Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jan 27:13:805184.
doi: 10.3389/fimmu.2022.805184. eCollection 2022.

Immunogenomic Landscape in Breast Cancer Reveals Immunotherapeutically Relevant Gene Signatures

Affiliations

Immunogenomic Landscape in Breast Cancer Reveals Immunotherapeutically Relevant Gene Signatures

Tao Wang et al. Front Immunol. .

Erratum in

Abstract

Breast cancer is characterized by some types of heterogeneity, high aggressive behaviour, and low immunotherapeutic efficiency. Detailed immune stratification is a prerequisite for interpreting resistance to treatment and escape from immune control. Hence, the immune landscape of breast cancer needs further understanding. We systematically clustered breast cancer into six immune subtypes based on the mRNA expression patterns of immune signatures and comprehensively depicted their characteristics. The immunotherapeutic benefit score (ITBscore) was validated to be a superior predictor of the response to immunotherapy in cohorts from various datasets. Six distinct immune subtypes related to divergences in biological functions, signatures of immune or stromal cells, extent of the adaptive immune response, genomic events, and clinical prognostication were identified. These six subtypes were characterized as immunologically quiet, chemokine dominant, lymphocyte depleted, wounding dominant, innate immune dominant, and IFN-γ dominant and exhibited features of the tumor microenvironment (TME). The high ITBscore subgroup, characterized by a high proportion of M1 macrophages:M2 macrophages, an activated inflammatory response, and increased mutational burden (such as mutations in TP53, CDH1 and CENPE), indicated better immunotherapeutic benefits. A low proportion of tumor-infiltrating lymphocytes (TILs) and an inadequate response to immune treatment were associated with the low ITBscore subgroup, which was also associated with poor survival. Analyses of four cohorts treated with immune checkpoint inhibitors (ICIs) suggested that patients with a high ITBscore received significant therapeutic advantages and clinical benefits. Our work may facilitate the understanding of immune phenotypes in shaping different TME landscapes and guide precision immuno-oncology and immunotherapy strategies.

Keywords: breast cancer; immune escape; immune subtype; immunotherapy; tumor microenvironment.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Landscape of immune subtypes in breast cancer. (A) Module distribution and cluster results. Top: Spearman correlation coefficients of 83 immune gene signatures. Boxes display five modules with shared associations. Middle: Six immune subtypes clustered by five representative signatures. Bottom: Distributions of signature scores within the six subtypes, with a dashed line indicating the median. (B) t-SNE plot showing the different immune subtypes. (C) Survival analysis grouped by immune subtype. (D) Distribution of immune subtypes and TCGA subtypes. (E) Summary of crucial characteristics by immune subtype.
Figure 2
Figure 2
Functional annotation of immune subtypes. (A) KEGG functional categories and pathways of each immune subtype. (B–D) ssGSEA was applied to test the significance of differentially expressed genes in the immune-associated signatures, breast cancer signatures, and oncogenic pathways in specific immune subtypes compared with others.
Figure 3
Figure 3
Potential extrinsic regulation of immune escape in breast cancer. (A) Proportions of 22 immune cell types within the TME according to the immune subtype (B) Mean trend scores of MDSCs, Tregs, the IFN-γ response, and mRNA expression of STING among subtypes. (C) The proportions of main categories of immune cells estimated by CIBERSORT according to distinct immune subtypes. (D) Leukocyte proportions (ρ=LF/SF) of distinct immune subtypes (E) From left to right: mRNA expression (median normalized expression levels), expression versus methylation (gene expression correlation with DNA methylation beta-value), amplification frequency, and deletion frequency for regulators.
Figure 4
Figure 4
Potential intrinsic regulation and genomic alterations in breast cancer. (A) Correlation between DNA damage and immune infiltrates. From left to right: all BRCA samples; averaged by immune subtype; grouped by immune subtype. (B) Top 10 genes with the highest mutation frequency across immune subtypes. (C) Gene expression patterns of STAT3 and BCL2 grouped by TP53 mutation. (D) Difference in DNA repair signalling pathways. (E) Median expression levels of 24 co-inhibitory and 37 co-stimulatory genes across immune subtypes. (F) Correlation of immunomodulators and tumor immunogenicity indicators. *P < 0.05; **P < 0.01; ***P < 0.001; ns, not significant.
Figure 5
Figure 5
Construction of the immune biological signature. (A) DEGs were used to construct the immune biological signature. (B) Representative immunohistochemical images of infiltrated immune cells in Bengbu cohort between high- and low- ITBscore groups. (C) Kaplan-Meier curves for patients in the BRCA cohort divided into high and low ITBscore subgroups. (D) Alluvial diagram indicating immune subtypes in groups with different BRCA subtypes (basal, Her2, LumA, LumB, and normal), ITBscores, and survival outcomes. (E) Prognostic value of the ITBscore and classic clinicopathological covariates in the high/low ITBscore subgroups. (F) Distribution of the ITBscore among TCGA-BRCA molecular subtypes.
Figure 6
Figure 6
Role of the ITBscore in predicting immunotherapeutic prognosis. (A) The ITBscore was used to evaluate clinical prognosis in independent cohorts of breast cancer patients with high/low ITBscores. (B) Differential putative immunotherapeutic response in the high/low ITBscore subgroups. (C) Kaplan–Meier curves of the anti-PD-L1 response in the high and low ITBscore subgroups of patients in the IMvigor210 cohort. (D) The percentage of patients with high and low ITBscore groups with an anti-PD-L1 immunotherapeutic response. CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease. (E) Distribution of the ITBscore in diverse anti–PD-L1 clinical response subtypes. (F) AUC value shows the predictive performance of the ITBscore and TMB in the IMvigor210 cohort. (G) Kaplan–Meier curves of the anti-PD-1 response in the high (n = 6) and low (n = 20) ITBscore subgroups in the GSE78220 cohort. (H) The percentage of patients with high and low ITBscore groups with an anti-PD-1 immunotherapeutic response. (I) Distribution of the ITBscore in diverse anti–PD-1 clinical response subtypes. (J) AUC value showing the predictive performance of the ITBscore in the GSE78220 cohort. (K) Distribution of the ITBscore in every patient with a clinical response to anti-PD-1. (L) Differences in PD1 in the high/low ITBscore subgroups. (M) Differential chemotherapeutic responses of the high/low ITBscore subgroups based on the IC50 value available in the GDSC database.

Similar articles

Cited by

References

    1. Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P, et al. . Subclonal Diversification of Primary Breast Cancer Revealed by Multiregion Sequencing. Nat Med (2015) 21(7):751–9. doi: 10.1038/nm.3886 - DOI - PMC - PubMed
    1. Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, et al. . The Clonal and Mutational Evolution Spectrum of Primary Triple-Negative Breast Cancers. Nature (2012) 486(7403):395–9. doi: 10.1038/nature10933 - DOI - PMC - PubMed
    1. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. . The Genomic and Transcriptomic Architecture of 2,000 Breast Tumours Reveals Novel Subgroups. Nature (2012) 486(7403):346–52. doi: 10.1038/nature10983 - DOI - PMC - PubMed
    1. Dvinge H, Git A, Graf S, Salmon-Divon M, Curtis C, Sottoriva A, et al. . The Shaping and Functional Consequences of the microRNA Landscape in Breast Cancer. Nature (2013) 497(7449):378–82. doi: 10.1038/nature12108 - DOI - PubMed
    1. Cancer Genome Atlas N. Comprehensive Molecular Portraits of Human Breast Tumours. Nature (2012) 490(7418):61–70. doi: 10.1038/nature11412 - DOI - PMC - PubMed

Publication types

Substances