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Tertiary lymphoid structures (TLS) are organized immune cell aggregates that form within tissues under chronic inflammation, including the tumor microenvironment. Unlike secondary lymphoid organs such as lymph nodes and spleen, TLS develop ectopically at sites of persistent antigenic stimulation, and their presence within solid tumors has emerged as one of the most clinically relevant features of the tumor immune microenvironment. TLS serve as local hubs where antigen presentation, T cell activation, and B cell maturation converge, directly shaping the antitumor immune response.

The clinical significance of TLS spans multiple cancer types. In non-small cell lung cancer, melanoma, triple-negative breast cancer, and colorectal cancer, the presence of mature TLS correlates with improved survival and better responses to immune checkpoint inhibitors. In pancreatic ductal adenocarcinoma — a tumor type traditionally considered immunologically “cold” — neoadjuvant immunotherapy has been shown to induce TLS formation, converting the tumor microenvironment into a more immune-responsive state. These findings have positioned TLS characterization as a priority area in immuno-oncology research.

For researchers designing translational studies, understanding whether a tumor harbors TLS — and if so, whether those TLS are mature, functional, or dysfunctional — has become critical for interpreting immunotherapy response patterns. NGS-based approaches, including transcriptomic profiling, immune repertoire sequencing, and spatial transcriptomics, have been instrumental in moving TLS characterization beyond histology alone, enabling researchers to query the molecular composition, functional state, and spatial organization of these structures at unprecedented resolution.

What TLS Signatures Reveal About Immune Activity

The presence of TLS in the tumor microenvironment is consistently associated with improved survival and better response to immune checkpoint inhibitors across multiple cancer types, including non-small cell lung cancer, melanoma, triple-negative breast cancer, and head and neck squamous cell carcinoma. However, not all TLS are functionally equivalent. Their maturation state — ranging from early aggregates without B and T cell zone segregation, to fully mature structures with germinal centers, follicular dendritic cells, and high endothelial venules — determines their immunological impact.

Mature TLS, characterized by segregated B cell and T cell zones, functional germinal centers, and plasma cell niches, are consistently associated with robust antitumor immunity. Immature TLS, lacking these organizational features, may reflect incomplete or suppressed immune activation. The ability to distinguish these states at the molecular level has become a priority for immuno-oncology research, and NGS-based approaches provide the primary toolkit for this characterization.

A comprehensive 2025 review in the Journal of Hematology and Oncology proposed a harmonized TLS maturation classification and highlighted transcriptomic signatures that distinguish each stage:

• E-TLS (early aggregates) — loose immune cell clusters without segregated B/T zones; limited germinal center activity
• PFL-TLS (partial B/T zones) — emerging organization with partial compartmentalization; developing follicular dendritic cell networks
• SFL-TLS (fully segregated, germinal center-positive) — mature structures with functional germinal centers, high endothelial venules, and plasma cell niches

These signatures, encompassing chemokine gradients, B cell maturation markers, and T follicular helper cell gene programs, form the basis for NGS-based TLS assessment in research cohorts.

Transcriptomic Signatures of TLS Activity

Bulk RNA-seq provides the most accessible NGS-based approach for characterizing TLS status in tumor samples. TLS gene signatures typically include:

• Chemokines — CXCL13, CCL19, CCL21, CXCL9, CXCL10 (immune cell recruitment)
• B cell and plasma cell markers — MS4A1/CD20, CD79A, SDC1/CD138, TNFRSF17
• T follicular helper cell genes — CXCL13, PDCD1, ICOS
• Germinal center markers — BCL6, AICDA

The coordinated expression of these genes correlates with histologically confirmed TLS presence and provides a quantitative readout of TLS activity.

Beyond simple detection, transcriptomic profiling reveals functional heterogeneity. In the NADIM trial studying neoadjuvant chemoimmunotherapy in locally advanced NSCLC, bulk RNA-seq combined with spatial transcriptomics demonstrated that complete pathological responders had mature TLS enriched for plasma cell content, T follicular helper cells, and MHC antigen processing gene expression. Non-responders, despite having histologically identifiable TLS, showed a different transcriptomic profile dominated by naive B cell signatures, indicating that the absence of B cell maturation within TLS was associated with treatment resistance.

Transcriptomic deconvolution methods further extend the utility of RNA-seq for TLS research. Computational tools that infer immune cell composition from bulk expression data can estimate the relative abundance of B cells, plasma cells, T follicular helper cells, and other TLS-associated populations, enabling TLS-related analyses in studies where spatial information or histology is unavailable. For researchers working with archived tumor specimens or publicly available datasets, these approaches provide a valuable entry point for TLS characterization.

The translational impact of transcriptomic TLS characterization is evident across cancer types. In triple-negative breast cancer, a 2025 study using single-cell RNA-seq combined with bulk transcriptomic analysis demonstrated that TLS-high tumors expressed higher levels of immunoglobulin genes (IGHM, IGHG1) and showed enhanced CD8+ T cell cytotoxicity against neo-antigens. TLS maturation status — assessed through gene signatures rather than histology alone — was significantly associated with neoadjuvant treatment response, while total CD8+ T cell levels were not predictive. This finding underscores the specific value of TLS-focused transcriptomic analysis over general immune profiling for immunotherapy research.

In gastric cancer, a comprehensive 2025 study by Shen and colleagues used single-cell RNA-seq (12 patients) combined with spatial transcriptomics and multiplex immunofluorescence to map TLS spatial heterogeneity. The study found that TLS located in the tumor center — rather than the invasive margin — were most significantly associated with immunotherapy outcomes. By integrating transcriptomic and spatial data, the researchers constructed a risk score model based on immune cell density and spatial distribution that effectively predicted overall survival and progression-free survival, demonstrating that TLS spatial context adds independent prognostic information beyond TLS presence alone.

Immune Repertoire Sequencing Within TLS

The B cell and T cell compartments within TLS are not merely structural components — they are functionally active, undergoing clonal expansion, affinity maturation, and class switching. Immune repertoire sequencing of BCR and TCR repertoires within TLS-infiltrated tumors provides direct evidence of these processes and links TLS activity to the broader antitumor immune response.

In Merkel cell carcinoma, a 2025 study combining spatial transcriptomics with TCR clonotype mapping revealed that TLS-positive tumors harbored significantly richer and more diverse TCR repertoires compared to TLS-negative tumors. TCR clones within TLS-infiltrated regions included populations reactive against Merkel cell polyomavirus antigens, demonstrating that TLS facilitate the recruitment and expansion of tumor-reactive T cells. The study also identified high endothelial venules within TLS as entry points for naive and central memory T cells, suggesting that TLS serve as both recruitment platforms and activation sites for antitumor T cell responses.

BCR sequencing within TLS has yielded complementary insights. A 2024 study of colorectal cancer liver metastases using single-cell RNA-seq combined with single-cell BCR-seq found that TLS-positive tumors were enriched for IgG-producing plasma cells that generated tumor-reactive antibodies. These antibodies enhanced macrophage-mediated phagocytosis, providing a humoral mechanism of antitumor immunity that is absent in TLS-negative tumors. The study further identified CCL19-expressing cancer-associated fibroblasts as key organizers of B cell recruitment to TLS, a finding with implications for therapeutic TLS induction.

For researchers designing immune monitoring studies, incorporating immune repertoire sequencing alongside transcriptomic profiling provides a more complete picture of TLS functional status. Clonal expansion within the B cell compartment, in particular, distinguishes immunologically active TLS from inactive lymphoid aggregates and correlates with immunotherapy benefit across multiple cancer types. BCR and TCR sequencing approaches that capture both repertoire diversity and clonal structure are increasingly incorporated into TLS-focused research protocols.

Spatial Transcriptomics Resolves TLS Architecture

While bulk RNA-seq and immune repertoire sequencing provide molecular profiles of TLS activity, they lack spatial resolution. The organization of immune cells within TLS — and critically, the spatial relationship between TLS and the tumor — determines immunological function. Spatial transcriptomics has emerged as a transformative technology for TLS research, enabling researchers to map gene expression programs directly onto TLS-containing tissue sections. Key findings from recent studies illustrate the value of spatial resolution:

• Head and neck squamous cell carcinoma (2024) — Nanostring GeoMx digital spatial profiling demonstrated that TLS proximity to tumor cells was a critical determinant of immunotherapy response. Tumor-proximal TLS exhibited elevated interferon response gene signatures, while distal TLS showed reduced immune activation and less clinical benefit
• Hepatocellular carcinoma (2025) — Stereo-seq mapped 937 individual TLS across a large cohort, identifying two subtypes of immature TLS: “conforming” TLS (retaining maturation potential) and “deviant” TLS (blocked by TDO2-mediated tryptophan metabolism). TDO2 inhibition combined with anti-PD-1 therapy promoted TLS maturation in preclinical models
• Pancreatic ductal adenocarcinoma (2025) — Multi-omics analysis using spatial transcriptomics, imaging mass cytometry, and AI-based H&E classification revealed that TLS-associated B cell maturation and IgG antibody dissemination throughout the tumor microenvironment were significantly associated with improved survival following neoadjuvant GVAX + nivolumab

For research teams without direct access to spatial transcriptomics platforms, integrating TLS-related gene signatures from bulk RNA-seq with histological assessment provides a practical alternative. However, for studies where spatial context is essential — particularly those investigating TLS-tumor interactions or the organization of immune cells within TLS — spatial transcriptomic approaches provide information that cannot be inferred from homogenized tissue. Tumor microenvironment profiling services that incorporate spatial analysis are increasingly available to academic and pharma researchers.

Metabolic Pathways Shaping TLS Maturation

The discovery that metabolic pathways regulate TLS maturation has opened a new dimension in TLS research. Key metabolic axes identified to date include:

• Tryptophan-kynurenine pathway (TDO2) — The 2025 HCC study using spatial transcriptomics demonstrated that TDO2-mediated tryptophan metabolism creates a localized immunosuppressive microenvironment that prevents immature TLS from progressing to fully mature structures. Malignant cells in TDO2-high regions were associated with “deviant” immature TLS lacking germinal centers and functional B cell maturation. Dietary tryptophan restriction and pharmacological TDO2 inhibition both promoted TLS maturation and enhanced anti-PD-1 efficacy
• Cholesterol metabolism (ACAT1) — ACAT1-driven ROS production suppresses B cell activation within TLS, representing a second metabolic checkpoint in TLS maturation
• Glutamine metabolism — Glutamine-to-alpha-ketoglutarate signaling promotes immature TLS formation, linking metabolic state to TLS initiation

These findings have implications beyond HCC. The tryptophan metabolic axis is active across multiple cancer types, and the spatial relationship between metabolic enzyme expression and TLS localization may influence immunotherapy outcomes. Researchers investigating TLS in their tumor models of interest can use transcriptomic profiling to assess TDO2 expression relative to TLS gene signatures, identifying potential metabolic barriers to TLS maturation. The integration of metabolomic and transcriptomic data within spatial contexts represents a frontier in TLS research, with potential to identify new therapeutic targets for enhancing TLS-mediated antitumor immunity.

Translating TLS Insights Into Immunotherapy Research

For researchers designing studies that incorporate TLS analysis, several methodological considerations are worth noting. TLS assessment can be incorporated into existing NGS workflows with appropriate study design, sample handling, and analytical planning.

RNA-seq analysis for TLS gene signatures requires careful selection of reference signatures that capture the specific TLS features of interest. Pan-TLS signatures (CXCL13, CCL19, CCL21, MS4A1) provide a general readout of TLS presence, while maturation-specific signatures (BCL6, AICDA for germinal centers; SDC1, TNFRSF17 for plasma cells) enable functional classification. For studies using archived FFPE samples with potentially degraded RNA, targeted transcriptomic panels may provide more robust TLS assessment than whole-transcriptome approaches.

For spatial transcriptomics studies, platform selection depends on the resolution required. Imaging-based platforms provide cellular or subcellular resolution ideal for mapping individual TLS, while sequencing-based platforms offer whole-transcriptome coverage at spot-level resolution suitable for broader TLS-TME characterization. Sample preparation, including tissue selection and section orientation, should account for TLS heterogeneity within the tumor — TLS distribution can be highly variable, and sampling bias is a significant concern.

For immune repertoire sequencing within TLS studies, paired BCR and TCR analysis provides the most comprehensive picture of adaptive immune responses. Immune repertoire sequencing approaches that capture both receptor diversity and clonal structure enable researchers to track TLS-associated clonal expansion and assess the functional status of the B cell and T cell compartments.

The emerging picture from these integrated NGS approaches is that TLS represent much more than histological curiosities. They are functional immune hubs whose maturation state, spatial organization, and metabolic environment determine their contribution to antitumor immunity. For immuno-oncology researchers, incorporating TLS characterization into study designs — whether through transcriptomic signatures, immune repertoire analysis, or spatial profiling — provides a dimension of tumor microenvironment analysis that complements traditional immune cell phenotyping and offers insights relevant to immunotherapy response prediction and mechanism of action studies.

FAQ

How can I assess TLS presence without spatial transcriptomics?

Bulk RNA-seq gene signatures provide a practical alternative for TLS assessment when spatial data is unavailable. TLS gene signatures typically include chemokines (CXCL13, CCL19, CCL21), B cell markers (MS4A1/CD20, CD79A), and germinal center markers (BCL6, AICDA). Computational deconvolution methods can further estimate immune cell composition and TLS-associated populations from bulk expression data.

What gene signatures are most reliable for detecting mature TLS?

Mature TLS signatures combine germinal center B cell markers (BCL6, AICDA), plasma cell markers (SDC1/CD138, TNFRSF17), and T follicular helper cell genes (CXCL13, PDCD1, ICOS). The coordinated expression of these gene groups, rather than any single marker, provides the most reliable transcriptomic indicator of mature, functional TLS.

Can TLS be induced therapeutically?

Yes. Neoadjuvant immunotherapy has been shown to induce TLS formation in previously TLS-negative tumors, including pancreatic ductal adenocarcinoma. TDO2 inhibition combined with immune checkpoint blockade promotes TLS maturation in preclinical models. Additional strategies targeting metabolic checkpoints (ACAT1, glutamine metabolism) and chemokine signaling pathways are under investigation.

Why does TLS maturation state matter for immunotherapy response?

Mature TLS with functional germinal centers support B cell affinity maturation, plasma cell differentiation, and tumor-reactive antibody production. Immature TLS lacking these features correlate with treatment resistance even when histologically identifiable. TLS maturation state, assessed through gene signatures rather than histology alone, has been shown to predict neoadjuvant treatment response where total CD8+ T cell levels could not.

What is the relationship between TLS spatial location and clinical outcome?

TLS located in close proximity to tumor cells are more strongly associated with immunotherapy benefit than TLS located at the invasive margin or distant from tumor. Tumor-proximal TLS exhibit elevated interferon response gene signatures and enhanced immune activation programs. In gastric cancer, TLS in the tumor center rather than the invasive margin were most significantly associated with immunotherapy outcomes.

Key Takeaways for TLS Research

• TLS are organized immune aggregates whose maturation state (early, partially organized, fully mature with germinal centers) correlates with immunotherapy response across multiple cancer types
• Transcriptomic signatures capturing chemokine gradients, B cell maturation, and T follicular helper cell programs enable NGS-based TLS assessment from bulk RNA-seq data
• Immune repertoire sequencing of BCR and TCR repertoires within TLS-infiltrated tumors reveals clonal expansion, affinity maturation, and tumor-reactive antibody production
• Spatial transcriptomics resolves TLS organization and TLS-tumor proximity at unprecedented resolution, revealing metabolic and structural determinants of TLS function
• Metabolic pathways, particularly the TDO2-tryptophan-kynurenine axis, regulate TLS maturation and represent potential therapeutic targets
• Study design for TLS characterization should account for intratumoral TLS heterogeneity, platform resolution requirements, and the complementary value of multi-omic approaches