Density and maturity ratio of tertiary lymphoid structures in stage II–III non-small cell lung cancer predict postoperative recurrence risk

Introduction

Lung cancer is currently the leading cause of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for over 85% of lung cancer cases. Approximately 30% of NSCLC cases are diagnosed at locally advanced stages, including IIA, IIB, IIIA, and IIIB (1,2). These stages are characterized by advanced disease progression, larger tumor burden, aggressive biological behavior, and frequent mediastinal lymph node metastasis, leading to high postoperative recurrence rates and consequently reduced survival. Patients at these stages typically require multimodal combination therapies, such as neoadjuvant therapy (chemotherapy or immunotherapy) to reduce tumor size and improve resectability, or adjuvant therapy (chemotherapy or radiotherapy) to decrease recurrence risk. However, there is currently no standardized protocol for determining optimal treatment sequencing and timing, necessitating personalized approaches based on individual patient characteristics (3,4). The existing tumor node metastasis (TNM) staging-based prognostic system fails to accurately identify high-risk recurrence populations, resulting in some patients suffering unnecessary toxicity from overtreatment, while others may experience disease recurrence due to insufficient treatment.

In recent years, biomarker-guided risk stratification has emerged as a pivotal strategy for optimizing the management of locally advanced NSCLC. Although biomarkers such as programmed cell death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and circulating tumor DNA (ctDNA) have demonstrated certain predictive value, their clinical application remains limited by spatial heterogeneity and dynamic evolution characteristics (5,6). Furthermore, heat shock protein 70 (Hsp70), as a stress-responsive protein, has been significantly correlated with adverse prognosis in NSCLC: elevated serum Hsp70 levels show positive associations with tumor volume, indicating increased tumor burden and heightened metastatic risk (7,8).

Tertiary lymphoid structures (TLSs), also known as tertiary lymphoid organs or ectopic lymphoid structures, are organized aggregates of immune cells that form in non-lymphoid tissues under pathological conditions. Structurally, mature TLSs consist of three distinct compartments: T-cell zones (CD3⁺/CD8⁺ cytotoxic T lymphocytes), germinal centers (GCs) [interactive regions of CD20⁺/CD23⁺ B cells and follicular dendritic cells (FDCs)], and high endothelial venules (HEVs). Their developmental process closely mirrors that of secondary lymphoid organs (SLOs) (9,10). Beyond structural organization, TLSs coordinate spatially-organized immune responses through two synergistic pathways: (I) within T-cell zones, dendritic cell-mediated antigen presentation directly activates tumor-specific cytotoxic T lymphocytes, circumventing immunosuppressive barriers; (II) through GC formation, TLSs drive B-cell differentiation into antibody-secreting plasma cells that generate opsonizing antibodies, thereby enhancing phagocytosis and antibody-dependent cellular cytotoxicity (ADCC). Evidence indicates that TLSs demonstrate robust predictive value for immune checkpoint inhibitor efficacy (11-15). Multiple studies have demonstrated that mature TLSs containing GCs are significantly associated with improved survival outcomes in patients with solid tumors such as colorectal and breast cancers, suggesting their potential as a pan-cancer prognostic biomarker (16,17).

Previous studies of TLSs in NSCLC have been extensive, and most of these studies have focused on the impact of TLSs on neoadjuvant therapy and the function of TLS components, such as protective B cells, infiltrating T cells, and immunosuppressive regulatory T cells or possible regulatory strategies and the relationship between TLSs and immunotherapy (13,18-21). It is not clear whether the number and maturation of TLSs in their original state in patients undergoing surgery for intermediate and advanced lung cancer without preoperative treatment follow the same pattern as in other tumors, and whether TLS in its original state can reflect anti-tumor immunity in stage II–III NSCLC and serve as a biomarker for the prediction of recurrence risk.

Therefore, this study investigates the correlation between the quantitative features and maturation ratio of TLSs in tumor tissues of stage II–III NSCLC patients and their clinicopathological characteristics/prognosis. By establishing an immune scoring system based on TLS density and maturation ratio, we aim to provide novel theoretical foundations for prognostic stratification and personalized treatment optimization in stage II–III NSCLC. We present this article in accordance with the REMARK reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-477/rc).

Methods

Study design & patient population

This study is a single-center, retrospective cohort study. A total of 118 patients were included, who underwent radical lung cancer surgery at the Department of Thoracic Surgery, The First Medical Center of Chinese PLA General Hospital, between 2010 and 2020, and were pathologically diagnosed with primary stage II–III NSCLC. None of the patients had received chemotherapy or radiotherapy before surgery, and all underwent more than two cycles of adjuvant chemotherapy postoperatively. Patients with a history of autoimmune diseases or those who received immunosuppressive drugs or targeted molecular therapy were excluded. All patients were followed up regularly via phone, with follow-up starting from the date of surgery until the patient’s death or the last follow-up (January 2024). A total of 8 patients were lost to follow-up, with a median follow-up duration of 53 months. Clinical staging was determined based on the 8th edition of the TNM classification guidelines by the International Association for the Study of Lung Cancer (IASLC).

The main clinical and pathological characteristics of the patients included age, gender, smoking history, tumor location, TNM stage, histological type (squamous carcinoma, adenocarcinoma, adenosquamous carcinoma), and adjuvant treatment (platinum-based chemotherapy).

This retrospective study was approved by the Ethics Committee of The First Medical Center of Chinese PLA General Hospital (approval No. S2024-377-01) and conducted according to the Declaration of Helsinki and its subsequent amendments; informed consent was waived due to the study’s retrospective design.

Histopathological assessment of TLS

Two experienced pathologists independently evaluated TLS morphology using hematoxylin and eosin (HE)-stained slides. TLS density and spatial distribution (intratumoral vs. stromal) were quantified across all tumor specimens. Intratumoral TLS density was calculated as the total TLS count normalized to the tumor-invasive margin area (TLS/mm²). Discordant assessments were resolved by adopting the higher score. Given the non-normal distribution of the data and consistent with the majority of TLS studies in solid tumors, we selected the median value as the cutoff for defining high/low TLS density groups, as it optimally balances statistical power and clinical interpretability. Patients were stratified into TLS-negative, TLS-low, and TLS-high groups using median-based cutoffs (22-24).

TLS maturity, analogous to SLOs, relies on coordinated interactions between innate and adaptive immune cells, characterized by increased FDCs and mature B cells extravasating through HEVs; maturity progresses through three distinct stages: early TLS (E-TLS) consisting of dense lymphocyte aggregates lacking CD21/CD23 expression, FDCs, or segregated T/B-cell zones; primary follicle-like TLS (PFL-TLS) exhibiting immature FDCs without GCs; and secondary follicle-like TLS (SFL-TLS) featuring dense lymphocyte clusters with active GC reactions resembling SLOs, with SFL-TLS representing fully mature TLS (14,25,26).

Multiplex immunohistochemical validation

Since CD3, CD20, CD21, and CD23 antibodies can label T cells, B cells, FDC grids, and GCs, respectively, we selected HE-stained sections showing TLS positivity that were further stained with CD3, CD20, CD21, and CD23 antibodies to validate TLS maturity (27). TLSs were classified as mature when CD3, CD20, CD21, and CD23 staining were all positive, indicating the presence of mature B cell clusters and GC-containing FDC networks. The maturity of TLSs was determined through multiplex immunohistochemistry (mIHC) analysis of paraffin-embedded tumor tissue sections. Sections (4 µm thick) from formalin-fixed, paraffin-embedded tumor tissues were dewaxed, hydrated, microwave antigen-repaired, and blocked with hydrogen peroxide and serum. The sections were incubated with primary antibodies, including anti-CD3 epsilon antibody [SP7] (ab16669, 1:150 dilution), anti-CD20 antibody [SP32] (ab64088, 1:100 dilution), anti-CD21 antibody [SP186]-C-terminal (ab227662, 1:100 dilution), anti-CD23 antibody [BU38] (ab254162, 1.114 µg/mL), anti-Ki67 antibody (ab15580, 0.5 µg/mL), and anti-pan Cytokeratin antibody [PAN-CK (Cocktail)] (ab215838, 2 µg/mL), and stored overnight at 4 °C in a transparent humid chamber, protected from light. The slides were then incubated with the corresponding secondary antibodies conjugated with HRP-ELISA and fluorescent dyes in the following sequence: iF488-TSA, iF440-TSA, iF546-TSA, iF647-TSA, iF594-TSA, iF700-TSA. Nuclei were stained with DAPI, autofluorescence was quenched, and the slides were mounted with anti-fade mounting medium and scanned for panoramic imaging.

TLS maturity ratio assessment

We observed that the count of fully mature TLS and the proportion of all observable TLS varied among patients. Patients were divided into high, middle, and low maturity ratio groups based on the proportion of mature TLSs among all TLSs in the slide. Various subgroups of patients were created based on all TLS variables (TLS density and maturity ratio) to compare prognosis. There were no validated cut-off values, so we used the median and quartiles of the data distribution as empirical cut-off values for stratifying these variables. For all time-to-event analyses, we used ratios instead of crude counts of E-TLS, PFL-TLS, and SFL-TLS, as this better reflects the relative composition of TLSs in the patient’s body.

Analyzing indicators

(I) TLS characteristics: differences in clinical indicators were compared between high- and low-density groups, stratified by the median density of intratumoral TLSs. (II) The relationship between intratumoral TLS density and 3-year OS, 3-year RFS, 5-year OS, and 5-year RFS in patients with stage II–III NSCLC. (III) The relationship between the proportion of mature TLSs and 3-year OS, 3-year RFS, 5-year OS, and 5-year RFS in patients with stage II–III NSCLC. (IV) Grouping based on the density and maturity ratio of TLSs, forming D score and M score, and combining them to establish an immune score. Analyze the differences in 5-year RFS among patients with different scoring categories. Follow-up time began from the date of surgery to the date of death or the last follow-up.

Statistical analysis

SPSS software V.26.0 (IBM) and GraphPad Prism (V.8.0) were used for statistical analysis. Categorical variables were compared using the χ² test or Fisher’s exact test, and general clinicopathological characteristics of the patients, including gender, age, smoking history, location of the lesion, T stage, N stage, post-treatment nodal margin staging (pTNM) stage, and type of pathology, were analyzed in relation to the density of TLS and the proportion of TLS maturation. Survival analyses were performed using the Kaplan-Meier method of plotting curves and log-rank tests to compare differences between groups. Univariate and multivariate Cox regression analyses were used to assess independent factors associated with recurrence-free survival (RFS) and overall survival (OS). A P<0.05 was considered statistically significant.

Results

Baseline analysis

This study enrolled 118 patients with stage II–III NSCLC undergoing surgical resection, comprising 81 males (68.64%) and 37 females (31.36%), with a mean age of 56.19±8.05 years. Pathological staging was as follows: stage II (40.68%, n=48), stage III (59.32%, n=70). Histopathological subtypes included adenocarcinoma (68.64%, n=81), squamous cell carcinoma (29.66%, n=35), and adenosquamous carcinoma (1.70%, n=2), with clinicopathological characteristics detailed in Table 1.

There were 8 patients (6.78%) who were lost to follow-up and excluded from survival analyses. Among the 110 evaluable patients, 45 recurrence events (40.91%) and 12 cancer-specific deaths (10.91%) were documented. Patients discontinuing follow-up for non-study-related reasons were right-censored at their last verified clinical encounter, including radiographic or pathologic assessments. In the OS analysis, 26 cases (22.00% of the cohort) were right-censored within 3 years, and 47 cases (39.83%) were censored by the 5-year endpoint. For RFS, 24 patients (20.34%) were censored prior to 3 years, with 44 cases (37.29%) censored within the 5-year follow-up period.

Impact of intratumoral TLS density

TLSs were observed in the tumors of 77 patients (Figure 1A). Patients were divided into three groups based on the presence and density of TLS: TLS-negative, TLS-low, and TLS-high, with the median TLS density used to differentiate the TLS-low and TLS-high groups (Figure 1B). Comparison revealed that TLS density was significantly associated with the T stage of patients with stage II–III NSCLC (Figure 1C) but not with the N stage (Table 2).

Figure 1 TLS density stratification and pathological features. (A) Arrangement of study cohort densities. Patients were divided into the TLS-high and TLS-low groups according to the median density of 0.037/mm² (density range 0.308–0.005/mm²) in TLS-positive patients. (B) Histopathological features of high- vs. low-density TLSs in HE-stained sections at 1× and 40× magnification. TLS identification: green demarcated areas indicate outlined TLS. (C) Differences in TLS density across T stages in the TLS-positive patient subgroup. HE, hematoxylin and eosin; TLS, tertiary lymphoid structure.

In subsequent survival analysis, patients who were lost to follow-up were excluded from the study cohort. We further analyzed the relationship between TLS density and OS and RFS in post-surgical patients with stage II–III NSCLC. In univariate analysis, patients in the TLS-high group had significantly better 3-year RFS, 3-year OS, 5-year RFS, and 5-year OS compared to the other two groups (Figure 2A-2D). In multivariate Cox regression analysis, TLS density was also an independent risk factor for 5-year RFS, with patients in the high-density TLS group showing a significantly lower recurrence risk [hazard ratio (HR) 0.344, 95% confidence interval (CI): 0.157–0.756] compared to the TLS-negative group (Table 3).

Figure 2 The relationship between TLS and postoperative prognosis of stage II–III NSCLC patients. (A) Correlation between TLS density and patients’ 3-year RFS. (B) Correlation between TLS density and patients’ 5-year RFS. (C) Correlation between TLS density and patients’ 3-year OS. (D) Correlation between TLS density and patients’ 5-year OS. NSCLC, non-small cell lung cancer; OS, overall survival; RFS, recurrence-free survival; TLS, tertiary lymphoid structure.

Characteristics of mature TLS ratio

Further immunofluorescence staining revealed three different TLS phenotypes, representing different maturation stages in NSCLC: early TLS, PFL-TLS and SFL-TLS (Figure 3). Immunofluorescence staining of some HE sections showed that mature TLSs were also present in normal lung tissue surrounding the tumor. Our study on TLS maturation ratios was based on the proportion of TLS staining in the entire section.

Figure 3 Multiplex immunohistochemical characterization of TLS maturity in stage II–III NSCLC. (A) Representative mIHC images showing the expression of CD3, CD20, CD21, CD23, Pan-CK, Ki-67 in TLSs at different maturity levels. (B) The mIHC staining (CD3, CD20, CD21, CD23, Ki-67) images of high and low maturity ratio TLSs. Red arrowheads indicate maturity. mIHC, multiplex immunohistochemistry; NSCLC, non-small cell lung cancer; TLS, tertiary lymphoid structure.

The ratio of mature SFL-TLSs was associated with overall TLS density (Figure 4A). A detailed analysis of TLS maturation differences in patients with varying TLS densities revealed that TLS maturation was arrested at the E-TLS stage in tumors with low TLS density, resulting in a significant reduction in the proportions of PFL-TLS and SFL-TLS stages.

Figure 4 The ratio of mature TLS correlated with density and with patients’ RFS at 3 and 5 years, but OS variability was small. (A) Correlation between TLS density and ratio of TLS maturity. (B) Correlation between the ratio of TLS maturity and patients’ 3-year RFS. (C) Correlation between the ratio of TLS maturity and patients’ 5-year RFS. (D) Correlation between the ratio of TLS maturity and patients’ 3-year OS. (E) Correlation between the ratio of TLS maturity and patients’ 5-year OS. OS, overall survival; RFS, recurrence-free survival; TLS, tertiary lymphoid structure.

We calculated the ratio of mature TLSs for each patient, identifying 54 patients with mature TLSs. These patients were divided into high, middle, and low maturity ratio groups based on tertiles (Table 4), and their 5-year outcomes were analyzed. While the 5-year OS analysis did not yield significant results, further analysis showed that a higher TLS maturity ratio was associated with better 3-year and 5-year RFS (Figure 4B-4E). In multivariate Cox regression analysis, the maturity ratio of TLS was also an independent risk factor for 5-year RFS, with patients in the high group having a significantly lower risk of recurrence compared with the low ratio group (HR 0.148, 95% CI: 0.031–0.695) (Table 5).

Immune scoring combining TLS density and maturity ratio

Considering the combined role of TLS density and maturation ratio in predicting recurrence risk, we developed a scoring system that stratifies post-surgical patients with stage II–III NSCLC into different risk categories. Patients were further divided into 6 categories (Score 0–5). Each category had different TLS densities and maturity ratios. In terms of density, a D score was assigned, with 0 points for no TLS, 1 point for low TLS density, and 2 points for high TLS density. In terms of maturity ratio, an M score was assigned, with 0 points for no mature TLS, 1 point for low maturity, 2 points for intermediate maturity, and 3 points for high maturity. A score of 0 represented patients without TLS, and a score of 5 represented patients with high TLS density and high maturity ratio. Scores 1–4 represented heterogeneous distributions of TLS, depending on the relative changes in the two scores. Grouping based on simple score addition provided some stratification of postoperative recurrence risk, though further differentiation could be achieved (Figure 5A).

Figure 5 Predictive value of immune score and immune class for postoperative recurrence risk in patients with stage II–III NSCLC. (A) Correlation between different immune scores and patients’ 5-year RFS. (B) Correlation between different immune classes and patients’ 5-year RFS. The immune score (0–5) provides a composite representation of the relative gradient in both density and maturation proportion of TLS within patient tumors, spanning from minimal to maximal levels. Classification (class 1–3) corresponds to recurrence risk stratification: class 1 (low-risk), class 3 (high-risk). NSCLC, non-small cell lung cancer; RFS, recurrence-free survival; TLS, tertiary lymphoid structure.

To integrate TLS density (D score) and maturity ratio (M score) into a unified prognostic framework, categorical variables were transformed into continuous scales and incorporated into a multivariable Cox proportional hazards regression model. The resulting regression coefficients were utilized as weighting factors to derive a composite immune grading score: immune score = (−0.5× D score) + (−0.4× M score). The negative coefficients reflect the protective association of higher TLS density and maturity with reduced recurrence risk. The magnitude of these coefficients corresponds to the relative contribution of each parameter to the protective effect. Patients were subsequently stratified into three prognostic tiers. Patients in class 1 exhibited the lowest recurrence risk, while those in class 3 demonstrated the highest recurrence risk (Figure 5B).

Discussion

Predicting recurrence risk in stage II–III NSCLC patients is of significant clinical importance, as it can help personalize postoperative adjuvant chemotherapy decisions and adjust follow-up intervals accordingly. This immune scoring system can be used to classify patients into different risk categories and guide the intensity and duration of adjuvant therapy. Patients with high immune scores had higher levels of intratumoural TLSs density and maturation ratios, indicating effective anti-tumour capacity and treatment response. For example, patients with low immune scores may benefit from more aggressive adjuvant chemotherapy or immunotherapy, while patients with high scores may be treated with milder regimens.

Our data reveal that high T-stage NSCLC exhibits diminished TLS density even among TLS-positive patients, suggesting active tumor volume-driven TLS suppression. Diminished TLS density in high T-stage NSCLC patients may be attributed to the hypoxic microenvironment and extensive necrotic regions characteristic of large tumors. This pathological milieu disrupts lymphoid architecture through dual mechanisms, impairing lymphocyte aggregation and inhibiting TLS formation. It can be further explored in the subsequent research.

The tumor stroma consists of the extracellular matrix and various non-tumor cells, including immune cells. TLSs provide a crucial microenvironment for the cellular immune response against tumor cells and are considered a favorable prognostic indicator in various solid cancers (28,29). In our study, we demonstrated that TLS density and maturation level are predictive of prognosis in stage II–III NSCLC patients. A study on colorectal cancer liver metastases (CRCLM) showed that the abundance of intratumoral TLSs is an indicator of favorable clinical outcomes, while peritumoral TLSs were significantly associated with poor prognosis. However, the characteristics of peritumoral TLSs in NSCLC and their impact on prognosis, as well as the underlying mechanisms through which TLSs influence prognosis, remain unclear (30).

TLSs are key players in antitumor immune responses, yet their interactions and dynamics throughout tumor progression remain to be fully deciphered (31). The presence of TLSs or their induction following cancer treatment is typically associated with favorable clinical outcomes and can predict treatment efficacy. Further research into TLS formation and composition may help to elucidate the molecular mechanisms at play within the tumor microenvironment (TME). Currently, B cell-related pathways within TLSs, such as the CXCL13/CXCR5 axis and the CCL19/CCL21/CCR7 axis, are being widely studied (32-34). Inducing the formation of TLSs and inhibiting tumor development via TLS mechanisms holds promise for future research. TLSs and the corresponding B cell pathways may represent novel antitumor targets in the era of T cell therapy (35).

Lastly, there is heterogeneity in the methods used to quantify TLSs. Cell types associated with TLSs, such as mature dendritic cells, B cells, follicular helper T cells, and HEVs, have been used as substitutes for quantifying TLSs in cancer tissues. However, most studies consistently find that the density of TLSs correlates with patient prognosis across various cancer types (24,36-38). In our study, we did not choose to use surrogate markers but instead applied pathological methods to detect TLSs as a whole structure. We observed substantial phenotypic differences in TLSs both within and between patients, based on their ability to recruit FDCs and generate GCs. This supports the concept that TLSs mature progressively as B-cell activity increases. Moreover, our study demonstrated that high-density native TLSs are associated with better outcomes in stage II–III NSCLC. Additionally, beyond density, most studies on TLS maturation in tumors focus solely on patients with antibody-positive TLSs as representing high maturity (20,39). However, the number of mature and immature TLSs can vary. Therefore, our study designed a method for quantifying the maturation ratio, finding that patients with a higher ratio of mature TLSs had better prognoses. Both the TLS density and maturation ratio, as well as the combined immune scoring equation, have predictive value for postoperative recurrence risk in stage II–III NSCLC. Our HE-based approach optimized clinical applicability. Emerging techniques like digital spatial profiling may resolve TLS niche interactions in future work.

This study does have some limitations. Firstly, a larger sample size would undoubtedly improve the accuracy of the results. The statistical validity of the survival analyses may be limited by the proportion of right-censored cases, and future studies with larger sample sizes will be needed for validation. Specifically, some associations that do not meet the pre-specified threshold of statistical significance (P<0.05) may show different results with higher sample sizes or event rates. In addition, external validation of immunological scores and recommended grouping thresholds in clinical cohorts is needed if the concepts of TLS density and maturation are to be applied in the clinic.

Conclusions

In summary, this comprehensive analysis of clinical, molecular, and tissue parameters in post-surgical stage II–III NSCLC patients revealed three novel findings. First, our data show that TLS density is associated with tumor T stage and is an independent prognostic indicator for this group of patients. Second, the TLS maturation ratio is an important parameter of the tumor immune microenvironment, with significant prognostic and potential predictive value. Third, integrating TLS quantity and maturation ratio into a dual-protective factor immune score can identify high-risk and low-risk patient subgroups for tumor recurrence.

Acknowledgments

We sincerely acknowledge Dr. Jie Gao and Dr. Zhouhuan Dong from the Department of Pathology at The First Medical Center of Chinese PLA General Hospital for their independent histological assessment of TLS status.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-477/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-477/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-477/prf

Funding: This work was supported by National Natural Science Foundation of China (grant No. 62076254), and Beijing Municipal Natural Science Foundation (grant No. 7222164).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-477/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Medical Center of Chinese PLA General Hospital (approval No. S2024-377-01). Informed consent was waived due to the study’s retrospective design.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Goldstraw P, Chansky K, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2016;11:39-51. [Crossref] [PubMed]
2. Detterbeck FC, Boffa DJ, Kim AW, et al. The Eighth Edition Lung Cancer Stage Classification. Chest 2017;151:193-203.
3. Galon J, Mlecnik B, Bindea G, et al. Towards the introduction of the 'Immunoscore' in the classification of malignant tumours. J Pathol 2014;232:199-209. [Crossref] [PubMed]
4. Fridman WH, Zitvogel L, Sautès-Fridman C, et al. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol 2017;14:717-34. [Crossref] [PubMed]
5. Normanno N, Morabito A, Rachiglio AM, et al. Circulating tumour DNA in early stage and locally advanced NSCLC: ready for clinical implementation? Nat Rev Clin Oncol 2025;22:215-31. [Crossref] [PubMed]
6. Tsai YT, Schlom J, Donahue RN. Blood-based biomarkers in patients with non-small cell lung cancer treated with immune checkpoint blockade. J Exp Clin Cancer Res 2024;43:82. [Crossref] [PubMed]
7. Gunther S, Ostheimer C, Stangl S, et al. Correlation of Hsp70 Serum Levels with Gross Tumor Volume and Composition of Lymphocyte Subpopulations in Patients with Squamous Cell and Adeno Non-Small Cell Lung Cancer. Front Immunol 2015;6:556. [Crossref] [PubMed]
8. Sha G, Jiang Z, Zhang W, et al. The multifunction of HSP70 in cancer: Guardian or traitor to the survival of tumor cells and the next potential therapeutic target. Int Immunopharmacol 2023;122:110492. [Crossref] [PubMed]
9. Dieu-Nosjean MC, Goc J, Giraldo NA, et al. Tertiary lymphoid structures in cancer and beyond. Trends Immunol 2014;35:571-80. [Crossref] [PubMed]
10. Cai D, Yu H, Wang X, et al. Turning Tertiary Lymphoid Structures (TLS) into Hot Spots: Values of TLS in Gastrointestinal Tumors. Cancers (Basel) 2023;15:367. [Crossref] [PubMed]
11. Chandnani N, Gupta I, Mandal A, et al. Participation of B cell in immunotherapy of cancer. Pathol Res Pract 2024;255:155169. [Crossref] [PubMed]
12. Prelaj A, Tay R, Ferrara R, et al. Predictive biomarkers of response for immune checkpoint inhibitors in non-small-cell lung cancer. Eur J Cancer 2019;106:144-59. [Crossref] [PubMed]
13. Kim A, Lee SJ, Ahn J, et al. The prognostic significance of tumor-infiltrating lymphocytes assessment with hematoxylin and eosin sections in resected primary lung adenocarcinoma. PLoS One 2019;14:e0224430. [Crossref] [PubMed]
14. Schumacher TN, Thommen DS. Tertiary lymphoid structures in cancer. Science 2022;375:eabf9419. [Crossref] [PubMed]
15. Schalper KA, Brown J, Carvajal-Hausdorf D, et al. Objective measurement and clinical significance of TILs in non-small cell lung cancer. J Natl Cancer Inst 2015;107:dju435. [Crossref] [PubMed]
16. Wang Q, Yu Y, Wang C, et al. Heterogeneity of tertiary lymphoid structures predicts the response to neoadjuvant therapy and immune microenvironment characteristics in triple-negative breast cancer. Br J Cancer 2025;132:295-310. [Crossref] [PubMed]
17. Posch F, Silina K, Leibl S, et al. Maturation of tertiary lymphoid structures and recurrence of stage II and III colorectal cancer. Oncoimmunology 2018;7:e1378844. [Crossref] [PubMed]
18. Germain C, Gnjatic S, Tamzalit F, et al. Presence of B cells in tertiary lymphoid structures is associated with a protective immunity in patients with lung cancer. Am J Respir Crit Care Med 2014;189:832-44. [Crossref] [PubMed]
19. Joshi NS, Akama-Garren EH, Lu Y, et al. Regulatory T Cells in Tumor-Associated Tertiary Lymphoid Structures Suppress Anti-tumor T Cell Responses. Immunity 2015;43:579-90. [Crossref] [PubMed]
20. Sun X, Liu W, Sun L, et al. Maturation and abundance of tertiary lymphoid structures are associated with the efficacy of neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer. J Immunother Cancer 2022;10:e005531. [Crossref] [PubMed]
21. Xu F, Zhu H, Dong Y, et al. Combined inflammatory parameters and tertiary lymphoid structure predict prognosis in patients with resectable non-small cell lung cancer treated with neoadjuvant chemoimmunotherapy. Front Immunol 2023;14:1244256. [Crossref] [PubMed]
22. Vanhersecke L, Bougouin A, Crombé A, et al. Standardized Pathology Screening of Mature Tertiary Lymphoid Structures in Cancers. Lab Invest 2023;103:100063. [Crossref] [PubMed]
23. Kinker GS, Vitiello GAF, Diniz AB, et al. Mature tertiary lymphoid structures are key niches of tumour-specific immune responses in pancreatic ductal adenocarcinomas. Gut 2023;72:1927-41. [Crossref] [PubMed]
24. Wang Y, Lin H, Yao N, et al. Computerized tertiary lymphoid structures density on H&E-images is a prognostic biomarker in resectable lung adenocarcinoma. iScience 2023;26:107635. [Crossref] [PubMed]
25. Xiaoxu D, Min X, Chengcheng C. Immature central tumor tertiary lymphoid structures are associated with better prognosis in non-small cell lung cancer. BMC Pulm Med 2024;24:155. [Crossref] [PubMed]
26. Flippot R, Teixeira M, Rey-Cardenas M, et al. B cells and the coordination of immune checkpoint inhibitor response in patients with solid tumors. J Immunother Cancer 2024;12:e008636. [Crossref] [PubMed]
27. Berthe J, Poudel P, Segerer FJ, et al. Exploring the impact of tertiary lymphoid structures maturity in NSCLC: insights from TLS scoring. Front Immunol 2024;15:1422206. [Crossref] [PubMed]
28. N J. Tertiary lymphoid structures and B lymphocytes in cancer prognosis and response to immunotherapies. Oncoimmunology 2021;10:1900508. [Crossref] [PubMed]
29. Sautès-Fridman C, Lawand M, Giraldo NA, et al. Tertiary Lymphoid Structures in Cancers: Prognostic Value, Regulation, and Manipulation for Therapeutic Intervention. Front Immunol 2016;7:407. [Crossref] [PubMed]
30. Zhang C, Wang XY, Zuo JL, et al. Localization and density of tertiary lymphoid structures associate with molecular subtype and clinical outcome in colorectal cancer liver metastases. J Immunother Cancer 2023;11:e006425. [Crossref] [PubMed]
31. Vaghjiani RG, Skitzki JJ. Tertiary Lymphoid Structures as Mediators of Immunotherapy Response. Cancers (Basel) 2022;14:3748. [Crossref] [PubMed]
32. Rouanne M, Arpaia N, Marabelle A. CXCL13 shapes tertiary lymphoid structures and promotes response to immunotherapy in bladder cancer. Eur J Cancer 2021;151:245-8. [Crossref] [PubMed]
33. Ukita M, Hamanishi J, Yoshitomi H, et al. CXCL13-producing CD4+ T cells accumulate in the early phase of tertiary lymphoid structures in ovarian cancer. JCI Insight 2022;7:e157215. [Crossref] [PubMed]
34. Groeneveld CS, Fontugne J, Cabel L, et al. Tertiary lymphoid structures marker CXCL13 is associated with better survival for patients with advanced-stage bladder cancer treated with immunotherapy. Eur J Cancer 2021;148:181-9. [Crossref] [PubMed]
35. Zhao H, Wang H, Zhou Q, et al. Insights into tertiary lymphoid structures in the solid tumor microenvironment: anti-tumor mechanism, functional regulation, and immunotherapeutic strategies. Cancer Biol Med 2021;18:981-91. [Crossref] [PubMed]
36. Sun R, Liu Z, Zhang Y, et al. Density of tertiary lymphoid structures predict clinical outcome in hepatoblastoma. Pediatr Res 2025; Epub ahead of print. [Crossref]
37. Zhai K, Ma Y, Gao X, et al. Tertiary lymphoid structures in esophageal cancer: a novel target for immunotherapy. Front Immunol 2025;16:1543322. [Crossref] [PubMed]
38. Xiang Z, Wei X, Zhang Z, et al. Efficacy, safety and single-cell analysis of neoadjuvant immunochemotherapy in locally advanced oral squamous cell carcinoma: a phase II trial. Nat Commun 2025;16:3968. [Crossref] [PubMed]
39. Trüb M, Zippelius A. Tertiary Lymphoid Structures as a Predictive Biomarker of Response to Cancer Immunotherapies. Front Immunol 2021;12:674565. [Crossref] [PubMed]