRECIST:

Immune-related response evaluation criteria in solid tumors

iRECIST:

Immune response evaluation criteria in solid tumors

WGCNA:

Weighted gene co-expression network analysis

GO:

Gene ontology

KEGG:

Kyoto encyclopedia of genes and genomes

DO:

Disease ontology

ROC:

Receiver operating characteristic

AUC:

Area under the curve

RMS:

Root mean square

GSVA:

Gene set variation analysis

GSEA:

Gene set enrichment analysis

SNP:

Single-nucleotide polymorphism

SNV:

Single-nucleotide variant

AMPK:

Adenosine 5′-monophosphate (AMP)-activated protein kinase

FOX:

Forkhead box

CPTAC:

Clinical proteomic tumor analysis consortium

AAT:

Alpha 1-antitrypsin

ACT:

Alpha 1-antichymotrypsin

References

  1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  2. McDonald ES, Clark AS, Tchou J, Zhang P, Freedman GM. Clinical diagnosis and management of breast cancer. J Nucl Med. 2016;57 Suppl 1(1535–5667 (Electronic)):9S-16S.

    Article  PubMed  Google Scholar 

  3. Condorelli R, Vaz-Luis I. Managing side effects in adjuvant endocrine therapy for breast cancer. Expert Rev Anticancer Ther. 2018;18(11):1101–12.

    Article  CAS  PubMed  Google Scholar 

  4. Tan S, Li D, Zhu X. Cancer immunotherapy: pros, cons and beyond. Biomed Pharmacother. 2020;124(1950–6007 (Electronic)):109821.

    Article  PubMed  Google Scholar 

  5. O’Donnell JS, Teng MWL, Smyth MJ. Cancer immunoediting and resistance to T cell-based immunotherapy. Nat Rev Clin Oncol. 2019;16(3):151–67.

    Article  CAS  PubMed  Google Scholar 

  6. Lei X, Lei Y, Li JK, et al. Immune cells within the tumor microenvironment: biological functions and roles in cancer immunotherapy. Cancer Lett. 2020;470(1872-7980 (Electronic)):126–33.

    Article  CAS  PubMed  Google Scholar 

  7. Bates JP, Derakhshandeh R, Jones L, Webb TJ. Mechanisms of immune evasion in breast cancer. BMC Cancer. 2018;18(1):556.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Barzaman K, Moradi-Kalbolandi S, Hosseinzadeh A, et al. Breast cancer immunotherapy: current and novel approaches. Int Immunopharmacol. 2021;98(1878–1705 (Electronic)):107886.

    Article  CAS  PubMed  Google Scholar 

  9. Binnewies M, Roberts EW, Kersten K, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24(5):541–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Alsaab HO, Sau S, Alzhrani R, et al. PD-1 and PD-L1 checkpoint signaling inhibition for cancer immunotherapy: mechanism, combinations, and clinical outcome. Front Pharmacol. 2017;8(1663–9812 (Print)):561.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Frelaut M, du Rusquec P, de Moura A, Le Tourneau C, Borcoman E. Pseudoprogression and hyperprogression as new forms of response to immunotherapy. BioDrugs. 2020;34(4):463–76.

    Article  PubMed  Google Scholar 

  12. Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol. 2017;18(3):e143–52.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Li L, Li L, Liu M, Li Y, Sun Q. Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA. Front Genet. 2022;13(1664–8021 (Print)):957675.

    CAS  PubMed  Google Scholar 

  14. Lv LH, Lu JR, Zhao T, Liu JL, Liang HQ. A CD8(+) T Cell-related genes expression signature predicts prognosis and the efficacy of immunotherapy in breast cancer. J Mammary Gland Biol Neoplasia. 2022;27(1):53–65.

    Article  PubMed  Google Scholar 

  15. Yao Y, Kong X, Liu R, Xu F, Liu G, Sun C. Development of a novel immune-related gene prognostic index for breast cancer. Front Immunol. 2022;13(1664–3224 (Electronic)):845093.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chandrashekar DS, Karthikeyan SK, Korla PK, et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia. 2022;25(1476–5586 (Electronic)):18–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ferris RL. Immunology and immunotherapy of head and neck cancer. J Clin Oncol. 2015;33(29):3293–304.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Pfail JL, Katims AB, Alerasool P, Sfakianos JP. Immunotherapy in non-muscle-invasive bladder cancer: current status and future directions. World J Urol. 2021;39(5):1319–29.

    Article  CAS  PubMed  Google Scholar 

  19. Cha JH, Chan LC, Song MS, Hung MC. New approaches on cancer immunotherapy. Cold Spring Harb Perspect Med. 2020;10(8):a036863.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Coutzac C, Pernot S, Chaput N, Zaanan A. Immunotherapy in advanced gastric cancer, is it the future? Crit Rev Oncol/Hematol. 2019;133(1879-0461 (Electronic)):25–32.

    Article  CAS  PubMed  Google Scholar 

  21. Keenan TE, Tolaney SM. Role of immunotherapy in triple-negative breast cancer. J Natl Compr Canc Netw. 2020;18(4):479–89.

    Article  CAS  PubMed  Google Scholar 

  22. Li Y, Zhang H, Merkher Y, et al. Recent advances in therapeutic strategies for triple-negative breast cancer. J Hematol Oncol. 2022;15(1):121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bernard V, Young J, Binart N. Prolactin: a pleiotropic factor in health and disease. Nat Rev Endocrinol. 2019;15(6):356–65.

    Article  CAS  PubMed  Google Scholar 

  24. Karayazi Atici O, Govindrajan N, Lopetegui-Gonzalez I, Shemanko CS. Prolactin: a hormone with diverse functions from mammary gland development to cancer metastasis. Semin Cell Dev Biol. 2021;114(1096–3634 (Electronic)):159–70.

    Article  CAS  PubMed  Google Scholar 

  25. Tabibzadeh S. Signaling pathways and effectors of aging. Front Biosci (Landmark Ed). 2021;26(1):50–96.

    Article  CAS  PubMed  Google Scholar 

  26. Soria-Bretones I, Saez C, Ruiz-Borrego M, Japon MA, Huertas P. Prognostic value of CtIP/RBBP8 expression in breast cancer. Cancer Med. 2013;2(6):774–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ashour N, Angulo JC, Gonzalez-Corpas A, et al. Epigenetic regulation of gfi1 in endocrine-related cancers: a role regulating tumor growth. Int J Mol Sci. 2020;21(13):4687.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Downs-Canner SM, Meier J, Vincent BG, Serody JS. B cell function in the tumor microenvironment. Annu Rev Immunol. 2022;40(1545–3278 (Electronic)):169–93.

    Article  CAS  PubMed  Google Scholar 

  29. Pan Y, Yu Y, Wang X, Zhang T. Tumor-associated macrophages in tumor immunity. Front Immunol. 2020;11(1664–3224 (Electronic)):583084.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lee YS, Radford KJ. The role of dendritic cells in cancer. Int Rev Cell Mol Biol. 2019;348(1937–6448 (Print)):123–78.

    Article  CAS  PubMed  Google Scholar 

  31. Silwal-Pandit L, Langerod A, Borresen-Dale AL. TP53 mutations in breast and ovarian cancer. Cold Spring Harb Perspect Med. 2017;7(1):a026252.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Schon K, Tischkowitz M. Clinical implications of germline mutations in breast cancer: TP53. Breast Cancer Res Treat. 2018;167(2):417–23.

    Article  CAS  PubMed  Google Scholar 

  33. Pollock NC, Ramroop JR, Hampel H, et al. Differences in somatic TP53 mutation type in breast tumors by race and receptor status. Breast Cancer Res Treat. 2022;192(3):639–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu CL, Cheng SP, Huang WC, et al. Aberrant expression of solute carrier family 35 member A2 correlates with tumor progression in breast cancer. In Vivo. 2023;37(1):262–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kabakov AE, Gabai VL. HSP70s in breast cancer: promoters of tumorigenesis and potential targets/tools for therapy. Cells. 2021;10(12):3446.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Na Y, Kaul SC, Ryu J, et al. Stress chaperone mortalin contributes to epithelial-mesenchymal transition and cancer metastasis. Cancer Res. 2016;76(9):2754–65.

    Article  CAS  PubMed  Google Scholar 

  37. Stephens PJ, Tarpey PS, Davies H, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486(7403):400–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jeong YJ, Oh HK, Choi HR, Park SH. Methylation of the NT5E gene is associated with poor prognostic factors in breast cancer. Diagnostics (Basel). 2020;10(11):939.

    Article  CAS  PubMed  Google Scholar 

  39. Godbole M, Togar T, Patel K, et al. Up-regulation of the kinase gene SGK1 by progesterone activates the AP-1-NDRG1 axis in both PR-positive and -negative breast cancer cells. J Biol Chem. 2018;293(50):19263–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We extend our heartfelt appreciation to home-for-researchers (https://www.home-for-researchers.com) for providing a valuable resource for data analysis.

Funding

This study received funding from the National Natural Science Foundation of China (Grant Number 82003802), the Science and Technology Program of Hunan Health Commission (Grant Number 20201978), the China Scholarship Council (Grant Number 201808430085) and Clinical Research 4310 Program of the First Affiliated Hospital of the University of South China (Grant Number 20224310NHYCG04), Science and technology innovation Program of Hengyang City (Grant Number 202250045223), and the Natural Science Foundation of Hunan Province (Grant Numbers 2019JJ50542 and 2023JJ50156).

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Author notes

  1. Haihong Hu and Ting Yan are equally contributed to this work.

Authors and Affiliations

  1. Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, 69 Chuanshan Road, Hengyang, 421001, Hunan, People’s Republic of China

    Haihong Hu, Hongxia Zhu, Wendi Zhan, Jingdi Zhang, Siyu Wang & Taolan Zhang

  2. School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, 421001, Hunan, People’s Republic of China

    Haihong Hu, Hongxia Zhu, Wendi Zhan, Jingdi Zhang, Siyu Wang & Taolan Zhang

  3. Phase I Clinical Trial Center, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, People’s Republic of China

    Taolan Zhang

  4. Department of Breast and Thyroid Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, People’s Republic of China

    Ting Yan

  5. Department of Radiation Oncology, Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA

    Lingxiang Jiang

  6. Department of Cancer Biology, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, 43606, USA

    David Escobar

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  1. Haihong Hu
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Contributions

TLZ and HHH conceived and designed the study. TY and HHH drafted the manuscript and conducted data analysis. TLZ, LXJ, and DE strictly revised the manuscript. HXZ, WDZ, JDZ, and SYW contributed to write figure legends. The final manuscript was reviewed and approved by all authors.

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Correspondence to Taolan Zhang.

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10238_2023_1247_MOESM1_ESM.tif

Relationship between ICRSBC and tumor progression. (A) Heatmap showing the relationship between ICRSBC and multiple clinical variables (*P < 0.05; **P < 0.01; ***P < 0.001). Correlation between ICRSBC and N stage (B), T stage (C), Age (D), stage (E), PAM50 subtypes (F). (TIF 28184 KB)

10238_2023_1247_MOESM2_ESM.jpg

Comparison between ICRSBC and other prognostic signatures for breast cancer. Survival analysis of distinct risk groups in breast cancer using ICRSBC (A), Li-signature (B), Lv-signature (C), and Yao-signature (D). The ROC curves for 1-, 3-, and 5-year survival prediction using ICRSBC (E), Li-signature (F), Lv-signature (G), and Yao-signature (H) in breast cancer. The ROC curves for 5-year survival prediction employing ICRSBC, Nomogram, and 3 other signatures in breast cancer (I). The C-index (J), RMS (K), and DCA (L) for the evaluation of 4 prognostic signatures in breast cancer. (JPG 3496 KB)

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Hu, H., Yan, T., Zhu, H. et al. A novel immune checkpoint-related signature for prognosis and immune analysis in breast cancer. Clin Exp Med 23, 5139–5159 (2023). https://doi.org/10.1007/s10238-023-01247-2

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  • DOI: https://doi.org/10.1007/s10238-023-01247-2

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