Spatial distribution analysis of tertiary lymphoid structures in esophageal squamous cell carcinoma predicts patient survival and response to  neoadjuvant chemo-immunotherapy

Spatial distribution analysis of tertiary lymphoid structures in esophageal squamous cell carcinoma predicts patient survival and response to neoadjuvant chemo-immunotherapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial distribution analysis of tertiary lymphoid structures in esophageal squamous cell carcinoma predicts patient survival and response to neoadjuvant chemo-immunotherapy lingxiong wang, Jinfeng Li, Yanyun Ao, Yanju Yu, Jinzhao Zhai, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7634389/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background: The prognostic and predictive significance of tertiary lymphoid structures (TLSs) exhibits spatial specificity in various cancers. However, the spatial distribution, phenotypic characteristics of TLSs in esophageal squamous cell carcinoma (ESCC) and their impact on prognosis and prediction are not yet fully understood. Methods: We performed multiplex immunofluorescence staining on 87 untreated ESCC specimens to simultaneously analyse TLS expression and phenotypic characteristics, CD8+ T cells and PNAD+ high endothelial venules (HEVs) in different spatial regions of ESCC tissues using Panel-1 (CD20/CD21/CD23/PNAD/CD8/Pan CK/DAPI). Panel-2 (CD20/ CD3/Foxp3/DC-LAMP/Pan CK/DAPI) was used to evaluate the spatial distribution of TLS-related immune cells (CD20+ B cells, CD3+ T cells, Foxp3+ Treg cells, and LAMP+ mature DCs) within the tumor microenvironment of ESCC. Furthermore, the predictive value of TLSs and the clinical prognostic significance of TLSs at different spatial locations were assessed in an independent cohort of 15 ESCC patients who received neoadjuvant chemo- immunotherapy (NACI). Results: In untreated ESCC, a high number/density of mature follicular TLSs (F-TLSs) at the distal (＞500μm) was significantly associated with better overall survival (OS) in patients (number: p=0.0092; density: p=0.0268). Patients with distal high F-TLSs not only exhibited high densities of CD3+ T cells, CD8+ T cells, CD20+ B cells, LAMP+ mature DCs, and PNAD+HEVs in the stromal regions, but this was also associated with increased CD8+ T cell infiltration within the tumor nests (p<0.05). Additionally, it was correlated with a reduced proportion of Foxp3+ Treg cells in the distal stromal regions. In patients with ESCC receiving NACI, the partial response (PR) group exhibited higher numbers and densities of F-TLSs post-treatment than the non-PR group (p<0.05). High numbers/densities of total TLSs, early-TLSs, and F-TLSs in the proximal stromal region (≤500μm) of post-treatment specimens were significantly associated with better OS (p<0.05). Conclusions: In untreated ESCC, distal mature F-TLSs are critical prognostic indicator, driving "favorable" immune infiltration by modulating the spatially heterogeneous immune microenvironment. After NACI, TLS quantity and spatial distribution were reshaped; mature F-TLSs predicted treatment response. Treatment-induced expansion of TLSs localised at the proximal tumour-stroma interface is a key indicator for predicting favourable long-term survival. Esophageal squamous cell carcinoma tertiary lymphoid structures spatial distribution digital analysis immune microenvironment neoadjuvant chemo-immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Eesophageal cancer (EC) is the seventh leading cause of cancer-related deaths worldwide, and epidemiological studies have shown that its incidence is gradually increasing [ 1 ]. Surgical resection is the foundational strategy for localised and locally advanced EC, with a 5-year survival rate of 15–39% [ 1 , 2 ]. Neoadjuvant therapy (radiotherapy, chemotherapy, or a combination of both) administered before surgery offers the advantage of addressing micrometastases and improving the rate of complete resection [ 2 ]. With the increasing use of immunotherapy in clinical practice in recent years, immune checkpoint inhibitors have become crucial treatments for patients with EC, and programmed cell death 1 (PD-1) inhibitors combined with chemotherapy have been established as the standard first-line treatment strategy for recurrent and metastatic oesophageal squamous cell carcinoma (ESCC) [ 3 , 4 ]. Currently, the use of neoadjuvant chemo-immunotherapy (NACI) has become widespread, and multiple phase I and II clinical trials have shown good efficacy in locally advanced resectable ESCC [ 5 – 9 ]. However, the biomarkers for predicting the efficacy of NACI in ESCC and alterations in the intratumoural immune microenvironment (IME) remain unclear. Tertiary lymphoid structures (TLSs) are organised aggregates of multiple immune cells, including T lymphocytes, B lymphocytes, and high endothelial venules in nonlymphoid tissues [ 10 ]. This structure has been associated with better clinical outcomes and responses to immunotherapy in many human tumours such as melanoma, head and neck squamous cell carcinoma, and ovarian cancer [ 11 – 14 ]. Recent studies have indicated that the function and prognostic significance of TLSs are related to their location [ 15 , 16 ]. For instance, in clear cell renal cell carcinoma [ 16 ], mature peritumoural TLSs were associated with a poorer prognosis, whereas the presence of distal TLSs in patients with endometrial cancer was significantly correlated with prolonged overall survival (OS) [ 17 ]. In ESCC, three studies examined the prognostic value of TLSs [ 18 – 20 ], and another study reported that TLSs can predict the response to immune checkpoint inhibitors in recurrent ESCC [ 21 ]. However, most of these studies used simple haematoxylin and eosin (HE)-stained sections or single-stain immunohistochemistry (IHC) of T and B cells to determine TLS presence and maturity, which may underestimate the true number of TLSs [ 22 , 23 ]. Furthermore, although these observations preliminarily explored the prognostic and predictive value of TLSs, the criteria for including TLSs in the quantification were inconsistent regarding their location. For instance, Hayashi et al. [ 21 ] only counted TLSs located within 1000 µm of the tumour nest boundary, whereas Rutao Li et al. [ 19 ] considered both intratumoural and peritumoural TLSs. Therefore, it is necessary to map the detailed spatial tissue architecture based on the cellular composition, maturity, location, and functional characteristics of TLSs to further explore their clinical correlation with patients with ESCC and comprehensively understand their role in anti-tumour immune responses within the tumour microenvironment. Multiplex immunofluorescence imaging can quantify the expression of multiple protein markers in the same tissue slice while maintaining spatial localisation [ 24 ]. In this study, we set two multiplexed immunofluorescence panels: an opal 7-colour dyes panel-1 (CD20/CD21/CD23/PNAD/CD8/Pan CK/DAPI) was used for simultaneous analysis of TLSs expression, phenotypic characterisation, and as well as tumour-infiltrating CD8 + T cells and PNAD + high endothelial venules in different spatial regions of ESCC tissues; and a opal 6-colour dyes panel-2 (CD20/CD3/Foxp3/DC-Lamp/Pan CK/DAPI) was used to the evaluate the spatial distribution of TLS-associated immune cells—CD20 + B cells, CD3 + T cells, Foxp3 + Treg cells, and LAMP + mature DCs—within the tumour microenvironment of ESCC tissue. Using the open-source Qupath software [ 25 ] for digital image analysis of fluorescent whole-tissue slices, we established relatively standardised operational approaches for TLSs acquisition, quantification, and spatial location. We analysed the spatial locations of TLSs with different phenotypes to explore the relationship between TLSs with distinct phenotypes at different locations and patients with ESCC prognosis. Additionally, we investigated the spatial distribution of TLSs in patients with ESCC receiving NACI and explored the predictive and prognostic value of spatially distinct TLS phenotypes for treatment efficacy. Materials and methods Patient cohorts and specimens In this study, we obtained surgically removed tumour specimens from 87 previously untreated consecutive patients with ESCC who underwent curative surgical resection at the First Medical Center of the PLA General Hospital between May 2011 and June 2012. These patients did not receive any tumour-related preoperative chemoradiotherapy or immunotherapy and received a routine cisplatin-based combination chemotherapy regimen for postoperative recurrence and progression. The patient's mean follow-up time was 30.67 months (interquartile range [IQR] 12.7–41.47 months), and basic clinicopathological characteristics are shown in Supplementary Table S1 . Herein, we also enrolled another cohort that received PD-1 blockade pembrolizumab in combination with nab-paclitaxel and cisplatin as neoadjuvant therapy for 15 patients with resectable ESCC. Between July 2020 and March 2022, patients diagnosed with ESCC were administered two cycles of preoperative neoadjuvant therapy, and oesophagectomy for ESCC was performed within a time frame of 4–8 weeks after neoadjuvant therapy. Postoperatively, 73% (11/15) of the patients completed two cycles of adjuvant therapy with an identical regimen as the neoadjuvant therapy. In patients with recurrence and progression, standard therapeutic strategies should be implemented according to the patient’s situation. These patients will be followed-up until 14 February 2025. The radiological RECIST criteria were used to evaluate the clinical response to neoadjuvant therapy. We collected paired tumour samples, which were baseline biopsies and surgically resected tissues, from pre- and post-NACI patients. Supplementary Table S2 presents the basic clinicopathological features of the patients. The study was approved by the Institutional Review Board of the PLA General Hospital (Approval No. S2019-228-02), and informed consent for relevant clinical data and tissue samples was obtained from the patients prior to surgery. Multiplexed immunofluorescence assay ( MIF) MIF assay was performed using an OPAL™ Polaris 7-Colour Manual IHC Kit (NEL861001KT; Akoya Biosciences). Specimen preparation was performed according to the manufacturer’s instructions using a previously published procedure [ 26 ]. Opal 7 colour dyes panel-1 (CD20/CD21/CD23/PNAD/CD8/Pan CK/DAPI) and opal 6 colour dyes panel-2 (CD20/CD3/Foxp3/DC-Lamp/Pan CK/DAPI) were stained in two sequential sections from the same patient. The primary antibodies and dilutions were anti-CD3 (1:300, Abcam, clone SP7), anti-CD8 (1:100, ZSBio, clone SP16), anti-CD20 (1:350, Abcam, clone SP16), anti-CD21 (1:300, ZSBio, clone EP64), anti-CD23 (1:200, ZSBio, clone EP75), anti-Foxp3 (1:100, Abcam, clone 236A/E7), anti-DC-Lamp (1:500, Abcam, clone EPR24265-8), anti-PNAD (1:100, BioLegend, clone MECA-79), and anti-Pan CK (1:2000, Abcam, clone, PAN-CK). Tyramide signal amplification was performed using Opal 480 (CD21), Opal 520 (CD3 and CD23), Opal 570 (CD8 and DC-Lamp), Opal 620 (Foxp3 and PNAD), Opal 690 (CD20), and Opal 780 (CK) fluorescent dyes. Nuclear counterstaining of the sections was used by spectral DAPI. Digital image analysis of MIF The whole-tissue section (WTS) stained with MIF was scanned using the Vectra Polaris platform on a x20 multispectral microscopy (Akoya Biosciences, USA), and the images were visualised using Akoya Phenochart software (v.1.1.0, Akoya Biosciences, USA). Quantitative digital image analysis and spatial annotation of MIF images were performed using QuPath (v.0.5.1) software [ 25 ]. For the quantitative analysis of markers, the DAPI channel for nuclear staining was selected for cell segmentation, the positive threshold of pixel fluorescence intensity was set to 15, and the nuclear area and cell parameters were the default options. In the single measurement classifier module, manually adjust the log histograms and real-time preview options to set a positive threshold for each marker: CD3 (dye-cell-positive threshold: mean MIF = 25), CD8 (dye-cell-positive threshold: mean MIF = 25), CD20 (dye-cell-positive threshold: mean MIF = 25), CD21 (dye-cell-positive threshold: mean MIF = 35), CD23 (dye-cell-positive threshold: mean MIF = 28), Foxp3 (dye-nucleus-positive threshold: mean MIF = 30), DC-Lamp (dye-cell-positive threshold: mean MIF = 30), PNAD (dye-cell-positive threshold: mean MIF = 30), and Pan CK (dye-cell-positive threshold: mean MIF = 22). A composite classifier for the MIF images was then created according to the individual classifiers for each marker. A composite classifier was used to quantify positive intensity values of each marker in each MIF image. For the acquisition of TLS, we employed automated annotation using CD20 channel density maps, followed by manual secondary validation and edge adjustment by two experienced pathologists. During this manual review, pathologists considered the T-cell zone of TLSs [ 27 ], such as the visualised CD3 or CD8 channels. In the density map module, the object type main class performed the CD20 channel, the density radius was set to 50 µm, the density threshold was set to 25, and other parameters were the default option. In spatial analysis, tumour nests and stromal regions were first annotated in tissue sections by Pan CK channel. Then select the outer boundary annotation line of the tumour nest as the interface, the tumour nests and stroma region of all markers were spatially annotated with a diameter of 100 µm by expand annotation module, covering all whole tissue. A hierarchical insertion approach was used for the spatial annotation of each TLS. In the annotation module, all TLSs that have been fully annotated are selected. Subsequently, all TLSs were hierarchically inserted, and the relative spatial positions were determined. Statistical analysis All Statistical analyses and data descriptions were performed using IBM SPSS Statistics (version 26). Data visualisation was performed using GraphPad Prism (version 8.0). Visualisation of cell proportional distribution analysis was performed using Chiplot online. Descriptions of the patients’ clinicopathological data are presented as percentages. Statistical comparisons between two or more groups were performed using two-tailed t-tests and one-way ANOVA. Correlation analysis of TLSs annotations in serial sections was performed using Pearson’s correlation coefficients. The correlation coefficient of ≥ 0.8 indicates a very strong correlation. The association between the distal mature follicular TLSs (F-TLSs) and clinicopathological features was analysed using the chi-square test. Patient survival analysis was conducted using the log-rank test and Kaplan-Meier method. All cases were stratified into high- and low-TLS groups based on an optimal cutoff value determined using the X-Tile tool [ 28 ] through minimal p-value analysis. Statistical significance was done with the following conventions: *p* < 0.05, **p* < 0.01, ***p* < 0.001, and ****p* 0.05) results were denoted as 'ns'. Results Quantification and spatial localisation strategies of TLSs and phenotypic classification TLSs differ from specific structured secondary lymphoid organs (SLOs) in that they exhibit different organisational patterns, which can be simple lymphocyte aggregates or more organised structures [ 10 , 23 ]. Therefore, in the TLS acquisition process, CD20 + B cells were used to identify as many aggregates as possible. Subsequently, we expanded the region around each B cell aggregate to include the surrounding T cell zone and ultimately obtained the TLSs of the WTS (Fig. 1 A, left). Previous studies only considered the total cell population of TLS aggregates [ 29 , 30 ]. In this study, in addition to setting the minimum radius (50 µm) of positive B cell dense aggregates when obtaining TLSs, we also set a threshold of at least 10 CD20 + positive B cells per TLS during data statistical analysis to quantify TLS. Here, we analysed the consistency of quantitative TLSs between two consecutive slices, and the results showed good agreement (Supplementary Fig. 1). In the spatial position annotation of TLS, we referred to the approach of Werner, Wagner, Simon, Glatz, Mertz, Läubli, Griss & Wagner [ 31 ] in melanoma. We used the outer boundary line of the tumour nest as the interface and expanded the inner and outer spaces of the tumour nest at a distance of 100 µm in diameter. Spatial annotation of TLSs within the tumour nest and stroma in the WTS was subsequently performed using a hierarchical insertion approach (Fig. 1 A, middle). When calculating the number of TLSs in adjacent spatial regions, if the relative area of the TLSs was significantly large, TLSs intersecting multiple perimeters were assigned to the perimeter with a dominant proportional area (Fig. 1 A, right). Based on molecular signal characterisation and structural features reported in previous studies [ 20 , 21 ], TLS phenotypes were divided into early TLSs (E-TLSs) and mature follicular TLSs (F-TLSs). Early TLSs were characterised by relatively dense CD20 + lymphocyte-dominated immune cell aggregations without CD21 or CD23 expression and with scattered T cells interspersed within the aggregates (Fig. 1 B, top). The F-TLSs can be further classified into two distinct subtypes: primary follicle-like TLSs (PFL-TLSs) and secondary follicle-like TLSs (SFL-TLSs). PFL-TLSs present as well-defined round or oval clusters of small lymphocytes containing PNAD + high endothelial vessels expressing CD21 but lacking CD23. (Fig. 1 B, middle), and SFL-TLSs exhibiting large round or oval follicles with definite germinal centres expressing CD23 and the variable presence of CD21. (Fig. 1 B, bottom). In the quantitative analysis of PFL-TLSs and SFL-TLSs, we set a minimum threshold of no fewer than five positive cells for both CD21 + B cells and CD23 + B cells. Spatial distribution analysis of TLSs in patients with ESCC In surgical resection tumour tissues from 87 untreated patients with ESCC, spatial distribution analysis of TLSs was performed in both intra-tumoural regions within 1000 µm (i.e., the tumour nests) and extra-tumoural regions located within a range of 7700 µm from the outer boundary of the tumour nests (i.e., the stroma) (Fig. 2 A). The intra- to extra-tumoural continuum was stratified into 87 consecutive sub-region with a diameter of 100 µm, with TLSs present in 95% of cases (83/87) and a total of 2,746 TLSs were present in 95% of the cases (83/87), and a total of 2,746 TLSs were detected (Supplementary Fig. 2A). To calculate the density of TLSs, we divided the number of TLS in each subregion of the tumour nest and stroma by the corresponding area of that region. This approach ensures the precise measurement of TLS distribution relative to the tissue area. No discernible TLSs were observed within the tumour nests, which is consistent with previous reports. We found TLSs were mainly distributed in the stromal area within a range of 4200 µm outside the tumour nests, exhibiting a gradual decrease in both number and density with increasing distance from the tumour margin (Fig. 2 A-B) Phenotypic stratification was performed for each TLSs of each patient. A total of 2, 370 E-TLSs and 376 F-TLSs were detected (171 PFL-TLSs and 205 SFL-TLSs) (Supplementary Fig. 2A). Next, we analysed the spatial distributions of E-TLSs, PFL-TLSs, SFL-TLSs, and F-TLSs. The E-TLSs population was predominantly localised within the 1,500 µm stroma region, and the number and density showed obvious peri-tumoural accumulation (Fig. 2 C; Supplementary Fig. S2B). The highest density was observed in the 100–300 µm subregion, accounting for 49.41% (1,171/2,370) of total E-TLSs. In contrast, although F-TLSs have a density peak at 500 µm within stroma region, its overall spatial distribution was relatively dispersed (Fig. 2 D; Supplementary Fig. S2C). PFL-TLSs and SFL-TLSs can detect up to 3500 µm from the outer boundary of the tumour nests (Fig. 2 E-F, Supplementary Fig. S2D-E). Notably, the stroma of 600–1200 µm subregion also exhibited a relatively high F-TLS density (mean density: 0.017/mm², [± 0.034]), despite their abundance progressively diminishing with increasing distance from the tumour nests (Fig. 2 D). Assessment of TLSs' spatial distribution relative to the tumour is important for the efficacy of its predictive outcomes [ 16 , 32 ]. Here, based on the above observations and previous reports [ 17 , 21 ], we further categorised TLSs within the stroma into proximal TLSs (≤ 500 µm from the tumour nests) and distal TLSs (> 500 µm from the tumour nests). We examined differences in the quantity and density of TLSs with different phenotypes in the proximal and distal regions. We found that proximal TLSs were mainly composed of early stage subtypes, accounting for 88.85% (1844/2073) of the total proximal TLSs (Fig. 2 G, left). In contrast, the proportion of mature phenotype F-TLSs significantly increased in the distal TLSs, reaching 21.84% (147/673) (Fig. 2 G, right). The densities (/mm2, [± SD]) of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in the proximal TLSs were 0.284 [± 0.241], 0.041 [± 0.055], 0.017 [± 0.029], and 0.023 [± 0.038], respectively (Fig. 2 H, left). The densities of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in the distal TLSs were 0.050 [± 0.060], 0.014 [± 0.039], 0.006 [± 0.024], and 0.010 [± 0.023], respectively (Fig. 2 H, right). Prognostic value of different spatial resident TLS and maturation status in patients with ESCC In research on the relationship between TLSs and the prognosis of ESCC, some researchers have utilised the number of TLSs within the tumour tissue [ 20 ], whereas others have considered the median density of TLSs per unit tissue area [ 21 ]. In this study, we used the optimal cutoff value to divide patients with ESCC into high and low groups and simultaneously analysed the association between both the number and density of TLSs and patient prognosis. We first investigated the prognostic relationship between total TLSs and TLSs with distinct maturation states (E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs) in patients with ESCC within individual subregions (Fig. 3 A; Supplementary Fig. S3A). We observed a prognostic difference between high and low density TLS groups for total TLSs within the 300–400 µm (p = 0.00964) and 600–800µm (p = 0.00355, p = 0.00652) subregions. Furthermore, survival analysis within subregions for different phenotypes revealed statistically significant differences: patients with high-density E-TLSs in the 300–400 µm (p = 0.00584) and 600–900 µm (p = 0.02686, p = 0.01902, p = 0.0455) subregions, and high-density F-TLSs in the 800–900 µm (p = 0.04288) subregion, showed significantly better survival outcomes compared to their respective low-density TLS groups. Subsequently, we counted the number and density of TLSs in the cumulative subspace spatial regions to investigate the prognostic relationship between TLSs and patients with ESCC. We found that in the cumulative regions beyond 500 µm from the outer boundary of tumour nests, patients with ESCC with high-density total TLSs showed significantly better survival compared to those with low density TLSs, with a statistically significant p-value (Fig. 3 B; p < 0.05 for cumulative regions beyond 500 µm). The impact of TLSs with different phenotypes on survival in cumulative regions was analysed. We found that mature F-TLSs, whether assessed by number or density in the cumulative regions 600 µm away from the outer boundary of tumour nest, compared with the low number or density TLSs group, the high number or density TLSs group showed a consistent significant correlation with the prognosis of patients with ESCC (Fig. 3 B; Supplementary Fig. S3). This finding is also consistent with the secondary spatial density distribution observed in our analysis of the F-TLS density patterns. Based on the above results, we speculate that the distal TLSs (> 500 µm) have a more significant impact on the survival of patients with ESCC (Fig. 3 C-D, supplementary Fig. S3C-D). We further examined the prognostic relationship between different TLS phenotypes within the distal TLSs in patients with ESCC. We found that patients with ESCC with a high number or density of mature F-TLSs at the distal end had better OS compared with those with a low number or density of TLSs at the distal (Fig. 3 C-D, TLSs high number, p = 0.0092; TLSs high density, p = 0.0268). Here, we also analysed the correlation between remote F-TLS and clinicopathological features, and the results are shown in Supplementary Table S3-4. Overall, through multi-level spatially stratified analysis of survival, we revealed the differential prognostic impact of TLSs based on their spatial localisation in patients with ESCC. Distal TLSs demonstrated superior prognostic value, particularly as the stromal regions beyond 500 µm from tumour nests contained a higher abundance of mature F-TLSs, which showed a statistically significant correlation with improved OS. Analysis of spatial distinct IMEs related to TLSs in ESCC patients and its relationship with TLSs Previous studies have shown that mature TLSs shape intratumoural IME in ESCC. ESCC with mature TLSs shows higher intratumoural CD8 + T cell infiltration compared to absent mature TLSs [ 18 ]. In this study, we observed that spatially distinct TLSs with different maturation states located beyond the boundary of tumour nests differentially impacted the prognosis of patients with ESCC. Therefore, we hypothesised that the distribution of TLSs with different maturation states within the spatially heterogeneous IME of ESCC may exhibit functional divergence. The antitumour efficacy of TLSs is likely influenced by both their spatial localisation and maturation status in ESCC. Using MIF staining, we simultaneously analysed the spatial distribution of CD20 + B cells, CD3 + T cells, CD8 + cytotoxic T cells, Foxp3 + regulatory T cells (Tregs), LAMP + dendritic cells (mature DCs), and PNAD + high endothelial venules (HEVs) within both the tumour nests and stroma of the ESCC specimens (Fig. 4 A). Based on the density distribution of TLSs, we also stratified the stromal distribution of the above-mentioned immune cells and HEVs into proximal (≤ 500 µm) and distal (> 500 µm) regions. We observed that CD20 + B cells, CD3 + T cells, CD8 + cytotoxic T cells, Foxp3 + Tregs, and LAMP + mature DCs exhibited varying densities of infiltration in both tumour nests and stroma, whereas PNAD + HEVs were mainly located within the stroma (Supplementary Fig. S4A). Spatial stratification analysis of the stroma revealed that the proximal infiltration densities of CD20 + B cells, CD3 + T cells, CD8 + T cells, and Foxp3 + Tregs were significantly higher than the distal infiltration densities(Fig. 4 B). The density distribution relationship among TLSs, different immune cells, and PNAD + HEVs in different subspatial regions was investigated. We found that in patients with ESCC with a high density of total TLSs compared to those with a low density of total TLS, CD20 + B cells, CD3 + T cells, CD8 + T cells, Foxp3 + Tregs, and LAMP3 + DCs exhibited distinct proportional clustering within tumour nests (Fig. 4 C). Statistical analysis of cell densities revealed a significant difference in CD8 + T cells among patients with ESCC with a high density of total TLSs compared with those with a low density of total TLS (Fig. 4 D). Overall, in the stromal region, patients with a high density of total TLSs exhibited increased infiltration of CD20 + B cells, CD3 + T cells, CD8 + T cells, Foxp3 + Tregs, and LAMP3 + DCs, along with enhanced PNAD + HEV distribution (Fig. 4 D). Notably, regional stratification analysis revealed that in the high-density total TLSs group, proximal CD3 + T cells and CD8 + T cells were significantly higher than their distal counterparts. Furthermore, a greater distribution of PNAD + HEVs was observed at the proximal end than at the distal end (Fig. 4 D). Additionally, in patients with a high density of total TLSs, the increase in Foxp3 + Tregs in both the proximal and distal areas did not reach statistical significance (Fig. 4 D). Cellular proportion analysis showed significantly higher clustering of Foxp3 + Tregs in the low-density total TLSs group than in the high-density total TLSs group (Fig. 4 C). Our spatial survival analysis revealed that distal TLSs demonstrated a better prognostic value, with distal F-TLSs specifically showing a significant association with improved OS. Therefore, we explored the relationship among distal TLSs, different immune cell subsets, and PNAD + HEVs within distinct spatial regions. We observed that in the patient group with a high number or density of distal E-TLSs compared to the group with a low number or density, although there was an obvious dense infiltration of CD8 + T cells within the stromal regions, the tumour nests did not exhibit the corresponding high-density infiltration of CD8 + T cells (Fig. 4 E, Supplementary Fig. S4B). However, in the patient group with a high number or density of distal F-TLSs, compared to the group with a low number or density, there was a significantly higher density of CD8 + T cells not only in both the proximal and distal stromal regions, but also within the tumour nests, with the differences reaching statistical significance (Fig. 4 F, Supplementary Fig. S4C). Furthermore, in the patient group with a high number or density of distal F-TLSs, compared to the group with a low number or density, there was a significantly greater distribution of PNAD + HEVs in both the proximal and distal stromal regions (Fig. 4 F, Supplementary Fig. S4C). Additionally, patients with a high number or density of distal F-TLSs showed lower numbers or density of Foxp3 + Tregs in the distal region (Fig. 4 F, Supplementary Fig. S4C). The spatial distribution of TLSs in patients with ESCC with NACI and the predictive value of TLS for their therapeutic efficacy and prognosis The spatial distribution of TLSs was examined in 15 paired ESCC tumour specimens collected pre- and post-NACI. In the pretreatment biopsy samples, 15 consecutive subregions were identified, extending from the tumour nests to the surrounding stroma (Supplementary Fig. S5A). Phenotypic analysis of TLSs revealed a predominance of E-TLSs (46/49), primarily located within 600 µm of the outer periphery of the tumour nests (Supplementary Fig. S5 B-E). After NACI, a significant increase in TLSs was observed, with a total TLSs, E-TLSs, and F-TLSs (PFL-TLSs and SFL-TLSs) of 272, 162, and 110 (63 and 47), respectively (Fig. 5 A). Spatial analysis revealed that high densities of both E-TLSs and F-TLSs were mainly concentrated within 1000 µm outside the tumour nests, although there was heterogeneous TLSs distribution in the compartment within 3300 µm outside the tumour nests (Supplementary Fig. S5 F-J). Recent studies have shown a positive correlation between mature TLSs and treatment efficacy in patients with ESCC receiving either neoadjuvant chemotherapy or neoadjuvant immunotherapy alone [ 29 ]. In this study, we investigated the association between the TLSs and the efficacy of NACI in ESCC. In the pre-treatment baseline, among patients with radiological responses (PR) and non-PR, whether it was total TLSs or TLSs of different phenotypes, there was no statistically significant difference in their number and density between the two groups (Fig. 5 B; Supplementary Fig. S6A). However, after treatment, both the number and density of F-TLSs were significantly increased in patients with PR compared to those in non-PR patients (Fig. 5 C; Supplementary Fig. S6B). Comparison of TLSs number and density of TLSs between PR and non-PR patients before and after treatment revealed that in patients with PR, both the number and density of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs increased significantly after treatment (Fig. 5 D; Supplementary Fig. S6C). In contrast, among non-PR patients, although the number of E-TLSs and F-TLSs increased post-treatment, their density showed no statistically significant difference (Fig. 5 E; Supplementary Fig. S6D). In this study, we examined the IME of TLS-related cells in patients with ESCC undergoing NACI and observed that within the tumour nests, the non-PR patient group exhibited a significantly higher infiltration of Treg cells compared to the PR group at baseline, indicating an immunosuppressive microenvironmental bias (Fig. 5 F). Following treatment, PR patients demonstrated prominent infiltration of CD3 + T cells within regions proximal to the stroma (Fig. 5 G). Survival analysis was performed on a cohort of patients with ESCC who underwent NACI. No association was found between the TLS levels in baseline biopsy specimens and OS (Supplementary Fig. S6E). This finding is consistent with the tumour microenvironment analysis, which revealed high intratumoural Treg cell infiltration in patients exhibiting high TLSs density (Supplementary Fig. S7A). In post-treatment specimens, patients with a higher number or density of total TLSs had significantly improved survival rates compared to those with a lower number or density of total TLSs. Following phenotypic stratification, statistically significant differences were observed in both E-TLSs and F-TLSs among patient cohorts with a high number or density of total TLSs (Supplementary Fig. S6F). Spatial regionalisation analysis of TLSs revealed that compared to the group with low number and density of proximal TLSs, high number and density of total TLSs, E-TLSs, and F-TLSs in the proximal region were all significantly associated with improved patient survival, with statistically significant differences (Fig. 5 H; Supplementary Fig. S6 G). However, patients with a high number and density of distal TLSs showed a trend toward better survival than those with a low number and density of distal TLS; however, this difference was not statistically significant. This finding corresponded to the IME analysis across different subspatial regions (Supplementary Fig. S7A). Discussion TLSs are ectopic lymphoid structures formed within non-lymphoid tissues, including tumours. Antitumour immunity and prognostic impact of TLSs in different cancers vary significantly depending on their spatial distribution, maturity, and abundance within the tumour tissue. Intratumoural TLSs abundance serves as an effective prognostic marker for favourable outcomes in intrahepatic cholangiocarcinoma, whereas the presence of peritumoural TLSs is positively correlated with poor prognosis [ 15 ]. TLSs in breast cancer, hepatocellular carcinoma, and clear cell renal cell carcinoma also demonstrate dual prognostic functions based on their spatial location, although its underlying mechanisms require further elucidated [ 16 , 33 , 34 ]. This study established a standardised operational procedure for the acquisition, quantification, and spatial localisation of TLSs using MIF and spatial digital image analysis. Spatial stratification analysis revealed that, in ESCC, the spatial location and maturation status of TLSs jointly shape their prognostic value and immunomodulatory function. Our data show that mature F-TLSs located in the distal region above 500 µm on the outer edge of the tumour nest have the most significant positive impact on the OS of patients with ESCC. This finding is consistent with observations in endometrial cancer, in which the presence of distal TLSs was associated with significantly prolonged OS [ 17 ]. Our analysis of the spatial distribution of IME and its relationship with TLSs suggests that well-structured TLSs located distal to the tumour core may represent more effective antitumour immune induction centres. They exert enhanced immune surveillance and antitumour effects by promoting extensive infiltration of CD8 + cytotoxic T cells within both tumour nests and stromal regions, particularly by overcoming the infiltration barrier inside tumour nests [ 35 – 37 ]. Notably, the presence of distally located high-density F-TLSs was associated with a richer distribution of PNAD + HEVs in the stromal regions (particularly distally) and a lower proportion of Foxp3 + Treg cells. Studies have shown that HEVs are present around TLSs, are associated with tumour infiltration by T and B cells [ 38 , 39 ], and serve as the primary site for the extravasation of naïve CD8 + T cell subsets [ 40 ]. However, as the abundance of TLSs around the tumour increased, the frequency of Treg cells within intratumoural TLSs significantly increased, suggesting a potential link between peri-tumoural TLSs and the immunosuppressive environment inside the tumour [ 15 ]. In conclusion, these data support the crucial role of mature distal TLSs in facilitating efficient lymphocyte trafficking and maintaining the effector IME. Previous studies have assessed the predictive value of mature TLSs for treatment efficacy in patients with ESCC receiving either neoadjuvant chemotherapy or neoadjuvant immunotherapy alone [ 29 , 41 ]. In addition, high-density TLSs in patients with recurrent ESCC treated with anti-PD-1 antibodies predicted the clinical response to anti-PD-1 antibodies and patient survival [ 21 ]. The analysis of our NACI cohort provides important insights into the dynamic changes in TLSs and their predictive value. We found that although the baseline TLS characteristics (both number and density) before treatment showed no significant association with the initial treatment response (PR vs. non-PR), the post-treatment response status (PR) was closely associated with a significant increase in F-TLSs. More importantly, the high density of TLSs in post-treatment specimens—specifically those localised to the peritumoural regions near the tumour margin (≤ 500 µm), including total TLSs, E-TLSs, and F-TLSs—demonstrated a strong prognostic association, predicting improved survival outcomes. This contrasts with the core prognostic value of distal TLSs observed in baseline analyses, suggesting that neoadjuvant therapy greatly reshaped the distribution pattern and functional focus of TLSs. The significant infiltration of CD3 + T cells in the proximal stromal regions of post-treatment PR patients, as well as the pre-treatment tendency for higher Treg cell infiltration within the tumour nests of non-PR patients, collectively depict the spatial correlation between treatment response and activation of the local IME. Consequently, the treatment-induced amplification and maturation of proximal TLSs may serve as effective biomarkers for evaluating the long-term benefits of NACI in patients with ESCC. Early studies on melanoma [ 12 ], sarcoma [ 42 ] and triple-negative breast cancer [ 43 ] have shown that the presence of TLSs, especially mature or germinal centre-positive TLSs, in needle biopsy tissues is associated with better OS and a positive response to immune checkpoint inhibitor therapy. However, in our study, although TLSs were detected within a 600 µm region at the outer edge of the tumour nest in pre-NACI biopsy samples of ESCC, they did not show statistically significant differences in predicting treatment response or in survival analysis. One possible reason for this is that [ 44 ], and a single biopsy may not capture regions richer in TLSs, requiring repeated samplings or imaging guidance. However, current methods for detecting TLSs may require refinement, for instance, by incorporating the detection of TLS-related gene signatures (such as B cell-related genes and chemokines, such as CXCL13, CCL19, and CCL21 [ 12 ]) to indirectly infer the presence and functional status of TLSs. Studies examining the association between TLSs and prognosis have not reached a consensus on whether TLSs should be evaluated based on their absolute quantity or their density. This is because there is currently no unified methodology for the morphological definition, maturity classification, and counting of TLSs [ 11 , 27 ]. Additionally, the analysis areas are inconsistent; some use the invasive margin [ 21 ], others the tumour nest [ 16 , 45 ], and others the whole tissue section [ 18 ], making it difficult to establish a standardised analysis. Our study simultaneously analysed the relationship between the number and density of TLSs in relative tissue regions and patient prognosis in two cohort studies involving patients with untreated ESCC and NACI. The results showed that both the number and density of TLSs can predict the OS of patients. In contrast, the relative density of TLSs in the corresponding tissue regions was more specific to patient survival. This study has several limitations. First, our spatially stratified analysis revealed the impact of different spatial localisations and maturation statuses of TLSs on the prognosis and response to NACI, but the sample sizes in both the retrospective and NACI cohorts were limited. Therefore, these findings need to be validated in larger multicentre studies. Second, although our analysis of IME examined the number or density of total TLSs, categorised by subtype and spatial distribution, and their correlation with immune cell infiltration across various tumour regions (such as tumour nests, stroma, and proximal and distal areas), it did not specifically assess how the variation in immune cell composition within each TLS subtype in these different regions is related to the overall tumour microenvironment infiltration. Thirdly, current research indicates that the proportion of CD138 + plasma cells is significantly associated with TLS maturity [ 21 ], with mature TLSs exhibiting a higher percentage of IgM-containing cells [ 29 ] compared to immature TLSs. However, owing to limitations in the number of markers included in the MIF panel, this study did not incorporate markers related to mature B cells and their functional states. Therefore, future investigations are necessary to further explore the relationship between mature B cells, spatial distribution of TLSs, and tumour IME. In summary, by integrating spatial localisation, maturation phenotypes, and the dynamic dimensions of therapeutic intervention, this study deepens our understanding of the complex functions of TLSs in ESCC. Our results indicate that, in untreated ESCC, distal mature F-TLSs serve as key positive prognostic factors, potentially mediated by the establishment of an effective distal immune surveillance hub. In contrast, among patients receiving NACI, treatment-induced amplification of TLSs located at the tumour–stroma interface (proximal region) emerged as a key predictor of favourable long-term survival. This spatial- and phenotype-dependent functional difference emphasises that when evaluating the clinical significance of TLSs, their precise anatomical location and maturity status must be considered. In addition, there have been several preclinical antitumour immunotherapy reports on TLS-induced interventions in mouse models [ 46 – 48 ], which may provide new ideas for establishing individualised antitumour immunotherapy strategies for cancer through the spatial induction and modulation of TLSs. Abbreviations Esophageal squamous cell carcinoma, ESCC Neoadjuvant chemo- immunotherapy, NACI Programmed cell death 1, PD-1 Tertiary lymphoid structures, TLSs Immune microenvironment, IME Overall survival, OS Whole-tissue section, WTS Multiplexed immunofluorescence, MIF High endothelial venules, HEVs Declarations Author contributions Lingxiong Wang: Conceptualization, Writing - review & editing, Writing - original draft, Visualization, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation. Jinfeng Li: Investigation, Visualization, Validation, Software, Methodology. Yanyun Ao: Investigation, Validation, Supervision, Resources. Yanju Yu: Methodology, Data curation, Jinzhao Zhai: Methodology, Formal analysis. Yinjie Zhang : Methodology, Software. Liangliang Wu : Investigation, Software. Jianqing Hao: Formal analysis. Yanyan Hu: Supervision. Qiong Wang: Conceptualization, Writing - review & editing, Validation, Fan Yin: Conceptualization, Writing - review & editing, Resources, Investigation. Tianyi Liu: Conceptualization, Project Administration, Writing - review & editing, Data curation, Formal analysis. Funding None. Data availability statement All data generated from the use or analysis in this study are available by the corresponding author upon reasonable request, except the patients data. Ethics approval and consent to participate The study conformed to the ethical approval processes and was approved by the Institutional ReviewCommittee of Chinese PLA General Hospital (Approval No. S2019-228-02). 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Cancer Cell2025, 43(6):1025-1044.e1014. Teillaud JL, Regard L, Martin C, Sibéril S, Burgel PR: Exploring the Role of Tertiary Lymphoid Structures Using a Mouse Model of Bacteria-Infected Lungs. Methods Mol Biol2025, 2864:281-297. Supplementary Files 2SupplementaryMaterial.doc Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 23 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor assigned by journal 17 Sep, 2025 First submitted to journal 16 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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14:33:00","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2100496,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/2330278f28919e65ac5a64b3.png"},{"id":92876095,"identity":"19b76e34-e8ec-408c-8f5a-5451e348edef","added_by":"auto","created_at":"2025-10-06 14:41:00","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":352134,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/d05a11e5dccecedf94e5a013.png"},{"id":92874080,"identity":"bece5dff-ee42-4577-b93f-611ab45c8fb8","added_by":"auto","created_at":"2025-10-06 14:25:01","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":560547,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/e3e1a09b3d11458e4a2f1343.png"},{"id":92874078,"identity":"6db7ded2-8b58-431e-9c12-20a4ec4f7ee2","added_by":"auto","created_at":"2025-10-06 14:25:01","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119543,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/2c32790c19383dc3a1dfd9d7.png"},{"id":92874076,"identity":"172bfa3b-1e5a-402f-933b-4e2ffc9d8fe3","added_by":"auto","created_at":"2025-10-06 14:25:01","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137905,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/04ce5ee460dc51e89503a324.png"},{"id":92874083,"identity":"6561ccdf-eb70-43f5-ad9a-dd7c7aee11fd","added_by":"auto","created_at":"2025-10-06 14:25:01","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":397977,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/0b90c003f56a022b036d8702.png"},{"id":92875080,"identity":"60d056c1-53d6-4450-9b50-de6532f8e00b","added_by":"auto","created_at":"2025-10-06 14:33:00","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165058,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/23e552a7be576749071ee097.png"},{"id":92874077,"identity":"dc51d8fb-a68a-4d9b-a359-c05d02111e01","added_by":"auto","created_at":"2025-10-06 14:25:01","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153122,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD25162590structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/bb9de2999cd326913584bace.xml"},{"id":92874081,"identity":"f0b0c274-d145-434e-b3eb-a185f5f6da37","added_by":"auto","created_at":"2025-10-06 14:25:01","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162390,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/7d101a2da6122d555f40537e.html"},{"id":92874062,"identity":"f87d8fbc-a3ec-4267-9c87-7444127f0881","added_by":"auto","created_at":"2025-10-06 14:25:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3193155,"visible":true,"origin":"","legend":"\u003cp\u003eQuantification, spatial localization and phenotypic classification of tertiary lymphoid structures (TLSs) in whole tissue sections (WTS) stained with multiplex immunofluorescence (MIF) in patients with esophageal squamous cell carcinoma (ESCC). (A) Representative digital image analysis for quantification and spatial localization of TLSs in MIF-stained WTS. The left panel shows the WTS divided into tumor nests and stromal regions via the Pan CK channel, with all TLSs annotated based on the density of CD20 positive B cells. The red shaded areas represent tumor nests, the blue-gray shaded areas indicate stromal regions, and the blue elliptical outlines denote TLSs. The middle panel illustrates spatial annotation of the entire section based on the outer boundary of the tumor nests, performed at intervals of 100 μm from the tumor margin into both the intra-tumoral nest and stromal regions. Hierarchical insertion was applied to determine the spatial distribution of TLSs within the tumor nests and stromal regions. The trapezoidal lines represent spatial annotation guides: yellow trapezoidal lines for the tumor nests region (red) and light yellow trapezoidal lines for the stromal region (blue-gray). The right panel displays a 100 μm magnified view of the area indicated by the white square in the left/middle panels, clearly showing the spatial location of an individual TLSs within the stromal region along the outer edge of a tumor nest. The blue elliptical outline highlights a single TLS. (B) Representative images of early E-TLSs and mature F-TLSs (PFL-TLSs, SFL-TLSs) in MIF staining. CD20+ B cells (red) , CD21+ FDCs (cyan-blue), CD23+ GC cells (green), CD8+ T cells (yellow), PNAD+ HVEs (orange), Pan CK+ tumor cells (white), and DAPI+ nuclei (blue).FDC, follicular dendritic cell; GC, germinal center; HVEs, high endothelial venules.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/39511839b5b03e4c58bf43e0.png"},{"id":92874057,"identity":"7b91ccb1-19d3-44a3-8193-95b49e09407c","added_by":"auto","created_at":"2025-10-06 14:25:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":279724,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution of tertiary lymphoid structures (TLSs) within tumor nests and stromal regions in surgical resection samples from 87 untreated esophageal squamous cell carcinoma (ESCC) patients. (A) and (B) show the number and density distribution of total TLSs in different subspatial regions from tumor nests and stromal regions. (C-F) display the density distribution of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in different subspatial regions respectively. (G) presents the number and proportion of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in proximal versus distal regions of the stroma. (H) shows the density of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in proximal and distal stromal regions.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/66f48f748e623e6e3f3cdc24.png"},{"id":92875071,"identity":"6aae0f11-b3d8-4564-9fe4-32572f4bd82e","added_by":"auto","created_at":"2025-10-06 14:33:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257223,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of tertiary lymphoid structures (TLSs) with different spatial and maturation states in 87 untreated esophageal squamous cell carcinoma (ESCC) patients. (A) Log-rank test results showing p-values for overall survival (OS) in 87 patients based on the density of total TLSs, E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs across each subregion. (B) Cumulative subregional p-values for OS in 87 patients derived from the log-rank test based on the density of total TLSs, E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs. (C) Kaplan-Meier survival curves for OS in 87 patients based on the number of total TLSs, E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in the distal stromal region, analyzed using the log-rank test. (D) Kaplan-Meier survival curves for OS in 87 patients based on the density of total TLSs, E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in the distal stromal region, analyzed using the log-rank test.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/d3541a86d92923b1f0941202.png"},{"id":92875074,"identity":"80e0d518-1496-4c32-ae46-d828d3317e14","added_by":"auto","created_at":"2025-10-06 14:33:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2100496,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the spatial immune microenvironment (IME) related to tertiary lymphoid structures (TLSs) in 87 untreated esophageal squamous cell carcinoma (ESCC) patients and its relationship with TLSs. (A) Representative images of multiplex immunofluorescence (MIF) staining of TLSs-associated immune cells and high endothelial venules (HEVs). CD20+ B cells (red), CD21+ FDCs (cyan-blue), CD23+ GC cells (green), CD3+ T cells (green), CD8+ T cells (yellow), Foxp3+ Treg cells (orange), Lamp+ DCs (yellow), PNAD+ HEVs (orange), Pan CK+ tumor cells (white), DAPI+ nuclei (blue). The yellow line indicates the boundary between the proximal and distal stromal regions. The red arrow marks the tumor nest area, the white arrow points to the proximal region, and the yellow arrow designates the distal region. The white oval follicles represent proximal TLSs, while the yellow oval follicles indicate distal TLSs. Analysis of the density (B) and cellular proportions (C) of CD20+ B cells, CD3+ T cells, CD8+ T cells, Foxp3+ Treg cells, Lamp+ mature DCs, and PNAD+ HEVs in different spatial regions: the tumor nest and the stroma (both proximal and distal). (D) Analysis of the correlation between the density of total TLSs in WTS and the density of CD20+ B cells, CD3+ T cells, CD8+ T cells, Foxp3+ Treg cells, Lamp+ mature DCs, and PNAD+ HEVs in different spatial regions: the tumor nest and the stroma (both proximal and distal). (E) and (F) respectively show the analysis of the correlation between the density of E-TLSs and F-TLSs in the distal region and the density of CD20+ B cells, CD3+ T cells, CD8+ T cells, Foxp3+ Treg cells, Lamp+ mature DCs, and PNAD+ HEVs in different spatial regions: the tumor nest and the stroma (both proximal and distal).\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/f83c86671c02002440b3bd14.png"},{"id":92875075,"identity":"81a97d46-ddb7-4fa1-a96c-188a21e16456","added_by":"auto","created_at":"2025-10-06 14:33:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":352134,"visible":true,"origin":"","legend":"\u003cp\u003eTo evaluate the predictive value of tertiary lymphoid structures (TLSs) for efficacy and prognosis in the neoadjuvant chemo-immunotherapy (NACI) cohort. (A) Analyze the quantitative changes of total TLSs, E-TLSs, F-TLSs, PFL-TLSs and SFL-TLSs in the samples before and after NACI. (B) and (C) show the density differences of total TLSs, E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs between PR and non-PR groups in samples before and after NACI, respectively. (D) Analysis of density changes in E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in pre- and post-treatment samples from the PR patient group. (E) Analysis of density changes in E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in pre- and post-treatment samples from the non-PR patient group. In the pre- (F) and post-treatment samples (G), the density differences of CD20+B cells, CD3+T cells, CD8+T cells, Foxp3+Treg cells, Lamp+ mature DCs, and PNAD+HVEs in the PR and non-PR groups were analyzed. (H) Kaplan-Meier survival curves for OS of 15 patients receiving NACI were plotted based on the density of total TLSs, E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in stroma-proximal regions of post-treatment samples, using the Log-rank test. (I) Kaplan-Meier survival curves for OS of 15 patients receiving NACI were plotted based on the density of total TLSs, E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in stroma-distal regions of post-treatment samples, using the Log-rank test.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/a5dc3d37ac58738849f7dd0c.png"},{"id":104250601,"identity":"6e1c135f-19d7-45eb-804b-fbcffbad2810","added_by":"auto","created_at":"2026-03-09 16:01:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6863871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/4fbdeb7f-6cdc-47e2-af00-5e821482bcf9.pdf"},{"id":92875081,"identity":"8f21858d-a230-4395-a4c8-fd3c0ede1461","added_by":"auto","created_at":"2025-10-06 14:33:01","extension":"doc","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":2211840,"visible":true,"origin":"","legend":"","description":"","filename":"2SupplementaryMaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-7634389/v1/3e726c03997d58ba22ae2293.doc"}],"financialInterests":"","formattedTitle":"Spatial distribution analysis of tertiary lymphoid structures in esophageal squamous cell carcinoma predicts patient survival and response to neoadjuvant chemo-immunotherapy","fulltext":[{"header":"Background","content":"\u003cp\u003eEesophageal cancer (EC) is the seventh leading cause of cancer-related deaths worldwide, and epidemiological studies have shown that its incidence is gradually increasing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Surgical resection is the foundational strategy for localised and locally advanced EC, with a 5-year survival rate of 15\u0026ndash;39% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Neoadjuvant therapy (radiotherapy, chemotherapy, or a combination of both) administered before surgery offers the advantage of addressing micrometastases and improving the rate of complete resection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With the increasing use of immunotherapy in clinical practice in recent years, immune checkpoint inhibitors have become crucial treatments for patients with EC, and programmed cell death 1 (PD-1) inhibitors combined with chemotherapy have been established as the standard first-line treatment strategy for recurrent and metastatic oesophageal squamous cell carcinoma (ESCC) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Currently, the use of neoadjuvant chemo-immunotherapy (NACI) has become widespread, and multiple phase I and II clinical trials have shown good efficacy in locally advanced resectable ESCC [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the biomarkers for predicting the efficacy of NACI in ESCC and alterations in the intratumoural immune microenvironment (IME) remain unclear.\u003c/p\u003e\u003cp\u003eTertiary lymphoid structures (TLSs) are organised aggregates of multiple immune cells, including T lymphocytes, B lymphocytes, and high endothelial venules in nonlymphoid tissues [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This structure has been associated with better clinical outcomes and responses to immunotherapy in many human tumours such as melanoma, head and neck squamous cell carcinoma, and ovarian cancer [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Recent studies have indicated that the function and prognostic significance of TLSs are related to their location [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For instance, in clear cell renal cell carcinoma [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], mature peritumoural TLSs were associated with a poorer prognosis, whereas the presence of distal TLSs in patients with endometrial cancer was significantly correlated with prolonged overall survival (OS) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In ESCC, three studies examined the prognostic value of TLSs [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and another study reported that TLSs can predict the response to immune checkpoint inhibitors in recurrent ESCC [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, most of these studies used simple haematoxylin and eosin (HE)-stained sections or single-stain immunohistochemistry (IHC) of T and B cells to determine TLS presence and maturity, which may underestimate the true number of TLSs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, although these observations preliminarily explored the prognostic and predictive value of TLSs, the criteria for including TLSs in the quantification were inconsistent regarding their location. For instance, Hayashi et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] only counted TLSs located within 1000 \u0026micro;m of the tumour nest boundary, whereas Rutao Li et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] considered both intratumoural and peritumoural TLSs. Therefore, it is necessary to map the detailed spatial tissue architecture based on the cellular composition, maturity, location, and functional characteristics of TLSs to further explore their clinical correlation with patients with ESCC and comprehensively understand their role in anti-tumour immune responses within the tumour microenvironment.\u003c/p\u003e\u003cp\u003eMultiplex immunofluorescence imaging can quantify the expression of multiple protein markers in the same tissue slice while maintaining spatial localisation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, we set two multiplexed immunofluorescence panels: an opal 7-colour dyes panel-1 (CD20/CD21/CD23/PNAD/CD8/Pan CK/DAPI) was used for simultaneous analysis of TLSs expression, phenotypic characterisation, and as well as tumour-infiltrating CD8\u0026thinsp;+\u0026thinsp;T cells and PNAD\u0026thinsp;+\u0026thinsp;high endothelial venules in different spatial regions of ESCC tissues; and a opal 6-colour dyes panel-2 (CD20/CD3/Foxp3/DC-Lamp/Pan CK/DAPI) was used to the evaluate the spatial distribution of TLS-associated immune cells\u0026mdash;CD20\u0026thinsp;+\u0026thinsp;B cells, CD3\u0026thinsp;+\u0026thinsp;T cells, Foxp3\u0026thinsp;+\u0026thinsp;Treg cells, and LAMP\u0026thinsp;+\u0026thinsp;mature DCs\u0026mdash;within the tumour microenvironment of ESCC tissue. Using the open-source Qupath software [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] for digital image analysis of fluorescent whole-tissue slices, we established relatively standardised operational approaches for TLSs acquisition, quantification, and spatial location. We analysed the spatial locations of TLSs with different phenotypes to explore the relationship between TLSs with distinct phenotypes at different locations and patients with ESCC prognosis. Additionally, we investigated the spatial distribution of TLSs in patients with ESCC receiving NACI and explored the predictive and prognostic value of spatially distinct TLS phenotypes for treatment efficacy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient cohorts and specimens\u003c/h2\u003e\u003cp\u003e In this study, we obtained surgically removed tumour specimens from 87 previously untreated consecutive patients with ESCC who underwent curative surgical resection at the First Medical Center of the PLA General Hospital between May 2011 and June 2012. These patients did not receive any tumour-related preoperative chemoradiotherapy or immunotherapy and received a routine cisplatin-based combination chemotherapy regimen for postoperative recurrence and progression. The patient's mean follow-up time was 30.67 months (interquartile range [IQR] 12.7\u0026ndash;41.47 months), and basic clinicopathological characteristics are shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eHerein, we also enrolled another cohort that received PD-1 blockade pembrolizumab in combination with nab-paclitaxel and cisplatin as neoadjuvant therapy for 15 patients with resectable ESCC. Between July 2020 and March 2022, patients diagnosed with ESCC were administered two cycles of preoperative neoadjuvant therapy, and oesophagectomy for ESCC was performed within a time frame of 4\u0026ndash;8 weeks after neoadjuvant therapy. Postoperatively, 73% (11/15) of the patients completed two cycles of adjuvant therapy with an identical regimen as the neoadjuvant therapy. In patients with recurrence and progression, standard therapeutic strategies should be implemented according to the patient\u0026rsquo;s situation. These patients will be followed-up until 14 February 2025. The radiological RECIST criteria were used to evaluate the clinical response to neoadjuvant therapy. We collected paired tumour samples, which were baseline biopsies and surgically resected tissues, from pre- and post-NACI patients. Supplementary Table S2 presents the basic clinicopathological features of the patients.\u003c/p\u003e\u003cp\u003e The study was approved by the Institutional Review Board of the PLA General Hospital (Approval No. S2019-228-02), and informed consent for relevant clinical data and tissue samples was obtained from the patients prior to surgery.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultiplexed immunofluorescence assay (\u003c/b\u003eMIF)\u003c/p\u003e\u003cp\u003eMIF assay was performed using an OPAL\u0026trade; Polaris 7-Colour Manual IHC Kit (NEL861001KT; Akoya Biosciences). Specimen preparation was performed according to the manufacturer\u0026rsquo;s instructions using a previously published procedure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Opal 7 colour dyes panel-1 (CD20/CD21/CD23/PNAD/CD8/Pan CK/DAPI) and opal 6 colour dyes panel-2 (CD20/CD3/Foxp3/DC-Lamp/Pan CK/DAPI) were stained in two sequential sections from the same patient. The primary antibodies and dilutions were anti-CD3 (1:300, Abcam, clone SP7), anti-CD8 (1:100, ZSBio, clone SP16), anti-CD20 (1:350, Abcam, clone SP16), anti-CD21 (1:300, ZSBio, clone EP64), anti-CD23 (1:200, ZSBio, clone EP75), anti-Foxp3 (1:100, Abcam, clone 236A/E7), anti-DC-Lamp (1:500, Abcam, clone EPR24265-8), anti-PNAD (1:100, BioLegend, clone MECA-79), and anti-Pan CK (1:2000, Abcam, clone, PAN-CK). Tyramide signal amplification was performed using Opal 480 (CD21), Opal 520 (CD3 and CD23), Opal 570 (CD8 and DC-Lamp), Opal 620 (Foxp3 and PNAD), Opal 690 (CD20), and Opal 780 (CK) fluorescent dyes. Nuclear counterstaining of the sections was used by spectral DAPI.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDigital image analysis of MIF\u003c/h3\u003e\n\u003cp\u003eThe whole-tissue section (WTS) stained with MIF was scanned using the Vectra Polaris platform on a x20 multispectral microscopy (Akoya Biosciences, USA), and the images were visualised using Akoya Phenochart software (v.1.1.0, Akoya Biosciences, USA). Quantitative digital image analysis and spatial annotation of MIF images were performed using QuPath (v.0.5.1) software [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the quantitative analysis of markers, the DAPI channel for nuclear staining was selected for cell segmentation, the positive threshold of pixel fluorescence intensity was set to 15, and the nuclear area and cell parameters were the default options. In the single measurement classifier module, manually adjust the log histograms and real-time preview options to set a positive threshold for each marker: CD3 (dye-cell-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;25), CD8 (dye-cell-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;25), CD20 (dye-cell-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;25), CD21 (dye-cell-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;35), CD23 (dye-cell-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;28), Foxp3 (dye-nucleus-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;30), DC-Lamp (dye-cell-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;30), PNAD (dye-cell-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;30), and Pan CK (dye-cell-positive threshold: mean MIF\u0026thinsp;=\u0026thinsp;22). A composite classifier for the MIF images was then created according to the individual classifiers for each marker. A composite classifier was used to quantify positive intensity values of each marker in each MIF image.\u003c/p\u003e\u003cp\u003eFor the acquisition of TLS, we employed automated annotation using CD20 channel density maps, followed by manual secondary validation and edge adjustment by two experienced pathologists. During this manual review, pathologists considered the T-cell zone of TLSs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], such as the visualised CD3 or CD8 channels. In the density map module, the object type main class performed the CD20 channel, the density radius was set to 50 \u0026micro;m, the density threshold was set to 25, and other parameters were the default option.\u003c/p\u003e\u003cp\u003eIn spatial analysis, tumour nests and stromal regions were first annotated in tissue sections by Pan CK channel. Then select the outer boundary annotation line of the tumour nest as the interface, the tumour nests and stroma region of all markers were spatially annotated with a diameter of 100 \u0026micro;m by expand annotation module, covering all whole tissue. A hierarchical insertion approach was used for the spatial annotation of each TLS. In the annotation module, all TLSs that have been fully annotated are selected. Subsequently, all TLSs were hierarchically inserted, and the relative spatial positions were determined.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll Statistical analyses and data descriptions were performed using IBM SPSS Statistics (version 26). Data visualisation was performed using GraphPad Prism (version 8.0). Visualisation of cell proportional distribution analysis was performed using Chiplot online. Descriptions of the patients\u0026rsquo; clinicopathological data are presented as percentages. Statistical comparisons between two or more groups were performed using two-tailed t-tests and one-way ANOVA. Correlation analysis of TLSs annotations in serial sections was performed using Pearson\u0026rsquo;s correlation coefficients. The correlation coefficient of \u0026ge;\u0026thinsp;0.8 indicates a very strong correlation. The association between the distal mature follicular TLSs (F-TLSs) and clinicopathological features was analysed using the chi-square test. Patient survival analysis was conducted using the log-rank test and Kaplan-Meier method. All cases were stratified into high- and low-TLS groups based on an optimal cutoff value determined using the X-Tile tool [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] through minimal p-value analysis. Statistical significance was done with the following conventions: *p* \u0026lt; 0.05, **p* \u0026lt; 0.01, ***p* \u0026lt; 0.001, and ****p* \u0026lt; 0.0001; non-significant (*p* \u0026gt;0.05) results were denoted as 'ns'.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eQuantification and spatial localisation strategies of TLSs and phenotypic classification\u003c/h2\u003e\u003cp\u003eTLSs differ from specific structured secondary lymphoid organs (SLOs) in that they exhibit different organisational patterns, which can be simple lymphocyte aggregates or more organised structures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, in the TLS acquisition process, CD20\u0026thinsp;+\u0026thinsp;B cells were used to identify as many aggregates as possible. Subsequently, we expanded the region around each B cell aggregate to include the surrounding T cell zone and ultimately obtained the TLSs of the WTS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, left). Previous studies only considered the total cell population of TLS aggregates [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, in addition to setting the minimum radius (50 \u0026micro;m) of positive B cell dense aggregates when obtaining TLSs, we also set a threshold of at least 10 CD20\u0026thinsp;+\u0026thinsp;positive B cells per TLS during data statistical analysis to quantify TLS. Here, we analysed the consistency of quantitative TLSs between two consecutive slices, and the results showed good agreement (Supplementary Fig.\u0026nbsp;1). In the spatial position annotation of TLS, we referred to the approach of Werner, Wagner, Simon, Glatz, Mertz, L\u0026auml;ubli, Griss \u0026amp; Wagner [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] in melanoma. We used the outer boundary line of the tumour nest as the interface and expanded the inner and outer spaces of the tumour nest at a distance of 100 \u0026micro;m in diameter. Spatial annotation of TLSs within the tumour nest and stroma in the WTS was subsequently performed using a hierarchical insertion approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, middle). When calculating the number of TLSs in adjacent spatial regions, if the relative area of the TLSs was significantly large, TLSs intersecting multiple perimeters were assigned to the perimeter with a dominant proportional area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, right).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on molecular signal characterisation and structural features reported in previous studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], TLS phenotypes were divided into early TLSs (E-TLSs) and mature follicular TLSs (F-TLSs). Early TLSs were characterised by relatively dense CD20\u0026thinsp;+\u0026thinsp;lymphocyte-dominated immune cell aggregations without CD21 or CD23 expression and with scattered T cells interspersed within the aggregates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, top). The F-TLSs can be further classified into two distinct subtypes: primary follicle-like TLSs (PFL-TLSs) and secondary follicle-like TLSs (SFL-TLSs). PFL-TLSs present as well-defined round or oval clusters of small lymphocytes containing PNAD\u0026thinsp;+\u0026thinsp;high endothelial vessels expressing CD21 but lacking CD23. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, middle), and SFL-TLSs exhibiting large round or oval follicles with definite germinal centres expressing CD23 and the variable presence of CD21. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, bottom). In the quantitative analysis of PFL-TLSs and SFL-TLSs, we set a minimum threshold of no fewer than five positive cells for both CD21\u0026thinsp;+\u0026thinsp;B cells and CD23\u0026thinsp;+\u0026thinsp;B cells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSpatial distribution analysis of TLSs in patients with ESCC\u003c/h2\u003e\u003cp\u003eIn surgical resection tumour tissues from 87 untreated patients with ESCC, spatial distribution analysis of TLSs was performed in both intra-tumoural regions within 1000 \u0026micro;m (i.e., the tumour nests) and extra-tumoural regions located within a range of 7700 \u0026micro;m from the outer boundary of the tumour nests (i.e., the stroma) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The intra- to extra-tumoural continuum was stratified into 87 consecutive sub-region with a diameter of 100 \u0026micro;m, with TLSs present in 95% of cases (83/87) and a total of 2,746 TLSs were present in 95% of the cases (83/87), and a total of 2,746 TLSs were detected (Supplementary Fig.\u0026nbsp;2A). To calculate the density of TLSs, we divided the number of TLS in each subregion of the tumour nest and stroma by the corresponding area of that region. This approach ensures the precise measurement of TLS distribution relative to the tissue area. No discernible TLSs were observed within the tumour nests, which is consistent with previous reports. We found TLSs were mainly distributed in the stromal area within a range of 4200 \u0026micro;m outside the tumour nests, exhibiting a gradual decrease in both number and density with increasing distance from the tumour margin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePhenotypic stratification was performed for each TLSs of each patient. A total of 2, 370 E-TLSs and 376 F-TLSs were detected (171 PFL-TLSs and 205 SFL-TLSs) (Supplementary Fig.\u0026nbsp;2A). Next, we analysed the spatial distributions of E-TLSs, PFL-TLSs, SFL-TLSs, and F-TLSs. The E-TLSs population was predominantly localised within the 1,500 \u0026micro;m stroma region, and the number and density showed obvious peri-tumoural accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; Supplementary Fig. S2B). The highest density was observed in the 100\u0026ndash;300 \u0026micro;m subregion, accounting for 49.41% (1,171/2,370) of total E-TLSs. In contrast, although F-TLSs have a density peak at 500 \u0026micro;m within stroma region, its overall spatial distribution was relatively dispersed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD; Supplementary Fig. S2C). PFL-TLSs and SFL-TLSs can detect up to 3500 \u0026micro;m from the outer boundary of the tumour nests (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F, Supplementary Fig. S2D-E). Notably, the stroma of 600\u0026ndash;1200 \u0026micro;m subregion also exhibited a relatively high F-TLS density (mean density: 0.017/mm\u0026sup2;, [\u0026plusmn;\u0026thinsp;0.034]), despite their abundance progressively diminishing with increasing distance from the tumour nests (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eAssessment of TLSs' spatial distribution relative to the tumour is important for the efficacy of its predictive outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Here, based on the above observations and previous reports [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], we further categorised TLSs within the stroma into proximal TLSs (\u0026le;\u0026thinsp;500 \u0026micro;m from the tumour nests) and distal TLSs (\u0026gt;\u0026thinsp;500 \u0026micro;m from the tumour nests). We examined differences in the quantity and density of TLSs with different phenotypes in the proximal and distal regions. We found that proximal TLSs were mainly composed of early stage subtypes, accounting for 88.85% (1844/2073) of the total proximal TLSs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG, left). In contrast, the proportion of mature phenotype F-TLSs significantly increased in the distal TLSs, reaching 21.84% (147/673) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG, right). The densities (/mm2, [\u0026plusmn;\u0026thinsp;SD]) of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in the proximal TLSs were 0.284 [\u0026plusmn;\u0026thinsp;0.241], 0.041 [\u0026plusmn;\u0026thinsp;0.055], 0.017 [\u0026plusmn;\u0026thinsp;0.029], and 0.023 [\u0026plusmn;\u0026thinsp;0.038], respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, left). The densities of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs in the distal TLSs were 0.050 [\u0026plusmn;\u0026thinsp;0.060], 0.014 [\u0026plusmn;\u0026thinsp;0.039], 0.006 [\u0026plusmn;\u0026thinsp;0.024], and 0.010 [\u0026plusmn;\u0026thinsp;0.023], respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, right).\u003c/p\u003e\u003c/div\u003e\u003cdiv class=\"Heading\"\u003e\u003cb\u003ePrognostic value of different spatial resident TLS and maturation status in patients with ESCC\u003c/b\u003e\u003c/div\u003e\u003cp\u003eIn research on the relationship between TLSs and the prognosis of ESCC, some researchers have utilised the number of TLSs within the tumour tissue [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], whereas others have considered the median density of TLSs per unit tissue area [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, we used the optimal cutoff value to divide patients with ESCC into high and low groups and simultaneously analysed the association between both the number and density of TLSs and patient prognosis. We first investigated the prognostic relationship between total TLSs and TLSs with distinct maturation states (E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs) in patients with ESCC within individual subregions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; Supplementary Fig. S3A). We observed a prognostic difference between high and low density TLS groups for total TLSs within the 300\u0026ndash;400 \u0026micro;m (p\u0026thinsp;=\u0026thinsp;0.00964) and 600\u0026ndash;800\u0026micro;m (p\u0026thinsp;=\u0026thinsp;0.00355, p\u0026thinsp;=\u0026thinsp;0.00652) subregions. Furthermore, survival analysis within subregions for different phenotypes revealed statistically significant differences: patients with high-density E-TLSs in the 300\u0026ndash;400 \u0026micro;m (p\u0026thinsp;=\u0026thinsp;0.00584) and 600\u0026ndash;900 \u0026micro;m (p\u0026thinsp;=\u0026thinsp;0.02686, p\u0026thinsp;=\u0026thinsp;0.01902, p\u0026thinsp;=\u0026thinsp;0.0455) subregions, and high-density F-TLSs in the 800\u0026ndash;900 \u0026micro;m (p\u0026thinsp;=\u0026thinsp;0.04288) subregion, showed significantly better survival outcomes compared to their respective low-density TLS groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, we counted the number and density of TLSs in the cumulative subspace spatial regions to investigate the prognostic relationship between TLSs and patients with ESCC. We found that in the cumulative regions beyond 500 \u0026micro;m from the outer boundary of tumour nests, patients with ESCC with high-density total TLSs showed significantly better survival compared to those with low density TLSs, with a statistically significant p-value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for cumulative regions beyond 500 \u0026micro;m). The impact of TLSs with different phenotypes on survival in cumulative regions was analysed. We found that mature F-TLSs, whether assessed by number or density in the cumulative regions 600 \u0026micro;m away from the outer boundary of tumour nest, compared with the low number or density TLSs group, the high number or density TLSs group showed a consistent significant correlation with the prognosis of patients with ESCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Supplementary Fig. S3). This finding is also consistent with the secondary spatial density distribution observed in our analysis of the F-TLS density patterns.\u003c/p\u003e\u003cp\u003eBased on the above results, we speculate that the distal TLSs (\u0026gt;\u0026thinsp;500 \u0026micro;m) have a more significant impact on the survival of patients with ESCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D, supplementary Fig. S3C-D). We further examined the prognostic relationship between different TLS phenotypes within the distal TLSs in patients with ESCC. We found that patients with ESCC with a high number or density of mature F-TLSs at the distal end had better OS compared with those with a low number or density of TLSs at the distal (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D, TLSs high number, p\u0026thinsp;=\u0026thinsp;0.0092; TLSs high density, p\u0026thinsp;=\u0026thinsp;0.0268). Here, we also analysed the correlation between remote F-TLS and clinicopathological features, and the results are shown in Supplementary Table S3-4.\u003c/p\u003e\u003cp\u003eOverall, through multi-level spatially stratified analysis of survival, we revealed the differential prognostic impact of TLSs based on their spatial localisation in patients with ESCC. Distal TLSs demonstrated superior prognostic value, particularly as the stromal regions beyond 500 \u0026micro;m from tumour nests contained a higher abundance of mature F-TLSs, which showed a statistically significant correlation with improved OS.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of spatial distinct IMEs related to TLSs in ESCC patients and its relationship with TLSs\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have shown that mature TLSs shape intratumoural IME in ESCC. ESCC with mature TLSs shows higher intratumoural CD8\u0026thinsp;+\u0026thinsp;T cell infiltration compared to absent mature TLSs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, we observed that spatially distinct TLSs with different maturation states located beyond the boundary of tumour nests differentially impacted the prognosis of patients with ESCC. Therefore, we hypothesised that the distribution of TLSs with different maturation states within the spatially heterogeneous IME of ESCC may exhibit functional divergence. The antitumour efficacy of TLSs is likely influenced by both their spatial localisation and maturation status in ESCC.\u003c/p\u003e\u003cp\u003eUsing MIF staining, we simultaneously analysed the spatial distribution of CD20\u0026thinsp;+\u0026thinsp;B cells, CD3\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells, Foxp3\u0026thinsp;+\u0026thinsp;regulatory T cells (Tregs), LAMP\u0026thinsp;+\u0026thinsp;dendritic cells (mature DCs), and PNAD\u0026thinsp;+\u0026thinsp;high endothelial venules (HEVs) within both the tumour nests and stroma of the ESCC specimens (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Based on the density distribution of TLSs, we also stratified the stromal distribution of the above-mentioned immune cells and HEVs into proximal (\u0026le;\u0026thinsp;500 \u0026micro;m) and distal (\u0026gt;\u0026thinsp;500 \u0026micro;m) regions. We observed that CD20\u0026thinsp;+\u0026thinsp;B cells, CD3\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells, Foxp3\u0026thinsp;+\u0026thinsp;Tregs, and LAMP\u0026thinsp;+\u0026thinsp;mature DCs exhibited varying densities of infiltration in both tumour nests and stroma, whereas PNAD\u0026thinsp;+\u0026thinsp;HEVs were mainly located within the stroma (Supplementary Fig. S4A). Spatial stratification analysis of the stroma revealed that the proximal infiltration densities of CD20\u0026thinsp;+\u0026thinsp;B cells, CD3\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and Foxp3\u0026thinsp;+\u0026thinsp;Tregs were significantly higher than the distal infiltration densities(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe density distribution relationship among TLSs, different immune cells, and PNAD\u0026thinsp;+\u0026thinsp;HEVs in different subspatial regions was investigated. We found that in patients with ESCC with a high density of total TLSs compared to those with a low density of total TLS, CD20\u0026thinsp;+\u0026thinsp;B cells, CD3\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, Foxp3\u0026thinsp;+\u0026thinsp;Tregs, and LAMP3\u0026thinsp;+\u0026thinsp;DCs exhibited distinct proportional clustering within tumour nests (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Statistical analysis of cell densities revealed a significant difference in CD8\u0026thinsp;+\u0026thinsp;T cells among patients with ESCC with a high density of total TLSs compared with those with a low density of total TLS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Overall, in the stromal region, patients with a high density of total TLSs exhibited increased infiltration of CD20\u0026thinsp;+\u0026thinsp;B cells, CD3\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, Foxp3\u0026thinsp;+\u0026thinsp;Tregs, and LAMP3\u0026thinsp;+\u0026thinsp;DCs, along with enhanced PNAD\u0026thinsp;+\u0026thinsp;HEV distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Notably, regional stratification analysis revealed that in the high-density total TLSs group, proximal CD3\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells were significantly higher than their distal counterparts. Furthermore, a greater distribution of PNAD\u0026thinsp;+\u0026thinsp;HEVs was observed at the proximal end than at the distal end (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Additionally, in patients with a high density of total TLSs, the increase in Foxp3\u0026thinsp;+\u0026thinsp;Tregs in both the proximal and distal areas did not reach statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Cellular proportion analysis showed significantly higher clustering of Foxp3\u0026thinsp;+\u0026thinsp;Tregs in the low-density total TLSs group than in the high-density total TLSs group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eOur spatial survival analysis revealed that distal TLSs demonstrated a better prognostic value, with distal F-TLSs specifically showing a significant association with improved OS. Therefore, we explored the relationship among distal TLSs, different immune cell subsets, and PNAD\u0026thinsp;+\u0026thinsp;HEVs within distinct spatial regions. We observed that in the patient group with a high number or density of distal E-TLSs compared to the group with a low number or density, although there was an obvious dense infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells within the stromal regions, the tumour nests did not exhibit the corresponding high-density infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, Supplementary Fig. S4B). However, in the patient group with a high number or density of distal F-TLSs, compared to the group with a low number or density, there was a significantly higher density of CD8\u0026thinsp;+\u0026thinsp;T cells not only in both the proximal and distal stromal regions, but also within the tumour nests, with the differences reaching statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Supplementary Fig. S4C). Furthermore, in the patient group with a high number or density of distal F-TLSs, compared to the group with a low number or density, there was a significantly greater distribution of PNAD\u0026thinsp;+\u0026thinsp;HEVs in both the proximal and distal stromal regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Supplementary Fig. S4C). Additionally, patients with a high number or density of distal F-TLSs showed lower numbers or density of Foxp3\u0026thinsp;+\u0026thinsp;Tregs in the distal region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Supplementary Fig. S4C).\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe spatial distribution of TLSs in patients with ESCC with NACI and the predictive value of TLS for their therapeutic efficacy and prognosis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe spatial distribution of TLSs was examined in 15 paired ESCC tumour specimens collected pre- and post-NACI. In the pretreatment biopsy samples, 15 consecutive subregions were identified, extending from the tumour nests to the surrounding stroma (Supplementary Fig. S5A). Phenotypic analysis of TLSs revealed a predominance of E-TLSs (46/49), primarily located within 600 \u0026micro;m of the outer periphery of the tumour nests (Supplementary Fig. S5 B-E). After NACI, a significant increase in TLSs was observed, with a total TLSs, E-TLSs, and F-TLSs (PFL-TLSs and SFL-TLSs) of 272, 162, and 110 (63 and 47), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Spatial analysis revealed that high densities of both E-TLSs and F-TLSs were mainly concentrated within 1000 \u0026micro;m outside the tumour nests, although there was heterogeneous TLSs distribution in the compartment within 3300 \u0026micro;m outside the tumour nests (Supplementary Fig. S5 F-J).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRecent studies have shown a positive correlation between mature TLSs and treatment efficacy in patients with ESCC receiving either neoadjuvant chemotherapy or neoadjuvant immunotherapy alone [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, we investigated the association between the TLSs and the efficacy of NACI in ESCC. In the pre-treatment baseline, among patients with radiological responses (PR) and non-PR, whether it was total TLSs or TLSs of different phenotypes, there was no statistically significant difference in their number and density between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB; Supplementary Fig. S6A). However, after treatment, both the number and density of F-TLSs were significantly increased in patients with PR compared to those in non-PR patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC; Supplementary Fig. S6B). Comparison of TLSs number and density of TLSs between PR and non-PR patients before and after treatment revealed that in patients with PR, both the number and density of E-TLSs, F-TLSs, PFL-TLSs, and SFL-TLSs increased significantly after treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD; Supplementary Fig. S6C). In contrast, among non-PR patients, although the number of E-TLSs and F-TLSs increased post-treatment, their density showed no statistically significant difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE; Supplementary Fig. S6D). In this study, we examined the IME of TLS-related cells in patients with ESCC undergoing NACI and observed that within the tumour nests, the non-PR patient group exhibited a significantly higher infiltration of Treg cells compared to the PR group at baseline, indicating an immunosuppressive microenvironmental bias (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Following treatment, PR patients demonstrated prominent infiltration of CD3\u0026thinsp;+\u0026thinsp;T cells within regions proximal to the stroma (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eSurvival analysis was performed on a cohort of patients with ESCC who underwent NACI. No association was found between the TLS levels in baseline biopsy specimens and OS (Supplementary Fig. S6E). This finding is consistent with the tumour microenvironment analysis, which revealed high intratumoural Treg cell infiltration in patients exhibiting high TLSs density (Supplementary Fig. S7A). In post-treatment specimens, patients with a higher number or density of total TLSs had significantly improved survival rates compared to those with a lower number or density of total TLSs. Following phenotypic stratification, statistically significant differences were observed in both E-TLSs and F-TLSs among patient cohorts with a high number or density of total TLSs (Supplementary Fig. S6F). Spatial regionalisation analysis of TLSs revealed that compared to the group with low number and density of proximal TLSs, high number and density of total TLSs, E-TLSs, and F-TLSs in the proximal region were all significantly associated with improved patient survival, with statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH; Supplementary Fig. S6 G). However, patients with a high number and density of distal TLSs showed a trend toward better survival than those with a low number and density of distal TLS; however, this difference was not statistically significant. This finding corresponded to the IME analysis across different subspatial regions (Supplementary Fig. S7A).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTLSs are ectopic lymphoid structures formed within non-lymphoid tissues, including tumours. Antitumour immunity and prognostic impact of TLSs in different cancers vary significantly depending on their spatial distribution, maturity, and abundance within the tumour tissue. Intratumoural TLSs abundance serves as an effective prognostic marker for favourable outcomes in intrahepatic cholangiocarcinoma, whereas the presence of peritumoural TLSs is positively correlated with poor prognosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. TLSs in breast cancer, hepatocellular carcinoma, and clear cell renal cell carcinoma also demonstrate dual prognostic functions based on their spatial location, although its underlying mechanisms require further elucidated [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This study established a standardised operational procedure for the acquisition, quantification, and spatial localisation of TLSs using MIF and spatial digital image analysis. Spatial stratification analysis revealed that, in ESCC, the spatial location and maturation status of TLSs jointly shape their prognostic value and immunomodulatory function. Our data show that mature F-TLSs located in the distal region above 500 \u0026micro;m on the outer edge of the tumour nest have the most significant positive impact on the OS of patients with ESCC. This finding is consistent with observations in endometrial cancer, in which the presence of distal TLSs was associated with significantly prolonged OS [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our analysis of the spatial distribution of IME and its relationship with TLSs suggests that well-structured TLSs located distal to the tumour core may represent more effective antitumour immune induction centres. They exert enhanced immune surveillance and antitumour effects by promoting extensive infiltration of CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells within both tumour nests and stromal regions, particularly by overcoming the infiltration barrier inside tumour nests [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Notably, the presence of distally located high-density F-TLSs was associated with a richer distribution of PNAD\u0026thinsp;+\u0026thinsp;HEVs in the stromal regions (particularly distally) and a lower proportion of Foxp3\u0026thinsp;+\u0026thinsp;Treg cells. Studies have shown that HEVs are present around TLSs, are associated with tumour infiltration by T and B cells [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and serve as the primary site for the extravasation of na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T cell subsets [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, as the abundance of TLSs around the tumour increased, the frequency of Treg cells within intratumoural TLSs significantly increased, suggesting a potential link between peri-tumoural TLSs and the immunosuppressive environment inside the tumour [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In conclusion, these data support the crucial role of mature distal TLSs in facilitating efficient lymphocyte trafficking and maintaining the effector IME.\u003c/p\u003e\u003cp\u003ePrevious studies have assessed the predictive value of mature TLSs for treatment efficacy in patients with ESCC receiving either neoadjuvant chemotherapy or neoadjuvant immunotherapy alone [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In addition, high-density TLSs in patients with recurrent ESCC treated with anti-PD-1 antibodies predicted the clinical response to anti-PD-1 antibodies and patient survival [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The analysis of our NACI cohort provides important insights into the dynamic changes in TLSs and their predictive value. We found that although the baseline TLS characteristics (both number and density) before treatment showed no significant association with the initial treatment response (PR vs. non-PR), the post-treatment response status (PR) was closely associated with a significant increase in F-TLSs. More importantly, the high density of TLSs in post-treatment specimens\u0026mdash;specifically those localised to the peritumoural regions near the tumour margin (\u0026le;\u0026thinsp;500 \u0026micro;m), including total TLSs, E-TLSs, and F-TLSs\u0026mdash;demonstrated a strong prognostic association, predicting improved survival outcomes. This contrasts with the core prognostic value of distal TLSs observed in baseline analyses, suggesting that neoadjuvant therapy greatly reshaped the distribution pattern and functional focus of TLSs. The significant infiltration of CD3\u0026thinsp;+\u0026thinsp;T cells in the proximal stromal regions of post-treatment PR patients, as well as the pre-treatment tendency for higher Treg cell infiltration within the tumour nests of non-PR patients, collectively depict the spatial correlation between treatment response and activation of the local IME. Consequently, the treatment-induced amplification and maturation of proximal TLSs may serve as effective biomarkers for evaluating the long-term benefits of NACI in patients with ESCC.\u003c/p\u003e\u003cp\u003eEarly studies on melanoma [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], sarcoma [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and triple-negative breast cancer [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] have shown that the presence of TLSs, especially mature or germinal centre-positive TLSs, in needle biopsy tissues is associated with better OS and a positive response to immune checkpoint inhibitor therapy. However, in our study, although TLSs were detected within a 600 \u0026micro;m region at the outer edge of the tumour nest in pre-NACI biopsy samples of ESCC, they did not show statistically significant differences in predicting treatment response or in survival analysis. One possible reason for this is that [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and a single biopsy may not capture regions richer in TLSs, requiring repeated samplings or imaging guidance. However, current methods for detecting TLSs may require refinement, for instance, by incorporating the detection of TLS-related gene signatures (such as B cell-related genes and chemokines, such as CXCL13, CCL19, and CCL21 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]) to indirectly infer the presence and functional status of TLSs.\u003c/p\u003e\u003cp\u003eStudies examining the association between TLSs and prognosis have not reached a consensus on whether TLSs should be evaluated based on their absolute quantity or their density. This is because there is currently no unified methodology for the morphological definition, maturity classification, and counting of TLSs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, the analysis areas are inconsistent; some use the invasive margin [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], others the tumour nest [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and others the whole tissue section [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], making it difficult to establish a standardised analysis. Our study simultaneously analysed the relationship between the number and density of TLSs in relative tissue regions and patient prognosis in two cohort studies involving patients with untreated ESCC and NACI. The results showed that both the number and density of TLSs can predict the OS of patients. In contrast, the relative density of TLSs in the corresponding tissue regions was more specific to patient survival.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, our spatially stratified analysis revealed the impact of different spatial localisations and maturation statuses of TLSs on the prognosis and response to NACI, but the sample sizes in both the retrospective and NACI cohorts were limited. Therefore, these findings need to be validated in larger multicentre studies. Second, although our analysis of IME examined the number or density of total TLSs, categorised by subtype and spatial distribution, and their correlation with immune cell infiltration across various tumour regions (such as tumour nests, stroma, and proximal and distal areas), it did not specifically assess how the variation in immune cell composition within each TLS subtype in these different regions is related to the overall tumour microenvironment infiltration. Thirdly, current research indicates that the proportion of CD138\u0026thinsp;+\u0026thinsp;plasma cells is significantly associated with TLS maturity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], with mature TLSs exhibiting a higher percentage of IgM-containing cells [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] compared to immature TLSs. However, owing to limitations in the number of markers included in the MIF panel, this study did not incorporate markers related to mature B cells and their functional states. Therefore, future investigations are necessary to further explore the relationship between mature B cells, spatial distribution of TLSs, and tumour IME.\u003c/p\u003e\u003cp\u003eIn summary, by integrating spatial localisation, maturation phenotypes, and the dynamic dimensions of therapeutic intervention, this study deepens our understanding of the complex functions of TLSs in ESCC. Our results indicate that, in untreated ESCC, distal mature F-TLSs serve as key positive prognostic factors, potentially mediated by the establishment of an effective distal immune surveillance hub. In contrast, among patients receiving NACI, treatment-induced amplification of TLSs located at the tumour\u0026ndash;stroma interface (proximal region) emerged as a key predictor of favourable long-term survival. This spatial- and phenotype-dependent functional difference emphasises that when evaluating the clinical significance of TLSs, their precise anatomical location and maturity status must be considered. In addition, there have been several preclinical antitumour immunotherapy reports on TLS-induced interventions in mouse models [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which may provide new ideas for establishing individualised antitumour immunotherapy strategies for cancer through the spatial induction and modulation of TLSs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEsophageal squamous cell carcinoma, ESCC\u003c/p\u003e\n\u003cp\u003eNeoadjuvant chemo- immunotherapy, NACI\u003c/p\u003e\n\u003cp\u003eProgrammed cell death 1, PD-1\u003c/p\u003e\n\u003cp\u003eTertiary lymphoid structures, TLSs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImmune microenvironment, IME\u003c/p\u003e\n\u003cp\u003eOverall survival, OS\u003c/p\u003e\n\u003cp\u003eWhole-tissue section, WTS\u003c/p\u003e\n\u003cp\u003eMultiplexed immunofluorescence, MIF\u003c/p\u003e\n\u003cp\u003eHigh endothelial venules, HEVs\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLingxiong Wang: Conceptualization, Writing - review \u0026amp; editing, Writing - original draft, Visualization, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation. Jinfeng Li: Investigation, Visualization, Validation, Software, Methodology. Yanyun Ao: Investigation, Validation, Supervision, Resources. Yanju Yu: Methodology, Data curation, Jinzhao Zhai: Methodology, Formal analysis. Yinjie Zhang : Methodology, Software. Liangliang Wu : Investigation, Software. Jianqing Hao: Formal analysis. Yanyan Hu: Supervision. Qiong Wang: Conceptualization, Writing - review \u0026amp; editing, Validation, \u0026nbsp;Fan Yin: Conceptualization, Writing - review \u0026amp; editing, Resources, Investigation. Tianyi Liu: Conceptualization, Project Administration, Writing - review \u0026amp; editing, Data curation, Formal analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated from the use or analysis in this study are available by the corresponding author upon reasonable request, except the patients data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study conformed to the ethical approval processes and was approved by the Institutional ReviewCommittee of Chinese PLA General Hospital (Approval No. S2019-228-02). Before the surgery, informed consent from patients regarding relevant clinical data and tissue samples was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYang H, Wang F, Hallemeier CL, Lerut T, Fu J: Oesophageal cancer. Lancet (London, England)2024, 404(10466):1991-2005.\u003c/li\u003e\n\u003cli\u003eLewis S, Lukovic J: Neoadjuvant Therapy in Esophageal Cancer. Thoracic Surgery Clinics2022, 32(4):447-456.\u003c/li\u003e\n\u003cli\u003eDoki Y, Ajani JA, Kato K, Xu J, Wyrwicz L, Motoyama S, Ogata T, Kawakami H, Hsu C-H, Adenis A\u003cem\u003e et al\u003c/em\u003e: Nivolumab Combination Therapy in Advanced Esophageal Squamous-Cell Carcinoma. 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Methods Mol Biol2025, 2864:281-297.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Esophageal squamous cell carcinoma, tertiary lymphoid structures, spatial distribution, digital analysis, immune microenvironment, neoadjuvant chemo-immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-7634389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7634389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The prognostic and predictive significance of tertiary lymphoid structures (TLSs) exhibits spatial specificity in various cancers. However, the spatial distribution, phenotypic characteristics of TLSs in esophageal squamous cell carcinoma (ESCC) and their impact on prognosis and prediction are not yet fully understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe performed multiplex immunofluorescence staining on 87 untreated ESCC specimens to simultaneously analyse TLS expression and phenotypic characteristics, CD8+ T cells and PNAD+ high endothelial venules (HEVs) in different spatial regions of ESCC tissues using Panel-1 (CD20/CD21/CD23/PNAD/CD8/Pan CK/DAPI). Panel-2 (CD20/ CD3/Foxp3/DC-LAMP/Pan CK/DAPI) was used to evaluate the spatial distribution of TLS-related immune cells (CD20+ B cells, CD3+ T cells, Foxp3+ Treg cells, and LAMP+ mature DCs) within the tumor microenvironment of ESCC. Furthermore, the predictive value of TLSs and the clinical prognostic significance of TLSs at different spatial locations were assessed in an independent cohort of 15 ESCC patients who received neoadjuvant chemo- immunotherapy (NACI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn untreated ESCC, a high number/density of mature follicular TLSs (F-TLSs) at the distal (＞500μm) was significantly associated with better overall survival (OS) in patients (number: p=0.0092; density: p=0.0268). Patients with distal high F-TLSs not only exhibited high densities of CD3+ T cells, CD8+ T cells, CD20+ B cells, LAMP+ mature DCs, and PNAD+HEVs in the stromal regions, but this was also associated with increased CD8+ T cell infiltration within the tumor nests (p\u0026lt;0.05). Additionally, it was correlated with a reduced proportion of Foxp3+ Treg cells in the distal stromal regions. In patients with ESCC receiving NACI, the partial response (PR) group exhibited higher numbers and densities of F-TLSs post-treatment than the non-PR group (p\u0026lt;0.05). High numbers/densities of total TLSs, early-TLSs, and F-TLSs in the proximal stromal region (≤500μm) of post-treatment specimens were significantly associated with better OS (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn untreated ESCC, distal mature F-TLSs are critical prognostic indicator, driving \"favorable\" immune infiltration by modulating the spatially heterogeneous immune microenvironment. After NACI, TLS quantity and spatial distribution were reshaped; mature F-TLSs predicted treatment response. Treatment-induced expansion of TLSs localised at the proximal tumour-stroma interface is a key indicator for predicting favourable long-term survival.\u003c/p\u003e","manuscriptTitle":"Spatial distribution analysis of tertiary lymphoid structures in esophageal squamous cell carcinoma predicts patient survival and response to neoadjuvant chemo-immunotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 14:24:55","doi":"10.21203/rs.3.rs-7634389/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-09-24T02:58:11+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T00:59:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T14:29:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-09-16T21:09:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31b036a8-f13a-4a69-9846-50bdac6f9f46","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:00:47+00:00","versionOfRecord":{"articleIdentity":"rs-7634389","link":"https://doi.org/10.1186/s12967-026-07950-4","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2026-03-03 15:57:32","publishedOnDateReadable":"March 3rd, 2026"},"versionCreatedAt":"2025-10-06 14:24:55","video":"","vorDoi":"10.1186/s12967-026-07950-4","vorDoiUrl":"https://doi.org/10.1186/s12967-026-07950-4","workflowStages":[]},"version":"v1","identity":"rs-7634389","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7634389","identity":"rs-7634389","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}