Beyond TLS Presence: Functional States (TLS-A/B/C) Integrating Maturity, Location, and Immune Context

1. Introduction

Immune responses are classically localized within primary lymphoid organs, such as the bone marrow and thymus, and secondary lymphoid organs, including lymph nodes, the spleen, and mucosa-associated lymphoid tissue, where lymphocyte development and antigen presentation are organized in a highly structured manner. However, under conditions of chronic inflammation, autoimmunity, or cancer, lymphoid aggregates may arise in non-lymphoid tissues. These ectopic lymphoid aggregates, referred to as tertiary lymphoid structures (TLS), recapitulate certain features of secondary lymphoid organs and may organize local immune responses [1,2,3].

1.1. Definition, Formation and Architecture of Tls

TLS are ectopic, non-encapsulated aggregates of immune cells that develop de novo in non-lymphoid tissues in response to persistent antigenic stimulation and chronic inflammation [2,3,4]. These structures frequently arise in perivascular niches and display varying degrees of organization, ranging from loose lymphocytic aggregates to highly ordered structures with partial or well-defined segregation of B-cell and T-cell zones. In more mature TLS, the B-cell area is typically located more centrally and may contain germinal centers (GCs), which serve as sites of B-cell proliferation, selection, and maturation. In turn, the more peripheral T-cell zone contains mature dendritic cells (DCs), which participate in antigen presentation to T lymphocytes, as well as fibroblastic reticular cells (FRCs), which form the stromal scaffold of this niche and support T-cell maintenance [4,5,6]. Endothelial remodeling and the ectopic expression of lymphoid chemokines, such as CCL19, CCL21, and CXCL13, promote the formation of high endothelial venules (HEVs), which play a key role in the recruitment of lymphocytes and DCs by regulating their trafficking from the bloodstream into TLS and contributing to the organization of their architecture [1,4]. In the microenvironment of solid tumors, TLS may therefore constitute specialized sites of local activation and coordination of antitumor immune responses [4].

1.2. Tls in Solid Tumors

TLS have been identified across a broad spectrum of solid tumors, including those of the lung, colon, ovary, kidney, stomach, esophagus, and liver [7,8]. However, their biological and clinical relevance varies across tumour types and appears to depend on maturation, localization and cellular composition [9].

An increasing number of observations indicate that mature TLS, particularly structures resembling secondary follicles and containing GCs, are of particular clinical relevance, as in selected solid tumors they have been associated with more favorable outcomes, including prolonged progression-free survival (PFS) and overall survival (OS). In addition, meta-analytic and cohort-based data suggest that TLS may reflect a more immunologically active tumor microenvironment and may serve as a useful predictive biomarker of response to immunotherapy, especially immune checkpoint inhibitor (ICI) treatment [10,11]. This is particularly relevant in the context of B-cell immunity, as TLS may support local antigen presentation, germinal center reactions, and B–T cell cooperation, although the extent of this activity may vary across individual structures [12].

1.3. From Tls Presence to Functional Interpretation

Previous approaches to TLS have focused primarily on quantitative parameters and simple maturity scales, which only partially capture the biological complexity of these structures. Moreover, depending on tumor type and microenvironmental organization, intratumoral and peritumoral TLS may be associated with different clinical outcomes, and TLS abundance alone does not always translate into the same biological effect. This indicates that conventional quantitative and spatial parameters are insufficient for a comprehensive interpretation of the significance of TLS in solid tumors [2]. An important dimension of TLS heterogeneity is their cellular composition, as B lymphocytes, plasma cells, dendritic cells, macrophages, neutrophils, and myeloid-derived suppressor cells present within these structures may exert distinct and sometimes opposing biological effects. Accordingly, the functional significance of TLS depends not only on their presence and architecture, but also on the dominant cellular subsets and the nature of their interactions [12]. An additional, often overlooked dimension of this heterogeneity is the local cytokine, chemokine, and metabolic context, which may either promote TLS induction and maturation or direct them toward immature, dysfunctional, or suppressive states [6]. Furthermore, TLS are not static structures, but rather dynamic immunological niches whose organization, cellular composition, and functional activity may be reshaped over time under the influence of tumor microenvironment evolution and treatment [6]. Taken together, these observations suggest that TLS in solid tumors do not represent a uniform histological category, but rather form a functional spectrum with diverse biological and clinical properties, thereby justifying the need for a more integrated classification [2,6].

1.4. Aim of the Review

The aim of this review is to synthesize current biological, spatial, and clinical evidence on TLS in solid tumors and to reframe TLS not as a purely binary, density-based, or maturity-based biomarker, but as a spectrum of functional states shaped by architectural organization, anatomical location, and immune context. Based on this integrative perspective, we propose a pragmatic, pathology-oriented conceptual framework that groups TLS into three simplified functional states (TLS-A, TLS-B, and TLS-C), intended not as a definitive biological taxonomy, but as an conceptual model for organizing existing evidence across heterogeneous tumor settings. We further outline the rationale for a “functional TLS score” that combines histopathologic and molecular features and may serve as a foundation for future translational studies, biomarker refinement, and more standardized TLS reporting.

2. Methodological Approach

This narrative and conceptual review was based on a targeted literature search conducted in PubMed/MEDLINE, Scopus and Google Scholar between 1 February 2010 and 1 April 2026. The final update was performed on 1 April 2026, which served as the cut-off date for included literature. Search terms combined phrases related to “tertiary lymphoid structures”, “TLS”, “solid tumours”, “tumour microenvironment”, “B cells”, “germinal centres”, “TLS maturation”, “intratumoral TLS”, “peritumoral TLS”, “immunotherapy”, “multiplex immunohistochemistry”, “spatial transcriptomics”, “digital pathology” and “TLS gene signature”.

We included original studies, translational analyses, clinical cohorts and recent reviews addressing TLS morphology, maturation, localization, immune composition, prognostic or predictive relevance, therapeutic modulation, or assessment methods in solid tumours. We excluded studies unrelated to solid tumours, articles without relevant TLS-specific data, non-English publications, conference abstracts with insufficient detail, duplicates, and studies focused exclusively on non-neoplastic inflammatory or autoimmune diseases, unless they provided essential mechanistic context.

The evidence was synthesized around three axes: structural maturity, spatial localization and functional immune context. On this basis, we proposed a pathology-oriented framework dividing TLS into TLS-A, TLS-B and TLS-C, and a modular Functional TLS Scoring Framework integrating histopathology, TLS burden, localization, immune-cell composition and optional molecular signatures. The framework is intended as a conceptual model for translational studies and standardized reporting, rather than as a validated clinical scoring system.

3. Biology of Tls and B-Cell Immunity in Solid Tumours

TLS are formations that develop in chronically inflamed tissues including the tumor microenvironment (TME). They are spatially organized compartments similar to secondary lymphoid organs (SLO), but developed in distinct pathological conditions. Their presence suggests a continuous local immune response. The structural and cellular heterogeneity of TLS leads directly to diverse roles in tumour progression and anti-tumour immunity. Therefore understanding the mechanisms regulating TLS formation, maturation and function are critical.

3.1. Induction and Development of Tls

TLS development is an inflammatory reaction to various conditions such as infections, autoimmune diseases, tissue transplantation, and malignancies. This is one of the key differences observed in TLS formation in contrast to SLO, where development occurs in a sequential manner during embryogenesis. Moreover, SLO are characterized by clearly segregated T and B-cell compartments, whereas TLS display considerable heterogeneity in their development, ranging from a diffuse infiltrate composed only of T cells to T and B cell accumulations without distinct compartmentalization, to mature and well organized structures containing germinal centers (GC) [13,14,15].

TLS are composed predominantly of T cells (CD3+), B cells (CD20+), fibroblasts, follicular dendritic cells (FDCs, CD21+), macrophages, and high endothelial venules (HEVs) expressing peripheral node addressin (PNAd). The main factor in initiating TLS formation is chronic inflammation, leading to infiltration by leukocytes. Subsequent activation of these cells, together with the activation of stromal cells, causes the production of a wide variety of pro-inflammatory and pro-lymphangiogenic cytokines. In addition, stromal cells undergo phenotypic polarization in the TME [13,14,15,16].

Cytokine-recruited lymphoid tissue inducer (LTi) cells trigger TLS formation through interaction with lymphotoxin beta receptor (LTβR) and tumor necrosis factor receptor 1 (TNFR1) expressed on mesenchymal stromal cells. LTi cells subsequently give signals to acquire lymphoid tissue organizer (LTo) phenotype by stromal cells mainly through lymphotoxin α1β2 (LTα1β2) and tumor necrosis factor (TNF) signalling. This interaction induces LTo cells to produce adhesion molecules such as vascular endothelial growth factor C (VEGF-C), vascular cell adhesion molecule 1 (VCAM-1) and intercellular adhesion molecule 1 (ICAM-1), as well as chemokines, including IL-7, C-C chemokine ligand type 12 (CCL12), CCL19, CCL21, and C-X-C chemokine ligand type 13 (CXCL13). These molecules are important for HEV formation, lymphocyte recruitment and B and T cells compartmentalization in TLS. In addition, it has been reported that a minimum density of 70 B cells/mm2 is needed to induce lymphoneogenesis and TLS formation [13,14,16].

Stromal elements are particularly important for TLS development in the tumor setting. Fibroblastic reticular cell-like populations and cancer-associated fibroblasts (CAFs) have the potential to gain LTo properties and contribute to chemokine production and structural support of TLS. Furthermore, endothelial remodeling and the generation of specialized perivascular niches promote lymphocyte recruitment and the formation of high endothelial venules (HEVs), thereby further enhancing lymphoid neogenesis inside tumors [13,14,17].

Notably, TLSs have dual roles, pro-tumorigenic and anti-tumorigenic, depending on the composition of immune cells in the tissue [13,14]. Mature TLS containing germinal centers (GC), dendritic cells (DC), and T and B cells are associated with improved patient survival and enhanced responses to immunotherapy. In contrast, TLS with the presence of regulatory T cells (Treg, FOXP3+) or immature HEVs follow poor immune infiltration of tumor and increased metastatic potential. Moreover, intratumoral B cells may contribute to tumor growth and progression through the production of immunosuppressive cytokines, such as IL-10 [15,16,17].

3.2. Structural Maturation of Tls (From Aggregates to Germinal Center-Tls)

There are three tiers of organizational structure for TLS which relate to the stages of lymphoid neogenesis. The initial stage, referred to as early TLS (E-TLS), is characterized by dense aggregates of B (CD20+) and T (CD3+) lymphocytes that do not contain FDC networks, HEVs, or GCs and are negative for both CD21 and CD23 expression. These structures are frequently detected during the early stages of tumorigenesis. E-TLS also contains Tregs and immunosuppressive macrophages that help to establish a tolerogenic microenvironment. The presence of E-TLS in a tumour is related to both a poorer patient prognosis and reduced responsiveness to treatment with immune checkpoint inhibitors (ICIs). Finally, since E-TLS represent a transitional developmental stage, their specific function in anti-tumor immunity is still unclear [13,18,19,20].

The next organization stage is primary follicle-like TLS (PFL-TLS), which represent partial compartmentalization into B-cell and T-cell areas. These dense lymphocyte aggregates create a CD21+ FDC network but lack CD23 expression. HEVs may be present but are often irregular or poorly developed. Therefore, only a limited amount of B-cell class switching occurs within PFL-TLS, and they do not facilitate the maturation of high-affinity antibodies. In addition, tumors that contain PFL-TLS tend to display transient anti-tumor immune responses [13,18,19,20].

The last stage, corresponding to TLS maturity, is secondary follicle-like TLS (SFL-TLS). They display a highly structured architecture, including compartments containing CXCR5+ B-cell follicles, CCR7+ T-cell zones, active GC, CD21+CD23+ FDC networks, and HEVs. Functionally, SFL-TLS shares key features with SLO, particularly the ability to support the activation of GC-dependent B cells to produce high-affinity antibodies targeting tumor antigens. In addition to promoting humoral immune responses, SFL-TLS also promotes effector T-cell and NK-cell responses. In many cancer types, the detection of SFL-TLS is related to a positive clinical prognosis and enhanced responsiveness to ICIs, highlighting their status as the most biologically active TLS subtype [13,18,19,20].

Some researchers have developed an alternative TLS classification into immature and mature stages based on positive CD23 staining observed in immunohistochemistry (IHC) or immunofluorescence (IF) [13]. The mature TLS was defined by the presence of CD23+ FDCs, BCL6+ GC B cells and PNAd+ HEVs while immature TLS only contained CD20+ B cells (Meylan et al.) [13,21]. In addition, Ahn et al. described an upregulation of 13 proteins (CYRIA, ENG, GPI, HLA-A, LIMA1, LRBA, LST1, MCAM, MGLL, NID1, NME2, PIK3R1, STARD7) in the mature TLS compared to the immature TLS, suggesting their potential as biomarkers of TLS maturation [13,22].

Immature TLS may also trigger effective immune responses, but these often depend on T-cell exhaustion, inflammatory signaling, or the immunosuppressive nature of the TME. Interestingly, in immature TLS, B cells tend to be less numerous and usually produce antibodies with limited affinity. In contrast, mature TLS contain BCL6+ B cells within GCs, where selective activation and expansion of specific B-cell clones occur, promoting immunoglobulin class switching and somatic hypermutation. As a result, the antibodies produced exhibit high affinity and demonstrate more effective immune functions [13].

The organization of T and B lymphocytes into separate compartments depends on increased chemokine secretion. Notably, the CCL19-CCR7 or CCL21-CCR7 axis are essential for guiding T-cell zones, while the CXCL13-CXCR5 pathway plays a similar role for B-cell areas [19]. Studies indicate that HEVs and DCs are present throughout all TLS maturity stages, as well as in unorganized regions rich in lymphocytes, suggesting that these components might have a meaningful role in TLS development [18].

Thus, TLS maturation should be regarded not only as a morphological continuity, but also as a functional transition from poorly organized lymphoid aggregates to GC-active immune niches that actively support local adaptive immune responses. This distinction provides a biological reason to differentiate functionally immature TLS from mature GC-positive TLS with increased immunological activity and clinical relevance.

3.3. B-Cell Immunity Within Tls

Mature B cells in TME display various degrees of differentiation, including phenotypes that are characteristic of the marginal zone, germinal center, and extrafollicular compartments. The activation of mature B cells in peripheral lymphoid tissues is associated with or occurs independently of T cells. TLS provide a local niche for B-cell activation, clonal expansion and differentiation toward plasma-cell and memory-cell phenotypes, although the quality of this response depends strongly on TLS maturation and immune context [15,18,23,24,25].

Besides GCs, B-cell activation also occurs through extrafollicular pathways. In this case, naive B cells are stimulated in association with FDCs and CD4+ T peripheral helper (Tph) cells. This leads to the production of atypical memory B cells and short-lived plasma cells. The antibodies synthesized under these conditions are characterized by lower affinity and autoreactivity, playing a diminished role in effective anti-tumor immunity [23].

B cells in TLS also function as professional antigen-presenting cells (APCs). By expressing major histocompatibility complex (MHC) class II molecules and costimulatory molecules like CD80 and CD86, they are able to activate helper and cytotoxic T cells, thereby contributing to the coordination of anti-tumor immunity in the TME [15,23,24,25].

The overall impact of tumor-infiltrating B cells (TIBs) is complex and may be regarded as a “double-edged sword”. TIBs may contribute to the anti-tumor responses through the secretion of cytokines (TNF, IL-2, IL-6, IFN-γ), recruitment of effector cells, and the production of antibodies capable of mediating different effector functions such as opsonization, cell-mediated cytotoxicity, and complement activation [23,24,25]. However, certain subtypes of TIBs are known to have immunosuppressive activity. Among these, regulatory B cells (Bregs) have been reported to suppress immune responses through the release of varied mediators such as anti-inflammatory cytokines (IL-10, IL-35, TGF-β), inhibitory ligands (programmed death-ligand 1 (PD-L1), Fas ligand (FasL), TNF-related apoptosis-inducing ligand (TRAIL)), and metabolically active compounds (gamma-aminobutyric acid (GABA), adenosine (ADO), indoleamine 2,3-dioxygenase (IDO)). The immunosuppressive role of Bregs may result in the inhibition of effector T cells and NK cells function while enhancing the expansion of Tregs, and suppressing APCs activity. Consequently, they may stimulate tumor growth, proliferation, and immune evasion. The presence of Bregs is associated with unfavorable clinical outcomes and resistance to therapy [18,23,24,25,26,27].

Moreover, the functional role of TIBs is linked to TLS maturation. B cells in immature TLS are more frequently associated with the formation of a suppressive TME, whereas B cells in mature TLS (with GCs) help to provide effective anti-tumor responses. The clinical implications of B-cell infiltration depend on the tumor type, as well as the ratio of effector and regulatory B cells. In several malignancies, including breast cancer, melanoma, and high-grade serous ovarian carcinoma (HGSC), the spatial proximity of B cells and CD8+ T cells in the tumor correlates with favorable prognosis. Similarly, the presence of mature TLS and aggregations of B cells correlate with improved OS and enhanced efficacy of immune checkpoint blockade (ICB) therapy in melanoma, renal cell carcinoma, and soft tissue sarcomas. However, the enrichment of immunoregulatory cells (Bregs and Tregs) contributed to shorter survival without metastases among cancer patients [15,23,24,25,28].

In summary, the functional role of B cells in tumors is largely dependent on context and closely associated with the maturation status of TLS. Since immature TLS are often connected with immunosuppressive or ineffective humoral responses, mature TLS containing GCs are sites of active B-cell differentiation, affinity maturation, and antibody production. Thus, the qualitative status of B-cell organization in TLS, rather than only their presence, is a key determinant of their role in anti-tumor immunity.

3.4. Cellular Ecosystem of Tls

3.4.1. Tfh-B Cell Axis

TLS represent a complex cellular system where specialized immune subsets play a vital role. Among these, the T follicular helper (Tfh)-B cell axis is crucial for GCs function. Tfh cells are a distinct population of CD4+ T cells that are fundamental for GC development, affinity maturation, B-cell differentiation into plasma cells and memory B cells, and the production of high affinity antibodies. B-cell lymphoma-6 (BCL6) is a transcriptional regulator of Tfh cell differentiation and functions. The factor induces the expression of C-X-C chemokine receptor type 5 (CXCR5), which is the selective marker for Tfh cells and guides their migration toward B-cell follicles [20,29,30,31,32].

Furthermore, the activation and differentiation of B cells are driven by Tfh cells through interleukin-21 (IL-21) secretion and the expression of CD40 ligand (CD40L, CD154). The binding of Tfh cells with B cells via the CD40L-CD40 signaling pathway plays a key role in initiation and maintenance of GC reactions, like somatic hypermutation (SHM), class-switch recombination (CSR), and plasma cell differentiation. TLS primarily produce IgG and IgA antibodies, while IgM is less abundant, indicating that B cells in TLS are highly differentiated. Additionally, Tfh cells are also involved in IgE production. Consequently, a higher number of Tfh cells is considered a marker of GC activity in TLS [20,29,32].

However, the involvement of Tfh cells in the cancerous processes is conditional. In certain malignancies, such as gastric cancer and osteosarcoma, a higher prevalence of Tfh cells is associated with immunosuppressive effects and tumor progression. Moreover, dysfunctional Tfh cells may impair the maturation and differentiation of B cells in osteosarcoma patients. In gastric cancer, increased Tfh cell number, together with elevated regulatory B cell (Breg) count, can indicate immune suppression, lymphatic metastasis, and poor clinical outcomes. Therefore, these findings suggest that, despite their elementary role in managing humoral immune responses, Tfh cells may also contribute to an immunosuppressive TME and act as a negative prognostic marker in specific cancer types [30,33].

In mature TLS, B-cell immunity is not limited to antibody production. B lymphocytes can capture antigens through the B-cell receptor (BCR), process it intracellularly, and present antigen-derived peptides on major histocompatibility complex class II (MHC II) molecules to CD4+ T cells, particularly Tfh cells [14,23]. Cognate interactions between Tfh cells and B lymphocytes, mediated by the T-cell receptor (TCR)–peptide–MHC II axis, cluster of differentiation 40 ligand (CD40L)–cluster of differentiation 40 (CD40) signaling, inducible T-cell co-stimulator (ICOS)-dependent costimulation, and interleukin-21 (IL-21), initiate and maintain germinal center (GC) reactions [13,20,29,32]. Within this niche, B-cell lymphoma 6 (BCL6) sustains the GC and Tfh-cell programs, whereas activation-induced cytidine deaminase (AID) enables somatic hypermutation (SHM) and class-switch recombination (CSR), resulting in affinity maturation, plasma cell differentiation, and memory B-cell generation [23,29,31,32]. Therefore, GC-positive TLS should be interpreted as active sites of local adaptive immune maturation rather than passive lymphoid aggregates [13,17]. In cancer-associated TLS, these processes may support the generation of anti-tumor antibody-producing plasma cells and help explain why mature GC-positive TLS often carry stronger prognostic and predictive significance than immature lymphoid aggregates [18,21,23].

3.4.2. Regulatory T Lymphocytes (Treg)

Treg lymphocytes represent a specialized T cell subset with the CD4+CD25+FoxP3+ profile, that infiltrates tumor-associated TLS (TA-TLS) and contributes to the suppression of anti-tumor immune response in the TME, thereby promoting tumor growth. The presence of Treg cells correlates with unfavorable prognosis in several malignancies, such as non-small cell lung cancer (NSCLC), where they inhibit T cell activation [34,35].

In Kras/p53-driven lung adenocarcinoma (LUAD), activated Treg cells restrained DC-T cell interactions. Although T cell proliferation is restored in Treg depletion, resulting in tumor regression. In TLS, Treg cells frequently express CD62L, a homing molecule that mediates their migration and accumulation in these structures [20,34].

Importantly, Treg cells located outside TLS may exhibit an even greater immunosuppressive potential. These Tregs often demonstrate overexpression of multiple immune checkpoint proteins (ICPs) and immunosuppressive cytokines, suggesting that non-TLS Tregs may show a more immunosuppressive capacity compared to TLS-Tregs [34].

From a therapeutic standpoint, selective targeting of immunosuppressive components in TA-TLS (like Treg cells) represents a highly promising approach for improving local anti-tumor immunity without disrupting systemic immune homeostasis [34,35].

3.4.3. Regulatory B Lymphocytes (Breg)

Bregs represent a specialized and relatively small subset of B cells, which are characterized by immunosuppressive functions, unlike the antibody-producing B cells. Instead of enhancing humoral immunity, Bregs inhibit T cell differentiation and promote tumor growth. They play a critical role in the regulation of immune responses especially in chronic inflammation, serious infections, autoimmune disorders, and malignancies. Functionally, Bregs suppress cytotoxic CD8+ T cells while inducing Tregs, thus establishing an immunosuppressive TME [20,33].

The functional role of Bregs is regulated by T cell immunoglobulin and mucin domain 1 (TIM-1), whose signaling is essential for optimal production of IL-10 in Breg cells. Accumulation of TIM-1+ Breg cells is observed in multiple tumor types (hepatocellular carcinoma, esophageal squamous cell carcinoma, cervical cancer, gastric cancer) and is frequently associated with advanced disease stage, metastasis, suppression of anti-tumor immune responses and poor prognosis [33].

Immunosuppressive effects exerted by Bregs occur mostly through the secretion of cytokines, like interleukin-10 (IL-10) and transforming growth factor-β (TGF-β), that facilitate the regulation of inflammatory responses and promote Tregs differentiation. Additionally, Bregs can suppress effector T cells directly by expressing surface receptors, like PD-L1, which allows them to act on effector T cells via checkpoint pathways such as PD-1/PD-L1 and VISTA-PSGL-1 [20,33].

3.4.4. Plasma Cells (Iga/igg Dependents)

Plasma cells (PCs) in TLS show considerable diversity, with the predominance of one particular immunoglobulin class, especially IgG or IgA, depending on the affected tissue and pathological state. Both IgG+ and IgA+ plasma cells have the capacity to migrate from TLS towards tumor stroma through tracks rich in fibroblasts. This process is controlled in part by the CXCL12-CXCR4 pathway, in which fibroblasts associated with cancer release CXCL12, creating chemokine gradients for attracting plasma cells [21,23].

IgG+ plasma cells commonly predominate in inflamed tumors. For instance, in colorectal liver metastases (CRLM), the SFL-TLS regions contain IgG+ plasma cells that contribute to anti-tumor antibody activity. In contrast, the abundance of IgA+ plasma cells is observed in TLS-deficient tumors. Generally the presence of IgG-producing plasma cells correlates with a more favorable prognosis, including prolonged progression-free survival (PFS), particularly during ICI therapy. IgG+ plasma cells in TLS produce antibodies that opsonize malignant cells, thus initiating anti-tumor immune responses. Additionally, tumor cells can be eliminated directly due to IgG-mediated activation of antibody-dependent cellular cytotoxicity (ADCC) [20,21,23].

Notably, not all IgG subclasses are biologically equal. In fact, IgG1 and IgG3 are associated with effective anti-tumor responses, including in renal cell carcinoma (RCC), due to their strong interaction with the Fcγ receptors, thus activating effector pathways. However, IgG4 is described as immunosuppressive, due to their poor binding capacity for Fcγ receptors. In some malignancies, such as melanoma, prostate cancer, colorectal cancer, and esophageal cancer, IgG4 can suppress the activity of other antibody subclasses and promote macrophages transition to an immunosuppressive M2-like type, contributing to worse prognosis [20,23,37].

In contrast, plasma cells producing IgA are typical for mucosal epithelial tumors, like ovarian, colorectal, gastric, and liver cancers. IgA is a key factor in protecting the mucosa from various pathogens and antigens, thus the presence of IgA+ plasma cells is often associated with chronic inflammation and localized humoral immunity [20,23,36].

However, the prognostic role of IgA+ plasma cells seems to be conditional and, in many malignancies, is connected with poor clinical outcomes. An accumulation of IgA+ plasma cells is associated with adverse impact on the course of prostate cancer, esophageal cancer, hepatocellular carcinoma (HCC), and colorectal cancer (CRC), presumably due to their immunosuppressive potential. These cells may disrupt anti-tumor responses by secreting inhibitory cytokines like interleukin-10 (IL-10) and also by expressing immune checkpoint molecules, including programmed death-ligand 1 (PD-L1), leading to T cell dysfunction in the TME. Regardless, in certain tumor types, such as ovarian cancer, IgA+ plasma cells may play a protective role. In this case, IgA antibodies are capable of interacting with polymeric immunoglobulin receptors (pIgR) present on tumor cells, sensitizing them to T cell-mediated cytotoxicity [20,21,23,37].

3.4.5. Myeloid Compartment: M2-like Macrophages, Neutrophils, and Dendritic Cells

Myeloid cells, including macrophages, myeloid dendritic cells (mDCs), monocytes, and granulocytes, constitute a significant fraction of the TME and play an essential role not only in the onset of innate immunity, but also in the modulation of tumor development and metastasis [38,39]. The TME is characterized by a rich supply of cytokines, chemokines, and metabolites that can affect the proliferation, differentiation, maturation, and survival of circulating and infiltrating myeloid cells. These signals frequently reprogram myeloid populations toward immunosuppressive and pro-tumorigenic properties [40].

Tumor-associated macrophages (TAMs) are the alternatively activated macrophages that mainly exert anti-inflammatory effects, usually in an M2-like phenotype. Tumor progression leads to M2 status in macrophages following exposure to cytokines in the TME [39]. M2 macrophages promote tumor cell proliferation, angiogenesis, invasion, metastasis, and exert strong immunosuppressive effects [39,42]. Phenotypically, they display increased expression of scavenger receptors like CD163 and CD206, and enhanced production of anti-inflammatory cytokines, angiogenic factors, and proteases [39,41]. Most of all, TAMs are highly plastic and exist on a spectrum between pro-inflammatory, anti-tumor M1-like states, and immunosuppressive, pro-tumor M2-like states [41].

Similarly, tumor-associated neutrophils (TANs) can be divided into anti-tumorigenic (N1) and pro-tumorigenic (N2) subsets in response to the stimulation of local cytokines and chemokines [39,43]. N2 neutrophils promote tumor progression via angiogenesis and metastasis, in part through secretion of matrix metalloproteinases (MMPs). Moreover, the role of TANs in tumor dissemination is associated with the formation of neutrophil extracellular traps (NETs), which might capture circulating tumor cells (CTCs) to promote metastasis. NETs activate Toll-like receptor (TLR) signaling that stimulates tumor cell migration, adhesion, invasion, and colonization. A distinct subset of neutrophils, low-density neutrophils (LDNs), has been identified in the mononuclear cell fraction of cancer patients. These cells mediate immunosuppressive functions including immature myeloid-derived suppressor cells (MDSCs), in particular polymorphonuclear MDSCs (PMN-MDSCs), which share features with N2 TANs and contribute to local immune suppression in the TME [39,40].

DCs are professional antigen-presenting cells required for initiation and regulation of T cell-mediated cancer immunity, thereby linking innate and adaptive immune responses [39,40]. Of particular importance among DC subsets are conventional type 1 dendritic cells (cDC1) due to their ability to cross-present tumor antigens and prime cytotoxic T lymphocyte responses [38]. This function is largely mediated by the secretion of interleukin-12 (IL-12), which supports stronger CD8+ T cell immune responses. However, DC function is often impaired in the TME. For instance, macrophages secrete interleukin-10 (IL-10) which suppresses IL-12 expression by DCs and thus T cell cytotoxicity. DCs are a key target in cancer immunotherapy because of their central role in antigen presentation. In addition, DCs vaccination approaches are underway to improve anti-tumor immunity [41].

The interplay between myeloid cell populations in the TME is well coordinated and plays an important role in immune evasion. Tumors secrete factors such as C-C motif chemokine ligand (CCL) 2 and colony-stimulating factor 1 (CSF-1) that attract myeloid cells from the bone marrow. Hypoxic conditions can intensify this process through hypoxia-inducible factor (HIF) that supports the production of a variety of chemokines and cytokines [39,41]. Such signals promote the accumulation of immunosuppressive populations like TAMs and MDSCs. Cancer cells produce secretome that also drive the differentiation of myeloid progenitors to MDSCs, which facilitate macrophages and neutrophils switch to anti-inflammatory, pro-tumorigenic status. MDSCs and M2 TAMs induce generation of Tregs and suppress activity of cytotoxic T and NK cells, thus reinforcing mechanisms of immune escape [39].

Myeloid cells play a key role in tumor progression and immune suppression, making them attractive therapeutic targets. Currently, strategies include the use of myeloid cells as anti-tumor effector cells, depletion of myeloid cell populations or blockage of their recruitment into the TME, and reprogramming of myeloid cells to restore their anti-tumor functions [39,44]. These approaches are being actively explored and have great potential to improve outcomes in cancer immunotherapy.

In conclusion, TLS are very dynamic and heterogeneous immune niches within the TME, whose biological importance depends on their structural maturity and composition of the microenvironment. Transition from immature lymphoid aggregates to organized TLS with GCs is associated with a shift from inefficient or suppressive immune activity to effective local adaptive immunity. In particular, the interaction between B cells, Tfh cells, and stromal components define the outcome of TLS functionality, affecting both humoral and cellular anti-tumor immunity. Thus, TLS can be considered not only as biomarkers of immune activity but also as promising therapeutic targets.

4. Architectural Heterogeneity and “Functional States” of Tls

4.1. Three Axes of Tls: Maturity, Localization, and Functional Context

Binary TLS scoring (present vs absent) and approaches only by density fail to capture the biological heterogeneity of TLS. Therefore, TLS have shown inconsistent prognostic and predictive associations across solid tumours and clinical settings. We argue that discrepancies largely reflect the conflation of at least three independent axes: structural maturity, spatial location, and functional immune context.

The first axis is structural maturity. Across tumours, lymphoid infiltrates range from loose lymphoid aggregates to organized non-germinal center (non-GC) TLS and fully mature germinal center (GC)-positive TLS [45,46,47]. Importantly, GC formation is a qualitative switch that reflects coordinated Tfh-B interactions, affinity maturation, and sustained antigen-driven immune activity. Thus, mature GC-TLS often carry different clinical meaning than immature aggregates or non-GC TLS [47]. Consequently, density-only TLS scoring conflates distinct maturation states and can obscure true associations.

The second axis is spatial localization. TLS may be intratumoural, concentrated at the invasive margin (marginal), or located in peritumoural or parenchymal tissue (among normal cells) [48]. Intratumoural and margin TLS are more likely to capture direct tumour-immune engagement, whereas peritumoural TLS may reflect organ specific background immunity, chronic inflammation, or tissue remodeling processes that are not necessarily tumour-directed [47,48,49,50]. Because region definitions and sampling strategies vary widely across studies, location is a major (often underreported) source of heterogeneity in TLS-associated outcomes [45,47].

The third axis is functional immune context. TLS architecture doesn’t map one-to-one with immune functions - structurally similar TLS can host effector-prone programs (Tfh-B interactions, cytotoxic T cells, activated B cells and plasma cells) or, conversely, regulatory/suppressive programs characterized by enrichment of regulatory T cells, regulatory B cell phenotypes, and suppressive myeloid populations. This functional polarization provides a mechanistic basis for why some tumours with a lot of TLS exhibit robust anti-tumor immunity, whereas others display attenuated or even adverse immune states despite the presence of organized lymphoid structures [34,51].

Taken together, structural maturity, spatial localization, and functional immune context represent sources of TLS heterogeneity. Different combinations of these axes can produce divergent clinical associations under the same TLS-positive label. In the next section, we link these axes to pro- vs suppressive immune mechanisms, and then formalize them into functional TLS states (TLS-A/B/C) to support standardized interpretation.

4.2. From Structure to Function: Mechanistic Pathways Shaping Tls Activity

Rather than repeating information about cellular components and developmental steps described in Section 2, we focus here on how recurrent immune programs emerging within TLS translate into distinct functional outputs. In brief, TLS can act as productive hubs of antigen-driven adaptive immunity when maturation and effector programs dominate; they can remain transitional when organization is incomplete; and they can become immunoregulatory when suppressive programs prevail [13,45,47,52]. The mechanistic framing provides the rationale for interpreting TLS beyond morphology and motivates the functional TLS state framework formalized in Subsection 3.3.

A central mechanism through which TLS promote antitumour immunity is the establishment of an antigen-driven adaptive immune hub within non-lymphoid tissue [53]. When maturation proceeds to GC activity, TLS support coordinated Tfh-B cell interactions that enable affinity maturation and the generation of memory and antibody-secreting programs, while simultaneously sustaining effector T cell activity in the surrounding microenvironment. In this state, TLS are not merely histologic aggregates but reflect an organized immune microenvironment capable of amplifying and maintaining adaptive responses [18,51,54]. This provides a mechanistic explanation for why mature, GC-positive TLS often carry different functional and clinical meaning than non-GC or immature structures.

A complementary determinant of TLS function is the supporting stromal and recruitment infrastructure that stabilizes lymphoid organization over time [55]. Lymphoid-organizing programs can promote compartmentalization and sustained immune trafficking, enabling TLS to behave as persistent immune niches rather than transient clusters [53,56]. Recent mechanistic work has shown that specific stromal lineages can actively regulate TLS neogenesis and shape downstream adaptive outputs, providing a possible explanation for why morphologically similar lymphoid aggregates diverge in stability and functional capacity [55].

Not all TLS mature into durable, GC-active immune hubs. A frequent intermediate outcomes is a transitional state in which TLS architecture is initiated but remains functionally incomplete (e.g., organized yet non-GC), resulting in variable immune output and context-dependent associations [45]. Conversely, TLS may adopt immunoregulatory programs despite recognizable organization, when regulatory lymphoid or suppressive myeloid pathways dominate. It’s explaining TLS-positive tumours that nevertheless exhibit attenuated or even adverse immune phenotypes [34]. Tumour-based analyses that stratify TLS by maturation stage support this continuum by demonstrating that immature and mature TLS can coexist and align with distinct immune microenvironments within the same disease context [57]. Independent clinical observations further show that regulatory cell infiltration within tumor-induced TLS can associate with unfavorable outcomes in selected settings, reinforcing that TLS should be interpreted through functional context rather than morphology alone [34]. Together, these mechanisms motivate interpreting TLS through functional context rather than morphology alone and set the stage for operationalizing TLS into functional states in Subsection 3.3.

4.3 Functional Tls States (Tls-A/tls-B/tls-C): Definitions and Minimal Classification Rules

The mechanistic continuum described above motivates a pragmatic, pathology-oriented framework to interpret TLS beyond “present/absent” scoring. Rather than introducing a new biomarker in isolation, we propose functional TLS states that integrate structural maturity, spatial localization, and functional immune context into a standardized vocabulary. This approach is intentionally conservative: it aims to separate (i) mature effector hubs, (ii) transitional structures with incomplete functional maturation, and (iii) immunoregulatory niches that may attenuate antitumor immunity despite recognizable TLS architecture. By explicitly distinguishing these states, we aim to resolve inconsistent TLS-associated outcomes reported across tumors and study designs.

4.3.1. Tls-a: Pro-Immunogenic Effector Hubs

Definition. TLS-A are mature, antigen-driven TLS that exhibit germinal center (GC) activity and an effector-skewed immune program, functioning as local hubs of adaptive antitumor immunity.

Required features:

(i) Evidence of structural maturity consistent with active GC-TLS;

(ii) an effector-skewed context

(iii) typically intratumoural or at the invasive margin (supportive but not strictly defining)

Additional molecular/functional features.

TLS-A commonly aligns with (i) chemokine programs associated with TLS organization and B-T cell cooperation (e.g., CXCL13-driven axis), (ii) GC-related transcriptional programs, and (iii) broader inflamed immune signatures showing sustained antigen-driven activity. These features should be interpreted as supportive rather than defining and are operationalized in Section 6.

Important remarks.

Density-only TLS-high calls can mix mature and non-mature structures; TLS-A should not be assigned without maturity and functional context.

4.3.2. Tls-B: Transitional/intermediate Tls with Organized but Not Fully Active Gc

Definition.

TLS-B are organized TLS that have initiated lymphoid architecture but lack strong and confident evidence of a fully mature GC program and/or display an intermediate (mixed) functional context, such that neither TLS-A nor TLS-C can be assigned conservatively.

Required features:

(i) Evidence of TLS organization beyond a loose aggregate (e.g., a recognizable lymphoid structure)

(ii) non-GC/immature/incomplete maturation or uncertainty about GC activity;

(iii) Functional context mixed or unassigned (no clear effector dominance required for TLS-A and no clear regulatory dominance required for TLS-C)

Additional molecular/functional features.

TLS-B often aligns with initiation-type lymphoid organizing programs and partial compartmentalization without robust GC transcriptional activity. In transcriptomic terms, TLS-B may show intermediate TLS signatures and/or incomplete GC-related programs, consistent with a state that is biologically “in progress” and potentially convertible under appropriate cues. These molecular features are supportive rather than defining and are operationalized in Section 6.

Important remarks.

TLS-B can occur across compartments; in some settings, partially organized/non-GC TLS are more frequently detected in peritumoral or stromal regions, potentially reflecting initiation without full stabilization in close proximity to tumor antigen encounter.

Do not interpret TLS-B as non-functional by default: it is a transitional category that acknowledges incomplete maturation or insufficient evidence.

4.3.3. Tls-C: Immunoregulatory/suppressive Tls Niches

Definition.

TLS-C are TLS in which regulatory/suppressive immune programs dominate, resulting in attenuated effective antitumor immunity despite recognizable TLS architecture; crucially, TLS-C is defined by functional context rather than location alone.

Required features:

(i) TLS organization is present (i.e., not merely a loose aggregate)

(ii) evidence - direct or strongly supported - of regulatory/suppressive dominance within the TLS niche (lymphoid and/or myeloid suppressive programs);

(iii) assignment is independent of location (peritumoral location may be supportive but is not sufficient without suppressive context)

Additional molecular/functional features.

TLS-C may align with transcriptomic and spatial patterns consistent with immunoregulation, including regulatory lymphoid programs and/or suppressive myeloid milieus. In practice, this can manifest as TLS-associated signals that do not co-segregate with classic effector/GC programs and may coincide with broader suppressive signatures; these supportive readouts are detailed and operationalized in Section 6.

Important remarks.

In selected tumor contexts, TLS-C-like patterns may be enriched in compartmentalized regions (e.g., peritumoral or adjacent non-malignant tissue), where chronic inflammation and tissue remodeling can shape a regulatory milieu. However, spatial compartment is treated as a modifier, not a definition.

5. Tumor Microenvironment Archetypes: Context-Dependent Functional States of Tls

To translate the TLS-A/B/C framework into biologically recognizable tumour settings, we organize the evidence into four recurrent tumour microenvironment archetypes. These archetypes are not intended as mutually exclusive tumour categories, but as interpretative patterns that explain why TLS may carry favourable, neutral, context-dependent, or adverse clinical significance. In this section, we integrate TLS maturity, spatial localization, immune context, specimen type, treatment exposure, and tumour-stage heterogeneity to illustrate how similar TLS-positive findings may correspond to distinct functional states.

5.1. Archetype I: Immune-Inflamed Tumors with Predominant Pro-Immunogenic Tls (Tls-a)

Immune-inflamed tumours are characterized by infiltration of effector immune cells into the tumour bed, particularly CD8+ T cells, together with active antigen presentation and interferon-driven inflammatory signalling [58,59]. In this setting, TLS are most likely to correspond to pro-immunogenic functional states when their structural maturation is accompanied by germinal centre activity, organized B- and T-cell zones, mature dendritic-cell niches, and effector immune polarization [13,19,60]. We therefore interpret this archetype as the prototypical setting for TLS-A, while emphasizing that TLS-A assignment requires concordance between maturity and immune context rather than TLS presence alone.

Archetype I is characterized by high T- and B-cell infiltration, interferon-associated inflammatory signalling, active antigen presentation, and the presence of organized lymphoid niches. The presence of specialised dendritic cells and organised T and B cell zones favour appearing of mature structures with GC, being a key factor distinguishing TLS-A [13,61]. Domination of interferon signaling pathways (IFN-I and IFN-II) further promotes recruitment and activation of immune cells. Moreover, the cytokine-chemokine inflammatory axis (CXCL12, CXCL13, CCL 19/21) enhances spatial organization of those cells into organized lymphoid structures, formatting highly organized immune niches [62,63]. Mature TLS (mTLS) show particularly high activity of antigen presentation, leading to effective T cell response within the tumor [64]. Therefore a key, distinguishing characteristic of TLS-A is coexisting with intensive immune infiltration, activation of interferon pathway and presence of organized niches presenting antigen. Altogether, they define an environment favourable to the appearing and mature function of TLS-A as well as effective anti-tumor response.

In TLS-A archetype dominate highly organized, mTLS structures. They contain GC, which functions as their characteristic of an active immunological state. TLS-A locate both intratumoral and in invasive margin, where they have a direct contact with tumor cells and effector immunological populations [19]. TLS-A architecture mirrors organization of secondary lymphatic organs with visible T- and B- cell zones, presence of specialised dendritic cells, and HEVs. It helps the local antigen presentation and initiation of adaptive immune response [65]. GC-positive mTLS function as local centers of generating antitumor response. It is where clonal lymphocyte expansion and differentiation of effector cells occur. Modern studies report that intratumoral TLS are especially enhanced in functioning as active populations of B- and T- cells, and favour local activation of Tfh–B pathway, leading to effective antitumor response [13,66]. Consequently, TLS-A should be seen as both indicator and active generator of an immune response.

From mechanistic perspectives, the TLS-A function is organized around the Tfh–B pathway, which initiates and keeps the GC reaction on. Therefore, it is a key mechanism of a local maturity of humoral response. Interactions between Tfh and B cells lead to somatic hypermutation, class switching and selection of clones with a high affinity, making it unable to generate plasma cells and immune memory [61,63]. This process impacts not only humoral response, but also supports the effector response, enhancing activation and maintenance function of the T cells CD8+ within the TME [67]. What is more, modern studies report that clonal expansion, differentiation towards plasma cells and their organization within the TLS correlate strictly with the efficiency of immunotherapy response, especially ICB [68]. In this context, TLS-A represent dynamic niches selecting and amplifying B and T cell clones, integrating the maturation of the humoral response with an effective effector response, and determining the tumor's susceptibility to immunotherapy.

Multiple studies on the clinical effects of the TLS have been conducted. They conclusively report that mTLS are associated with better prognosis in various solid tumors, being a favourable biomarker for better response to immunotherapy [54]. A 2022 study by Brunet et al. [69] showed that mTLS are a useful biomarker helping to foresee a response to the immunotherapy in patients with non-small-cell lung cancer [69]. Moreover, studies highlight their importance as biomarkers, correlating with better OS and more efficient treatment response [70]. However, corticosteroids disturb their maturation and prognostic value [18]. Another 2020 study by Cabrita et al. [51] revealed that the presence of TLS favours efficient immune response in melanoma and can be a biomarker for a better OS as well as the response to immune checkpoint inhibitors (ICIs) [51]. More studies support the thesis. A better response to anti-PD1 immunotherapy in renal cancer can be found when there is a high number of TLS [71,72]. Furthermore, it has been proven that in renal cancer mTLS enhances local immune response, leading to visually higher OS and better treatment response [21]. At the same time, a 2024 study by Gil-Jimenez et al. [73] found that in urothelial cancer the efficacy of the immunotherapy relies more on spatial relationship of immune cells. Close proximity of T cells CD8+ and macrophages to tumor cells favour treatment response [73]. Therefore, the presence of mTLS in the TME is an independent biomarker for a better response and OS in patients treated with ICIs, regardless of PD-L1 expression and number of T cells CD8+ [74,75]. Studies on the impact of TLS on CRC have shown that both the presence of mTLS and their high density can be linked to better survival and lower risk of tumor recurrence. At the same time, lack of mTLS or their low number especially alongside high Ki-67 is an unfavourable factor [76,77].

In summary, TLS-A represents the most consistently pro-immunogenic TLS state. In immune-inflamed tumours, mature GC-positive TLS may act both as biomarkers and active organizers of local antitumour immunity, supporting improved survival and enhanced responsiveness to immune checkpoint blockade. However, their clinical interpretation should remain dependent on maturity, spatial context, and effector immune polarization rather than TLS density alone.

5.2. Archetype Ii: Stromal-Barrier / Immune-Excluded Tumours with Inducible or Transitional Tls (Tls-B → Tls-a)

Stromal-barrier/ immune-excluded tumors are characterized by a strongly desmoplastic stroma, poor antigen presentation to T and B cells, and the presence of immunosuppressive cell populations within the TME. In this archetype, the immune system is not completely absent, but due to the environmental signals and specific cytokines, it might be functionally limited. As a result immune cells remain poorly coupled to tumour nests, as microenvironment impedes the effective migration of lymphocytes to tumor sites [78,79].

In this setting, TLS are not necessarily absent, but they more commonly occur as intermediate structures with limited functionality. In addition to lymphoid aggregates and GC-containing TLS, an intermediate subgroup can be identified in which TLS are structurally organized but lack the functionality of GCs [80]. We propose to classify these structures as TLS-B, defined as organized TLS that have begun to form a lymphatic structure, but do not show evidence of a fully mature GC program. They may occur both in the peritumoral regions and within the tumor center. Although spatial localization is not itself defining for TLS-B, such structures frequently align with peritumoral regions [79,81]. This model is supported by studies showing that TLS may arise early during tumor development yet remain functionally immature. In early-stage liver cancer, TLS were almost exclusively immature, lacking the morphologically mature follicular features. Moreover they coexisted with inhibitory and immunosuppressive programs, indicating that local immune activation does not necessarily translate into effective tumor control [82].

Importantly, TLS maturation should not be regarded as either an automatic or spontaneous process. Even after lymphoid organization has been initiated, the transition toward fully mature TLS containing GCs may remain incomplete. In this context, Posch et al. showed that in tumors with low TLS density, the process of lymphoid organization may be initiated but does not achieve full maturation, resulting in persistence of early-stage TLS rather than the development of fully mature GC-containing structures [83]. In addition to the follicular architecture alone, the functional efficiency of the TLS also depends on the organization of dendritic cell niches, as these cells are the primary antigen-presenting cells that coordinate adaptive immune responses. In this context, incomplete TLS maturation may indicate not only a lack of GC activity, but also insufficient consolidation of local interactions between antigen-presenting cells and T cells [84].

The fate of these partially organized TLS may also depend on local cellular composition. Mature TLS were associated with Th17-like cells, whereas immature TLS states align with CD163+ macrophages [85]. At the same time, the prognostic significance of TLS appears to depend not only on their degree of maturity but also on their intratumoral localization. In non-small cell lung cancer, immature TLS located in the central tumor compartment were associated with a favorable prognosis [85].

As tumor progression continues, the functional maturation of TLS may weaken, even when these structures remain morphologically detectable, their biological activity might become increasingly underdeveloped [86]. At the same time, because immature TLS may also favor differentiation of B-cells toward Breg phenotypes, they may also evolve toward a more immunosuppressive state, which in our model may correspond to the transition from TLS-B to TLS-C [87].

From a mechanistic perspective, CXCL13 appears to be a key signaling molecule linking early lymphoid organization with subsequent TLS maturation. In the early phase of lymphoid follicle development, CXCL13 is produced mainly by CD4+ T cells, whereas in more mature follicular structures, its expression is associated with CD21+ lymphoid follicle dendritic cells. This supports the interpretation that partially organized TLS-B may require sustained CXCL13-dependent signaling in order to progress to more mature TLS-A states [15].

Given the clinical significance of TLS, available evidence suggests that the formation of mature TLS can be induced by a variety of therapeutic interventions, including, vaccination, immunotherapy, chemotherapy, radiotherapy, and cytokine delivery approaches. For this reason, TLS should be viewed not as a static histological finding, but as inducible immune structures whose maturation state may remain biologically plastic under appropriate therapeutic conditions [13].

This plasticity is central to the TLS-B concept: intermediate TLS should not be interpreted as biologically inert structures, but as potentially convertible immune niches whose final orientation depends on stromal, inflammatory, antigenic, and therapeutic cues.

5.3. Archetype Iii: Location-Split Tumours — Spatially Divergent Tls Function and the Risk of Suppressive Tls-C-like Niches

TLS can be found not only intratumoral, but also in invasive margin and peritumoral in location-split tumors. Their spatial distribution within the TME is highly heterogeneous and functionally relevant. Therefore, the role of TLS should be discussed not solely based on their presence or maturity but also on their spatial location in the TME [88,89]. The difference in function is an effect of distinct inflammatory profile, composition and antigen availability in particular parts of the tumor [90]. Intratumoral zone is usually rich in tumoral antigens and IFN signal, supporting activation of effector T cells, Tfh-B pathway and formation of GC. In the invasive margin, tumoral and stromal signals are combined, leading to mixed cell phenotypes and functions. In contrast, the peritumoral zone is similar to chronic inflammation with the recruitment of myeloid cells of regulatory function. Those significant differences affect cytokine and chemokine profiles as well as cell interactions, further determining local organization and function of the immune response [20,50,91]. Nevertheless, the literature often unifies the TLS, leading to heterogenous data, false results and mixed information. Moreover, analysing all TLS as one archetype may miss real functional differences between TLS-A, TLS-B, and TLS-C. Therefore, location-split tumours should not be interpreted through TLS presence alone. Instead, they illustrate how intratumoural, invasive-margin, and peritumoural TLS may occupy distinct antigenic and inflammatory niches, leading to divergent functional states. In this archetype, TLS-C-like interpretation should be considered only when peritumoural or compartmentalized TLS are accompanied by regulatory lymphoid, suppressive myeloid, or checkpoint-dominated immune features.

The difference in TLS function in archetype III is based on the varied access to the tumoral antigen, inflammation signals and cell composition between intratumoral and peritumoral environments [20]. Accumulating evidence highlights that intratumoral TLS are more often connected to active antitumoral response, higher infiltration of T and B cells, visual Th1 and Th17 pro-inflammatory polarization. They are more mature, associated with improved survival, and have developed HEVs. Moreover, the frequency of Tfh-B cells is higher in intratumoral than peritumoral TLS [81,92,93]. On the contrary, peritumoral TLS emerge as an effect of chronic antigen stimulation and inflammation, which does not have to correlate with the tumor itself. They can appear after organ damage or fibrosis processes, partially independent of the antitumor response. Peritumoral zone is associated with the presence of distinct cell populations, including neutrophils and myeloid cells with regulatory potential [50]. In the peritumoral zone the key role is played by stromal cells- fibroblastic reticular cells, dendritic cells and endothelial cells. Together they organize the migration of lymphocytes to TLS. T cells CD4+ type Tfh are also important in the zone as they initiate the formation of GC and maturation of TLS via secretion of CXCL13 and interaction with dendritic and B cells. In such an environment, Tfh-B pathway may be directed towards autoantigenes, leading to GC reactions of limited antitumoral specificity [91]. In peritumoral TLS, B cells may differentiate into plasma cells producing antitumoral antibodies or, in less mature TLS, adopt a regulatory phenotype promoting immunosuppression. Therefore, depending on the cellular component and level of organization, peritumoral TLS may favour antitumoral response or with dominating Treg, M2 macrophages and immature dendritic cells, promote tumor progression and invasion. Another factor which may modulate and limit the TLS function is the richness of myeloidal regulatory cells [8,14]. At the same time, clinical data regarding the role of peritumoral roles remain inconsistent. Peritumoral TLS may be neutral or even have a positive impact on prognosis. On the other hand, in some cases, they can correlate with worse disease courses, highlighting their context dependent biological function [81].Therefore, the presence of TLS in the peritumoral zone should not be interpreted as immunosuppressive phenotype TLS-C, but rather as an enhanced signal of the TME heterogeneity and potentially different immune response regulation dependent on location.

Consequently, during such complex interactions, a functional split within the TLS function may occur. Intratumoral TLS may act as effective niches for antitumor response- TLS-A, while peritumoral TLS may present a context dependent role. Therefore, the key difference is based not on the presence, but spatial localization within the antigenic and immunological gradients of the TME.

However, it needs to be highlighted that the peritumoral location should not be automatically linked to the TLS-C. In many studies, TLS located peritumoral show neutral or even positive prognostic importance. At the same time, the presence of peritumoral TLS should be interpreted as a marker of inflammatory response rather than a unified immunosuppression marker [94,95].

In clinical data, peritumoral TLS are usually described as immunosuppressive, pro-tumorigenic TLS-C, connected to worse OS and treatment outcomes. A 2025 study by Xu et al. [96] reported that the presence of peritumoral TLS in head and neck squamous cell carcinoma was linked to worse OS and higher risk of lymph nodes metastasis. They were noted to be an unfavorable prognostic factor, supporting tumor progression [96]. In a 2025 study by Yu et al. [97] peritumoral TLS were often connected with highly advanced esophageal squamous cell carcinoma, suggesting their impact on disease progression and enhanced inflammation around the tumor. At the same time, TLS presence may also be an effect of an active immune response. However, this response does not translate directly into tumor growth control as effective as intratumoral mTLS. Therefore, peritumoral TLS may have an ambivalent biological role, potentially combining the tumor progression promotion and limited efficacy of the antitumor response [97]. On the other hand, peritumoral TLS were also linked with a lower tumor stage and long-term survival in esophageal cancer in a 2025 study by Zhai et al. [98]. It further highlights the possible dual role of peritumoral TLS.

A 2022 study by Devi-Marulkar et al. [34] reported that the TLS are a place of activation and differentiation of T cells, which correlates with Th1 immune response, cytotoxicity and better OS in non-small cell lung cancer. Regulatory T (Tregs) cells are also present within these structures and can also be a prognostic marker. A high density of Tregs are associated with worse OS and may weaken the positive effect of TLS. Therefore, a balance between cytotoxic CD8+ T cells and Tregs may be critical for the prognosis [34].

In breast cancer, peritumoral TLS are linked with an especially unfavourable prognosis. They occur with high density, in multiple peritumoral locations, highlighting more aggressive and advanced stages. The researchers highlight that peritumoral TLS may reflect an immunosuppressive or dysfunctional TME that promotes disease progression. However, more studies are needed to be conducted to determine their biological role in breast cancer [49,99].

In a research by Merali et al. (2024) TLS rich in cytotoxic T cells and B cells are related to better OS and immunotherapy response. However, TLS rich in Tregs and Bregs may limit antitumor response and promote further disease progression. Moreover, it has been showed that TLS enhance the ICIs response through support of the local activation of T cells and production of tumor specific antigens. In pancreatic ductal adenocarcinoma, the presence of intratumoral TLS is often correlated with better OS and PFS. However, some therapies such chemotherapy or steroids may impair their formation and function [100].

In summary, standardized reporting of TLS location and the integration of spatial data seems to be a vital factor of precise interpretation of the TLS role in tumor biology and treatment response. It needs to be highlighted that TLS cannot be interpreted without spatial context as it is equally important to their maturation. Moreover, TLS’ spatial heterogeneity leads to their different biological functions and prognosis. Therefore, ways for detecting and describing TLS should be unified to avoid further misinformation and provide the best help for patients. They should combine TLS localization, maturity level and TME context.

5.4. Archetype Iv: Intermediate and Heterogeneous Tumors - Mixed Tls States and High Sensitivity to Bias

In Archetype IV, there is significant heterogeneity among patients with the same disease. For this reason, intermediate and heterogeneous tumors are particularly susceptible to analytical bias. In particular, the interpretation of TLS depends on the treatment context. Neoadjuvant therapies can significantly affect TLS density and maturity, thereby modifying or even invalidating their predictive value of TLS. In some settings, this is associated with a significant reduction in TLS density and maturation, whereas in others it promotes the formation of mature tertiary lymphoid structures [101,102].

Importantly, a single tumor may contain mixed TLS states, including more effector-prone TLS programs and less mature ones, which is conceptually consistent with the coexistence of TLS-A and TLS-B like structures in single tumor [20].

The clinical significance of TLS is also influenced by treatment modality. In patients with non-small cell lung cancer (NSCLC) receiving chemotherapy, no association has been demonstrated between TLS and either treatment response or progression-free survival [103]. In contrast, earlier studies have shown that TLS are specifically predictive of response to immune checkpoint blockade [104,105].

The assessment of the presence of TLS structures in a tumor depends largely on the specimen type. According to Wang et al. (2024) TLS was found in 98.3% of untreated patients with locally advanced rectal cancer who had resection, but only 18.1% of those who underwent biopsy prior to treatment. This suggests that biopsy-based assessment may be related to a high percentage of false-negative classifications and that the specimen type may have a significant impact on the outcome. This suggests that the samples obtained during the biopsy may not fully reflect the spatial distribution of TLS within the tumor [101].

Methodological differences in the assessment of TLS add further to this heterogeneity. Within individual studies, TLS is not always classified using the same maturity criteria, spatial definitions, or sets of markers. For example, in the case of advanced colorectal cancer, mature TLS has been defined as an organized infiltration of T and B cells with Ki-67-positive proliferating germinal centers, whereas other studies focused primarily on broader architectural criteria, including the presence of follicular dendritic cells with evidence of a germinal center reaction. It means that TLS-positive tumors that appear similar at first glance may in fact correspond to biologically distinct conditions [76,83,84].

In summary, these observations indicate that variability in TLS-related findings in intermediate and heterogeneous tumors stems not only from biological diversity but also from methodological inconsistencies. Part of this heterogeneity may already stem from pathological interpretation, as different pathologists do not always identify TLS in the same way, particularly when the assessment depends on subtle features of maturity or location. For this reason, standardization of TLS maturity assessment in routine practice is essential. This provides a strong rationale for the framework proposed in Section 6, in which TLS is described using a more structured and modular approach [106].

Overall, the four archetypes illustrate why TLS cannot be interpreted as a universal biomarker independent of tumour context. The same TLS-positive status may reflect mature effector immunity, incomplete lymphoid organization, suppressive immune polarization, or sampling-related heterogeneity. This provides the rationale for the functional and modular assessment strategy proposed in the following sections.

6. Therapeutic Implications of Tls Functional States

6.1. Tls Functional States as Predictive Biomarkers for Immunotherapy and Neoadjuvant Treatments

TLS functional states are beginning to be viewed as potential biomarkers for predicting treatment response. They may prove particularly useful in the context of immune checkpoint inhibitor therapies and neoadjuvant therapy [66,74,101,106,107]. In this context, a shift in the perception of functional states is also necessary; they should not be viewed in binary terms. An increasing number of studies indicate that the clinical value of TLS does not lie in their mere presence, but rather in their functional state, and above all in their degree of maturity [66,74,107]. Mature TLS appear to represent the state with the greatest prognostic value; in a landmark multi-cohort analysis conducted by Vanhersecke et al. (2021), the presence of mature TLS was associated with a better objective response rate, progression-free survival and overall survival in patients treated with anti-PD-1/PD-L1 therapy, regardless of PD-L1 expression and CD8+ T-cell density [74]. The results of this study highlight the value of TLS as a biomarker that goes beyond strictly immunological parameters [74].

Recent studies confirm this interpretation. Hao Li et al. (2025) demonstrated in their work that the presence of mature TLS correlates with better overall survival and response to ICBs, and that tumours lacking mature TLS do not support local anti-tumour immunity [66]. Mature TLS are enriched in T cells with stem cell-like properties, as well as differentiating B cells, which together sustain local anti-tumour immunity, whereas immature TLS are more frequently associated with immunosuppressive immune activity [66,107]. This highlights the potential for using TLS maturity as a biomarker. However, TLS should not simply be classified as present or absent, but rather stratified according to their functional status [66,74,107].

The concepts outlined above take on particular significance in the case of gastrointestinal cancers. In rectal cancer, the detection of TLS has been shown to predict a better response to neoadjuvant therapy [101]. Similarly, in the case of colorectal adenocarcinoma, a CRC-specific TLS signature has enabled the identification of patients who are more likely to benefit from both neoadjuvant chemotherapy and PD-1 blockade [108]. The findings from the aforementioned studies indicate that mature TLS are not merely biomarkers of pre-existing immune activation, but can serve as tools for assessing prognosis and response to neoadjuvant treatment [66,74,101,108].

6.2. Therapeutic Induction of Pro-Immunogenic Tls States (Tls-a)

The application of treatments utilising TLS should focus not on increasing the number of lymphoid clusters, but on shifting their stage of development towards mature forms [14,16,109,110,111]. TLS-A can be understood as mature, organised clusters capable of developing and sustaining a local anti-tumour response [14,109] . Preclinical evidence suggests that such a shift in the functional state of TLS can be achieved in several ways, including stromal remodelling, local inflammatory stimulation, oncolytic virotherapy, and conventional chemotherapeutic and radiotherapeutic interventions [14,109,110,111].

Stromal remodelling appears to be the most compelling method of TLS induction. The work of Johansson-Percival et al. (2021) shows that TLS induction is closely linked to the reprogramming of the tumour stroma, particularly through the normalisation of the vascular system, the modulation of fibroblast-related organisational functions, and the activation of chemokine programmes involving LIGHT/LTα/LTβ, TNFα and CCL/CXCL signalling [109]. This is of particular significance in tumours with dense desmoplasia, one of which is pancreatic ductal adenocarcinoma (PDAC). Francesca R Delvecchio et al. demonstrated in a mouse model that the proximity of dendritic cells and B lymphocytes within the organised structure of mature TLS may facilitate better communication between these cells, which in turn promotes the development of a more effective anti-tumour response. Induced TLS in this model enhances the anti-tumour effect of chemotherapy [16].

Another possible route for inducing TLS is oncolytic virotherapy. The use of oncolytic viruses has a beneficial effect on the release of chemokines. This intensifies local inflammation and helps transform tumours that weakly activate the inflammatory response into those that activate it strongly [110,111]. The concept has been verified in mouse models. In a study, Meng-Jie Zhang et al. (2024) demonstrated that the oncolytic herpes simplex virus type 1 (oHSV) induces TLS formation and enhances anti-tumour immunity via the CXCL10/CXCR3 axis. It is also worth noting that blocking this axis impaired TLS formation [111].

Recent reviews indicate that chemotherapy, radiotherapy and chemoradiotherapy promote TLS formation, particularly when used in combination with immunomodulatory interventions that facilitate the recruitment of immune cells and local lymphoid organisation [14]. However, the aim should not be to induce TLS per se, but to promote states corresponding to TLS-A, which favour an anti-tumour response [14,109]. Consequently, future research should focus on quality-oriented TLS induction. By transforming poorly organised or absent lymphoid responses into mature, pro-immunogenic TLS, rather than merely maximising their numbers [14,16,109,110,111].

6.3. Avoiding or Reprogramming Adverse / Pro-Tumor Tls States (Tls-C)

TLS is often associated with a more favourable prognosis and a better response to treatment, although a growing body of evidence suggests that not all TLS is beneficial [35,82,91,112]. Immature TLS may act as niches that promote tumour persistence rather than as effective sites for anti-tumour immune responses [82,91,112]. Experimental studies on lung cancer have shown that tumour-associated TLS can be infiltrated by regulatory T cells (Tregs), which actively suppress the responses of effector T cells, supporting the concept that some TLS have an immunosuppressive rather than a protective effect [35,112]. Similarly, in the case of hepatocarcinogenesis, it has been shown that immature TLS act as micro-niches for tumour progenitor cells, suggesting that these forms, under specific conditions, facilitate tumour initiation, maintenance or recurrence rather than immune control [91].

The adverse effect of immature TLS forms is also confirmed by data from translational studies in humans. In early-stage liver tumours, immature TLS forms were associated with higher expression of immunosuppressive molecules. This suggests that immature TLS may contribute to immunosuppression [82]. Recent reviews emphasise that the heterogeneity of TLS is not limited to density or anatomical location, but extends to their cellular composition, degree of maturity and regulatory balance, all of which can shift these structures towards anti-tumour immunity or immunosuppression [112].

Given the above, the most important clinical objective should be the functional transformation of immature states (TLS-C) into mature states (TLS-A), i.e. the remodelling of suppressive or tumour-promoting lymphoid aggregates into mature, immunologically active TLS capable of supporting an anti-tumour response [35,82,91,112]. This concept is more biologically sound than strategies aimed solely at maximising TLS numbers, as the clinical value of TLS depends on quality, organisation and immunological orientation, rather than solely on quantity [82,112].

7. Methodological Approaches to Assess TLS Functional States and Proposal of a “Functional TLS Score”

7.1. Histopathologic Assessment of Tls: From H&e Screening to Standardized Mtls Identification

Conventional histopathology remains the first-line approach for TLS assessment because it is feasible in routine tumour specimens and allows initial recognition of lymphoid aggregates, follicle-like organization, and GC formation [18,80,106,113]. On hematoxylin and eosin (H&E) or hematoxylin, eosin and saffron (HES)-stained sections, TLS should first be stratified along a structural maturation axis: loose lymphoid aggregates without clear follicular architecture; organized non-GC TLS with partial lymphoid compartmentalization; and mTLS showing GC-like morphology [13,18,80,106,113]. This distinction is biologically relevant, because GC formation reflects a transition toward coordinated Tfh–B-cell cooperation, B-cell selection, somatic hypermutation, class-switch recombination, and local adaptive immune activity [18,19,48,81]. Standardized pathology approaches suggest that mTLS can be operationally identified by visible GC formation on H&E/HES or by immunohistochemical evidence of CD23-positive follicular dendritic cell networks [106]. A minimal confirmatory panel may include CD20/CD19 for B-cell areas, CD3/CD8 for T-cell zones, CD21/CD23 for follicular dendritic cells, BCL6 and Ki-67 for GC activity, and PNAd/MECA-79 for HEVs [13,80,81,106]. Several studies have translated TLS abundance and maturation into pathological scores, supporting the prognostic value of density- and maturity-based assessment in gastrointestinal and colorectal cancers [77,113,114]. However, these approaches remain primarily structural and do not fully capture cellular composition, spatial localization, or suppressive versus effector immune polarization [20,48,81,115]. Therefore, in the proposed TLS-A/B/C framework, GC-positive mTLS should be interpreted as structural candidates for TLS-A, organized non-GC TLS as candidates for TLS-B, whereas TLS-C should not be assigned by morphology or peritumoural location alone and requires additional evidence of regulatory or suppressive immune dominance. Thus, histopathology provides the structural backbone of functional TLS classification, but immune-context and spatial analyses are required to determine whether a structurally defined TLS corresponds to an effector, transitional, or suppressive functional state. Table 1 summarizes the proposed structural assessment module, including basic histopathologic and immunohistochemical markers for TLS detection, maturation grading, and preliminary assignment of structural TLS-A/TLS-B candidates before immune-context evaluation.

7.2. Multiplex Immunohistochemistry, Digital Pathology and Spatial Technologies: Defining the Immune-Context Module

Although conventional histopathology and basic immunohistochemistry define the structural backbone of TLS assessment, they do not fully determine whether a given TLS acts as an effector, transitional, or suppressive immune niche. Morphologically similar TLS may differ substantially in cellular composition, spatial organization, and immune polarization; therefore, structural maturity should be complemented by an immune-context module [20,48,81,115]. Multiplex immunohistochemistry and multiplex immunofluorescence allow simultaneous assessment of B-cell, T-cell, dendritic-cell, plasma-cell, vascular, regulatory, and myeloid compartments within the same tissue section [20,80,88]. In practical terms, an effector-oriented multiplex panel may combine B-cell and T-cell markers such as CD20/CD19, CD3 and CD8 with GC and Tfh-associated markers including BCL6, Ki-67, CXCR5, ICOS, PD-1 and CD40L, plasma-cell markers such as CD138, mature dendritic-cell markers such as DC-LAMP/CD208, cytotoxicity-associated markers such as granzyme B, and vascular markers such as PNAd/MECA-79 [19,20,66,80,81,86,88]. Such profiles support TLS-A assignment when they occur within GC-positive TLS and are spatially coupled to tumour-facing immune activation. Conversely, a suppressive-context panel may include FOXP3 and CD25 for regulatory T cells, CD68, CD163 and CD206 for macrophage-rich or M2-like myeloid compartments, MPO or CD66b for neutrophils, and immune checkpoint or metabolic markers such as PD-L1, CTLA-4, LAG-3, TIM-3 and IDO1 [20,34,35,48,57,81]. These features may support TLS-C assignment only when they indicate regulatory or suppressive dominance within the TLS niche, rather than merely reflecting background inflammation or peritumoural location. TLS-B should remain a conservative intermediate category for organized TLS with incomplete effector maturation or mixed immune-context features.

Importantly, functional TLS assessment should not rely only on marker abundance, but also on spatial relationships. The biological meaning of a given immune subset depends on whether it is located inside the TLS, at the TLS border, in the tumour nest, at the invasive margin, or in peritumoural stroma [20,88,115]. Spatially resolved analyses can quantify immune neighbourhoods, cell-cell proximity, TLS-tumour distance, and compartment-specific immune programs. For example, the proximity of Tfh cells to B-cell follicles, CD8+ T cells to tumour nests, plasma cells to TLS borders, or mature dendritic cells to T-cell areas may support an effector-oriented TLS-A-like configuration, whereas accumulation of FOXP3+ Tregs, CD163+ macrophages, neutrophils, or checkpoint-positive cells within or around TLS may suggest a suppressive TLS-C-like niche [34,35,86,88,57,66]. Digital pathology may further support this process by automating TLS detection, density measurement, maturity grading, regional annotation, marker quantification, and spatial distance analyses; however, such algorithms require standardized TLS definitions and careful biological validation before routine use [20,77,106,114]. Spatial transcriptomics and spatial proteomics add another layer by linking TLS morphology to localized chemokine, interferon, GC-related, exhaustion, myeloid, and suppressive programs while preserving anatomical context [20,66,86,88]. Thus, multiplex and spatial approaches provide the immune-context information required to move from structural TLS recognition toward functional TLS-A/B/C assignment: TLS-A as mature effector TLS, TLS-B as intermediate or mixed TLS, and TLS-C as suppressive TLS requiring direct evidence of regulatory immune dominance.

7.3. Transcriptomic Tls Signatures and Pan-Cancer Tls Scores: Molecular Support for Functional Assignment

Transcriptomic TLS signatures provide an additional molecular layer for evaluating TLS, particularly in large cohorts where detailed spatial or multiplex tissue analysis is not always feasible. These signatures usually combine genes reflecting lymphoid organization, B-cell recruitment, Tfh–B-cell cooperation, GC activity, plasma-cell differentiation, antigen presentation, interferon-driven inflammation, cytotoxicity, and immune regulation [20,48,81,115]. Core TLS-associated signals commonly include chemokines and lymphoid-organizing molecules such as CXCL13, CCL19, CCL21, CXCL9, CXCL10, CXCL11, LTA, LTB, LTBR, CCR7 and CXCR5, B-cell markers such as MS4A1/CD20, CD19, CD79A, CD79B and CD40, Tfh/GC-related markers such as BCL6, ICOS, PDCD1/PD-1, IL21, CD40LG and AICDA, and plasma-cell or antibody-associated genes such as MZB1, JCHAIN, XBP1, IGHG1 and IGHA1 [19,20,48,81,108,115,116]. In addition, effector TLS programs may include antigen-presentation and inflammatory genes, including HLA-DRA, HLA-DRB1, CD74, IFNG, STAT1, CXCL9, CXCL10, GZMB and PRF1, whereas suppressive or TLS-C-like molecular contexts may be supported by regulatory or myeloid-associated markers such as FOXP3, IL10, TGFB1, IDO1, CD274/PD-L1, CTLA4, LAG3, HAVCR2/TIM-3, MRC1/CD206, CD163, VSIG4 and ARG1 [20,34,35,48,57,81,115,116,117,118]. For example, in colorectal cancer, one TLS-related model included genes such as CCL19/CCL21, CD8A, IGHG1, MRC1, SELL, VSIG4 and PRRX1, while PRRX1, SELL and VSIG4 were highlighted as important hub genes linked to immune infiltration and tumour microenvironmental regulation [116]. In advanced non-small cell lung cancer, a TLS-derived gene signature was reported to predict response to chemoimmunotherapy and correlated with PD-L1/CD274 expression and “hot” immune phenotypes [118]. In the proposed framework, TLS-A would be expected to align with high TLS-related chemokine, GC/Tfh/B-cell, plasma-cell, antigen-presentation and interferon/cytotoxic signatures. TLS-B may show intermediate lymphoid-organizing signals, such as CXCL13–CCL19/CCL21 activity, without robust GC, plasma-cell or cytotoxic co-signatures. TLS-C should not be inferred from a TLS-high signature alone, but rather from coexistence of TLS-associated genes with regulatory, suppressive myeloid, checkpoint, metabolic or T-cell dysfunction programs. However, transcriptomic TLS scores have important limitations: bulk RNA sequencing does not directly show whether immune cells are organized into true TLS or dispersed throughout the tumour microenvironment, cannot reliably distinguish intratumoural from peritumoural TLS, and may be affected by tumour purity, stromal content, necrosis, sampling region and treatment timing [20,86,115,118]. Therefore, transcriptomic signatures should be interpreted as an optional molecular module of the functional TLS score: useful for supporting and scaling TLS assessment, but insufficient to replace histopathologic maturity grading and spatial immune-context evaluation.

7.4. Towards A Pathology-Oriented Functional Tls Scoring Framework: Modular Architecture and Assignment Rules

First, TLS should be confirmed morphologically and separated from non-organized lymphoid infiltrates. Second, TLS maturity should be graded as loose aggregate, organized non-GC TLS, or GC-positive mature TLS. Third, TLS localization should be recorded as intratumoural, invasive-margin, or peritumoural. Fourth, immune-context markers should be used to determine whether the TLS niche is effector-skewed, mixed, or suppressive. Finally, molecular signatures may support but should not override morphology and spatial immune-context assessment.

The preceding sections indicate that TLS assessment cannot rely on a single parameter, such as presence, density, or maturity alone. Histopathology provides the structural backbone of TLS evaluation, multiplex and spatial approaches define the immune-context module, and transcriptomic signatures may provide additional molecular support [20,48,81,106,115]. However, these dimensions are often analysed separately, which may contribute to inconsistent clinical interpretation across tumour types, specimen types, and treatment settings [20,48,81,115]. Therefore, we propose a pathology-oriented Functional TLS Score as a structured framework integrating structural maturity, TLS burden, spatial localization, immune-cell composition, and optional molecular signatures. Importantly, this proposal should not be interpreted as a validated clinical scoring system, but as a modular conceptual model intended for translational studies, harmonized reporting, and future validation.

The rationale for a modular score is that each layer of TLS assessment captures a different biological dimension:

• The structural module distinguishes loose lymphoid aggregates, organized non-GC TLS, and GC-positive mTLS, thereby identifying whether lymphoid neogenesis has progressed toward follicle-like maturation [13,18,80,106,113];
• The density module quantifies TLS burden by TLS count, TLS density, TLS area, or maturity-weighted scores, which have shown prognostic relevance in gastrointestinal and colorectal cancers but do not independently define function [77,113,114];
• The spatial module records whether TLS are intratumoural, located at the invasive margin, or peritumoural, and should be interpreted as a modifier rather than as a stand-alone functional criterion [20,48,81,115];
• The immune-context module evaluates the balance between effector components, such as CD8+ T cells, T follicular helper cells, GC B cells, plasma cells, mature dendritic cells and high endothelial venules, and suppressive components, such as FOXP3+ regulatory T cells, regulatory B-cell phenotypes, CD163+/CD206+ macrophages, neutrophils, immune checkpoint expression or IDO1-related metabolic suppression [19,20,34,35,48,57,66,81,86,88];
• Finally, the molecular module may support assignment using TLS-related, GC/Tfh/B-cell, interferon/cytotoxic or suppressive gene signatures, but should not replace histopathologic and spatial validation [108,116,117,118].

For practical use, the Functional TLS Score could be applied at two levels:

• A minimal version would rely on H&E/HES screening, basic immunohistochemistry for B-cell and T-cell compartments, follicular dendritic cell networks and GC activity, together with regional annotation of TLS localization [13,18,80,106,113]. This version would be feasible in routine or near-routine pathology workflows and would mainly support structural classification and preliminary TLS-A/TLS-B candidate assignment;
• An extended version, intended primarily for translational studies, would incorporate multiplex immunohistochemistry or immunofluorescence, digital pathology, spatial proteomics, spatial transcriptomics, or targeted RNA-based TLS signatures [20,66,86,88,108,116,117,118]. This extended version would allow more confident functional assignment by testing whether structurally defined TLS are effector-skewed, mixed/intermediate, or suppressive. Thus, the proposed score should be understood as a stepwise framework: from morphology, through spatial and cellular context, toward optional molecular confirmation.

Table 2. Proposed minimal and extended assessment pathways within the Functional TLS Score framework.

Module	Minimal assessment	Extended assessment	Contribution to TLS-A/B/C assignment
Structural maturity	H&E/HES; recognition of lymphoid aggregates, organized non-GC TLS and GC-positive mature TLS; basic IHC: CD20/CD19, CD3, CD21/CD23, BCL6, Ki-67 [13,18,80,106,113]	Digital pathology-assisted maturity grading; multiplex confirmation of follicular structure and germinal centre activity [20,66,80,86,88]	Distinguishes loose aggregates, TLS-B candidates, and structural TLS-A candidates
TLS burden / density	TLS count, TLS density per area, semi-quantitative estimation of TLS abundance [77,113,114]	Maturity-weighted density scores, automated quantification, whole-slide analysis [20,77,114]	Quantifies TLS burden, but does not define functional state alone
Spatial localization	Regional annotation as intratumoural, invasive-margin, or peritumoural TLS [20,48,81,115]	Distance-to-tumour measurements, neighbourhood mapping, compartment-specific spatial analysis [20,66,86,88]	Modifies interpretation of TLS function; does not independently define TLS-A/B/C
Immune - context module	Limited single-plex IHC focused on key effector/suppressive markers, e.g. CD8, FOXP3, CD68/CD163, CD138, PNAd where available [19,20,34,35,48,57,81]	Multiplex IHC/IF, spatial proteomics, simultaneous assessment of Tfh, GC B cells, plasma cells, dendritic cells, myeloid and checkpoint markers [20,34,35,57,66,80,86,88]	Supports distinction between TLS-A, TLS-B and TLS-C
Molecular module	Optional targeted TLS-related RNA panel when available [108,116,117,118]	Bulk RNA TLS signatures, spatial transcriptomics, integrated molecular profiling [20,86,108,116,117,118]	Provides supportive molecular evidence, but does not replace morphology or spatial validation

Table 2. Proposed minimal and extended assessment pathways within the Functional TLS Score framework.

Module	Minimal assessment	Extended assessment	Contribution to TLS-A/B/C assignment
Structural maturity	H&E/HES; recognition of lymphoid aggregates, organized non-GC TLS and GC-positive mature TLS; basic IHC: CD20/CD19, CD3, CD21/CD23, BCL6, Ki-67 [13,18,80,106,113]	Digital pathology-assisted maturity grading; multiplex confirmation of follicular structure and germinal centre activity [20,66,80,86,88]	Distinguishes loose aggregates, TLS-B candidates, and structural TLS-A candidates
TLS burden / density	TLS count, TLS density per area, semi-quantitative estimation of TLS abundance [77,113,114]	Maturity-weighted density scores, automated quantification, whole-slide analysis [20,77,114]	Quantifies TLS burden, but does not define functional state alone
Spatial localization	Regional annotation as intratumoural, invasive-margin, or peritumoural TLS [20,48,81,115]	Distance-to-tumour measurements, neighbourhood mapping, compartment-specific spatial analysis [20,66,86,88]	Modifies interpretation of TLS function; does not independently define TLS-A/B/C
Immune - context module	Limited single-plex IHC focused on key effector/suppressive markers, e.g. CD8, FOXP3, CD68/CD163, CD138, PNAd where available [19,20,34,35,48,57,81]	Multiplex IHC/IF, spatial proteomics, simultaneous assessment of Tfh, GC B cells, plasma cells, dendritic cells, myeloid and checkpoint markers [20,34,35,57,66,80,86,88]	Supports distinction between TLS-A, TLS-B and TLS-C
Molecular module	Optional targeted TLS-related RNA panel when available [108,116,117,118]	Bulk RNA TLS signatures, spatial transcriptomics, integrated molecular profiling [20,86,108,116,117,118]	Provides supportive molecular evidence, but does not replace morphology or spatial validation

The central output of the proposed framework should be functional assignment into TLS-A, TLS-B, TLS-C, or unclassifiable TLS, rather than a single universal numerical value. TLS-A should be assigned when structural maturity and immune context are concordant with an effector-oriented state: GC-positive mTLS, evidence of Tfh–B-cell cooperation, enrichment of CD8+ T cells, GC B cells, plasma cells, mature dendritic cells or HEVs, and, when available, supportive TLS/GC, interferon or cytotoxic transcriptomic signatures [18,19,20,66,81,106,108,116,117,118]. Intratumoural or invasive-margin localization may strengthen TLS-A interpretation, but should not replace evidence of mature architecture and effector polarization [20,88,115].

TLS-B should represent an organized but incompletely matured or functionally intermediate state. It should be assigned to TLS with lymphoid organization beyond a loose aggregate, but without convincing GC-positive maturation and/or without clear effector or suppressive dominance. This category may include non-GC TLS with partial B/T-cell compartmentalization, incomplete follicular dendritic cell networks, intermediate lymphoid-organizing signals, or mixed immune-context features [13,18,20,58,80,81,115]. Importantly, TLS-B should be defined by intermediate biology, not by insufficient evidence; poorly sampled, technically limited, or uninterpretable cases should be reported as unclassifiable rather than automatically categorized as TLS-B.

TLS-C should be assigned only when TLS organization is accompanied by direct evidence of regulatory or suppressive immune dominance. This may include enrichment of FOXP3+ regulatory T cells, regulatory B-cell phenotypes, CD163+/CD206+ macrophages, neutrophils, immune checkpoint-dominated or IDO1-related suppressive programs, or spatial configurations in which suppressive immune cells accumulate within or around the TLS niche [20,34,35,48,57,81,117,118]. Peritumoural localization, immature morphology, or high TLS density alone should not be considered sufficient for TLS-C assignment, because these features may reflect tissue-specific inflammation, sampling patterns, or transitional lymphoid neogenesis rather than true immunosuppressive function [20,48,81,115]. In this framework, TLS-C is therefore defined by suppressive dominance, not by anatomical location alone. Conversely, structurally mature TLS should not be assumed to be TLS-A if the immune-context module demonstrates predominance of regulatory or suppressive programs. This distinction is essential because the proposed model aims to classify TLS according to functional orientation, rather than simply relabeling existing maturity or location categories.

Table 3. Modular criteria for functional assignment of TLS-A, TLS-B and TLS-C.

Assessment module	TLS-A	TLS-B	TLS-C
Structural maturity module	GC-positive mature TLS; organized follicular architecture; follicular dendritic cell network; GC activity [13,18,80,106,113]	Organized non-GC TLS or incompletely matured TLS; partial B/T-cell compartmentalization; weak or incomplete FDC network [13,80,113]	Organized TLS may be present, with or without mature morphology; structure alone is insufficient for TLS-C assignment [20,48,81,115]
Density / burden module	High density of mature TLS may support TLS-A interpretation, especially if concordant with effector context [77,113,114]	Variable TLS density; maturity-weighted scoring may suggest intermediate TLS burden [77,113,114]	High TLS density alone does not define TLS-C; density must be interpreted with suppressive context [20,48,81,115]
Spatial localization module	Intratumoural or invasive-margin localization may strengthen TLS-A interpretation when combined with maturity and effector features [20,88,115]	Any location; spatial context may modify interpretation but does not define TLS-B [20,88,115]	Peritumoural localization may support TLS-C interpretation only when accompanied by suppressive immune-context evidence [20,48,81,115]
Immune-context module	Effector-oriented profile: CD8+ T cells, Tfh cells, GC B cells, plasma cells, mature dendritic cells, HEVs, cytotoxic markers [19,20,66,81,86,88]	Mixed or intermediate immune context; partial effector features without clear TLS-A dominance and without suppressive dominance [20,48,81,115]	Suppressive-dominant profile: FOXP3+ Tregs, Breg-like phenotypes, CD163+/CD206+ macrophages, neutrophils, checkpoint-rich or IDO1-related suppressive context [20,34,35,48,57,81]
Molecular support module	TLS/GC/Tfh/B-cell, plasma-cell, antigen-presentation, interferon and cytotoxic transcriptomic signatures [108,116,117,118]	Intermediate lymphoid-organizing signals without robust GC, plasma-cell, cytotoxic or suppressive co-signature [108,116,117,118]	TLS-associated signals combined with regulatory, suppressive myeloid, checkpoint, metabolic or T-cell dysfunction programs [108,116,117,118]
Final assignment	Mature effector TLS; high confidence when structural, spatial, immune-context and molecular features are concordant	Transitional/intermediate TLS; should reflect intermediate biology, not insufficient evidence	Suppressive TLS; requires direct evidence of regulatory or suppressive dominance

Table 3. Modular criteria for functional assignment of TLS-A, TLS-B and TLS-C.

Assessment module	TLS-A	TLS-B	TLS-C
Structural maturity module	GC-positive mature TLS; organized follicular architecture; follicular dendritic cell network; GC activity [13,18,80,106,113]	Organized non-GC TLS or incompletely matured TLS; partial B/T-cell compartmentalization; weak or incomplete FDC network [13,80,113]	Organized TLS may be present, with or without mature morphology; structure alone is insufficient for TLS-C assignment [20,48,81,115]
Density / burden module	High density of mature TLS may support TLS-A interpretation, especially if concordant with effector context [77,113,114]	Variable TLS density; maturity-weighted scoring may suggest intermediate TLS burden [77,113,114]	High TLS density alone does not define TLS-C; density must be interpreted with suppressive context [20,48,81,115]
Spatial localization module	Intratumoural or invasive-margin localization may strengthen TLS-A interpretation when combined with maturity and effector features [20,88,115]	Any location; spatial context may modify interpretation but does not define TLS-B [20,88,115]	Peritumoural localization may support TLS-C interpretation only when accompanied by suppressive immune-context evidence [20,48,81,115]
Immune-context module	Effector-oriented profile: CD8+ T cells, Tfh cells, GC B cells, plasma cells, mature dendritic cells, HEVs, cytotoxic markers [19,20,66,81,86,88]	Mixed or intermediate immune context; partial effector features without clear TLS-A dominance and without suppressive dominance [20,48,81,115]	Suppressive-dominant profile: FOXP3+ Tregs, Breg-like phenotypes, CD163+/CD206+ macrophages, neutrophils, checkpoint-rich or IDO1-related suppressive context [20,34,35,48,57,81]
Molecular support module	TLS/GC/Tfh/B-cell, plasma-cell, antigen-presentation, interferon and cytotoxic transcriptomic signatures [108,116,117,118]	Intermediate lymphoid-organizing signals without robust GC, plasma-cell, cytotoxic or suppressive co-signature [108,116,117,118]	TLS-associated signals combined with regulatory, suppressive myeloid, checkpoint, metabolic or T-cell dysfunction programs [108,116,117,118]
Final assignment	Mature effector TLS; high confidence when structural, spatial, immune-context and molecular features are concordant	Transitional/intermediate TLS; should reflect intermediate biology, not insufficient evidence	Suppressive TLS; requires direct evidence of regulatory or suppressive dominance

To improve interpretability and reproducibility, each TLS-A/B/C assignment should ideally be accompanied by a confidence level reflecting the completeness and concordance of available data. High-confidence TLS-A requires GC-positive maturity together with effector immune-context features and, when available, supportive spatial or molecular findings. High-confidence TLS-C requires organized TLS with direct evidence of regulatory or suppressive dominance. Cases with insufficient tissue, inadequate marker panels, missing regional annotation, or discordant results should be classified as unclassifiable or low-confidence rather than forced into one of the three functional states. Thus, the proposed Functional TLS Score should be viewed as a structured reporting framework rather than a validated clinical tool and will require tumour-specific validation, reproducibility testing, and prospective correlation with clinical outcomes [20,77,106,108,113,114,115,116,117,118].

To facilitate reproducible use of this framework across tumour types and study designs, we propose a minimum reporting checklist for TLS assessment, summarizing the essential variables that should accompany any TLS-A/B/C assignment in translational or pathology-oriented studies (Table 4).

8. Knowledge Gaps and Future Directions

In our work we have proposed a conception of functional states of TLS, answering the inconsistencies in literature findings about their tumoral role. We have moved from the present approach, which is mostly based on quantity parameters, to a more complex interpretation of TLS. Taking into consideration TLS maturity, location and immune context allows reasoning for their variable roles reported in the studies. In the proposed approach, TLS diversity mirrors their functional heterogeneity, which may help to fill the knowledge gap and succeed at facilitating TLS understanding [20].

Nevertheless, our division should be acknowledged carefully as there are no clear boundaries between the proposed framework. Moreover, in clinical practice, TLS-A, TLS-B and TLS-C may overlap and lead to false TLS classification, as there is a possibility of coexisting TLS of different function in one TME. This altogether furtherly complicates TLS classification and limits the usefulness of simplified schemes. In this context, a significant challenge remains distinguishing between the actual function of TLS and their phenotype captured in histological material, which does not reflect the dynamics of immunological processes occurring over time [3,4,20].

Another limitation is linked to the lack of standardized methods of TLS assessment, both molecular and histopathological. Differences in maturity, spatial analysis or tissue sampling make it difficult to compare results across studies. It seems that only the integration of data, which would include spatial imaging, transcriptomic profiling, and cellular population characteristics will allow for a reliable assignment of TLS to a specific functional state and their treatment correlation [3,4].

What is more, it will be crucial to further research into the plasticity of TLS. Available data suggests that these structures may undergo remodeling in the response to changes in the TME, including treatments. It allows a possibility that TLS are solely passive markers of immune response, but active components that can be therapeutically modulated. A particularly interesting aspect is the identification of factors promoting the transition from immature or suppressive phenotypes (TLS-B/TLS-C) to the effector phenotype (TLS-A), which could potentially enhance the efficacy of immunotherapy [4,11,23].

On the other hand, it is possible that in certain contexts, the induction or presence of TLS may have adverse effects. It concerns mainly immunosuppressive treatments [23]. This highlights the need for a careful approach to the concept of therapeutically inducing TLS without considering their functional quality. In such scenarios, future strategies should focus not only on increasing the number of TLS, but above all on the targeted modulation of their cellular composition and function.

Summing up, the proposed model of functional states of TLS is a useful interpretational scheme. However, its practical worth will greatly depend on its implementation possibilities in the clinical studies. A key set would be development of standardized, reproducible TLS assessment tools such as integrated indices. Only such an approach will allow for the full exploitation of the potential of TLS as biomarkers and therapeutic targets in modern oncology.

9. Conclusions

Tertiary lymphoid structures should not be interpreted solely as present or absent histological findings. Their biological and clinical significance depends on structural maturity, spatial localization and immune-cell composition. Mature germinal-centre-positive TLS are most consistently associated with coordinated antitumour immunity and improved response to immunotherapy, whereas incompletely matured or suppressive TLS may have neutral, context-dependent or even adverse significance.

In this review, we propose a pathology-oriented framework that groups TLS into three simplified functional states: TLS-A, TLS-B and TLS-C. This model is intended to organize heterogeneous evidence, improve interpretability across tumour types and support more standardized TLS reporting. The proposed Functional TLS Scoring Framework integrates histopathology, spatial assessment, immune-context markers and optional molecular signatures, but should be regarded as a conceptual and translational tool rather than a validated clinical score.

Future studies should validate this framework in prospective, tumour-specific cohorts using standardized definitions, reproducible pathology workflows and spatially resolved immune profiling. Such efforts may help clarify when TLS act as biomarkers of effective antitumour immunity, when they represent transitional immune niches, and when they may contribute to immunosuppression or therapeutic resistance. Ultimately, functional TLS assessment may support more precise biomarker development and guide future strategies aimed not only at inducing TLS, but at promoting their maturation toward pro-immunogenic states.