The Role of CD8A in the Immune Microenvironment of Breast Cancer

Background: Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer (BC), and it is often associated with a high tumor grade, a younger age at diagnosis, and a low survival rate. Conventional endocrine and anti-HER-2 therapies are usually ineffective against TNBC, creating treatment challenges and resulting in a poor prognosis. Hence, new targets and treatment strategies for TNBC are urgently required. Methods: The GSE102818 dataset was used to identify differentially expressed genes (DEGs) between primary BC and metastatic BC lesions. The Cancer Genome Atlas and the cBioPortal platform were employed to explore mutations in candidate genes. Utilizing the Tumor IMmune Estimation Resource (TIMER), the relationship between the expression of candidate genes and immune cell infiltration was assessed. Additionally, the cell-specific expression of the candidate genes was examined in the immune microenvironment of primary BC and metastatic BC lesions using the single-cell RNA sequencing (scRNA-seq) datasets GSE118389 and GSE202695. Finally, the protein expression of the candidate genes in clinical TNBC samples was evaluated. Results: CD8A was identified as a hub gene in the DEG network and was found to be down-regulated in metastatic BC lesions. CD8A expression was highly correlated with the infiltration of CD8 ${}^{+}$ T cells, and elevated CD8A expression was correlated with improved survival. Interestingly, scRNA-seq data revealed that CD8A was down-regulated in CD8 ${}^{+}$ T cells in the immune microenvironment of metastatic BC lesions. Finally, the evaluation of clinical samples confirmed the down-regulation of CD8A in the immune microenvironment of BC lung metastases. Conclusions: In patients with metastatic TNBC, high levels of CD8A (especially in the immune microenvironment) are associated with a good prognosis.

Keywords

triple-negative breast cancer

differentially expressed genes

breast cancer

tumor immune microenvironment

1. Introduction

Triple-negative breast cancer (TNBC) is a highly heterogeneous type of breast cancer (BC) [1]. Compared with other subtypes of BC, TNBC is aggressive and often has a high tumor grade and poor prognosis [2]. Notably, TNBC is associated with a high risk of metastasis, with the lungs, liver, and brain being the three most common sites of metastasis [3].

Owing to a lack of target receptors, TNBC is typically unresponsive to endocrine therapy and targeted therapy. Hence, chemotherapy is often employed as the primary treatment strategy for TNBC. At present, adjuvant chemotherapy with anthracyclines plus taxanes is typically used to treat TNBC. Further, it is recommended as a first-line therapeutic option for TNBC per international guidelines. However, some patients with BC do not respond to anthracyclines and taxanes, which results in a poor prognosis [4]. Owing to limited treatment options and drug resistance in TNBC, research on this malignancy has become a key priority in the field of oncology. However, so far, studies on the treatment of TNBC have largely focused on the discovery of new targets and the use of endocrine therapy and immunotherapy.

Tumor immunotherapy aims to enhance the body’s immune response against tumors, thereby reducing tumor immunosuppression and enhancing antitumor effects. Insights into the interaction between cancer and the immune system have helped in maximizing the benefits of immunotherapy. Notably, immunotherapy has achieved significantly stronger antitumor responses than monotherapy in several patients. Therefore, several immunotherapy-based treatment strategies have been proposed. The principle of immunotherapy is multifaceted. Immune checkpoint dysregulation is an important event during malignant transformation. This process allows tumor cells to resist immune responses, reduces the activation of T cells, prevents tumor surveillance, and enhances tumor survival. In this context, the clinical application of immune checkpoint inhibitors blocks immune checkpoint-related pathways, thus reactivating immune cells. Hence, treatment with immune checkpoint inhibitors disrupts immune resistance in tumor cells, strengthens the activity of T cells against cancer cells, and boosts the immune response [5]. Research has shown that both tumor-infiltrating lymphocytes (TILs) and checkpoint molecules can serve as indicators for the effectiveness of immune checkpoint inhibitors in BC [6]. Higher TIL levels can lead to improved immunotherapy outcomes, and the number of TILs is positively correlated with progression-free survival [7].

Interestingly, TNBC is the most immunogenic type of breast malignancy because it exhibits higher levels of TILs than other BC subtypes. The inherent heterogeneity of TNBC has important implications for drug development, clinical diagnosis, and treatment in this type of cancer. According to Oura et al. [8], the tumor immune microenvironment (TIME) plays a key role in TNBC metastasis. Identifying TIME biomarkers can help elucidate the causes of tumor heterogeneity in TNBC, enabling the development of targeted treatment strategies.

In this study, differentially expressed genes (DEGs) between primary and metastatic BC lesions were identified, with a focus on genes related to the TIME. After identifying the hub gene CD8A, immune cell infiltration (ICI) analysis was performed. The results showed that CD8A expression was closely related to the infiltration of CD8 ${}^{+}$ T cells. Finally, single-cell RNA sequencing (scRNA-seq) data were analyzed to visualize the distribution of CD8A expression in CD8 ${}^{+}$ T cells within the TIME of primary and metastatic BC. Notably, the expression of CD8A was found to be significantly down-regulated in cases of metastasis. Together, the findings confirmed that CD8A is a potential prognostic factor in metastatic BC.

2. Materials and Methods

2.1 Acquisition of Gene Expression Data

All gene expression data were obtained from the Gene Expression Omnibus (GEO) database. RNA-seq expression data from 31 patients with primary BC and 17 patients with metastatic BC were obtained from the GSE102818 dataset. Additionally, the scRNA-seq data of 1534 cells from six TNBC samples (GSE118389), as well as the scRNA-seq data of patient-derived BC xenograft tumors and matched lung macrometastases (GSE202695), were acquired.

2.2 DEG Screening

We used the “Limma” package and GSE102818 data to identify the genes differentially expressed between primary and metastatic BC lesions. DEGs were identified by comparing gene expression between the 31 primary BC samples and 17 metastatic BC samples.

2.3 Protein–Protein Interaction (PPI)

To explore the functional relationships and interactions among the identified DEGs, we constructed a PPI network using the STRING database. This network analysis helped us understand the potential roles of these genes in tumor-related biological processes.

2.4 Gene Enrichment Analysis

We conducted gene enrichment analysis on the identified DEGs using R software (version 4.3.2, The R Foundation, https://www.r-project.org/). The analysis was performed using the “clusterProfiler” package, “org.Hs.eg.db” package, and “enrichplot” package. Accordingly, we explored the biological functions and pathways associated with the identified DEGs.

2.5 Microsatellite Instability (MSI) and Tumor Mutational Burden (TMB)

We obtained MSI and TMB scores from The Cancer Genome Atlas (TCGA). Spearman’s method was employed to analyze the relationship of MSI and TMB with the expression of specific genes.

2.6 Online cBioPortal Analysis

We used the cBioPortal platform and BC data from TCGA for gene mutation analysis and explored the genetic alterations in the genes of interest to better understand their potential roles in BC.

2.7 Online Tumor IMmune Estimation Resource (TIMER) Analysis

To investigate the correlation between ICI and CD8A expression, we utilized the TIMER platform (https://cistrome.shinyapps.io/timer/). Pearson correlation coefficients were calculated to explore the role of CD8A in the modulation of immune responses.

2.8 scRNA-seq Analysis

We applied the “SingleR” package to analyze the scRNA-seq datasets GSE118389 and GSE202695. This analysis allowed us to determine the specific expression patterns of CD8A within the TIME, as previously described [9].

2.9 Immunohistochemical Staining

Immunohistochemical staining was performed to examine primary and metastatic BC tissue. Tissue sections were dewaxed, and antigen retrieval was performed in citrate buffer (pH 6.0) at 95 °C for 20 min. Subsequently, the sections were treated with 3% peroxidase for 20 min and then use endogenous peroxidase to blocked for 30 min. Finally, the sections were incubated with the anti-CD8A antibody (ab237709, Abcam) overnight at 4 °C. The following day, the sections were washed and incubated with HRP-Conjugated Streptavidin and biotinylated goat anti-mouse IgG (H+L). DAB staining was performed, followed by hematoxylin counterstaining. The protein expression levels were assessed by five pathologists.

2.10 Statistical Analysis

All data were subjected to statistical analysis using R software (version 4.0.3, https://www.r-project.org/). Statistical tests were performed, as appropriate, for the specific analyses conducted. A p value of 0.05 or below indicated statistical significance.

3. Results

3.1 Screening of DEGs Between Primary and Metastatic BC Lesions

To understand the differences in gene expression between primary BC and metastatic lesions, we analyzed differential gene expression using data from 31 primary BC samples and 17 metastatic BC lesions (obtained from the GSE102818 dataset). The analysis yielded 63 DEGs (Fig. 1A,B). Subsequently, we attempted to identify the potential biological functions of these DEGs. Gene enrichment analyses—including both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses—revealed the association of DEGs with various tumor-related processes. These included the humoral immune response, lymphocyte differentiation, G protein-coupled receptor binding, and chemokine activity (Fig. 1C,D).

Fig. 1.

Genes differentially expressed between primary and metastatic breast cancer (BC) lesions. (A) Volcano plot showing the distribution of differentially expressed genes (DEGs). (B) Heatmap denoting the DEGs (primary vs. metastatic BC lesions). (C,D) Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene enrichment analysis of the DEGs.

3.2 Expression and Role of CD8A in BC

After the DEGs were identified, we visualized their interactions using a PPI network (Fig. 2). In-depth analysis revealed that CD8A was a key hub gene in this network and had the highest number of nodes (i.e., 74), which highlighted its centrality within this network (Fig. 3A). Furthermore, we validated the expression of CD8A using TCGA data. Interestingly, our analysis indicated that there was no significant difference in CD8A expression levels between BC and normal tissues (Fig. 3B). Moreover, in our pan-cancer analysis, CD8A expression was not significantly correlated with TMB or MSI (Fig. 3C,D).

Fig. 2.

Protein–protein interaction network of differentially expressed genes.

Fig. 3.

CD8A is an important hub gene. (A) Node analysis of the differentially expressed gene–protein network. (B) Expression levels of CD8A in 33 types of tumors. (C) Association of CD8A expression with tumor mutational burden in 33 types of tumors. (D) Association of CD8A expression with microsatellite instability in 33 types of tumors. *p $<$ 0.05, **p $<$ 0.01, ***p $<$ 0.001.

3.3 CD8A Gene Mutations in BC

To further understand the role of CD8A in BC, we explored the mutations in this gene using the cBioPortal platform. The overall mutation rate of CD8A was 0.6% (Fig. 4A), and a detailed examination revealed various mutation types (Fig. 4B). Moreover, several genes showed co-alterations in the presence of CD8A mutations, including DNAH6, MAT2A, and PTCD3 (Fig. 4C). Notably, K92N was the primary type of CD8A mutation (Fig. 4D). Interestingly, CD8A mutations were correlated with copy number alterations such as deep deletions, high amplifications, and arm-level gains in BC macrophages (Fig. 4E).

Fig. 4.

Mutation status of CD8A in breast cancer (BC). (A) Oncoprint of CD8A in BC. (B) Types of CD8A mutations in BC. (C) Frequency of gene alterations in the presence and absence of CD8A mutations. (D) Site of CD8A mutations in BC. (E) Association of CD8A mutations in BC with copy number alterations in immune cells. *p $<$ 0.05, ***p $<$ 0.001.

3.4 Correlation between CD8A Expression and ICI

We explored the correlations between CD8A expression and ICI. CD8A expression exhibited a strong correlation with PD-L1 (CD274, R = 0.581) and PD1 (PDCD1, R = 0.841) expression (Fig. 5A,B). Additionally, we analyzed the association between CD8A expression and the infiltration of various immune cells. CD8A expression exhibited a negative correlation with tumor purity. Moreover, it exhibited a positive correlation with the infiltration of B cells, CD8 ${}^{+}$ T cells, CD4 ${}^{+}$ T cells, macrophages, neutrophils, and dendritic cells (Fig. 5C). Importantly, survival analysis revealed that BC patients with higher B cell infiltration and CD8A levels had a more favorable prognosis (Fig. 5D).

Fig. 5.

Correlation between CD8A expression and immune cell infiltration (ICI). (A) Correlation between CD8A and PD-L1 (CD274) expression. (B) Correlation between CD8A and PD1 (PDCD1) expression. (C) Correlation between CD8A expression and ICI. (D) Survival analysis based on CD8A expression and ICI in BC patients.

3.5 Distribution of CD8A in Immune Cells within the TIME of Metastatic and Primary BC Lesions

Using scRNA-seq data, we identified CD8A-expressing immune cells to understand the distribution of CD8A expression within the TIME of BC. Our findings showed that CD8A was predominantly expressed in CD8 ${}^{+}$ T cells within primary BC foci (Fig. 6A–C). Interestingly, other hub genes also exhibited similar expression patterns in CD8 ${}^{+}$ T cells (Fig. 6D). However, in BC lung metastases, CD8A expression was significantly down-regulated in CD8 ${}^{+}$ T cells (Fig. 7A–D).

Fig. 6.

Immune cell distribution of CD8A in primary breast cancer (BC) lesions. (A) tSNE plot illustrating the scRNA-seq data obtained from immune cells in the BC microenvironment. (B,C) Cellular distribution of CD8A expression in the BC immune microenvironment. (D) Cellular distribution of the top 10 differentially expressed genes in the BC immune microenvironment.

Fig. 7.

Immune cell distribution of CD8A in lung metastases of breast cancer (BC). (A) tSNE plot illustrating the scRNA-seq data obtained from immune cells in BC lung metastases. (B,C) Cellular distribution of CD8A in the the Tumor IMmune Estimation Resource (TIME) of BC lung metastases. (D) Cellular distribution of DEGs in the TIME of BC lung metastases.

3.6 Validation of CD8A Expression in Primary and Metastatic BC Lesions

Finally, we validated our findings using clinical BC tissue samples. The results were consistent with our previous analyses, revealing high CD8A expression levels in the TIME of primary BC foci (Fig. 8A) and a significant down-regulation of CD8A expression in the TIME of BC lung metastases (Fig. 8B). These results confirmed the significance of CD8A in BC progression.

Fig. 8.

Immunohistochemical staining for CD8A in clinical breast cancer and lung metastasis samples. Black box, cancerous tissue; red box, adjacent normal tissue; blue box, immune cells. High CD8A expression levels in the TIME of primary BC foci (A) ,down-regulation of CD8A expression in the TIME of BC lung metastases (B).

4. Discussion

TNBC, which accounts for 15% to 20% of all BC cases, is a highly aggressive breast malignancy. Pathologically invasive ductal carcinoma is the most common subtype of TNBC [10]. Clinically, TNBC is often accompanied by lung and brain metastasis, which typically occurs within the first 3 years after the initial diagnosis. TNBC is prone to recurrence even after neoadjuvant and adjuvant treatments, including chemotherapy, and it has a lower overall survival rate than other types of BC [11].

The present study demonstrates that CD8A plays a critical role in the progression of TNBC. CD8A, which is primarily expressed in cytotoxic T lymphocytes and natural killer cells, is a key mediator of the immune system’s defense against cancer cells. In this study, we discovered that CD8A is significantly up-regulated in metastatic TNBC. This highlights the role of CD8A in the advanced stages of TNBC, particularly during its spread to distant sites.

In this study, we re-mined high-throughput data on tumor characteristics from existing public databases. We analyzed and integrated these data to obtain novel insights regarding TNBC [12]. Previously, a genomic study of 158 TNBC patients (TCGA dataset) revealed that some TNBC tumors only exhibited a few genomic changes, while others contained hundreds of somatic mutations and showed the simultaneous activation of multiple signaling pathways. This suggests that a single drug or therapeutic agent would likely be ineffective against TNBC [13]. Further detailed RNA-seq showed that about 36% of mutations in TNBC were expressed, and the gene mutations were most commonly concentrated in TP53 (80%), followed by PIK3CA and PTEN.

The increased expression of CD8A in metastatic TNBC is not merely a correlative finding. Instead, it has profound prognostic implications. Our analysis revealed that higher CD8A expression levels are associated with more favorable survival outcomes. This finding suggests CD8A could serve as a valuable prognostic marker in TNBC. Hence, patients with elevated CD8A expression may have a better prognosis, potentially due to the heightened immune response against cancer cells in the metastatic microenvironment. The presence of CD8A-expressing CD8 ${}^{+}$ T cells within the tumor microenvironment is clinically significant, given that CD8 ${}^{+}$ T cells are essential for the immune system’s defense against cancer. These cells are responsible for recognizing and targeting tumor-specific antigens, thus enabling the direct elimination of cancer cells. Furthermore, CD8 ${}^{+}$ T cells secrete cytokines such as granzyme B and interferon-gamma (IFN- $\gamma{}$ ), which can inhibit the growth of cancer cells and even induce apoptosis. This dual functionality of CD8+ T cells makes them instrumental in the suppression of tumor progression [14].

Immune evasion is a well-established strategy that allows tumors to survive and spread in the body. In this context, the role of CD8A in recognizing and eliminating cancer cells is quite important. Antigen-presenting cells present cancer antigens to tumor-infiltrating CD8 ${}^{+}$ T cells, activating their cytotoxic potential. These activated CD8 ${}^{+}$ T cells then destroy tumor cells, inhibiting tumor progression [15]. Together, these mechanisms modulate antitumor responses to prevent tumor growth and progression [16]. In TNBC, TILs can predict the response to immunotherapy, and high levels of TILs are associated with more inert tumor behavior [17]. An increase in TILs is associated with pathological complete response after neoadjuvant chemotherapy. Further, it has been linked to improved prognosis in both patients treated with and without neoadjuvant therapy. Moreover, TNBC patients with high levels of TILs also tend to show reduced rates of disease recurrence [18].

Subgroup analysis can reveal the relationship between the number/proportion of different subtypes of TIL and the prognosis of cancer patients. Hence, different subgroups of TILs have different prognostic values for cancer. Of all the TIL subgroups, tumor-infiltrating CD8 ${}^{+}$ T cells are the most closely related to cancer prognosis, and this finding has been extensively confirmed in various types of solid tumors. Tumor-infiltrating CD8 ${}^{+}$ T cells play an active role in adaptive immune defense and kill cancer cells via both direct and indirect pathways [19]. More than 60% of TILs in TNBC are lymphocytes, and ICI in these tumors is dominated by CD8 ${}^{+}$ T cells [20]. Interestingly, studies have confirmed that a high level of CD8 ${}^{+}$ T cell infiltration in tumor tissue predicts a longer recurrence-free survival [21, 22, 23].

Consistent with these results, we found that CD8A is specifically expressed in CD8+ T cells within the TIME of primary BC. Moreover, high CD8A expression leads to better patient outcomes in BC, and CD8A expression is significantly related to PD1 expression, PD-L1 expression, and ICI. Subsequently, we explored the relationship between CD8A expression and genetic alterations in infiltrating immune cells, especially deep deletions. Examinations of a patient-derived xenograft model using scRNA-seq technology revealed that the temporal and spatial evolution of tumor genomes largely conforms to Darwinian evolutionary laws during in vivo BC progression. That is, the change in genome copy number is relatively stable even during the malignant transformation of BC cells. However, somatic mutations exhibit great heterogeneity during tumor development, and this acquired heterogeneity is even more pronounced in TNBC [24, 25].

In this study, we also analyzed the distribution of CD8A expression using scRNA-seq data from a patient-derived TNBC xenograft tumor and matched lung metastases model. The findings showed that CD8A expression is down-regulated in BC lung metastases. These results suggest that high CD8A expression in tumor-infiltrating CD8 ${}^{+}$ T cells may improve survival and prognosis in TNBC patients. Tumor-infiltrating CD8 ${}^{+}$ T cells can improve survival in cancer patients through two key mechanisms, as mentioned previously. First, antigen-presenting cells can present the cancer antigens to tumor-infiltrating CD8 ${}^{+}$ T cells, causing them to transform into cytotoxic T cells. These cytotoxic T cells can then eliminate cancer cells. Furthermore, CD8 ${}^{+}$ T cells can also secrete cytokines such as granzyme B and IFN- $\gamma{}$ , which inhibit the growth of cancer cells and induce tumor cell apoptosis, enabling tumor suppression [26, 27].

The strength of this study lies in the combined transcriptome and scRNA-seq analysis of TNBC metastasis-associated biomarkers. Nevertheless, this study also has certain shortcomings. First, it was difficult to find a comparable BC metastasis transcriptome dataset for validation. Second, we only verified protein expression using clinical samples of primary and secondary BC lesions and did not establish an independent BC metastasis model to further clarify the role of CD8A in BC metastasis. Further experiments to clarify the mechanisms underlying the role of CD8A during BC metastasis are warranted.

5. Conclusions

Here, we comprehensively analyzed transcriptome and scRNA-seq datasets to identify the key genes involved in TNBC. Our results indicated that CD8A expression was correlated with BC metastasis, and a high level of CD8A expression was associated with improved survival outcomes in TNBC patients. Hence, CD8A might be a potential prognostic marker in TNBC and could also play a role in BC metastasis.

Abbreviations

TNBC, triple-negative breast cancer; BC, breast cancer; DEGs, differentially expressed genes; scRNA-seq, single-cell RNA sequencing; TIME, tumor immune microenvironment; TILs, tumor-infiltrating lymphocytes; GEO, Gene Expression Omnibus; PPI, protein–protein interaction; TMB, tumor mutational burden; MSI, microsatellite instability; ICI, immune cell infiltration.

Availability of Data and Materials

The corresponding author will provide the datasets upon reasonable request.

Author Contributions

JC and ST drafted the manuscript and revised it critically and finally approved the version to be published; JC, ST, TL, and HF designed the study and analyzed the data. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

Ethics Approval and Consent to Participate

The study’s protocol was approved by the Affiliated Hospital of Nanjing University Medical School (protocol No. 2021220374).

Acknowledgment

Not applicable.

Funding

This research received no external funding.

Conflict of Interest

The authors declare no conflict of interest.

References

[1]

Bianchini G, De Angelis C, Licata L, Gianni L. Treatment landscape of triple-negative breast cancer - expanded options, evolving needs. Nature Reviews. Clinical Oncology. 2022; 19: 91–113.

| Google Scholar

[2]

Lyons TG. Targeted Therapies for Triple-Negative Breast Cancer. Current Treatment Options in Oncology. 2019; 20: 82.

| Google Scholar

[3]

Yu B, Luo F, Sun B, Liu W, Shi Q, Cheng SY, et al. KAT6A Acetylation of SMAD3 Regulates Myeloid-Derived Suppressor Cell Recruitment, Metastasis, and Immunotherapy in Triple-Negative Breast Cancer. Advanced Science. 2021; 8: e2100014.

| Google Scholar

[4]

Kim C, Gao R, Sei E, Brandt R, Hartman J, Hatschek T, et al. Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing. Cell. 2018; 173: 879–893.e13.

| Google Scholar

[5]

Bagchi S, Yuan R, Engleman EG. Immune Checkpoint Inhibitors for the Treatment of Cancer: Clinical Impact and Mechanisms of Response and Resistance. Annual Review of Pathology. 2021; 16: 223–249.

| Google Scholar

[6]

Yamashita N, Long M, Fushimi A, Yamamoto M, Hata T, Hagiwara M, et al. MUC1-C integrates activation of the IFN-

\gamma

pathway with suppression of the tumor immune microenvironment in triple-negative breast cancer. Journal for Immunotherapy of Cancer. 2021; 9: e002115.

| Google Scholar

[7]

Wang X, Barrera C, Bera K, Viswanathan VS, Azarianpour-Esfahani S, Koyuncu C, et al. Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors. Science Advances. 2022; 8: eabn3966.

| Google Scholar

[8]

Oura K, Morishita A, Tani J, Masaki T. Tumor Immune Microenvironment and Immunosuppressive Therapy in Hepatocellular Carcinoma: A Review. International Journal of Molecular Sciences. 2021; 22: 5801.

| Google Scholar

[9]

Ren X, Zhang L, Zhang Y, Li Z, Siemers N, Zhang Z. Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment. Annual Review of Immunology. 2021; 39: 583–609.

| Google Scholar

[10]

Wang H, Rong X, Zhao G, Zhou Y, Xiao Y, Ma D, et al. The microbial metabolite trimethylamine N-oxide promotes antitumor immunity in triple-negative breast cancer. Cell Metabolism. 2022; 34: 581–594.e8.

| Google Scholar

[11]

Marra A, Curigliano G. Adjuvant and Neoadjuvant Treatment of Triple-Negative Breast Cancer With Chemotherapy. Cancer Journal. 2021; 27: 41–49.

| Google Scholar

[12]

Higa A, Takahashi N, Hiyama G, Tamura H, Hoshi H, Shimomura K, et al. High-Throughput In Vitro Assay using Patient-Derived Tumor Organoids. Journal of Visualized Experiments. 2021; e62668.

| Google Scholar

[13]

Jiang YZ, Liu Y, Xiao Y, Hu X, Jiang L, Zuo WJ, et al. Molecular subtyping and genomic profiling expand precision medicine in refractory metastatic triple-negative breast cancer: the FUTURE trial. Cell Research. 2021; 31: 178–186.

| Google Scholar

[14]

Liu Y, Hu Y, Xue J, Li J, Yi J, Bu J, et al. Correction: Advances in immunotherapy for triple-negative breast cancer. Molecular Cancer. 2023; 22: 154.

| Google Scholar

[15]

Ye F, Dewanjee S, Li Y, Jha NK, Chen ZS, Kumar A, et al. Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer. Molecular Cancer. 2023; 22: 105.

| Google Scholar

[16]

Dieci MV, Miglietta F, Guarneri V. Immune Infiltrates in Breast Cancer: Recent Updates and Clinical Implications. Cells. 2021; 10: 223.

| Google Scholar

[17]

Sukumar J, Gast K, Quiroga D, Lustberg M, Williams N. Triple-negative breast cancer: promising prognostic biomarkers currently in development. Expert Review of Anticancer Therapy. 2021; 21: 135–148.

| Google Scholar

[18]

Huertas-Caro CA, Ramirez MA, Gonzalez-Torres HJ, Sanabria-Salas MC, Serrano-Gómez SJ. Immune Lymphocyte Infiltrate and its Prognostic Value in Triple-Negative Breast Cancer. Frontiers in Oncology. 2022; 12: 910976.

| Google Scholar

[19]

Edwards DN, Ngwa VM, Raybuck AL, Wang S, Hwang Y, Kim LC, et al. Selective glutamine metabolism inhibition in tumor cells improves antitumor T lymphocyte activity in triple-negative breast cancer. The Journal of Clinical Investigation. 2021; 131: e140100.

| Google Scholar

[20]

Singh S, Lee N, Pedroza DA, Bado IL, Hamor C, Zhang L, et al. Chemotherapy Coupled to Macrophage Inhibition Induces T-cell and B-cell Infiltration and Durable Regression in Triple-Negative Breast Cancer. Cancer Research. 2022; 82: 2281–2297.

| Google Scholar

[21]

Binnewies M, Pollack JL, Rudolph J, Dash S, Abushawish M, Lee T, et al. Targeting TREM2 on tumor-associated macrophages enhances immunotherapy. Cell Reports. 2021; 37: 109844.

| Google Scholar

[22]

Federico L, McGrail DJ, Bentebibel SE, Haymaker C, Ravelli A, Forget MA, et al. Distinct tumor-infiltrating lymphocyte landscapes are associated with clinical outcomes in localized non-small-cell lung cancer. Annals of Oncology. 2022; 33: 42–56.

| Google Scholar

[23]

Lan C, Kitano Y, Yamashita YI, Yamao T, Kajiyama K, Yoshizumi T, et al. Cancer-associated fibroblast senescence and its relation with tumour-infiltrating lymphocytes and PD-L1 expressions in intrahepatic cholangiocarcinoma. British Journal of Cancer. 2022; 126: 219–227.

| Google Scholar

[24]

Rong G, Yi Z, Ma F, Guan Y, Xu Y, Li L, et al. DNA damage response as a prognostic indicator in metastatic breast cancer via mutational analysis. Annals of Translational Medicine. 2021; 9: 220.

| Google Scholar

[25]

Sun WY, Lee J, Kim BK, Kim JO, Park J. Distinct Somatic Alteration Features Identified by Gene Panel Sequencing in Korean Triple-Negative Breast Cancer with High Ki67 Expression. Diagnostics. 2021; 11: 416.

| Google Scholar

[26]

Bonnal RJP, Rossetti G, Lugli E, De Simone M, Gruarin P, Brummelman J, et al. Clonally expanded EOMES

{}^{+}

Tr1-like cells in primary and metastatic tumors are associated with disease progression. Nature Immunology. 2021; 22: 735–745.

| Google Scholar

[27]

Tcyganov EN, Hanabuchi S, Hashimoto A, Campbell D, Kar G, Slidel TW, et al. Distinct mechanisms govern populations of myeloid-derived suppressor cells in chronic viral infection and cancer. The Journal of Clinical Investigation. 2021; 131: e145971.

| Google Scholar

Publisher’s Note: IMR Press stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Front. Biosci. (Landmark Ed) Print ISSN 2768-6701 Electronic ISSN 2768-6698