Research Article

Expression, Clinical Significance, and Role in Immunotherapy Efficacy Evaluation of the Co-stimulatory Molecule LLT1 in Gastric Cancer

DOI:

10.3791/68827

⸱

October 10th, 2025

 ,  ,  ,  ,  ,  ,  , 
1Department of General Surgery, The First Affiliated Hospital of Soochow University, 2Department of Pathology, The First Affiliated Hospital of Soochow University, 3Jiangsu Institute of Clinical Immunology, The First Affiliated Hospital of Soochow University, 4Jiangsu Key Laboratory of Clinical Immunology, Soochow University, 5Jiangsu Key Laboratory of Gastrointestinal Tumor Immunology, The First Affiliated Hospital of Soochow University

In This Article

Summary

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This research investigates LLT1 as a predictive immunotherapy biomarker in gastric cancer through multi-omics and clinical analyses. The study evaluates LLT1's role in immune evasion and tumor microenvironment modulation to facilitate precision immunotherapy approaches for enhanced treatment outcomes.

Abstract

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LLT1 is a novel immune checkpoint molecule and potential therapeutic target. Immunotherapy has improved outcomes in advanced gastric cancer; however, many patients do not respond or develop resistance, and predictive biomarkers are lacking. The role of LLT1 in gastric cancer immune evasion and its effect on immunotherapy response remain unclear. To assess this, LLT1 expression was analyzed in the Cancer Genome Atlas (TCGA) gastric adenocarcinoma dataset using multiple algorithms (TIMER, CIBERSORT, and ESTIMATE) to quantify immune cell infiltration. Protein expression was validated by immunohistochemistry (IHC) in 268 gastric adenocarcinoma samples and by multiplex immunofluorescence (MIF) in 115 post-immunotherapy tissues. Integrated transcriptomic and proteomic data were analyzed for associations with tumor microenvironment features and clinical immunotherapy outcomes. The results showed that high LLT1 protein expression was significantly associated with less advanced tumor characteristics (lower invasion depth and nodal metastasis; P < 0.05) and longer overall survival (P = 0.012). LLT1 mRNA and protein levels correlated with greater CD8+ T-cell and M1 macrophage infiltration and elevated PD-L1 expression in the tumor microenvironment. Among the first cohort of 198 patients with available prognostic data, patients with high LLT1 expression (n=117) demonstrated significantly longer overall survival compared to those with low expression (n=81). In the second cohort of patients treated with combined immunotherapy and chemotherapy, tumors with high LLT1 expression (n=48) had longer progression-free survival (P = 0.014) than those with low LLT1 expression (n=67). A significant increase (P < 0.05) in PD-1+CD161+ immune cell populations was observed in Immunotherapy-effective patients (n=62) relative to treatment-resistant patients (n=53). Conclusively, LLT1 expression is a promising biomarker of a favorable prognosis in gastric cancer. To our knowledge, this is the first study to demonstrate that LLT1 expression predicts improved response to combined chemotherapy and immunotherapy in gastric cancer, supporting its potential in guiding immunotherapeutic strategies.

Introduction

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Gastroesophageal cancer, particularly gastric cancer (GC), holds the distinction of being the 5th most common form of cancer worldwide and the 5th most deadly. Global statistics reveal that roughly 1 million new cases are identified each year, with East Asia shouldering a disproportionate share of the disease, contributing to more than half of the global incidence1. For those diagnosed with early-stage GC, the prospect of survival is significantly favorable, with over 90% achieving 5-year survival rates following endoscopic submucosal dissection (ESD) or radical surgical intervention2. However, the subtle presentation of initial symptoms often leads to the diagnosis of a locally advanced or metastatic phase3,4. The outlook for patients at these advanced stages is dire, with those left untreated experiencing a median overall survival (OS) of barely 6 months. Even with the administration of combination chemotherapy regimens based on fluorouracil and platinum, the median OS is merely extended to approximately a year5.

Recently, there has been a significant change in the treatment of late-stage stomach cancer, owing to these new drugs called immune checkpoint inhibitors. They are similar to a magic bullet for cancer cells that have a certain marker, PD-L1, showing up on them - at least a score of 1 on the Combined Positive Score (CPS) scale. These treatments are also highly efficacious for cancers that are all mixed up in their DNA, which is called high microsatellite instability (MSI-H). This new approach makes a difference in providing patients with more time6,7. Pivotal clinical trials indicate that within the PD-L1-positive cohort, individuals with a CPS of 10 or higher gain a more pronounced increase in survival after treatment with PD-1 monoclonal antibodies8,9. Nevertheless, current immunotherapy still faces multiple clinical challenges: objective response rates (ORR) generally below 20%, secondary resistance rates as high as 40%-60%, imperfect biomarker prediction systems (existing biomarkers can identify only 30%-40% of potential beneficiaries), and difficulties in managing immune-related adverse events (irAEs)10,11,12,13,14. Although dual immune checkpoint blockade (e.g., combined PD-1/CTLA-4 inhibition) or chemoimmunotherapy regimens can increase response rates to 40%-60%, approximately half of the patients still fail to achieve sustained clinical benefits15. Therefore, establishing precision classification systems based on multi-omics features, discovery of novel predictive biomarkers, and identification of new immune checkpoints have become critical research directions for improving the clinical outcomes of GC immunotherapy.

Lectin-like Transcript 1 (LLT1) is a C-type lectin-related protein, with CD161, a constituent of the natural killer (NK) cell receptor family, which serves as its identified binding partner. The CLEC2D gene, which is responsible for its synthesis, gives rise to five distinct isoforms through alternative splicing events that bypass certain exons. Variant 1, known as LLT1, has been validated as the only variant present on the cell membrane16,17. LLT1 expression is predominantly observed in various immune cell types, such as B lymphocytes, NK cells, dendritic cells (DCs), specific T-cell populations (including activated and regulatory T cells), monocytes, and macrophages, and is also observed in certain malignant cells and during inflammatory episodes18. Initial findings indicate that LLT1 is upregulated in DCs and B cells following Toll-like receptor (TLR) stimulation. The engagement of LLT1 with CD161 has been shown to suppress NK cell-mediated cytotoxicity, suggesting its involvement in modulating immune response18. In B cells, LLT1 expression is upregulated by viral infection or inflammatory stimulation. When functioning as antigen-presenting cells (APCs), the LLT1-CD161 interaction exerts dual immunomodulatory effects by suppressing NK cell function while co-stimulating T-cell proliferation or IFN-γ production19.

LLT1 is known to be present in a range of cancerous cells, including triple-negative breast cancer, glioblastoma, and B-cell non-Hodgkin lymphoma. This protein engages with the CD161 receptor found on NK cells, effectively blocking their ability to destroy cancer cells, thus aiding in the evasion of the immune response20,21,22. A notable observation in HPV-negative oropharyngeal squamous cell carcinoma is the variable expression of LLT1, which, according to immunohistochemical studies, indicates a positive prognostic significance when highly expressed within tertiary lymphoid structures (TLS), whereas its presence on tumor cells is linked to a poorer outcome23. Furthermore, in the context of non-small cell lung cancer (NSCLC), LLT1 is not detected in the cancer cells themselves but is found within the tumor microenvironment (TME), and its mRNA expression level is associated with a better prognosis24.

As demonstrated by Mathewson et al. in glioma research, blocking CD161 enhances T cell-mediated killing of glioma cells in vitro and improves their anti-tumor function in vivo, highlighting the therapeutic potential of CD161 as an immune checkpoint target25. Given that LLT1 is the canonical ligand of CD16118, its role as an immunotherapeutic target warrants serious consideration. Analogous to the critical interaction between PD-1 and PD-L1 in immunotherapy, investigating the biological functions of LLT1 in gastric cancer and its correlation with immunotherapy outcomes holds significant clinical value.

This study provides a comprehensive analysis of LLT1 expression patterns within the gastric cancer tumor microenvironment, integrating both mRNA and protein-level investigations. It systematically assesses the potential of LLT1 as a prognostic biomarker in advanced gastric cancer and evaluates its predictive value for immunotherapy efficacy. These insights contribute to a deeper understanding of its clinical translational potential.

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Protocol

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The research involving human subjects was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Soochow University (Approval No. 2019026). The ethical review board of the First Affiliated Hospital of Soochow University has approved or granted an exemption for the ethical review of this data usage. Additionally, informed consent was obtained from all participants.

Bioinformatics data analysis
After a thorough Pan-cancer expression analysis examination of the TCGA and Tumor IMmune Estimation Resource (TIMER) databases, the behavior of LLT1 was investigated across various cancer types, with a particular focus on gastric cancer. The TISIDB database was utilized to investigate the correlation between LLT1 expression levels and different molecular subtypes of gastric cancer. The correlation between patients' clinical characteristics and LLT1 expression was further analyzed with TCGA data through R programming. GO and KEGG analyses were employed to investigate the functional role of LLT1 in gastric carcinogenesis. The ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm was additionally employed to quantify immune and stromal cell infiltration levels in tumor tissues according to LLT1 expression patterns. Further analysis utilizing the CIBERSORT algorithm was conducted to evaluate correlations between LLT1 expression levels and specific immune cell infiltration patterns within the tumor microenvironment. Comprehensive bioinformatic analysis was performed using TCGA datasets and R statistical programming (v4.4.1) to interrogate potential correlations between LLT1 expression profiles and immunoregulatory biomarkers. Finally, the TCIA (The Cancer Immunome Atlas) database was utilized to investigate whether LLT1 expression could predict response to PD-1/PD-L1 immune checkpoint blockade therapy.

GAC specimens and tissue microarray (TMA) construction
Two sets of tissue microarray data were generated as follows:

The first set retrospectively analyzed specimens from 268 patients with GAC who underwent surgery in 2011. Pathologists selected paraffin blocks, identified tumor core regions, and constructed them into tissue microarrays. Every individual sample site was allocated a distinct identifier to meticulously monitor associated clinical parameters such as sex, age, neoplasm dimensions, degree of neoplastic differentiation, phase of cancer progression, invasion depth, lymphatic involvement, and the presence of distant spread. The duration of overall survival (OS) was defined as the interval from the initiation of therapy until the occurrence of mortality or the cutoff date of December 2017. It should be highlighted that comprehensive OS data was only available for a subset of 198 participants.

The second set included 115 treatment-naïve patients with advanced GC, who underwent surgical/endoscopic resection at the First Affiliated Hospital of Soochow University between 2016 and 2023. Pathologists evaluated the paraffin blocks and constructed tissue microarrays with close follow-up of disease progression. Notably, all specimens were collected before any preoperative treatment, and postoperative treatment primarily consisted of PD-1 monoclonal antibodies combined with chemotherapy.

All the surgical resection specimens were collected from the antrum of the stomach. All samples were fixed in neutral buffered formalin and embedded in paraffin. Based on the evaluation of H&E-stained sections, two senior pathologists independently identified the most representative tissue regions from the paraffin-embedded specimens. The tissue microarrays (TMAs) were constructed using an automated tissue arrayer, with tissue cores precisely positioned in the TMA blocks according to predefined modules. Finally, the completed TMA blocks were sectioned at 4 µm thickness by an experienced histotechnologist.

Immunohistochemistry (IHC) and multiplex immunofluorescence (mIHC)
Immunohistochemical and multiplex immunofluorescence assays were conducted on a constructed tissue microarray. IHC staining was carried out in accordance with a previously established methodology26. The first set of microarray paraffin sections was transferred onto glass slides and baked at 72°C for 1 h. After the sections were deparaffinized with xylene and rehydrated in successively increasing ethanol dilutions, endogenous peroxidase activity was blocked by the application of peroxidase blocking solution for 30 min. Antigen retrieval was performed by boiling the slides at 110 °C in Citrate Antigen Retrieval Solution (pH 6.0) for 3 min. After blocking with 5% BSA blocking buffer, the sections were incubated with mouse anti-human CLEC2D monoclonal, mouse anti-human CD3 mono clonal (ready to use), mouse anti-human CD4 monoclonal (ready to use), rabbit anti-human CD8 monoclonal (ready to use), mouse anti-human CD11c monoclonal (1:50) mouse anti-human CD19 monoclonal (ready to use), mouse anti-human CD31 monoclonal (ready to use), mouse anti-human CD56 monoclonal (ready to use), mouse anti-human CD68 monoclonal (ready to use), rabbit anti-human Foxp3 monoclonal (1:50), mouse anti-human IL-17 monoclonal (1:50), mouse anti-human IFN-γ monoclonal (1:50), mouse anti-human Ki67 monoclonal (1:50), mouse anti-human PD-L1 monoclonal (ready to use) at 4 °C overnight27,28. The slides were then washed and incubated with horseradish peroxidase-labeled secondary antibodies (anti-mouse and rabbit) at room temperature for 30 min. After incubation, the samples were washed, and the EnVision detection system was used for immunodetection. Under the microscope, when brown areas begin to appear on the chip, careful observation is required. The chromogenic reaction should be stopped once the brown areas no longer increase. Slides were counterstained with Mayer's hematoxylin and mounted with neutral glue. In the negative control, mouse or rabbit IgG was used in place of the primary antibody. The positive control was human tonsil tissue. The second set of microarray sections is used for multiplex immunohistochemistry (mIHC). Reusing the aforementioned antigen retrieval and blocking conditions, the antibody panel is spectrally optimized as follows: PD-L1 (480 nm), CD161 (520 nm), LLT1 (570 nm), PD-1 (620 nm), CD8 (690 nm), and PAN-CK (780 nm). Primary antibodies were incubated under the same conditions as in the original protocol29 (except for PANCK and CD161, which were newly introduced), at the recommended 1:100 dilution, followed by tyramide signal amplification (TSA) fluorescence staining. After each round of fluorescence staining, antibody stripping is performed, followed by repeated antigen retrieval, endogenous peroxidase blocking, and non-specific target blocking before incubating the next primary antibody. When performing multiplex immunohistochemistry, three points are crucial: pre-experiments must be conducted for each fluorescent dye, strongly expressed antibodies should be paired with weak fluorescent signals, while weakly expressed antibodies should be paired with strong fluorescent signals, and after staining each fluorescent marker, the staining results must be observed under a microscope under light-protected conditions. After staining with the 6th fluorescent dye, DAPI counterstaining is applied to label cell nuclei, and the slides are mounted. Finally, all tissue microarrays were scanned using a digital pathology scanner and analyzed with an AI-powered digital pathology image analysis software.

IHC/mIHC staining evaluation
The staining outcomes were interpreted by two expert pathologists in a double-blind fashion. They gauged the depth of staining using a high-power objective (200x magnification) or matching fluorescent setting (200x), assigning scores ranging from 0 (no staining) to 3 (intense staining). The fractions of LLT1-positive cells within gastric carcinoma samples were divided into four stratifications: 0 (<5%), 1 (5-25%), 2 (26-50%), and 3 (>50%). The Immunoreactivity Score (IRS) was derived from the product of the staining depth and proportion of LLT1-positive cells. Specimens scoring zero were regarded as negative, whereas scores of 1-4 indicated weak expression, 5-7 moderate, and 8-9 strong expression; the same criteria were applied to evaluate the expression of other markers such as Ki67 and CD3. Based on the evaluation and grouping methodology, the samples were labeled as having strong (IRS8-9), moderate (IRS5-7), weak (IRS1-4), or no expression (IRS0). Patients with the lowest scores (IRS≤4) were assigned to the low-expression group, and those with higher scores (IRS≥5) were assigned to the high-expression group.

However, for the PD-L1 marker, a different approach was adopted using the CPS. This score was determined by the ratio of PD-L1-positive cells (including tumor, immune, and stromal cells) to the total number of viable tumor cells scaled up by a factor of 100. Individuals with a CPS exceeding five were placed in the high-expression cohort, whereas those with a CPS of five or below were grouped into the low-expression cohort.

Immunotherapy response grouping
In line with the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), the evaluation of target lesions in patients falls into four distinct categories: (a) Complete Response (CR): all target lesions have vanished entirely, and the short axis of all pathological lymph nodes (both target and non-target) must measure less than 10 mm; (b) Partial Response (PR): the cumulative diameter of all measurable target lesions has reduced by at least 30% from the baseline measurements; (c) Progressive Disease (PD): this category is applicable when the cumulative diameter of target lesions has increased by at least 20% compared to the smallest recorded value during the study (using the baseline as a reference if it represents the smallest value), with the absolute increase being no less than 5 mm; additionally, the emergence of new lesions is also categorized as Progressive Disease (PD); (d) Stable Disease (SD): the cumulative diameter of the target lesions neither achieves the reduction criteria for PR nor the increase criteria for PD throughout the study. Two stratification techniques were implemented. The first method involved segregating patients based on their response status, where PD (n=53) and SD (n=46) patients comprised the non-responder group (n=99), whereas PR (n=15) and CR (n=1) patients formed the responder group (n=16). The second approach entailed categorizing patients according to disease progression, with CR, PR, and SD patients classified as the immunotherapy-effective cohort (n=62) and PD patients as the immunotherapy non-effective cohort (n=53).

Statistical analysis
Empirical evaluations were performed using the R computing platform (version 4.4.1) and the SPSS Statistical software package (version 27.0). Disparities among study cohorts were investigated by the application of the chi-square contingency table analysis. Spearman's non-parametric correlation technique was employed to probe the strength and direction of the associations between variables. Survival time variations were analyzed through the construction of Kaplan-Meier estimators complemented by log-rank testing for significance. Subsequently, Cox proportional hazards regression models were employed to identify factors potentially influencing patient survival outcomes. Statistical significance was predefined as a two-sided p-value threshold of <0.05. To ensure the robustness of the findings, comprehensive sensitivity analyses were conducted to evaluate potential confounding variables, with multivariate models adjusted accordingly. Furthermore, explicit interpretations of hazard ratios were provided to facilitate clinical understanding, emphasizing the practical implications of identified risk factors on patient prognosis.

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Results

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Association between CLEC2D mRNA expression and gastric cancer subclassification/clinical parameters
An initial comprehensive cancer examination utilizing the TCGA dataset, facilitated by the TIMER database, revealed a notable elevation in CLEC2D transcript levels across various cancerous tissues, as depicted in Figure 1A. This observation implies that CLEC2D may serve as a general oncogenic factor implicated in...

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Discussion

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Our study showed higher CLEC2D mRNA expression in tumor tissues than in normal tissues. Immunohistochemical results of gastric cancer revealed that high LLT1 protein expression in tumor tissues was associated with better prognosis and positively correlated with immune cell infiltration in the tumor microenvironment. Analysis of tissues from gastric cancer patients receiving immunotherapy indicated that high LLT1 expression predicts better immunotherapeutic response. Our research explored the expression and clini...

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Disclosures

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No potential conflict of interest is reported by the authors.

Acknowledgements

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Grateful acknowledgment is extended to the TCGA database for providing data support. This study was supported by the National Natural Science Foundation of China (Grant No. 81974375) and Grants of Suzhou Science and Technology Development Plan (SKY2022129). The study adheres to ARRIVE guidelines.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
5% BSA Blocking BufferBeijing Solarbio Science & Technology Co., Ltd.,ChinaSW3015
anti-CD11c ZSGB-BIO,China5D11
Anti-CD161Abcam,Cambridge, UKab302564
anti-CD19  ZSGB-BIO,ChinaEPl69
anti-CD3 ZSGB-BIO,ChinaLNl0
anti-CD31ZSGB-BIO,China UMAB30
anti-CD4ZSGB-BIO,ChinaUMAB64
anti-CD56 ZSGB-BIO,ChinaUMAB83
anti-CD68 ZSGB-BIO,ChinaKP1
anti-CD8ZSGB-BIO,ChinaEP334
Anti-CLEC2DAbcam,Cambridge, UKab197341
anti-Foxp3R&D Systems, USA1054C
anti-IFN-γ Santa Cruz Biotechnologysc-74108
anti-IL-17R&D Systems, USA MAB317 
anti-Ki67 ZSGB-BIO,China8H5
Anti-pan Cytokeratin Abcam,Cambridge, UKab7753
Anti-PD-1Abcam,Cambridge, UKab137132
Anti-PD-L1DAKO,Denmark22C3
automated tissue array equipmentEstigen OU, Tartu, EstoniaBeecher ATA 27This instrument is primarily used for the construction of tissue microarrays.
Citrate Antigen Retrieval SolutionAladdin Biochemical Technology Co., Ltd , ChinaC752119
DAB chromogen kit AiFang Biological,ChinaAFIHC004
Digital pathology scannerNingbo Jiangfeng Bio-Information Technology Co., Ltd,ChinaKF-FL-020This instrument is primarily used for scanning immunohistochemistry and multiplex immunohistochemistry chips.
GraphPad Prism 10GraphPad Software,USAv10.0.3This software is for Plotting Kaplan-Meier curves (with log-rank statistical analysis),Scientific data visualization, and Figure Preparation.
PBSAladdin Biochemical Technology Co., Ltd , ChinaP743267
peroxidase blocking solutionAiFang Biological,ChinaAFIHC012
Polymer-HRP conjugated goat anti-mouse/rabbit secondary antibodyAiFang Biological,ChinaAFIHC001
Six-color seven-marker multiplex immunofluorescence staining kitAiFang Biological,ChinaAFIHC037This reagent kit is designed for multiplex immunohistochemistry (mIHC)
SPSSIBM Corporation,USAIBM SPSS Statistics 27The software was utilized for statistical analysis of the data.
The R ProjectR Core TeamV4.4.1All R packages, related code, and original files have been uploaded to the public database.
VISIOPHARM softwareVisiopharm,Denmark2025.02.2.18022 x64This software is primarily designed for the quantitative analysis of pathological images.

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LLT1 ExpressionGastric CancerImmune CheckpointImmunotherapy EfficacyTumor MicroenvironmentImmune Cell InfiltrationPD L1 ExpressionCD8 T CellsImmunohistochemistryMultiplex Immunofluorescence

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