Gastric cancer (GC) is an aggressive and heterogeneous disease with poor survival outcomes. The progression of GC involves complex, multi-step processes. Endothelial cells (ECs) play a crucial role in tumor angiogenesis, proliferation, invasion, and metastasis, particularly through the process of endothelial-to-mesenchymal transition (EndoMT). However, the specific role and mechanisms of EndoMT in gastric cancer remain unclear. Based on 6 GC single-cell RNA-sequencing (scRNA-seq) cohorts (samples = 97), we established an EndoMT-related gene signature, termed EdMTS. Leveraging this gene signature, ssGSEA was applied to calculate sample scores across multiple bulk RNA-seq datasets, which include information on immunotherapy, metastasis, GC progression, and survival. Moreover, we applied the Monocle2 method to calculate cell pseudotime and used CellChat to analyze interactions between malignant and EC cells. We verified the molecular mechanism by multiple immunofluorescence and cell function experiments. Findings In this study, we established a single-cell atlas of ECs in GC and identified a subpopulation of COL1A1+ ECs that play a critical role in tumor progression and metastasis. These COL1A1+ ECs were significantly associated with worse clinical outcomes in GC patients. Further analysis revealed that COL1A1+ ECs originated from lymphatic ECs and underwent EndoMT through the upregulation of CEBPB, driving tumor invasiveness. Moreover, COL1A1+ ECs interacted with malignant cells via ANGPTL4-SDC4 axis, enhancing invasion and migration. These findings provide a deeper understanding of the role of COL1A1+ ECs in GC progression and highlight potential therapeutic targets for disrupting the EndoMT process in these cells to provide a benefit for GC patients.

Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.

Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively. A deep learning neural network, using a multimodal ensemble approach based on CT imaging data before immunotherapy, was trained in the training cohort to predict survival, and an internal validation cohort was constructed to select the optimal ensemble model. Data from another cohort were used for external validation. The area under the receiver operating characteristic curve was analyzed to evaluate performance in predicting survival. Detailed clinicopathological data and peripheral blood prior to immunotherapy were collected for each patient. Univariate and multivariable logistic regression analysis of imaging models and clinicopathological variables was also applied to identify the independent predictors of survival. A nomogram based on multivariable logistic regression was constructed.

A total of 79 GC patients in the training cohort and 97 patients in the external validation cohort were enrolled in this study. A multi-model ensemble approach was applied to train a model to predict the 1-year survival of GC patients. Compared to individual models, the ensemble model showed improvement in performance metrics in both the internal and external validation cohorts. There was a significant difference in overall survival (OS) among patients with different imaging models based on the optimum cutoff score of 0.5 (HR = 0.20, 95 % CI: 0.10-0.37, P < 0.001). Multivariate Cox regression analysis revealed that the imaging models, PD-L1 expression, and lung immune prognostic index were independent prognostic factors for OS. We combined these variables and built a nomogram. The calibration curves showed that the C-index of the nomogram was 0.85 and 0.78 in the training and validation cohorts.

The deep learning model in combination with several clinical factors showed predictive value for survival in patients with unresectable GC receiving immunotherapy.

Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments.

Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients.

The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer.

Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu.

IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

Epstein-Barr virus (EBV)-associated gastric cancers (EBVaGCs) account for about 10% of gastric cancers globally, with higher prevalence in East Asia and Latin America. These cancers develop through a "gastritis-infection-cancer sequence" and are characterized by unique molecular signatures, including CpG island methylator phenotype and mutations in ARID1A and PIK3CA genes. EBVaGCs typically present in the proximal stomach with diffuse-type histology and dense lymphocytic infiltration. Key viral proteins EBNA1 and LMP2A drive oncogenesis by altering cellular processes and immune responses. The IFN-γ signature and extensive epigenetic modifications contribute to their distinct profile. Despite often presenting at advanced stages, EBVaGCs generally have a more favorable prognosis. EBV employs sophisticated strategies to evade immune detection, utilizing latent proteins and noncoding RNAs. Paradoxically, despite an immune-hot environment, EBVaGCs demonstrate effective immune evasion, partly due to the expression of immune checkpoint molecules like PD-L1 and LAG3. Treatment approaches vary based on disease stage, from endoscopic resection for early-stage cancers to systemic therapies for advanced cases. Immunotherapy, particularly PD-1/PD-L1 inhibitors, shows promising results. Emerging research suggests combining these with LAG3 inhibitors may enhance efficacy. Ongoing research and advanced genomic techniques continue to reveal new insights, paving the way for personalized therapies and novel diagnostic approaches.

Chaperonin containing TCP1 subunit 5 (CCT5), a vital component of the molecular chaperonin complex, has been implicated in tumorigenesis, cancer stemness maintenance, and therapeutic resistance. Nevertheless, its comprehensive roles in pan-cancer progression, underlying biological functions, and potential as a predictor of immunotherapy response remains poorly understood.

We performed a comprehensive multi-omics pan-cancer analysis of CCT5 across 33 cancer types, integrating bulk RNA-seq, single-cell RNA-seq (scRNA-seq), and spatial transcriptomics data. CCT5 expression patterns, prognostic relevance, stemness association, and immune microenvironment relationships were evaluated. A novel CCT5-based signature (CCT5.Sig) was developed using machine learning on 23 immune checkpoint blockade (ICB) cohorts (n = 1394) spanning eight cancer types. Model performance was assessed using AUC metrics and survival analyses.

CCT5 was significantly overexpressed in tumor tissues and primarily localized to malignant and cycling cells. High CCT5 expression correlated with poor prognosis in multiple cancers and was enriched in oncogenic, cell cycle, and DNA damage repair pathways. CCT5 expression was positively associated with mRNAsi, mDNAsi, and CytoTRACE scores, indicating a role in stemness maintenance. Furthermore, CCT5-high tumors exhibited immune-cold phenotypes, with reduced TILs and CD8⁺ T cell activity. The CCT5.Sig model, based on genes co-expressed with CCT5, achieved superior predictive accuracy for ICB response (AUC = 0.82 in validation and 0.76 in independent testing), outperforming existing pan-cancer signatures.

This study reveals the multifaceted oncogenic roles of CCT5 and highlights its potential as a pan-cancer biomarker for prognosis and immunotherapy response. The machine learning-derived CCT5.Sig model provides a robust tool for patient stratification and may inform personalized immunotherapy strategies.

Gastric cancer (GC) remains a major global health burden. Despite the advancements in immunotherapy for patients with GC, the heterogeneity of GC limits response rates, especially in immune "cold" subtypes, including genomically stable and epithelial-mesenchymal transition GC. Common lymphatic endothelial and vascular endothelial receptor-1 (CLEVER-1), a newly recognized immune checkpoint molecule predominantly expressed on tumor-associated macrophages (TAMs), remains poorly understood in GC. This study aims to explore the clinical significance of CLEVER-1+TAM infiltration, elucidate its role in modulating the tumor immune landscape, and investigate the therapeutic potential of CLEVER-1 blockade in enhancing immunotherapy.

This study analyzed two independent GC cohorts and single-cell RNA sequencing data (GSE183904). CLEVER-1 expression in TAMs was assessed via multiplex immunofluorescence, flow cytometry, and RNA sequencing. The clinical relevance of CLEVER-1+TAM infiltration was evaluated in relation to tumor, node, metastases staging, molecular subtypes, prognosis, and immunochemotherapy response. Transcriptomic and pathway analyses characterized the immunophenotype of CLEVER-1+TAMs. Functional assays examined their suppression on CD8+T cells, while interventional experiments assessed the impact of CLEVER-1 blockade alone or with programmed cell death protein-1 (PD-1) inhibition.

CLEVER-1 was predominantly expressed on TAMs in GC and was associated with worse clinical outcomes. Transcriptomic and phenotypic analyses revealed that CLEVER-1+TAMs display a dynamic immunophenotype and critically suppress T-cell function, fostering an immunosuppressive microenvironment. High CLEVER-1+TAM infiltration was linked to poor response or adaptive resistance to PD-1 blockade therapy. CLEVER-1 blockade reprogrammed TAMs toward a pro-inflammatory phenotype, resulting in enhanced CD8+T cell cytotoxicity and proliferation. Co-targeting CLEVER-1 and PD-1 synergistically enhanced antitumor responses.

High infiltration of CLEVER-1+TAMs indicates immune suppression and poor prognosis in GC. The combination of CLEVER-1 and PD-1 blockade emerges as a dual-pronged strategy to boost immune-mediated tumor control and prevent treatment relapse in GC.

Gastric cancer (GC) with distant metastases has a poor prognosis, and immune checkpoint inhibitors (ICIs) effectively improve the survival time of patients with this disease. This study aimed to identify effective prognostic markers that can predict the treatment effect of ICIs in patients with stage IV GC.

This study included 256 patients with GC with distant metastases who had received treatment with ICIs. A receiver operating characteristic (ROC) curve was used to analyze the predictive ability and optimal cutoff values of immune-inflammatory markers. Kaplan‒Meier survival curves were used to analyze the differences in progression-free survival (PFS) and overall survival (OS) among patients. Cox proportional hazard regression analysis was used to identify independent prognostic factors for PFS and OS.

By comparing the area under the ROC curve (AUC) of immune-inflammatory markers, we selected the preoperative platelet count/(lymphocyte count × prealbumin count) ratio and fibrinogen/albumin ratio to form a combined score (PLPR-FAR score). The ROC curve revealed that when the PLPR-FAR score was used to predict patient PFS and OS, the AUC were 0.614 and 0.672, respectively. The Kaplan‒Meier survival curve revealed that patients with higher PLPR-FAR scores had significantly shorter PFS and OS than those with lower PLPR-FAR scores. Cox proportional hazard regression analysis revealed that the PLPR-FAR score was an independent risk factor for PFS and OS in stage IV GC patients.

The PLPR-FAR score may help identify which patients are more likely to benefit from ICIs treatment, and could serve as a novel and promising prognostic biomarker.

This multicenter phase Ib/II trial aimed to evaluate the safety and efficacy of combining durvalumab, tremelimumab, and paclitaxel as second-line treatment for biomarker-selected patients with metastatic gastric cancer.

In phase Ib, the standard 3 + 3 dose escalation method was used. Durvalumab and tremelimumab were administered every 4 weeks for 13 and 4 cycles, respectively, combining paclitaxel 80 mg/m2 (dose level 2) or 60 mg/m2 (dose level 1) on days 1, 8, and 15. The primary outcome for phase II was the objective response rate (ORR).

In phase Ib (n = 7), dose level-1 was selected as the recommended phase II dose. In phase II, 48 patients were enrolled: microsatellite instability-high or deficient mismatch repair protein tumors (n = 16); EBV-positive tumors (n = 15); high tumor mutation burden ( ≥ 5/Mb) (n = 11); CD274 amplification (n = 5); and POLD1 mutation (n = 1). The ORR was 52.1%, meeting the primary endpoint. The median progression-free survival and overall survival were 5.3 and 13.1 months, respectively. The most common any-grade and grade 3-4 adverse events were anemia (41.7%) and neutropenia (10.4%), respectively.

Durvalumab-tremelimumab with paclitaxel was tolerable and efficacious in biomarker-selected gastric cancer patients as a second-line treatment, highlighting the importance of biomarker-based approaches for immunotherapy in gastric cancer.

NCT03751761.

The tumor microenvironment (TME) plays a crucial role in the occurrence and progression of gastric cancer. Yet, we still don't understand how immune and stromal components of TMEs are modulated. In this study, we applied the ESTIMATE algorithm to calculate the number of immune and stromal components in 410 STAD cases in the Cancer Genome Atlas (TCGA) database. COX regression analysis and protein-protein interaction (PPI) network construction were used to analyze differentially expressed genes (DEGs). Then, P-selectin (SELP) was identified as a predictor by cross-analysis of univariate COX and PPI. After verifying the clinical significance of SELP for study, we performed an immune infiltration analysis and identified 54 immunomodulators associated with SELP through public data. Immunomodulation associated with gastric cancer prognosis was then confirmed by LASSO regression, and the previous results were further validated with single-cell data. Finally, we verified that SELP can promote EMT on gastric cancer cells. In conclusion, we validated that SELP may affect the biological phenotype of gastric cancer with the immune microenvironment alteration of gastric cancer.

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN+ iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN+ iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN+ iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN+ iCAFs and CD8+ Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN+ iCAFs and CD8+ exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8+ T cells, downregulation of GSN on CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

Immune checkpoint inhibitors (ICIs) are becoming more prominent in the treatment of gastric cancer (GC). However, predictive biomarkers of response to ICIs in HER2-negative patients remain incompletely understood.

A total of 47 patients diagnosed with HER2-negative advanced GC who underwent ICI regimens were eligible for this study. Plasma samples with paired white blood cells prior to treatments were collected from these 47 patients. Variations of circulating tumor DNA (ctDNA) was evaluated by next-generation sequencing followed by its significance analysis.

A total of 658 somatic mutations involving 203 genes were identified in all ctDNA. Mutations in MEN1, MLH1, CEBPA, ATR, GNAQ, and FOXL2 genes were more frequent in responders (P < 0.05). Compared with wild-type patients, patients with CEBPA or IRS2 mutations had prolonged median progression-free survival (mPFS, P = 0.0056). Patients with co-occurring mutations in IRS2/CEBPA, IRS2/POLD1, TP53/PIK3CA, or POLD1/CEBPA had longer mPFS compared with others (P = 0.003; 0.006; 0.0166; 0.0315; respectively). Both alteration of CDKN2A alone and co-mutations with MSH6 were significantly associated with superior overall survival (OS, P = 0.0289; 0.0355; respectively). In addition, higher on-treatment ctDNA concentration or variant allele frequency (VAF) were associated with poorer response (P < 0.05). Additionally, the increased molecular alterations of POLE, FGFR2 and MDC1 seemed to indicate the acquired resistance to ICIs.

Variation signatures captured by ctDNA as well as the kinetics of ctDNA could predict the clinical benefits of ICB in HER2-negative GC patients, which was worth further validated in large cohort.

The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (MSI), Epstein-Barr virus (EBV)-associated, genomically stable (GS), and chromosomally instable (CIN). However, an association between molecular subtypes and clinical outcomes has not been clearly demonstrated. Given few actionable biomarkers, we investigated the clinical relevance of TCGA classification system.

We identified all patients with esophagogastric adenocarcinoma whose tumors underwent prospective next-generation sequencing using the Memorial Sloan Kettering-IMPACT assay from 2014 to 2023. We classified all tumors in accordance with TCGA methodology and correlated molecular subtypes with high-quality clinicopathologic data.

Among 1,438 included patients, 941 had CIN, 344 had GS, 103 had MSI, and 50 had EBV tumors. Accounting for the clinical stage and tumor grade, molecular classification was independently associated with overall cancer-specific survival (P < 0.001) on Cox multivariable analysis. Furthermore, genomic signatures, patient demographics, pathologic responses to neoadjuvant therapy, patterns of recurrence, and metastatic organotropism differed significantly by molecular subtype. Although most distal esophageal and gastroesophageal junction tumors were CIN, up to 25% of these included GS, MSI, or EBV subtypes in contrast to TCGA. Random forest machine learning demonstrated that the molecular subtype is more influential in predicting response to treatment than tumor location.

Molecular classification is independently prognostic and may warrant inclusion in future staging and treatment guidelines. Routine molecular profiling is clinically feasible and may play a role in the management of patients to help guide appropriate treatment selection and clinical trial enrollment in the place of tumor location.

The year 2024 marks the 60th anniversary of the discovery of the Epstein-Barr virus (EBV), the first virus confirmed to cause human cancer. Viral infections significantly contribute to the global cancer burden, with seven known Group 1 oncogenic viruses, including hepatitis B virus (HBV), human papillomavirus (HPV), EBV, Kaposi sarcoma-associated herpesvirus (KSHV), hepatitis C virus (HCV), human T-cell leukemia virus type 1 (HTLV-1), and human immunodeficiency virus (HIV). These oncogenic viruses induce cellular transformation and cancer development by altering various biological processes within host cells, particularly under immunosuppression or co-carcinogenic exposures. These viruses are primarily associated with hepatocellular carcinoma, gastric cancer, cervical cancer, nasopharyngeal carcinoma, Kaposi sarcoma, lymphoma, and adult T-cell leukemia/lymphoma. Understanding the mechanisms of viral oncogenesis is crucial for identifying and characterizing the early biological processes of virus-related cancers, providing new targets and strategies for treatment or prevention. This review first outlines the global epidemiology of virus-related tumors, milestone events in research, and the process by which oncogenic viruses infect target cells. It then focuses on the molecular mechanisms by which these viruses induce tumors directly or indirectly, including the regulation of oncogenes or tumor suppressor genes, induction of genomic instability, disruption of regular life cycle of cells, immune suppression, chronic inflammation, and inducing angiogenesis. Finally, current therapeutic strategies for virus-related tumors and recent advances in preclinical and clinical research are discussed.

Gastric cancer (GC) is a malignant tumor with one of the highest morbidity and death rates in the world. Neoadjuvant therapy, including neoadjuvant chemotherapy (NAC) and NAC combined with immunotherapy, can improve the resection and long-term survival rates. However, not all patients respond well to neoadjuvant therapy. It has been confirmed that immune cells in the tumor immune microenvironment, including T cells, B cells, and natural killer cells, can affect the efficacy of neoadjuvant therapy. This paper summarizes current preclinical and clinical evidence to more fully describe the effects of neoadjuvant therapy on the immune microenvironment of GC, to provide the impetus to identify biomarkers to predict the potency of neoadjuvant therapy, and to identify the mechanisms of drug resistance, which should promote the development of individualized and accurate treatments for GC patients.

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical benefits in cancer treatment, but only a minority of patients exhibit favorable response, highlighting the importance of determining patients who will benefit from immunotherapy. Currently, patient datasets regarding immunotherapy response are scarce, while ample experiments can be performed on syngeneic mouse tumor models to generate valuable data. Therefore, how to effectively utilize mouse data to identify predictors of immunotherapy response and subsequently transfer relevant knowledge to predict human response to ICIs is a question worth studying. In this study, we propose a novel methodology to address this issue. Firstly, we identify gene modules associated with immunotherapy response from mouse tumor profiles based on cancer gene panels. Subsequently, these identified modules are employed to build prediction models for immunotherapy response based on mouse data. Furthermore, we transfer these models to predict ICIs responses of human cancer patients. Experimental results demonstrate that the gene modules identified from mouse data are reliable predictors of immunotherapy response. The mouse-based models built on these modules could be transferred to humans, effectively predicting drug responses and survival outcomes for cancer patients. Compared to conventional cancer biomarkers and existing prediction models based on mouse data, our method exhibits superior performance. These findings provide a valuable reference for further in-depth research on immunotherapy response prediction model based on mouse tumor profiles, with the potential for transfer applications in human cancer therapy.

PD-1/PD-L1 inhibitors have been used to treat gastric cancer, and PD-L1 expression has been identified as a biomarker for predicting the effectiveness of immunotherapy in the treatment of gastric cancer. However, PD-L1 expression prediction for immunotherapy response is inaccurate, and improved response biomarkers are required. Thus, it is important to identify additional biomarkers that can predict the responses to PD-1/PD-L1 monoclonal antibodies in gastric cancer. In this study, GO and KEGG enrichment analysis of 142 DEGs co-expressed with PD-L1 were performed, and 41 genes were identified based on the intersection of the mRNA-significant GO term network and the mRNA-significant signalling pathway network. Further intersection analysis of the 41 candidate genes and 137 positive immunotherapy response genes indicated that BATF2 significantly affects the overall survival of GC patients. The transcription factor prediction for BATF2 identified additional potential predictors and therapeutic targets for GC. STAT and IRF family members were predicted to be transcription factors for BATF2. In addition, BATF2 knockdown significantly promoted GC cell growth, and PD-L1 expression was upregulated in si-BATF2-treated MKN-45 cells. Thus, BATF2 may serve as a biomarker for predicting the efficacy of PD-L1 blockade therapy in GC. BATF2 acts as a tumour suppressor gene during the development of GC. BATF2 is closely related to PD-L1 expression in GC, and high BATF2 expression positively correlates with low PD-L1 expression. BATF2 can be used as a potential biomarker and therapeutic target for responding to anti-PD-1 and anti-PD-L1 immunotherapies in GC.

The traditional action of Rabdosia rubescens (Hemsl.) H. Hara is heat-clearing and detoxifying, relieve sore throat, dissipate binds and disperse swelling. DLC, as an extract prepared from Rabdosiae Rubescentis Herba, could regulate the polarization of tumor associated macrophages (TAMs). For TAMs play an important role in the tumor microenvironment. It is worthy to further explore the mechanism of DLC on the polarized function of macrophages.

The aim of this study is to investigate the activity and molecular mechanisms of DLC on dissipating binds and dispersing swelling by modulating the gastric cancer microenvironment and macrophage polarization.

We conducted comprehensive qualitative and quantitative chromatographic analyses to characterize the main components of DLC. To evaluate its anti-tumor effects, immunofluorescence, MTT assay, plate cloning, transcriptomics analysis, western blotting, and siRNA knockdown experiments were performed to assess DLC's action on gastric cancer cell proliferation. Additionally, we utilized Trypan blue staining, a THP-1 and MGC-803 co-culture model, flow cytometry, enzyme-linked immunosorbent assay (ELISA), and a mouse xenograft model with five distinct dosage groups to systematically investigate DLC's effects on macrophage polarization.

Key compounds in DLC were identified. The vivo tests demonstrated the tumor inhibition rate of the 5 g/kg DLC group reached 66.99 %, surpassing that of the 5-fluorouracil group (59.94 %). Mechanistically, DLC upregulated SIRT1 expression and suppressed NF-κB pathway, thereby preventing p65 from translocating into nuclear and modulating downstream p53/MDM2/USP7 signaling. Moreover, DLC enhanced M1 macrophage factors such as TNF-α, IL-6 while inhibiting M2 marker TGF-β, effectively repolarizing M2 TAMs toward an M1 phenotype. This effect was associated with suppressed protein expression of HIF-1α, p-p65, and p-PI3K.

This study provides insights into DLC's mechanisms in regulating tumor microenvironment remodeling and promoting macrophage polarization toward an anti-tumor phenotype. These results provide a solid basis for DLC's potential clinical treament in gastric cancer, highlighting its promise as a natural therapeutic agent.

Immune checkpoint inhibitors have become standard care across many cancers, but most patients do not respond. Predicting response remains challenging due to complex tumor-immune interactions and the poor generalizability of current biomarkers and models. Predictors such as tumor mutational burden, PD-L1 expression, and transcriptomic signatures often fail across cancer types, therapies, and clinical settings. There is a clear need for a robust, interpretable model that captures shared immune response principles and adapts to diverse clinical contexts. We present Compass, a foundation model for predicting immunotherapy response from pan-cancer transcriptomic data using a concept bottleneck architecture. Compass encodes tumor gene expression through 44 biologically grounded immune concepts representing immune cell states, tumor-microenvironment interactions, and signaling pathways. Trained on 10,184 tumors across 33 cancer types, Compass outperforms 22 baseline methods in 16 independent clinical cohorts spanning seven cancers and six immune checkpoint inhibitors, increasing precision by 8.5%, Matthews correlation coefficient by 12.3%, and area under the precision-recall curve by 15.7%, with minimal or no additional training. The model generalizes to unseen cancer types and treatments, supporting indication selection and patient stratification in early-phase clinical trials. Survival analysis shows that Compass-stratified responders have significantly longer overall survival (hazard ratio = 4.7, p < 0.0001). Personalized response maps link gene expression to immune concepts, revealing distinct mechanisms of response and resistance. For example, among immune-inflamed non-responders, Compass identifies distinct resistance programs involving TGF- β signaling, endothelial exclusion, CD4+ T cell dysfunction, and B cell deficiency. By combining mechanistic interpretability with transfer learning, Compass provides mechanistic insights into treatment response variability, supports clinical decision-making, and informs trial design.

Predicting immune checkpoint inhibitor (ICI) response remains a significant challenge in cancer immunotherapy. Many existing approaches rely on differential gene expression analysis or predefined immune signatures, which may fail to capture the complex regulatory mechanisms underlying immune response. Network-based models attempt to integrate biological interactions, but they often lack a quantitative framework to assess how individual genes contribute within pathways, limiting the specificity and interpretability of biomarkers. Given these limitations, we developed PathNetDRP, a framework that integrates biological pathways, protein-protein interaction networks, and machine learning to identify functionally relevant biomarkers for ICI response prediction.

We introduce PathNetDRP, a novel biomarker discovery approach that applies the PageRank algorithm to prioritize ICI-associated genes, maps them to relevant biological pathways, and calculates PathNetGene scores to quantify their contribution to immune response. Unlike conventional methods that focus solely on gene expression differences, PathNetDRP systematically incorporates biological context to improve biomarker selection. Validation across multiple independent cancer cohorts showed that PathNetDRP achieved strong predictive performance, with cross-validation the area under the receiver operating characteristic curves increasing from 0.780 to 0.940. Interestingly, PathNetDRP did not merely improve predictive accuracy; it also provided insights into key immune-related pathways, reinforcing its potential for identifying clinically relevant biomarkers.

The biomarkers identified by PathNetDRP demonstrated robust predictive performance across cross-validation and independent validation datasets, suggesting their potential utility in clinical applications. Furthermore, enrichment analysis highlighted key immune-related pathways, providing a deeper understanding of their role in ICI response regulation. While these findings underscore the promise of PathNetDRP, future work will explore the integration of additional predictive features, such as tumor mutational burden and microsatellite instability, to further refine its applicability.

Cancer has long been associated with genetic and environmental factors, but recent studies reveal the important role of gut microbiota in its initiation and progression. Around 13% of cancers are linked to infectious agents, highlighting the need to identify the specific microorganisms involved. Gut microbiota can either promote or inhibit cancer growth by influencing oncogenic signaling pathways and altering immune responses. Dysbiosis can lead to cancer, while certain probiotics and their metabolites may help reestablish micro-ecological balance and improve anti-tumor immune responses. Research into targeted approaches that enhance therapy with probiotics is promising. However, the effects of probiotics in humans are complex and not yet fully understood. Additionally, methods to counteract harmful bacteria are still in development. Early clinical trials also indicate that modifying gut microbiota may help manage side effects of cancer treatments. Ongoing research is crucial to understand better how gut microbiota can be used to improve cancer prevention and treatment outcomes.

SERPINE1 is a serine protease inhibitor upregulated in various malignancies and pivotal in gastric cancer (GC) metastasis and the tumor microenvironment (TME). This study elucidates the mechanisms by which SERPINE1 mediates anoikis resistance and fosters an immunosuppressive TME in advanced GC. SERPINE1 is highly expressed in GC tissues and metastatic lesions and serves as an independent risk factor for poor prognosis. The transcriptional activation of SERPINE1 by CEBPB triggers the PI3K/AKT and EMT signaling pathway via autocrine mechanisms, enhancing anoikis resistance and metastatic potential in GC cells. Furthermore, SERPINE1 facilitates M2 macrophage polarization by binding to lipoprotein receptor-related protein 1 (LRP1) in a paracrine manner, suppressing CD8+ T-cell infiltration and functionality in the TME. Therapeutic intervention combining SERPINE1 inhibition with PD-1 blockade exhibits synergistic antitumor effects. Clinically, high SERPINE1 expression is associated with an increased risk of recurrence following immune checkpoint inhibitor therapy in patients with advanced GC. These findings suggest that SERPINE1 is a critical driver of GC progression through anoikis resistance and TME remodeling. Hence, SERPINE1 can offer a promising therapeutic target and represent a predictive biomarker for immunotherapy outcomes in GC.

Circulating tumor DNA (ctDNA) has emerged as a promising biomarker with prognostic and potentially predictive value for several tumor types, including gastric cancer (GC). This study uses real-world data (RWD) to investigate the association of pretreatment ctDNA burden with clinical outcomes among patients with first-line (1L)-treated metastatic gastric cancer (mGC) in the United States.

Patients were identified from the GuardantINFORM real-world clinical-genomic database. Adult patients with mGC who underwent testing with the Guardant360 assay from June 2014 to March 2022 and within 60 days before 1L treatment were included. The median of the maximum variant allele fraction (MVAF) was used to classify patients into high or low ctDNA burden groups, with undetectable ctDNA burden included in the low group. Associations with real-world outcome variables derived from claims, including time to treatment discontinuation (rwTTD), time to next treatment (rwTTNT), and overall survival (rwOS), were assessed using log-rank tests and multivariable Cox models.

A cohort of 824 patients with mGC was identified. Median MVAF was 2.9%, with 91% having detectable ctDNA. Among patients receiving chemo-based treatment (n = 537), rwTTDs were similar in low and high ctDNA burden groups, while those with high ctDNA burden showed significantly shorter rwTTNT and rwOS (median rwTTNT = 4.8 months v 7.4 months, P < .001; median rwOS = 13.2 months v 19.1 months, P < .001). Multivariable Cox analyses showed similar results. ctDNA burden in immunotherapy (n = 100) and trastuzumab-based (n = 99) treatment groups did not have significant associations with outcomes.

We used RWD to demonstrate that high pretreatment ctDNA burden was associated with worse clinical outcomes in a mGC population receiving 1L chemotherapy-based treatments. Our analysis suggests ctDNA burden could be used as a prognostic biomarker for mGC.

Epstein-Barr virus-associated gastric cancer (EBVaGC) represents a distinct molecular subgroup with potential responsiveness to immunotherapy approved for programmed death-ligand 1 (PD-L1)-positive gastric cancer. This retrospective study aimed to assess the prevalence and association between EBVaGC and PD-L1 positivity among patients with gastric adenocarcinoma treated at a university hospital in Southern Thailand from January 2017 to October 2023.

The EBV status of the patients and PD-L1 expression were determined using in situ hybridization and immunohistochemistry, respectively.

The prevalence of EBVaGC was 4.5% among 132 patients, whereas 9.1% of patients exhibited a PD-L1 combined positive score (CPS) of ≥1, with no significant association observed between them. EBVaGC was more prevalent in males, non-antral tumors, diffuse/mixed histologic subtypes, and poorly differentiated tumors. Median overall survival for patients with EBVaGC and PD-L1 CPS ≥ 1 was 9.48 and 14.19 months, respectively, compared with 10.32 and 9.79 months for those with non-EBVaGC (hazard ratio: 1.24; 95% CI: 0.50-3.04; p = 0.645) and PD-L1 CPS < 1 (hazard ratio: 0.82; 95% CI: 0.40-1.69; p = 0.590), respectively.

Our findings revealed a low prevalence of EBVaGC and PD-L1 positivity in Thailand, with no significant association or survival impact observed. These findings highlight the regional variation in these biomarkers and support EBV as an independent biomarker from PD-L1. However, further research, particularly studies evaluating immunotherapy outcomes, is warranted to clarify the predictive and clinical significance of EBV in gastric cancer.

Epstein-Barr virus (EBV) positivity is a potential predictive biomarker for immune checkpoint inhibitors (ICIs) in gastric cancer patients, but its value in first-line ICI-based chemotherapy remains unclear. This study aimed to evaluate the predictive value of EBV positivity in patients treated with first-line nivolumab plus chemotherapy.

This single-center study included advanced gastric cancer patients treated with first-line nivolumab plus chemotherapy (n = 293). Patients with EBV positivity treated with chemotherapy alone (n = 12) served as the control group. EBV positivity was confirmed by in situ hybridization.

Among patients treated with nivolumab plus chemotherapy, 18 (6.1%) had EBV-positive tumors, and these were associated with high PD-L1 combined positive score (CPS) expression levels. Progression-free survival (PFS) and overall survival (OS) tended to be more favorable in those with EBV-positive tumors. Multivariate analysis of patients treated with nivolumab-chemotherapy revealed that EBV positivity, combined with PD-L1 CPS ≥ 5, was an independent factor for PFS. In patients with EBV-positive tumors, nivolumab-chemotherapy was associated with significantly favorable PFS and OS compared to chemotherapy alone. Similar results were observed in the subgroup with PD-L1 CPS ≥ 5. However, survival outcomes did not differ between patients treated with nivolumab plus chemotherapy versus chemotherapy alone in the subgroup with PD-L1 CPS < 5.

EBV positivity predicts favorable survival outcomes in patients with gastric cancer treated with nivolumab plus chemotherapy. The benefit of nivolumab plus chemotherapy over chemotherapy alone for patients with EBV positivity appears to be associated with high PD-L1 expression levels.

Programmed cell death (PCD) genes play crucial roles in cancer progression and response to therapies, yet their impact on gastric cancer remains inadequately elucidated. This study aimed to create a prognostic cell death signature (PCDs) for gastric cancer, providing insights into potential therapeutic targets and survival predictors.

We utilized TCGA-STAD and five GEO datasets, representing thousands of gastric cancer samples, for a comprehensive analysis of PCD genes. Differential gene expression, functional enrichment, survival, and machine learning analyses were conducted to construct a PCD-based prognostic model.

A total of 249 differentially expressed PCD genes were identified between cancerous and noncancerous gastric tissues. Subsequently, a PCD signature based on seven genes was developed and cross-validated across multiple cohorts. The high-PCD subtype correlated with poorer survival outcomes, lower tumor mutational burden, higher infiltration of M2 macrophages, lower levels of immune checkpoint expression, and decreased response to immunotherapy. A nomogram incorporating the PCDs provided accurate survival rate predictions. Additionally, nine machine learning algorithms were implemented for recurrence prediction, with the random forest model displaying high effectiveness. In this model, thrombospondin 1 (THBS1) showed the highest weight, and its knockdown significantly reduced gastric cancer cell proliferation and invasion.

This study underscores the significance of PCD genes, particularly THBS1, in gastric cancer progression and highlights their value as potential therapeutic targets. The predictive models developed here can aid in assessing patient prognosis and tailoring personalized treatment strategies.

Gastric cancer (GC) is a prevalent digestive system tumor, the fifth most diagnosed cancer worldwide, and a leading cause of cancer deaths. GC is distinguished by its pronounced heterogeneity and a dynamically evolving tumor microenvironment (TME). The lack of accurate disease models complicates the understanding of its mechanisms and impedes the discovery of novel drugs. A growing body of evidence suggests that GC organoids, developed using organoid culture technology, preserve the genetic, phenotypic, and behavioral characteristics. GC organoids hold significant potential for predicting treatment responses in individual patients. This review provides a comprehensive overview of the current clinical treatment strategies for GC, as well as the history, construction and clinical applications of organoids. The focus is on the role of organoids in simulating the TME to explore mechanisms of immune evasion and intratumoral microbiota in GC, as well as their applications in guiding clinical drug therapy and facilitating novel drug screening. Furthermore, we summarize the limitations of GC organoid models and underscore the need for continued technological advancements to benefit both basic and translational oncological research.

Alpha-fetoprotein-producing gastric carcinoma (AFPGC) is an aggressive subtype of gastric cancer characterized by a primitive cellular phenotype and poor prognosis. The tumor immunology of AFPGC remains largely unexplored. Given its embryonic-like properties, AFPGC is hypothesized to employ distinct immune evasion strategies, with the oncofetal protein human leukocyte antigen (HLA)-G-a key mediator of maternal-fetal immune tolerance-likely playing a pivotal role. To test this, we assessed the expression of HLA-G, along with other key immune evasion markers, including HLA class I (HLA-I) deficiency and PD-L1 expression, in 39 cases of AFPGC, and compared them with those of 44 Epstein-Barr virus (EBV)-positive, 57 microsatellite instability (MSI), 54 intestinal-type, and 45 diffuse-type gastric carcinomas. HLA-G expression was significantly higher in AFPGCs (71%) than in other subtypes (7-28%; P < 0.001). HLA-I deficiency (≥ 1% of tumor cells) was most prevalent in AFPGC (69%), followed by MSI tumors (56%), with lower rates in other subtypes (22-29%). PD-L1 positivity (combined positive score ≥ 5) was observed in 41% of AFPGCs, lower than in EBV-positive (77%) and MSI tumors (44%), but higher than in intestinal-type (13%) and diffuse-type (9%) carcinomas. Furthermore, CD8-positive T-cell infiltration was found to be lowest in AFPGC compared to the other subtypes. These findings suggest that AFPGC employs multiple immune evasion mechanisms, notably through increased HLA-G expression and HLA-I deficiency, likely linked to its primitive cellular phenotype and reactivation of immunogenic oncofetal antigens. Such immune evasion features may underlie the aggressiveness of AFPGC and present promising targets for immunotherapeutic interventions.

More than 90% of advanced gastric cancers (GC) are microsatellite-stable (MSS). Compared to the high response rate of immune checkpoint inhibitors (ICI) in microsatellite-instability-high (MSI-H) GCs, only 10% of unstratified MSS GCs respond to ICIs. In this study, we apply semi-supervised learning to stratify potential ICI responders in MSS GCs, achieving high accuracy, quantified by an area under the curve of 0.924. Spatial analysis of the tumor microenvironment of ICI-sensitive GCs reveals a high level of T-bet+ CD8 + T cell infiltration in their tumor compartments. T-bet+ CD8 + T cells exhibit superior anti-tumor activity due to their increased ability to infiltrate tumors and secrete cytotoxic molecules. Adoptive transfer of T-bet+ CD8 + T cells boosts anti-tumor immunity and confers susceptibility to ICIs in immune-ignorant MSS GCs in a humanized mouse model. Spatial RNA sequencing suggests a positive-feedback loop between T-bet+ T cells and PD-L1+ tumor cells, which eventually drives T cell exhaustion and can therefore be leveraged for ICI therapy. In summary, our research provides insights into the underlying mechanism of anti-tumor immunity and deepens our understanding of varied ICI responses in MSS GCs.

Multiple primary malignant tumors refer to the occurrence of two or more primary malignant tumors in the same organ or multiple organs or tissues at the same time or successively in the same patient, and can occur anywhere in the body. The treatment guidelines for patients with multiple primary malignant tumors are currently controversial.

A 51-year-old male patient with liver cancer and portal hypertension received 42 months of co-treatment with atezolizumab and bevacizumab. After that, the disease was rated stable disease. The patient was then diagnosed with gastric cancer. Since the patient was not sensitive to anti-programmed death ligand 1 immunosuppressive agents, a co-treatment with oxaliplatin, tegafur, apatinib, and cadonilimab was selected after multidisciplinary consultation and the patient's agreement. After four cycles of treatment, partial response and stable disease were observed in gastric and liver cancers, respectively. Surgical treatment was performed considering the high-risk factors of gastrointestinal bleeding in patients with gastroesophageal varices. Postoperative pathology showed that the Tumor Regression Grade was 1. Moreover, the genetic testing of postoperative tumor specimens indicated negative programmed death ligand 1 and microsatellite stability. In addition, the latest follow-up indicated an 8 and 40-month progression-free survival in gastric and liver cancer patients, respectively. Currently, the patient is receiving postoperative immunotherapy with cadonilimab.

Cadonilimab not only treats microsatellite stability gastric cancer patients but can also be used for liver cancer treatment.

Efficacy of 2nd line treatment in advanced gastric or gastro-esophageal junction (GEJ) adenocarcinoma remains limited with no identified strong predictor of treatment efficacy. We evaluated the prognostic value of circulating tumor DNA (ctDNA) in predicting the efficacy of immune checkpoint inhibitors (ICI) plus chemotherapy in the randomized PRODIGE 59-FFCD 1707-DURIGAST trial.

ctDNA was evaluated before treatment (baseline) and at 4 weeks (before the third cycle of treatment, C3) using droplet-digital PCR assays based on the detection of CpG methylation.

Progression-free survival (PFS) and overall survival (OS) were shorter in patients with a high (>1.1 ng/mL) versus low (<1.1 ng/mL) ctDNA concentration at baseline (2.3 vs. 5.8 months; HR = 2.19; 95% CI, 1.09-4.41; p = 0.03 and 4.5 vs. 12.9 months; HR = 2.73; 95% CI, 1.29-5.75; p < 0.01), respectively, after adjustment for identified prognostic variables. Patients with a ctDNA decrease ≤75% between baseline and C3 versus a ctDNA decrease >75% had a worse objective response rate (p = 0.007), shorter PFS (2.2 vs. 7.4 months, HR = 1.90; 95% CI, 1.03-3.51; p = 0.04) and OS (6.6 vs 16.0 months; HR = 2.18; 95% CI, 1.09-4.37; p = 0.03).

An early decrease in ctDNA concentration is a strong predictor of the therapeutic efficacy of ICI plus chemotherapy in advanced gastric/GEJ adenocarcinoma. Clinical Trial Information NCT03959293 (DURIGAST).

Microsatellite stable (MSS) gastric cancer (GC) is largely unresponsive to immunotherapy, presenting a persistent and formidable challenge in the field. Patients with advanced GC and Helicobacter pylori (H. pylori) infection have shown benefits from immunotherapy. However, it remains unreported whether neoadjuvant immunotherapy is beneficial for H. pylori-positive MSS GC patients.

This retrospective cohort study analyzed data from GC patients treated at three medical centers in China between January 1, 2014, and July 1, 2024. Patients with gastric adenocarcinoma or adenocarcinoma of the gastroesophageal junction underwent testing for H. pylori infection prior to receiving neoadjuvant therapy.

In this retrospective analysis, those positive for H. pylori had a higher objective response rate of 63.77% (95% CI, 51.98-74.11%) compared to 47.73% (95% CI, 39.39-56.19%) in H. pylori-negative patients. Pathological complete remission was higher in H. pylori-positive patients at 17.39% (95% CI, 10.24-27.98%) versus 15.91% (95% CI, 10.65-23.10%). Logistic regression analysis revealed a strong correlation between H. pylori positivity and increased objective remission rate (P = 0.031, OR = 1.928, 95% CI 1.06-3.51). In H. pylori-positive MSS GC patients receiving neoadjuvant immunotherapy pCR rates can reach 27.27% (95% CI, 15.07-44.21%), much higher than the 8.33% (95% CI, 2.87-21.82%) in neoadjuvant chemotherapy patients. Survival analysis showed a 3-year OS rate of 74.2% (95% CI, 56.75-86.30%) in the H. pylori-positive group and 64.3% (95% CI, 51.20-75.55%) in the H. pylori-negative group, and the hazard ratio (HR) of these two groups was 0.50 (95% CI, 0.28-0.87; P <.001). Multivariable analysis for OS further showed the survival benefit of H. pylori, with HRs of 0.51 (95% CI, 0.29-0.91; P = 0.02).

H. pylori infection has emerged as a favorable factor for neoadjuvant immunotherapy in MSS GC, underscoring the importance of considering H. pylori status in preoperative treatment strategies.

This study conducted a network meta-analysis to evaluate and rank the safety and efficacy of programmed cell death protein-1 (PD-1) inhibitors for patients with advanced gastric or gastroesophageal junction cancer (GC/GEJC).

A systematic search was conducted in PubMed, Embase, and Cochrane Library databases to compare the efficacy and safety of different treatment regimens, including overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and treatment-related adverse events (TRAEs) in patients with advanced GC/GEJC.

A total of six RCT studies were ultimately included in the analysis, involving 6,294 patients. Among them, 256 patients received PD-1 inhibitor monotherapy (pembrolizumab), 3,029 patients received a PD-1 inhibitor plus chemotherapy (1,047 with pembrolizumab, 1,154 with nivolumab, 327 with sintilimab, and 501 with tislelizumab), and 3,009 received either chemotherapy or chemotherapy plus placebo. Sintilimab plus chemotherapy had the highest SUCRA value for OS (85.2%), while nivolumab plus chemotherapy had the highest SUCRA values for both PFS and ORR (96.8% and 82.9%). Four PD-1 inhibitors plus chemotherapy significantly improved median OS and ORR compared with chemotherapy. Sintilimab plus chemotherapy, pembrolizumab plus chemotherapy, and nivolumab plus chemotherapy significantly improved median PFS compared with chemotherapy. For TRAEs of grade 3 or worse, pembrolizumab monotherapy had the highest SUCRA value. Tislelizumab plus chemotherapy, as well as sintilimab plus chemotherapy, did not increase the overall incidence of TRAEs and the incidence of grade 3 or worse TRAEs.

In the first-line treatment of advanced GC/GEJC, PD-1 inhibitors plus chemotherapy have been demonstrated to significantly improve OS, PFS, and ORR compared with chemotherapy. Among them, sintilimab plus chemotherapy achieved the highest SUCRA value for OS, and nivolumab plus chemotherapy achieved the highest SUCRA values for PFS and ORR. Regarding safety, tislelizumab plus chemotherapy and sintilimab plus chemotherapy did not increase the overall incidence of TRAEs and the incidence of grade 3 or worse TRAEs, with good tolerability and safety.

Synchronous multiple early gastric cancer (SMEGC) is a relatively uncommon variant of early gastric cancer (EGC). In this report, we present a case of SMEGC accompanied by a microsatellite instability-high (MSI-H) phenotype. The patient was a 69-year-old man who presented to our hospital with abdominal pain. The endoscopic examination revealed two lesions. Both lesions were pathologically confirmed as EGC, then the patient subsequently underwent endoscopic submucosal dissection (ESD). Nine months post-procedure, the patient returned with recurrent abdominal pain, leading to the diagnosis of a new EGC. Immunohistochemical analysis demonstrated that all lesions exhibited an MSI-H phenotype and BRAF mutant expression, suggesting that these lesions are not associated with Lynch syndrome-related EGC. The case was ultimately diagnosed as SMEGC with an MSI-H phenotype. The current evidence and clinical experience suggest that patients with advanced MSI-H are likely to benefit from immunotherapy and should be considered for early systemic treatment with immunotherapy as a central component. At present, research studies on the molecular characteristics of SMEGC are limited, underscoring the importance of conducting comprehensive molecular diagnostics of each EGC patient, which could help clinicians thoroughly understand the lesion's characteristics.

The immune composition of solid tumors is typically inferred from biomarkers, such as histologic and molecular classifications, somatic mutational burden, and PD-L1 expression. However, the extent to which these biomarkers predict the immune landscape in gastric adenocarcinoma-an aggressive cancer often linked to chronic inflammation-remains poorly understood. We leveraged high-dimensional spectral cytometry to generate a comprehensive single-cell immune landscape of tumors, normal tissue, and lymph nodes from patients in the Western Hemisphere with gastric adenocarcinoma. The immune composition of gastric tumors could not be predicted by traditional metrics such as tumor histology, molecular classification, mutational burden, or PD-L1 expression via IHC. Instead, our findings revealed that innate immune surveillance within tumors could be anticipated by the immune profile of the normal gastric mucosa. Additionally, distinct T-cell states in the lymph nodes were linked to the accumulation of activated and memory-like CD8+ tumor-infiltrating lymphocytes. Unbiased reclassification of patients based on tumor-specific immune infiltrate generated four distinct subtypes with varying immune compositions. Tumors with a T cell-dominant immune subtype, which spanned The Cancer Genome Atlas molecular subtypes, were exclusively associated with superior responses to immunotherapy. Parallel analysis of metastatic gastric cancer patients treated with immune checkpoint blockade showed that patients who responded to immunotherapy had a pretreatment tumor composition that corresponded to a T cell-dominant immune subtype from our analysis. Taken together, this work identifies key host-specific factors associated with intratumoral immune composition in gastric cancer and offers an immunological classification system that can effectively identify patients likely to benefit from immune-based therapies.

Targeting MALT1's paracaspase activity has been explored for B cell lymphoma and solid tumors. While the role of MALT1 in promoting cancer cell proliferation has been investigated, its involvement in immune evasion is unclear. Here we report that MALT1 promotes immune evasion through its paracaspase and death domain. In a paracaspase-dependent manner, MALT1 protects CD274 mRNA from degradation by its cleavage of ROQUIN1 and ROQUIN2. In a death-domain-dependent manner, MALT1 promotes the proliferation and polarization of tumor-associated macrophages to generate an immunosuppressive tumor microenvironment. Targeting MALT1 with antisense oligonucleotides inhibits PD-L1 expression in patient-derived tumor cells and suppresses the proliferation and M2-like polarization of tumor-associated macrophages isolated from patients with cancer. In preclinical models of solid tumors in female mice, treatment with MALT1 antisense oligonucleotides overcomes resistance to immune-checkpoint inhibitors. Together, our study demonstrates that targeting MALT1 is a potential strategy to overcome immune-checkpoint inhibitor resistance.

Gastric cancer (GC) is linked to high morbidity and mortality rates. Approximately two-thirds of GC patients are diagnosed at an advanced or metastatic stage. Conventional treatments for GC, including surgery, radiotherapy, and chemotherapy, offer limited prognostic improvement. Recently, immunotherapy has gained attention for its promising therapeutic effects in various tumors. Immunotherapy functions by activating and regulating the patient's immune cells to target and eliminate tumor cells, thereby reducing the tumor burden in the body. Among immunotherapies, immune checkpoint inhibitors (ICIs) are the most advanced. ICIs disrupt the inhibitory protein-small molecule (PD-L1, CTLA4, VISTA, TIM-3 and LAG3) interactions produced by immune cells, reactivating these cells to recognize and attack tumor cells. However, adverse reactions and resistance to ICIs hinder their further clinical and experimental development. Therefore, a comprehensive understanding of the advancements in ICIs for GC is crucial. This article discusses the latest developments in clinical trials of ICIs for GC and examines combination therapies involving ICIs (targeted therapy, chemotherapy, radiotherapy), alongside ongoing clinical trials. Additionally, the review investigates the tumor immune microenvironment and its role in non-responsiveness to ICIs, highlighting the function of tumor immune cells in ICI efficacy. Finally, the article explores the prospects and limitations of new immunotherapy-related technologies, such as tumor vaccines, nanotechnologies, and emerging therapeutic strategies, aiming to advance research into personalized and optimized immunotherapy for patients with locally advanced gastric cancer.