In situ analysis of CCR8 + regulatory T cells and cytotoxic CD8 + T cells in human lung squamous cell carcinoma: biological insights and clinical implications

In situ analysis of CCR8 + regulatory T cells and cytotoxic CD8 + T cells in human lung squamous cell carcinoma: biological insights and clinical implications | 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 In situ analysis of CCR8 + regulatory T cells and cytotoxic CD8 + T cells in human lung squamous cell carcinoma: biological insights and clinical implications Yoshinori Hayashi, Azumi Ueyama, Soichiro Funaki, Koichi Jinushi, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4121046/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background CCR8-expressing regulatory T cells (Tregs) are selectively localized within tumors and have gained attention as potent suppressors of anti-tumor immunity. This study focused on CCR8 + Tregs and their interaction with CD8 + T cells in the tumor microenvironment of human lung cancer. We evaluated their spatial distribution impact on CD8 + T cell effector function, specifically granzyme B (GzmB) expression, and clinical outcomes. Methods A total of 81 patients with lung squamous cell carcinoma (LSCC) who underwent radical surgical resection without preoperative treatment were enrolled. Histological analyses were performed, utilizing an automated image analysis system for double-stained immunohistochemistry assays of CCR8/Foxp3 and GzmB/CD8. We investigated the association of CCR8 + Tregs and GzmB + CD8 + T cells in tumor tissues and further evaluated the prognostic impact of their distribution profiles. Results Histological evaluation using the region of interest (ROI) protocol showed that GzmB expression levels in CD8 + T cells were decreased in areas with high infiltration of CCR8 + Tregs, suggesting a suppressive effect of CCR8 + Tregs on T cell cytotoxicity in the local tumor microenvironment. Analysis of the association with clinical outcomes showed that patients with more CCR8 + Tregs and lower GzmB expression, represented by a low GzmB/CCR8 ratio, had worse progression-free survival. Conclusions Our data suggest that local CCR8 + Treg accumulation is associated with reduced CD8 + T cell cytotoxic activity and poor prognosis in LSCC patients, highlighting the biological role and clinical significance of CCR8 + Tregs in the tumor microenvironment. The GzmB/CCR8 ratio may be a useful prognostic factor for future clinical applications in LSCC. Tumor immunity regulatory T cells CCR8 cytotoxic T cells lung cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Lung cancer is estimated to have 2.2 million new cases and cause 1.8 million deaths each year worldwide, making this disease a major contributor to cancer-related mortality [ 1 , 2 ]. Among the non-small cell lung cancer subtypes, which constitute approximately 85% of all lung cancer cases, lung adenocarcinoma (LAD)and lung squamous cell carcinoma (LSCC) are the most common [ 1 ]. LSCC patients have historically seen scarce benefits from targeted therapies because of the low frequency of driver mutations, such as in EGFR , and limited therapeutic efficacy compared with LAD patients [ 3 , 4 ]. The advent of immune checkpoint inhibitors (ICIs), such as anti-PD-1/PD-L1 or anti-CTLA4 agents, has markedly improved the prognosis of LSCC patients [ 5 , 6 ]. However, despite these advances, ICIs have limited efficacy and most LSCC patients still do not respond to current immunotherapy methods. This has led to an increased interest in research aiming to understand the tumor immune microenvironment of LSCC. Regulatory T cells (Tregs) are an immunosuppressive CD4 + T cell subset that express the transcription factor Foxp3. They play an important role in maintaining immune tolerance and homeostasis to prevent autoimmune diseases and allergies [ 7 – 9 ]. Tregs also act as immunosuppressors in tumor immunity, potentially inhibiting immune responses against tumor cells and supporting tumor progression [ 10 – 12 ]. CC motif chemokine receptor 8 (CCR8) is a chemokine receptor that has recently been identified using advanced comprehensive transcriptomic analysis and flow cytometry as a novel marker that is selectively expressed on intratumor Tregs [ 13 , 14 ]. CCR8 is mainly expressed in clonally expanded Tregs activated by tumor-associated antigens, with CCR8 + Tregs being an "effector-like" cell population with a stable anti-tumor immunosuppressive function [ 15 ]. Our previous study also reported that CCR8 + Tregs are involved in the tumor immunosuppressive microenvironment in lung cancer, including LSCC [ 16 ]. Furthermore, preclinical studies in several murine tumor models, including lung carcinoma, have demonstrated that depletion of CCR8 + Tregs by anti-CCR8 antibody administration resulted in a marked anti-tumor effect [ 16 – 22 ]. Their mechanism of action has been suggested to be via enhancement of CD8 + T cell function, as evidenced by increased expression levels of granzyme B (GzmB) and interferon-γ in CD8 + T cells following antibody administration and abolished in vivo efficacy from CD8 + T cell depletion [ 17 , 18 ]. Tumor-infiltrating lymphocytes (TILs) are a vital component of the tumor microenvironment (TME) and are closely related to the progression and prognosis of malignant tumors [ 23 , 24 ]. CD8 + cytotoxic T lymphocytes (CTLs), which can directly kill cancer cells, are a particularly significant T cell subset in anti-tumor immunity. We have previously demonstrated that human CCR8 + Tregs from lung cancer TILs or expanded from peripheral blood mononuclear cells have potent inhibitory activity against CTL function in vitro [ 16 ]. However, there is no clinical evidence of the impact of CCR8 + Tregs on CTL activity in the TME of human cancer patients, which we aimed to evaluate in this study. Here, we performed histological investigations of human LSCC samples to explore the levels and distribution of CCR8 + Tregs and their impact on surrounding CD8 + T cells in the local TME. In addition, we analyzed their association with patient clinicopathological features and prognosis to verify their clinical significance. METHODS Patients We retrospectively identified serial patients who were diagnosed with LSCC and underwent radical resection without preoperative treatment at Osaka University Hospital from March 2010 to May 2017. Excluded individuals included those who underwent palliative or non-curative resection and those with multiple concurrent cancers. A total of 81 patients were included in the study. Medical data of the patients, such as clinicopathological characteristics, surgical findings, and clinical course, were collected retrospectively using the clinical database and pathological examination reports of the institute. Tumor staging was performed according to the eighth edition of the UICC TNM classification system [ 25 ]. All patients were monitored for recurrence and death or survival to the last follow-up (at least 5 years) for recurrence-free cases. This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Osaka University Hospital (approval number: 13266). All participants provided informed consent using the opt-out methodology from the retrospective design of the study. Double staining for immunohistochemistry (IHC) analysis Formalin-fixed paraffin-embedded (FFPE) surgical specimens from each patient were collected and prepared. Three serial 4 µm thin sections were cut for hematoxylin and eosin (H&E) staining and double IHC staining for CCR8/Foxp3 and GzmB/CD8, respectively. The sections were deparaffinized using xylene, subjected to a stepwise ethanol dilution series for hydration, and then immersed in an EDTA-based antigen retrieval buffer (pH 9.0), followed by incubation in a pressure cooker at 110°C for 15 minutes. The sections were washed in distilled water, incubated for 10 minutes in 3% hydrogen peroxide (H 2 O 2 ) to block endogenous peroxidase activity, washed three times in 0.05% Tween-20/TBS, and then incubated for 20 minutes in a 3% bovine serum albumin or 5% goat serum solution/PBS for non-specific antigen reaction blocking. The antibodies were diluted in 1% bovine serum albumin/PBS. The sections were incubated 2 hours at room temperature with primary antibodies (for CCR8 and CD8). After washing three times, detection was performed using a polymer reagent (Histofine Simple Stain MAX PO, Nichirei Bioscience Inc., Tokyo, Japan) and 3’-3-diamnobenzidine (DAB) substrate according to the manufacturer's protocol. Subsequently, antigen retrieval was performed again, followed by 3% H 2 O 2 treatment and blocking. The samples were then incubated 2 hours at room temperature with other primary antibodies (for Foxp3 and GzmB). They were washed three times, followed by detection with a polymer reagent and Vina Green Chromogen kit (BRR807AH, Biocare Medical LLC, Pacheco, CA, USA). The sections were counterstained with hematoxylin, washed, dehydrated with ethanol, and then mounted. The primary antibodies used were as follows: CCR8 (433H, mouse monoclonal, BD Biosciences, Franklin Lakes, NJ, USA, 1:40 dilution), CD8 (L26, mouse monoclonal, Nichirei Bioscience, Tokyo, Japan, ready to use), Foxp3 (236A/E, mouse monoclonal, Abcam, Cambridge, UK, 1:100 dilution), and GzmB (D6E9W, rabbit monoclonal, Cell Signaling Technology, Danvers, MA, USA, 1:50 dilution). Analysis of IHC staining images The double-stained glass slides were scanned and merged into digital slide images at 40× magnification using a microscope slide scanner system (VENTANA iScan HT, Roche-Ventana Medical Systems Inc., Tucson, AZ, USA). Images were imported into a pathological image analysis software (HALO version 3.5, Indica Labs, Albuquerque, NM, USA), then all sequential steps for automated analysis, including annotation, training, and analysis, were performed[ 26 ]. The analysis step was carried out using the auto-cell counting algorithm generated in the training step. Tumor lesions in each slide were discriminated by observation of H&E-stained images by an experienced pathologist. The following measurements within the tumor lesions were obtained according to the Whole Tumor Area (WTA) protocol or Region of Interest (ROI) protocol: [CCR8 + Treg] = CCR8 + Foxp3 + cell counts per area [Total CD8 + T] = CD8 + cell count per area [GzmB + CD8 + T] = GzmB + CD8 + cell count per area [%GzmB + in CD8 + T] = percentage of GzmB + cells out of CD8 + cells In the WTA protocol, the overall tumor area of each tissue section was assessed. In the ROI protocol, cell density heat maps were drawn from CCR8/Foxp3-stained images using the spatial analysis mode of HALO. Multiple fields (360 × 270 micrometer in size) with high positive cell counts were obtained and the top five fields were selected as representative “Hot Spots” for analysis. Five fields with few CCR8 + Tregs were also selected as representative “Cold Spots” for analysis, except for fields with less than 10 positive cells for both CD8 and Foxp3 staining. CCR8/Foxp3-stained and CD8/GzmB-stained images of the matched fields were analyzed. For the association analysis of clinical features and prognosis, measurements obtained from overall tissue analysis by the WTA protocol or the average of the measurements obtained from five Hot Spots per case by the ROI protocol were used as representative values for each patient. The [GzmB/CCR8 ratio] is defined as [%GzmB + in CD8 + T] divided by [CCR8 + Treg]. Statistical analysis Statistical analysis and data description were performed using JMP Pro software version 16.2.0 (SAS Institute Inc., Cary, NC, USA) and R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). A linear regression model was used for correlation analysis. Comparisons between two groups were analyzed using the Mann-Whitney U test for continuous variables and Fisher’s exact test for categorical variables. Progression-free survival (PFS) was estimated using the Kaplan-Meier method and compared using the log-rank test. Hazard ratios (HRs) with a 95% confidence interval (CI) were calculated using the Cox proportional hazards model. A P -value < 0.05 was considered statistically significant. RESULTS Whole tumor tissue assessment of CCR8 + Tregs and GzmB + CD8 + T cells Serial tissue sections from 81 LSCC patients were double-stained for CCR8/Foxp3 and GzmB/CD8. Representative images of the IHC staining results for two typical cases with high and low CCR8 + Treg infiltration are shown in Fig. 1 . First, we examined the relationship between CCR8 + Treg infiltration and CD8 + T cell infiltration or GzmB expression by using WTA analysis. The results showed that [CCR8 + Treg (cells/mm 2 )] had a weak positive correlation with [Total CD8 + T (cells/mm 2 )], [GzmB + CD8 + T (cells/mm 2 )], and [%GzmB + in CD8 + T] (Fig. 2 A). When patients were divided into high and low groups relative to the [CCR8 + Treg (cells/mm 2 )] median value, the [CCR8 + Treg]-high group had higher infiltration of [Total CD8 + T (cells/mm 2 )] and [GzmB + CD8 + T (cells/mm 2 )], while no significant association was observed with [%GzmB + in CD8 + T] (Fig. 2 B). Analysis of CCR8 + Tregs and GzmB + CD8 + T cells in ROIs with high immune cell infiltration The overall tumor assessment with the WTA protocol showed a weak positive correlation between CCR8 + Treg infiltration and GzmB expression in CD8 + T cells. However, the stained images represented in Fig. 1 suggest that GzmB expression levels in CD8 + T cells appear to be lower in high CCR8 + Treg cases compared with in low CCR8 + Treg cases. Observation of the whole tissue scan image, shown in Fig. 3 A, revealed that the immune cell distribution is heterogeneous, with areas of high-density, low-density, and no lymphocytes. Therefore, to investigate the effects of local interactions between CCR8 + Tregs and CD8 + T cells, we focused our analysis on specific ROIs where the CCR8 + Tregs are highly accumulated. Using the density heat map generated by spatial analysis, Hot Spots with high CCR8 + Treg infiltration were selected for five fields in each case (Fig. 3 A) and GzmB/CD8-stained images of the matched fields were analyzed. This ROI analysis showed that [CCR8 + Treg (cells/field)] positively correlated with [Total CD8 + T (cells/field)], but negatively correlated with [GzmB + CD8 + T (cells/ field)] and [%GzmB + in CD8 + T] (Fig. 3 B). In a two-group comparison, [Total CD8 + T (cells/ field)] was significantly higher, whereas [%GzmB + in CD8 + T] was significantly lower, in the high [CCR8 + Treg (cells/ field)] areas than in the low areas (Fig. 3 C). In contrast, the analysis of total Foxp3 + Tregs showed that [Treg (cells/field)] had a positive correlation with [Total CD8 + T (cells/field)] and [GzmB + CD8 + T (cells/field)], and a weaker negative correlation with [%GzmB + in CD8 + T] than [CCR8 + Treg (cells/field)] (Supplementary Figure S1 A). Furthermore, there was no significant difference in [%GzmB + in CD8 + T] between areas with high and low [Total Treg (cells/field)] (Supplementary Figure S1 B). To further evaluate the impact of CCR8 + Treg local accumulation on neighboring CD8 + T cells in the TME, we analyzed GzmB + CD8 + T cells in areas of high and low CCR8 + Treg infiltration within the same case. In addition to Hot Spots, low-density fields of CCR8 + Tregs (excluding areas with no lymphocytes) were selected as Cold Spots for five fields in each case. Scatter plots of [CCR8 + Treg (cells/field)] and each CD8 + T cell parameter in each case are shown in Supplementary Figure S2 . A correlation analysis using the Hot and Cold Spot data from 40 cases with high CCR8 + Treg infiltration showed that [CCR8 + Treg (cells/field)] was positively correlated with [Total CD8 + T (cells/field)], but negatively correlated with [%GzmB + in CD8 + T] (Supplementary Figure S2 ). Furthermore, [%GzmB + in CD8 + T] in Hot Spots was significantly decreased compared with in Cold Spots ( P = 0.0035 and 0.013 respectively, Supplementary Figure S3 ). These results suggest that CD8 + T cell cytotoxicity may be suppressed in areas of the TME with CCR8 + Treg accumulation. Patients with more CCR8 + Tregs and lower GzmB expression levels had a poorer prognosis Finally, we investigated the association between the immunosuppressive profile indicated by our histological analysis and clinical outcomes, specifically PFS. All patients were divided into two groups relative to the median value of [CCR8 + Treg (cells/field)] or [%GzmB + in CD8 + T] at Hot Spots from the ROI protocol. As shown in Fig. 4 , the [CCR8 + Treg (cells/field)]-high group had worse PFS than the low group (3-year PFS 60.2% vs. 70.7%, respectively, P = 0.25) and the [%GzmB + in CD8 + T]-low group had worse PFS than the high group (3-year PFS 59.7% vs. 71.7%, respectively, P = 0.15), but neither result was statistically significant. We then divided the patients based on the GzmB/CCR8 ratio, representing local CCR8 + Treg accumulation and reduced CD8 + T cell cytotoxicity. The data suggested that the low patient group had significantly worse PFS than the high group (3-year PFS 57.1% vs. 74.5%, respectively, P = 0.032). A comparable trend was observed with the WTA protocol, but there were no significant differences in any of the indices (Supplementary Figure S4 ). Furthermore, similar analyses were performed for the CD8/Foxp3 ratio, GzmB/Foxp3 ratio, and CD8/CCR8 ratio, none of which showed significant differences in PFS (Supplementary Figure S5 ). When comparing clinicopathological characteristics, the low GzmB/CCR8 ratio group from the ROI protocol had significantly higher rates of lymph node metastasis (34.1% vs. 12.5%, respectively, P = 0.035) and pleural invasion (22.0% vs. 5.0%, respectively, P = 0.048) (Table 1 ). Multivariate analyses for PFS demonstrated that the GzmB/CCR8 ratio from the ROI protocol was an independent prognostic factor (HR = 2.13, 95% CI = 1.05–4.32, P = 0.036), along with pStage (HR = 2.08, 95% CI = 1.02–4.23, P = 0.044) (Table 2 ). Table 1 Clinicopathological characteristics of lung squamous cell carcinoma (LSCC) patients according to CCR8 + Treg, %GzmB + in CD8 + T, and the GzmB/CCR8 ratio. Variables CCR8 + Treg high (n = 40) CCR8 + Treg low (n = 41) P -value %GzmB + in CD8 + T high (n = 40) %GzmB + in CD8 + T low (n = 41) P -value GzmB/CCR8 ratio high (n = 40) GzmB/CCR8 ratio low (n = 41) P -value Age in years, median (range) 71.5 (42—85) 72 (38—85) 0.94 71 (42—85) 74 (38—83) 0.12 71.5 (38–85) 72 (58–83) 0.65 Sex Female Male 6 (15.0) 34 (85.0) 2 (4.9) 39 (95.1) 0.15 6 (15.0) 34 (85.0) 2 (4.9) 39 (95.1) 0.15 5 (12.5) 35 (87.5) 3 (7.3) 38 (92.7) 0.48 Smoking status Never Current/former 2 (5.0) 38 (95.0) 1 (2.4) 40 (97.6) 0.62 2 (5.0) 38 (95.0) 1 (2.44) 40 (97.6) 0.62 2 (5.0) 38 (95.0) 1 (2.4) 40 (97.6) 0.62 Brinkmann index, median (range) 1120 (0–3900) 1032 (0–3430) 0.50 1036 (0–3900) 1060 (0–3500) 0.94 1000 (0–3900) 1200 (0–3500) 0.22 Tumor laterality Left Right 19 (47.5) 21 (52.5) 18 (43.9) 23 (56.1) 0.82 18 (45.0) 22 (55.0) 19 (46.3) 22 (53.7) 1.00 19 (47.5) 21 (52.5) 18 (43.9) 23 (56.1) 0.82 Tumor location Upper lobe/Middle lobe Lower lobe 22 (55.0) 18 (45.0) 25 (61.0) 16 (39.0) 0.66 23 (57.5) 17 (42.5) 24 (58.5) 17 (41.5) 1.00 25 (61.0) 16 (39.0) 22 (55.0) 18 (45.0) 0.66 Surgical procedure Wedge resection/segmentectomy Lobectomy/pneumonectomy 5 (12.5) 35 (87.5) 6 (14.6) 35 (85.4) 1.00 4 (10.0) 36 (90.0) 7 (17.1) 34 (82.9) 0.52 6 (15.0) 34 (85.0) 5 (12.2) 36 (87.8) 0.76 Histological differentiation (SCC) Well/moderate Poor 38 (95.0) 2 (5.0) 35 (85.4) 6 (14.6) 0.26 34 (85.0) 6 (15.0) 39 (95.1) 2 (4.9) 0.15 31 (77.5) 9 (22.5) 30 (73.2) 11 (26.8) 0.80 pT T1—2 T3—4 33 (82.5) 7 (17.5) 31 (75.6) 10 (24.4) 0.59 33 (82.5) 7 (17.5) 31 (75.6) 10 (24.4) 0.59 32 (80.0) 8 (20.0) 35 (78.1) 10 (22.0) 1.00 pN N0 N1—2 26 (65.0) 14 (35.0) 36 (87.8) 5 (12.2) 0.019 33 (82.5) 7 (17.5) 29 (70.7) 12 (29.3) 0.30 35 (87.5) 5 (12.5) 27 (65.9) 14 (34.1) 0.035 pStage Stage I Stage II/III 20 (50.0) 20 (50.0) 26 (63.4) 15 (36.6) 0.27 25 (62.5) 15 (37.5) 21 (51.2) 20 (48.8) 0.37 25 (62.5) 15 (37.5) 21 (51.2) 20 (48.8) 0.37 Lymphatic invasion Absent Present 33 (82.5) 7 (17.5) 37 (90.2) 4 (9.8) 0.35 38 (95.0) 2 (5.0) 32 (78.1) 9 (22.0) 0.048 37 (92.5) 3 (7.5) 33 (80.5) 8 (19.5) 0.19 Vascular invasion Absent Present 33 (82.5) 7 (17.5) 38 (92.7) 3 (7.3) 0.19 38 (95.0) 2 (5.0) 33 (80.5) 8 (19.5) 0.088 38 (95.0) 2 (5.0) 33 (80.5) 8 (19.5) 0.088 Pleural invasion Absent Present 33 (82.5) 7 (17.5) 37 (90.2) 4 (9.8) 0.35 39 (97.5) 1 (2.5) 31 (75.6) 10 (24.4) 0.0070 38 (95.0) 2 (5.0) 32 (78.0) 9 (22.0) 0.048 Data are presented as n (%) unless noted otherwise. Abbreviations: CCR8, CC motif chemokine receptor 8; Treg, regulatory T cell; GzmB, Granzyme B Bolded P -values indicate statistical significance ( P < 0.05). Table 2 Univariate and multivariate analyses of progression-free survival in lung squamous cell carcinoma (LSCC) patients. Variables Univariate analysis Multivariate analysis HR (95% CI) P -value HR (95% CI) P -value Age < 72 years ≥ 72 years 1 [Reference] 1.69 (0.85–3.35) 0.13 Sex Female Male 1 [Reference] 4.33 (0.59–31.71) 0.15 Smoking status Never/former Current 1 [Reference] 1.19 (0.16–8.73) 0.86 Brinkmann index < 1040 ≥ 1040 1 [Reference] 1.61 (0.81–3.19) 0.18 Tumor laterality Left Right 1 [Reference] 1.21 (0.62–2.39) 0.58 Tumor Location Ul/Ml Ll 1 [Reference] 2.32 (1.17–4.60) 0.016 1 [Reference] 1.87 (0.91–3.82) 0.086 Surgical procedure Wedge resection/segmentectomy Lobectomy/pneumonectomy 1.06 (0.41–2.74) 1 [Reference] 0.91 Histological differentiation (SCC) Well/moderate Poor 1 [Reference] 1.30 (0.46–3.70) 0.64 pStage I II, III 1 [Reference] 2.62 (1.32–5.17) 0.0056 1 [Reference] 2.08 (1.02–4.23) 0.044 GzmB/CCR8 ratio High Low 1 [Reference] 2.12 (1.05–4.30) 0.037 1 [Reference] 2.13 (1.05–4.32) 0.036 Abbreviations: HR, hazard ratio; CI, confidence interval; Ul, upper lobe; Ml, middle lobe; Ll, lower lobe; SCC, squamous cell carcinoma; GzmB, Granzyme B; CCR8, CC motif chemokine receptor 8. DISCUSSION This study focused on the interactions between CCR8 + Tregs and CD8 + T cells in the LSCC TME, evaluating the impact of their spatial distribution on anti-tumor immunity by histological analysis. We found that local accumulation of CCR8 + Tregs within tumors was associated with reduced GzmB expression levels in closely infiltrating CD8 + T cells, as well as that the GzmB/CCR8 ratio was an independent prognostic factor in LSCC. CCR8 + Tregs have recently attracted attention as immunosuppressive cells that are highly concentrated in malignant tumor tissues compared with in peripheral blood and normal tissues in both humans and mice [ 19 – 22 ]. Using flow cytometry and public mRNA database analysis, our previous study showed high CCR8 + Treg infiltration within tumors that was associated with poor prognosis in lung cancer patients [ 16 ]. Ex vivo culture studies suggested that CCR8 + Tregs can suppress the cytotoxic function of CD8 + T cells [ 16 ]. Mouse studies have also corroborated that CCR8 + Tregs can suppress anti-tumor immunity via regulation of CD8 + T cell function [ 17 , 18 ]. However, to our knowledge, our data in this study provide the first evidence suggesting that CCR8 + Tregs can suppress CD8 + T cell cytotoxic activity in vivo within the TME of human cancer patients. Notably, the results were more robust for the analysis of CCR8 + Tregs than for the analysis of total Tregs, highlighting the importance of CCR8 + Tregs for the suppressive effect. In this study, tumor-infiltrating immune cells were evaluated using two protocols: the WTA protocol and ROI protocol. However, the results of the two protocols were not consistent. An overall assessment using the WTA protocol showed that high CCR8 + Treg infiltration was positively correlated with both high GzmB + CD8 + T cell infiltration and high GzmB expression in CD8 + T cells, with no findings suggesting immunosuppression by CCR8 + Tregs. This would indicate that patients with a high number of TILs have both functional and suppressive T cell infiltration and activation throughout the tumor tissue, which is consistent with several previous reports [ 27 – 29 ]. However, full-frame imaging of tumor sections showed heterogeneous accumulation of TILs. In general, immune cell infiltration within tumors is heterogeneously distributed, making spatial analysis of TILs in the local microenvironment important for fully assessing anti-tumor immune activity [ 30 , 31 ]. Several previous reports have highlighted the importance of examining local TIL infiltration, focusing on the Hot Spots of high infiltration, tumor stroma, and tumor periphery. These serve as biomarkers that are relevant to prognosis and therapeutic efficacy in various types of cancer [ 32 – 36 ]. Therefore, we performed an ROI analysis to investigate the effect of CCR8 + Tregs on the surrounding CD8 + T cells in the local TME. Our work with the ROI protocol in Hot Spots revealed a negative correlation between CCR8 + Treg accumulation and GzmB expression patterns in CD8 + T cells, supporting the possibility of in situ CTL suppression. Many studies have reported the clinical utility of TIL evaluation. The most well-known approach is the Immunoscore, which reflects the level of CD3 + and CD8 + T lymphocyte infiltration and has been established as a powerful prognostic indicator in colorectal cancer [ 37 ]. In lung cancer, in situ TIL assessment has also been recognized as an important prognostic tool and similar approaches focusing on CD3 + or CD8 + T cell infiltration have been investigated [ 38 – 40 ]. Conversely, the high Foxp3/CD3 or Foxp3/CD8 ratio in tumor tissues has been reported as a poor prognostic factor [ 41 – 43 ], suggesting that the balance between CD8 + T cells and Tregs is important in tumor immunity. Here, we propose that the GzmB/CCR8 ratio, which combines CCR8 + Treg accumulation and GzmB expression in CD8 + T cells, is a more advanced prognostic marker. Importantly, the GzmB/CCR8 ratio was more strongly associated with prognosis in LSCC patients than the CD8/Foxp3 ratio, GzmB/Foxp3 ratio, or CD8/CCR8 ratio. This indicator is expected to sensitively reflect the activity of anti-tumor immunity from the perspective of both immune promoters and suppressors, with the potential to provide reasonable and good prognostic prediction. This study has several limitations. First, it is an observational study with a limited number of cases from a single institution. Further prospective verification with a larger sample size is necessary in the future. Second, our analysis did not separate the stromal and intra-tumoral areas despite there being a potential relationship between the distribution of TILs and histological structure, as discussed above. While the automated ROI acquisition method used here is valid, we did not define the tumor area within the ROI. Third, the study did not fully investigate the mechanism by which CCR8 + Tregs can suppress GzmB expression in CD8 + T cells. In our previous study, we experimentally demonstrated that the CCR8 + Treg-mediated inhibition of CD8 + T cell function can be canceled by blocking major histocompatibility complex (MHC) molecules [ 16 ], suggesting that the suppressive effect of CCR8 + Tregs is mediated by antigen-presenting cells (APCs). However, we did not assess APCs or MHC molecules in this study. This will be explored in future research. CONCLUSIONS Our histological study illustrated that the accumulation of CCR8 + Tregs in the TME may lead to reduced cytotoxic function of the adjacent CD8 + T cells and poor prognosis in LSCC patients. This highlights the biological importance and clinical relevance of CCR8 + Tregs in anti-tumor immunity. The GzmB/CCR8 ratio is a potential prognostic predictor for LSCC and may benefit future clinical applications. Abbreviations APC antigen-presenting cell CCR8 CC motif chemokine receptor 8 CI Confidential interval CTL Cytotoxic T lymphocyte DAB 3’-3-diamnobenzidine FFPE formalin-fixed paraffin embedded FoxP3 Forkhead box protein 3 GzmB Granzyme B H 2 O 2 hydrogen peroxide HR Hazard Ratio H&E Hematoxylin and eosin ICI immune checkpoint inhibitors IHC immunohistochemistry LAD lung adenocarcinoma LSCC lung squamous cell carcinoma MHC major histocompatibility complex PD-L1 Programmed death ligand 1 PD-1 Programmed cell death 1 PFS progression-free survival ROI Regions of Interest Treg Regulatory T cell TIL Tumor-infiltrating lymphocyte TME tumor microenvironment WTA Whole tumor area Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Osaka University Hospital (approval number: 13266) and was performed in accordance with the Declaration of Hersinki. Informed consent was obtained from all patients. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, Osaka University has a joint research laboratory with Shionogi & Co., Ltd. Funding This study did not receive specific funding. Author’s contributions HW conceived the study. HW, AU, and YH designed the study. YH, KJ, NH, and HM collected the data. YH wrote the initial draft of the manuscript. SF, YS, MH, YN, TS, AK and KI contributed to data interpretation and critical revision of the manuscript for important intellectual content. All authors have read and approved the final version of the manuscript and have agreed to the accountability of all aspects of the study, thereby ensuring that any queries related to the accuracy or integrity of any part of the work are answerable. Acknowledgments We thank J. Iacona, Ph.D., from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209–49. Campbell JD, Alexandrov A, Kim J, Wala J, Berger AH, Pedamallu CS, et al. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat Genet. 2016;48:607–16. Planchard D, Popat S, Kerr K, Novello S, Smit EF, Faivre-Finn C, et al. Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2018;29(Suppl 4):iv192–237. Herbst RS, Giaccone G, de Marinis F, Reinmuth N, Vergnenegre A, Barrios CH, et al. 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International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391:2128–39. Donnem T, Hald SM, Paulsen EE, Richardsen E, Al-Saad S, Kilvaer TK, et al. Stromal CD8 + T-cell density—A promising supplement to TNM staging in non-small cell lung cancer. Clin Cancer Res. 2015;21:2635–43. Schalper KA, Brown J, Carvajal-Hausdorf D, McLaughlin J, Velcheti V, Syrigos KN, et al. Objective measurement and clinical significance of TILs in non-small cell lung cancer. J Natl Cancer Inst. 2015;107:dju435. Donnem T, Kilvaer TK, Andersen S, Richardsen E, Paulsen EE, Hald SM, et al. Strategies for clinical implementation of TNM-Immunoscore in resected nonsmall-cell lung cancer. Ann Oncol. 2016;27:225–32. Suzuki K, Kadota K, Sima CS, Nitadori JI, Rusch VW, Travis WD, et al. Clinical impact of immune microenvironment in stage i lung adenocarcinoma: Tumor interleukin-12 receptor β2 (IL-12Rβ2), IL-7R, and stromal FoxP3/CD3 ratio are independent predictors of recurrence. J Clin Oncol. 2013;31:490–8. Suzuki H, Chikazawa N, Tasaka T, Wada J, Yamasaki A, Kitaura Y, et al. Intratumoral CD8 + T/FOXP3 + cell ratio is a predictive marker for survival in patients with colorectal cancer. Cancer Immunol Immunother. 2010;59:653–61. Sato E, Olson SH, Ahn J, Bundy B, Nishikawa H, Qian F, et al. Intraepithelial CD8 + tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc Natl Acad Sci U S A. 2005;102:18538–43. Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf Supplementary Figure S1. Association of total Foxp3 + Tregs with CD8 + T cell parameters by the region of interest (ROI) analysis protocol. A. Five Hot Spots with high Treg infiltration were selected per case for 81 patients. The correlation plots with linear regression model of the data from 405 fields are presented. B. The data from all 405 fields were divided into high and low groups relative to the [Total Treg (cells/filed)] median value. The two groups were compared by the Mann-Whitney U test. FigureS2.pdf Supplementary Figure S2. Association of CCR8 + Tregs with CD8 + T cell parameters in each case. Five Hot Spots and five Cold Spots were selected per case for 81 lung squamous cell carcinoma (LSCC) patients and the CCR8/Foxp3 and GzmB/CD8 stained images of the matched fields were analyzed. The left panels show the scatter plots of patients with high CCR8 + Treg infiltration in the Hot Spots and the right panels show the scatter plots of patients with low CCR8 + Treg infiltration in the Hot Spots. FigureS3.pdf Supplementary Figure S3. Association of CCR8 + Tregs with CD8 + T cell parameters in Hot and Cold Spots. Both the Hot Spots and Cold Spots (five fields per case) were analyzed for the top 41 lung squamous cell carcinoma (LSCC) patients with high CCR8 + Treg infiltration in Hot Spots. A. The correlation plots with the linear regression model of the data from 410 fields are displayed. B. Each CD8 + T cell parameter in the Hot Spots and Cold Spots is shown in boxplots and compared by the Mann-Whitney U test. FigureS4.pdf Supplementary Figure S4. Association of Treg and CD8 + T cell profiles by the whole tumor area (WTA) analysis protocol with prognosis. The 81 lung squamous cell carcinoma (LSCC) patients were divided into high and low groups relative to the median value of each measurement by the WTA analysis. The Kaplan-Meier survival curves for progression-free survival (PFS) are presented. Group comparisons were conducted using the log-rank test. The hazard ratios (HRs) with the 95% confidence interval (CI) were calculated using the Cox proportional hazards model. FigureS5.pdf Supplementary Figure S5. Association of various Treg and CD8 + T cell balance indicators with prognosis. Using the average measurements obtained from the five Hot Spots per case by the region of interest (ROI) analysis protocol, 81 lung squamous cell carcinoma (LSCC) patients were divided into high and low groups relative to the median value of each measurement. The Kaplan-Meier survival curves for progression-free survival (PFS) are presented. Group comparisons were conducted using the log-rank test. The hazard ratios (HRs) with the 95% confidence interval (CI) were calculated using the Cox proportional hazards model. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Mar, 2024 Editor assigned by journal 20 Mar, 2024 Submission checks completed at journal 20 Mar, 2024 First submitted to journal 18 Mar, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4121046","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281780842,"identity":"38d4960f-9ba4-4a58-b221-214b93a1c8b9","order_by":0,"name":"Yoshinori Hayashi","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Yoshinori","middleName":"","lastName":"Hayashi","suffix":""},{"id":281780843,"identity":"588425ea-bc25-4f81-b68f-e250f2512bf7","order_by":1,"name":"Azumi 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assays. The left panel shows CCR8 (brown) and Foxp3 (blue) staining, the middle panel shows CD8 (brown) and GzmB (blue) staining, and the right panel shows hematoxylin and eosin (H\u0026amp;E)-stained images. The three top images are from the CCR8\u003csup\u003e+\u003c/sup\u003e Treg high case and the three bottom images are from the CCR8\u003csup\u003e+\u003c/sup\u003e Treg low case.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/f2cb0cb2fe6677a13741045d.jpeg"},{"id":53417751,"identity":"04d5e2fe-cad2-48f5-a29c-78dd02449071","added_by":"auto","created_at":"2024-03-25 18:05:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":423387,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs with CD8\u003csup\u003e+\u003c/sup\u003e T cell parameters by the whole tumor area (WTA) analysis protocol. The overall tissue area of CCR8/Foxp3 and CD8/GzmB double-stained tissue sections from 81 lung squamous cell carcinoma (LSCC) patients were analyzed. \u003cstrong\u003eA.\u003c/strong\u003e Correlation plots with the linear regression model are displayed. \u003cstrong\u003eB.\u003c/strong\u003e All 81 patients were divided into high and low groups relative to the [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (mm\u003csup\u003e2\u003c/sup\u003e)] median value. The two groups were compared by the Mann-Whitney U test.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/a2012dc49a19b6b4549c5c6b.jpeg"},{"id":53417757,"identity":"c8e7aeda-40b2-433a-be17-db82ce0984fe","added_by":"auto","created_at":"2024-03-25 18:05:01","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":874014,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs with CD8\u003csup\u003e+\u003c/sup\u003e T cell parameters by the region of interest (ROI) analysis protocol. \u003cstrong\u003eA.\u003c/strong\u003e Representative whole tissue scan image of CCR8/Foxp3-stained sections. The left panel shows a double-stained immunohistochemistry image and the right panel shows a density heat map of positive cells. Positive cells exhibit heterogeneous distribution, with areas of high density (red box) and low density (blue box). \u003cstrong\u003eB.\u003c/strong\u003e Five Hot Spots with high CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration were selected per case for 81 lung squamous cell carcinoma (LSCC) patients and the CCR8/Foxp3 and GzmB/CD8 stained images of the matched fields were analyzed. The correlation plots with the linear regression model of the data from 405 fields are presented. \u003cstrong\u003eC.\u003c/strong\u003e Data from all 405 fields were divided into high and low groups relative to the [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/filed)] median value. The two groups were compared by the Mann-Whitney U test.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/a1494e7b7217ea980c6077d5.jpeg"},{"id":53417754,"identity":"2beebec2-d424-45a9-8065-2dd4f49f63fa","added_by":"auto","created_at":"2024-03-25 18:05:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":329695,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic impact of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and GzmB\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in the tumor microenvironment (TME). Using the average measurements obtained from five Hot Spots per case with the region of interest (ROI) analysis, 81 lung squamous cell carcinoma (LSCC) patients were divided into high and low groups relative to the median value of each measurement. The Kaplan-Meier survival curves for progression-free survival (PFS) are presented. The GzmB/CCR8 ratio is defined as [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] divided by [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/field)]. Group comparisons were conducted using the log-rank test. Hazard ratios (HRs) with the 95% confidence interval (CI) were calculated using the Cox proportional hazards model.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/68a3a2cffbfd1c6f1aa7f8b7.jpeg"},{"id":53419680,"identity":"e6009903-d418-40a4-88f8-8fd79cb70f8e","added_by":"auto","created_at":"2024-03-25 18:13:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":897727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/e504faf8-112d-4991-97c3-93b5957c72de.pdf"},{"id":53417703,"identity":"c6f195e6-b162-43c0-853d-30b7b5e19827","added_by":"auto","created_at":"2024-03-25 18:04:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":797226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. \u003c/strong\u003eAssociation of total Foxp3\u003csup\u003e+\u003c/sup\u003e Tregs with CD8\u003csup\u003e+\u003c/sup\u003e T cell parameters by the region of interest (ROI) analysis protocol.\u003cstrong\u003e A. \u003c/strong\u003eFive Hot Spots with high Treg infiltration were selected per case for 81 patients. The correlation plots with linear regression model of the data from 405 fields are presented. \u003cstrong\u003eB.\u003c/strong\u003e The data from all 405 fields were divided into high and low groups relative to the [Total Treg (cells/filed)] median value. The two groups were compared by the Mann-Whitney U test.\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/e49c2bcca02a2f1c81129a66.pdf"},{"id":53419666,"identity":"2787e10a-8f84-48ae-838d-1e5e0f04a8d0","added_by":"auto","created_at":"2024-03-25 18:13:01","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1085438,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2. \u003c/strong\u003eAssociation of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs with CD8\u003csup\u003e+\u003c/sup\u003e T cell parameters in each case. Five Hot Spots and five Cold Spots were selected per case for 81 lung squamous cell carcinoma (LSCC) patients and the CCR8/Foxp3 and GzmB/CD8 stained images of the matched fields were analyzed. The left panels show the scatter plots of patients with high CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration in the Hot Spots and the right panels show the scatter plots of patients with low CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration in the Hot Spots.\u003c/p\u003e","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/907cbe50b46784c2dfab7aca.pdf"},{"id":53417756,"identity":"d6510c1b-62fe-470e-8c57-f082ba0be591","added_by":"auto","created_at":"2024-03-25 18:05:01","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":713411,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S3. \u003c/strong\u003eAssociation of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs with CD8\u003csup\u003e+\u003c/sup\u003e T cell parameters in Hot and Cold Spots. Both the Hot Spots and Cold Spots (five fields per case) were analyzed for the top 41 lung squamous cell carcinoma (LSCC) patients with high CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration in Hot Spots. \u003cstrong\u003eA.\u003c/strong\u003e The correlation plots with the linear regression model of the data from 410 fields are displayed. \u003cstrong\u003eB.\u003c/strong\u003e Each CD8\u003csup\u003e+\u003c/sup\u003e T cell parameter in the Hot Spots and Cold Spots is shown in boxplots and compared by the Mann-Whitney U test.\u003c/p\u003e","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/90445d2abcde3c2c4bbc1a89.pdf"},{"id":53417759,"identity":"63007210-8966-42db-84ec-26328be3adad","added_by":"auto","created_at":"2024-03-25 18:05:01","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":507305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S4. \u003c/strong\u003eAssociation of Treg and CD8\u003csup\u003e+\u003c/sup\u003e T cell profiles by the whole tumor area (WTA) analysis protocol with prognosis. The 81 lung squamous cell carcinoma (LSCC) patients were divided into high and low groups relative to the median value of each measurement by the WTA analysis. The Kaplan-Meier survival curves for progression-free survival (PFS) are presented. Group comparisons were conducted using the log-rank test. The hazard ratios (HRs) with the 95% confidence interval (CI) were calculated using the Cox proportional hazards model.\u003c/p\u003e","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/b1c2654595a688fdb5cbeeb0.pdf"},{"id":53417753,"identity":"5cbb9bab-610c-4020-b918-419a4c9f396b","added_by":"auto","created_at":"2024-03-25 18:05:01","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":511463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S5. \u003c/strong\u003eAssociation of various Treg and CD8\u003csup\u003e+\u003c/sup\u003e T cell balance indicators with prognosis. Using the average measurements obtained from the five Hot Spots per case by the region of interest (ROI) analysis protocol, 81 lung squamous cell carcinoma (LSCC) patients were divided into high and low groups relative to the median value of each measurement. The Kaplan-Meier survival curves for progression-free survival (PFS) are presented. Group comparisons were conducted using the log-rank test. The hazard ratios (HRs) with the 95% confidence interval (CI) were calculated using the Cox proportional hazards model.\u003c/p\u003e","description":"","filename":"FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4121046/v1/5c99cbd783388c8c17d556c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"In situ analysis of CCR8 + regulatory T cells and cytotoxic CD8 + T cells in human lung squamous cell carcinoma: biological insights and clinical implications","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eLung cancer is estimated to have 2.2\u0026nbsp;million new cases and cause 1.8\u0026nbsp;million deaths each year worldwide, making this disease a major contributor to cancer-related mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among the non-small cell lung cancer subtypes, which constitute approximately 85% of all lung cancer cases, lung adenocarcinoma (LAD)and lung squamous cell carcinoma (LSCC) are the most common [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. LSCC patients have historically seen scarce benefits from targeted therapies because of the low frequency of driver mutations, such as in \u003cem\u003eEGFR\u003c/em\u003e, and limited therapeutic efficacy compared with LAD patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The advent of immune checkpoint inhibitors (ICIs), such as anti-PD-1/PD-L1 or anti-CTLA4 agents, has markedly improved the prognosis of LSCC patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, despite these advances, ICIs have limited efficacy and most LSCC patients still do not respond to current immunotherapy methods. This has led to an increased interest in research aiming to understand the tumor immune microenvironment of LSCC.\u003c/p\u003e \u003cp\u003eRegulatory T cells (Tregs) are an immunosuppressive CD4\u003csup\u003e+\u003c/sup\u003e T cell subset that express the transcription factor Foxp3. They play an important role in maintaining immune tolerance and homeostasis to prevent autoimmune diseases and allergies [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Tregs also act as immunosuppressors in tumor immunity, potentially inhibiting immune responses against tumor cells and supporting tumor progression [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. CC motif chemokine receptor 8 (CCR8) is a chemokine receptor that has recently been identified using advanced comprehensive transcriptomic analysis and flow cytometry as a novel marker that is selectively expressed on intratumor Tregs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. CCR8 is mainly expressed in clonally expanded Tregs activated by tumor-associated antigens, with CCR8\u003csup\u003e+\u003c/sup\u003e Tregs being an \"effector-like\" cell population with a stable anti-tumor immunosuppressive function [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our previous study also reported that CCR8\u003csup\u003e+\u003c/sup\u003e Tregs are involved in the tumor immunosuppressive microenvironment in lung cancer, including LSCC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, preclinical studies in several murine tumor models, including lung carcinoma, have demonstrated that depletion of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs by anti-CCR8 antibody administration resulted in a marked anti-tumor effect [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Their mechanism of action has been suggested to be via enhancement of CD8\u003csup\u003e+\u003c/sup\u003e T cell function, as evidenced by increased expression levels of granzyme B (GzmB) and interferon-γ in CD8\u003csup\u003e+\u003c/sup\u003e T cells following antibody administration and abolished \u003cem\u003ein vivo\u003c/em\u003e efficacy from CD8\u003csup\u003e+\u003c/sup\u003e T cell depletion [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Tumor-infiltrating lymphocytes (TILs) are a vital component of the tumor microenvironment (TME) and are closely related to the progression and prognosis of malignant tumors [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. CD8\u003csup\u003e+\u003c/sup\u003e cytotoxic T lymphocytes (CTLs), which can directly kill cancer cells, are a particularly significant T cell subset in anti-tumor immunity. We have previously demonstrated that human CCR8\u003csup\u003e+\u003c/sup\u003e Tregs from lung cancer TILs or expanded from peripheral blood mononuclear cells have potent inhibitory activity against CTL function \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, there is no clinical evidence of the impact of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs on CTL activity in the TME of human cancer patients, which we aimed to evaluate in this study.\u003c/p\u003e \u003cp\u003eHere, we performed histological investigations of human LSCC samples to explore the levels and distribution of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and their impact on surrounding CD8\u003csup\u003e+\u003c/sup\u003e T cells in the local TME. In addition, we analyzed their association with patient clinicopathological features and prognosis to verify their clinical significance.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe retrospectively identified serial patients who were diagnosed with LSCC and underwent radical resection without preoperative treatment at Osaka University Hospital from March 2010 to May 2017. Excluded individuals included those who underwent palliative or non-curative resection and those with multiple concurrent cancers. A total of 81 patients were included in the study. Medical data of the patients, such as clinicopathological characteristics, surgical findings, and clinical course, were collected retrospectively using the clinical database and pathological examination reports of the institute. Tumor staging was performed according to the eighth edition of the UICC TNM classification system [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. All patients were monitored for recurrence and death or survival to the last follow-up (at least 5 years) for recurrence-free cases. This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Osaka University Hospital (approval number: 13266). All participants provided informed consent using the opt-out methodology from the retrospective design of the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDouble staining for immunohistochemistry (IHC) analysis\u003c/h2\u003e \u003cp\u003eFormalin-fixed paraffin-embedded (FFPE) surgical specimens from each patient were collected and prepared. Three serial 4 \u0026micro;m thin sections were cut for hematoxylin and eosin (H\u0026amp;E) staining and double IHC staining for CCR8/Foxp3 and GzmB/CD8, respectively. The sections were deparaffinized using xylene, subjected to a stepwise ethanol dilution series for hydration, and then immersed in an EDTA-based antigen retrieval buffer (pH 9.0), followed by incubation in a pressure cooker at 110\u0026deg;C for 15 minutes. The sections were washed in distilled water, incubated for 10 minutes in 3% hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) to block endogenous peroxidase activity, washed three times in 0.05% Tween-20/TBS, and then incubated for 20 minutes in a 3% bovine serum albumin or 5% goat serum solution/PBS for non-specific antigen reaction blocking. The antibodies were diluted in 1% bovine serum albumin/PBS. The sections were incubated 2 hours at room temperature with primary antibodies (for CCR8 and CD8). After washing three times, detection was performed using a polymer reagent (Histofine Simple Stain MAX PO, Nichirei Bioscience Inc., Tokyo, Japan) and 3\u0026rsquo;-3-diamnobenzidine (DAB) substrate according to the manufacturer's protocol. Subsequently, antigen retrieval was performed again, followed by 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e treatment and blocking. The samples were then incubated 2 hours at room temperature with other primary antibodies (for Foxp3 and GzmB). They were washed three times, followed by detection with a polymer reagent and Vina Green Chromogen kit (BRR807AH, Biocare Medical LLC, Pacheco, CA, USA). The sections were counterstained with hematoxylin, washed, dehydrated with ethanol, and then mounted. The primary antibodies used were as follows: CCR8 (433H, mouse monoclonal, BD Biosciences, Franklin Lakes, NJ, USA, 1:40 dilution), CD8 (L26, mouse monoclonal, Nichirei Bioscience, Tokyo, Japan, ready to use), Foxp3 (236A/E, mouse monoclonal, Abcam, Cambridge, UK, 1:100 dilution), and GzmB (D6E9W, rabbit monoclonal, Cell Signaling Technology, Danvers, MA, USA, 1:50 dilution).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of IHC staining images\u003c/h2\u003e \u003cp\u003eThe double-stained glass slides were scanned and merged into digital slide images at 40\u0026times; magnification using a microscope slide scanner system (VENTANA iScan HT, Roche-Ventana Medical Systems Inc., Tucson, AZ, USA). Images were imported into a pathological image analysis software (HALO version 3.5, Indica Labs, Albuquerque, NM, USA), then all sequential steps for automated analysis, including annotation, training, and analysis, were performed[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The analysis step was carried out using the auto-cell counting algorithm generated in the training step. Tumor lesions in each slide were discriminated by observation of H\u0026amp;E-stained images by an experienced pathologist. The following measurements within the tumor lesions were obtained according to the Whole Tumor Area (WTA) protocol or Region of Interest (ROI) protocol:\u003c/p\u003e \u003cp\u003e[CCR8\u003csup\u003e+\u003c/sup\u003e Treg]\u0026thinsp;=\u0026thinsp;CCR8\u003csup\u003e+\u003c/sup\u003eFoxp3\u003csup\u003e+\u003c/sup\u003e cell counts per area\u003c/p\u003e \u003cp\u003e[Total CD8\u003csup\u003e+\u003c/sup\u003e T]\u0026thinsp;=\u0026thinsp;CD8\u003csup\u003e+\u003c/sup\u003e cell count per area\u003c/p\u003e \u003cp\u003e[GzmB\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T]\u0026thinsp;=\u0026thinsp;GzmB\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e cell count per area\u003c/p\u003e \u003cp\u003e[%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T]\u0026thinsp;=\u0026thinsp;percentage of GzmB\u003csup\u003e+\u003c/sup\u003e cells out of CD8\u003csup\u003e+\u003c/sup\u003e cells\u003c/p\u003e \u003cp\u003eIn the WTA protocol, the overall tumor area of each tissue section was assessed. In the ROI protocol, cell density heat maps were drawn from CCR8/Foxp3-stained images using the spatial analysis mode of HALO. Multiple fields (360 \u0026times; 270 micrometer in size) with high positive cell counts were obtained and the top five fields were selected as representative \u0026ldquo;Hot Spots\u0026rdquo; for analysis. Five fields with few CCR8\u003csup\u003e+\u003c/sup\u003e Tregs were also selected as representative \u0026ldquo;Cold Spots\u0026rdquo; for analysis, except for fields with less than 10 positive cells for both CD8 and Foxp3 staining. CCR8/Foxp3-stained and CD8/GzmB-stained images of the matched fields were analyzed.\u003c/p\u003e \u003cp\u003e For the association analysis of clinical features and prognosis, measurements obtained from overall tissue analysis by the WTA protocol or the average of the measurements obtained from five Hot Spots per case by the ROI protocol were used as representative values for each patient. The [GzmB/CCR8 ratio] is defined as [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] divided by [CCR8\u003csup\u003e+\u003c/sup\u003e Treg].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and data description were performed using JMP Pro software version 16.2.0 (SAS Institute Inc., Cary, NC, USA) and R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). A linear regression model was used for correlation analysis. Comparisons between two groups were analyzed using the Mann-Whitney U test for continuous variables and Fisher\u0026rsquo;s exact test for categorical variables. Progression-free survival (PFS) was estimated using the Kaplan-Meier method and compared using the log-rank test. Hazard ratios (HRs) with a 95% confidence interval (CI) were calculated using the Cox proportional hazards model. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWhole tumor tissue assessment of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and GzmB\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells\u003c/h2\u003e \u003cp\u003eSerial tissue sections from 81 LSCC patients were double-stained for CCR8/Foxp3 and GzmB/CD8. Representative images of the IHC staining results for two typical cases with high and low CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. First, we examined the relationship between CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration and CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration or GzmB expression by using WTA analysis. The results showed that [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/mm\u003csup\u003e2\u003c/sup\u003e)] had a weak positive correlation with [Total CD8\u003csup\u003e+\u003c/sup\u003e T (cells/mm\u003csup\u003e2\u003c/sup\u003e)], [GzmB\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T (cells/mm\u003csup\u003e2\u003c/sup\u003e)], and [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). When patients were divided into high and low groups relative to the [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/mm\u003csup\u003e2\u003c/sup\u003e)] median value, the [CCR8\u003csup\u003e+\u003c/sup\u003e Treg]-high group had higher infiltration of [Total CD8\u003csup\u003e+\u003c/sup\u003e T (cells/mm\u003csup\u003e2\u003c/sup\u003e)] and [GzmB\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T (cells/mm\u003csup\u003e2\u003c/sup\u003e)], while no significant association was observed with [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and GzmB\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in ROIs with high immune cell infiltration\u003c/h2\u003e \u003cp\u003eThe overall tumor assessment with the WTA protocol showed a weak positive correlation between CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration and GzmB expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells. However, the stained images represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e suggest that GzmB expression levels in CD8\u003csup\u003e+\u003c/sup\u003e T cells appear to be lower in high CCR8\u003csup\u003e+\u003c/sup\u003e Treg cases compared with in low CCR8\u003csup\u003e+\u003c/sup\u003e Treg cases. Observation of the whole tissue scan image, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, revealed that the immune cell distribution is heterogeneous, with areas of high-density, low-density, and no lymphocytes. Therefore, to investigate the effects of local interactions between CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and CD8\u003csup\u003e+\u003c/sup\u003e T cells, we focused our analysis on specific ROIs where the CCR8\u003csup\u003e+\u003c/sup\u003e Tregs are highly accumulated.\u003c/p\u003e \u003cp\u003eUsing the density heat map generated by spatial analysis, Hot Spots with high CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration were selected for five fields in each case (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and GzmB/CD8-stained images of the matched fields were analyzed. This ROI analysis showed that [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/field)] positively correlated with [Total CD8\u003csup\u003e+\u003c/sup\u003e T (cells/field)], but negatively correlated with [GzmB\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T (cells/ field)] and [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In a two-group comparison, [Total CD8\u003csup\u003e+\u003c/sup\u003e T (cells/ field)] was significantly higher, whereas [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] was significantly lower, in the high [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/ field)] areas than in the low areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eIn contrast, the analysis of total Foxp3\u003csup\u003e+\u003c/sup\u003e Tregs showed that [Treg (cells/field)] had a positive correlation with [Total CD8\u003csup\u003e+\u003c/sup\u003e T (cells/field)] and [GzmB\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T (cells/field)], and a weaker negative correlation with [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] than [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/field)] (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Furthermore, there was no significant difference in [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] between areas with high and low [Total Treg (cells/field)] (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo further evaluate the impact of CCR8\u003csup\u003e+\u003c/sup\u003e Treg local accumulation on neighboring CD8\u003csup\u003e+\u003c/sup\u003e T cells in the TME, we analyzed GzmB\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in areas of high and low CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration within the same case. In addition to Hot Spots, low-density fields of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs (excluding areas with no lymphocytes) were selected as Cold Spots for five fields in each case. Scatter plots of [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/field)] and each CD8\u003csup\u003e+\u003c/sup\u003e T cell parameter in each case are shown in Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. A correlation analysis using the Hot and Cold Spot data from 40 cases with high CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration showed that [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/field)] was positively correlated with [Total CD8\u003csup\u003e+\u003c/sup\u003e T (cells/field)], but negatively correlated with [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Furthermore, [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] in Hot Spots was significantly decreased compared with in Cold Spots (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0035 and 0.013 respectively, Supplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). These results suggest that CD8\u003csup\u003e+\u003c/sup\u003e T cell cytotoxicity may be suppressed in areas of the TME with CCR8\u003csup\u003e+\u003c/sup\u003e Treg accumulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatients with more CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and lower GzmB expression levels had a poorer prognosis\u003c/h2\u003e \u003cp\u003eFinally, we investigated the association between the immunosuppressive profile indicated by our histological analysis and clinical outcomes, specifically PFS. All patients were divided into two groups relative to the median value of [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/field)] or [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T] at Hot Spots from the ROI protocol. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the [CCR8\u003csup\u003e+\u003c/sup\u003e Treg (cells/field)]-high group had worse PFS than the low group (3-year PFS 60.2% vs. 70.7%, respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25) and the [%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T]-low group had worse PFS than the high group (3-year PFS 59.7% vs. 71.7%, respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15), but neither result was statistically significant. We then divided the patients based on the GzmB/CCR8 ratio, representing local CCR8\u003csup\u003e+\u003c/sup\u003e Treg accumulation and reduced CD8\u003csup\u003e+\u003c/sup\u003e T cell cytotoxicity. The data suggested that the low patient group had significantly worse PFS than the high group (3-year PFS 57.1% vs. 74.5%, respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). A comparable trend was observed with the WTA protocol, but there were no significant differences in any of the indices (Supplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Furthermore, similar analyses were performed for the CD8/Foxp3 ratio, GzmB/Foxp3 ratio, and CD8/CCR8 ratio, none of which showed significant differences in PFS (Supplementary Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). When comparing clinicopathological characteristics, the low GzmB/CCR8 ratio group from the ROI protocol had significantly higher rates of lymph node metastasis (34.1% vs. 12.5%, respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) and pleural invasion (22.0% vs. 5.0%, respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Multivariate analyses for PFS demonstrated that the GzmB/CCR8 ratio from the ROI protocol was an independent prognostic factor (HR\u0026thinsp;=\u0026thinsp;2.13, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;4.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), along with pStage (HR\u0026thinsp;=\u0026thinsp;2.08, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;4.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics of lung squamous cell carcinoma (LSCC) patients according to CCR8\u003csup\u003e+\u003c/sup\u003e Treg, %GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T, and the GzmB/CCR8 ratio.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCR8\u003csup\u003e+\u003c/sup\u003e Treg high\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCR8\u003csup\u003e+\u003c/sup\u003e Treg low\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T high (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%GzmB\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T low (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGzmB/CCR8 ratio high\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGzmB/CCR8 ratio low\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.5 (42\u0026mdash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (38\u0026mdash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71 (42\u0026mdash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74 (38\u0026mdash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.5 (38\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72 (58\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (15.0)\u003c/p\u003e \u003cp\u003e34 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.9)\u003c/p\u003e \u003cp\u003e39 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (15.0)\u003c/p\u003e \u003cp\u003e34 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (4.9)\u003c/p\u003e \u003cp\u003e39 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (12.5)\u003c/p\u003e \u003cp\u003e35 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 (7.3)\u003c/p\u003e \u003cp\u003e38 (92.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003cp\u003eNever\u003c/p\u003e \u003cp\u003eCurrent/former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003cp\u003e38 (95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.4)\u003c/p\u003e \u003cp\u003e40 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003cp\u003e38 (95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.44)\u003c/p\u003e \u003cp\u003e40 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003cp\u003e38 (95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (2.4)\u003c/p\u003e \u003cp\u003e40 (97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrinkmann index, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1120 (0\u0026ndash;3900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1032 (0\u0026ndash;3430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1036 (0\u0026ndash;3900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1060 (0\u0026ndash;3500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1000 (0\u0026ndash;3900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1200 (0\u0026ndash;3500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor laterality\u003c/p\u003e \u003cp\u003eLeft\u003c/p\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (47.5)\u003c/p\u003e \u003cp\u003e21 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (43.9)\u003c/p\u003e \u003cp\u003e23 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (45.0)\u003c/p\u003e \u003cp\u003e22 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (46.3)\u003c/p\u003e \u003cp\u003e22 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19 (47.5)\u003c/p\u003e \u003cp\u003e21 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18 (43.9)\u003c/p\u003e \u003cp\u003e23 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003cp\u003eUpper lobe/Middle lobe\u003c/p\u003e \u003cp\u003eLower lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (55.0)\u003c/p\u003e \u003cp\u003e18 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (61.0)\u003c/p\u003e \u003cp\u003e16 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (57.5)\u003c/p\u003e \u003cp\u003e17 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (58.5)\u003c/p\u003e \u003cp\u003e17 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25 (61.0)\u003c/p\u003e \u003cp\u003e16 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22 (55.0)\u003c/p\u003e \u003cp\u003e18 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical procedure\u003c/p\u003e \u003cp\u003eWedge resection/segmentectomy\u003c/p\u003e \u003cp\u003eLobectomy/pneumonectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (12.5)\u003c/p\u003e \u003cp\u003e35 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (14.6)\u003c/p\u003e \u003cp\u003e35 (85.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (10.0)\u003c/p\u003e \u003cp\u003e36 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (17.1)\u003c/p\u003e \u003cp\u003e34 (82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (15.0)\u003c/p\u003e \u003cp\u003e34 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5 (12.2)\u003c/p\u003e \u003cp\u003e36 (87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological differentiation (SCC)\u003c/p\u003e \u003cp\u003eWell/moderate\u003c/p\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (95.0)\u003c/p\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (85.4)\u003c/p\u003e \u003cp\u003e6 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (85.0)\u003c/p\u003e \u003cp\u003e6 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (95.1)\u003c/p\u003e \u003cp\u003e2 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31 (77.5)\u003c/p\u003e \u003cp\u003e9 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30 (73.2)\u003c/p\u003e \u003cp\u003e11 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epT\u003c/p\u003e \u003cp\u003eT1\u0026mdash;2\u003c/p\u003e \u003cp\u003eT3\u0026mdash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (82.5)\u003c/p\u003e \u003cp\u003e7 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (75.6)\u003c/p\u003e \u003cp\u003e10 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (82.5)\u003c/p\u003e \u003cp\u003e7 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (75.6)\u003c/p\u003e \u003cp\u003e10 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32 (80.0)\u003c/p\u003e \u003cp\u003e8 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35 (78.1)\u003c/p\u003e \u003cp\u003e10 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epN\u003c/p\u003e \u003cp\u003eN0\u003c/p\u003e \u003cp\u003eN1\u0026mdash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (65.0)\u003c/p\u003e \u003cp\u003e14 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (87.8)\u003c/p\u003e \u003cp\u003e5 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (82.5)\u003c/p\u003e \u003cp\u003e7 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (70.7)\u003c/p\u003e \u003cp\u003e12 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35 (87.5)\u003c/p\u003e \u003cp\u003e5 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27 (65.9)\u003c/p\u003e \u003cp\u003e14 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epStage\u003c/p\u003e \u003cp\u003eStage I\u003c/p\u003e \u003cp\u003eStage II/III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (50.0)\u003c/p\u003e \u003cp\u003e20 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (63.4)\u003c/p\u003e \u003cp\u003e15 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (62.5)\u003c/p\u003e \u003cp\u003e15 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (51.2)\u003c/p\u003e \u003cp\u003e20 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25 (62.5)\u003c/p\u003e \u003cp\u003e15 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21 (51.2)\u003c/p\u003e \u003cp\u003e20 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphatic invasion\u003c/p\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (82.5)\u003c/p\u003e \u003cp\u003e7 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (90.2)\u003c/p\u003e \u003cp\u003e4 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (95.0)\u003c/p\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (78.1)\u003c/p\u003e \u003cp\u003e9 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37 (92.5)\u003c/p\u003e \u003cp\u003e3 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33 (80.5)\u003c/p\u003e \u003cp\u003e8 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular invasion\u003c/p\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (82.5)\u003c/p\u003e \u003cp\u003e7 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (92.7)\u003c/p\u003e \u003cp\u003e3 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (95.0)\u003c/p\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (80.5)\u003c/p\u003e \u003cp\u003e8 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38 (95.0)\u003c/p\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33 (80.5)\u003c/p\u003e \u003cp\u003e8 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleural invasion\u003c/p\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (82.5)\u003c/p\u003e \u003cp\u003e7 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (90.2)\u003c/p\u003e \u003cp\u003e4 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (97.5)\u003c/p\u003e \u003cp\u003e1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (75.6)\u003c/p\u003e \u003cp\u003e10 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0070\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38 (95.0)\u003c/p\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32 (78.0)\u003c/p\u003e \u003cp\u003e9 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eData are presented as n (%) unless noted otherwise.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: CCR8, CC motif chemokine receptor 8; Treg, regulatory T cell; GzmB, Granzyme B\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eBolded \u003cem\u003eP\u003c/em\u003e-values indicate statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses of progression-free survival in lung squamous cell carcinoma (LSCC) patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;72 years\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;72 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e1.69 (0.85\u0026ndash;3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e4.33 (0.59\u0026ndash;31.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003cp\u003eNever/former\u003c/p\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e1.19 (0.16\u0026ndash;8.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrinkmann index\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1040\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e1.61 (0.81\u0026ndash;3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor laterality\u003c/p\u003e \u003cp\u003eLeft\u003c/p\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e1.21 (0.62\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Location\u003c/p\u003e \u003cp\u003eUl/Ml\u003c/p\u003e \u003cp\u003eLl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e2.32 (1.17\u0026ndash;4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e1.87 (0.91\u0026ndash;3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical procedure\u003c/p\u003e \u003cp\u003eWedge resection/segmentectomy\u003c/p\u003e \u003cp\u003eLobectomy/pneumonectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.41\u0026ndash;2.74)\u003c/p\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological differentiation (SCC)\u003c/p\u003e \u003cp\u003eWell/moderate\u003c/p\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e1.30 (0.46\u0026ndash;3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epStage\u003c/p\u003e \u003cp\u003eI\u003c/p\u003e \u003cp\u003eII, III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e2.62 (1.32\u0026ndash;5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e2.08 (1.02\u0026ndash;4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGzmB/CCR8 ratio\u003c/p\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e2.12 (1.05\u0026ndash;4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [Reference]\u003c/p\u003e \u003cp\u003e2.13 (1.05\u0026ndash;4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; Ul, upper lobe; Ml, middle lobe; Ll, lower lobe; SCC, squamous cell carcinoma; GzmB, Granzyme B; CCR8, CC motif chemokine receptor 8.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study focused on the interactions between CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and CD8\u003csup\u003e+\u003c/sup\u003e T cells in the LSCC TME, evaluating the impact of their spatial distribution on anti-tumor immunity by histological analysis. We found that local accumulation of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs within tumors was associated with reduced GzmB expression levels in closely infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cells, as well as that the GzmB/CCR8 ratio was an independent prognostic factor in LSCC.\u003c/p\u003e \u003cp\u003eCCR8\u003csup\u003e+\u003c/sup\u003e Tregs have recently attracted attention as immunosuppressive cells that are highly concentrated in malignant tumor tissues compared with in peripheral blood and normal tissues in both humans and mice [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Using flow cytometry and public mRNA database analysis, our previous study showed high CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration within tumors that was associated with poor prognosis in lung cancer patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cem\u003eEx vivo\u003c/em\u003e culture studies suggested that CCR8\u003csup\u003e+\u003c/sup\u003e Tregs can suppress the cytotoxic function of CD8\u003csup\u003e+\u003c/sup\u003e T cells [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Mouse studies have also corroborated that CCR8\u003csup\u003e+\u003c/sup\u003e Tregs can suppress anti-tumor immunity via regulation of CD8\u003csup\u003e+\u003c/sup\u003e T cell function [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, to our knowledge, our data in this study provide the first evidence suggesting that CCR8\u003csup\u003e+\u003c/sup\u003e Tregs can suppress CD8\u003csup\u003e+\u003c/sup\u003e T cell cytotoxic activity \u003cem\u003ein vivo\u003c/em\u003e within the TME of human cancer patients. Notably, the results were more robust for the analysis of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs than for the analysis of total Tregs, highlighting the importance of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs for the suppressive effect.\u003c/p\u003e \u003cp\u003eIn this study, tumor-infiltrating immune cells were evaluated using two protocols: the WTA protocol and ROI protocol. However, the results of the two protocols were not consistent. An overall assessment using the WTA protocol showed that high CCR8\u003csup\u003e+\u003c/sup\u003e Treg infiltration was positively correlated with both high GzmB\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration and high GzmB expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells, with no findings suggesting immunosuppression by CCR8\u003csup\u003e+\u003c/sup\u003e Tregs. This would indicate that patients with a high number of TILs have both functional and suppressive T cell infiltration and activation throughout the tumor tissue, which is consistent with several previous reports [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, full-frame imaging of tumor sections showed heterogeneous accumulation of TILs. In general, immune cell infiltration within tumors is heterogeneously distributed, making spatial analysis of TILs in the local microenvironment important for fully assessing anti-tumor immune activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Several previous reports have highlighted the importance of examining local TIL infiltration, focusing on the Hot Spots of high infiltration, tumor stroma, and tumor periphery. These serve as biomarkers that are relevant to prognosis and therapeutic efficacy in various types of cancer [\u003cspan additionalcitationids=\"CR33 CR34 CR35\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, we performed an ROI analysis to investigate the effect of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs on the surrounding CD8\u003csup\u003e+\u003c/sup\u003e T cells in the local TME. Our work with the ROI protocol in Hot Spots revealed a negative correlation between CCR8\u003csup\u003e+\u003c/sup\u003e Treg accumulation and GzmB expression patterns in CD8\u003csup\u003e+\u003c/sup\u003e T cells, supporting the possibility of \u003cem\u003ein situ\u003c/em\u003e CTL suppression.\u003c/p\u003e \u003cp\u003eMany studies have reported the clinical utility of TIL evaluation. The most well-known approach is the Immunoscore, which reflects the level of CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T lymphocyte infiltration and has been established as a powerful prognostic indicator in colorectal cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In lung cancer, \u003cem\u003ein situ\u003c/em\u003e TIL assessment has also been recognized as an important prognostic tool and similar approaches focusing on CD3\u003csup\u003e+\u003c/sup\u003e or CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration have been investigated [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Conversely, the high Foxp3/CD3 or Foxp3/CD8 ratio in tumor tissues has been reported as a poor prognostic factor [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], suggesting that the balance between CD8\u003csup\u003e+\u003c/sup\u003e T cells and Tregs is important in tumor immunity. Here, we propose that the GzmB/CCR8 ratio, which combines CCR8\u003csup\u003e+\u003c/sup\u003e Treg accumulation and GzmB expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells, is a more advanced prognostic marker. Importantly, the GzmB/CCR8 ratio was more strongly associated with prognosis in LSCC patients than the CD8/Foxp3 ratio, GzmB/Foxp3 ratio, or CD8/CCR8 ratio. This indicator is expected to sensitively reflect the activity of anti-tumor immunity from the perspective of both immune promoters and suppressors, with the potential to provide reasonable and good prognostic prediction.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it is an observational study with a limited number of cases from a single institution. Further prospective verification with a larger sample size is necessary in the future. Second, our analysis did not separate the stromal and intra-tumoral areas despite there being a potential relationship between the distribution of TILs and histological structure, as discussed above. While the automated ROI acquisition method used here is valid, we did not define the tumor area within the ROI. Third, the study did not fully investigate the mechanism by which CCR8\u003csup\u003e+\u003c/sup\u003e Tregs can suppress GzmB expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells. In our previous study, we experimentally demonstrated that the CCR8\u003csup\u003e+\u003c/sup\u003e Treg-mediated inhibition of CD8\u003csup\u003e+\u003c/sup\u003e T cell function can be canceled by blocking major histocompatibility complex (MHC) molecules [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], suggesting that the suppressive effect of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs is mediated by antigen-presenting cells (APCs). However, we did not assess APCs or MHC molecules in this study. This will be explored in future research.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOur histological study illustrated that the accumulation of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs in the TME may lead to reduced cytotoxic function of the adjacent CD8\u003csup\u003e+\u003c/sup\u003e T cells and poor prognosis in LSCC patients. This highlights the biological importance and clinical relevance of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs in anti-tumor immunity. The GzmB/CCR8 ratio is a potential prognostic predictor for LSCC and may benefit future clinical applications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAPC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantigen-presenting cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCCR8\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCC motif chemokine receptor 8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidential interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCTL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCytotoxic T lymphocyte\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDAB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e3\u0026rsquo;-3-diamnobenzidine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFFPE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eformalin-fixed paraffin embedded\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFoxP3\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForkhead box protein 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGzmB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGranzyme B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehydrogen peroxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eH\u0026amp;E\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHematoxylin and eosin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune checkpoint inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIHC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLAD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elung adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLSCC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elung squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMHC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emajor histocompatibility complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePD-L1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgrammed death ligand 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePD-1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgrammed cell death 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePFS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegions of Interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTreg\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegulatory T cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTIL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor-infiltrating lymphocyte\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTME\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWTA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole tumor area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Osaka University Hospital (approval number: 13266) and was performed in accordance with the Declaration of Hersinki. Informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, Osaka University has a joint research laboratory with Shionogi \u0026amp; Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive specific funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHW conceived the study. HW, AU, and YH designed the study. YH, KJ, NH, and HM collected the data. YH wrote the initial draft of the manuscript. SF, YS, MH, YN, TS, AK and KI contributed to data interpretation and critical revision of the manuscript for important intellectual content. All authors have read and approved the final version of the manuscript and have agreed to the accountability of all aspects of the study, thereby ensuring that any queries related to the accuracy or integrity of any part of the work are answerable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank J. Iacona, Ph.D., from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. 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J Inflamm (London). 2015;12:63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Tian S, Sun J, Zhang J, Lin L, Hu C. The presence of tumour-infiltrating lymphocytes (TILs) and the ratios between different subsets serve as prognostic factors in advanced hypopharyngeal squamous cell carcinoma. BMC Cancer. 2020;20:731.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLing A, Edin S, Wikberg ML, \u0026Ouml;berg \u0026Aring;, Palmqvist R. The intratumoural subsite and relation of CD8\u0026thinsp;+\u0026thinsp;and FOXP3\u0026thinsp;+\u0026thinsp;T lymphocytes in colorectal cancer provide important prognostic clues. Br J Cancer. 2014;110:2551\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGartrell RD, Marks DK, Hart TD, Li G, Davari DR, Wu A, et al. Quantitative Analysis of Immune Infiltrates in Primary Melanoma. Cancer Immunol Res. 2018;6:481\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorredor G, Wang X, Zhou Y, Lu C, Fu P, Syrigos K, et al. Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non\u0026ndash;small cell lung cancer. Clin Cancer Res. 2019;25:1526\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayashi Y, Makino T, Sato E, Ohshima K, Nogi Y, Kanemura T, et al. Density and maturity of peritumoral tertiary lymphoid structures in oesophageal squamous cell carcinoma predicts patient survival and response to immune checkpoint inhibitors. Br J Cancer. 2023;128:2175\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoma T, Makino T, Ohshima K, Sugimura K, Miyata H, Honma K, et al. Immunoscore Signatures in Surgical Specimens and Tumor-Infiltrating Lymphocytes in Pretreatment Biopsy Predict Treatment Efficacy and Survival in Esophageal Cancer. Ann Surg. 2023;277:e528\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoi S, Michiels S, Adams S, Loibl S, Budczies J, Denkert C, et al. The journey of tumor-infiltrating lymphocytes as a biomarker in breast cancer: clinical utility in an era of checkpoint inhibition. Ann Oncol. 2021;32:1236\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHendry S, Salgado R, Gevaert T, Russell PA, John T, Thapa B, the International Immunooncology Biomarkers Working Group. Assessing Tumor-infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method from : Part 1: Assessing the Host Immune Response, TILs in Invasive Breast Carcinoma and Ductal Carcinoma in Situ, Metastatic Tumor Deposits and Areas for Further Research. Adv Anat Pathol. 2017;24:235\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcar E, Esendağlı G, Yazıcı O, Dursun A. Tumor-Infiltrating Lymphocytes (TIL), Tertiary Lymphoid Structures (TLS), and Expression of PD-1, TIM-3, LAG-3 on TIL in Invasive and In Situ Ductal Breast Carcinomas and Their Relationship with Prognostic Factors. Clin Breast Cancer. 2022;22:e901\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePag\u0026egrave;s F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391:2128\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonnem T, Hald SM, Paulsen EE, Richardsen E, Al-Saad S, Kilvaer TK, et al. Stromal CD8\u0026thinsp;+\u0026thinsp;T-cell density\u0026mdash;A promising supplement to TNM staging in non-small cell lung cancer. 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J Clin Oncol. 2013;31:490\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki H, Chikazawa N, Tasaka T, Wada J, Yamasaki A, Kitaura Y, et al. Intratumoral CD8\u0026thinsp;+\u0026thinsp;T/FOXP3\u0026thinsp;+\u0026thinsp;cell ratio is a predictive marker for survival in patients with colorectal cancer. Cancer Immunol Immunother. 2010;59:653\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSato E, Olson SH, Ahn J, Bundy B, Nishikawa H, Qian F, et al. Intraepithelial CD8\u0026thinsp;+\u0026thinsp;tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc Natl Acad Sci U S A. 2005;102:18538\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tumor immunity, regulatory T cells, CCR8, cytotoxic T cells, lung cancer","lastPublishedDoi":"10.21203/rs.3.rs-4121046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4121046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCCR8-expressing regulatory T cells (Tregs) are selectively localized within tumors and have gained attention as potent suppressors of anti-tumor immunity. This study focused on CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and their interaction with CD8\u003csup\u003e+\u003c/sup\u003e T cells in the tumor microenvironment of human lung cancer. We evaluated their spatial distribution impact on CD8\u003csup\u003e+\u003c/sup\u003e T cell effector function, specifically granzyme B (GzmB) expression, and clinical outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 81 patients with lung squamous cell carcinoma (LSCC) who underwent radical surgical resection without preoperative treatment were enrolled. Histological analyses were performed, utilizing an automated image analysis system for double-stained immunohistochemistry assays of CCR8/Foxp3 and GzmB/CD8. We investigated the association of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and GzmB\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in tumor tissues and further evaluated the prognostic impact of their distribution profiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHistological evaluation using the region of interest (ROI) protocol showed that GzmB expression levels in CD8\u003csup\u003e+\u003c/sup\u003e T cells were decreased in areas with high infiltration of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs, suggesting a suppressive effect of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs on T cell cytotoxicity in the local tumor microenvironment. Analysis of the association with clinical outcomes showed that patients with more CCR8\u003csup\u003e+\u003c/sup\u003e Tregs and lower GzmB expression, represented by a low GzmB/CCR8 ratio, had worse progression-free survival.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur data suggest that local CCR8\u003csup\u003e+\u003c/sup\u003e Treg accumulation is associated with reduced CD8\u003csup\u003e+\u003c/sup\u003e T cell cytotoxic activity and poor prognosis in LSCC patients, highlighting the biological role and clinical significance of CCR8\u003csup\u003e+\u003c/sup\u003e Tregs in the tumor microenvironment. The GzmB/CCR8 ratio may be a useful prognostic factor for future clinical applications in LSCC.\u003c/p\u003e","manuscriptTitle":"In situ analysis of CCR8 + regulatory T cells and cytotoxic CD8 + T cells in human lung squamous cell carcinoma: biological insights and clinical implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 18:04:51","doi":"10.21203/rs.3.rs-4121046/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-22T05:01:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-20T11:07:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-20T11:06:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-03-18T07:47:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84afb054-fa4e-4a69-962b-0449d56d436c","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T10:07:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-25 18:04:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4121046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4121046","identity":"rs-4121046","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}