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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

The Immune-based Prognostic Score for the Immunogenomic Landscape Aanalysis and Application of Chemotherapy in Breast Cancer

Author(s): Qianzi Lu*, Shiyuan Wang, Yi Pan, Yao Yu, Yuqiang Xiong, Haodong Wei, Dongqing Su, Yongchun Zuo* and Lei Yang*

Volume 17, Issue 7, 2022

Published on: 26 August, 2022

Page: [624 - 631] Pages: 8

DOI: 10.2174/1574893617666220524123825

Price: $65

Abstract

Background: Breast cancer is one cancer that develops from breast tissue and one of the major reasons of deaths in of women all over the world. The tumor-infiltrating lymphocytes in tumor immune microenvironment are correlated with the prognosis in breast cancer patients, and play an important role in the occurrence and development of breast cancer.

Methods: In this study, by integrating the immune gene expression of 20 breast cancer cohorts from the public dataset, an immune-based prognostic score was established. This immune-based prognostic score was found to be correlated with prognosis, stromal score, tumor purity, three famous immune checkpoints, and immune escape mechanism in breast cancer patients.

Results: The clinical application of the prognostic score was verified by the breast cancer patients treated with chemotherapy, and good therapeutic benefit of the prognostic score was obtained. In addition, the XGBoost classifier was used to construct for predicting the high and low prognostic score subtypes, and the predictive results indicated that the XGBoost was suitable to predict these two subtypes in breast cancer patients.

Conclusion: Based on these results, we believed that the prognostic score may be used as an effective prognostic marker and may provide great help for chemotherapy treatment of breast cancer patients.

Keywords: Breast cancer, prognosis, immune landscape, immune escape, prediction model, gene expression.

Graphical Abstract
[1]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020; 70(1): 7-30.
[http://dx.doi.org/10.3322/caac.21590] [PMID: 31912902]
[2]
DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics, 2019. CA Cancer J Clin 2019; 69(6): 438-51.
[http://dx.doi.org/10.3322/caac.21583] [PMID: 31577379]
[3]
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68(6): 394-424.
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[4]
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.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[5]
Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin 2021; 71(1): 7-33.
[http://dx.doi.org/10.3322/caac.21654] [PMID: 33433946]
[6]
McDonald ES, Clark AS, Tchou J, Zhang P, Freedman GM. Clinical diagnosis and management of breast cancer. J Nucl Med 2016; 57 (Suppl. 1): 9S-16S.
[http://dx.doi.org/10.2967/jnumed.115.157834] [PMID: 26834110]
[7]
Aleskandarany MA, Vandenberghe ME, Marchiò C, Ellis IO, Sapino A, Rakha EA. Tumour heterogeneity of breast cancer: From morphology to personalised medicine. Pathobiology 2018; 85(1-2): 23-34.
[http://dx.doi.org/10.1159/000477851] [PMID: 29428954]
[8]
Denkert C, von Minckwitz G, Darb-Esfahani S, et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: A pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol 2018; 19(1): 40-50.
[http://dx.doi.org/10.1016/S1470-2045(17)30904-X] [PMID: 29233559]
[9]
Denkert C, Loibl S, Noske A, et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol 2010; 28(1): 105-13.
[http://dx.doi.org/10.1200/JCO.2009.23.7370] [PMID: 19917869]
[10]
Ali HR, Provenzano E, Dawson SJ, et al. Association between CD8+ T-cell infiltration and breast cancer survival in 12,439 patients. Ann Oncol 2014; 25(8): 1536-43.
[http://dx.doi.org/10.1093/annonc/mdu191] [PMID: 24915873]
[11]
Bindea G, Mlecnik B, Tosolini M, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 2013; 39(4): 782-95.
[http://dx.doi.org/10.1016/j.immuni.2013.10.003] [PMID: 24138885]
[12]
Charoentong P, Finotello F, Angelova M, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 2017; 18(1): 248-62.
[http://dx.doi.org/10.1016/j.celrep.2016.12.019] [PMID: 28052254]
[13]
Wang S, Xiong Y, Zhang Q, 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(4): bbaa311.
[PMID: 33302293]
[14]
Korde LA, Lusa L, McShane L, et al. Gene expression pathway analysis to predict response to neoadjuvant docetaxel and capecitabine for breast cancer. Breast Cancer Res Treat 2010; 119(3): 685-99.
[http://dx.doi.org/10.1007/s10549-009-0651-3] [PMID: 20012355]
[15]
Miller WR, Larionov A. Changes in expression of oestrogen regulated and proliferation genes with neoadjuvant treatment highlight heterogeneity of clinical resistance to the aromatase inhibitor, letrozole. Breast Cancer Res 2010; 12(4): R52.
[http://dx.doi.org/10.1186/bcr2611] [PMID: 20646288]
[16]
George B, Seals S, Aban I. Survival analysis and regression models. J Nucl Cardiol 2014; 21(4): 686-94.
[http://dx.doi.org/10.1007/s12350-014-9908-2] [PMID: 24810431]
[17]
Lee YH. An overview of meta-analysis for clinicians. Korean J Intern Med (Korean Assoc Intern Med) 2018; 33(2): 277-83.
[http://dx.doi.org/10.3904/kjim.2016.195] [PMID: 29277096]
[18]
Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14(1): 7.
[http://dx.doi.org/10.1186/1471-2105-14-7] [PMID: 23323831]
[19]
Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013; 4(1): 2612.
[http://dx.doi.org/10.1038/ncomms3612] [PMID: 24113773]
[20]
Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102(43): 15545-50.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[21]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[22]
Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43(7): e47.
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[23]
Wang S, Zhang Q, Yu C, Cao Y, Zuo Y, Yang L. Immune cell infiltration-based signature for prognosis and immunogenomic analysis in breast cancer. Brief Bioinform 2021; 22(2): 2020-31.
[http://dx.doi.org/10.1093/bib/bbaa026] [PMID: 32141494]
[24]
Buechler MB, Turley SJ. A short field guide to fibroblast function in immunity. Semin Immunol 2018; 35: 48-58.
[http://dx.doi.org/10.1016/j.smim.2017.11.001] [PMID: 29198601]
[25]
Corrales L, McWhirter SM, Dubensky TW Jr, Gajewski TF. The host STING pathway at the interface of cancer and immunity. J Clin Invest 2016; 126(7): 2404-11.
[http://dx.doi.org/10.1172/JCI86892] [PMID: 27367184]
[26]
Zhao Y, Lee CK, Lin C-H, et al. PD-L1:CD80 cis-heterodimer triggers the co-stimulatory receptor CD28 while repressing the inhibitory PD-1 and CTLA-4 pathways. Immunity 2019; 51(6): 1059-1073.e9.
[http://dx.doi.org/10.1016/j.immuni.2019.11.003] [PMID: 31757674]
[27]
Iorgulescu JB, Braun D, Oliveira G, Keskin DB, Wu CJ. Acquired mechanisms of immune escape in cancer following immunotherapy. Genome Med 2018; 10(1): 87-7.
[http://dx.doi.org/10.1186/s13073-018-0598-2] [PMID: 30466478]
[28]
Schreiber RD, Old LJ, Smyth MJ. Cancer immunoediting: Integrating immunity’s roles in cancer suppression and promotion. Science 2011; 331(6024): 1565-70.
[http://dx.doi.org/10.1126/science.1203486] [PMID: 21436444]
[29]
Turajlic S, Litchfield K, Xu H, et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: A pan-cancer analysis. Lancet Oncol 2017; 18(8): 1009-21.
[http://dx.doi.org/10.1016/S1470-2045(17)30516-8] [PMID: 28694034]
[30]
Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015; 12(5): 453-7.
[http://dx.doi.org/10.1038/nmeth.3337] [PMID: 25822800]
[31]
Ogunleye A, Wang QG. XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Trans Comput Biol Bioinform 2020; 17: 2131-40.
[http://dx.doi.org/10.1109/TCBB.2019.2911071]
[32]
Harbeck N, Gnant M. Breast cancer. Lancet 2017; 389(10074): 1134-50.
[http://dx.doi.org/10.1016/S0140-6736(16)31891-8] [PMID: 27865536]

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