An Integrated Analysis of HAVCR1 with a Focus on Immunological and Prognostic Roles in Breast Cancer

Abstract

Background:

Breast cancer remains a predominant malignancy and a leading cause of oncologic mortality among women globally. The discovery of novel biomarkers is crucial for improving therapeutic outcomes.

Methods:

We conducted a comprehensive analysis of the immunological and prognostic significance of hepatitis A virus cellular receptor 1 (HAVCR1) in breast cancer using publicly available datasets.

Results:

HAVCR1 expression was markedly downregulated in breast cancer tissues. Significantly, lower expression levels of HAVCR1 in pre-treatment tumor samples were associated with poorer prognosis among pan-cancer patients undergoing immunotherapy, and a higher incidence of metastasis was observed in the breast cancer subgroup. Subtype-specific DEG analyses further indicated that distinct patterns of immune infiltration may underlie this association. Moreover, gene set enrichment analysis (GSEA) highlighted the immunological relevance of HAVCR1, particularly its involvement in T cell activation within the TNBC subtype. Clinically, elevated levels of HAVCR1 expression in pre-treatment T cells were indicative of a more favorable response to PD-1 blockade therapy compared to those with diminished expression.

Conclusion:

The expression of HAVCR1 exhibits a strong correlation with immune infiltration and holds potential as a prognostic biomarker for breast cancer, offering predictive insight into the efficacy of immunotherapeutic interventions.

Keywords

breast cancer HAVCR1 immunotherapy immune infiltration prognostic biomarker

Introduction

Breast cancer, representing the most commonly diagnosed malignancy among women worldwide, accounts for 30% of all female cancers, with its incidence exhibiting a consistent annual increase of approximately 0.5%.^1,2 The disease is characterized by its significant heterogeneity, classified into various molecular profiles including hormone receptor-positive (luminal A, luminal B), human epidermal growth factor receptor-2 (HER-2) positive, and triple-negative breast cancer (TNBC), which lacks expression of estrogen, progesterone receptors, and HER-2.³ Despite considerable advances in surgery and adjuvant therapies in recent years, the 5-year survival rate for metastatic breast cancer remains below 30%,⁴ with TNBC exhibiting a particularly poor prognosis compared to other subtypes.⁵

Immune checkpoint inhibitors (ICIs), which target molecules such as PD-1, PD-L1, and CTLA-4, have revolutionized cancer therapy and yielded substantial clinical benefits in various malignancies.^6,7 However, in breast cancer, the therapeutic efficacy of ICIs is limited to a subset of patients,⁸ underscoring the critical role of the tumor microenvironment (TME) in modulating immune response and determining the success of immunotherapy. The dynamic interplay within the TME, including the depletion or transient activation of immune cells and suppression of favorable microenvironment formation, frequently contribute to resistance against ICIs.⁹ Immune-associated genes and the infiltration of immune cells within the TME are pivotal in influencing tumor characteristics such as proliferation and development.¹⁰ Therefore, comprehensive characterization of immune-associated genes is essential for elucidating factors that influence survival and guiding therapy and prognosis in breast cancer.

Hepatitis A virus cellular receptor 1 (HAVCR1), also known as T-cell immunoglobulin and mucin domain 1 (TIM-1), is a member of the TIM gene family and plays a multifaceted role in immune regulation and tumor progression.¹¹ Predominantly expressed on T helper 2 cells, HAVCR1 facilitates T-cell activation and hinders the development of peripheral tolerance.^12,13 Recent evidence also highlights the significance of HAVCR1 expression on B cells in modulating anti-tumor immune responses and influencing tumor growth in vivo.¹⁴ The involvement of HAVCR1 in tumor growth and metastasis, primarily through interactions with immune cells and contributing to an immunosuppressive tumor microenvironment (TME) conducive to immune evasion, underscores its potential as a therapeutic and prognostic marker. Notably, elevated HAVCR1 expression has been reported in breast cancer, particularly in situ carcinoma.¹⁵

This study aims to delineate the prognostic and immunological implications of HAVCR1 expression in breast cancer through comprehensive analysis of publicly available databases, positing HAVCR1 as a promising biomarker for prognosis, especially in TNBC, and as a guide for immunotherapy efficacy.

Materials and Methods

Pan-Cancer and Clinical Variable Analyses

UCSC XENA (https://xenabrowser.net/datapages/) and GDC (https://portal.gdc.cancer.gov) are cancer-associated web portals, which are linked to the TCGA and GTEx databases.¹⁶ These web portals obtain RNA-seq and clinical data of TCGA and GTEx datasets to analyze the expression of the gene(s) of interest. In our study, we used data obtained from both the web portal for the pan-cancer and clinical variable-based differential expression analysis of HAVCR1. P-value < .05.

Kaplan-Meier Plotter Analysis

Overall survival data were obtained from the Kaplan-Meier Plotter (http://kmplot.com/analysis), which compiles and standardizes RNA expression and clinical outcome data from publicly available datasets, including TCGA, GEO, and EGA. In our study, the impact of HAVCR1 expression on the overall survival (OS) in patients with various cancers treated with immune checkpoint inhibitors (ICIs) was analyzed using this tool, and the cut-off value was determined by the best-performing threshold. Statistical significance was assessed using the log-rank test, and hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. The ICI-treated cohorts analyzed comprised 91 cases of melanoma treated with CTLA-4 inhibitors (GSE165278), 309 cases of melanoma and bladder cancer treated with PD-1 inhibitors (GSE91061, GSE78220, GIDE2019, GSE176307), and 424 cases of bladder cancer, esophageal adenocarcinoma, and urothelial carcinoma treated with PD-L1 inhibitors (GSE176307, GSE165252, GSE183924).

Differential Expression and Gene Set Enrichment Analysis Based on HAVCR1 Expression in TCGA Breast Cancer Subtypes

Gene expression profiles and corresponding clinical data of breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) database. Differential expression analysis between the high and low HAVCR1 expression groups was performed separately for each subtype using the DESeq2 package (v1.48.1) in R. Differentially expressed genes (DEGs) were defined as those with a false discovery rate (FDR) <.05 and absolute log2 fold change (|log2FC|) >1.5.¹⁷ To prioritize genes for enrichment analysis, the DEG list was ranked by the product of FDR and the direction of change (assigned as 1 for genes upregulated in the high-expression group and −1 for downregulated genes), ordered from highest to lowest. Gene Set Enrichment Analysis (GSEA) was conducted using the clusterProfiler package (v4.16.0), based on KEGG pathway gene sets, with signaling pathways related to viral infection and disease explicitly excluded.¹⁸ Visualization of differential expression results was performed using volcano plots, while GSEA results were visualized with bar plots. All plots were generated using the ggplot2 package (v3.5.2).

Clinicopathological Correlation and Prognosis Analysis

We downloaded clinical information of triple-negative breast cancer in the TCGA dataset and used multivariate Cox analysis to discern the expression of HAVCR1 that was significantly related to the OS using survival (R package).

Statistical Analysis

All statistical analyses were conducted using R software, with a P-value of less than .05 deemed to indicate statistical significance. To assess differences in clinical characteristics and immune cell scores based on HAVCR1 expression levels, T-tests, Wilcoxon tests, and chi-square tests were utilized. Furthermore, multivariate Cox regression analyses were applied to ascertain the clinical characteristics associated with OS.

Results

Different Expression of HAVCR1 in Various Types of Cancers

The flow chart of this study is shown in Figure 1. The mRNA expression profile of HAVCR1 was assessed across multiple cancer types using TCGA datasets by comparing tumor tissues with their corresponding adjacent normal tissues (Figure 2a). This analysis revealed that HAVCR1 expression was significantly downregulated in breast cancer tissues compared with normal counterparts, whereas it was markedly upregulated in 17 other human tumor types, including bladder urothelial carcinoma (BLCA) and lung squamous cell carcinoma (LUSC; Figure 2b-d). To investigate the prognostic relevance of HAVCR1 expression in the context of ICI therapy, univariate survival analyses were conducted using integrated datasets,¹⁹ with the Kaplan-Meier plotter employed for visualization (http://kmplot.com/analysis). The ICI-treated cohort encompassed multiple malignancies, including melanoma, bladder cancer, esophageal adenocarcinoma, and urothelial carcinoma. The survival curves demonstrated that low expression of HAVCR1 in pre-treatment tumor tissues was significantly associated with shorter OS in pan-cancer cohorts treated with CTLA-4 inhibitor (HR = 0.4, 95% CI: 0.23-0.71, P = .0013), PD-1 inhibitor (HR = 0.65, 95% CI: 0.47-0.9, P = .0085), and PD-L1 inhibitor (HR = 0.71, 95% CI:0.55-0.93, P = .011), respectively (Figure 3). Conversely, patients with high HAVCR1 expression tended to have a better response to ICI therapy and prolonged OS.

Figure 1.

Workflow chart for this study.

Figure 2.

The mRNA expression levels of HAVCR1 were analyzed using TCGA datasets by comparing tumor tissues with their matched adjacent normal tissues (a). The protein expression level of HAVCR1 in breast invasive carcinoma (BRCA) tissue and normal breast tissues (b). The protein expression level of HAVCR1 in bladder urothelial carcinoma (BLCA) tissue and normal breast tissues (c). The protein expression level of HAVCR1 in lung squamous cell carcinoma (LUSC) tissue and normal breast tissues (d). Statistical significance was determined using the Wilcoxon rank-sum test and a 2-tailed, unpaired Student’s t test. *P < .05; ***P < .001.

Figure 3.

Patients were stratified into high- and low-HAVCR1 expression groups based on the median expression level in pre-treatment tumor samples. Survival outcomes were analyzed for patients receiving CTLA-4 (a), PD-1 (b), and PD-L1 (c) inhibitors, respectively. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using univariate Cox proportional hazards regression. Statistical significance was assessed using the log-rank test, with P < .05 indicating a significant difference.

HAVCR1 Expression and Pathological Features in Breast Cancer

Additionally, the association between HAVCR1 expression levels and various clinicopathological features of breast cancer was systematically investigated, including patient age, pathological tumor size (T), lymph node involvement (N), distant metastasis (M), as defined by the TNM classification system, as well as OS, disease-specific survival (DSS), and progression-free interval (PFI; Table 1). Based on the median HAVCR1 expression level, patients were divided into high-expression and low-expression cohorts. Notably, the low-expression HAVCR1 group exhibited a higher incidence of metastasis compared to the high-expression group. However, within the TCGA cohort, no statistically significant differences were observed in terms of age, pathologic T and N classification, OS, DSS, and PFI across all breast cancer patients, irrespective of molecular subtype.

Table 1.

The Correlations Between HAVCR1 Expression and Clinical Characteristics Based on TCGA Datasets.

Variables	Low expression of HAVCR1	High expression of HAVCR1	P value	Statistics	Methods
n	543	544
Pathologic T stage, n (%)			.627	0.93455	Chisq test
T1	136 (12.5%)	142 (13.1%)
T2	311 (28.7%)	320 (29.5%)
T3&T4	93 (8.6%)	82 (7.6%)
Pathologic N stage, n (%)			.889	0.63255	Chisq test
N0	256 (24%)	260 (24.3%)
N1	181 (16.9%)	178 (16.7%)
N2	58 (5.4%)	58 (5.4%)
N3	35 (3.3%)	42 (3.9%)
Pathologic M stage, n (%)			.049	38 851	Chisq test
M0	432 (46.7%)
M1	14 (1.5%)
Age, n (%)					Chisq test
⩽60	303 (27.9%)
>60	240 (22.1%)
OS event, n (%)			.158	1.9924	Chisq test
Alive	459 (42.2%)	476 (43.8%)
Dead	84 (7.7%)	68 (6.3%)
DSS event, n (%)			.403	0.6997	Chisq test
No	485 (45.5%)
Yes	46 (4.3%)
PFI event, n (%)			.323	0.9754	Chisq test
No	464 (42.7%)	476 (43.8%)
Yes	79 (7.3%)	68 (6.3%)

DEGs Identification Based on HAVCR1 Expression in Breast Cancer

To investigate the transcriptional alterations associated with HAVCR1 expression in breast cancer, we performed differential gene expression analysis across 3 molecular subtypes: luminal (n = 685), HER-2 positive (n = 162), and triple-negative breast cancer (TNBC, n = 177). Within each subtype, patients were stratified into high-and low-HAVCR1 expression groups based on the median expression level within each subtype (Figure 4).

Figure 4.

Volcano plots illustrating differentially expressed genes between HAVCR1 high- and low-expression samples within luminal (a), HER-2 positive (b), and TNBC (c) subtypes of breast cancer, as derived from the TCGA database. Each plot displays genes with log2 fold changes on the x-axis versus -log10 (FDR) on the y-axis. Points exceeding the horizontal threshold (indicating a false discovery rate (FDR) < 0.05 for statistical significance) and demonstrating an absolute log2 fold change (|log2FC|) > 1.5 are identified as significantly differentially expressed genes.

In the luminal subtype, immune-related transcripts such as FCRL1, IL21, and GNAT3 were significantly upregulated in the high-expression group, suggesting potential roles in B cell activation and function, T cell signaling and cytokine response.^20
-25 In the HER2-positive subgroup, a total of 145 DEGs were identified. Key genes included CR2, EYA1, and CHRNA9, which are known to regulate B cell function, epithelial differentiation, and immune modulation.^26
-28 In TNBC, a more robust immune signature was observed, with high HAVCR1 expression linked to the upregulation of TXK, ALOX15B, TRAV26-1, and TRBV27, many of which are central to T cell receptor signaling and cytotoxic activity.^29
-33

Gene set enrichment analysis (GSEA; Figure 5) further confirmed significant enrichment of immune-related pathways across all subtypes. Specifically, in the luminal subtype, enriched pathways included cytokine-cytokine receptor interaction, chemokine signaling, lymphocyte differentiation, JAK-STAT signaling. In HER-2 positive breast cancer, enriched pathways were primarily associated with pathways related to lymphocyte activation and differentiation, antigen processing and presentation, T/B cell receptor signaling, and cytokine-cytokine receptor interactions. For the TNBC subtype, the enrichment analysis pointed toward T cell differentiation, cytokine-cytokine receptor interaction, chemokine signaling and T cell receptor signaling. Notably, TNBC exhibited the most prominent enrichment of immune-related pathways, underscoring a strong association between HAVCR1 expression and immune activation in this subtype. These findings suggest that HAVCR1 may serve as a critical immunomodulatory factor in TNBC, potentially shaping the tumor immune microenvironment and influencing immunotherapeutic responsiveness.

Figure 5.

Bar plots of the top KEGG pathway enrichment analysis was performed separately for luminal (a), HER2-positive (b), and TNBC (c) subtypes using GSEA, as analyzed using the TCGA database. The x-axis represents the normalized enrichment score (NES), and the color gradient corresponds to the −log10 adjusted P-value.

HAVCR1 Expression and Immune Cell Score in TNBC

Given the pronounced correlation between HAVCR1 expression and immune response in the TNBC subtype, our investigation delved deeper into the relationship between HAVCR1 and immune cell infiltration and immunomodulatory gene expression. Using the TCGA-TNBC cohort, we performed CIBERSORT analysis to estimate immune cell composition based on HAVCR1 expression levels.

Figure 6a showed the infiltration levels of 28 immune cell subtypes between the high and low HAVCR1 expression groups. Activated B cell, activated CD8 T cell, activated dendritic cell, central memory CD4 T cell, effector memory CD8 T cell, and T follicular helper cell exhibited significantly higher infiltration in the HAVCR1 high-expression group, suggesting that HAVCR1 may contribute to shaping a more immune-active tumor microenvironment.

Figure 6.

(a) The infiltration levels of 28 immune cells in high- and low-HAVCR1 expression groups calculated by The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis (Wilcoxon test, ns: not significant; *P < .05; **P < .01; ***P < .001; ***P < .0001). (b) The heatmap shows the expression patterns of different immune-related genes in TNBC at various clinical stages.

To further investigate the immunological landscape associated with HAVCR1, we analyzed the expression patterns of immune-related genes (Figure 6b). The HAVCR1 high-expression group showed upregulation of several key chemokines (CCL5, CCL19), chemokine receptors (CCR5, CCR7, CXCR3), cytokines (IL-15, IL-6), cytotoxic effector genes (GZMA, GZMB, GZMK, GZMM), and major histocompatibility complex (MHC) class molecules, including HLA-E, HLA-G, and HLA-DRA.

Collectively, these findings underscore the complex immunoregulatory role of HAVCR1 in TNBC. Its high expression is associated with increased immune cell infiltration, enhanced antigen presentation via MHC molecules, and activation of stimulatory immune signals, highlighting its potential as a biomarker for predicting immunotherapy response and guiding immune-targeted strategies in TNBC.

Expression of HAVCR1 and Response of PD-1 Treatment in TNBC

To further evaluate the predictive value of HAVCR1 expression for PD-1 blockade efficacy in TNBC, we analyzed the data from a neoadjuvant phase II clinical trial (NCT03366844, clinicaltrials.gov). As shown in Figure 7, HAVCR1 was predominantly expressed in T cells. Furthermore, patients with high level of HAVCR1 in T cells before treatment exhibited a favorable response to the pembrolizumab combined with radiotherapy (Figure 8a). Consistently, in the model of mouse treatment with PD-1 inhibitors, elevated pre-treatment HAVCR1 levels were also associated with enhanced immunotherapeutic response (Figure 8b). These results suggest that pre-treatment HAVCR1 expression is positively correlated with sensitivity to immunotherapy in TNBC and may serve as a predictive biomarker for therapeutic response.

Figure 7.

Violin plot generated from single-cell RNA sequencing data (GSE176078), revealing HAVCR1 expression patterns across cell types in breast cancer. Predominantly, HAVCR1 is expressed in T-cell clusters, indicating its specific role in the immune landscape of the tumor.

Figure 8.

Association between elevated HAVCR1 expression and enhanced response to immunotherapy in TNBC. Analysis of data derived from GSE246613 reveals that in both clinical samples (a) and mouse model tissues (b) of TNBC, samples with superior immunotherapeutic outcomes exhibit markedly higher HAVCR1 levels in immune cells.

Discussion

Breast cancer remains the most common and fatal malignancy among women globally, characterized by its intricate TME and pronounced heterogeneity.³⁴ Despite advances in treatment, clinical outcomes—particularly in TNBC remain unsatisfactory.³⁵ In light of the limitations of conventional treatments, immunotherapy has emerged as a promising strategy, highlighting the need for reliable immune-related biomarkers.

HAVCR1, a transmembrane glycoprotein implicated in immune regulation within the TME, has been identified in several cancer types.^14,36 However, its expression patterns and functional implications in breast cancer remain unexplored. In this study, we present a comprehensive analysis of HAVCR1 expression and its association with immune features across breast cancer subtypes. HAVCR1 was significantly downregulated in tumor tissues, and high expression correlated with better outcomes following immune checkpoint inhibitor (ICI) therapy across multiple cancer types, highlighting its potential as a predictive biomarker for immunotherapy responsiveness.

Given HAVCR1′s association with immunotherapy efficacy, we delved deeper into its relationship between HAVCR1 expression and immune infiltration in breast cancer. Notably, a strong correlation was observed in TNBC. DEGs and GSEA revealed that high HAVCR1 expression is linked to enhanced T cell activation and a pro-inflammatory TME. T cell activation is a central component of antitumor immunity, particularly in TNBC, where robust T cell responses are critical for effective tumor suppression.^37
-39 ICIs that promote T cell activation have demonstrated therapeutic potential in breast cancer by promoting immune-mediated tumor eradication.⁴⁰

Further analysis of DEGs in TNBC identified 529 upregulated genes primarily involved in immune regulation, including chemokines, chemokine receptors, and cytokines. The upregulation of key mediators such as CCL5 and its receptor CCR5 points to enhanced chemotaxis of effector T cells,⁴¹ while upregulation of cytotoxic markers such as GZMB and IFNG indicates potent antitumor immune activity. Taken together, these findings suggest that HAVCR1 may serve as a key modulator of the immune landscape in TNBC, potentially enhancing responsiveness to immunotherapy through the promotion of T cell-mediated immune surveillance.

Tumor-infiltrating lymphocytes (TILs) are recognized as independent prognostic factors in cancer.⁴² Our analysis reveals that high HAVCR1 expression also showed a strong positive correlation with tumor-infiltrating immune cells in TNBC, including CD8+ T cells, NK cells, macrophages, dendritic cells, and B cells. This immune-enriched, or “hot” tumor microenvironment is associated with favorable prognosis and enhanced immunotherapy responsiveness. The cytotoxic activities of NK cells and CD8+ T cells play a pivotal role in tumor suppression. The anticancer activity of CD8+ T cells, particularly through interferon-gamma production, correlates with improved clinical outcomes in breast cancer, suggesting that elevated HAVCR1 expression may enhance the efficacy of ICIs, thereby extending OS by activating CD8+ T cells.

Beyond the significant relationship between HAVCR1 and immune infiltration in TNBC, our findings also indicate a moderate to strong correlation in the luminal subtype. Research by Jin et al identified a subset of luminal breast cancer, termed SNF2 tumors, characterized by an increased abundance of immune cells and higher expression levels of immune activation signatures, notably for CD8+ T cells.⁴³ This discovery is consistent with our results, further emphasizing the role of HAVCR1 in modulating the immune landscape across breast cancer subtypes.

Tumor immunotherapy has recently garnered significant attention. Our research raises questions about the potential to enhance immunotherapy efficacy through the upregulation of HAVCR1 expression, and whether HAVCR1 could serve as a reliable biomarker for predicting the efficacy of immune checkpoint blockade therapy. Further research is necessary to thoroughly understand the underlying mechanisms. This study contributes new insights into the molecular interactions between HAVCR1 and breast cancer, particularly the dynamics between HAVCR1 and immune-related genes, which could explain the variable responses to immunotherapies observed among breast cancer patients and aid in the development of more precise treatments.

Despite the comprehensive nature of our analysis, several limitations should be acknowledged. Firstly, the absence of breast cancer-specific ICI treatment datasets prevented a direct assessment of the prognostic impact of HAVCR1 expression in this clinical context. Secondly, the immunological functions of HAVCR1 inferred from bioinformatic analyses require validation through mechanistic studies in vitro and in vivo. Lastly, although we established a strong association between HAVCR1 expression, immune cell infiltration, and metastasis, the downstream pathways by which HAVCR1 modulates the immune response remain to be elucidated.

Conclusion

This study is the inaugural report suggesting a significant association between HAVCR1 overexpression and improved OS in breast cancer, highlighting a strong link with immune infiltration. These findings propose HAVCR1 as a potential prognostic biomarker for breast cancer and a predictor of immunotherapy efficacy.

Footnotes

Acknowledgements

We acknowledge the contributions of all authors to the completion of this study.

Abbreviations

Hepatitis A virus cellular receptor 1 (HAVCR1); differentially expressed genes (DEGs); gene set enrichment analysis (GSEA); human epidermal growth factor receptor-2 (HER-2); triple-negative breast cancer (TNBC); immune checkpoint inhibitors (ICIs); tumor microenvironment (TME); T-cell immunoglobulin and mucin domain 1 (TIM-1); overall survival (OS); tumor, nodes, and metastases (TNM); disease-specific survival (DSS); progression-free interval (PFI); granzyme B (GZMB); interferon-gamma (IFNG); cytotoxic T lymphocytes (CTLs); natural killer (NK); tumor-infiltrating lymphocytes (TILs); myeloid-derived suppressor cells (MDSCs); major histocompatibility complex (MHC).

ORCID iDs

Wenbin Zhou

Yue Sun

Ethical Considerations

This article does not contain any studies with human participants or animals performed by any of the authors. All data used in our study is publicly accessible.

Consent to Participate

Informed consent was not applicable to this study.

Author Contributions

WS, JYZ and MYS collected the data and drafted the manuscript. WS, WYZ, JYZ, QXF, YS and WBZ conducted statistical analysis and offered technical support. YS, WBZ, WYZ and WS revised and finalized the manuscript. All authors contributed to the article and approved the final submitted version.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (82403331 Y.S.).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All data generated and analyzed in this study are presented within the published article.

References

Siegel

Miller

Wagle

Jemal

Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48.

Trapani

Ginsburg

Fadelu

, et al Global challenges and policy solutions in breast cancer control. Cancer Treat Rev. 2022;104:102339.

The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61-70.

Riggio

Varley

Welm

AL.

The lingering mysteries of metastatic recurrence in breast cancer. Br J Cancer. 2021;124(1):13-26.

Foulkes

Smith

Reis-Filho

JS.

Triple-negative breast cancer. N Engl J Med. 2010;363(20):1938-1948.

Rastin

Javid

Oryani

Rezagholinejad

Afshari

Karimi-Shahri

Immunotherapy for colorectal cancer: Rational strategies and novel therapeutic progress. Int Immunopharmacol. 2024;126:111055.

Doroshow

Bhalla

Beasley

, et al PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol. 2021;18(6):345-362.

Miles

Gligorov

André

, et al Primary results from IMpassion131, a double-blind, placebo-controlled, randomised phase III trial of first-line paclitaxel with or without atezolizumab for unresectable locally advanced/metastatic triple-negative breast cancer. Ann Oncol. 2021;32(8):994-1004.

Tang

Qiao

YX.

Immunotherapy and tumor microenvironment. Cancer Lett. 2016;370(1):85-90.

10.

González

Volkova

Beer

Gerstung

Immuno-oncology from the perspective of somatic evolution. Semin Cancer Biol. 2018;52(Pt 2):75-85.

11.

Kim

Eyheramonho

Pichavant

, et al A polymorphism in TIM1 is associated with susceptibility to severe hepatitis A virus infection in humans . J Clin Investig. 2011;121(3):1111-1118.

12.

Mariat

Degauque

Balasubramanian

, et al Tim-1 signaling substitutes for conventional signal 1 and requires costimulation to induce T cell proliferation. J Immunol. 2009;182(3):1379-1385.

13.

Xiao

Najafian

Reddy

, et al Differential engagement of Tim-1 during activation can positively or negatively costimulate T cell expansion and effector function. J Exp Med. 2007;204(7):1691-1702.

14.

Bod

Kye

Shi

, et al B-cell-specific checkpoint molecules that regulate anti-tumour immunity. Nature. 2023;619(7969):348-356.

15.

Diniz

Pulular

Solakoğlu Kahraman

, et al Tissue expression of neutrophil gelatinase-associated lipocalin and kidney injury molecule-1 in breast cancers. Eur J Breast Health. 2022;18(4):336-342.

16.

Bao

Ren

, et al Comprehensive pan-cancer analysis indicates UCHL5 as a novel cancer biomarker and promotes cervical cancer progression through the Wnt signaling pathway. Biol Direct. 2024;19(1):139.

17.

Zheng

Zhang

, et al Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in colon adenocarcinoma. Aging (Albany NY), 2023, 15(18): 9521-9543.

18.

Bao

Zhang

Wang

Jiang

Identification and verification of a PANoptosis-related long noncoding ribonucleic acid signature for predicting the clinical outcomes and immune landscape in lung adenocarcinoma. Heliyon. 2024;10(8):e29869.

19.

Kovács

Fekete

Győrffy

Predictive biomarkers of immunotherapy response with pharmacological applications in solid tumors. Acta Pharmacol Sin. 2023;44(9):1879-1889.

20.

Zhao

Xie

Zhao

, et al Fc receptor–like 1 intrinsically recruits c-Abl to enhance B cell activation and function. Sci Adv. 2019;5(7):eaaw0315.

21.

Davis

RS.

Roles for the FCRL6 immunoreceptor in tumor immunology. Front Immunol. 2020;11:575175.

22.

Zander

Kasmani

Chen

, et al Tfh cell-derived interleukin 21 sustains effector CD8+ T cell responses during chronic viral infection. Immunity. 2022;55(3):475-493.e5.

23.

Hamouda

AEI

Filtjens

Brabants

, et al Intratumoral delivery of lipid nanoparticle-formulated mRNA encoding IL-21, IL-7, and 4-1BBL induces systemic anti-tumor immunity. Nat Commun. 2024;15(1):10635.

24.

Zhou

Tan

, et al Chronic social defeat stress broadly inhibits gene expression in the peripheral taste system and alters taste responses in mice. Physiol Behav. 2023;275:114446.

25.

Hollenhorst

Jurastow

Nandigama

, et al Tracheal brush cells release acetylcholine in response to bitter tastants for paracrine and autocrine signaling. FASEB J. 2020;34(1):316-332.

26.

Kanekiyo

Joyce

, et al Rational design of an Epstein-barr virus vaccine targeting the receptor-binding site. Cells. 2015;162(5):1090-1100.

27.

Liu

Nie

Zhang

Deng

The value of EYA1/3/4 in clear cell renal cell carcinoma: a study from multiple databases. Sci Rep. 2023;13(1):7442.

28.

Geng

Zhao

, et al CHRNA9 as a new prognostic marker and potential therapeutic target in glioma. J Cancer. 2024;15(8):2095-2109.

29.

Gomez-Rodriguez

Kraus

Schwartzberg

PL.

Tec family kinases Itk and Rlk / Txk in T lymphocytes: cross-regulation of cytokine production and T-cell fates. FEBS J. 2011;278(12):1980-1989.

30.

Weigert

Strack

Snodgrass

Brüne

MPGES-1 and ALOX5/-15 in tumor-associated macrophages. Cancer Metastasis Rev. 2018;37(2-3):317-334.

31.

Gunnarsen

Høydahl

Risnes

, et al A TCRα framework–centered codon shapes a biased T cell repertoire through direct MHC and CDR3β interactions. JCI Insight. 2017;2(17):e95193.

32.

Yang

Chen

Weng

Mariuzza

RA.

Structural basis for clonal diversity of the human T-cell response to a dominant influenza virus epitope. J Biol Chem. 2017;292(45):18618-18627.

33.

Jia

Zhai

Wang

Zhang

Song

RH.

High-throughput T cell receptor sequencing reveals differential immune repertoires in autoimmune thyroid diseases. Mol Cell Endocrinol. 2022;550:111644.

34.

Sousa

Ribeiro

Paredes

Heterogeneity and plasticity of breast cancer stem cells. Adv Exp Med Biol. 2019;1139:83-103.

35.

Jerusalem

Collignon

Schroeder

Lousberg

Triple-negative breast cancer: treatment challenges and solutions. Breast Cancer. 2016;8:93.

36.

Rocha

Bach

Masfarré

, et al Molecular and immunological features associated with long-term benefits in metastatic NSCLC patients undergoing immune checkpoint blockade. OncoImmunology. 2025;14(1):2469377.

37.

Pantelidou

Sonzogni

De Oliveria Taveira

, et al PARP inhibitor efficacy depends on CD8+ T cell recruitment via intratumoral STING pathway activation in BRCA-Deficient models of triple-negative breast cancer. Cancer Discov. 2019;9(6):722-737.

38.

Baldominos

Barbera-Mourelle

Barreiro

, et al Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche. Cells. 2022;185(10):1694-1708.e19.

39.

Park

Hsueh

PC.

Microenvironment-driven metabolic adaptations guiding CD8+ T cell anti-tumor immunity. Immunity. 2023;56(1):32-42.

40.

Jerby-Arnon

Shah

Cuoco

, et al A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cells. 2018;175(4):984-997.e24.

41.

Jacobs

Shah

, et al HSF1 inhibits antitumor immune activity in breast cancer by suppressing CCL5 to block CD8+ T-cell recruitment. Cancer Res. 2023;84(2):276-290.

42.

Oshi

Asaoka

, et al Intratumoral tumor infiltrating lymphocytes (TILs) are associated with cell proliferation and better survival but not always with chemotherapy response in breast cancer. Ann Surg. 2023;278(4):587-597.

43.

Jin

Zhou

, et al Molecular classification of hormone receptor-positive HER2-negative breast cancer. Nat Genet. 2023;55(10):1696-1708.