Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single-cell sequencing analysis.

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single-cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high-AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high-AIDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated AIDPS-associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, AIDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer.

Comprehensive multi-omics analysis of pyroptosis for optimizing neoadjuvant immunotherapy in patients with gastric cancer.

Background: Pyroptosis plays a crucial role in immune responses. However, the effects of pyroptosis on tumor microenvironment remodeling and immunotherapy in gastric cancer (GC) remain unclear. Patients and Methods: Large-sample GEO data (GSE15459, GSE54129, and GSE62254) were used to explore the immunoregulatory roles of pyroptosis. TCGA cohort was used to elucidate multiple molecular events associated with pyroptosis, and a pyroptosis risk score (PRS) was constructed. The prognostic performance of the PRS was validated using postoperative GC samples from three public databases (n=925) and four independent Chinese medical cohorts (n=978). Single-cell sequencing and multiplex immunofluorescence were used to elucidate the immune cell infiltration landscape associated with PRS. Patients with GC who received neoadjuvant immunotherapy (n=48) and those with GC who received neoadjuvant chemotherapy (n=49) were enrolled to explore the value of PRS in neoadjuvant immunotherapy. Results: GC pyroptosis participates in immune activation in the tumor microenvironment and plays a powerful role in immune regulation. PRS, composed of four pyroptosis-related differentially expressed genes (BATF2, PTPRJ, RGS1, and VCAN), is a reliable and independent biomarker for GC. PRSlow is associated with an activated pyroptosis pathway and greater infiltration of anti-tumor immune cells, including more effector and CD4+ T cells, and with the polarization of tumor-associated macrophages in the tumor center. Importantly, PRSlow marks the effectiveness of neoadjuvant immunotherapy and enables screening of GC patients with combined positive score ≥1 who benefit from neoadjuvant immunotherapy. Conclusion: Our study demonstrated that pyroptosis activates immune processes in the tumor microenvironment. A low PRS correlates with enhanced infiltration of anti-tumor immune cells at the tumor site, increased pyroptotic activity, and improved patient outcomes. The constructed PRS can be used as an effective quantitative tool for pyroptosis analysis to guide more effective immunotherapeutic strategies for patients with GC.

An On-Treatment Decreased Trend of Serum IL-6 and IL-8 as Predictive Markers Quickly Reflects Short-Term Efficacy of PD-1 Blockade Immunochemotherapy in Patients with Advanced Gastric Cancer.

Objective: Immunotherapy has proven effective in treating advanced gastric cancer (AGC), yet its benefits are limited to a subset of patients. Our aim is to swiftly identify prognostic biomarkers using cytokines to improve the precision of clinical guidance and decision-making for PD-1 inhibitor-based cancer immunotherapy in AGC. Materials and Methods: The retrospective study compared 36 patients with AGC who received combined anti-PD-1 immunotherapy and chemotherapy (immunochemotherapy) with a control group of 20 patients who received chemotherapy alone. The concentrations of TNF-α, IL-1ß, IL-2R, IL-6, IL-8, IL-10, and IL-17 in the serum were assessed using chemiluminescence immunoassay at three distinct time intervals following the commencement of immunochemotherapy. Results: When compared to controls, patients undergoing immunochemotherapy demonstrated a generalized rise in cytokine levels after the start of treatment. However, patients who benefited from immunochemotherapy showed a decrease in IL-6 or IL-8 concentrations throughout treatment (with varied trends observed for IL-1ß, IL-2R, IL-10, IL-17, and TNF-α) was evident in patients benefiting from immunochemotherapy but not in those who did not benefit. Among these markers, the combination of IL-6, IL-8, and CEA showed optimal predictive performance for short-term efficacy of immunochemotherapy in AGC patients. Conclusion: Reductions in IL-6/IL-8 levels observed during immunochemotherapy correlated with increased responsiveness to treatment effectiveness. These easily accessible blood-based biomarkers are predictive and rapid and may play a crucial role in identifying individuals likely to derive benefits from PD-1 blockade immunotherapy.

New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model.

OBJECTIVE: The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients. METHODS: Differentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights. RESULTS: High METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1. CONCLUSION: The random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.

Determinants of gastric cancer immune escape identified from non-coding immune-landscape quantitative trait loci.

The landscape of non-coding mutations in cancer progression and immune evasion is largely unexplored. Here, we identify transcrptome-wide somatic and germline 3' untranslated region (3'-UTR) variants from 375 gastric cancer patients from The Cancer Genome Atlas. By performing gene expression quantitative trait loci (eQTL) and immune landscape QTL (ilQTL) analysis, we discover 3'-UTR variants with cis effects on expression and immune landscape phenotypes, such as immune cell infiltration and T cell receptor diversity. Using a massively parallel reporter assay, we distinguish between causal and correlative effects of 3'-UTR eQTLs in immune-related genes. Our approach identifies numerous 3'-UTR eQTLs and ilQTLs, providing a unique resource for the identification of immunotherapeutic targets and biomarkers. A prioritized ilQTL variant signature predicts response to immunotherapy better than standard-of-care PD-L1 expression in independent patient cohorts, showcasing the untapped potential of non-coding mutations in cancer.

Unraveling the unfolded protein response signature: implications for tumor immune microenvironment heterogeneity and clinical prognosis in stomach cancer.

BACKGROUND: Stomach cancer is a leading cause of cancer-related deaths globally due to its high grade and poor response to treatment. Understanding the molecular network driving the rapid progression of stomach cancer is crucial for improving patient outcomes. METHODS: This study aimed to investigate the role of unfolded protein response (UPR) related genes in stomach cancer and their potential as prognostic biomarkers. RNA expression data and clinical follow-up information were obtained from the TCGA and GEO databases. An unsupervised clustering algorithm was used to identify UPR genomic subtypes in stomach cancer. Functional enrichment analysis, immune landscape analysis, and chemotherapy benefit prediction were conducted for each subtype. A prognostic model based on UPR-related genes was developed and validated using LASSO-Cox regression, and a multivariate nomogram was created. Key gene expression analyses in pan-cancer and in vitro experiments were performed to further investigate the role of the identified genes in cancer progression. RESULTS: A total of 375 stomach cancer patients were included in this study. Analysis of 113 UPR-related genes revealed their close functional correlation and significant enrichment in protein modification, transport, and RNA degradation pathways. Unsupervised clustering identified two molecular subtypes with significant differences in prognosis and gene expression profiles. Immune landscape analysis showed that UPR may influence the composition of the tumor immune microenvironment. Chemotherapy sensitivity analysis indicated that patients in the C2 molecular subtype were more responsive to chemotherapy compared to those in the C1 molecular subtype. A prognostic signature consisting of seven UPR-related genes was constructed and validated, and an independent prognostic nomogram was developed. The gene IGFBP1, which had the highest weight coefficient in the prognostic signature, was found to promote the malignant phenotype of stomach cancer cells, suggesting its potential as a therapeutic target. CONCLUSIONS: The study developed a UPR-related gene classifier and risk signature for predicting survival in stomach cancer, identifying IGFBP1 as a key factor promoting the disease's malignancy and a potential therapeutic target. IGFBP1's role in enhancing cancer cell adaptation to endoplasmic reticulum stress suggests its importance in stomach cancer prognosis and treatment.

GGT5: a potential immunotherapy response inhibitor in gastric cancer by modulating GSH metabolism and sustaining memory CD8+ T cell infiltration.

PURPOSE: The variable responses to immunotherapy observed in gastric cancer (GC) patients can be attributed to the intricate nature of the tumor microenvironment. Glutathione (GSH) metabolism significantly influences the initiation and progression of gastric cancer. Consequently, targeting GSH metabolism holds promise for improving the effectiveness of Immune checkpoints inhibitors (ICIs). METHODS: We investigated 16 genes related to GSH metabolism, sourced from the MSigDB database, using pan-cancer datasets from TCGA. The most representative prognosis-related gene was identified for further analysis. ScRNA-sequencing analysis was used to explore the tumor heterogeneity of GC, and the results were confirmed by  Multiplex immunohistochemistry (mIHC). RESULTS: Through DEGs, LASSO, univariate and multivariate Cox regression analyses, and survival analysis, we identified GGT5 as the hub gene in GSH metabolism with the potential to promote GC. Combining CIBERSORT, ssGSEA, and scRNA analysis, we constructed the immune architecture of GC. The subpopulations of T cells were isolated, revealing a strong association between GGT5 and memory CD8+ T cells. Furthermore, specimens from 10 GC patients receiving immunotherapy were collected. mIHC was used to assess the expression levels of GGT5 and memory CD8+ T cell markers. Our results established a positive correlation between GGT5 expression, the enrichment of memory CD8+ T cells, and a suboptimal response to immunotherapy. CONCLUSIONS: Our study identifies GGT5, a hub gene in GSH metabolism, as a potential therapeutic target for inhibiting the response to immunotherapy in GC patients. These findings offer new insights into strategies for optimizing immunotherapy of GC.

Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer.

BACKGROUND: Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI). METHODS: This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model's predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model's predictions. RESULTS: Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity. CONCLUSIONS: Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy.

Comprehensive analysis of T-cell regulatory factors and tumor immune microenvironment in stomach adenocarcinoma.

BACKGROUND: Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and is associated with high morbidity and mortality rates. However, the specific biomarkers used to predict the postoperative prognosis of patients with gastric cancer remain unknown. Recent research has shown that the tumor microenvironment (TME) has an increasingly positive effect on anti-tumor activity. This study aims to build signatures to study the effect of certain genes on gastric cancer. METHODS: Expression profiles of 37 T cell-related genes and their TME characteristics were comprehensively analyzed. A risk signature was constructed and validated based on the screened T cell-related genes, and the roles of hub genes in GC were experimentally validated. RESULTS: A novel T cell-related gene signature was constructed based on CD5, ABCA8, SERPINE2, ESM1, SERPINA5, and NMU. The high-risk group indicated lower overall survival (OS), poorer immune efficacy, and higher drug resistance, with SERPINE2 promoting GC cell proliferation, according to experiments. SERPINE2 and CXCL12 were significantly correlated, indicating poor OS via the Youjiang cohort. CONCLUSIONS: This study identified T cell-related genes in patients with stomach adenocarcinoma (STAD) for prognosis estimation and proposed potential immunotherapeutic targets for STAD.

CPT1C-positive cancer-associated fibroblast facilitates immunosuppression through promoting IL-6-induced M2-like phenotype of macrophage.

Cancer-associated fibroblasts (CAFs) exhibit remarkable phenotypic heterogeneity, with specific subsets implicated in immunosuppression in various malignancies. However, whether and how they attenuate anti-tumor immunity in gastric cancer (GC) remains elusive. CPT1C, a unique isoform of carnitine palmitoyltransferase pivotal in regulating fatty acid oxidation, is briefly indicated as a protumoral metabolic mediator in the tumor microenvironment (TME) of GC. In the present study, we initially identified specific subsets of fibroblasts exclusively overexpressing CPT1C, hereby termed them as CPT1C+CAFs. Subsequent findings indicated that CPT1C+CAFs fostered a stroma-enriched and immunosuppressive TME as they correlated with extracellular matrix-related molecular features and enrichment of both immunosuppressive subsets, especially M2-like macrophages, and multiple immune-related pathways. Next, we identified that CPT1C+CAFs promoted the M2-like phenotype of macrophage in vitro. Bioinformatic analyses unveiled the robust IL-6 signaling between CPT1C+CAFs and M2-like phenotype of macrophage and identified CPT1C+CAFs as the primary source of IL-6. Meanwhile, suppressing CPT1C expression in CAFs significantly decreased IL-6 secretion in vitro. Lastly, we demonstrated the association of CPT1C+CAFs with therapeutic resistance. Notably, GC patients with high CPT1C+CAFs infiltration responded poorly to immunotherapy in clinical cohort. Collectively, our data not only present the novel identification of CPT1C+CAFs as immunosuppressive subsets in TME of GC, but also reveal the underlying mechanism that CPT1C+CAFs impair tumor immunity by secreting IL-6 to induce the immunosuppressive M2-like phenotype of macrophage in GC.

Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Identification of TAP1 as a T-cell related therapeutic target in gastric cancer by mediating oxalipliatin-related synergistic enhancement of immunotherapy.

BACKGROUND: Given the intricate molecular complexities and heterogeneity inherent in T-cell immunotherapy of gastric cancer (GC), elucidative T-cell-related biomarkers were imperative needed for facilitating the prediction of GC patient prognosis and identify potential synergistic therapeutic targets. METHODS: We conducted COX regression analysis in TISIDB, TCGA-STAD, and GEO databases to identify 19 GC T-cell-mediated sensitivity tumor killing (TTK) genes (key GCTTKs). Based on key GCTTKs, we constructed two TTK patterns and analyzed their metabolic pathways, mutation features, clinical data distribution, immune cell infiltration, and prognosis. LASSO regression was performed to develop a T-cell-mediated GC Prognosis (TGCP) model. We validated the TGCP model in GC patients. TAP1 was further selected for investigation of its biological functions and molecular mechanisms. We assessed the potential of TAP1 as a promising therapeutic target for GC using Patient-derived organoids (PDOs)-derived xenografts (PDOXs) models of GC. RESULTS: The TTK patterns display notable disparities. The TGCP model showcases its proficiency in predicting immune response efficacy, effectively distinguishes immunotherapy difference GC patients. Our findings find further confirmation in PDOX models, affirming TAP1 can enhance immunotherapy facilitated by PDL1 inhibitors. Furthermore, Oxaliplatin, by promoting TAP1 expression, augments PDL1 expression, thereby enhancing the efficacy of immunotherapy. CONCLUSIONS: We constructed a TGCP model, which demonstrates satisfactory predictive accuracy. Out of 9 prognostic genes, TAP1 was validated as a synergistic target for Oxaliplatin and PDL1 inhibitors, offering a genetic-level explanation for the synergy observed in GC treatment involving Oxaliplatin in combination with PDL1 inhibitors.

Aldehyde Dehydrogenese-1 High Cancer Stem-like Cells/Cancer-initiating Cells Escape from Cytotoxic T Lymphocytes due to Lower Expression of Human Leukocyte Antigen Class 1.

BACKGROUND/AIM: Human gastric cancer stem-like cells (CSCs)/cancer-initiating cells can be identified as aldehyde dehydrogenase-high (ALDHhigh) cells. Cancer immunotherapy employing immune checkpoint blockade has been approved for advanced gastric cancer cases. However, the effectiveness of cancer immunotherapy against gastric CSCs/CICs remains unclear. This study aimed to investigate the susceptibility of gastric CSCs/CICs to immunotherapy. MATERIALS AND METHODS: Gastric CSCs/CICs were isolated as ALDHhigh cells using the human gastric cancer cell line, MKN-45. ALDHhigh clone cells and ALDHlow clone cells were isolated using the ALDEFLUOR assay. ALDH1A1 expression was assessed via qRT-PCR. Sphere-forming ability was evaluated to confirm the presence of CSCs/CICs. A model neoantigen, AP2S1, was over-expressed in ALDHhigh clone cells and ALDHlow clone cells, and susceptibility to AP2S1-specific TCR-T cells was assessed using IFNγ ELISPOT assay. RESULTS: Three ALDHhigh clone cells were isolated from MKN-45 cells. ALDHhigh clone cells exhibited a stable phenotype in in vitro culture for more than 2 months. The High-36 clone cells demonstrated the highest sphere-forming ability, whereas the Low-8 cells showed the lowest sphere-forming ability. High-36 cells exhibited lower expression of HLA-A24 compared to Low-8 cells. TCR-T cells specific for AP2S1 showed lower reactivity to High-36 cells compared to Low-8 cells. CONCLUSION: High-36 cells and Low-8 cells represent novel gastric CSCs/CICs and non-CSCs/CICs, respectively. ALDHhigh CSCs/CICs evade T cells due to lower expression of HLA class 1.

Somatic mutations that affect early genetic progression and immune microenvironment in gastric carcinoma.

Gastric carcinoma (GC) is a high heterogeneity and malignant tumor with a poor prognosis. The current implementation of immunotherapy in GC is limited due to the insufficient exploration of immune-related mutations and speculated early mutation events. Therefore, we performed whole-exome sequencing on 40 patients with GC to explore their genetic characteristics, shedding light on the order of genetic events, somatic mutations impacting the immune microenvironment, and potential biomarkers for immunotherapy. Regarding genetic events, TP53 disruptions were identified as frequent and early events in GC progression, often occurring alongside other gene mutations. The mutations occurring in GANS, SMAD4, and POLE were early independent events. Patients harboring CSMD3, FAT4, FLG, KMT2C, LRP1B, MUC5B, MUC16, PLEC, RNF43, SYNE1, TP53, TTN, XIRP2, and ZFHX4 mutations tended to have decreased B cells, T cells, macrophage, neutrophil, and dendritic cells infiltration, except for the ARID1A gene mutations. We also found patients with microsatellite instability-high tumors had higher homologous recombination deficiency (HRD) scores. HRD showed a positive correlation with tumor mutational burden, which might serve as indirect evidence supporting the potential of HRD as a biomarker for GC. These findings highlighted GC's high heterogeneity and complexity and provided valuable insights into the somatic mutations that affect early genetic progression and immune microenvironment.

Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.

BACKGROUND: Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms. METHODS: Gene expression data for gastric cancer were gathered from TCGA and GEO databases. DEGs associated with immunotherapy response came from ICBatlas. KEGG and GO analyses investigated pathways. Hub genes identification employed multiple machine algorithms. Associations between hub genes and signaling pathways, disease genes, immune cell infiltration, drug sensitivity, and prognostic predictions were explored via multi-omics analysis. Hub gene expression was validated through HPA and CCLE. Multiple algorithms pinpointed Cancer-Associated Fibroblasts genes (CAFs), with ten machine-learning methods generating CAFs scores for prognosis. Model gene expression was validated at the single-cell level using the TISCH database. RESULTS: We identified 201 upregulated and 935 downregulated DEGs. Three hub genes, namely CDH6, EGFLAM, and RASGRF2, were unveiled. These genes are implicated in diverse disease-related signaling pathways. Additionally, they exhibited significant correlations with disease-associated gene expression, immune cell infiltration, and drug sensitivity. Exploration of the HPA and CCLE databases exposed substantial expression variations across patients and cell lines for these genes. Subsequently, we identified CAFs-associated genes and established a robust prognostic model. The analysis in the TISCH database showed that the genes in this model were highly expressed in CAFs. CONCLUSIONS: The results unveil an association between CDH6, EGFLAM, and RASGRF2 and the immunotherapeutic response in gastric cancer. These genes hold potential as predictive biomarkers for gastric cancer immunotherapy resistance and prognostic assessment.

The tumor immune composition of mismatch repair deficient and Epstein-Barr virus-positive gastric cancer: A systematic review.

BACKGROUND: Gastric cancer (GC), known for its unfavorable prognosis, has been classified in four distinct molecular subtypes. These subtypes not only exhibit differences in their genome and transcriptome but also in the composition of their tumor immune microenvironment. The microsatellite instable (MSI) and Epstein-Barr virus (EBV) positive GC subtypes show clear clinical benefits from immune checkpoint blockade, likely due to a neoantigen-driven and virus-driven antitumor immune response and high expression of immune checkpoint molecule PD-L1. However, even within these subtypes response to checkpoint inhibition is variable, which is potentially related to heterogeneity in the tumor immune microenvironment (TIME) and expression of co-inhibitory molecules. We conducted a systematic review to outline the current knowledge about the immunological features on the TIME of MSI and EBV + GCs. METHODS: A systematic search was performed in PubMed, EMBASE and Cochrane Library. All articles from the year 1990 and onwards addressing immune features of gastric adenocarcinoma were reviewed and included based on predefined in- and exclusion criteria. RESULTS: In total 5962 records were screened, of which 139 were included that reported immunological data on molecular GC subtypes. MSI and EBV + GCs were reported to have a more inflamed TIME compared to non-MSI and EBV- GC subtypes. Compared to microsatellite stable (MSS) tumors, MSI tumors were characterized by higher numbers of CD8 + and FoxP3 + T cells, and tumor infiltrating pro- and anti-inflammatory macrophages. HLA-deficiency was most common in MSI tumors compared to other molecular GC subtypes and associated with lower T and B cell infiltrates compared to HLA-proficient tumors. EBV + was associated with a high number of CD8 + T cells, Tregs, NK cells and macrophages. Expression of PD-L1, CTLA-4, Granzyme A and B, Perforin and interferon-gamma was enriched in EBV + tumors. Overall, MSI tumors harbored a more heterogeneous TIME in terms of immune cell composition and immune checkpoints compared to the EBV + tumors. DISCUSSION AND CONCLUSION: MSI and EBV + GCs are highly Handbook for Conducting a Literature-Based Health Assessment Using OHAT Approach for Systematic Review and Evidence Integration.; 2019pro-inflammatory immune cell populations. Although studies on the direct comparison of EBV + and MSI tumors are limited, EBV + tumors show less intra-subgroup heterogeneity compared to MSI tumors. More studies are needed to identify how Intra-subgroup heterogeneity impacts response to immunotherapy efficacy.

The Clinical Impact of the Prognostic Immune and Nutritional Index in Gastric Cancer Patients Who Received Curative Treatment.

BACKGROUND/AIM: The prognostic immune and nutritional index (PINI) was developed and reported as a promising prognostic factor. This study aimed to clarify the clinical impact of the PINI in gastric cancer (GC) patients who received curative treatment. PATIENTS AND METHODS: Patients who underwent curative resection for GC at Yokohama City University between 2005 and 2020 were selected based on their medical records. The PINI was calculated by dividing the serum albumin concentration (g/dl) by the serum monocyte concentration. Both measurements were performed prior to surgery. RESULTS: A total of 262 patients were included in this study. Based on the 3- and 5-year overall survival (OS), we set the cutoff value of the PINI at 3.4 in the present study. The 3- and 5-year OS rates were 69.0% and 66.1%, respectively, in the PINI-low group and 90.5% and 82.8% in the PINI-high group. There were significant differences between the two groups (p<0.001). A multivariate analysis of factors associated with OS identified PINI as an independent prognostic factor (hazard ratio=1.996; 95% confidence interval=1.096-3.636, p=0.024). Similar results were observed for RFS. In addition, the PINI status affected the recurrence pattern, postoperative anastomotic leakage, and the introduction of postoperative adjuvant chemotherapy. CONCLUSION: The PINI is a promising nutritional and inflammatory marker for patients with GC and might be a useful marker for the treatment and management of patients with GC.

Análisis coste-efectividad de una vacuna para Helicobacter pylori en el sur de Europa / Cost-effectiveness analysis of a vaccine for Helicobacter pylori in southern Europe

Fundamentos: existen pruebas de la factibilidad de una vacuna para prevenir la infección por helicobacter pylori. Modelizacio-nes en entornos de baja prevalencia informan de una muy probable coste-efectividad a largo plazo. El objetivo de este estudio fue cuantificar su eficiencia en un contexto local.métodos: se simuló la evolución de una cohorte de nacidos a través de un modelo compartimental representativo de varios estados clínicos en relación a la infección por h. Pylori. Se ejecutó dicho modelo bajo las premisas de vacunación en el periodo de lactante y de no intervención. El horizonte temporal fue equivalente a la esperanza de vida y se tuvo en cuenta la perspectiva del sistema de salud.resultados: la vacunación frente a h. Pylori costaría de media 2.168 €/persona más que la no intervención. Con ello se obten-drían 0,32 años de vida ganados ajustados por calidad (avac), lo que implicaría una razón de coste-efectividad incremental (rcei) media de 7.196 €/avac. Para una disposición a pagar de 24.506 €/avac, el 99,96% de las simulaciones resultaron coste-efectivas al alcanzar el horizonte temporal y se cruzó dicho umbral a partir de los treinta años de la vacunación. Las variables que más peso tuvieron para explicar la variabilidad de la rcei fueron, en este orden, la efectividad vacunal, la incidencia de la infección en la primera infancia y el precio de la vacuna. La vacunación dejaría de ser coste-efectiva con un precio mayor de 3.634€/vial o con una cobertura poblacional efectiva menor del 11%.conclusiones: una vacunación frente a la infección por h. Pylori administrada en la infancia sería coste-efectiva a largo plazo en un entorno con las características epidemiológicas y económicas del sur de europa.(AU)

Background: there is sufficient evidence on the feasibility of a vaccine to prevent helicobacter pylori infection. Modeling studies in low prevalence environments report a very probable long-term cost-effectiveness. The objective of this study was to quantify its efficiency in a local context.methods: the evolution of a cohort of newborns was simulated through a compartmental model representing a series of clinical situations regarding h. Pylori infection and related diseases. The model was run under the assumption of both vaccination in the first year of life and no intervention. The time horizon was set as equivalent to the life expectancy and the perspective of the health system was taken into account.results: vaccination against h. Pylori would cost an average of €2,168/person more than no intervention. This would yield an average additional 0.32 quality-adjusted life years gained (qaly), which would entail an incremental cost-effectiveness ratio (icer) of €7,196/qaly. For a willingness to pay of €24,506/qaly, 99.96% of the simulations were cost-effective at eighty-four years old. This threshold was crossed thirty years after vaccination. The variables that carried the most weight in explaining the variability of the icer were, in this order, vaccine effectiveness, the incidence of infection in young children, and the price of the vaccine. Vaccination would cease to be cost-effective with a price greater than €3,634/dose or with effective population coverage less than 11%.(AU)

Low-dose radiotherapy synergizes with iRGD-antiCD3-modified T cells by facilitating T cell infiltration.

BACKGROUND AND PURPOSE: Poor penetration of transferred T cells represents a critical factor impeding the development of adoptive cell therapy in solid tumors. We demonstrated that iRGD-antiCD3 modification promoted both T cell infiltration and activation in our previous work. Interest in low-dose radiotherapy has recently been renewed due to its immuno-stimulatory effects including T cell recruitment. This study aims to explore the synergistic effects between low-dose radiotherapy and iRGD-antiCD3-modified T cells. MATERIALS AND METHODS: Flow cytometry was performed to assess the expression of iRGD receptors and chemokines. T cell infiltration was evaluated by immunohistofluorescence and in vivo real-time fluorescence imaging and antitumor effects were investigated by in vivo bioluminescence imaging in the gastric cancer peritoneal metastasis mouse model. RESULTS: We found that 2 Gy irradiation upregulated the expression of all three iRGD receptors and T-cell chemokines. The addition of 2 Gy low-dose irradiation boosted the accumulation and penetration of iRGD-antiCD3-modified T cells in peritoneal tumor nodules. Combining 2 Gy low-dose irradiation with iRGD-antiCD3-modified T cells significantly inhibited tumor growth and prolonged survival in the peritoneal metastasis mouse model with a favorable safety profile. CONCLUSION: Altogether, we demonstrated that low-dose radiotherapy could improve the antitumor potency of iRGD-antiCD3-modified T cells by promoting T cell infiltration, providing a rationale for exploring low-dose radiotherapy in combination of other adoptive T cell therapies in solid tumors.

Exploration of signature based on T cell-related genes in stomach adenocarcinoma by analysis of single cell sequencing data.

BACKGROUND: Gastric cancer (GC) is a leading reason for the death of cancer around the world. The immune microenvironment counts a great deal in immunotherapy of advanced tumors, in which T cells exert an indispensable function. METHODS: Single-cell RNA sequencing data were utilized to characterize the expression profile of T cells, followed by T cell-related genes (TCRGs) to construct signature and measure differences in survival time, enrichment pathways, somatic mutation status, immune status, and immunotherapy between groups. RESULTS: The complex tumor microenvironment was analyzed by scRNA-seq data of GC patients. We screened for these T cell signature expression genes and the TCRGs-based signature was successfully constructed and relied on the riskscore grouping. In gene set enrichment analysis, it was shown that pro-tumor and suppressive immune pathways were more abundant in the higher risk group. We also found different infiltration of immune cells in two groups, and that the higher risk samples had a poorer response to immunotherapy. CONCLUSION: Our study established a prognostic model, in which different groups had different prognosis, immune status, and enriched features. These results have provided additional insights into prognostic evaluation and the development of highly potent immunotherapies in GC.