Recent progress in immune evasion mechanisms of triple-negative breast cancer

Abstract

Significant progress has been made in understanding the complex immune evasion mechanisms of triple-negative breast cancer (TNBC), paving the way for more effective immunotherapies. This review highlights key advances in elucidating the molecular basis of TNBC immune escape, including aberrant immune checkpoint expression, metabolic reprogramming, epigenetic regulation, immune evasion by associated cellular components, and clinical trials of emerging immunotherapies. Specifically, overexpression of immune checkpoint inhibitors such as PD-L1 on TNBC cells and within the tumor microenvironment (TME) plays a critical role in suppressing antitumor immunity. Secondly, TNBC cells evade immune surveillance through metabolic reprogramming. For instance, upregulated glutamine metabolism supports tumor growth and modulates the TME toward immunosuppression by limiting nutrient availability to immune cells. Competitive consumption of amino acids such as tryptophan and arginine further compromises immune cell function, promoting immune escape. Epigenetic modifications, including DNA methylation and histone modifications, are increasingly recognized as key contributors to immune evasion in TNBC. These mechanisms can silence genes involved in antigen presentation and immune activation while promoting the expression of immunosuppressive factors. Long non-coding RNAs (lncRNAs) have been identified as central regulators of immune evasion in TNBC, offering new therapeutic targets for intervention. Moreover, TNBC actively shapes its microenvironment to establish immunosuppression, including recruitment of regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and M2-polarized macrophages, which collectively inhibit effector T cell function. Building on these mechanistic insights, this review also integrates findings from clinical trials evaluating next-generation immunotherapies, including bispecific antibodies targeting PD-1/CTLA-4, LAG-3 inhibitors, and CD47-SIRPα blockers, as well as potential biomarkers. These novel combination strategies aim to overcome resistance to single-agent checkpoint inhibitors, while research explores monoclonal antibodies, bispecific antibodies, and antibody-drug conjugates (ADCs) within biomarker-driven personalized treatment frameworks. The ultimate goal is to improve survival and quality of life for TNBC patients through tailored immunotherapies.

Introduction

Epidemiologic and clinical features of triple-negative breast cancer

Breast cancer (BC) is molecularly stratified into four principal subtypes: Luminal A, Luminal B, HER2-enriched, and triple-negative. TNBC is defined by the absence of HER2 amplification and immunohistochemical evidence of ER and PR expression < 1%. Accounting for 10–15% of newly diagnosed breast cancers, TNBC represents a biologically aggressive entity characterized by moderate-to-high histologic grade, highly proliferative tumor cells, and limited therapeutic options. These features collectively contribute to early disease onset, elevated metastatic propensity, increased risk of distant recurrence, and the poorest clinical outcomes among all BC subtypes. Compared with non-TNBC counterparts, TNBC patients face a nearly threefold higher risk of early distant recurrence within 5 years of diagnosis. Conversely, late recurrence risk beyond 5 years declines to < 3%. In TNBC, both progression-free survival (PFS) and overall survival (OS) are markedly inferior. For early-stage TNBC, 5-year OS is only 77%, compared to 91% across all BC subtypes. In metastatic TNBC, 5-year OS drops to 12% [1–3].

Recent research has classified TNBC into two major epigenetic subtypes—basal-like and non-basal-like—further subdivided into five molecular subgroups. The basal-like subtype is categorized into Basal1, Basal2, and Basal3 subgroups, while the non-basal-like group is divided into nonBasal1 and nonBasal2 subgroups. Approximately 80% of TNBC cases belong to the basal-like subtype, which is generally characterized by higher immune cell infiltration, particularly CD8⁺ T cells, B cells, and Tregs. Notably, the Basal3 subgroup exhibits elevated PD-L1 expression and more pronounced immune infiltration, suggesting that basal-like TNBC, especially the Basal3 subgroup, may derive greater benefit from immune checkpoint inhibitor therapy. However, even within this subgroup, primary or secondary resistance may occur, potentially mediated by other immunosuppressive mechanisms. The non-basal-like subtype accounts for approximately 20% of TNBC cases and typically demonstrates lower immune cell infiltration. Importantly, this subtype—particularly the nonBasal2 subgroup—is enriched in luminal androgen receptor (LAR)-type tumors and displays activated fatty acid metabolic networks, driven in part by promoter hypomethylation (and consequent overexpression) of key metabolic genes such as FASN. These features imply that although immune checkpoint inhibitors may show limited efficacy in this context, targeting aberrant fatty acid metabolism—for instance, using FASN inhibitors—could reverse immunosuppression and potentially improve outcomes in this TNBC subgroup [4].

The standard of care for TNBC primarily relies on cytotoxic agents such as taxanes, anthracyclines, and platinum compounds for both early and advanced disease. However, several targeted therapies—including bevacizumab and sunitinib—have failed to demonstrate significant clinical benefit in TNBC. Furthermore, while triple-negative breast cancer (TNBC) is theoretically poised to benefit from immune checkpoint inhibitor (ICI) therapy, clinical data indicate that only a subset of patients (approximately 20–30%) exhibit durable responses to ICIs, whereas the majority still develop resistance. This variability in treatment response largely stems from the pronounced intratumoral heterogeneity (ITH) and ongoing clonal evolution characteristic of TNBC.

Tumor heterogeneity and clonal evolution in triple-negative breast cancer

Tumor heterogeneity and clonal evolution represent one of the foremost challenges in the treatment of TNBC, particularly in the realm of immunotherapy. Tumor heterogeneity refers to the considerable genomic, epigenomic, transcriptomic, and proteomic diversity among cells within the same tumor. Clonal evolution serves as the central driver of this heterogeneity, enabling tumor cells to evade immune recognition and attack through branched evolutionary patterns and adaptation under selective pressures. Modes of clonal evolution—such as linear, punctuated, and convergent evolution—are closely associated with mechanisms of immune escape. Under the selective pressure of immunotherapy, subclones with immune-evasive phenotypes may be preferentially enriched, thereby promoting immune evasion and conferring treatment resistance in TNBC.

TNBC employs diverse mechanisms to evade recognition and attack by the immune system. These mechanisms can be broadly categorized into antigen-dependent and antigen-independent pathways. Tumor heterogeneity contributes to both, collectively shaping the complex landscape of immune evasion. Antigen-dependent immune evasion refers to strategies wherein tumor cells alter molecules involved in antigen presentation to avoid immune detection. Different subclones harbor distinct neoantigen profiles, leading to fragmented local immune responses [5]. In TNBC, only a subset of subclones expresses shared neoantigens, while the majority express subclone-specific neoantigens. This neoantigen heterogeneity implies that even if the immune system successfully identifies and eliminates clones expressing immunogenic neoantigens, other clones lacking these antigens may survive and proliferate. Furthermore, tumor cells often downregulate MHC-I expression through epigenetic silencing (e.g., promoter methylation) or genetic mutations (e.g., B2M deletion), thereby escaping recognition by CD8⁺ T cells. Notably, even when MHC-I expression is restored to normal levels using IFNγ pretreatment, some immunologically “cold” clones remain unrecognized by T cells, suggesting the involvement of more complex mechanisms. It is also important to emphasize that impaired expression of antigen-processing machinery components—such as the transporter associated with antigen processing (TAP) and proteasome subunits (e.g., PSMB8/9)—further contributes to immune evasion in certain TNBC subclones. Antigen-independent immune evasion involves the creation of an immunosuppressive microenvironment or direct suppression of immune cell function. Tumor cells can overexpress immune checkpoint molecules like PD-L1 and CTLA-4, which engage corresponding receptors on T cells and induce exhaustion. Additionally, through the secretion of chemokines such as CCL2, CCL5, and CSF1, tumor cells recruit immunosuppressive cells including Tregs, myeloid-derived suppressor cells (MDSCs), and M2-like tumor-associated macrophages (TAMs). Studies have also shown that cancer-associated fibroblasts (CAFs) secrete abundant collagen fibers, forming a physical barrier that impedes T cell infiltration. Moreover, tumor cells undergo metabolic reprogramming and engage in nutrient competition, resulting in T cell “starvation” and functional impairment, while simultaneously supporting their own proliferation and immune suppression. The causal relationship between clonal evolution and immune evasion remains debated. Some perspectives view clonal evolution as a consequence of immune evasion, whereas others consider it a driving cause. Emerging evidence suggests that clonal evolution and immune evasion constitute a dynamically interactive process, wherein evolutionary dynamics shape diverse immune escape mechanisms that ultimately influence treatment response.

Immunoediting in TNBC

During tumor evolution, immune pressure not only selects for clones with intrinsic immune escape capabilities, but may also actively induce tumor cells to acquire immune-resistant phenotypes—a process termed “immunoediting.” The process of cancer immune editing, wherein the host immune system shapes tumor development through innate and adaptive immune mechanisms, is classically divided into three phases: Elimination, Equilibrium, and Escape [6]. This paradigm underscores the dual role of the immune system as both a tumor suppressor and a sculptor of immune-evasive tumor clones. Initially, innate and adaptive immune responses effectively control tumor growth by eliminating malignant cells via the perforin-granzyme pathway [7] and death receptor-mediated apoptosis, defining the elimination phase. Subsequently, while some tumor cells are eradicated, others survive. These residual cells exploit genomic instability to generate variant clones with reduced immunogenicity or acquire mechanisms such as secretion of immunosuppressive factors and recruitment of immunosuppressive cells to establish a state of equilibrium. During this phase, tumor cells evade complete clearance by the immune system, yet immune surveillance prevents overt tumor outgrowth. Immune escape, recognized as one of the “hallmarks of cancer” [8], represents the final phase where tumor cells actively subvert immune recognition through diverse mechanisms, including defects in antigen presentation, expression of immunosuppressive molecules, and construction of an immunosuppressive TME.

However, recent studies propose a novel conceptual framework for TNBC immune evasion: the “three C” model—Camouflage (evasion of immune recognition), Coercion (active suppression of immune effector functions), and Cytoprotection (resistance to immune-mediated cytotoxicity) [9]. This model highlights the limitations of the traditional immune editing theory in capturing the complexity of TNBC immunobiology, particularly its integration of metabolic reprogramming, epigenetic regulation, and immunosuppressive TME dynamics. Integrating multifaceted factors—including metabolic reprogramming, epigenetic dysregulation, and immunosuppressive microenvironments—into a synergistic network to establish a rational foundation for combination therapy design represents a pivotal step toward advancing novel immune-targeted therapeutics for TNBC. This review systematically dissects the molecular and cellular drivers of immune evasion in TNBC, with the aim of providing a foundation for developing next-generation ICIs, optimizing synergistic immunotherapy regimens, engineering targeted drug delivery systems to overcome TME barriers, and identifying robust biomarkers for patient stratification. These strategies are critical to overcoming the major clinical challenges in TNBC immunotherapy: low response rates, acquired resistance, and the lack of predictive biomarkers.

Mechanisms of immune evasion in TNBC

Dysregulation of immune checkpoint molecules and immune escape in TNBC

While TNBC exhibits a high TMB and immunogenic potential—features that theoretically render it susceptible to immunotherapy—clinical data reveal suboptimal response rates to ICIs, such as PD-1/PD-L1 blockers. This paradox underscores the presence of complex immune evasion mechanisms in TNBC, among which dysregulation of immune checkpoint molecules serves as a critical driver. Here, we review the mechanistic roles of the PD-1/PD-L1 axis, CTLA-4/B7 signaling pathway, and emerging immune checkpoints—including LAG-3, TIM-3, and TIGIT—in promoting immune evasion in TNBC (Fig. 1).

Fig. 1.

This Figure illustrates the regulatory mechanisms and suppressive effects of PD-L1 on T cell function in TNBC. In tumor cells, PD-L1 gene transcription is modulated by transcription factors (e.g., HIF-1α, MYC) and epigenetic complexes (e.g., ZNF652-NuRD). Activation of signaling pathways such as MAPK/PI3K/Akt further enhances PD-L1 expression via STAT3. PD-L1 protein binds to PD-1 on T cells, recruiting SHP-2 to inhibit ZAP70 phosphorylation. This blocks downstream TCR signaling through RAS-MEK-ERK and PI3K-Akt-mTOR pathways, leading to suppressed T cell proliferation, cytotoxic activity, and IL-2 secretion. It also interacts with B7.1 on dendritic cells (DCs), suppressing T cell activation. Macrophage polarization is linked to PD-L1: M2 macrophages enhance immunosuppression, while M1 promotes anti-tumor immunity. Figure B highlights that high PD-L1 expression promotes M2 macrophage polarization, reduces NK cell cytotoxicity, and suppresses T cell activation via B7.1 binding. Figure C describes the CTLA-4/B7 pathway: lactate promotes Treg function via MCT1 and Foxp3-USP39 signaling, enhances fatty acid oxidation, and suppresses T cell activity. B7-H4 induces Treg conversion and blocks effector T cell function via Akt/Foxo signaling and ERK1/2/PI3K inhibition, leading to G1/G0 phase arrest. Figure D outlines the roles of LAG-3, TIGIT, and TIM-3 in immune escape. LAG-3 binds MHC-II and enhances Treg function. TIGIT suppresses TCR signaling and cytokine production via SHP-1/SHP-2 and inhibits NK cell activity. TIM-3 induces CD8 + T cell apoptosis via Galectin-9, promotes M2 polarization through NF-κB-mediated IL-6 expression, and enhances immunosuppressive cytokine secretion. Together, these checkpoints form a coordinated immunosuppressive network in TNBC

The PD-1/PD-L1 axis: a key driver of immune evasion in TNBC

Programmed Death Receptor-1 (PD-1) and its ligand PD-L1 represent the most extensively studied immune checkpoint molecules to date. PD-L1 interacts with diverse immune cell populations within the TME of TNBC, establishing a systemic immunosuppressive network. Upon binding to its receptor PD-1 on T cells, PD-L1 recruits the phosphatase SHP-2, which inhibits phosphorylation of ZAP70—a critical kinase in T cell receptor (TCR) signaling. This blockade disrupts downstream activation of the RAS-MEK-ERK and PI3K-Akt-mTOR pathways, leading to T cell dysfunction, reduced proliferation, and diminished cytotoxicity. Consequently, this interaction enables tumor cells to evade immune surveillance, fostering tumor growth and metastasis. Dendritic cells (DCs), pivotal mediators bridging innate and adaptive immunity, also play a critical role in the TNBC TME. PD-L1 on DCs binds to B7.1, competitively inhibiting B7.1-CD28 co-stimulation, thereby suppressing T cell activation and expansion. Moreover, There is a close relationship between PD-L1 expression and macrophage polarization. Studies indicate that elevated PD-L1 levels may drive macrophages toward an immunosuppressive M2 phenotype, dampening antitumor responses. Conversely, PD-L1 depletion or low expression skews macrophage polarization toward a pro-inflammatory M1 state, amplifying antitumor immunity [10]. This dual regulatory mechanism positions PD-L1 as a double-edged sword in the TNBC TME, where its expression level and macrophage polarization status jointly dictate tumor immunogenicity. Natural killer (NK) cells, critical first-line defenders against viral infections and malignant transformation, are also impaired by PD-L1. Research shows that PD-1 on NK cells engages PD-L1 on cancer cells, recruiting SHP-2 to inhibit the PI3K-Akt pathway. This suppression reduces secretion of cytotoxic molecules such as granzyme B and perforin, compromising NK cell-mediated tumor cell lysis.

TNBC employs epigenetic regulatory mechanisms to upregulate PD-L1 expression, thereby suppressing T cell activity and enabling immune evasion. PD-L1 overexpression in TNBC is closely linked to the loss of the transcriptional repressor ZNF652. ZNF652 physically interacts with the nucleosome remodeling and deacetylase (NuRD) complex, forming a ZNF652-NuRD repressive complex that enriches at the PD-L1 promoter region and suppresses its transcription. In TNBC, ZNF652 deficiency prevents the NuRD complex from effectively repressing the PD-L1 promoter, thereby derepressing PD-L1 expression [11]. Additionally, histone deacetylase 2(HDAC2) regulates PD-L1 expression in TNBC through activation of the IFNγ-JAK/STAT signaling pathway and chromatin remodeling, ultimately impacting tumor immune evasion and metastasis [12]. Multi-omics analyses reveal that PD-L1 expression in TNBC is governed by multilayered epigenetic regulators, including DNA methylation, histone modifications, and chromatin remodeling. At the transcriptional level, PD-L1 expression is primarily modulated by key transcription factors such as STAT, MYC, NF-κB, IRF1, AP-1, and HIF-1α, as well as signaling effectors from pathways including MAPK/PI3K/Akt, JAK/STAT3, and EGFR/MAPK [13].

The CTLA-4/B7 signaling pathway promotes immune evasion in TNBC

The CTLA-4/B7 signaling pathway, a pivotal immune checkpoint, plays a central role in TNBC immune evasion by suppressing T cell activation and fostering an immunosuppressive tumor microenvironment. CTLA-4 enhances Tregs function through metabolic reprogramming. Studies reveal that the lactate-Foxp3-USP39-CTLA-4 signaling axis enables Treg cells to prioritize fatty acid oxidation (FAO) and oxidative phosphorylation (OXPHOS) for energy production while suppressing glycolysis, thereby sustaining their immunosuppressive activity [14]. Furthermore, B7-H4 promotes Treg differentiation via a Foxp3-dependent mechanism. B7-H4 modulates the Akt/Foxo pathway to induce conventional CD4⁺ T cells to express Foxp3 and differentiate into immunosuppressive Treg cells [15]. Concurrently, B7-H4 impairs effector T cell (Teff) inflammatory functions by reducing cytokine production (e.g., IFNγ) and inhibiting proliferation, establishing a positive feedback loop that reinforces immune evasion. The synergistic interaction between CTLA-4 and B7-H4 represents a hallmark of TNBC immune escape. Research demonstrates that tumor-expressed B7-H4 diminishes the efficacy of anti-CTLA-4 therapy, primarily by altering CTLA-4 expression on Treg surfaces and driving Treg expansion [15]. Additionally, B7-H4 induces cell cycle arrest at the G1/G0 phase by suppressing T cell cycle regulators (e.g., cyclin-dependent kinases [CDKs] and cyclins), while simultaneously inhibiting T cell function through downregulation of ERK1/2 and PI3K signaling pathways.

Emerging immune checkpoint molecules and immune evasion in TNBC

In recent years, emerging immune checkpoint molecules such as LAG-3, TIM-3, and TIGIT have garnered increasing attention. These molecules are upregulated to varying degrees in TNBC, where they may act synergistically to enhance immunosuppressive effects, thereby facilitating immune evasion by tumor cells [16].

Lymphocyte activation gene-3 (LAG-3; CD223) is an inhibitory receptor predominantly expressed on the surface of activated T cells, B cells, NK cells, and plasmacytoid dendritic cells. In TNBC, LAG-3 promotes immune evasion through two major mechanisms: (1) suppression of effector T cell function via binding to MHC class II (MHC-II), and (2) enhancement of the immunosuppressive capacity of regulatory Tregs. LAG-3 is highly expressed on both Treg and effector T cells in TNBC, where it suppresses T cell activity by engaging either MHC-II or its soluble ligand, fibrinogen-like protein 1 (FGL-1) [17, 18]. Studies have shown that LAG-3 binds to MHC-II with significantly higher affinity than CD4, allowing it to competitively block the CD4–MHC-II interaction. Moreover, the LAG-3–MHC-II complex induces rigidification of the MHC-II β-chain, thereby preventing effective engagement of the TCR with the antigenic peptide presented by the pMHC-II complex. A decrease in synaptic pH further reduces the binding affinities of both TCR-pMHC-II and CD4-MHC-II interactions. These molecular changes result in reduced recruitment of Lck to the TCR signaling complex and impaired phosphorylation of ZAP70. Notably, upon MHC-II binding, the EP motif within LAG-3 releases free Zn²⁺ ions, which subsequently bind to the TCRζ chain and completely disrupt downstream signal transduction. Additionally, the cytoplasmic domain of LAG-3, containing a conserved “KIEELE” motif, inhibits calcium ion channel activity, leading to reduced intracellular Ca²⁺ levels and further dampening T cell functionality [19].Beyond its effects on effector T cells, LAG-3 also modulates Treg function by enhancing their suppressive activity and altering cytokine secretion profiles. Within the tumor microenvironment, LAG-3 synergizes with the PD-1/PD-L1 axis to cooperatively suppress effector T cell responses, reinforcing immune evasion and contributing to tumor immune resistance.

TIGIT (T-cell immunoglobulin and ITIM domain protein), an emerging immune checkpoint molecule, plays a pivotal role in immune evasion in TNBC. Expressed on the surface of T cells and NK cells, TIGIT functions as an inhibitory receptor that forms a counter-regulatory axis with CD226 (DNAM-1). Upon engagement with its ligands CD155 (PVR) or CD112 (PVRL2) — which are often upregulated on tumor cells and APCs — TIGIT competes with CD226 for binding and recruits SHP-1/SHP-2 phosphatases via its immunoreceptor tyrosine-based inhibitory motif (ITIM). This interaction leads to suppression of TCR signaling pathways, including blockade of ZAP70 phosphorylation, and downregulation of pro-inflammatory cytokines such as IL-2 and IFN-γ, ultimately impairing T cell activation and effector function [20, 21]. In addition, high expression of TIGIT on NK cells results in diminished degranulation, cytokine production, and cytotoxic activity against tumor cells following ligand engagement with CD155 or CD112 [22]. Moreover, TIGIT enhances the secretion of inhibitory cytokines such as IL-10 and TGF-β by Tregs, suppresses pro-inflammatory Th1 and Th17 responses, and facilitates M2 macrophage polarization. Collectively, these effects further undermine anti-tumor immunity and contribute to immune evasion in TNBC.

T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) is significantly upregulated in patients with TNBC, and its expression strongly correlates with tumor aggressiveness and poor clinical outcomes. In TNBC, TIM-3 primarily contributes to immune evasion through the suppression of T cell function. Studies have shown that TIM-3 binds to its ligand galectin-9, inducing apoptosis and functional exhaustion of tumor-infiltrating CD8⁺ T cells [23]. TIM-3 also exerts inhibitory effects on T cell signaling pathways. Upon ligand engagement, BAT3 dissociates from the cytoplasmic tail of TIM-3, leading to a conformational change that converts TIM-3 into an activated state. This switch results in the suppression of T cell activation and effector function [24, 25]. In addition, TIM-3 is highly expressed on Tregs, where it enhances their immunosuppressive capacity. Notably, TIM-3⁺ Tregs predominate in TNBC patients and actively promote immune evasion by secreting inhibitory cytokines such as IL-10 [26]. Moreover, TIM-3 modulates macrophage polarization. It promotes infiltration and functional differentiation of M2-polarized TAMs. Mechanistically, TIM-3 activates the NF-κB signaling pathway, which upregulates IL-6 expression and subsequently drives macrophage polarization toward the immunosuppressive M2 phenotype [27].

DNA methylation regulates TNBC immune escape

TNBC is characterized by genomic instability and elevated mutation rates, which contribute to immune evasion through mechanisms involving both intrinsic tumor cell properties and extrinsic immune microenvironment remodeling [28]. Recent studies have highlighted the critical role of aberrant DNA methylation patterns in TNBC immune escape, marked by a paradoxical combination of genome-wide hypomethylation and localized CpG island hypermethylation [29, 30]. This epigenetic imbalance leads to silencing of tumor suppressor genes and activation of immunosuppressive pathways, culminating in the establishment of an immunologically “cold tumor” phenotype with reduced T-cell infiltration and impaired antitumor immunity [29]. A landmark study recently mapped the DNA methylome of primary TNBC, identifying two distinct epigenetic subtypes: Basal-like and non-Basal-like [4]. This classification underscores how TNBC exploits dysregulated DNA methylation to modulate the expression of immune evasion-related genes. For instance: Hypermethylation of promoter regions in key immune regulators (e.g., MHC class I molecules, chemokine receptors) suppresses antigen presentation and T-cell recruitment. Hypomethylation-induced activation of oncogenic pathways (e.g., Wnt/β-catenin, TGF-β) further reinforces immunosuppression by promoting tumor cell-intrinsic immune evasion and stromal remodeling. These findings reveal a direct link between epigenetic reprogramming and immune checkpoint modulation, positioning DNA methylation as a pivotal driver of TNBC immune evasion.

In TNBC, aberrant DNA methylation orchestrates immune evasion through multifaceted gene-specific regulatory mechanisms(Fig. 2), with several key epigenetically silenced or activated genes emerging as critical mediators: The transcriptional repressor ZBTB28 is frequently silenced by promoter hypermethylation in TNBC, correlating with poor clinical outcomes in breast cancer patients [31]. Mechanistically, ectopic ZBTB28 expression in breast cancer cells downregulates CD47 and CD24—key “don’t eat me” signals—enhancing macrophage phagocytosis and restoring immune surveillance. This highlights ZBTB28 as a methylation-dependent regulator of tumor-macrophage interactions. In contrast to ZBTB28, the lectin receptor ligand LGALS2 exhibits promoter hypomethylation and consequent overexpression in TNBC compared to normal mammary tissue [32]. LGALS2 promotes M2-like macrophage polarization and proliferation by activating the colony-stimulating factor 1 (CSF1)/CSF1 receptor (CSF1R) axis, thereby establishing an immunosuppressive tumor microenvironment (TME) in TNBC. Targeting this axis may synergize with macrophage reprogramming strategies in immunotherapy. As a core subunit of the immunoproteasome, PSMB9 (β1i) plays a pivotal role in antigen processing and MHC class I presentation. While its expression is typically induced by IFN-γ to facilitate immunoproteasome assembly, recent studies reveal that D-xylose metabolism by DHDH drives PSMB9 promoter hypermethylation, suppressing its activation [33, 34]. This methylation defect compromises antigen presentation efficiency, reducing tumor immunogenicity—a critical vulnerability in TNBC where antigenicity is already low. These methylation-driven immune evasion mechanisms underscore the therapeutic potential of: Epigenetic modulators (e.g., DNMT inhibitors to reactivate ZBTB28 and suppress LGALS2); Metabolic-immune axis targeting (e.g., DHDH inhibition to restore PSMB9-mediated antigenicity); Combination therapies integrating methylation reversal with immune checkpoint blockade (ICB) or adoptive cell transfer (ACT).Future research should prioritize single-cell methylome-immunome profiling to map spatial and temporal dynamics of these regulatory networks in TNBC.

Fig. 2.

This figure illustrates the core mechanisms by which TNBC cells exploit DNA methylation to regulate immune-related genes and shape an immunosuppressive microenvironment. In TNBC cells, ZBTB28 undergoes promoter hypermethylation, silencing its expression and leading to upregulation of its target genes CD47 and CD24—key “don’t eat me” signals. These molecules bind to SIRPa and Siglec10 on macrophages, respectively, suppressing phagocytosis. Concurrently, LGALS2 exhibits promoter hypomethylation and overexpression, driving secretion of CSF1, which activates macrophage CSF1R to promote M2-like macrophage polarization and proliferation, amplifying immunosuppression. Additionally, PSMB9 (β1i), critical for antigen processing and MHC-I presentation, is silenced by promoter hypermethylation, causing defective 20 S proteasome function and reduced antigenic peptide generation. This impairs MHC-I-mediated antigen presentation, hindering CD8 + T cell recognition and cytotoxicity. These methylation-driven gene expression alterations synergistically weaken both innate and adaptive immune responses, facilitating TNBC immune escape

Non-coding RNAs regulates TNBC immune escape

Recent studies have highlighted that ncRNAs, particularly miRNAs and lncRNAs(Table 1), act as key regulators of tumor progression and immune escape through their ability to modulate multiple genes and signaling pathways. miRNAs are deeply involved in fundamental biological processes, including cell proliferation, differentiation, migration, angiogenesis, and apoptosis. Dysregulation of miRNA networks has been implicated in oncogenic transformation, immune evasion, and therapeutic resistance in TNBC. By modulating genomic architecture, epigenetic landscapes, and signaling pathways, lncRNAs serve as central orchestrators of cell fate decisions. Their aberrant expression drives malignant transformation, metastasis, and therapy resistance in TNBC through multifactorial mechanisms, including immune checkpoint activation and TME remodeling [35].

Table 1.

The mechanisms by which selected NcRNAs drive immune evasion in TNBC

Molecular Name	Type	Mechanism	Target/Regulatory Biomarker	Ref(s)
miR-200 family	TS-miRNA	Transcriptional silencing of EMT regulators (ZEB1/ZEB2) impairs tumor cell invasiveness and metastatic capacity	ZEB1/2、EMT	[37]
miR-34a/b/c	TS-miRNA	miRNAs induced by wild-type p53 promote the degradation of PD-L1 mRNA	PD-L1 and T cell activation	[39, 40]
miR-142-5p	TS-miRNA	WWP1/PI3K/AKT axis-mediated PD-L1 downregulation potentiates T cell function	WWP1、PI3K/AKT、PD-L1	[41, 42]
miR-21	oncomiR	Suppression of PTEN triggers PI3K/AKT pathway activation	PTEN	[43]
miR-155	oncomiR	Promotion of regulatory T cell (Treg) differentiation	FOXP3、Treg infiltration	[44]
miR-148a	oncomiR	Epigenetic silencing of Gadd45α, PTEN, and Bim subverts B cell tolerance checkpoints	Gadd45α、PTEN、Bim	[45]
miR-27b	oncomiR	Inhibition of PDHX leads to acidification of the tumor microenvironment	PDHX、Lactate accumulation	[46]
LINC00665	lncRNA(Oncogenes)	Mechanistically facilitates cancer progression through Wnt/β-catenin, TGF-β, NF-κB, PI3K/AKT, and MAPK signaling pathways	Wnt/β-catenin,、TGF-β、NF-κB,、PI3K/AKT、 MAPK	[48]
HOTAIR	lncRNA(Oncogenes)	Modulates PKM2 to reprogram tumor glucose metabolism, promoting oncogenic progression	PKM2	[49]
LncSNHG5	lncRNA(Oncogenes)	Modulates ZNF281 activity to drive pro-angiogenic factor release and vascular basement membrane disruption, enhancing neovascularization and aberrant permeability that expedite premetastatic niche formation	ZNF281	[50]
LINC00514	lncRNA(Oncogenes)	Activates the STAT3-Jagged1-Notch signaling cascade, thereby driving aberrant tumor vascularization, immune-evasive microenvironment remodeling, and invasive metastatic progression	STAT3-Jagged1-Notch	[51]
KRT19P3	lncRNA(Tumor suppressor)	Downregulates PD-L1 expression in TNBC, potentiating CD8 + T-cell cytotoxic function, thereby suppressing breast cancer malignant progression	PD-L1	[52]
BM466146	lncRNA(Tumor suppressor)	Upregulates CXCL13 expression to drive directed migration and tumor infiltration of CD8⁺ T cells, enforcing immunosurveillance that constrains neoplastic progression	CXCL13	[53]

miRNAs play an extremely important role in the oncogenesis, proliferation, migration, and metastasis of TNBC. miRNAs exhibit a dual role in cancer, acting as both oncogenic miRNAs (oncomiRs) that promote tumor development and tumor-suppressive miRNAs (TS-miRNAs) that inhibit tumor progression [36]. OncomiRs primarily function to suppress endogenous tumor suppressor genes in TNBC, while tumor-suppressive miRNAs target oncogenes. Numerous studies have shown that members of the miR-200 family can inhibit EMT, thereby reducing metastatic potential [37]. The human miR-205 gene is located on chromosome 1q32.2 and can target and inhibit EMT-related genes such as ZEB1 and ZEB2, thus suppressing the invasion and metastasis of tumor cells and exerting an anti-cancer effect [38]. Research has also demonstrated that miR-205 can directly target RCP, leading to reduced RCP-mediated recycling of integrin β1 and thereby inhibiting breast cancer metastasis. The human miR-34a gene is situated on chromosome 1p36 and is directly regulated by the p53 transcription factor. p53 activates miR-34a expression by binding to its promoter, forming the “p53 - miR-34a” tumor suppressor axis, which induces tumor cell apoptosis [39, 40]. The miR-142–5p gene, located on chromosome 17q58.3, may inhibit immune evasion - related factors (such as PD - L1 and TGF - β) by targeting the WWP1/PI3K/AKT pathway in TNBC, thereby restoring T cell function and reducing the infiltration of immunosuppressive cells [41, 42]. The miR-21 gene is located on chromosome 17q23.2, embedded within the intron region of the protein - coding gene VMP1. It is highly expressed in breast cancer and promotes tumor cell proliferation and metastasis by inhibiting PTEN and activating the PI3K/AKT pathway [43]. The human miR-155 gene is situated on chromosome 21q21.3, embedded within the intron region of the ncRNA gene BIC (B - cell integration cluster). It can target SOCS1, enhance STAT3 signaling, and drive tumorigenesis [44]. Elevated miR-148a expression promotes the survival of immature B cells following B cell antigen receptor engagement by inhibiting the expression of the immune - inhibitory factor Gadd45α, the tumor suppressor PTEN, and the pro - apoptotic protein Bim, thereby impairing B cell tolerance and facilitating tumor immune evasion [45]. Overexpressed miR-27b in TNBC can induce extracellular acidification by targeting PDHX, thereby inhibiting T cell function [46]. miRNAs regulate breast cancer immune evasion through multiple dimensions and serve as a key link between tumor cells and the immune microenvironment. Despite challenges in clinical translation, such as delivery and safety concerns, precision intervention strategies based on miRNAs (e.g., miR-34a mimics targeting PD-L1) hold promise for overcoming therapeutic barriers when combined with existing immunotherapies. Future efforts should focus on innovations in delivery technology, subtype - specific mechanistic elucidation, and interdisciplinary collaboration to facilitate the translation of miRNAs from basic research to clinical practice, ultimately improving the prognosis of breast cancer patients.

Emerging evidence underscores that lncRNAs not only participate in oncogenic mechanisms of malignancies but also serve as critical modulators of immune cell function, including neutrophils, monocytes, macrophages, DCs, T cells, and B cells. These lncRNA-immune interactions contribute to tumor immune escape, highlighting their dual roles in shaping the tumor-immune crosstalk. Owing to their tissue-specific expression, stability, and key regulatory roles in disease pathogenesis, lncRNAs hold dual potential in diagnostic, prognostic, and therapeutic applications for TNBC [47]. They function both as promising biomarkers for early detection and outcome prediction, and as innovative therapeutic targets for precision oncology. lncRNAs orchestrate tumor immune evasion through multifaceted signaling networks, including: LINC00665: A newly identified oncogenic lncRNA that promotes TNBC progression via activation of Wnt/β-catenin, TGF-β, NF-κB, PI3K/AKT, and MAPK pathways [48]. HOTAIR: By targeting pyruvate kinase M2 (PKM2), a rate-limiting enzyme in glycolysis, HOTAIR reprograms glucose metabolism in TNBC cells, enhancing metabolic fitness and malignant progression [49]. CAFs are pivotal stromal components driving metastasis. For instance, lncSNHG5 is highly expressed in CAFs within the breast TME. It promotes pre-metastatic niche formation by: Targeting ZNF281 to enhance angiogenic factor release; Disrupting vascular basement membrane integrity; Inducing aberrant angiogenesis and vascular permeability [50]. lncRNAs modulate immune cell subsets with distinct functional outcomes: LINC00514: Induces TAMs toward M2 anti-inflammatory polarization, synergistically activating the STAT3-Jagged1-Notch cascade. This drives immunosuppressive microenvironment remodeling, aberrant angiogenesis, and metastasis in BC [51]. KRT19P3: Suppresses PD-L1 expression in TNBC, thereby enhancing CD8⁺ T-cell-mediated antitumor immunity and inhibiting BC progression [52]. BM466146: Upregulates CXCL13 in the TME, promoting CD8⁺ T-cell infiltration via CXCR5-mediated chemotaxis. CXCL13-bound CD8⁺ T cells recognize tumor antigens and activate perforin-granzyme B and Fas/FasL cytotoxic pathways, reinforcing tumor immune surveillance [53]. lncRNAs represent central hubs in TNBC immune evasion, offering actionable targets for novel interventions. Strategies such as lncRNA mimics/inhibitors, CRISPR-based editing, and nanoparticle-delivered therapeutics could synergize with existing immunotherapies to overcome treatment resistance. Future directions include: Single-cell lncRNA profiling to identify subtype-specific regulators; Engineered delivery systems for tumor-specific targeting; Multidisciplinary collaborations bridging computational biology, nanotechnology, and immuno-oncology. By unraveling lncRNA-mediated regulatory networks, we can unlock new paradigms for precision immunotherapy in TNBC, ultimately improving clinical outcomes for patients with this aggressive disease.

Metabolomics in triple-negative breast cancer (TNBC)

Metabolomics also plays a significant role in immune evasion in TNBC. In contrast to other breast cancer subtypes, TNBC exhibits particularly distinct metabolic features, including elevated glycolytic activity, enhanced fatty acid synthesis, and altered amino acid metabolism. These unique metabolic signatures not only reflect TNBC’s aggressive biology but also offer promising opportunities for targeted therapy and biomarker development.

HIF-1α in TNBC

Hypoxia is a hallmark of many solid tumors, particularly prevalent in TNBC. Among the molecular subtypes of BC, TNBC exhibits the most pronounced hypoxic features [54]. The HIF-1α, as a master transcriptional regulator under hypoxic conditions, has been extensively studied for its pivotal role in tumor initiation and progression. Molecular studies have revealed that HIF-1α functions as a central regulatory hub in TNBC, orchestrating a wide spectrum of oncogenic processes through the transcriptional activation of downstream gene networks. These include: Metabolic reprogramming toward glycolysis; Pro-angiogenic signaling; Metastatic phenotype acquisition; Enrichment of cancer stem cell populations and Establishment of an immunosuppressive tumor microenvironment. Collectively, these mechanisms drive the maintenance of TNBC’s aggressive malignant phenotype and disease progression (Fig. 3) [55]. Notably, HIF-1α activity is not solely dictated by oxygen levels—it can also be modulated by inflammatory mediators. Inflammatory cells secrete specific signaling molecules, such as ROS, which induce genetic mutations in neighboring cancer cells, promoting their transformation into more aggressive and invasive tumor phenotypes. The tumor vasculature often fails to meet the high oxygen demand of rapidly proliferating cancer cells, further exacerbating intratumoral hypoxia. In TNBC, elevated production of ROS and NO contributes to both genomic instability and metabolic adaptation .Under hypoxic conditions, the loss of prolyl hydroxylase domain protein 2 (PHD-2) function prevents the ubiquitin-mediated degradation of HIF-1α, leading to its stabilization and nuclear translocation. There, HIF-1α dimerizes with HIF-1β to form a functional transcriptional complex that regulates the expression of hypoxia-responsive genes [56]. Moreover, HIF-1α expression and activity are tightly regulated by several oncogenic signaling cascades, including the RAS-RAF-MEK-ERK, PI3K/Akt/mTOR, and JAK-STAT pathways. These pathways converge on HIF-1α to promote key malignant traits in TNBC, such as: Expansion of tumor-initiating cell populations; Activation of angiogenic signals and Enhanced proliferative and survival capacities of cancer cells.

Fig. 3.

This Figure illustrates the regulatory mechanisms of HIF-1α in mediating TME in TNBC under hypoxia. Hypoxia-induced accumulation of reactive ROS and NO activates signaling pathways such as RAS/RAF/MEK/ERK, PI3K/Akt/mTOR, and JAK-STAT, thereby promoting HIF-1α expression. Simultaneously, the hypoxic environment inhibits the activity of PHD-2, thereby blocking the VHL-mediated ubiquitination and degradation process of HIF-1α, which leads to its stabilization and nuclear translocation. Within the nucleus, HIF-1α dimerizes with HIF-1β to form a heterodimeric complex that binds to HREs in target gene promoters, orchestrating the expression of genes involved in immune evasion. This includes upregulation of CSF1 and CCR5 to recruit MDSCs, enhanced production of immunosuppressive molecules (ARG1, Cxcl12) in MDSCs, activation of FoxP3 transcription to drive regulatory Treg differentiation, and induction of PD-L1 and IL-10 to suppress T cell activity. Additionally, HIF-1α downregulates effector molecules in NK cells (e.g., GZMB, CD107a, NKp30/NKp46/NKG2D), impairing their cytotoxic function. Through these multi-target regulatory mechanisms, HIF-1α reshapes the TME into an immunosuppressive landscape, ultimately driving TNBC progression

Non-coding RNAs can regulate HIF-1 expression and stabilize it in TNBC, promoting tumor cell survival, metastasis, and immune evasion. DNA methylation also impacts HIF-1α. HIF-1α, along with glycolytic enzymes (PKM2, LDHA), induces low DNA methylation in fibroblasts, enhancing CAF glycolysis and reshaping the tumor microenvironment [57]. P300/CBP acetyltransferases boost the transcription of HIF-1 downstream targets (VEGF, GLUT1) by modifying chromatin structure [58].

Under hypoxic conditions, both the infiltration, activation, and functional capacity of immune cells within the tumor microenvironment are profoundly compromised. HIF-1α, a central mediator of cellular adaptation to low oxygen, plays a pivotal role in orchestrating immunosuppressive mechanisms that favor tumor immune evasion and progression. In response to hypoxia, HIF-1α collaborates with chemokines secreted by mesenchymal stem cells (MSCs) to induce the expression of CSF1 and CCR5 in breast cancer cells, thereby promoting the recruitment of MDSCs into the tumor microenvironment [59].Moreover, HIF-1α enhances the immunosuppressive activity of MDSCs by upregulating miR-210, which in turn increases the expression of ARG1, Cxcl12, and IL-16, reinforcing their ability to suppress anti-tumor immunity [60]. HIF-1α also directly binds to the hypoxia-responsive element (HRE) in the promoter region of PD-L1, leading to its upregulation on MDSC surfaces. This PD-L1 expression contributes to T cell exhaustion and suppression of effector function, further mediated through the secretion of inhibitory cytokines such as IL-6 and IL-10 [61]. Beyond its effects on myeloid cells, HIF-1α actively promotes the differentiation and functional polarization of Tregs by directly enhancing FoxP3 transcription, thereby amplifying their immunosuppressive capabilities [62]. Simultaneously, HIF-1α inhibits the proliferation and differentiation of CD4⁺ and CD8⁺ T cells, weakening adaptive immune responses against the tumor. Additionally, hypoxic tumor cells exhibit increased glucose uptake, effectively competing with CD8⁺ T cells for this essential metabolic substrate. This results in reduced glycolytic activity and ATP production in T cells, directly impairing their effector functions. HIF-1α also drives elevated adenosine production in tumor cells, which engages the A2A receptor on T cells, inducing apoptosis and suppressing proliferation [63]. Furthermore, HIF-1α recruits histone deacetylase 1 (HDAC1) and the polycomb repressive complex 2 (PRC2) to inhibit the transcription of key effector cytokines, including IFN-γ and TNF, thereby silencing the expression of immunostimulatory molecules in CD8⁺ T cells [64]. NK cells are also severely affected under hypoxic conditions. Hypoxia leads to a marked reduction in the secretion of granzyme B (GZMB) and IFN-γ, as well as decreased surface expression of degranulation marker CD107a and critical activating receptors such as NKp30, NKp46, and NKG2D [65]. These changes significantly diminish NK cell cytotoxicity. Mechanistically, hypoxia impairs ERK/STAT3 phosphorylation, weakening the killing capacity of NK cells and promoting their transition toward an immunosuppressive phenotype, further contributing to tumor immune escape.

Aerobic glycolysis (Warburg Effect) in TNBC

Aerobic glycolysis, also known as the Warburg effect, represents one of the hallmark metabolic features of cancer cells. Accumulating evidence indicates that the glycolytic pathway is frequently upregulated in various types of cancer and is closely associated with tumor aggressiveness, metastatic potential, and immune evasion. Among breast cancer subtypes, TNBC exhibits particularly robust aerobic glycolysis. TNBC cells demonstrate significantly elevated glucose uptake rates—often several-fold higher than those observed in other breast cancer subtypes. This metabolic shift is driven by multiple dysregulated signaling networks. In TNBC, the PI3K/Akt/mTOR pathway is constitutively activated, leading to the upregulation of key glycolytic enzymes such as GLUT1 (glucose transporter 1), HK2 (hexokinase 2), and PKM2 (pyruvate kinase M2 isoform), while simultaneously suppressing mitochondrial oxidative phosphorylation (OXPHOS). As a result, TNBC cells exhibit a strong preference for glycolytic metabolism to meet their bioenergetic and biosynthetic demands [66, 67]. Moreover, HIF-1α can be activated in an oxygen-independent manner. Under normoxic conditions, HIF-1α stability can be maintained via the ROS/NF-κB signaling pathway, which promotes LDHA (lactate dehydrogenase A) expression and enhances lactate production, thereby sustaining the Warburg effect [68].

TNBC cells exhibit a robust Warburg phenotype, characterized by enhanced aerobic glycolysis and excessive lactate production. This metabolic shift leads to the accumulation of lactate within the TME, establishing an acidic extracellular milieu (Fig. 4). Lactate efflux from tumor cells is primarily mediated by MCTs, whose dysregulated expression plays a central role in maintaining this immunosuppressive niche. This acidic environment suppresses anti-tumor immunity through multiple mechanisms, promoting immune evasion. Lactate exerts direct inhibitory effects on various immune cell populations, impairing their function and viability. Fructose-1,6-bisphosphate (F1,6BP), a key intermediate in glycolysis, activates the EGFR via direct binding, thereby triggering downstream signaling cascades that enhance both lactate production and extracellular release [69]. The resulting lactate overload acidifies the TME and directly impairs the function of CTLs—key mediators of adaptive anti-tumor immunity. Recent studies have shown that exhausted T cells (Tex cells) infiltrating TNBC tumors exhibit specific upregulation of Slc16a11, which encodes MCT11, a lactate transporter. Enhanced MCT11 expression increases lactate uptake, accelerating T cell exhaustion and compromising anti-tumor responses [70]. Lactate also modulates DC biology by engaging GPR81, a G protein-coupled receptor on DC surfaces. Activation of GPR81 initiates the cAMP/PKA signaling pathway, which suppresses DC maturation and antigen-presenting capacity. Specifically: Expression of MHC class II molecules and co-stimulatory receptors (CD80/CD86) is downregulated; Secretion of pro-inflammatory cytokines such as IL-6 and IL-12 is reduced and Production of immunosuppressive cytokine IL-10 is elevated [71]. These functional impairments prevent DCs from effectively activating naïve T cells, leading to defective tumor antigen-specific T cell responses. Consequently, anti-tumor immune surveillance is compromised, facilitating immune escape. Lactate serves as a metabolic and epigenetic regulator in TAMs. It induces histone lactylation, particularly at lysine 18 of histone H3 (H3K18la), which enhances the transcription of VEGF and ARG1—genes associated with angiogenesis and arginine depletion, respectively [72]. These changes promote the polarization of TAMs toward an immunosuppressive M2-like phenotype. Moreover, lactate accumulation in TAMs inhibits lactate dehydrogenase B (LDHB), reducing fatty acid synthesis and redirecting metabolic flux toward sterol regulatory element-binding protein 2 (SREBP2)-mediated cholesterol biosynthesis. The resulting cholesterol-enriched microenvironment further supports M2 polarization and tumor cell proliferation [73]. Natural killer (NK) cells are highly sensitive to lactate-mediated suppression. Extracellular lactate can penetrate NK cells and lower intracellular pH, causing cytoplasmic acidification. This disrupts both glycolysis and OXPHOS—metabolic pathways essential for NK cell effector functions. Since glycolysis fuels the production and release of cytotoxic granules such as perforin and granzymes, its inhibition directly compromises NK cell cytotoxicity [74]. Additionally, lactate suppresses IFN-γ production by interfering with the NFAT signaling pathway and inhibiting mTORC1 activity, thereby impairing NK cell activation of other immune subsets and limiting their ability to control tumor growth. Lactate not only recruits but also potentiates Tregs—key orchestrators of immune tolerance. HIF-1α-dependent secretion of chemokines such as CCL17, CCL22, and CCL28 facilitates Treg infiltration into the TME by forming concentration gradients recognized by Treg-expressed chemokine receptors, including CCR4, CCR8, and CCR10 [75]. Furthermore, lactate activates the NF-κB signaling pathway, enhancing the expression of these chemokines. In TNBC, tumor-derived cytokines such as IL-6, IL-1β, and TNF-α further activate NF-κB, reinforcing the expression of CCL17/CCL22 and forming a positive feedback loop that amplifies Treg recruitment [76]. Recent studies have also revealed that lactate induces histone lysine lactylation, especially at H3K18, which enhances the transcriptional activity of Foxp3, the master regulator of Treg identity and suppressive function [77].

Fig. 4.

This figure illustrates how TNBC cells utilize the Warburg effect (aerobic glycolysis) to produce lactate, which shapes an immunosuppressive TME. TNBC cells overexpress glucose transporters and glycolytic enzymes, enabling massive glucose uptake and conversion to LDHA, which is secreted extracellularly to acidify the TME. Lactate modulates multiple immune cell populations through diverse mechanisms: recruiting Tregs and enhancing their immunosuppressive activity by upregulating Foxp3; inhibiting DC maturation, antigen presentation, and pro-inflammatory cytokine production (e.g., IL-6, IL-12) while increasing immunosuppressive IL-10; polarizing TAMs toward an M2-like immunosuppressive phenotype and reprogramming their lipid metabolism; suppressing NK cell cytotoxicity and cytokine (e.g., IFN-γ) secretion; and inducing exhausted Tex to overexpress lactate transporters (e.g., MCT11), accelerating functional exhaustion. These coordinated effects collectively impair anti-tumor immunity, driving immune evasion in TNBC

In TNBC, out of the ten key glycolytic enzymes, six have been found to be upregulated, underscoring the critical role of glycolytic enzyme dysregulation in breast cancer progression (Fig. 5). The first committed step of glucose metabolism is catalyzed by hexokinases (HKs). HK2 has been shown to be overexpressed in various cancers, particularly in TNBC [78]. Compared to other breast cancer subtypes, TNBC exhibits elevated particulate HK activity (i.e., mitochondrial-bound fraction), which contributes to its increased invasiveness, lymph node involvement, and metastatic potential. Activation of the EGF/EGFR signaling axis in TNBC enhances glycolysis at its first irreversible step via HK2, while simultaneously suppressing the final irreversible step mediated by PKM2, thereby creating a condition referred to as a “glycolytic jam.” This metabolic bottleneck leads to the accumulation of glycolytic intermediates such as fructose-1,6-bisphosphate (F1,6BP) and lactate, promoting an aggressive tumor phenotype [69]. The second committed step of glycolysis is catalyzed by phosphofructokinase (PFK), a key rate-limiting enzyme. Similar to HK, total particulate PFK activity is significantly elevated in TNBC, and high PFK activity is also associated with increased lymph node metastasis and poor clinical outcomes [79]. Emerging evidence indicates that PFK-1 interacts with and stabilizes YAP/TAZ through its association with the TEAD transcription factor, thereby enhancing TNBC progression and metastasis [80]. Furthermore, Zancan et al. demonstrated that PFKL expression not only promotes breast cancer cell invasiveness but also enhances glycolytic efficiency—functions that HK alone cannot achieve [81]. Elevated PFKP expression has also been linked to reduced survival rates in breast cancer patients. Under hypoxic conditions, the ataxia-telangiectasia mutated (ATM) kinase, activated in response to oxidative stress, enhances the expression of both PFKP and citrate synthase (CS), leading to intracellular citrate accumulation. This metabolic shift further drives tumor cell invasion and metastasis in TNBC [82]. At the terminal stage of glycolysis, pyruvate kinase (PK) catalyzes the transfer of a high-energy phosphate group from phosphoenolpyruvate (PEP) to ADP, generating ATP and pyruvate. Four PK isoforms exist in humans: PKL, PKR, PKM1, and PKM2. Among these, PKM2 is frequently overexpressed in many cancers, especially in TNBC. Studies have shown that high PKM2 expression correlates with poor OS and PFS in breast cancer patients, and is associated with an increased risk of lymph node metastasis [83]. In a study by Zhifen Zhou et al., phosphorylated PKM2 at tyrosine 105 (pY105-PKM2) was found to be significantly elevated in TNBC cells (e.g., MDA-MB-231) compared to normal mammary epithelial cells (MCF10A). This post-translational modification, driven by tyrosine kinases such as YES, AXL, and JAK3, promotes the expansion of CD44hi/CD24neg and ALDH + cancer stem-like cells [84]. However, some studies suggest that PKM2 overexpression may enhance radiosensitivity as well as sensitivity to chemotherapeutic agents such as 5-fluorouracil (5-FU) and epirubicin in TNBC, indicating a dual role depending on context. Another critical enzyme in glycolysis is lactate dehydrogenase (LDH), which catalyzes the reversible conversion of pyruvate to lactate and regenerates NAD⁺ from NADH. LDH is composed of two major subunits: LDHA (muscle-type, M) and LDHB (heart-type, H). Both LDHA and LDHB are implicated in cancer aggressiveness, metastasis, and poor prognosis. Compared to non-TNBC subtypes, LDHA expression is markedly elevated in TNBC, and its overexpression is strongly correlated with advanced TNM stage, distant metastasis, high Ki67 proliferation index, and reduced patient survival [85]. In breast cancer patients, elevated LDHA levels are associated with decreased OS and DFS. Importantly, Dong et al. demonstrated that tumor LDHA expression and serum LDH levels are positively correlated with brain metastasis in TNBC, identifying them as independent predictive biomarkers [86]. Furthermore, miR-30a-5p has been shown to inhibit aerobic glycolysis mediated by LDHA, significantly suppressing TNBC cell growth and metastasis in vitro and in vivo by reducing lactate production, ATP levels, and glucose uptake in TNBC models such as MDA-MB-231. While LDHB may compensate for LDHA loss and maintain lactate metabolism, providing metabolic plasticity to cancer cells, its exact functional role remains less defined compared to LDHA [87].

Fig. 5.

This figure illustrates key glucose metabolic steps and lactate production mechanisms in TNBC cells. Glucose is taken up via the GLUT1 transporter and phosphorylated by HK to form G6P, progressing through glycolytic intermediates such as F1,6BP and Gly3P, catalyzed by GPI, PFK, and ALDO. Pyruvate is finally generated by PK. LDHA converts pyruvate to lactate, which is exported via MCT1 or imported by MCT4, forming a lactate shuttle. Enzymatic annotations and arrow thickness highlight upregulated glycolytic enzymes (HK, PFK, PKM2, LDHA) in TNBC, driving accelerated glycolysis and lactate accumulation, thereby promoting tumor invasion and metastasis. LDHA and other enzymes are significantly linked to poor prognosis in TNBC, highlighting the central role of glycolytic enzyme dysregulation in TNBC progression

Amino acid metabolism in TNBC

Amino acid metabolism represents one of the central metabolic processes essential for sustaining life activities in living organisms. Tumor cells exhibit significantly enhanced uptake and utilization of amino acids compared to normal cells [88], and like glucose, amino acids serve as critical metabolic fuels that support tumor cell growth and survival. Emerging evidence from recent studies highlights that reprogramming of amino acid metabolism not only drives the malignant proliferation of TNBC cells, but also acts as a key regulatory hub in tumor immune escape. In this section, we will focus on the metabolic roles of key amino acids—glutamine, arginine, tryptophan, serine and glycine—to explore the mechanistic involvement of amino acid metabolism in the immunoevasive strategies of TNBC (Fig. 6).

Fig. 6.

This figure illustrates the mechanisms by which TNBC cells exploit amino acid metabolic reprogramming to drive immune evasion. Glutamate transporters (SLC1A5/SLC7A5) on TNBC cells enhance glutamate uptake, promoting TAM polarization (via Arg1/IL-10 upregulation) and Treg activation (through mTORC1 signaling), while glutamate deprivation triggers IRE1α-JNK pathway activation, secreting G-CSF/GM-CSF to expand MDSCs. Arginine metabolism, mediated by ARG1, depletes L-arginine to suppress T cell function and promote M2 TAM polarization; ARG1 also collaborates with iNOS to generate ROS/RNS, inducing T cell apoptosis. MDSCs sense arginine scarcity via GCN2, suppressing T/NK cells, while GM-CSF activates STAT3/p38MAPK pathways to upregulate ARG1. Tryptophan catabolism, driven by IDO/TDO enzymes, converts tryptophan to Kyn, activating the AhR pathway to expand Tregs, inhibit Teffs, and upregulate PD-1 for T cell exhaustion; GM-CSF further amplifies ARG1-driven immunosuppression. Serine/glycine metabolism diverts 3PG via PHGDH/PSAT1/PSPH enzymes, fueling cancer cell proliferation and metastasis. Collectively, these amino acid metabolic rewirings establish an immunosuppressive TME to facilitate TNBC immune escape

Glutamine is the most abundant amino acid in cancer cells and serves as a critical metabolic substrate that donates both carbon and nitrogen atoms to a wide array of biosynthetic and bioenergetic reactions that support tumor cell proliferation, invasion, and metastasis. In TNBC, the overexpression of glutamine transporters such as SLC1A5 and the SLC7A5/SLC3A2 heterodimeric complex markedly enhances glutamine uptake efficiency, thereby promoting the polarization of TAMs toward a pro-tumorigenic M2 phenotype [89]. Notably, α-KG, a key metabolite derived from glutamine catabolism, acts not only as an intermediate in energy metabolism but also as an epigenetic regulator. It promotes histone demethylation, thereby sustaining the M2-like immunosuppressive phenotype of TAMs [89]. Moreover, glutamine deprivation activates the IRE1α-JNK stress signaling pathway, leading to the secretion of G-CSF and GM-CSF, which drive the expansion of MDSCs—a heterogeneous population of immunosuppressive myeloid cells that potently inhibit anti-tumor immunity [90]. Glutamine metabolism also profoundly influences T cell dynamics within the TME. It activates Tregs via the mTORC1 signaling axis, while simultaneously suppressing the mitochondrial metabolic activity of Teffs. This dual effect disrupts the balance between pro-inflammatory and immunosuppressive responses, ultimately contributing to immune evasion [89, 91].

A central player in arginine metabolism and TNBC immune escape is arginase 1 (ARG1), which is highly expressed in TNBC and contributes to immune evasion through multiple mechanisms [92]. ARG1 catalyzes the hydrolysis of L-arginine into L-ornithine and urea, thereby depleting L-arginine in the TME. This local depletion directly impairs T cell activation and proliferation, ultimately leading to tumor immune escape. In addition to its role in amino acid catabolism, ARG1 is co-expressed with iNOS, promoting the production of ROS and RNS. These reactive metabolites cause mitochondrial dysfunction and induce apoptosis in tumor-infiltrating T cells. Furthermore, the interplay between activated ARG1 and NOS in the TME suppresses NO production, which shifts the polarization of TAMs from the anti-tumorigenic M1 phenotype toward the pro-tumorigenic M2 phenotype, thereby facilitating tumor progression [93]. MDSCs are particularly sensitive to arginine depletion. These immunosuppressive cells utilize the GCN2 kinase pathway to sense low arginine levels, activating stress signaling cascades that inhibit T cell function while enhancing MDSC survival and expansion. Moreover, MDSCs impair tumor immune surveillance by suppressing the activity of NK cells and NKT cells, further dampening anti-tumor immunity [94]. Notably, GM-CSF has been shown to activate STAT3 and p38MAPK signaling pathways, which significantly upregulate ARG1 expression. This induction promotes M2 polarization of TAMs, reduces CD8 + T cell infiltration, and ultimately enhances immune evasion in TNBC [93].

Tryptophan catabolism plays a pivotal role in promoting immune escape in TNBC. This metabolic pathway primarily relies on key enzymes such as indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO). IDO1 is highly expressed in both tumor cells and infiltrating immune cells—particularly dendritic cells—where it catalyzes the conversion of L-tryptophan (Trp) to kynurenine (Kyn). This reaction establishes an immunosuppressive “metabolic checkpoint” that actively dampens anti-tumor immunity. Notably, GCH1 (GTP cyclohydrolase 1) is upregulated in TNBC and promotes the synthesis of 5-hydroxytryptophan (5-HTP), which activates the aryl hydrocarbon receptor (AhR). AhR signaling, in turn, further enhances IDO1 activity, forming a positive feedback loop characterized by Trp depletion and Kyn accumulation—a metabolic signature strongly associated with immune evasion and tumor progression. Metabolites generated via the tryptophan-kynurenine pathway exert profound immunomodulatory effects through AhR signaling. These include: Expansion of Tregs; Suppression of Teffs function༛Upregulation of PD-1 expression, leading to activation of the PD-1/PTEN axis and subsequent T cell exhaustion༛Direct inhibition of NK cell cytotoxicity and reduction of IFN-γ secretion [95, 96]. Moreover, overexpression of IDO1 drives the polarization of TAMs toward an immunosuppressive M2 phenotype and suppresses T cell activity [97]. Importantly, IDO inhibits Th1 differentiation, promotes T cell apoptosis, and enhances MDSC-mediated immunosuppression, often accompanied by increased secretion of IL-10 and TGF-β—two major immunosuppressive cytokines [98]. In addition to IDO1, TDO exhibits elevated enzymatic activity in TNBC, contributing to kynurenine production via the AhR signaling pathway. TDO-derived Kyn has been shown to impair CD8 + T cell function, further reinforcing immune evasion. Ultimately, tryptophan depletion and the accumulation of its downstream metabolites lead to: Inhibition of effector T and NK cell functions; Activation and expansion of immunosuppressive cell populations, including Tregs, MDSCs, and M2-polarized macrophages. This collectively establishes an immunosuppressive TME that facilitates tumor immune escape [99].

In TNBC, metabolic reprogramming redirects 3PG away from glycolysis toward the serine synthesis pathway, thereby conferring a proliferative advantage to tumor cells [100]. Key enzymes in serine biosynthesis—PHGDH (phosphoglycerate dehydrogenase), PSAT1 (phosphoserine aminotransferase 1), and PSPH (phosphoserine phosphatase)—are significantly upregulated in TNBC. Notably, depletion of extracellular serine and glycine has been shown to inhibit TNBC cell proliferation under culture conditions, highlighting the dependency of these tumors on endogenous serine synthesis [100]. Among these enzymes, PHGDH overexpression is a hallmark of TNBC, where it catalyzes the first and rate-limiting step in serine biosynthesis by converting 3PG into phosphohydroxypyruvate (pPYR). This reaction drives flux through the serine synthesis pathway and supports downstream metabolic processes critical for tumor growth. Importantly, α-ketoglutarate (α-KG) generated via the serine pathway contributes to approximately 50% of the carbon input into the TCA cycle in PHGDH-high TNBC cells, underscoring its central role in sustaining mitochondrial metabolism and tumor proliferation [101]. Moreover, PSAT1 plays a pivotal role in TNBC progression by promoting cyclin D1 expression, thereby enhancing both cell proliferation and invasiveness.

Lipid metabolism contributes to immune escape in TNBC

In recent years, advances in cancer metabolism research have revealed that lipid metabolic reprogramming plays a central role in shaping the immunosuppressive TME and promoting immune escape in TNBC.TNBC cells frequently undergo profound alterations in lipid metabolism, characterized by enhanced lipid uptake, de novo fatty acid synthesis, fatty acid oxidation (FAO), and lipid storage. These metabolic adaptations enable cancer cells to survive and proliferate under conditions of hypoxia and nutrient deprivation, which are hallmarks of the aggressive TNBC TME. One of the most notable features of TNBC metabolism is the upregulation of de novo fatty acid synthesis. Key lipogenic enzymes such as fatty acid synthase (FASN) and acetyl-CoA carboxylase (ACC) are significantly overexpressed in TNBC cells. This metabolic shift drives the synthesis of saturated fatty acids, which provide essential building blocks for membrane biogenesis, energy storage, and oncogenic signaling pathways. In addition to endogenous synthesis, TNBC cells enhance their uptake of extracellular fatty acids to support rapid proliferation and survival. Overexpression of the fatty acid transporter CD36 on the surface of TNBC cells facilitates the import of circulating free fatty acids (FFAs) or lipids released by CAFs. This exogenous lipid supply contributes to membrane biosynthesis, bioenergetics, and metastatic potential [102, 103]. TNBC cells also rely heavily on FAO for energy production. FAO involves the β-oxidation of long-chain fatty acids to generate acetyl-CoA, which enters the tricarboxylic acid (TCA) cycle and fuels OXPHOS. This pathway efficiently produces ATP, NADH, and NADPH, providing the energetic and redox support necessary for tumor cell proliferation, invasion, and metastasis. Notably, FAO has been implicated not only in sustaining tumor growth but also in supporting stemness, drug resistance, and immune evasion in TNBC.

During TNBC progression, tumor cells undergo metabolic reprogramming that enables them to abnormally sequester fatty acids, thereby creating a state of metabolic competition within the TME. This aberrant lipid uptake significantly reduces the bioavailability of fatty acids for immune cells, leading to profound metabolic and functional impairments. The resulting lipid metabolic imbalance disrupts the activation, infiltration, and effector functions of anti-tumor immune cells, ultimately weakening immunosurveillance and promoting immune escape (Fig. 7). In TNBC, this lipid metabolic rewiring also plays a pivotal role in shaping the polarization of TAMs toward the pro-tumorigenic M2 phenotype. Cancer cells modulate TAM lipid metabolism through multiple signaling pathways, enhancing both FAO and fatty acid uptake in these myeloid cells. Given that M2 macrophages rely heavily on fatty acids as their primary energy source for ATP production, this metabolic shift creates a permissive environment for M2 polarization, which in turn promotes tumor growth, metastasis, and angiogenesis. Emerging evidence indicates that upregulation of FASN and CD36 in TNBC cells enhances the synthesis of polyunsaturated fatty acids (PUFAs), which further drive the M2-like polarization of TAMs. Additionally, arachidonic acid (AA), catalyzed by cyclooxygenase-2 (COX-2) in cancer cells, generates PGE2, which activates EP2/EP4 receptors on TAMs and potently induces M2 polarization [104]. TAM-secreted cholesterol metabolites, such as 27-hydroxycholesterol (27-HC), can recruit CCR2+/CCR5 + monocytes and promote their differentiation into M2-polarized macrophages [105]. Moreover, lipid accumulation in cancer cells triggers ER stress in TAMs, activating the IRE1α–XBP1 pathway, which drives STAT3 phosphorylation and the expression of pro-oncogenic genes [106]. Apoptotic cancer cells release sphingosine-1-phosphate (S1P), which signals through S1PR1/3 to further enhance M2 macrophage polarization. In addition, adipocyte-type fatty acid-binding protein (A-FABP)—highly expressed in TAMs—amplifies IL-6/STAT3 signaling via the NF-κB/miR-29b axis, promoting tumor metastasis [107, 108]. Beyond TAMs, lipid metabolic reprogramming in TNBC also profoundly affects other components of the immune system: Fatty acid transport protein 2 (FATP2) in myeloid-derived suppressor cells (MDSCs) facilitates the accumulation of arachidonic acid, leading to increased PGE2 synthesis and enhanced immunosuppressive activity [109]; Lipid overload in the TME activates peroxisome proliferator-activated receptors (PPARs) in NK cells, impairing mitochondrial function and reducing cytotoxicity [110, 111]; In senescent CD8 + T cells, persistent activation of cytosolic phospholipase A2α (cPLA2α) leads to lipotoxic metabolite accumulation via MAPK/STAT signaling, further contributing to immune dysfunction and tumor immune escape [112]; Cancer-associated fibroblasts (CAFs) expressing FATP1 enhance exogenous lipid uptake, supporting cancer cell migration and metastasis [113, 114]. Importantly, PGE2 produced by TAMs via COX-2/mPGES1 signaling upregulates PD-L1 expression in a paracrine and autocrine manner, synergistically suppressing T cell function and reinforcing immune checkpoint blockade [115].

Fig. 7.

This diagram illustrates the core mechanisms by which TNBC cells reshape the immunosuppressive TME through lipid metabolic reprogramming. TNBC cells rely on CD36-mediated fatty acid uptake and FAO to generate ATP, NADH, and NADPH, fueling invasion, metastasis, and proliferation. Overexpressed FASN synthesizes PUFAs, which are converted into PGE₂ via COX-2. PUFAs and PGE₂ synergistically induce TAM polarization toward an M2 phenotype by engaging CD36 and EP₂/EP₄ receptors, respectively. TAMs enhance tumor aggressiveness through IL6/STAT3 signaling, A-FABP overexpression, and secretion of 27-HC to recruit CCR2⁺/CCR5⁺ monocytes. IRE1α-XBP1s activation in TNBC cells reinforces TAM M2 polarization via STAT signaling, while apoptotic cancer cell-derived S1P amplifies this process through S1PR1/3 receptors. Additionally, TAM-derived PGE₂ upregulates PD-L1 via the COX-2/mPGES1 axis, suppressing T cell function. MDSCs exploit FATP2 to accumulate AA, enhancing PGE₂ synthesis and immunosuppression. NK cells exhibit impaired cytotoxicity due to lipid overload activating PPARγ/δ pathways, while T cells suffer dysfunction from cPLA2α-mediated lipotoxic metabolite accumulation via MAPK/STAT signaling. Collectively, TNBC cells competitively deprive immune cells of lipids, orchestrating multidimensional suppression of anti-tumor immunity to drive immune evasion

The role of cellular players in TNBC immune escape

Immune cell-mediated immune evasion in TNBC

Immune evasion in TNBC is orchestrated through a multicellular network arising from synergistic interactions among diverse immune cell populations, which collectively shape an immunosuppressive TME via intercellular communication and regulatory crosstalk. From a cellular perspective, TAMs, T lymphocytes, MDSCs, NK cells, DCs, and TANs constitute the core immunosuppressive network driving TNBC immune evasion. This review focuses on elucidating the critical intercellular networks that govern immune escape mechanisms in TNBC, emphasizing the dynamic cross-regulation among these immune cell subsets and their functional integration within the tumor ecosystem.

The immunosuppressive network in triple-negative breast cancer (TNBC) involves multiple cellular components engaging in coordinated interactions. Among various potential initiators of pro-tumorigenic signaling, tumor-associated macrophages (TAMs) can secrete IL-1β, which in some contexts stimulates γδT cells to produce IL-17. This IL-17 response enhances granulocyte colony-stimulating factor (G-CSF) production and promotes the recruitment of tumor-associated neutrophils (TANs) [116]. Once recruited, TANs may reinforce M2-like TAM polarization through IL-6 and TGF-β signaling, potentially establishing a positive feedback loop that sustains an immunosuppressive microenvironment. TAN-derived TGF-β contributes to T cell dysfunction through downregulation of the NKG2D receptor and may additionally modulate TAM phagocytic activity, illustrating a bidirectional regulatory axis.

TAMs and myeloid-derived suppressor cells (MDSCs) frequently exhibit synergistic immunosuppressive effects. Both cell types utilize the IL-6/STAT3 signaling pathway to inhibit T and NK cell function. Furthermore, they jointly deplete arginine and tryptophan within the tumor microenvironment, thereby impairing T cell proliferation and effector responses [117, 118]. TAM-secreted TGF-β can enhance the immunosuppressive capacity of MDSCs, while MDSC-derived reactive oxygen species (ROS) may reciprocally suppress TAM phagocytic function, forming a complex regulatory network.

Tumor cell-derived TGF-β modulates dendritic cell (DC) function by inhibiting CD4 + T cell activation and altering DC differentiation, thereby compromising antigen presentation. TAMs exacerbate this suppression through IL-10-mediated STAT3 activation, a process associated with upregulation of DNA methylation-related enzymes and further dampening of antitumor immunity. TAMs also influence DC activity through CD40/CD40L interactions and PD-L1/PD-1 signaling. Additionally, TAM-expressed PD-L1 directly inhibits T cell activation, thereby undermining DC-mediated priming of effective antitumor responses [119].

Immunosuppressive cells collectively utilize multiple pathways to inhibit CD8 + T cell function: TAMs through IL-10 and TGF-β secretion, TANs via TGF-β-induced exhaustion, MDSCs through arginine depletion and ROS production, and regulatory T cells (Tregs) via cytokine-mediated suppression and direct elimination of effector T cells. TAMs and Tregs engage in bidirectional crosstalk, wherein TAM-derived TGF-β promotes Treg expansion, while Tregs can suppress TAM phagocytosis through TGF-β signaling, reinforcing an immunosuppressive circuit [120, 121].

This regulatory network extends to tumor cell-intrinsic mechanisms. TAM-secreted TGF-β and IL-10 impair CD8 + T cell function while promoting Treg differentiation. Tumor cells evade immune recognition through downregulation of MHC class I molecules and upregulation of PD-L1, with TAM-expressed PD-L1 further amplifying PD-1-mediated T cell inhibition. TAMs also support tumor stemness and progression through CCL2/AKT/β-catenin signaling, a pathway associated with immune-excluded phenotypes and impaired CD8 + T cell infiltration [119, 122].

Additionally, TANs promote immune evasion by releasing neutrophil extracellular traps (NETs) containing MMP-9, which can activate latent TGF-β from tumor cells and induce epithelial-mesenchymal transition (EMT) and chemoresistance. TAN-derived TGF-β further suppresses T cell NKG2D receptor expression, establishing an immunosuppressive loop that impairs tumor cell recognition. Clinically, an elevated intratumoral neutrophil-to-lymphocyte ratio (iNLR) correlates with reduced T cell infiltration, enhanced immune evasion, and poor prognosis in TNBC patients, underscoring the clinical relevance of these mechanisms [116].

Cancer-associated fibroblasts (CAFs) facilitates immune evasion in TNBC

In the TME, CAFs represent the most abundant non-epithelial stromal cell population, comprising over 50% of tumor volume. Compared to normal fibroblasts, CAFs exhibit distinct morphological, transcriptomic, and functional profiles, characterized by their capacity to secrete diverse cytokines, chemokines, and ECM proteins. These features enable CAFs to orchestrate immune evasion in TNBC through multilayered mechanisms that reshape the TME. This review will focus on three pivotal strategies employed by CAFs: ECM remodeling; Cytokine network modulation and Metabolic reprogramming. These mechanisms collectively establish an immunosuppressive niche that facilitates tumor immune escape and therapeutic resistance (Fig. 8).

Fig. 8.

This diagram illustrates the multidimensional mechanisms by which CAFs reshape the immunosuppressive TME in TNBC. Activation of the GPER on CAFs by estradiol (E2) triggers the cAMP/PKA/CREB pathway, driving Gln metabolism to produce immunosuppressive metabolites such as Kyn and Ado, which directly impair T-cell function. Simultaneously, Gln promotes M2-like polarization of TAMs via the CSF1/CSF1R axis. CAF-secreted CXCL12 engages CXCR4 on CD4⁺CD25⁺ T cells, driving their differentiation into regulatory Tregs. IL-6 activates JAK/STAT3 signaling in immune cells, expanding MDSCs and recruiting them to tumor sites, while CXCL1/2/5 and CCL2/3 further facilitate MDSC infiltration. GM-CSF promotes M2 TAM polarization, leading to secretion of anti-inflammatory cytokines (IL-10, TGF-β) and pro-angiogenic factors (VEGF), which suppress CD4⁺/CD8⁺ T-cell activity. TGF-β signaling via SMAD pathways induces EMT, reinforcing immune evasion. Collectively, these mechanisms highlight CAFs as central orchestrators of TNBC immune escape through cytokine networks and metabolic reprogramming, establishing a permissive TME for tumor progression

CAFs exhibit high heterogeneity in TNBC and can be divided into multiple subtypes based on different surface markers. α-SMA + CAFs are the most numerous and functionally potent subtype, forming a dense ECM structure by secreting type I collagen. FAP + CAFs participate in ECM remodeling by secreting fibroblast activation protein (FAP) and modulate immune cell functions through cytokines such as GM-CSF. In addition, there are several other subtypes, including iCAFs (inflammatory CAFs), myCAFs (myofibroblast-like CAFs), and apCAFs (activated CAFs), each contributing to TNBC immune evasion through distinct mechanisms. The orderly alignment of collagen fibers mediated by discoidin domain receptor 1 (DDR1) in CAFs is one of the key mechanisms of immune evasion. Studies have shown that DDR1-ECD shedding and cross-linking with collagen result in a more compact arrangement of collagen fibers in the ECM, forming a physical barrier that hinders T cell infiltration into the tumor core [117]. Increased stromal stiffness is another important mechanism of CAF-mediated immune evasion. Type I collagen secreted by α-SMA + CAFs not only increases ECM density but also enhances stromal stiffness [118]. Research indicates that stromal rigidity inhibits T cell pseudopod formation and migration capacity through the integrin β1/FAK signaling pathway [119]. The directional alignment of collagen fibers significantly impacts T cell infiltration. Recent studies have revealed that the directional alignment of collagen fibers forms tumor-associated collagen signatures (TACS), which are negatively correlated with T cell infiltration. Collagen fibers with orderly alignment act as a physical barrier to T cell migration, while disordered collagen fibers may facilitate T cell movement [120]. In TNBC, TGF-β and DDR1 secreted by CAFs jointly promote the orderly alignment of collagen fibers, creating an immune-excluded microenvironment [121].

CAFs orchestrate immune evasion in TNBC through a multifaceted cytokine network that dynamically reshapes the tumor microenvironment. The CAF-S1 subtype potently secretes C-X-C motif chemokine 12 (CXCL12), which engages C-X-C chemokine receptor type 4 (CXCR4) on CD4⁺CD25⁺ T cells to drive their differentiation into immunosuppressive Tregs, establishing a tolerogenic niche [122]. This axis not only enhances Treg infiltration but also induces T-cell exhaustion by suppressing activation markers (e.g., CD28, ICOS) and effector cytokines (e.g., IFN-γ, TNF-α) [123]. Concurrently, CAFs produce interleukin-6 (IL-6), which activates the JAK/STAT3 pathway in myeloid cells, promoting expansion of MDSCs and further reinforcing Treg dominance. IL-6 not only recruits MDSCs to primary and metastatic TNBC sites but also directly inhibits T-cell proliferation and cytokine production while driving EMT via STAT3-dependent upregulation of Snail and Twist, linking immunosuppression with metastatic competence [124]. Additionally, CAF-secreted chemokines—including CXCL1, CXCL2, CXCL5, CCL2, and CCL3—establish a gradient that attracts immunosuppressive leukocytes: CXCL1/2/5 promote neutrophil infiltration and N2 polarization, CCL2 recruits monocytes for TAM differentiation, and CCL3 accumulates regulatory dendritic cells [125]. CAF-derived GM-CSF further programs TAMs toward an M2-like phenotype, characterized by secretion of anti-inflammatory cytokines (IL-10, TGF-β) and pro-angiogenic factors (VEGF). These M2 macrophages inhibit CD4⁺/CD8⁺ T-cell activation via PD-1/PD-L1 upregulation, promote Treg expansion through CTLA-4-dependent mechanisms, and enhance tumor cell stemness via Wnt/β-catenin signaling. Recent studies demonstrate that GM-CSF blockade reduces M2 TAM infiltration while enhancing T-cell effector functions [126]. Moreover, TGF-β and IL-6 form a reciprocal amplification loop: TGF-β signals via SMAD2/3 to induce FoxP3⁺ Treg differentiation, whereas IL-6 activates JAK/STAT3 to expand MDSCs, collectively suppressing MHC class I expression and IFN-γ signaling in tumor cells [127, 128]. This synergistic axis correlates with resistance to ICIs and poor survival in TNBC, underscoring its therapeutic relevance as a central hub of immunosuppression.

CAFs further drive immune evasion in TNBC through metabolic reprogramming that alters the TME. The G protein-coupled estrogen receptor (GPER), highly expressed in TNBC stroma, synergizes with PD-L1 to promote immunosuppression. Upon activation by estradiol (E2), cytoplasmic GPER in CAFs enhances glutamine synthesis and secretion via the cAMP/PKA/CREB signaling axis, leading to accumulation of immunosuppressive metabolites such as kynurenine and adenosine, which directly impair T-cell function. This GPER-driven glutamine metabolism also cooperates with the CSF1/CSF1R pathway to polarize TAMs toward an M2-like phenotype, reinforcing a tolerogenic niche [129, 130]. CAFs engage in bidirectional metabolic exchange with cancer cells via lactate, lipids, and glutamine, forming a metabolic symbiosis that sustains tumor growth while suppressing anti-tumor immunity [131]. Emerging evidence reveals that CAF-secreted metabolites intersect with tumor cell-derived signals to construct a complex metabolic network, dynamically regulating immune cell function within the TME. These findings underscore the central role of stromal metabolic rewiring in immune evasion and highlight actionable targets for combination therapies aimed at restoring immunosurveillance in TNBC.

Clinical translation and emerging clinical trials

This comprehensive review integrates the multifaceted mechanisms underlying immune evasion in TNBC. However, emerging evidence highlights that the most therapeutically tractable pathways for intervention are the immune checkpoint axis, TME remodeling, and metabolic reprogramming [132]. Among immune checkpoints, the PD-1/PD-L1 axis remains the most extensively characterized immune evasion pathway in TNBC. Pembrolizumab combined with chemotherapy demonstrates significant clinical benefit in PD-L1-positive TNBC patients. The KEYNOTE-522 trial reported a pathological complete response (pCR) rate of 65.5% in PD-L1-positive TNBC patients treated with pembrolizumab plus chemotherapy, compared to 51.7% in the placebo arm. However, monotherapy with anti-PD-1 agents demonstrates limited efficacy in TNBC, with response rates of 5–23%, highlighting the inherent limitations of single-axis blockade [133]. Notably, combined analysis of PD-L1 expression and TIL density significantly enhances predictive accuracy. Furthermore, emerging co-inhibitory receptors such as LAG-3, TIM-3, and TIGIT also play pivotal roles in TNBC immune evasion. A meta-analysis revealed that high LAG-3 expression correlates with improved overall survival, suggesting context-dependent immunomodulatory functions. The CITYSCAPE trial evaluating the TIGIT inhibitor tiragolumab combined with atezolizumab in non-small cell lung cancer demonstrated an ORR of 37.3% in the combination group versus 20.6% in the control arm, with a striking 66% ORR in PD-L1-high (TPS ≥ 50%) patients compared to 24% in the monotherapy group [134]. Although these data originate from lung cancer studies, they underscore the potential for synergistic effects between LAG-3/TIGIT and PD-1/PD-L1 dual blockade in TNBC. Regarding TME remodeling, targeting immunosuppressive networks mediated by MDSCs and CAFs represents a critical strategy to overcome therapeutic resistance. Agents targeting MDSCs, including ARG1 inhibitors, CXCL5/2 inhibitors, and phosphodiesterase 5 (PDE5) inhibitors, are currently under clinical evaluation [135]. Additionally, CAF-targeting strategies are transitioning from preclinical models to clinical applications, with bispecific antibodies (e.g., TST005), FAP-targeted therapeutics, and Notch ligand JAG1 inhibitors offering novel avenues for TNBC immunotherapy [136]. Metabolic reprogramming further facilitates immune evasion through glutamine-dependent metabolism and amino acid depletion (e.g., tryptophan and arginine catabolism). Interventions targeting these metabolic pathways, such as glutaminase inhibitors and tryptophan metabolism modulators, hold promise for restoring immune cell functionality in the immunosuppressive TME.

Despite the complexity and diversity of immune escape mechanisms in TNBC, in-depth studies of its molecular mechanisms combined with advances in clinical trials of novel immunotherapies are gradually unveiling a range of potential therapeutic targets and optimized combination treatment strategies (Table 2). However, immune-related adverse events (irAEs) pose significant safety concerns for patients undergoing combination immunotherapy. The toxicity profiles exhibit marked heterogeneity across different drug combinations, with ICIs combined with chemotherapy being the most prevalent regimen. This combination significantly elevates irAE incidence compared to monotherapy, manifesting as dermatotoxicity, endocrinopathy, hepatotoxicity, pneumonitis, and infusion reactions. In the IMpassion130 trial, atezolizumab-containing regimens demonstrated a 64.7% irAE rate versus 42.0% in controls, with the most common events including rash (34.0% vs. 26.0%), hypothyroidism (18.0% vs. 5.0%), hyperthyroidism (5.0% vs. 1.0%), and pneumonitis (4.0% vs. <1.0%) [137]. Similarly, the KEYNOTE-355 study reported higher irAE rates with pembrolizumab (11.9% hypothyroidism vs. 3.2%, 4.8% pneumonitis vs. 2.3%, 2.6% colitis vs. 1.0%). Combining ICIs with anti-angiogenic agents like bevacizumab enhances antitumor efficacy but introduces additional toxicities [138]. In TNBC, these regimens carry a 5–9% risk of hypertension, alongside increased hemorrhagic events (e.g., gastrointestinal or pulmonary bleeding) and proteinuria, which may be exacerbated by ICI-induced inflammation. Emerging combinations, such as CD73 inhibitors (e.g., AB680) with ICIs, may induce hematologic toxicities (anemia, neutropenia) due to metabolic interference. CD73, expressed by tumor and stromal cells (e.g., CAFs, TAMs), sustains an immunosuppressive niche via A2BR signaling-driven “adenosine-CAFs” positive feedback loops. Disrupting this pathway may impair tumor glycolysis and NAD⁺ metabolism, inadvertently amplifying immune attacks on metabolically vulnerable tissues and causing anemia. PARP inhibitors, inherently associated with myelosuppression (anemia, thrombocytopenia, neutropenia) and fatigue, further elevate irAE risks when combined with ICIs. Their DNA repair inhibition may damage immune cells (e.g., T cells), triggering cytokine storms (elevated IFN-γ, TNF-α) post-immune hyperactivation, leading to pneumonitis or hepatotoxicity. Dual immune checkpoint blockade (e.g., CTLA-4 + PD-1 inhibition) increases risks of dermatotoxicity (e.g., immune-mediated bullous dermatosis), colitis, and pneumonitis. In TNBC, skin toxicity incidence exceeds 49%, predominantly grade 1–2, with rare severe (grade ≥ 3) events [139].

Table 2.

Recent clinical trials of immunotherapies in TNBC with main results

Experimental title/identifier	Phase	TNBC stage	Therapy	Main results	Ref(s)
KEYNOTE-355 (NCT02819518)	III	III-IV	ChT+/- pembrolizumab (PD-1)	Median OS = 23 m pembrolizumab vs. 16.1 m placebo (CPS > 10)	[138]
IMpassion130 (NCT02425891)	II	III-IV	Nab-paclitaxel+/-atezolizumab (PD-L1)	OS = 21 m atezolizumab vs. 18.7 m placebo	[137]
TORCHLIGHT (NCT04085276)	III	III-IV	Nab-paclitaxel+/- Toripalimab (PD-L1)	Median PFS 8.4 m Toripalimab vs. 5.6 m placebo; median OS 32.8 m Toripalimab vs. 19.5 m placebo (CPS ≥ 1)	[140]
GeparNuevo (NCT02685059)	II	II-III	Durvalumab (PD-L1) +/- nab-paclitaxel	ORR = 53.4% Durvalumab vs. 44.2% placebo pCR = 61% Durvalumab vs. 41.4% placebo	[141]
KEYNOTE-522 (NCT03036488)	III	II-III	Paclitaxel + carboplatin +/- pembrolizumab (PD-1)	pCR = 64.8 pembrolizumab vs. 51.2% placebo	[133]
SYNERGY (NCT03616886)	II	IV	Paclitaxel + carboplatin + Durvalumab (PD-1) +/- Oleclumab (CD73)	Median PFS = 5.9 m oleclumab vs. 7 m without oleclumab	[142]
NCT02768701	II	IV	Cyclophosphamide (T reg depletion) prior to pembrolizumab (PD-1)	ORR = 21% PFS = 1.8 m	[143]
NCT03121352	II	IV	Carboplatin + Paclitaxel + Pembrolizumab (PD-1)	ORR = 48% (2 CR, 11 PR, 8 SD) DOR = 6.4 m PFS = 5.8 m, OS = 13.4 m	[144]
ENHANCE 1 (NCT02513472)	Ib (1st line)/II (2nd – 3rd line)	IV	Eribulin + Pembrolizumab (PD-1)	ORR = 25.8% Ib vs. 21.8% II	[145]
NCT05382286	III	IV	sacituzumabgovitecan + pembrolizumabvs chemo + pembrolizumab	Median PFS 11.2 m sacituzumabgovitecan + pembrolizumab vs. 7.8 m chemo + pembrolizumab;	[146]
NCT04303741	II	III-IV	Eribulin + Camrelizumab (PD-1) + apatinib (VEGF2 tyrosine kinase)	ORR = 37% PFS = 8.1 m	[147]
FUTURE-C- Plus (NCT04129996)	II	III-IV	Famitinib (angiogenesis inhibitor) + nab-paclitaxel + Camrelizumab (PD-1)	ORR = 81.3% PFS = 13.6 m	[148]
NEWBEAT (WJOG9917B)	II	IV	Nivolumab (PD-1) + bevacizumab (VEGF) + paclitaxel	ORR = 59% PFS median = 7.8 m OS = 32.5 m	[149]
NCT04185311	I	I-III	Nivolumab (PD-1) + Ipilimumab (CTLA-4) + intratumor talimogenelaheparepvec (oncolytic virus)	1 pCR ((16.7%), 3 PR (50%), 1 SD (16.7%), 1 PD (16.7%) Increase of T cell infiltrate	[150]
NCT03256344	Ib	IV	intratumor talimogenelaheparepvec (oncolytic virus) + atezolizumab (PD-L1)	ORR = 10% in TNBC patients AEs = 90% of patients	[151]
NCT03060356	I	IV	RNA-electroporated c-Met directed chimeric antigen receptor (CAR) T cells	57.1% SD Increase of CD8 and CD3 T cells in TME. Decrease of p56 and Ki67	[152]

Multidimensional monitoring strategies are essential for mitigating irAE risks in TNBC patients undergoing combination immunotherapy. Comprehensive baseline assessments should be conducted prior to treatment initiation. During therapy, rigorous monitoring of hematological parameters, hepatic and renal function, thyroid function, pulmonary capacity, cardiac biomarkers, and imaging findings is required. Post-treatment long-term follow-up, coupled with dynamic adjustments based on toxicity severity and therapeutic response, remains critical. Emerging biomarker technologies further enhance irAE risk management, including exosomal CD73 profiling, circulating tumor DNA (ctDNA) dynamics, gut microbiome analysis, and TCR repertoire diversity monitoring. Personalized risk-mitigation approaches tailored to TNBC biology are pivotal for reducing irAEs. Molecular subtyping-guided toxicity management demonstrates clinical utility: the FUTURE-SUPER trial revealed that precision therapy based on the Fudan subtypes (IM, LAR, BLIS, MES) significantly improves survival in TNBC patients. Prophylactic pharmacological strategies also play a role—for instance, angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) (e.g., amlodipine) are recommended as first-line antihypertensive agents to counteract bevacizumab-ICI-induced hypertension. Treatment optimization encompasses sequential therapy adjustments, toxicity-driven regimen modifications, exploration of novel combination regimens, and adjuvant immunomodulatory therapies. Multidisciplinary team (MDT) collaboration, centered on biomarker-informed individualized care (Table 3), represents a cornerstone for minimizing irAE risks while ensuring patient safety.

Table 3.

Summary of predictive biomarkers in Triple-Negative breast cancer (TNBC)

Biomarker Category	Specific Biomarker(s)	Predictive value	Ref(s)
Immune Checkpoint Molecule	PD-L1(CPS/TPS)	Key predictor for response to anti-PD-1/PD-L1 inhibitors (e.g., Pembrolizumab, Atezolizumab).	[13]
	LAG-3	High expression is associated with immune suppression and prognosis; a potential target for combination therapy.	[153]
	TIM-3	High expression correlates with tumor aggressiveness and poor outcomes; induces T-cell exhaustion.	[26]
	TIGIT	Inhibits T and NK cell function; acts synergistically with PD-1; an emerging therapeutic target.	[154]
Tumor Mutation Burden	TMB	High TMB may generate more neoantigens, theoretically associated with better immunotherapy response.	[155]
Tumor-infiltrating Lymphocytes	TILs	High density of TILs, particularly CD8⁺ T cells, is positively correlated with better prognosis and response to immunotherapy.	[156]
Metabolism-related Biomarkers	LDHA	High expression/titer is associated with poor prognosis, brain metastasis, and is a convenient prognostic indicator.	[86]
	IDO1/GCH1	High expression creates an immunosuppressive microenvironment via tryptophan depletion; potential therapeutic target.	[96]
	FASN	Overexpressed in non-basal-like (e.g., LAR) TNBC; linked to fatty acid metabolism reprogramming and may indicate resistance to ICIs.	[157]
Metabolism-related Biomarkers	ZBTB28 Methylation	Promoter hypermethylation leads to silencing, resulting in upregulation of CD47/CD24 (“don’t eat me” signals) and inhibited macrophage phagocytosis.	[31]
	LGALS2 Hypomethylation	Promoter hypomethylation causes overexpression, promoting M2-like macrophage polarization via the CSF1/CSF1R axis.	[32]
	PSMB9 Methylation	Hypermethylation suppresses transcription, leading to defects in immunoproteasome assembly and antigen presentation.	[96]
Non-coding RNA	miRNAs (e.g., miR-200 family, miR-34a, miR-142-5p)	Function as oncogenes or tumor suppressors, regulating PD-L1, EMT, and T-cell function; potential prognostic and predictive biomarkers.	[158]
	lncRNAs (e.g., LINC00665, HOTAIR, KRT19P3)	Regulate immune checkpoints, metabolic reprogramming, and T-cell infiltration; expression levels correlate with prognosis and treatment response.	[159]
Cell-subset Biomarkers	CD163⁺/CD68⁺ M2-TAMs	High infiltration is significantly associated with reduced DFS, DMFS, and OS, indicating an immunosuppressive TME.	[160]
	Tregs(Foxp3⁺)	High infiltration is associated with immune evasion and therapy resistance.	[161]
	MDSCs(CD11b⁺CD33⁺HLA-DR⁻)	High proportion correlates with immunosuppression, metastasis, and poor prognosis.	[162]

Safety management of combination immunotherapy in TNBC represents a cornerstone of precision oncology. With the integration of PD-1/PD-L1 inhibitors into standard chemotherapeutic regimens, the distinct safety profile—characterized by multi-organ irAEs affecting skin, endocrine glands, liver, and lungs—poses unprecedented challenges for clinical practice. However, the ultimate objective remains to maximize antitumor efficacy while minimizing treatment-related toxicity through evidence-based risk stratification, proactive monitoring, and mechanistically guided interventions.

Summary and outlooks

In conclusion, the convergence of mechanistic immunology and clinical innovation has ushered TNBC treatment toward an era of precision and efficacy. Yet, the profound heterogeneity of TNBC and the dynamic interplay between tumor and immune components necessitate continued exploration. Addressing these challenges through interdisciplinary collaboration—spanning basic science, translational research, and clinical trials—will be critical to maximizing therapeutic benefit and overcoming the barriers posed by TNBC’s resilient immune evasion tactics.

Precision molecular subtyping systems serve as the foundation for enhancing immunotherapy outcomes in TNBC. Integrating genomic, transcriptomic, proteomic, and metabolomic data into TNBC classification establishes a comprehensive molecular subtyping framework. Developing a standardized workflow for multi-omics subtyping—including data acquisition, preprocessing, and analytical methodologies—is critical to ensuring cross-center consistency and enabling precise therapeutic decision-making tailored to individual tumor biology.

A comprehensive understanding of the molecular and cellular mechanisms underlying immune evasion—such as antigen presentation defects, dysregulation of immune checkpoints, metabolic reprogramming, and epigenetic modifications—provides critical insights for the development of novel therapeutic strategies. The TME, with its complex network of immune cells (including Tregs, MDSCs, M2 macrophages, DCs, TANs) and stromal components like CAFs, remains a central hub of investigation. Targeting these elements through combination immunotherapies, including bispecific antibodies, LAG-3 inhibitors, CD47-SIRPα blockers, and ICIs, offers a promising avenue to overcome resistance and enhance anti-tumor immunity. Combination therapy remains a central paradigm in future oncological strategies, leveraging synergistic interactions between mechanistically distinct agents while simultaneously targeting multifaceted immunosuppressive networks to undermine tumor immune evasion capacity.

Moreover, the integration of multi-omics data to refine predictive biomarkers—such as PD-L1 expression, TMB, and TIL density—will be crucial for identifying responders and guiding personalized treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution and comprehensive identification of rare immune cell subsets that evade detection by conventional methods. Through pseudotime trajectory analysis and cell-cell communication mapping, it deciphers dynamic immune cell differentiation processes and tumor-immune crosstalk patterns. Furthermore, scRNA-seq data facilitates the construction of prognostic models and therapeutic response prediction frameworks. Spatial transcriptomics, by preserving cellular spatial coordinates, reveals regional immune cell distribution within the tumor microenvironment. Analyzing tumor-immune spatial proximity elucidates immune evasion mechanisms, while spatially resolved gene expression profiling localizes key immunomodulatory markers to guide targeted therapy. Proteomic data, though lacking single-cell resolution and spatial context, directly reflects molecular functionality by validating transcriptomic findings through post-translational modifications and protein interaction networks. This layer identifies regulatory mechanisms and druggable targets, enabling the development of precision therapeutics such as small-molecule inhibitors and monoclonal antibodies. Integrating scRNA-seq, spatial transcriptomics, and proteomics into a multi-omics framework provides a multidimensional dissection of TNBC immune landscapes—from gene expression dynamics to cellular heterogeneity, spatial organization, and functional validation—while facilitating the discovery of novel biomarkers. This integrative approach offers transformative insights for designing innovative TNBC therapeutic strategies. Moreover, leveraging artificial intelligence (AI) and machine learning algorithms to harmonize genomic, transcriptomic, proteomic, and clinical imaging data will establish more precise TNBC immunotyping and therapeutic response prediction models, ultimately advancing truly personalized oncology care.

Furthermore, elucidating resistance mechanisms and implementing rigorous toxicity management constitute indispensable components for optimizing TNBC therapeutic strategies. A multidimensional dissection of resistance mechanisms is required, spanning tumor cell-autonomous defects (e.g., antigen presentation machinery disruption), immunological microenvironment alterations (e.g., suppressive immune cell infiltration, compensatory immune checkpoint upregulation), and systemic factors (e.g., gut microbiota dysbiosis). Targeting these mechanisms demands innovative approaches such as bispecific antibodies, next-generation immune agonists, and adoptive cellular therapies, alongside optimized treatment regimens like intermittent dosing schedules or sequential drug rotation to delay resistance emergence. With expanding immunotherapy applications and combination strategies, managing irAEs has become increasingly critical. Establishing predictive biomarker panels for irAE risk stratification and implementing standardized yet personalized toxicity management protocols are essential to ensure treatment safety and patient compliance.

The future of TNBC immunotherapy hinges on achieving deep mechanistic insights into its complex tumor-immune ecosystems and profound interpatient heterogeneity, while strategically bridging fundamental research with clinical translation to innovate combination therapeutic paradigms. Leveraging cutting-edge technologies—including multi-omics integration, spatially resolved transcriptomics, and artificial intelligence-driven analytics—will enable precise patient stratification and dynamic therapeutic adaptation. Concurrently, unraveling resistance mechanisms through tumor-immune co-evolution models and establishing predictive biomarker systems for irAEs will be pivotal in optimizing therapeutic indices. This holistic approach, combining mechanistic discovery, technological innovation, and precision medicine frameworks, holds transformative potential for overcoming TNBC’s therapeutic challenges. By unraveling the intricate crosstalk between cancer cells and the immune system, researchers and clinicians are steadily transforming TNBC from a once treatment-refractory malignancy into a disease with increasingly viable and durable therapeutic options.

Author contributions

YYY conceptualized the study, drafted the initial manuscript, critically revised the content, and gave final approval to the manuscript. WWD supervised the writing process, provided methodological guidance, and gave intellectual refinement to the academic content.

Funding

This work was supported by grants from Clinical Scientist Program of Sichuan Cancer Hospital (YB2022003), Key Projects of Sichuan Natural Science Foundation (2022NSFSC0051), Key R&D Plan of Chengdu Science and Technology Bureau (2021-YF05-01659-SN).

Data availability

Not applicable.

Declarations

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Competing interests

The authors declare no competing interests.