Analysis of T-Cell Subsets Using Multiplex Immunohistochemistry and Clinical Outcomes of Immune Checkpoint Inhibitors in Advanced Gastric Cancer Patients

Tae-Yong Kim; Jeesun Yoon; Dae-Won Lee; Yoonjin Kwak; Hye Seung Lee; Do-Youn Oh

doi:10.4143/crt.2025.458

Articles

Page Path: HOME > J Korean Cancer Assoc > Ahead-of print articles > Article

Original Article Analysis of T-Cell Subsets Using Multiplex Immunohistochemistry and Clinical Outcomes of Immune Checkpoint Inhibitors in Advanced Gastric Cancer Patients: Tae-Yong Kim^1,2, Jeesun Yoon¹, Dae-Won Lee^1,2, Yoonjin Kwak³, Hye Seung Lee³, Do-Youn Oh^1,2,4; DOI: https://doi.org/10.4143/crt.2025.458
Published online: September 9, 2025

¹Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea

²Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea

³Department of Pathology, Seoul National University Hospital, Seoul, Korea

⁴Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Korea

Correspondence: Do-Youn Oh, Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: 82-2-2072-0701 E-mail: ohdoyoun@snu.ac.kr

^*Tae-Yong Kim and Jeesun Yoon equally contributed to this work.

• Received: April 28, 2025 • Accepted: September 7, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

1,960 Views
94 Download
1 Crossref

Full Article

Download PDF

Abstract

Purpose
As immunotherapy has become essential in the treatment of gastric cancer (GC), there has been growing interest in T-cells, which play a key role in immunotherapy. In this study, we evaluated the impact of T-cell subsets on immune responsiveness to immune checkpoint inhibitors (ICIs) in GC using multiplex immunohistochemistry (mIHC).
Materials and Methods
Eighty-four GC patients treated with ICIs were enrolled, and we repeated the staining-scanning-stripping procedure nine times to assess different kinds of T-cells or cell-surface immune checkpoints in a single tissue section.
Results
The proportions of patients with microsatellite instability (MSI)–high, Epstein-Barr virus (EBV), and non-MSI/non-EBV were 8.3%, 3.6%, and 88.1%. A high cytotoxic T-cell (T_cyto) density was related to longer overall survival (OS). GC with a high ratio of T_cyto/total T-cells (T_total) and a low ratio of regulatory T-cells (T_reg)/T_total showed better OS. A high density of programmed death-1 (PD-1)– or TIM-3–expressing T_cyto were also associated with longer OS than a low density of those. Among memory T-cells (T_mem) subsets, GC with a high ratio of memory T_cyto/T_mem and a low ratio of memory T_reg/T_mem showed prolonged OS. Better tumor responses were observed in GC with a high ratio of T_cyto/T_total and memory T_cyto/T_mem.
Conclusion
T-cell subsets within the tumor microenvironment were associated with the clinical efficacy of ICIs in GC. PD-1– or TIM-3–expressing T-cells were also associated with response to ICIs, while T_mem subsets were associated with survival. mIHC is a feasible method for evaluating T-cell subsets in archival gastric tumor tissue.
Key words: Stomach neoplasms, T cell subsets, Multiplex immunohistochemistry, Immune checkpoint inhibitors, Cancer immunotherapy

Introduction

Metastatic or recurrent gastric cancer (GC) remains incurable, with chemotherapy being the key treatment [1]. Immunotherapy has emerged as an essential component in treating advanced GC patients [2]. The CheckMate-649 study demonstrated that adding the programmed cell death-1 (PD-1) monoclonal antibody (mAb), nivolumab to platinum-doublet chemotherapy showed an improvement of survival outcome as first-line treatment for human epidermal growth factor receptor 2 (HER2)–negative GC [3]. Following the success of the KEYNOTE-859 study [4] and the Rationale-305 study [5], immunotherapy in combination with chemotherapy has become the standard of care as first-line treatment for HER2-negative GC. Additionally, in HER2-positive GC, the KEYNOTE-811 study demonstrated significant improvement of clinical outcomes by adding pembrolizumab to chemotherapy and trastuzumab [6], thereby establishing immune checkpoint inhibitors (ICIs) as an essential component of treatment strategy in advanced GC regardless of HER2.

Cytotoxic T-lymphocyte–associated antigen 4 (CTLA4) and PD-1 inhibit T cell signaling by interacting with CD80 or CD86 and programmed cell death-ligand 1 (PD-L1) or PD-L2, finally exhausting T-cells [7]. Several biomarkers including microsatellite instability (MSI), tumor mutational burden, PD-L1 expression, and tumor infiltrating lymphocytes (TILs) have been suggested to predict the response to ICIs across tumor types [8]. In case of GC, MSI, PD-L1 expression or Epstein-Barr virus (EBV) have been reported to be associated with the clinical efficacy of ICIs [9]. Additionally, with the emergence of single-cell RNA sequencing, the role of TILs in response to ICIs has been actively underway in GC. As a central axis of immune response, T cells have a key role in directly killing cancer cells and coordinating immune cells through cytokines [10]. Tumor-infiltrating CD8⁺ T cells have been associated with overall survival (OS) in diverse tumor types including GC [10-12]. Intratumoral Foxp3+RORgammat+ T cells have been associated with impaired function of CD8⁺ T cells and may predict poor prognosis in GC [13]. Analysis of peripheral blood T cells and T-cell gene signatures are also suggested to predict response to ICIs in GC [14,15]. Although these testing methods provide a deep understanding of T-cell subsets and their roles in GC, there are challenges to be applied to daily practice setting, which includes requirement of fresh and large tissue samples and financial toxicity, etc. Therefore, the current study was designed to assess intratumoral T-cell subsets using multiplex immunohistochemistry (mIHC), which could be more feasible method to predict clinical outcomes of ICIs in GC.

Materials and Methods

1. Patients

The key eligibility criteria for enrolling patients included pathologically-confirmed advanced GC, availability of formalin-fixed paraffin-embedded (FFPE) block before initiation of ICIs treatment. ICIs included anti–PD-1, anti–PD-L1, or anti-CTLA4 mAbs. Patients treated with ICIs in combination with other kinds of immune checkpoint agents or targeted agents were included. However, cases wherein ICIs were combined with any kind of cytotoxic chemotherapy were excluded.

2. Multiplex immunohistochemistry

FFPE tissue samples were retrieved from the archives of the Department of Pathology, Seoul National University Hospital. Tissue microarray (TMA) slides were first subjected to immunohistochemistry (IHC) (anti–PD-L1) using the Envison FLEX+ kit (Dako) followed by ImmPact NovaRED (Vector Laboratories) and Meyer hematoxylin staining. After whole slide scanning with AT2 scanner (Leica Biosystems), the slides were treated with stripping buffer (20% sodium dodecyl sulfate, 0.5 M Tris-HCl pH 6.8, β-mercaptoethanol, and distilled water) and then microwaved. These staining-scanning-stripping procedures were repeated nine times with different primary antibodies (CD8, CD3, PD-1, FOXP3, cytokeratin, Ki-67, T-cell immunoglobulin and mucin domain-3 [TIM-3], LAG3, CD45RO) (S1 Table). Multiple staining of immune and immune checkpoint markers was established in the same section by using mIHC (S2 and S3 Figs.).

For counting immune cells, files of scanned whole slide image were divided into 60 images comprising a single TMA core. The alignment of differently-stained images of the same region enabled identification of every single cell. Alignment and measurement were done using CellProfiler ver. 3.1.8 (Broad Institute). Cytokeratin image was used to delineate the tumoral area from stromal area. Positively-stained cells (for each staining and double-stained cells) were also counted; detailed information regarding the mIHC procedure is described in previous study [15].

3. Experimental procedure

All study populations were categorized into three groups, depending on tumor biology: MSI-high (MSI-H) group, EBV group, and non-MSI/non-EBV group. To assess the EBV status, all samples were subject to EBV in situ hybridization using INFORM EBV-encoded RNA probe (Ventana Medical Systems). The MSI status of the tumors was assessed by comparing the allele profiles of NCI five markers (BAT-26, BAT-25, D5S346, D17S250, and S2S123) in tumor cells to those of matched non-tumor tissues. Expression of PD-L1 in tumor was assessed with the primary antibody used for PharmDx IHC assay (22C3, Agilent Technologies). The combined positive score (CPS) was calculated as total number of PD-L1–positive cells (tumor cells plus immune cells) divided by the total number of tumor cells, multiplied by 100. According to The Cancer Genome Atlas (TCGA) data [16], non-MSI/non-EBV group could be further classified into chromosomal instability (CIN) and genomic stability (GS) based on somatic copy-number variation analysis of next-generation sequencing (NGS) results; however, our dataset lacked NGS results, making accurate classification difficult. Therefore, we further divided the groups into two groups estimated as CIN and GS groups based on the clinical data of IHC for epidermal growth factor receptor (EGFR), c-MYC, c-MET, and histological features.

T-cell subsets were determined, depending on surface CD markers in mIHC (S4 Fig.); total T cells (T_total) were determined to be all CD3⁺ cells. Cytotoxic T cells (T_cyto), helper T cells (T_help), regulatory T cells (T_reg), and memory T cells (T_mem) were defined as CD3⁺CD8⁺, CD3⁺CD8^–, CD3⁺CD8^–FoxP3⁺ and CD3⁺CD45RO⁺ cells, respectively. T-cell subset density was defined to be the number of positively-stained cells per mm². The value of high and low in each variable were defined depending on the median value of each variable.

4. Statistical analysis

Categorical variants were analyzed using Pearson’s chi-square and continuous variables were evaluated using a Student’s t test. The comparison of mean of three independent categorical variants were analyzed by one-way analysis of variance with a post-hoc Tukey test. Tumor response was assessed by Response Evaluation Criteria in Solid Tumors criteria (RECIST) ver. 1.1. Survival was estimated using the Kaplan-Meier method and the log-rank test was used to compare survival. Statistical analyses were conducted using SPSS ver. 25.0 (IBM Corp.) and R ver. 4.4.2 (R Foundation for Statistical Computing). A p-value < 0.05 was considered as statistically significant difference.

Results

1. Patients

A total of 84 patients were analyzed (Table 1, S5 Table). The MSI-H, EBV, and non-MSI/EBV group included 7 (8.3%), 3 (3.6%), and 74 (88.1%) patients, respectively. Based on Lauren’s classification, all GCs harboring MSI-H were intestinal tumor type. GC harboring MSI-H and EBV showed a higher proportion of CPS ≥ 1 than non-MSI/non-EBV GC (85.7% and 100% vs. 40.5%, respectively). Majority of patients (63.1%) received ICI monotherapy. Among 72 patients who were available for tumor response assessment, 16 (22.2%) achieved complete response (CR) or partial response (PR), with two showing CR (2.8%) (Fig. 1A and B). Among the 16 responders, two were EBV-positive, six were MSI-H, and eight were in the CIN group, while there were no responders in the GS group (S5 Table, S6 Fig.). The mean OS and progression-free survival (PFS) of all study groups were 8.1 months (95% confidence interval [CI], 5.231 to 11.035) and 4.0 months (95% CI, 2.511 to 5.489), respectively. The median OS and PFS of MSI-H and EBV were longer than those of non-MSI/non-EBV group (Table 2). GC with CPS ≥ 1 showed longer OS than GC with CPS < 1 (17.7 vs. 5.9 months, p=0.010). Longer OS were also observed in early line of ICIs (17.7 vs. 4.1 months, p=0.010) and combination therapy (17.9 vs. 5.9 months, p=0.022).

2. Intratumoral T-cell subsets and Molecular subtypes

EBV group (442.97/mm²) showed higher T_cyto density than the MSI-H (145.25/mm²) and non-MSI/non-EBV (101.46/mm²) groups (S7 Fig.). The T_cyto/T_total ratio was relatively higher in MSI-H group than in non-MSI/non-EBV group. However, the T_help/T_total and memory T_help/T_mem ratios were relatively higher in non-MSI/non-EBV group than in MSI-H group.

3. Intratumoral T-cell subsets and tumor response

GC with a high T_cyto/T_total ratio showed better tumor response than those with a low ratio (34.2% vs. 8.8%, p=0.034) (Table 3). A high ratio of memory T_cyto/T_mem was also associated with better tumor response (34.2% vs. 8.8%, p=0.026). However, the densities of T_cyto and PD-1– or TIM-3–expressing T_cyto were not statistically different between CR/PR and stable disease (SD) or progressive disease (PD) groups. While the average density of T_cyto was not different between tumor response, the average ratio of T_cyto/T_total was higher in CR/PR (0.53) than in PD (0.31) (p=0.021) (Fig. 2A). The average ratio of T_reg/T_help was also higher in CR/PR (0.46) than in SD (0.33) group (Fig. 2C). However, the average ratio of T_help/T_total and memory T_help/T_mem was lower in CR/PR group than PD group (Fig. 2B and D).

4. Intratumoral T-cell subsets and survival

GC with a high density of T_cyto showed longer OS than those with a low density (18.2 vs. 6.5 months, p=0.001) (Table 4, Fig. 3A). A high ratio of T_cyto/T_total was associated with better OS (24.3 vs. 5.9 months, p < 0.001) (Fig. 3B). GC with a high density of PD-1 or TIM-3–expressing T_cyto showed better OS than those with a low density of such cells (17.9 vs. 7.0 months, p=0.024 and 18.2 vs. 7.0 months, p=0.003, respectively) (Fig. 3C and D). While no difference in OS was observed based on the ratio of T_reg/T_help (9.0 vs. 8.1 months, p=0.963), a low T_reg/T_total ratio was associated with prolonged OS (5.9 vs. 17.9 months, p=0.034) (Fig. 3E). Furthermore, a low density of memory T_reg were related with better OS (5.9 vs. 10.8 months, p=0.025), and a low ratio of T_mem/T_total was also related to prolonged OS (5.9 vs 10.4 months, p=0.027) (Fig. 3F). GC with a high ratio of memory T_cyto/T_mem and a low ratio of memory T_reg/T_mem showed longer OS, compared with a low or high ratio of those, respectively (18.1 vs. 5.9 months, p < 0.001 and 5.9 vs. 19.6 months, p=0.001, respectively) (Fig. 3G and H). PFS showed similar trends to OS, except for T_reg/T_total ratio, memory T_reg density, and memory T_reg/T_mem ratio (Table 4). Cox’s proportional hazard analysis indicated that the molecular subtypes (MSI/EBV vs. non-MSI/non-EBV), line of ICIs (first- or second-line vs. later), combination, and T_cyto density (high vs. low) were significantly associated with OS (S8 Table 3, S9 Fig.).

Discussion

Immunotherapy has become a key anti-cancer agent in the treatment of advanced GC. In particular, the role of T cells is fundamental, and various approaches are conducted to gain a deeper understanding of T cell role. Our study showed that intratumoral T-cell subsets were associated with response to ICIs (Table 3). T_cyto positively affected survival on ICIs treatment (S8 Table). Interestingly, the ratio of memory T-cell subsets, for example, memory T_cyto or memory T_reg over total T_mem were also associated with clinical efficacy of ICIs. These results are meaningful because this study evaluated the responsiveness to immunotherapy in patients with GC who received immunotherapy alone, thereby demonstrating the role of T-cell subsets as a predictive marker for immunotherapy. Additionally, these findings are consistent with recent analyses of T-cell subsets through single-cell RNA sequencing in patients with GC who received cytotoxic chemotherapy and immunotherapy combination [14,17,18].

CD8⁺ cytotoxic T lymphocytes (CTLs) have a major role in killing and eliminating cancer cells via release of granules or induction of FasL-mediated apoptosis [19]. Intratumoral CD8⁺ CTLs were associated with anti-tumor activity of ICIs in a preclinical study [20], which is in line with our findings. Tumor antigen (TA)–specific CTLs mainly have an exhausted phenotype, suggesting CTLs in tumor are easily reinvigorated by ICIs [21]. Although we did not directly evaluate TA-specific CTLs, intratumoral T_cyto were likely to be TA CTLs, contributing to better anti-tumor activity of ICIs.

Interestingly, a high density of PD-1– or TIM-3–expressing T_cyto was significantly associated with better clinical efficacy of ICIs in our study. CTL with PD-1^high were an exhausted phenotype and associated with worst prognosis [22]. However, some preclinical study suggested that PD-1^high CTL were more easily reinvigorated by anti–PD-1 monoclonal antibodies than PD-1^low CTL ex vivo [23], and patients with high levels of circulating PD-1^high CTLs showed better responses to immunotherapy [17]. These findings are consistent with our observation that a high density of PD-1– or TIM-3–expressing T_cyto was associated with better clinical efficacy of ICIs in our study. Therefore, in-depth functional studies are needed in the future to comprehensively understand the conflicting research results between the expression of T cell exhaustion markers such as PD-1 or TIM-3 and the response to immunotherapy.

Memory T-cell subsets, for example, memory T_cyto or memory T_reg, were associated with clinical outcomes of ICIs in our study. While a high density of CTLs linked to tissue-resident memory T cells (Trm) and CTL from Trm cells showed enhanced cytotoxicity, memory T_reg decreased anti-tumor immunity, leading to tumor progression [24,25]. Taken together, our findings that a high ratio of memory T_cyto/T_mem and a low T_reg/T_mem was associated with longer OS are reasonable.

TCGA studies have classified GC into four molecular subtypes: GC with MSI-H, EBV, GS, and CIN [16]. The NGS results in this study dataset were insufficient to accurately classify the samples according to the TCGA classification. To improve this limitation, we conducted an additional analysis to divide the samples into two groups, estimated to be CIN and GS groups, based on the IHC results (EGFR, c-MYC, c-MET) and histological features obtained from the clinical data of individual patients. GC harboring EBV was associated with PD-L1/L2 overexpression and immune cell signaling. As expected, GC with MSI-H and EBV showed a better tumor response and survival, compared with non-MSI/non-EBV GC in our study (Table 1, S10 Table), which was consistent with a previous study [9]. PD-L1, a well-known biomarker that can predict responses to ICIs in GC. Similarly, GC with CPS ≥ 1 also showed longer survival than GC with CPS < 1 in our study (17.7 and 5.9 months, p=0.010). Besides molecular subtypes and CPS, the line of treatment and single or combination treatment affected clinical outcomes of ICIs in our study. To exclude the influence of aforementioned factors, we re-analyzed OS according to tumor biology, CPS, the line of treatment, and with or without combination. As can be seen in S11-S13 Tables, T_cyto, PD-1, TIM-3, T_help, and T_mem cell subsets were still meaningful to OS, and especially T_cyto density was a meaningful marker regardless of aforementioned clinicopathologic factors (S8 Table).

For analysis of T-cell subsets in a tissue or blood, fluorescence-activated cell sorting (FACS) is a commonly used method. However, FACS needs a fresh and adequate amount of tissue, which is a major limitation on translational research using a human tissue. Recent advances in technology, from bulk transcriptome analysis to single-cell transcriptome analysis, have provided deeper insights into the role of intratumoral T-cell subsets. However, it is not realistic to apply these analyses to all patients in clinical practice setting and immediately utilize the results in treatment. IHC can simply assess T cell surface markers; however, it cannot read two or more surface markers in the same section, due to the cross-reactivity of secondary antibodies [26]. Multiplex IHC overcomes the limitation of conventional IHC and enables analysis of T-cell subsets even in a small biopsy FFPE tumor tissue [27]. In this context, our study suggested the use of cost-effective multiplex IHC to analyze T-cell subsets and predict responses to ICIs.

This study has some limitations. First, the number of samples was small. In the study planning stage, we excluded patients who had received immunotherapy in combination with chemotherapy in order to observe a homogeneous response to immunotherapy. Recently, most patients with advanced GC receive immunotherapy as a first-line setting in combination with chemotherapy. Therefore, we did not additionally include patients in the front-line setting after immunotherapy was widely applied. Due to these limitations, a large-scale external validation study using patient samples from actual clinical settings where immunotherapy monotherapy was administered is necessary to validate our findings in the future. Second, the target patient population is heterogeneous, consisting of patients with diverse treatment settings. Currently, ICI monotherapy for advanced GC is being conducted in the third- or later-line setting, with an objective response rate of approximately 10%. Considering that treatment response rates decrease as the later-line setting in advanced cancer, this study included patients who participated in clinical trials using immunotherapy as first- or second-line treatment. Because our study population was heterogeneous, careful interpretation was necessary to account for the influence of confounding variables. Further external validation in a homogeneous patient population is required. Third, during the analysis of T-cell subsets, the cutoff was standardized to the median, which may lack biological relevance. We attempted to apply receiver operating characteristic curves based on response status and maximize Log-rank statistics based on survival for each parameter; however, due to the small sample size mentioned earlier, it was difficult to present a statistically significant, unified cutoff determination method for all parameters. Fourth, line of treatment was not unified. Among 84 patients in this study, 38 (45.2%) were in the second-line setting, and 28 (33.3%) were in the third-line setting. To overcome the small sample size, we analyzed all patients without distinguishing treatment lines and classified each T cell parameter into high/low groups based on the median value. When classified in this manner, there were no significant differences in patient distribution between the high/low groups according to treatment line. Further studies are needed with a more homogeneous patient population within the same treatment line setting.

In conclusion, our study shows that intratumoral T_cyto play an important role in responses to ICIs, and PD-1 or TIM-3–expressing T_cyto are associated with clinical efficacy of ICIs. Memory T-cell subsets, including memory T_cyto or T_reg, are also essential for response to ICIs. These findings suggest that interaction between tumor and intratumoral T cells is crucial to determine the activity of ICIs in GC. To assess T cell subsets, multiplex IHC is a feasible method using archival gastric tumor tissue.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2025-458_S1_Table.pdf

crt-2025-458_S2_Fig.pdf

crt-2025-458_S3_Fig.pdf

crt-2025-458_S4_Fig.pdf

crt-2025-458_S5_Table.pdf

crt-2025-458_S6_Fig.pdf

crt-2025-458_S7_Fig.pdf

crt-2025-458_S8_Table.pdf

crt-2025-458_S9_Fig.pdf

crt-2025-458_S10_Table.pdf

crt-2025-458_S11_Table.pdf

crt-2025-458_S12_Table.pdf

crt-2025-458_S13_Table.pdf

NOTES

Ethical Statement

This study protocol was reviewed and approved by the Institutional Review Board of Seoul National University Hospital (IRB No. H-1809-010-970). The recommendations of the Declaration of Helsinki for biomedical research involving human subjects were also followed. The requirement for informed consent was waived by the Institutional Review Board due to the retrospective nature of the study and the use of de-identified data.

Author Contributions

Conceived and designed the analysis: Kim TY, Oh DY.

Collected the data: Kim TY, Yoon J, Lee DW, Kwak Y, Lee HS, Oh DY.

Contributed data or analysis tools: Kim TY, Yoon J, Lee DW, Kwak Y, Lee HS, Oh DY.

Performed the analysis: Kim TY, Yoon J, Kwak Y, Lee HS, Oh DY.

Wrote the paper: Kim TY, Yoon J, Lee DW, Kwak Y, Lee HS, Oh DY.

Conflicts of Interest

TYK has served in consultant/advisory board roles for Samsung Bioepis, Eisai, Novartis. HSL received research grant from Astellas, Ono-pharma. DYO has served in consultant/advisory board roles for AstraZeneca, Novartis, Genentech/Roche, Merck Serono, Bayer, Taiho, ASLAN, Halozyme, Zymeworks, BMS/Celgene, BeiGene, Basilea, Turning Point, Yuhan, Arcus Biosciences, IQVIA, MSD, LG Chem, Astellas, Abbvie, J-Pharma, Mirati Therapeutics, Eutilex, Moderna, Idience, Alligator Bioscience AB, Hana pharm, Revolution Medicine, Tallac Therapeutics, and received research grant from AstraZeneca, Novartis, Array, Eli Lilly, Servier, BeiGene, MSD, Handok. All remaining authors have declared no conflicts of interest.

Funding

This work was supported by the Institute of Smart Healthcare Innovative Medical Sciences, a Brain Korea 21 four program, Seoul National University.

Fig. 1.

Tumor response of immune checkpoint inhibitors. (A) The waterfall plot for best tumor reduction from baseline. Each yellow (microsatellite instability–high [MSI-H]), green (Epstein-Barr virus [EBV]) and light blue (non–microsatellite instability [MSI]/non-EBV) bar means best change of sum of target lesions’ diameter from baseline, based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 in each subject. Black star marks above or below each bar mean patients who received immune checkpoint inhibitors (ICs) as first- or second-line. (B) The spider plot for change of sum of target lesions’ diameter over time since the initiation of ICs, based on Response Evaluation Criteria in Solid Tumor 1.1. ICB, immune checkpoint blockades.

Fig. 2.

T-cell subsets and tumor response. (A) A T_cyto/T_total ratio according to tumor response. (B) A T_help/T_total ratio according to tumor response. (C) A T_reg/T_help ratio according to tumor response. (D) A memory T_help/T_mem ratio according to tumor response. CR, complete remission; NS, no significance; PD, progressive disease; PR, partial remission; SD, stable disease; T_cyto, cytotoxic T cells; T_help, helper T cells; T_mem, memory T cells; T_reg, regulatory T cells; T_total, total T cells.

Fig. 3.

Kaplan-Meier survival curves, depending on the density and ratio of T-cell subsets. Overall survival (OS), according to T_cyto density (A), T_cyto/T_total ratio (B), programmed cell death-1 (PD-1)–expressing T_cyto (C), TIM-3–expressing T_cyto (D), T_reg/T_total ratio (E), T_mem/T_total ratio (F), memory T_cyto/T_mem ratio (G), and memory T_reg/T_mem ratio (H). T_cyto, cytotoxic T cells; TIM-3, T-cell immunoglobulin and mucin domain-3; T_mem, memory T cells; T_reg, regulatory T cells; T_total, total T cells.

Table 1.

Baseline characteristics of study populations

	MSI-H	EBV	NMNE	Total	p-value
Total	7 (8.3)	3 (3.6)	74 (88.1)	84 (100)
Age (yr)	59 (55-82)	47 (46-66)	62 (28-88)	62 (28-88)	-
Sex
Male	3 (42.9)	3 (100)	51 (68.9)	57 (67.9)	0.177
Female	4 (57.1)	0	23 (31.1)	27 (32.1)
Lauren classification
Diffuse	0	1 (33.3)	29 (39.2)	30 (35.7)	0.030
Intestinal	7 (100)	1 (33.3)	42 (56.8)	50 (59.5)
Mixed	0	1 (33.3)	3 (4.1)	4 (4.8)
HER2
Negative	5 (71.4)	3 (100)	57 (77.0)	65 (77.4)	0.003
Positive	0	0	16 (21.6)	16 (19.0)
Unknown	2 (28.6)	0	1 (1.4)	3 (3.6)
CPS
< 1	1 (14.3)	0	44 (59.4)	45 (53.6)	0.012
≥ 1	6 (85.7)	3 (100)	30 (40.5)	39 (46.4)
Gastric surgery
Yes	6 (85.7)	1 (33.3)	36 (48.6)	43 (51.2)	0.380
No	1 (14.3)	2 (66.7)	38 (51.4)	41 (48.8)
Line of ICBs
1st- or 2nd-line	4 (57.1)	2 (66.7)	34 (45.9)	40 (47.6)	0.679
3rd- or later lines	3 (42.9)	1 (33.3)	40 (54.1)	44 (52.4)
Treatments
PD-1	5 (71.4)	2 (66.7)	37 (50.0)	44 (52.4)	0.665
PD-L1	2 (28.6)	0	4 (5.4)	6 (7.1)
CTLA4	0	0	3 (4.1)	3 (3.6)
PD-1+targeted agents	0	0	11 (14.9)	11 (13.1)
PD-L1+targeted agents	0	1 (33.3)	8 (10.8)	9 (10.7)
PD-L1+CTLA4	0	0	4 (5.4)	4 (4.8)
PD-L1+other kinds of ICIs	0	0	1 (1.4)	1 (1.2)
PD-1+ other kinds of ICIs	0	0	6 (8.1)	6 (7.1)

Values are presented as number (%) or median (range). CPS, combined positive score; CTLA4, cytotoxic T-lymphocyte-associated antigen 4; EBV, Epstein-Barr virus; HER2, human epidermal growth factor receptor 2; ICBs, immune checkpoint blockades; MSI-H, microsatellite instability–high; NMNE, non-MSI/non-EBV; PD-1, programmed cell death-1; PD-L1, programmed cell death-ligand 1.

Table 2.

Summary of overall survival and progression-free survival

	Overall survival			Progression-free survival
	Survival (mo)	95% CI	p-value	Survival (mo)	95% CI	p-value
Total	8.1	5.231-11.035	-	4.0	2.511-5.489	-
Molecular subtypes
MSI-H	-	-	-			0.001
EBV	27.3	-		11.1	0-22.462
Non-MSI/Non-EBV	7.0	5.273-8.794		2.7	0.908-4.492
CPS
< 1	5.9	3.430-8.370	0.010	2.8	0.564-5.036	0.014
≥ 1	17.7	7.113-28.287		4.7	1.947-7.453
Line of ICIs
1st- or 2nd-line	17.7	5.154-7.631	0.010	5.5	2.501-8.499	0.033
3rd- or later lines	4.1	0.888-2.392		2.2	1.179-3.221
Combination
Mono	5.9	3.244-8.489	0.022	3.7	2.143-5.257	0.179
Combo	17.9	7.48-28.253		5.5	1.561-9.439

CI, confidence interval; CPS, combined positive score; EBV, Epstein-Barr virus; ICIs, immune checkpoint inhibitors; MSI-H, microsatellite instability–high.

Table 3.

Summary of the response rate of ICIs

	CR/PR	SD	PD	p-value
Total	16 (22.2)	28 (38.9)	28 (38.9)
Molecular subtypes
MSI-H	6 (85.7)	1 (14.3)	0	< 0.001
EBV	2 (66.7)	1 (33.3)	0
NMNE	8 (12.9)	26 (41.9)	28 (45.2)
CPS
< 1	5 (13.5)	14 (37.8)	18 (48.6)	0.106
≥ 1	11 (31.4)	14 (40.0)	10 (28.6)
Line of ICIs
1st- or 2nd-line	12 (30.8)	16 (41.0)	11 (28.2)	0.067
3rd-or later	4 (12.1)	12 (36.4)	17 (51.5)
T_cyto
High	11 (28.2)	14 (35.9)	14 (35.9)	0.414
Low	5 (15.2)	14 (42.4)	14 (42.4)
PD-1–expressing T_cyto density
High	10 (27.0)	14 (37.8)	13 (35.1)	0.580
Low	6 (17.1)	14 (40.0)	15 (42.9)
TIM-3–expressing
T_cyto density
High	11 (28.2)	11 (28.2)	17 (43.6)	0.114
Low	5 (15.2)	17 (51.5)	11 (33.3)
T_cyto/T_total ratio
High	13 (34.2)	13 (34.2)	12 (31.6)	0.034
Low	3 (8.8)	15 (44.1)	16 (47.1)
T_reg/T_help ratio
High	11 (30.6)	12 (33.3)	13 (36.1)	0.227
Low	5 (13.9)	16 (44.4)	15 (41.7)
Memory T_cyto/T_mem
High	13 (34.2)	14 (36.8)	11 (28.9)	0.026
Low	3 (8.8)	14 (41.2)	17 (50.0)
Memory T_help/T_mem
High	3 (8.8)	13 (38.2)	18 (52.9)	0.014
Low	13 (34.2)	15 (39.5)	10 (26.3)

Values are presented as number (%). CPS, combined positive score; CR, complete remission; EBV, Epstein-Barr virus; ICIs, immune check-point inhibitors; MSI-H, microsatellite instability-high; NMNE, non-MSI/non-EBV; PD, progressive disease; PD-1, programmed cell death-1; PR, partial remission; SD, stable disease; T_cyto, cytotoxic T cells; T_help, helper T cells; TIM-3, T-cell immunoglobulin and mucin domain-3; T_mem, memory T cells; T_reg, regulatory T cells; T_total, total T cells.

Table 4.

Summary of overall survival and progression-free survival

	Overall survival			Progression-free survival
	Survival (mo)	95% CI	p-value	Survival (mo)	95% CI	p-value
T_cyto
Density
High	18.2	0.371-36.029	0.001	4.7	1.003-8.397	0.030
Low	6.5	3.76-9.24		2.5	0-5.184
PD-1–expressing T_cyto density
High	17.9	6.693-29.107	0.024	4.7	3.097-6.303	0.047
Low	7.0	4.785-9.215		2.2	0.99-3.41
TIM-3–expressing T_cyto density
High	18.2	1.468-34.932	0.003	4.4	2.485-6.315	0.033
Low	7.0	4.818-9.182		2.5	0.26-4.74
T_cyto/T_total ratio
High	24.3	6.912-41.688	< 0.001	5.4	0.704-10.096	0.003
Low	5.9	2.619-9.181		1.9	0.569-3.231
T_help
Density
High	6.3	4.554-8.046	0.234	3.8	1.634-5.966	0.248
Low	10.8	0-22.304		4.4	1.687-7.113
T_help/T_total ratio
High	6.3	4.395-8.205	0.001	2.4	0-5.422	0.005
Low	24.3	6.439-42.161		4.7	0-9.73
T_reg/T_total ratio
High	5.9	2.334-9.466	0.034	4.2	1.593-6.807	0.648
Low	17.9	2.196-33.604		4.0	2.379-5.621
T_reg/T_help ratio
High	9.0	4.11-13.89	0.963	4.4	2.254-6.546	0.391
Low	8.1	5.673-10.527		3.7	2.279-5.121
T_mem
Density
High	7.1	3.552-10.648	0.146	4.2	1.711-6.689	0.152
Low	10.4	6.133-14.667		4.0	1.705-6.295
Memory T_reg density
High	5.9	4.091-7.709	0.025	3.8	1.328-6.272	0.349
Low	10.8	0-22.929		4.0	2.278-5.722
T_mem/T_total ratio
High	5.9	3.713-8.087	0.027	3.8	0.652-6.948	0.049
Low	10.4	6.643-14.157		4.0	2.218-5.782
Memory T_cyto/T_mem ratio
High	18.1	2.563-33.637	< 0.001	5.5	0-11.043	0.004
Low	5.9	2.724-9.076		1.9	1.157-2.643
Memory T_help/T_mem ratio
High	6.3	4.712-7.888	< 0.001	1.9	0.946-2.854	0.002
Low	24.3	8.862-39.738		5.5	0-11.126
Memory T_reg/T_mem ratio
High	5.9	3.963-7.837	0.001	3.1	0.873-5.327	0.069
Low	19.6	1.458-37.742		4.2	1.169-7.231

CI, confidence interval; OS, overall survival; PD-1, programmed cell death-1; PFS, progression-free survival; T_cyto, cytotoxic T cells; T_help, helper T cells; TIM-3, T-cell immunoglobulin and mucin domain-3; T_mem, memory T cells; T_reg, regulatory T cells; T_total, total T cells.

REFERENCES

1. Wagner AD, Syn NL, Moehler M, Grothe W, Yong WP, Tai BC, et al. Chemotherapy for advanced gastric cancer. Cochrane Database Syst Rev. 2017;8:CD004064.Article PubMed PMC
2. Yoon J, Kim TY, Oh DY. Recent progress in immunotherapy for gastric cancer. J Gastric Cancer. 2023;23:207–23. Article PubMed PMC PDF
3. Janjigian YY, Shitara K, Moehler M, Garrido M, Salman P, Shen L, et al. First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial. Lancet. 2021;398:27–40. Article PubMed PMC
4. Rha SY, Oh DY, Yanez P, Bai Y, Ryu MH, Lee J, et al. Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for HER2-negative advanced gastric cancer (KEYNOTE-859): a multicentre, randomised, double-blind, phase 3 trial. Lancet Oncol. 2023;24:1181–95. PubMed
5. Qiu MZ, Oh DY, Kato K, Arkenau T, Tabernero J, Correa MC, et al. Tislelizumab plus chemotherapy versus placebo plus chemotherapy as first line treatment for advanced gastric or gastro-oesophageal junction adenocarcinoma: RATIONALE-305 randomised, double blind, phase 3 trial. BMJ. 2024;385:e078876Article PubMed
6. Janjigian YY, Kawazoe A, Bai Y, Xu J, Lonardi S, Metges JP, et al. Pembrolizumab plus trastuzumab and chemotherapy for HER2-positive gastric or gastro-oesophageal junction adenocarcinoma: interim analyses from the phase 3 KEYNOTE-811 randomised placebo-controlled trial. Lancet. 2023;402:2197–208. Article PubMed
7. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12:252–64. Article PubMed PMC PDF
8. Gibney GT, Weiner LM, Atkins MB. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 2016;17:e542–51. Article PubMed PMC
9. Kim ST, Cristescu R, Bass AJ, Kim KM, Odegaard JI, Kim K, et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat Med. 2018;24:1449–58. Article PubMed PDF
10. Fridman WH, Pages F, Sautes-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12:298–306. Article PubMed PDF
11. Lee HE, Chae SW, Lee YJ, Kim MA, Lee HS, Lee BL, et al. Prognostic implications of type and density of tumour-infiltrating lymphocytes in gastric cancer. Br J Cancer. 2008;99:1704–11. Article PubMed PMC PDF
12. Li R, Liu H, Cao Y, Wang J, Chen Y, Qi Y, et al. Identification and validation of an immunogenic subtype of gastric cancer with abundant intratumoural CD103(+)CD8(+) T cells conferring favourable prognosis. Br J Cancer. 2020;122:1525–34. Article PubMed PMC PDF
13. Fei Y, Cao Y, Gu Y, Fang H, Chen Y, Wang J, et al. Intratumoral Foxp3(+)RORgammat(+) T cell infiltration determines poor prognosis and immunoevasive contexture in gastric cancer patients. Cancer Immunol Immunother. 2022;71:1–11. Article PubMed PDF
14. Zhong J, Pan R, Gao M, Mo Y, Peng X, Liang G, et al. Identification and validation of a T cell marker gene-based signature to predict prognosis and immunotherapy response in gastric cancer. Sci Rep. 2023;13:21357.Article PubMed PMC PDF
15. Gao D, Liu L, Liu J, Liu J. Predictive response and outcome of peripheral CD4(+) T cell subpopulations to combined immunotherapy and chemotherapy in advanced gastric cancer patients. Int Immunopharmacol. 2024;129:111663.Article PubMed
16. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nat-ure. 2014;513:202–9. Article PDF
17. Liu J, Liu D, Hu G, Wang J, Chen D, Song C, et al. Circulating memory PD-1(+)CD8(+) T cells and PD-1(+)CD8(+)T/PD-1(+)CD4(+)T cell ratio predict response and outcome to immunotherapy in advanced gastric cancer patients. Cancer Cell Int. 2023;23:274.Article PubMed PMC PDF
18. Wang J, Liang Y, Xue A, Xiao J, Zhao X, Cao S, et al. Intratumoral CXCL13(+) CD160(+) CD8(+) T cells promote the formation of tertiary lymphoid structures to enhance the efficacy of immunotherapy in advanced gastric cancer. J Immunother Cancer. 2024;12:e009603Article PubMed PMC
19. Farhood B, Najafi M, Mortezaee K. CD8(+) cytotoxic T lymphocytes in cancer immunotherapy: a review. J Cell Physiol. 2019;234:8509–21. Article PubMed PDF
20. Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–71. Article PubMed PMC PDF
21. Duhen T, Duhen R, Montler R, Moses J, Moudgil T, de Miranda NF, et al. Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors. Nat Commun. 2018;9:2724.Article PubMed PMC PDF
22. Sakuishi K, Apetoh L, Sullivan JM, Blazar BR, Kuchroo VK, Anderson AC. Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. J Exp Med. 2010;207:2187–94. Article PubMed PMC PDF
23. Kim HD, Song GW, Park S, Jung MK, Kim MH, Kang HJ, et al. Association between expression level of PD1 by tumor-infiltrating CD8(+) T cells and features of hepatocellular carcinoma. Gastroenterology. 2018;155:1936–50. Article PubMed
24. Ganesan AP, Clarke J, Wood O, Garrido-Martin EM, Chee SJ, Mellows T, et al. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat Immunol. 2017;18:940–50. Article PubMed PMC PDF
25. Lin YC, Chang LY, Huang CT, Peng HM, Dutta A, Chen TC, et al. Effector/memory but not naive regulatory T cells are responsible for the loss of concomitant tumor immunity. J Immunol. 2009;182:6095–104. Article PubMed PDF
26. Gorris MAJ, Halilovic A, Rabold K, van Duffelen A, Wickramasinghe IN, Verweij D, et al. Eight-color multiplex immunohistochemistry for simultaneous detection of multiple immune checkpoint molecules within the tumor microenvironment. J Immunol. 2018;200:347–54. Article PubMed PDF
27. Tsujikawa T, Kumar S, Borkar RN, Azimi V, Thibault G, Chang YH, et al. Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. Cell Rep. 2017;19:203–17. Article PubMed PMC

Figure & Data

REFERENCES

Citations

Citations to this article as recorded by

T cell-mediated immune function in gastric cancer: a bibliometric overview of the last 20 years (2006-2025)
Tao Wang, Li-Li Li, Chun-Yan Wang, Wen-Jing Sui, Mai-Qing Yang
Frontiers in Oncology.2026;[Epub] CrossRef

PubReader
ePub Link

Cite

CITE: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Analysis of T-Cell Subsets Using Multiplex Immunohistochemistry and Clinical Outcomes of Immune Checkpoint Inhibitors in Advanced Gastric Cancer Patients

DOI: https://doi.org/10.4143/crt.2025.458

XML Download

Abstract
Introduction
Materials and Methods
Results
Discussion
Electronic Supplementary Material
NOTES
REFERENCES

Analysis of T-Cell Subsets Using Multiplex Immunohistochemistry and Clinical Outcomes of Immune Checkpoint Inhibitors in Advanced Gastric Cancer Patients

Fig. 1. Tumor response of immune checkpoint inhibitors. (A) The waterfall plot for best tumor reduction from baseline. Each yellow (microsatellite instability–high [MSI-H]), green (Epstein-Barr virus [EBV]) and light blue (non–microsatellite instability [MSI]/non-EBV) bar means best change of sum of target lesions’ diameter from baseline, based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 in each subject. Black star marks above or below each bar mean patients who received immune checkpoint inhibitors (ICs) as first- or second-line. (B) The spider plot for change of sum of target lesions’ diameter over time since the initiation of ICs, based on Response Evaluation Criteria in Solid Tumor 1.1. ICB, immune checkpoint blockades.

Fig. 2. T-cell subsets and tumor response. (A) A Tcyto/Ttotal ratio according to tumor response. (B) A Thelp/Ttotal ratio according to tumor response. (C) A Treg/Thelp ratio according to tumor response. (D) A memory Thelp/Tmem ratio according to tumor response. CR, complete remission; NS, no significance; PD, progressive disease; PR, partial remission; SD, stable disease; Tcyto, cytotoxic T cells; Thelp, helper T cells; Tmem, memory T cells; Treg, regulatory T cells; Ttotal, total T cells.

Fig. 3. Kaplan-Meier survival curves, depending on the density and ratio of T-cell subsets. Overall survival (OS), according to Tcyto density (A), Tcyto/Ttotal ratio (B), programmed cell death-1 (PD-1)–expressing Tcyto (C), TIM-3–expressing Tcyto (D), Treg/Ttotal ratio (E), Tmem/Ttotal ratio (F), memory Tcyto/Tmem ratio (G), and memory Treg/Tmem ratio (H). Tcyto, cytotoxic T cells; TIM-3, T-cell immunoglobulin and mucin domain-3; Tmem, memory T cells; Treg, regulatory T cells; Ttotal, total T cells.

Fig. 1.

Fig. 2.

Fig. 3.

Analysis of T-Cell Subsets Using Multiplex Immunohistochemistry and Clinical Outcomes of Immune Checkpoint Inhibitors in Advanced Gastric Cancer Patients

	MSI-H	EBV	NMNE	Total	p-value
Total	7 (8.3)	3 (3.6)	74 (88.1)	84 (100)
Age (yr)	59 (55-82)	47 (46-66)	62 (28-88)	62 (28-88)	-
Sex
Male	3 (42.9)	3 (100)	51 (68.9)	57 (67.9)	0.177
Female	4 (57.1)	0	23 (31.1)	27 (32.1)
Lauren classification
Diffuse	0	1 (33.3)	29 (39.2)	30 (35.7)	0.030
Intestinal	7 (100)	1 (33.3)	42 (56.8)	50 (59.5)
Mixed	0	1 (33.3)	3 (4.1)	4 (4.8)
HER2
Negative	5 (71.4)	3 (100)	57 (77.0)	65 (77.4)	0.003
Positive	0	0	16 (21.6)	16 (19.0)
Unknown	2 (28.6)	0	1 (1.4)	3 (3.6)
CPS
< 1	1 (14.3)	0	44 (59.4)	45 (53.6)	0.012
≥ 1	6 (85.7)	3 (100)	30 (40.5)	39 (46.4)
Gastric surgery
Yes	6 (85.7)	1 (33.3)	36 (48.6)	43 (51.2)	0.380
No	1 (14.3)	2 (66.7)	38 (51.4)	41 (48.8)
Line of ICBs
1st- or 2nd-line	4 (57.1)	2 (66.7)	34 (45.9)	40 (47.6)	0.679
3rd- or later lines	3 (42.9)	1 (33.3)	40 (54.1)	44 (52.4)
Treatments
PD-1	5 (71.4)	2 (66.7)	37 (50.0)	44 (52.4)	0.665
PD-L1	2 (28.6)	0	4 (5.4)	6 (7.1)
CTLA4	0	0	3 (4.1)	3 (3.6)
PD-1+targeted agents	0	0	11 (14.9)	11 (13.1)
PD-L1+targeted agents	0	1 (33.3)	8 (10.8)	9 (10.7)
PD-L1+CTLA4	0	0	4 (5.4)	4 (4.8)
PD-L1+other kinds of ICIs	0	0	1 (1.4)	1 (1.2)
PD-1+ other kinds of ICIs	0	0	6 (8.1)	6 (7.1)

	Overall survival			Progression-free survival
	Survival (mo)	95% CI	p-value	Survival (mo)	95% CI	p-value
Total	8.1	5.231-11.035	-	4.0	2.511-5.489	-
Molecular subtypes
MSI-H	-	-	-			0.001
EBV	27.3	-		11.1	0-22.462
Non-MSI/Non-EBV	7.0	5.273-8.794		2.7	0.908-4.492
CPS
< 1	5.9	3.430-8.370	0.010	2.8	0.564-5.036	0.014
≥ 1	17.7	7.113-28.287		4.7	1.947-7.453
Line of ICIs
1st- or 2nd-line	17.7	5.154-7.631	0.010	5.5	2.501-8.499	0.033
3rd- or later lines	4.1	0.888-2.392		2.2	1.179-3.221
Combination
Mono	5.9	3.244-8.489	0.022	3.7	2.143-5.257	0.179
Combo	17.9	7.48-28.253		5.5	1.561-9.439

	CR/PR	SD	PD	p-value
Total	16 (22.2)	28 (38.9)	28 (38.9)
Molecular subtypes
MSI-H	6 (85.7)	1 (14.3)	0	< 0.001
EBV	2 (66.7)	1 (33.3)	0
NMNE	8 (12.9)	26 (41.9)	28 (45.2)
CPS
< 1	5 (13.5)	14 (37.8)	18 (48.6)	0.106
≥ 1	11 (31.4)	14 (40.0)	10 (28.6)
Line of ICIs
1st- or 2nd-line	12 (30.8)	16 (41.0)	11 (28.2)	0.067
3rd-or later	4 (12.1)	12 (36.4)	17 (51.5)
T_cyto
High	11 (28.2)	14 (35.9)	14 (35.9)	0.414
Low	5 (15.2)	14 (42.4)	14 (42.4)
PD-1–expressing T_cyto density
High	10 (27.0)	14 (37.8)	13 (35.1)	0.580
Low	6 (17.1)	14 (40.0)	15 (42.9)
TIM-3–expressing
T_cyto density
High	11 (28.2)	11 (28.2)	17 (43.6)	0.114
Low	5 (15.2)	17 (51.5)	11 (33.3)
T_cyto/T_total ratio
High	13 (34.2)	13 (34.2)	12 (31.6)	0.034
Low	3 (8.8)	15 (44.1)	16 (47.1)
T_reg/T_help ratio
High	11 (30.6)	12 (33.3)	13 (36.1)	0.227
Low	5 (13.9)	16 (44.4)	15 (41.7)
Memory T_cyto/T_mem
High	13 (34.2)	14 (36.8)	11 (28.9)	0.026
Low	3 (8.8)	14 (41.2)	17 (50.0)
Memory T_help/T_mem
High	3 (8.8)	13 (38.2)	18 (52.9)	0.014
Low	13 (34.2)	15 (39.5)	10 (26.3)

	Overall survival			Progression-free survival
	Survival (mo)	95% CI	p-value	Survival (mo)	95% CI	p-value
T_cyto
Density
High	18.2	0.371-36.029	0.001	4.7	1.003-8.397	0.030
Low	6.5	3.76-9.24		2.5	0-5.184
PD-1–expressing T_cyto density
High	17.9	6.693-29.107	0.024	4.7	3.097-6.303	0.047
Low	7.0	4.785-9.215		2.2	0.99-3.41
TIM-3–expressing T_cyto density
High	18.2	1.468-34.932	0.003	4.4	2.485-6.315	0.033
Low	7.0	4.818-9.182		2.5	0.26-4.74
T_cyto/T_total ratio
High	24.3	6.912-41.688	< 0.001	5.4	0.704-10.096	0.003
Low	5.9	2.619-9.181		1.9	0.569-3.231
T_help
Density
High	6.3	4.554-8.046	0.234	3.8	1.634-5.966	0.248
Low	10.8	0-22.304		4.4	1.687-7.113
T_help/T_total ratio
High	6.3	4.395-8.205	0.001	2.4	0-5.422	0.005
Low	24.3	6.439-42.161		4.7	0-9.73
T_reg/T_total ratio
High	5.9	2.334-9.466	0.034	4.2	1.593-6.807	0.648
Low	17.9	2.196-33.604		4.0	2.379-5.621
T_reg/T_help ratio
High	9.0	4.11-13.89	0.963	4.4	2.254-6.546	0.391
Low	8.1	5.673-10.527		3.7	2.279-5.121
T_mem
Density
High	7.1	3.552-10.648	0.146	4.2	1.711-6.689	0.152
Low	10.4	6.133-14.667		4.0	1.705-6.295
Memory T_reg density
High	5.9	4.091-7.709	0.025	3.8	1.328-6.272	0.349
Low	10.8	0-22.929		4.0	2.278-5.722
T_mem/T_total ratio
High	5.9	3.713-8.087	0.027	3.8	0.652-6.948	0.049
Low	10.4	6.643-14.157		4.0	2.218-5.782
Memory T_cyto/T_mem ratio
High	18.1	2.563-33.637	< 0.001	5.5	0-11.043	0.004
Low	5.9	2.724-9.076		1.9	1.157-2.643
Memory T_help/T_mem ratio
High	6.3	4.712-7.888	< 0.001	1.9	0.946-2.854	0.002
Low	24.3	8.862-39.738		5.5	0-11.126
Memory T_reg/T_mem ratio
High	5.9	3.963-7.837	0.001	3.1	0.873-5.327	0.069
Low	19.6	1.458-37.742		4.2	1.169-7.231

Table 1. Baseline characteristics of study populations

Table 2. Summary of overall survival and progression-free survival

CI, confidence interval; CPS, combined positive score; EBV, Epstein-Barr virus; ICIs, immune checkpoint inhibitors; MSI-H, microsatellite instability–high.