Spatial analysis of NOS2 and COX2 interaction with T-effector cells reveals immunosuppressive landscapes associated with poor outcome in ER- breast cancer patients

Abstract

Multiple immunosuppressive mechanisms exist in the tumor microenvironment that drive poor outcomes and decrease treatment efficacy. The co-expression of NOS2 and COX2 is a strong predictor of poor prognosis in ER- breast cancer and other malignancies. Together, they generate pro-oncogenic signals that drive metastasis, drug resistance, cancer stemness, and immune suppression. Using an ER- breast cancer patient cohort, we found that the spatial expression patterns of NOS2 and COX2 with CD3+CD8+PD1- T effector (Teff) cells formed a tumor immune landscape that correlated with poor outcome. NOS2 was primarily associated with the tumor-immune interface, whereas COX2 was associated with immune desert regions of the tumor lacking Teff cells. A higher ratio of NOS2 or COX2 to Teff was highly correlated with poor outcomes. Spatial analysis revealed that regional clustering of NOS2 and COX2 was associated with stromal-restricted Teff, while only COX2 was predominant in immune deserts. Examination of other immunosuppressive elements, such as PDL1/PD1, Treg, B7H4, and IDO1, revealed that PDL1/PD1, Treg, and IDO1 were primarily associated with restricted Teff, whereas B7H4 and COX2 were found in tumor immune deserts. Regardless of the survival outcome, other leukocytes, such as CD4 T cells and macrophages, were primarily in stromal lymphoid aggregates. Finally, in a 4T1 model, COX2 inhibition led to a massive cell infiltration, thus validating the hypothesis that COX2 is an essential component of the Teff exclusion process and, thus, tumor evasion. Our study indicates that NOS2/COX2 expression plays a central role in tumor immunosuppression. Our findings indicate that new strategies combining clinically available NOS2/COX2 inhibitors with various forms of immune therapy may open a new avenue for the treatment of aggressive ER-breast cancers.

Keywords: Breast; COX2; Cancer; NOS2; immunosuppression; spatial.

Figure 1.. Survival Analysis of Univariant and NOS2 and COX2 with different factors.

Comparison of % cell with NOS2 and COX2 with immune markers with respect to survival in ER− breast cancer with deceased n=11 and alive n=10. A) Univariant analysis of the NOS2 and COX2 with PDL1, CD68, CD8, PD1, FOXP3, CK-SOX10, CD3, CD4, IFNγ, COX2, and NOS2 all. B) Ratio of NOS2all (above lowest threshold) (medium deceased = 0.27 : alive = 0.16), NOS2s (medium deceased = 0.048 : alive = 0.0067), and COX2all with tumor (CK-SOX10) (medium deceased = 0.365 : alive = 0.167). C) Linear regression analysis of NOS2 and COX2. D) Ratio of NOS2s with CD3CD8PD1- (T_eff) (medium deceased=0.54: alive=0.053) and IFNγ (medium deceased= 0.102 : alive=0.0109 ). E) Linear regression analysis of NOS2s or COX2 with IFNγ. F) Kaplan-Meir plot comparing the ratios of NOS2/CD8A-high and NOS2/CD8A-low; cohort (TCGA-BRCA) divided at median of gene expression ratio (*p <0.5, **p <0.01, *** p<0.001, Mann Whitney test, one-tail).

Figure 2.. Spatial distribution analysis of NOS2 COX2 with immune cellular phenotypes.

A) Density Heat Maps comparing spatial orientation of T_eff, IFNγ, tumor-NOS2 and tumor-COX2 in representative ER− breast cancer deceased patient. B) Distribution analysis of % cells compared to the CD3CD8 with different tumor and immune cellular phenotypes in Deceased and Alive. C) Linear regression analysis of T cell phenotypes with tumor-COX2 D) linear regression of tumor and macrophage phenotypes with CD3CD8PD1. E) Spatial S-UMAP of the clustering cellular phenotypes. F) cluster analysis comparing survival G) nearest neighborhoods analysis to identify cellular phenotypes for different phenotypes.

Figure 2.. Spatial distribution analysis of NOS2 COX2 with immune cellular phenotypes.

A) Density Heat Maps comparing spatial orientation of T_eff, IFNγ, tumor-NOS2 and tumor-COX2 in representative ER− breast cancer deceased patient. B) Distribution analysis of % cells compared to the CD3CD8 with different tumor and immune cellular phenotypes in Deceased and Alive. C) Linear regression analysis of T cell phenotypes with tumor-COX2 D) linear regression of tumor and macrophage phenotypes with CD3CD8PD1. E) Spatial S-UMAP of the clustering cellular phenotypes. F) cluster analysis comparing survival G) nearest neighborhoods analysis to identify cellular phenotypes for different phenotypes.

Figure 3.. Regional placement of cellular phenotypes in the tumor.

A-C) represents the 5 regions i) stromal/marginal lymphoid aggregates; Large Tumor nests (>0.1 mm²) with ii) NOS2⁺ tumor edges or iii) NOS2⁻ tumor edges, and iv) tumor interior (core) and tumor satellites (< 0.01mm²). The % cells in each of the 5 regions for cellular phenotypes D) CD8 E) CD4 F) tumor G) macrophage ((*p <0.5, **p <0.01, *** p<0.001, Ordinary one-way ANOVA).

Figure 3.. Regional placement of cellular phenotypes in the tumor.

A-C) represents the 5 regions i) stromal/marginal lymphoid aggregates; Large Tumor nests (>0.1 mm²) with ii) NOS2⁺ tumor edges or iii) NOS2⁻ tumor edges, and iv) tumor interior (core) and tumor satellites (< 0.01mm²). The % cells in each of the 5 regions for cellular phenotypes D) CD8 E) CD4 F) tumor G) macrophage ((*p <0.5, **p <0.01, *** p<0.001, Ordinary one-way ANOVA).

Figure 4.. Analysis of T cell exclusion in human ER− breast cancer and 4T1 murine breast cancer.

A) represents 3 types of CD8 tumor exclusion. B) is the analysis of the impact of NOS2- and indomethacin on infiltration of CD3CD8PD1- cells in 4T1 model. C) spatial distribution maps of CD3CD8PD1- (red) and CD3CD4 (green) upon treatment in 4T1 murine model. Comparison of CD4 and CD8 distribution with fluorescence imaging, annotation, and heat maps in human patients D) deceased and E) Alive. Ratio analysis of lymphoid aggregates of Teff with Treg and immune suppressive macrophage in F) human ER− breast cancer G) 4T1 tumors. H) Comparison of Kaplan Meir plot of FOXP3A/CD8A high vs FOXP3/CD8A low ratios, cohort (GSE37751) divided at median of gene expression ratio.

Figure 4.. Analysis of T cell exclusion in human ER− breast cancer and 4T1 murine breast cancer.

A) represents 3 types of CD8 tumor exclusion. B) is the analysis of the impact of NOS2- and indomethacin on infiltration of CD3CD8PD1- cells in 4T1 model. C) spatial distribution maps of CD3CD8PD1- (red) and CD3CD4 (green) upon treatment in 4T1 murine model. Comparison of CD4 and CD8 distribution with fluorescence imaging, annotation, and heat maps in human patients D) deceased and E) Alive. Ratio analysis of lymphoid aggregates of Teff with Treg and immune suppressive macrophage in F) human ER− breast cancer G) 4T1 tumors. H) Comparison of Kaplan Meir plot of FOXP3A/CD8A high vs FOXP3/CD8A low ratios, cohort (GSE37751) divided at median of gene expression ratio.

Figure 4.. Analysis of T cell exclusion in human ER− breast cancer and 4T1 murine breast cancer.

A) represents 3 types of CD8 tumor exclusion. B) is the analysis of the impact of NOS2- and indomethacin on infiltration of CD3CD8PD1- cells in 4T1 model. C) spatial distribution maps of CD3CD8PD1- (red) and CD3CD4 (green) upon treatment in 4T1 murine model. Comparison of CD4 and CD8 distribution with fluorescence imaging, annotation, and heat maps in human patients D) deceased and E) Alive. Ratio analysis of lymphoid aggregates of Teff with Treg and immune suppressive macrophage in F) human ER− breast cancer G) 4T1 tumors. H) Comparison of Kaplan Meir plot of FOXP3A/CD8A high vs FOXP3/CD8A low ratios, cohort (GSE37751) divided at median of gene expression ratio.

Figure 4.. Analysis of T cell exclusion in human ER− breast cancer and 4T1 murine breast cancer.

A) represents 3 types of CD8 tumor exclusion. B) is the analysis of the impact of NOS2- and indomethacin on infiltration of CD3CD8PD1- cells in 4T1 model. C) spatial distribution maps of CD3CD8PD1- (red) and CD3CD4 (green) upon treatment in 4T1 murine model. Comparison of CD4 and CD8 distribution with fluorescence imaging, annotation, and heat maps in human patients D) deceased and E) Alive. Ratio analysis of lymphoid aggregates of Teff with Treg and immune suppressive macrophage in F) human ER− breast cancer G) 4T1 tumors. H) Comparison of Kaplan Meir plot of FOXP3A/CD8A high vs FOXP3/CD8A low ratios, cohort (GSE37751) divided at median of gene expression ratio.

Figure 5.. The density heat map distribution comparisons.

The density heat map distribution comparing CD63CD8, CD4FOXP3^-Treg, tumor-PDL1, tumor-NOS2 and tumor-COX2 in whole tumor classification of CD8NOS2 and COX2. A) restricted CD8⁺NOS2⁺COX2⁺ B) restricted CD8⁺NOS2⁻COX2⁻ C) CD8⁻NOS2⁻COX2⁺ D) infiltrating CD8⁺NOS2⁻COX2⁻.

Figure 6.. The survival and spatial analysis of IDO and B7H4.

A) Compares survival as univariant the ratio of NOS2s with IDO1. B) Compares survival as univariant the ratio of NOS2s and B7H4. (*p <0.5, **p <0.01, *** p<0.001, Mann-Whitney test, one-tail). C) Density heat maps of CD3CD8, IDO1, B7H4 comparing to tumor-NOS2 and tumorCOX2. D-F) Density heat maps of CD3CD8, IDO1, B7H4, tumor-NOS2, and tumor-COX2 in restricted CD8⁺NOS2⁻COX2⁻, CD8⁻NOS2⁻COX2⁺, and Infiltrating CD8⁺NOS2⁻COX2⁻ regions.

Figure 7.. Graphic analysis of the impact of T cell interaction with NOS2 and COX2 in the tumor microenvironment and mechanism that cooperate to lead to immune suppression.