Unraveling Triple-Negative Breast Cancer Tumor Microenvironment Heterogeneity: Towards an Optimized Treatment Approach

Abstract

Background: Recent efforts of gene expression profiling analyses recognized at least four different triple-negative breast cancer (TNBC) molecular subtypes. However, little is known regarding their tumor microenvironment (TME) heterogeneity.

Methods: Here, we investigated TME heterogeneity within each TNBC molecular subtype, including immune infiltrate localization and composition together with expression of targetable immune pathways, using publicly available transcriptomic and genomic datasets from a large TNBC series totaling 1512 samples. Associations between molecular subtypes and specific features were assessed using logistic regression models. All statistical tests were two-sided.

Results: We demonstrated that each TNBC molecular subtype exhibits distinct TME profiles associated with specific immune, vascularization, stroma, and metabolism biological processes together with specific immune composition and localization. The immunomodulatory subtype was associated with the highest expression of adaptive immune-related gene signatures and a fully inflamed spatial pattern appearing to be the optimal candidate for immune check point inhibitors. In contrast, most mesenchymal stem-like and luminal androgen receptor tumors showed an immunosuppressive phenotype as witnessed by high expression levels of stromal signatures. Basal-like, luminal androgen receptor, and mesenchymal subtypes exhibited an immune cold phenotype associated with stromal and metabolism TME signatures and enriched in margin-restricted spatial pattern. Tumors with high chromosomal instability and copy number loss in the chromosome 5q and 15q regions, including genomic loss of major histocompatibility complex related genes, showed reduced cytotoxic activity as a plausible immune escape mechanism.

Conclusions: Our results demonstrate that each TNBC subtype is associated with specific TME profiles, setting the ground for a rationale tailoring of immunotherapy in TNBC patients.

Figure 1.

Tumor microenvironment (TME) features associated with triple-negative breast cancer (TNBC) molecular subtypes and overall survival (OS). A) Associations between TME gene expression signatures and TNBC molecular subtypes. A logistic regression model was used to evaluate associations between each specific gene signature and each TNBC molecular subtype. P values were obtained from parametric Mann-Whitney U tests and corrected for multi testing. Only statistically significant associations are shown (false discovery rate [FDR] ≤ .05), with negative and positive associations represented in red and green, respectively. The left half-circle and the right half-circle represent cohorts A and B, respectively. B) Associations between TME gene signatures and 10-year OS, using univariate and multivariable Cox regression models, adjusted for the dataset (The Cancer Genome Atlas Consortium vs Molecular Taxonomy of Breast Cancer International Consortium), patient age (≤40 y vs >40 y), nodal status (positive vs negative), tumor size (<2 cm vs ≥2 cm), and histological grade (I/II vs III). The x- and y-axis represent the hazard ratio and the −log10 (FDR), respectively. The horizontal bold dotted line represents the FDR threshold at .05 for statistically significant associations. BL = Basal-like; CAF = Cancer-associated fibroblast; IM = Immunomodulatory; LAR = Luminal androgen receptor; M = Mesenchymal; MSL = Mesenchymal stem-like.

Figure 2.

Characterization of the spatial immune landscape and immune composition in triple-negative breast cancer (TNBC) molecular subtypes. Associations between TNBC molecular subtypes and tumor immune microenvironment (TIME) subtypes in cohorts A (A) and B (B). C) Associations between tumor microenvironment (TME) gene expression signatures and TIME subtypes. Logistic regression model was used to evaluate the association between each feature and each subtype. D) Associations between 16 immune cell subsets scores with TNBC molecular subtypes. A logistic regression model was used to evaluate associations between each immune cell population and each tumor TNBC molecular subtype. P values were obtained from parametric Mann-Whitney U tests and corrected for multi testing. Only statistically significant associations are shown (FDR ≤ .05), with negative and positive associations represented in red and green, respectively. The left half-circle and the right half-circle represent cohorts A and B, respectively. aDC = Activated dendritic cells; B cell = B cell lymphocytes; BL = Basal-like; CAF = Cancer-associated fibroblast; FI = Fully-inflammed; iDC = Inactivated dendritic cells; IM = Immunomodulatory; LAR = Luminal androgen receptor; M = Mesenchymal; MR = Margin restricted; MSL = Mesenchymal stem-like; NK = Natural killer cells; SR = Stroma restricted; Tcm = Central memory T cells; Tem = Effector memory T cells; Tfh = Follicular helper T cells; Th = Helper T cells; Treg = Regulatory T cells.

Figure 3.

Chromosomal instability association with immune escape. A) Associations between tumor microenvironment (TME) and tumor-specific features with local cytotoxic immune activity (CYT) in cohorts A and B. Spearman correlation was used to evaluate associations between each TME and tumor-specific gene expression signature with the CYT gene signature. The x- and y-axis represent Spearman ρ and the −log10(FDR), respectively. The horizontal bold dotted line represents the FDR threshold at 0.05 for statistically significant association. Chromosomal instability (CIN) distribution within each triple-negative breast cancer (TNBC) molecular (B) and tumor immune microenvironment (TIME) (C) subtype in cohorts A and B. Differences between each subtype and the rest of the cohort were assessed using a two-sided Mann-Whitney U test. A black star was displayed when statistically significant CIN score enrichment was observed within a specific TNBC and TIME subtype. D) Gene ontology analyses of genes with mRNA expression statistically significant (FDR ≤ .05) negatively (left, in red) and positively (right, in blue) correlated with CIN scores (Spearman correlation) in cohorts A and B. BL = Basal-like; CAF = Cancer-associated fibroblast; CIN = Chromosomal instability; FI = Fully-inflammed; HRD = Homologous recombination deficiency; IM = Immunomodulatory; ITH = Intra-tumoral heterogeneity; LAR = Luminal androgen receptor; M = Mesenchymal; MR = Margin restricted; MSL = Mesenchymal stem-like; SR = Stroma restricted; TMB = Tumor mutational burden.

Figure 4.

Loss of chromosome 5q and 15q regions is associated with reduced cytotoxic immune activity (CYT) levels. A) Copy number aberration (CNA) status distribution according to the three different triple-negative breast cancer (TNBC) classifications of the 33 genes associated with high CNA loss and low CYT scores in chromosomal instability (CIN) high (CIN ≥ 0.38, third tertile) and CIN low (CIN ≤ 0.08, first tertile) tumors in cohort A. B) CNA status distribution of 27 of these 33 genes validated in cohort B associated with high CNA loss and low CYT scores in CIN high (CIN ≥ 0.39, third tertile) and CIN low (CIN ≤ 0.11, first tertile) tumors. Differences in CNA status between both CIN subgroups were assessed using a two-sided Fisher exact test. P values were adjusted for multiple testing using the Benjamini-Hochberg procedure. A logistic regression model was used to evaluate the association between each gene CNA loss with CYT activity. P values were obtained from parametric Mann-Whitney U tests and corrected for multi testing.

Figure 5.

Specific 5q and 15q region losses associated with reduced cytotoxic immune activity (CYT). Associations between chromosome 5q (A) and 15q (B) specific region logR with cytotoxic activity using a linear regression model. Forestplots displaying hazard ratios and 95% confidence intervals (CI). Horizontal bars represent the 95% CI of odds ratios (OR). Variables with statistically significant effect (P ≤ .05) are shown in red. Gray boxes highlight common regions significantly associated in both cohorts.

Figure 6.

Therapeutic immune targets according to triple-negative breast cancer (TNBC) molecular and tumor immune microenvironment (TIME) subtypes. Associations of 44 immune genes corresponding to immunomodulatory targets with TNBC molecular (A) and TIME (B) subtypes. A logistic regression model was used to evaluate associations between each gene expression with each subtype. P values were obtained from parametric Mann-Whitney U tests and corrected for multi testing. Only statistically significant associations are shown (FDR ≤ .05), with negative and positive associations represented in red and green, respectively. The left half-circle and the right half-circle represent cohorts A and B, respectively. BL = Basal-like; FI = Fully-inflammed; IM = Immunomodulatory; LAR = Luminal androgen receptor; M = Mesenchymal; MR = Margin restricted; MSL = Mesenchymal stem-like; SR = Stroma restricted.

Figure 7.

Validation of tumor microenvironment (TME) heterogeneity within triple-negative breast cancer (TNBC) molecular subtypes. A) Associations between TME gene expression signatures and TNBC molecular subtypes within cohort C. A logistic regression model was used to evaluate associations between each specific gene signature and each TNBC molecular subtype. P values were obtained from parametric Mann-Whitney U tests and corrected for multi testing. Only statistically significant associations are shown (FDR ≤ .05), with negative and positive associations represented in red and green, respectively. B) Associations between TME gene signatures and relapse-free survival (RFS) using a cox regression model. The x- and y-axis represent hazard ratio and the −log10 (false discovery rate [FDR]), respectively. The horizontal bold dotted line represents the FDR threshold at .05 for statistically significant associations. C) Associations between TNBC molecular subtypes and tumor immune microenvironment (TIME) subtypes in cohort C. D) Associations between TME gene expression signatures with TIME subtypes in cohort C. A logistic regression model was used to evaluate associations between each specific gene signature and each TNBC molecular subtype. P values were obtained from parametric Mann-Whitney U tests and corrected for multi testing. Only statistically significant associations are shown (FDR ≤ 0.05), with negative and positive associations represented in red and green, respectively. (E) Kaplan-meier analysis of relapse-free survival of cohort C stratified according to TIME subtypes (fully inflamed [FI] vs stroma restricted [SR] vs margin restricted [MR]). F) Associations between 16 immune cell population scores with TNBC molecular subtypes in cohort C. A logistic regression model was used to evaluate associations between each immune cell population and each tumor subtype. P values were obtained from parametric Mann-Whitney U tests and corrected for multi testing. Only statistically significant associations are shown (FDR ≤ .05), with negative and positive associations represented in red and green, respectively. BL = Basal-like; CAF = Cancer-associated fibroblast; FI = Fully-inflammed; IM = Immunomodulatory; LAR = Luminal androgen receptor; M = Mesenchymal; MR = Margin restricted; MSL = Mesenchymal stem-like; SR = Stroma restricted.

Figure 8.

Response to immunotherapy through targeting triple-negative breast cancer (TNBC) tumor microenvironment and genomic heterogeneity. The inner pie chart represents the relative proportion of the TNBC molecular subtypes. The outer pie chart represents the relative proportion of the tumor immune microenvironment (TIME) subtypes within each TNBC molecular subtype. Observed TNBC subtype-specific aberrations are listed in each quadrant with the corresponding rational therapeutic strategies presented in the dotted box. BL = Basal-like; DNMT = DNA methyltransferase; HDAC = Histone deacetylase; IM = Immunomodulatory; LAR = Luminal androgen receptor; M = Mesenchymal; MSL = Mesenchymal stem-like.