Clinical Significance of Four Molecular Subtypes of Gastric Cancer Identified by The Cancer Genome Atlas Project

Perioperative chemotherapy strategies in diffuse gastric cancer

This study reviews the findings of a recent study by Li et al, which demonstrated that perioperative chemotherapy benefits patients with diffuse-type gastric cancer compared to surgery alone. Despite potential biases, the study supports the inclusion of perioperative chemotherapy in treatment guidelines. Neoadjuvant and adjuvant chemotherapy may also provide similar survival outcomes, allowing for flexible treatment planning.

Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.

Mutant Pattern of p53 as a Feasible Predictor of Distant Metastasis Following Curative Gastrectomy for Advanced-stage Gastric Cancer

Objective: The TP53 mutation is a poor prognostic factor for malignant tumors in a number of organs. The present study primarily aimed to clarify the impact of the mutant pattern of p53 on the prognosis and recurrence of gastric cancer. Methods: For this purpose, 519 patients who underwent radical gastrectomy for cancer were enrolled in the present study. Immunohistochemistry (IHC) was used to examine p53 expression in tissues and a three-stage classification system was used to divide the patient tissues into three groups according to the expression of p53: Heterogeneous (wild-type), absent and overexpression (mutant). Results: After 5 years of follow-up, recurrence and metastasis occurred in 38.7% of patients with stomach cancer, with a p53 mutant pattern in 48.4% of these patients. Patients with a p53 mutant pattern had lower recurrence-free and overall survival rates at 5 years compared with those who were p53 wild-type (P<0.001). It was found that the p53 pattern differed significantly (P<0.001) between the wild-type and mutant patterns, including the pN0 and pN+ gastric cancer subgroups (P<0.001 and P=0.014, respectively). The p53 mutant pattern was also significant in the determination of the recurrence-free survival of patients with progressive stomach cancer (P<0.0001). The 5-year overall survival rates were 71.7 and 36.2%, and the recurrence-free survival rates were 71.2 and 35.2% in the pN0 and pN+ groups, respectively (P<0.001). The mutant pattern of p53 was a significant prognostic factor for both distant metastasis [relative risk (RR)=2.881, P<0.001] and overall survival (RR=2.809, P<0.001) in the univariate Cox regression analysis. In the multivariate analysis, distant metastasis (RR=2.767, P<0.001) remained significant in the mutant pattern of p53 staining. After propensity score matching, 189 patients with a p53 wild-type and 189 patients with a p53 mutant pattern were extracted for analysis. The 5-year overall survival rate in patients with the p53 mutant pattern (n = 189) was worse than that in the patients with p53 wild-type (n = 189) and with significant differences (log-rank P<0.01). The study was statistically significant after Cox univariate and multivariate regression analysis, which revealed that the mutant pattern of p53 is an independent prognostic factor impacting distant metastases following curative gastrectomy for advanced-stage gastric cancer (p = 0.48).

Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer

Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored. This study applied multiomics approaches, integrating transcriptomic, epigenetic, and somatic mutation data, with consensus clustering algorithms to classify GC molecular subtypes and assess their biological and immunological features. A machine learning model was developed to create the Gastric Cancer Multi-Omics Programmed Cell Death Signature (GMPS), targeting PCD-related genes. We verified the expression of the GMPS hub genes using the RT-qPCR method. The prognostic influence of GMPS on GC was then evaluated. Single-cell analysis was performed to examine the heterogeneity of PCD characteristics in GC. Findings indicate that GMPS notably correlates with patient survival rates, tumor mutational burden (TMB), and copy number variations (CNV), demonstrating substantial prognostic predictive power. Moreover, GMPS is closely associated with the tumor microenvironment (TME) and immune therapy response. This research elucidates the molecular subtypes of GC, highlighting PCD's critical role in prognosis assessment. The relationship between GMPS and immune therapy response, alongside gastric cancer's microenvironmental features, provides insights for personalized treatment.

Identification of gastric cancer subtypes based on disulfidptosis-related genes: GPC3 as a novel biomarker for prognosis prediction

Gastric cancer (GC) is the fourth most common cancer type. "Disulfidptosis," a distinct form of cell death, is initiated through aberrant intracellular disulfide metabolism. Here, we identified various GC subtypes based on disulfidptosis-related genes (DRGs) and constructed a risk score model to identify relevant genes to help predict patient prognosis and guide treatment. We downloaded RNA sequencing (RNA-seq) data from the TCGA-STAD database, performed a difference analysis, and combined the data with GSE84437 to successfully perform an unsupervised clustering analysis based on DRGs and differentially expressed genes (DEGs). Risk-scoring models were established by screening prognosis-related DEGs. The GC samples were segregated into high-risk (HR) and low-risk (LR) groups according to their risk scores. We then evaluated the genes screened with the model in terms of prognosis, tumor, and immune cell infiltration. The response of patients with GC to immunological therapy was assessed using tumor mutational burden, microsatellite instability, and tumor immune dysfunction and exclusion scores. Using unsupervised cluster analysis, we identified two DRG clusters and two gene clusters that differed in prognosis and tumor microenvironment. A six-gene model was developed for risk score assessment. The LR group demonstrated superior performance compared to the HR group in terms of immunity, exhibiting greater sensitivity to immunotherapy. Thereafter, we selected the model gene GPC3 for single-gene analysis and verified it by experimental validation. The results demonstrated that GPC3 can serve as a standalone biomarker with promising clinical applicability in the prognostic prediction and clinical management of GC.

Predictive significance of MPT-driven necrosis-related genes signature in gastric cancer and their impact on the tumor microenvironment

BACKGROUND

Gastric cancer (GC) presents significant management challenges. MPT-driven necrosis (MPTDN) plays a significant role in various conditions, but its connection with GC is unclear. This study aimed to investigate the predictive significance of MPTDN-related genes (MPTDNRGs) in GC and their effect on the tumor immune microenvironment (TIME).

METHODS

RNA sequencing data for GC were sourced from TCGA and GEO databases. The mutation profiles and MPTDNRG expression between tumor and normal samples were assessed. Prognostic mRNAs were identified using univariate Cox regression and LASSO regression. GC patients were classified into high- and low-risk groups according to risk scores, followed by survival analysis and evaluation of correlations between MPTDN score and clinicopathological features, functional pathway, TIME, and responses to immunotherapy.

RESULTS

MPTDNRGs exhibited a 64% mutation rate in GC, with 22 showing significant expression differences. Univariate Cox and LASSO regression identified 15 independently prognostic MPTDNRGs. The prognostic risk model stratified patients into two groups, revealing significant differences in overall and disease-free survival. A nomogram incorporating the signature and clinical characteristics showed strong specificity and sensitivity in predicting prognosis. The MPTDN score was significantly associated with clinical characteristics, functional pathways, and TIME. scRNA-seq analysis indicated higher MPTDN-signature expression in CD8 + T cells, malignant cells, and myofibroblasts. TIDE analysis suggested high-risk patients have reduced responses to immunotherapy, while low-risk patients could benefit more. Importantly, validation using urothelial carcinoma data confirmed a better prognosis for low-risk patients with immunotherapy.

CONCLUSION

This study highlights the importance of MPTDN-related signatures in predicting GC prognosis and guiding therapeutic decisions.

Cancer molecular subtyping using limited multi-omics data with missingness

Diagnosing cancer subtypes is a prerequisite for precise treatment. Existing multi-omics data fusion-based diagnostic solutions build on the requisite of sufficient samples with complete multi-omics data, which is challenging to obtain in clinical applications. To address the bottleneck of collecting sufficient samples with complete data in clinical applications, we proposed a flexible integrative model (CancerSD) to diagnose cancer subtype using limited samples with incomplete multi-omics data. CancerSD designs contrastive learning tasks and masking-and-reconstruction tasks to reliably impute missing omics, and fuses available omics data with the imputed ones to accurately diagnose cancer subtypes. To address the issue of limited clinical samples, it introduces a category-level contrastive loss to extend the meta-learning framework, effectively transferring knowledge from external datasets to pretrain the diagnostic model. Experiments on benchmark datasets show that CancerSD not only gives accurate diagnosis, but also maintains a high authenticity and good interpretability. In addition, CancerSD identifies important molecular characteristics associated with cancer subtypes, and it defines the Integrated CancerSD Score that can serve as an independent predictive factor for patient prognosis.

GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer

Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier. The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets.

Diffuse Gastric Cancer: A Comprehensive Review of Molecular Features and Emerging Therapeutics

Diffuse-type gastric cancer (DGC) accounts for approximately one-third of gastric cancer diagnoses but is a more clinically aggressive disease with peritoneal metastases and inferior survival compared with intestinal-type gastric cancer (IGC). The understanding of the pathogenesis of DGC has been relatively limited until recently. Multiomic studies, particularly by The Cancer Genome Atlas, have better characterized gastric adenocarcinoma into molecular subtypes. DGC has unique molecular features, including alterations in CDH1, RHOA, and CLDN18-ARHGAP26 fusions. Preclinical models of DGC characterized by these molecular alterations have generated insight into mechanisms of pathogenesis and signaling pathway abnormalities. The currently approved therapies for treatment of gastric cancer generally provide less clinical benefit in patients with DGC. Based on recent phase II/III clinical trials, there is excitement surrounding Claudin 18.2-based and FGFR2b-directed therapies, which capitalize on unique biomarkers that are enriched in the DGC populations. There are numerous therapies targeting Claudin 18.2 and FGFR2b in various stages of preclinical and clinical development. Additionally, there have been preclinical advancements in exploiting unique therapeutic vulnerabilities in several models of DGC through targeting of the focal adhesion kinase (FAK) and Hippo pathways. These preclinical and clinical advancements represent a promising future for the treatment of DGC.

Development of a streamlined NGS-based TCGA classification scheme for gastric cancer and its implications for personalized therapy

Background

The Cancer Genome Atlas (TCGA) has identified four distinct molecular subtypes of gastric cancer (GC) with prognostic significance: Epstein-Barr virus (EBV)-positive, microsatellite instability (MSI)-high, genomically stable (GS), and chromosomal instability (CIN). Unfortunately, the complex analysis required for TCGA classification limits its practical use in clinical settings. Our study sought to devise a next-generation sequencing (NGS)-based method to classify GC more efficiently, serving as a promising biomarker for prognosis and immunotherapy efficacy.

Methods

This study was a retrospective observation study, and we employed 2 independent GC cohorts. The 3DMed cohort (n=765), comprising data on 733 cancer-related genes along with 4 EBV-encoded genes, was utilized to develop the new NGS classification. Additionally, the secondary Korean cohort (n=55), which includes both genomic data and information on immune checkpoint inhibitor (ICI) treatment, was employed to establish a correlation between NGS subtypes and ICI responsiveness.

Results

In the 3DMed cohort, we identified 5.2% EBV, 4.6% MSI, 30.6% GS, and 59.6% CIN subtypes. The MSI subtype exhibited the highest number of mutation events, along with the highest tumor mutational burden (TMB) and strong programmed cell death ligand 1 (PD-L1) expression. CIN tumors showed extensive copy number variations (CNVs) and genomic heterogeneity. The EBV subtype presented recurrent ARID1A and PIK3CA mutations and fewer TP53 mutations. GS tumors exhibited specific mutations in CDH1 and ARID1A. In the Korean cohort, ICIs were most effective in MSI and EBV cases, showing disease control rates of 100%, compared to 62.9% in GS and 12.5% in CIN subtypes.

Conclusions

The NGS method successfully maps the mutational landscape of GC, providing a practical TCGA classification surrogate to optimize patient-specific treatment strategies.

Aberrant DNA polymerase beta expression is associated with dysregulated tumor immune microenvironment and its prognostic value in gastric cancer

BACKGROUND

Gastric cancer is caused by different exogenous risk factors. Polymerase beta (POLB) is critical to repair oxidative and alkylating-induced DNA damage in genome maintenance. It is unknown whether overexpression of POLB genes in GC modulates tumor immunogenicity and plays a role in its prognostic value.

METHODS

RNA-Seq of GC data retrieved from TCGA and GEO database and patient survival were compared using Kaplan-Meier statistical test. The TIMER algorithm was used to calculate the abundance of tumor-infiltrating immune cells. Furthermore, ROC analysis was applied to evaluate the prognostic value of POLB overexpression.

RESULTS

Our data analysis of TCGA and GEO gastric cancer genomics datasets reveals that POLB overexpression is significantly associated with intestinal subtypes of stomach cancer. In addition, POLB overexpression is associated with low expression of innate immune signaling genes. In contrast, POLB-overexpressed tumor harbors high mutation frequency and MSI score. Furthermore, POLB-overexpressed tumor with high immune score exhibits a better prognosis. Interestingly, our ROC analysis results suggested that POLB overexpression has a potential for prognostic markers for stomach cancer.

CONCLUSIONS

Our analysis suggests that aberrant POLB overexpression in stomach cancer impacts the diverse aspects of tumor immune microenvironment. In addition, POLB might be a potential prognosis marker and/or an attractive target for immune-based therapy in GC. However, our observation still requires further experimental-based scientific validation studies.

Research advances in the molecular classification of gastric cancer

Gastric cancer (GC) is a malignant tumor with one of the lowest five-year survival rates. Traditional first-line treatment regimens, such as platinum drugs, have limited therapeutic efficacy in treating advanced GC and significant side effects, greatly reducing patient quality of life. In contrast, trastuzumab and other immune checkpoint inhibitors, such as nivolumab and pembrolizumab, have demonstrated consistent and reliable efficacy in treating GC. Here, we discuss the intrinsic characteristics of GC from a molecular perspective and provide a comprehensive review of classification and treatment advances in the disease. Finally, we suggest several strategies based on the intrinsic molecular characteristics of GC to aid in overcoming clinical challenges in the development of precision medicine and improve patient prognosis.

Genomic biomarkers to predict response to atezolizumab plus bevacizumab immunotherapy in hepatocellular carcinoma: Insights from the IMbrave150 trial

BACKGROUND/AIMS

Combination immunotherapy, exemplified by atezolizumab plus bevacizumab, has become the standard of care for inoperable hepatocellular carcinoma (HCC). However, the lack of predictive biomarkers and limited understanding of response mechanisms remain a challenge.

METHODS

Using data from the IMbrave150plus cohort, we applied an immune signature score (ISS) predictor to stratify HCC patients treated with atezolizumab plus bevacizumab or with sorafenib alone into potential high and low response groups. By applying multiple statistical approaches including a Bayesian covariate prediction algorithm, we refined the signature to 10 key genes (ISS10) for clinical use while maintaining similar predictive power to the full model. We further validated ISS10 in an independent HCC cohort treated with nivolumab plus ipilimumab.

RESULTS

The study identified a significant association between the ISS and treatment response. Among patients classified as high responders, those treated with the atezolizumab plus bevacizumab combination exhibited improved overall and progression-free survival as well as better objective response rate compared to those treated with sorafenib. We also observed a significant correlation between ISS10 and response to nivolumab plus ipilimumab treatment. Analysis of immune cell subpopulations revealed distinct characteristics associated with ISS subtypes. In particular, the ISS10 high subtype displayed a more favorable immune environment with higher proportions of antitumor macrophages and activated T-cells, potentially explaining its better response.

CONCLUSION

Our study suggests that ISS and ISS10 are promising predictive biomarkers for enhanced therapeutic outcomes in HCC patients undergoing combination immunotherapy. These markers are crucial for refining patient stratification and personalized treatment approaches to advance the effectiveness of standard-of-care regimens.

Cell Migration-Proliferation Dichotomy in Cancer: Biological Fact or Experimental Artefact?

The migration-proliferation dichotomy (MPD) has long been observed in cultured cancer cells. This phenomenon is not only relevant to tumour progression but may also have therapeutic significance in clinical cancer. However, MPD has rarely been investigated in primary cancer. This study aimed to either confirm or disprove the existence of MPD in primary cancer. Using primary gastric, colorectal and prostate cancer (GC, CRC and PCa) cohorts from the Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center, this study interrogated the MPD phenomenon by utilising RNA-Seq-based proliferation (CIN70 signature) and migration (epithelial-mesenchymal transition) indices, as well as gene set enrichment analyses (GSEA). Alternative hypothetical migration-proliferation models-The simultaneous migration-proliferation (SMP) and phenotype-refractory (PR) models-were compared to the MPD model by probing the migration-proliferation relationships within cancer stages and between early- and late-stage diseases using chi-square and independent T tests, z-score statistics and GSEA. The results revealed an inverse relationship between migration and proliferation signatures overall in the GC, CRC and PCa cohorts, as well as in early- and late-stage diseases. Additionally, a shift in proliferation- to migration dominance was observed from early- to late-stage diseases in the GC and CRC cohorts but not in the PCa cohorts, which showed enhanced proliferation dominance in metastatic tumours compared to primary cancers. The above features exhibited by the cancer cohorts are in keeping with the MPD model of the migration-proliferation relationship at the cellular level and exclude the SMP and PR migration-proliferation models.

Potent therapeutic strategy in gastric cancer with microsatellite instability-high and/or deficient mismatch repair

Gastric cancer (GC) is a common malignancy that presents challenges in patient care worldwide. The mismatch repair (MMR) system is a highly conserved DNA repair mechanism that protects genome integrity during replication. Deficient MMR (dMMR) results in an increased accumulation of genetic errors in microsatellite sequences, leading to the development of a microsatellite instability-high (MSI-H) phenotype. Most MSI-H/dMMR GCs arise sporadically, mainly due to MutL homolog 1 (MLH1) epigenetic silencing. Unlike microsatellite-stable (MSS)/proficient MMR (pMMR) GCs, MSI-H/dMMR GCs are relatively rare and represent a distinct subtype with genomic instability, a high somatic mutational burden, favorable immunogenicity, different responses to treatment, and prognosis. dMMR/MSI-H status is a robust predictive biomarker for treatment with immune checkpoint inhibitors (ICIs) due to high neoantigen load, prominent tumor-infiltrating lymphocytes, and programmed cell death ligand 1 (PD-L1) overexpression. However, a subset of MSI-H/dMMR GC patients does not benefit from immunotherapy, highlighting the need for further research into predictive biomarkers and resistance mechanisms. This review provides a comprehensive overview of the clinical, molecular, immunogenic, and therapeutic aspects of MSI-H/dMMR GC, with a focus on the impact of ICIs in immunotherapy and their potential as neoadjuvant therapies. Understanding the complexity and diversity of the molecular and immunological profiles of MSI-H/dMMR GC will drive the development of more effective therapeutic strategies and molecular targets for future precision medicine.

Construction and evaluation of a polygenic hazard score for prognostic assessment in localized gastric cancer

To investigate whether genetic variants may provide additional prognostic value to improve the existing clinical staging system for gastric cancer (GC), we performed two genome-wide association studies (GWASs) of GC survival in the Jiangsu (N = 1049) and Shanghai (N = 1405) cohorts. By using a TCGA dataset, we validated genetic markers identified from a meta-analysis of these two Chinese cohorts to determine GC survival-associated loci. Then, we constructed a weighted polygenic hazard score (PHS) and developed a nomogram in combination with clinical variables. We also evaluated prognostic accuracy with the time-dependent receiver operating characteristic (ROC) curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We identified a single nucleotide polymorphism (SNP) of rs1618332 at 15q15.1 that was associated with the survival of GC patients with a P value of 4.12 × 10-8, and we also found additional 25 SNPs having consistent associations among these two Chinese cohort and TCGA cohort. The PHS derived from these 26 SNPs (PHS-26) was an independent prognostic factor for GC survival (all P < 0.001). The 5-year AUC of PHS-26 was 0.68, 0.66 and 0.67 for Jiangsu, Shanghai and their pooled cohorts, respectively, which increased to 0.80, 0.82 and 0.81, correspondingly, after being integrated into a nomogram together with variables of the clinical model. The PHS-26 could improve the NRIs by 16.20%, 4.90% and 8.70%, respectively, and the IDIs by 11.90%, 8.00% and 9.70%, respectively. The 26-SNP based PHS could substantially improve the accuracy of prognostic assessment and might facilitate precision medicine for GC patients.

Unlocking the Potential: Epstein-Barr Virus (EBV) in Gastric Cancer and Future Treatment Prospects, a Literature Review

Gastric cancer (GC) is a complex disease with various etiologies. While Helicobacter pylori infection is still one of the leading risk factors for GC, increasing evidence suggests a link between GC and other infective agents such as Epstein Bar Virus (EBV). EBV-associated gastric cancer (EBVaGC) is now recognized as a distinct subgroup of GC, and the complex interactions between the virus and gastric mucosa may influence its development. A recent integrative analysis of the genome and proteome of GC tissues by The Cancer Genome Atlas project has identified EBVaGC as a specific subtype characterized by PIK3CA and ARID1A mutations, extensive DNA hyper-methylation, and activation of immune signaling pathways. These molecular characteristics are markers of the unique molecular profile of this subset of GC and are potential targets for therapy. This review aims to provide an overview of the current knowledge on EBVaGC. It will focus on the epidemiology, clinic-pathological features, and genetic characteristics of EBVaGC. Additionally, it will discuss recent data indicating the potential use of EBV infection as a predictive biomarker of response to chemotherapy and immune checkpoint inhibitors. The review also delves into potential therapeutic approaches for EBVaGC, including targeted therapies and adoptive immunotherapy, highlighting the promising potential of EBV as a therapeutic target.

Diffuse-Type Histology Is Prognostic for All Siewert Types of Gastroesophageal Adenocarcinoma

PURPOSE

The optimal treatment for gastroesophageal junction adenocarcinoma (GEJA) remains controversial. We evaluated the treatment patterns and outcomes of patients with locally advanced GEJA according to the histological type.

MATERIALS AND METHODS

We conducted a single-institution retrospective cohort study of patients with locally advanced GEJA who underwent curative-intent surgical resection between 2010 and 2020. Perioperative therapies as well as clinicopathologic, surgical, and survival data were collected. The results of endoscopy and histopathological examinations were assessed for Siewert and Lauren classifications.

RESULTS

Among the 58 patients included in this study, 44 (76%) were clinical stage III, and all received neoadjuvant therapy (72% chemoradiation, 41% chemotherapy, 14% both chemoradiation and chemotherapy). Tumor locations were evenly distributed by Siewert Classification (33% Siewert-I, 40% Siewert-II, and 28% Siewert-III). Esophagogastrectomy (EG) was performed for 47 (81%) patients and total gastrectomy (TG) for 11 (19%) patients. All TG patients received D2 lymphadenectomy compared to 10 (21%) EG patients. Histopathological examination showed the presence of 64% intestinal-type and 36% diffuse-type histology. The frequencies of diffuse-type histology were similar among Siewert groups (37% Siewert-I, 36% Siewert-II, and 33% Siewert-III). Regardless of Siewert type and compared to intestinal-type, diffuse histology was associated with increased intraabdominal recurrence rates (P=0.03) and decreased overall survival (hazard ratio, 2.33; P=0.02). With a median follow-up of 31.2 months, 29 (50%) patients had a recurrence, and the median overall survival was 50.5 months.

CONCLUSIONS

Present in equal proportions among Siewert types of esophageal and gastric cancer, a diffuse-type histology was associated with high intraabdominal recurrence rates and poor survival. Histopathological evaluation should be considered in addition to anatomic location in the determination of multimodal GEJA treatment strategies.

Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer

Gastric cancer (GC) is the fifth most common cancer worldwide and is a heterogeneous disease. Among GC subtypes, the mesenchymal phenotype (Mes-like) is more invasive than the epithelial phenotype (Epi-like). Although gene expression of the epithelial-to-mesenchymal transition (EMT) has been studied, the regulatory landscape shaping this process is not fully understood. Here we use ATAC-seq and RNA-seq data from a compendium of GC cell lines and primary tumors to detect drivers of regulatory state changes and their transcriptional responses. Using the ATAC-seq data, we developed a machine learning approach to determine the transcription factors (TFs) regulating the subtypes of GC. We identified TFs driving the mesenchymal (RUNX2, ZEB1, SNAI2, AP-1 dimer) and the epithelial (GATA4, GATA6, KLF5, HNF4A, FOXA2, GRHL2) states in GC. We identified DNA copy number alterations associated with dysregulation of these TFs, specifically deletion of GATA4 and amplification of MAPK9 Comparisons with bulk and single-cell RNA-seq data sets identified activation toward fibroblast-like epigenomic and expression signatures in Mes-like GC. The activation of this mesenchymal fibrotic program is associated with differentially accessible DNA cis-regulatory elements flanking upregulated mesenchymal genes. These findings establish a map of TF activity in GC and highlight the role of copy number driven alterations in shaping epigenomic regulatory programs as potential drivers of GC heterogeneity and progression.

Organoid models: the future companions of personalized drug development

High failure rates of the current drug development process are driving exemplary changes toward methodologies centered on human diseasein-vitromodeling. Organoids are self-organized tissue sub-units resembling their organ of origin and are widely acknowledged for their unique potential in recapitulating human physio-pathological mechanisms. They are transformative for human health by becoming the platform of choice to probe disease mechanisms and advance new therapies. Furthermore, the compounds' validation as therapeutics represents another point of the drug development pipeline where organoids may provide key understandings and help pharma organizations replace or reduce animal research. In this review, we focus on gastrointestinal organoid models, which are currently the most advanced organoid models in drug development. We focus on experimental validations of their value, and we propose avenues to enhance their use in drug discovery and development, as well as precision medicine and diagnostics.

Progress and prospects of biomarker-based targeted therapy and immune checkpoint inhibitors in advanced gastric cancer

Gastric cancer and gastroesophageal junction cancer represent the leading cause of tumor-related death worldwide. Although advances in immunotherapy and molecular targeted therapy have expanded treatment options, they have not significantly altered the prognosis for patients with unresectable or metastatic gastric cancer. A minority of patients, particularly those with PD-L1-positive, HER-2-positive, or MSI-high tumors, may benefit more from immune checkpoint inhibitors and/or HER-2-directed therapies in advanced stages. However, for those lacking specific targets and unique molecular features, conventional chemotherapy remains the only recommended effective and durable regimen. In this review, we summarize the roles of various signaling pathways and further investigate the available targets. Then, the current results of phase II/III clinical trials in advanced gastric cancer, along with the superiorities and limitations of the existing biomarkers, are specifically discussed. Finally, we will offer our insights in precision treatment pattern when encountering the substantial challenges.

Management of Microsatellite Instability High (MSI-H) Gastroesophageal Adenocarcinoma

BACKGROUND

Gastroesophageal cancer is a major cause of cancer-related mortality worldwide. Treatment of both early stage and advanced disease remains highly reliant on cytotoxic chemotherapy. About 4-24% of gastroesophageal cancers are microsatellite instability high (MSI-H). The MSI-H subtype is associated with favorable prognosis, resistance to cytotoxic chemotherapy, and sensitivity to immune checkpoint inhibitors (ICI). Recent studies have demonstrated promising activity of ICIs in the MSI-H subtype, resulting in fundamental changes in the management of MSI-H gastroesophageal adenocarcinoma.

PURPOSE

In this review, we discuss the prevalence, characteristics, prognosis, and management of MSI-H gastroesophageal adenocarcinoma, with a focus on recent and ongoing studies that have changed the landscape of treatment for the MSI-H subtype. We also discuss current challenges in the management of resectable and advanced MSI-H gastroesophageal cancer, including the need for more accurate biomarkers of response to ICI therapy.

Molecular classification and intratumoral heterogeneity of gastric adenocarcinoma

Gastric cancers frequently harbor striking histological complexity and diversity between lesions as well as within single lesions, known as inter- and intratumoral heterogeneity, respectively. The latest World Health Organization Classification of Tumors designated more than 30 histological subtypes for gastric epithelial tumors, assigning 12 subtypes for gastric adenocarcinoma (GAD). Meanwhile, recent advances in genome-wide analyses have provided molecular aspects to the histological classification of GAD, and consequently revealed different molecular traits underlying these histological subtypes. Moreover, accumulating knowledge of comprehensive molecular profiles has led to establishing molecular classifications of GAD, which are often associated with clinical biomarkers for therapeutics and prognosis. However, most of our knowledge of GAD molecular profiles is based on inter-tumoral heterogeneity, and the molecular profiles underlying intratumoral heterogeneity are yet to be determined. In this review, recently established molecular classifications of GAD are introduced in the aspect of pathological diagnosis and are discussed in the context of intratumoral heterogeneity.

Clinical features and molecular landscape of cuproptosis signature-related molecular subtype in gastric cancer

Recent studies have highlighted the biological significance of cuproptosis in disease occurrence and development. However, it remains unclear whether cuproptosis signaling also has potential impacts on tumor initiation and prognosis of gastric cancer (GC). In this study, 16 cuproptosis-related genes (CRGs) transcriptional profiles were harnessed to perform the regularized latent variable model-based clustering in GC. A cuproptosis signature risk scoring (CSRS) scheme, based on a weighted sum of principle components of the CRGs, was used to evaluate the prognosis and risk of individual tumors of GC. Four distinct cuproptosis signature-based clusters, characterized by differential expression patterns of CRGs, were identified among 1136 GC samples across three independent databases. The four clusters were also associated with different clinical outcomes and tumor immune contexture. Based on the CSRS, GC patients can be divided into CSRS-High and CSRS-Low subtypes. We found that DBT, MTF1, and ATP7A were significantly elevated in the CSRS-High subtype, while SLC31A1, GCSH, LIAS, DLAT, FDX1, DLD, and PDHA1 were increased in the CSRS-Low subtype. Patients with CSRS-Low score were characterized by prolonged survival time. Further analysis indicated that CSRS-Low score also correlated with greater tumor mutation burden (TMB) and higher mutation rates of significantly mutated genes (SMG) in GC. In addition, the CSRS-High subtype harbored more significantly amplified focal regions related to tumorigenesis (3q27.1, 12p12.1, 11q13.3, etc.) than the CSRS-Low tumors. Drug sensitivity analyses revealed the potential compounds for the treatment of gastric cancer with CSRS-High score, which were experimentally validated using GC cells. This study highlights that cuproptosis signature-based subtyping is significantly associated with different clinical features and molecular landscape of GC. Quantitative evaluation of the CSRS of individual tumors will strengthen our understanding of the occurrence and development of cuproptosis and the treatment progress of GC.

ARID1A Mutations in Gastric Cancer: A Review with Focus on Clinicopathological Features, Molecular Background and Diagnostic Interpretation

AT-rich interaction domain 1 (ARID1A) is a pivotal gene with a significant role in gastrointestinal tumors which encodes a protein referred to as BAF250a or SMARCF1, an integral component of the SWI/SNF (SWItch/sucrose non-fermentable) chromatin remodeling complex. This complex is instrumental in regulating gene expression by modifying the structure of chromatin to affect the accessibility of DNA. Mutations in ARID1A have been identified in various gastrointestinal cancers, including colorectal, gastric, and pancreatic cancers. These mutations have the potential to disrupt normal SWI/SNF complex function, resulting in aberrant gene expression and potentially contributing to the initiation and progression of these malignancies. ARID1A mutations are relatively common in gastric cancer, particularly in specific adenocarcinoma subtypes. Moreover, such mutations are more frequently observed in specific molecular subtypes, such as microsatellite stable (MSS) cancers and those with a diffuse histological subtype. Understanding the presence and implications of ARID1A mutations in GC is of paramount importance for tailoring personalized treatment strategies and assessing prognosis, particularly given their potential in predicting patient response to novel treatment strategies including immunotherapy, poly(ADP) ribose polymerase (PARP) inhibitors, mammalian target of rapamycin (mTOR) inhibitors, and enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) inhibitors.

Unraveling metabolic characteristics and clinical implications in gastric cancer through single-cell resolution analysis

Background: Gastric cancer is a highly prevalent malignant neoplasm. Metabolic reprogramming is intricately linked to both tumorigenesis and cancer immune evasion. The advent of single-cell RNA sequencing technology provides a novel perspective for evaluating cellular metabolism. This study aims to comprehensively investigate the metabolic pathways of various cell types in tumor and normal samples at high resolution and delve into the intricate regulatory mechanisms governing the metabolic activity of malignant cells in gastric cancer. Methods: Utilizing single-cell RNA sequencing data from gastric cancer, we constructed metabolic landscape maps for different cell types in tumor and normal samples. Employing unsupervised clustering, we categorized malignant cells in tumor samples into high and low metabolic subclusters and further explored the characteristics of these subclusters. Results: Our research findings indicate that epithelial cells in tumor samples exhibit significantly higher activity in most KEGG metabolic pathways compared to other cell types. Unsupervised clustering, based on the scores of metabolic pathways, classified malignant cells into high and low metabolic subclusters. In the high metabolic subcluster, it demonstrated the potential to induce a stronger immune response, correlating with a relatively favorable prognosis. In the low metabolic subcluster, a subset of cells resembling cancer stem cells (CSCs) was identified, and its prognosis was less favorable. Furthermore, a set of risk genes associated with this subcluster was discovered. Conclusion: This study reveals the intricate regulatory mechanisms governing the metabolic activity of malignant cells in gastric cancer, offering new perspectives for improving prognosis and treatment strategies.

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.

Regulation mechanism of EBV-encoded EBER1 and LMP2A on YAP1 and the impact of YAP1 on the EBV infection status in EBV-associated gastric carcinoma

This study aims to explore the role and regulatory mechanism of Yes-associated protein 1 (YAP1) in the development of Epstein-Barr virus-associated gastric cancer (EBVaGC). Here we showed that EBV can upregulate the expression and activity of YAP1 protein through its encoded latent products EBV-encoded small RNA 1 (EBER1) and latent membrane protein 2A (LMP2A), enhancing the malignant characteristics of EBVaGC cells. In addition, we also showed that overexpression of YAP1 induced the expression of EBV encoding latent and lytic phase genes and proteins in the epithelial cell line AGS-EBV infected with EBV, and increased the copy number of the EBV genome, while loss of YAP1 expression reduced the aforementioned indicators. Moreover, we found that YAP1 enhanced EBV lytic reactivation induced by two known activators, 12-O-tetradecanoylhorbol-13-acetate (TPA) and sodium butyrate (NaB). These results indicated a bidirectional regulatory mechanism between EBV and YAP1 proteins, providing new experimental evidence for further understanding the regulation of EBV infection patterns and carcinogenic mechanisms in gastric cancer.

Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer

PURPOSE

To investigate the value of intratumoral and peritumoral radiomics based on contrast-enhanced computer tomography (CECT) to preoperatively predict microsatellite instability (MSI) status in gastric cancer (GC) patients.

METHODS

A total of 189 GC patients, including 63 patients with MSI-high (MSI-H) and 126 patients with MSI-low/stable (MSI-L/S), were randomly divided into the training cohort and validation cohort. Intratumoral and 5-mm peritumoral regions' radiomics features were extracted from CECT images. The features were standardized by Z-score, and the Inter- and intraclass correlation coefficient, univariate logistic regression analysis, and least absolute shrinkage and selection operator (LASSO) were applied to select the optimal radiomics features. Radiomics scores (Rad-score) based on intratumoral regions, peritumoral regions, and intratumoral + 5-mm peritumoral regions were calculated by weighting the linear combination of the selected features with their respective coefficients to construct the intratumoral model, peritumoral model, and intratumoral + peritumoral model. Logistic regression was used to establish a combined model by combining clinical characteristics, CT semantic features, and Rad-score of intratumoral and peritumoral regions.

RESULTS

Eleven radiomics features were selected to establish a radiomics intratumoral + peritumoral model. CT-measured tumor length and tumor location were independent risk factors for MSI status. The established combined model obtained the highest area under the receiver operating characteristic (ROC) curve (AUC) of 0.830 (95% CI, 0.727-0.906) in the validation cohort. The calibration curve and decision curve demonstrated its good model fitness and clinical application value.

CONCLUSION

The combined model based on intratumoral and peritumoral CECT radiomics features and clinical factors can predict the MSI status of GS with moderate accuracy before surgery, which helps formulate personalized treatment strategies.

RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation

BACKGROUND

Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg).

METHODS

Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets.

RESULTS

Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival.

CONCLUSION

The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.

Biological and targeting differences between the rare KRAS A146T and canonical KRAS mutants in gastric cancer models

BACKGROUND

Gastric cancer (GC) is the third leading cause of cancer-related death worldwide, with a poor prognosis for patients with advanced disease. Since the oncogenic role of KRAS mutants has been poorly investigated in GC, this study aims to biochemically and biologically characterize different KRAS-mutated models and unravel differences among KRAS mutants in response to therapy.

METHODS

Taking advantage of a proprietary, molecularly annotated platform of more than 200 GC PDXs (patient-derived xenografts), we identified KRAS-mutated PDXs, from which primary cell lines were established. The different mutants were challenged with KRAS downstream inhibitors in in vitro and in vivo experiments.

RESULTS

Cells expressing the rare KRAS A146T mutant showed lower RAS-GTP levels compared to those bearing the canonical G12/13D mutations. Nevertheless, all the KRAS-mutated cells displayed KRAS addiction. Surprisingly, even if the GEF SOS1 is considered critical for the activation of KRAS A146T mutants, its abrogation did not significantly affect cell viability. From the pharmacologic point of view, Trametinib monotherapy was more effective in A146T than in G12D-mutated models, suggesting a vulnerability to MEK inhibition. However, in the presence of mutations in the PI3K pathway, more frequently co-occurrent in A146T models, the association of Trametinib and the AKT inhibitor MK-2206 was required to optimize the response.

CONCLUSION

A deeper genomic and biological characterization of KRAS mutants might sustain the development of more efficient and long-lasting therapeutic options for patients harbouring KRAS-driven GC.

USP12 facilitates gastric cancer progression via stabilizing YAP

The dysregulation of Hippo signaling is a crucial factor driving the progression of gastric cancer, making the targeting of the Hippo pathway a promising therapeutic strategy. However, effective drugs targeting the Hippo/YAP axis remain unavailable. Thus, identifying potential therapeutic targets and mechanisms that inhibit the activity of the Hippo/YAP axis in gastric cancer is of paramount importance. The ubiquitination modification of the Hippo/YAP pathway plays a significant role in signaling transduction and cancer progression. In an effort to shed light on effective therapeutic targets, we conducted a screening using a deubiquitinase small interfering RNA library, leading to the identification of USP12 as an important deubiquitinase in the context of Hippo/YAP axis and the progression of gastric cancer. Our bioinformatic analysis further demonstrated a correlation between USP12 and poor survival, as well as a positive association with classical YAP target genes in gastric cancer samples. Notably, USP12 depletion was found to inhibit gastric cancer progression via the Hippo/YAP axis, whereas USP12 overexpression exhibited the opposite effect, promoting gastric cancer growth and enhancing YAP activity. Further studies through immuno-staining and immuno-precipitation assays indicated the nuclear localization of USP12 and its association with YAP to enhance YAP stability. Specifically, our findings revealed that USP12 could inhibit K48-linked poly-ubiquitination of YAP, predominantly at the K315 site. As a result, we have identified a novel regulatory mechanism involving USP12 and Hippo signaling in the progression of gastric cancer, with the potential for blockade of USP12 to materialize as a promising strategy for combating gastric cancer.

Tumor and Peritoneum-Associated Macrophage Gene Signature as a Novel Molecular Biomarker in Gastric Cancer

A spectrum of immune states resulting from tumor resident macrophages and T-lymphocytes in the solid tumor microenvironment correlates with patient outcomes. We hypothesized that in gastric cancer (GC), macrophages in a polarized immunosuppressive transcriptional state would be prognostic of poor survival. We derived transcriptomic signatures for M2 (M2TS, MRC1; MS4A4A; CD36; CCL13; CCL18; CCL23; SLC38A6; FGL2; FN1; MAF) and M1 (M1TS, CCR7; IL2RA; CXCL11; CCL19; CXCL10; PLA1A; PTX3) macrophages, and cytolytic T-lymphocytes (CTLTS, GZMA; GZMB; GZMH; GZMM; PRF1). Primary GC in a TCGA stomach cancer dataset was evaluated for signature expressions, and a log-rank test determined overall survival (OS) and the disease-free interval (DFI). In 341 TCGA GC entries, high M2TS expression was associated with histological types and later stages. Low M2TS expression was associated with significantly better 5-year OS and DFI. We validated M2TS in prospectively collected peritoneal fluid of a GC patient cohort (n = 28). Single-cell RNA sequencing was used for signature expression in CD68+CD163+ cells and the log-rank test compared OS. GC patients with high M2TS in CD68+CD163+ cells in their peritoneal fluid had significantly worse OS than those with low expression. Multivariate analyses confirmed M2TS was significantly and independently associated with survival. As an independent predictor of poor survival, M2TS may be prognostic in primary tumors and peritoneal fluid of GC patients.

Precision Oncology in Gastrointestinal and Colorectal Cancer Surgery

Precision medicine is used to treat gastrointestinal malignancies including esophageal, gastric, small bowel, colorectal, and pancreatic cancers. Cutting-edge assays to detect and treat these cancers are active areas of research and will soon become standard of care. Colorectal cancer is a prime example of precision oncology as disease site is no longer the final determinate of treatment. Here, the authors describe how leveraging an understanding of tumor biology translates to individualized patient care using evidence-based practices.

Implementing an integrated molecular classification for gastric cancer from endoscopic biopsies using on-slide tests

The availability of more effective biological therapy can improve outcomes of gastric cancer (GC), but most patients do not have access to personalized treatment. GC molecular classification helps identify patients suitable for specific therapies and provides useful prognostic information. To date, only a small number of patients have access to molecular classification. We proposed a working molecular classification that can be delivered using on-slide tests available in most histopathology laboratories. We used eight on-slide tests [in situ hybridization (ISH) for Epstein-Barr virus-encoded small ribonucleic acid (EBER) and immunohistochemistry (IHC) for MutL homolog 1 (MLH1), PMS1 homolog 2 (PMS2), MutS homolog 2 (MSH2), MutS homolog 6 (MSH6), E-cadherin, β-catenin and p53] to classify GC into one of six categories: GC associated with Epstein-Barr virus (GC-EBV), GC mismatch repair deficient (GC-dMMR), GC with epithelial-mesenchymal transition (GC-EMT), GC with chromosomal instability (GC-CIN), GC genomically stable (GC-GS) and GC not otherwise specified (GC-NOS)∕indeterminate. The classification has provision also for current and future on-slide companion diagnostic (CDx) tests necessary to select specific biological therapies and, as proof of principle, in this study we used three CDx tests currently required for the management of GC [human epidermal growth factor receptor 2 (Her2), programmed cell death-ligand 1 (PD-L1) 22C3 and Claudin18.2 (CLDN18.2)]. This paper describes the necessary tissue pathways and laboratory workflow and assesses the feasibility of using this classification prospectively on small endoscopic biopsies of gastric and gastroesophageal junction adenocarcinoma. This work demonstrates that such molecular classification can be implemented in the context of a histopathology diagnostic routine with little impact on turnaround times and laboratory capacity. The widespread adoption of a molecular classification for GC will help refine prognosis and guide the choice of more appropriate biological therapy for these patients.

Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data

BACKGROUND

Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific Quantile Normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance Normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature selection.

RESULTS

FSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models decreases.

CONCLUSIONS

In the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions.

Validation of CDC45 as a novel biomarker for diagnosis and prognosis of gastric cancer

Background

Cell division cycle protein 45 (CDC45) has been demonstrated to play vital roles in the progression of various malignancies. However, the clinical significance of CDC45 in gastric cancer (GC) remains unreported.

Method

In this study, we employed the TCGA database and the TCGA & GTEx dataset to compare the mRNA expression levels of CDC45 between gastric cancer tissues and adjacent or normal tissues (p < 0.05 was considered statistically significant), which was further validated in multiple datasets including GSE13911, GSE29272, GSE118916, GSE66229, as well as RT-qPCR. Furthermore, we harnessed the Human Protein Atlas (HPA) to evaluate the protein expression of CDC45, which was subsequently verified through immunohistochemistry (IHC). To ascertain the diagnostic utility of CDC45, receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were calculated in TCGA database, and further validated it in TCGA & GTEx and GSE66229 datasets. The Kaplan-Meier method was used to reveal the prognostic importance of CDC45 in The Cancer Genome Atlas (TCGA) database and authenticated through the GSE66229, GSE84433, and GSE84437 datasets. Through cBioPortal, we identified co-expressed genes of CDC45, and pursued enrichment analysis. Additionally, we availed gene set enrichment analysis (GSEA) to annotate the biological functions of CDC45.

Results

Differential expression analysis revealed that CDC45 was significantly upregulated at both the mRNA and protein levels in GC (all p < 0.05). Remarkably, CDC45 emerged as a promising prognostic indicator and a novel diagnostic biomarker for GC. In a comprehensive the drug susceptibility analysis, we found that patients with high expression of CDC45 had high sensitivity to various chemotherapeutic agents, among which 5-fluorouracil, docetaxel, cisplatin, and elesclomol were most evident. Furthermore, our findings suggested a plausible association between CDC45 and immune cell infiltration. Enrichment analysis revealed that CDC45 and its associated genes may play crucial roles in muscle biofunction, whereas GSEA demonstrated significant enrichment of gene sets pertaining to G protein-coupled receptor ligand binding and G alpha (i) signaling events.

Conclusion

Our study elucidates that upregulation of CDC45 is intricately associated with immune cell infiltration and holds promising potential as a favorable prognostic marker and a novel diagnostic biomarker for GC.

Sex-dependent different clinicopathological characterization of Epstein-Barr virus-associated gastric carcinoma: a large-scale study

BACKGROUND

Epstein-Barr virus (EBV)-associated gastric cancer (EBVaGC) has been reported to account for approximately 5-16% of all GCs with good prognosis compared to EBV-negative GC. We evaluated the clinicopathological characteristics of EBVaGC including survival rate in South Korea.

METHODS

A total of 4,587 patients with GC who underwent EBV in situ hybridization (EBV-ISH) were prospectively enrolled at the Seoul National University Bundang Hospital from 2003 to 2021. Age, sex, smoking status, cancer type and stage, tumor size and location, histological type, molecular features and survival information were analyzed.

RESULTS

A total of 456 patients with GC (9.9%) were positive for EBV. The EBVaGC group displayed a higher proportion of males (P < 0.001), a predominant presence in the proximal stomach (P < 0.001), a higher proportion of undifferentiated cancer (P < 0.001), and a lower cancer stage (P = 0.004) than the EBV-negative group. Cox multivariate analyses revealed age (hazard ratio [HR] = 1.025, P < 0.001), tumor size (HR = 1.109, P < 0.001), and cancer stage (stage2 HR = 4.761, P < 0.001; stage3 HR = 13.286, P < 0.001; stage4 HR = 42.528, P < 0.001) as significant risk factors for GC-specific mortality, whereas EBV positivity was inversely correlated (HR = 0.620, P = 0.022). Furthermore, the EBVaGC group displayed statistically significant survival advantages over the EBV-negative cancer group in terms of both overall (P = 0.021) and GC-specific survival (P = 0.007) on the Kaplan-Meier survival curve. However, this effect was evident only in males.

CONCLUSIONS

EBVaGC patients showed better prognoses despite their association with proximal location and poorly differentiated histology in male, probably due to the difference in immunity between males and females.

Prognosis of EBV-positive gastric cancer with lymphoid stroma: systematic review and meta-analysis

BACKGROUND

Although gastric cancer with lymphoid stroma (GCLS) presents better prognosis, uncertainty still exists regarding the association of Epstein-Barr virus (EBV) infection with prognosis of GCLS. Therefore, it is urgent to evaluate the outcome and characteristics of EBV-positive GCLS via a systematic review and meta-analysis.

METHODS

Three medical databases, with a period ranging from 2000 to so far, were searched for observational studies on EBV infection, clinical characteristics and prognosis. Odds ratio (OR) was used to evaluate the mortality and clinical characteristics of EBV-positive GCLS patients. Egger's test and subgroup analysis were conducted to identify the source of heterogeneity.

RESULTS

Nine retrospective studies were finally identified, which involved 618 EBV-positive and 153 EBV-negative GCLS patients. The forest plot indicated that EBV-positive GCLS patients had lower mortality (p = .009; 95% CI: 0.15-0.77; I2 = 48.6%). Both of funnel plot and Egger's tests suggested that there was no publication bias. Nonetheless, subgroup analysis indicated that T1-2 stage ratio more than 50% (p < .001; I2 = 6.7%) and male ratio more than 80% (p < .001; I2 = 0.0%) were valuable for eliminating the heterogeneity. Seven studies including valid information showed that TNM stage of EBV-positive and negative GCLS patients was not statistically different (p = .644; 95% CI: 0.50-1.53; I2 = 0.0%).

CONCLUSIONS

EBV-positive GCLS tends to have lower mortality, suggesting that detection of EBV infection is necessary to predict prognosis of GCLS.

Intensifying supportive care is associated with improved survival in gastric cancer patients with malignant ascites

BACKGROUND

Gastric cancer patients with malignant ascites often have poor functional status and malnutrition that preclude receipt of systemic therapies. Thus, these patients have a very poor prognosis. Beginning in 2019, our multidisciplinary gastric cancer disease-oriented team implemented a more aggressive supportive care plan for gastric cancer patients with malignant ascites. The initiative included measures such as supplemental enteral nutrition, ascites drainage, and initiation of chemotherapy on an inpatient basis. We compared outcomes for gastric cancer patients who presented with synchronous malignant ascites treated before and after the implementation of the care plan.

METHODS

We performed a retrospective review of our institutional database to identify patients diagnosed with gastric adenocarcinoma and synchronous malignant ascites between 2010 and 2022. We compared overall survival (OS) between patients diagnosed from 2010 to 2018, which will be referred to as the historical control era and patients diagnosed from 2019 to 2022, which will be called the aggressive supportive care era.

RESULTS

Fifty-four patients were included in our analysis; 31 patients were treated in the historical control time frame, and 23 patients were treated during the aggressive supportive care era. Demographic, clinical, and pathologic characteristics were similar between groups. 3% of historical controls received supplemental tube feeds at diagnosis as compared to 30% of the aggressive supportive care cohort (p < 0.01). 3% of historical controls received their first cycle of chemotherapy in the inpatient setting versus 39% of patients treated during the aggressive supportive care era (p < 0.01). The median number of chemotherapy cycles received was 5 among historical controls and 9.5 among aggressive supportive care era patients (p = 0.02). There was no difference in the number of days spent as an inpatient between the two groups. The median OS for historical control patients was 5.4 months as compared with 10.4 months for patients treated during aggressive supportive care era (p = 0.04).

CONCLUSIONS

Gastric cancer patients with synchronous malignant ascites treated during a timeframe when our multidisciplinary team implemented more aggressive supportive care measures had improved OS as compared with historic controls. Our results suggest that aggressive supportive measures for these patients with highly challenging clinical issues and poor prognosis can prolong survival. Specifically, initiation of chemotherapy in the inpatient setting and supplemental nutrition should be considered for patients at high risk for treatment intolerance.

Adjuvant and neoadjuvant chemotherapy for MSI early gastric cancer: a systematic review and meta-analysis

Background

Perioperative chemotherapy (CT) is an established therapeutic approach for patients diagnosed with stage IB-III gastric cancer (GC).

Objectives

This study aimed to investigate the efficacy of this approach in individuals with GC exhibiting high microsatellite instability (MSI-H).

Design

A systematic review was conducted, including studies that provided data on (neo)adjuvant CT outcomes in patients with MSI-H GC.

Methods

Systematic searches were conducted in PubMed, Cochrane Central of Controlled Trials, and Embase databases. Data were aggregated using hazard ratios (HRs) to compare overall survival between CT and surgery.

Results

Data analysis from 23 studies, including 22,011 patients, revealed that the prevalence of MSI-H is 9.8%. Administration of adjuvant or perioperative CT did not significantly reduce the risk of death or relapse in patients with MSI-H GC (HR = 0.8, 95% CI 0.54-1.16; p = 0.24 and HR = 0.84, 95% CI 0.59-1.18; p = 0.31, respectively).

Conclusion

Chemotherapy did not benefit patients diagnosed with MSI-H nonmetastatic GC but rather will be integrated with immune checkpoint inhibitors in the near future.

Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

Expression patterns of E2Fs identify tumor microenvironment features in human gastric cancer

Objective

E2F transcription factors are associated with tumor development, but their underlying mechanisms in gastric cancer (GC) remain unclear. This study explored whether E2Fs determine the prognosis or immune and therapy responses of GC patients.

Methods

E2F regulation patterns from The Cancer Genome Atlas (TCGA) were systematically investigated and E2F patterns were correlated with the characteristics of cellular infiltration in the tumor microenvironment (TME). A principal component analysis was used to construct an E2F scoring model based on prognosis-related differential genes to quantify the E2F regulation of a single tumor. This scoring model was then tested in patient cohorts to predict effects of immunotherapy.

Results

Based on the expression profiles of E2F transcription factors in GC, two different regulatory patterns of E2F were identified. TME and survival differences emerged between the two clusters. Lower survival rates in the Cluster2 group were attributed to limited immune function due to stromal activation. The E2F scoring model was then constructed based on the E2F-related prognostic genes. Evidence supported the E2F score as an independent and effective prognostic factor and predictor of immunotherapy response. A gene-set analysis correlated E2F score with the characteristics of immune cell infiltration within the TME. The immunotherapy cohort database showed that patients with a higher E2F score demonstrated better survival and immune responses.

Conclusions

This study found that differences in GC prognosis might be related to the E2F patterns in the TME. The E2F scoring system developed in this study has practical value as a predictor of survival and treatment response in GC patients.

Prognostic implications of tumor-infiltrating lymphocytes within the tumor microenvironment in gastric cancer

BACKGROUND

Tumor-infiltrating lymphocytes (TILs) play a regulatory role in the tumor-associated immune response and are important in the prognosis and treatment response of several cancers. However, because of its heterogeneity, the prognostic value of TILs in gastric cancer (GC) is still controversial. Thus, this study aimed to investigate the association between the density of TILs and patients' outcomes in GC.

METHODS

Patients with gastric adenocarcinoma who underwent curative intent gastrectomy were retrospectively investigated. The groups for analysis were determined on the basis of TIL intensity and percentage of CD3+ T-cell infiltration by immunohistochemical. Furthermore, Epstein-Barr virus (EBV), microsatellite instability (MSI), T-cell ratio of CD4 to CD8, and programmed death protein ligand 1 (PD-L1) status were evaluated.

RESULTS

A total of 345 patients were enrolled: 124 patients with GCs (35.9%) were classified as the low-CD3+ TIL group, and 221 patients with GCs (64.1%) were classified as the high-CD3+ TIL group. Poorly differentiated histology (P = .014), EBV-positive status (P < .001), PD-L1-positive status (P = .001), and CD4 < CD8 (P < .001) were associated with high-CD3+ GC. There was no difference regarding MSI status, the degree of tumor invasion (pT), the presence of lymph node metastasis, and pTNM stage between low- and high-CD3+ groups. In survival analysis, the high-CD3+ group had better disease-free survival and overall survival rates than had the low-CD3+ group (P = .055 and P = .041, respectively). In the multivariate analysis, total gastrectomy, lymph node metastasis, advanced pT stage, and low CD3+ levels were independent factors related to worse survival.

CONCLUSION

High CD3+ TILs levels were significantly associated with improved survival and could serve as prognostic biomarkers in GC. In addition, CD3+ T-cell infiltration was related to both EBV-positive and PD-L1-positive GC and may assist in the investigation of targets in immunotherapy.

Identification and characterization of CLEC11A and its derived immune signature in gastric cancer

Introduction

C-type lectin domain family 11 member A (CLEC11A) was characterized as a growth factor that mainly regulates hematopoietic function and differentiation of bone cells. However, the involvement of CLEC11A in gastric cancer (GC) is not well understood.

Methods

Transcriptomic data and clinical information pertaining to GC were obtained and analyzed from publicly available databases. The relationships between CLEC11A and prognoses, genetic alterations, tumor microenvironment (TME), and therapeutic responses in GC patients were analyzed by bioinformatics methods. A CLEC11A-derived immune signature was developed and validated, and its mutational landscapes, immunological characteristics as well as drug sensitivities were explored. A nomogram was established by combining CLEC11A-derived immune signature and clinical factors. The expression and carcinogenic effects of CLEC11A in GC were verified by qRT-PCR, cell migration, invasion, cell cycle analysis, and in vivo model analysis. Myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), M2 macrophages, and T cells in tumor samples extracted from mice were analyzed utilizing flow cytometry analysis.

Results

CLEC11A was over-expressed in GC, and the elevated CLEC11A expression indicated an unfavorable prognosis in GC patients. CLEC11A was involved in genomic alterations and associated with the TME in GC. Moreover, elevated CLEC11A was found to reduce the benefit of immunotherapy according to immunophenoscore (IPS) and the tumor immune dysfunction, exclusion (TIDE). After validation, the CLEC11A-derived immune signature demonstrated a consistent ability to predict the survival outcomes in GC patients. A nomogram that quantifies survival probability was constructed to improve the accuracy of prognosis prediction in GC patients. Using shRNA to suppress the expression of CLEC11A led to significant inhibitions of cell cycle progression, migration, and invasion, as well as a marked reduction of in vivo tumor growth. Moreover, the flow cytometry assay showed that the knock-down of CLEC11A increased the infiltration of cytotoxic CD8+ T cells and helper CD4+ T into tumors while decreasing the percentage of M2 macrophages, MDSCs, and Tregs.

Conclusion

Collectively, our findings revealed that CLEC11A could be a prognostic and immunological biomarker in GC, and CLEC11A-derived immune signature might serve as a new option for clinicians to predict outcomes and formulate personalized treatment plans for GC patients.

Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer

BACKGROUNDThe tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC).METHODSThis study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(-) or high(+)] with 4 radiomics immune subtypes: 1 (-/-), 2 (+/-), 3 (-/+), and 4 (+/+). We further evaluated the imaging biomarkers' predictive values on prognosis and immunotherapy response.RESULTSThe developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765-0.773) and myeloid (AUC range: 0.736-0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240-0.761 for LRS; 1.301-4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy (P < 0.001). Further, a highly heterogeneous outcome on objective response ​rate was observed in 4 imaging subtypes: 1 (-/-) with 27.3%, 2 (+/-) with 53.3%, 3 (-/+) with 10.2%, and 4 (+/+) with 30.0% (P < 0.0001).CONCLUSIONThe noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.

Construction of an immune-related risk score signature for gastric cancer based on multi-omics data

Early identification of gastric cancer (GC) is associated with a superior survival rate compared to advanced GC. However, the poor specificity and sensitivity of traditional biomarkers suggest the importance of identifying more effective biomarkers. This study aimed to identify novel biomarkers for the prognosis of GC and construct a risk score (RS) signature based on these biomarkers, with to validation of its predictive performance. We used multi-omics data from The Cancer Genome Atlas to analyze the significance of differences in each omics data and combined the data using Fisher's method. Hub genes were subsequently subjected to univariate Cox and LASSO regression analyses and used to construct the RS signature. The RS of each patient was calculated, and the patients were divided into two subgroups according to the RS. The RS signature was validated in two independent datasets from the Gene Expression Omnibus and subsequent analyses were subsequently conducted. Five immune-related genes strongly linked to the prognosis of GC patients were obtained, namely CGB5, SLC10A2, THPO, PDGFRB, and APOD. The results revealed significant differences in overall survival between the two subgroups (p < 0.001) and indicated the high accuracy of the RS signature. When validated in two independent datasets, the results were consistent with those in the training dataset (p = 0.003 and p = 0.001). Subsequent analyses revealed that the RS signature is independent and has broad applicability among various GC subtypes. In conclusion, we used multi-omics data to obtain five immune-related genes comprising the RS signature, which can independently and effectively predict the prognosis of GC patients with high accuracy.

An individualized stemness-related signature to predict prognosis and immunotherapy responses for gastric cancer using single-cell and bulk tissue transcriptomes

BACKGROUND

Currently, many stemness-related signatures have been developed for gastric cancer (GC) to predict prognosis and immunotherapy outcomes. However, due to batch effects, these signatures cannot accurately analyze patients one by one, rendering them impractical in real clinical scenarios. Therefore, we aimed to develop an individualized and clinically applicable signature based on GC stemness.

METHODS

Malignant epithelial cells from single-cell RNA-Seq data of GC were used to identify stemness-related signature genes based on the CytoTRACE score. Using two bulk tissue datasets as training data, the enrichment scores of the signature genes were applied to classify samples into two subtypes. Then, using the identified subtypes as criteria, we developed an individualized stemness-related signature based on the within-sample relative expression orderings of genes.

RESULTS

We identified 175 stemness-related signature genes, which exhibited significantly higher AUCell scores in poorly differentiated GCs compared to differentiated GCs. In training datasets, GC samples were classified into two subtypes with significantly different survival times and genomic characteristics. Utilizing the two subtypes, an individualized signature was constructed containing 47 gene pairs. In four independent testing datasets, GC samples classified as high risk exhibited significantly shorter survival times, higher infiltration of M2 macrophages, and lower immune responses compared to low-risk samples. Moreover, the potential therapeutic targets and corresponding drugs were identified for the high-risk group, such as CD248 targeted by ontuxizumab.

CONCLUSIONS

We developed an individualized stemness-related signature, which can accurately predict the prognosis and efficacy of immunotherapy for each GC sample.

Emerging Targets for Systemic Treatment of Gastric Cancer: HER2 and Beyond

In recent years, remarkable progress has been made in the molecular profiling of gastric cancer. This progress has led to the development of various molecular classifications to uncover subtype-specific dependencies that can be targeted for therapeutic interventions. Human epidermal growth factor receptor 2 (HER2) is a crucial biomarker for advanced gastric cancer. The recent promising results of novel approaches, including combination therapies or newer potent agents such as antibody-drug conjugates, have once again brought attention to anti-HER2 targeted treatments. In HER2-negative diseases, the combination of cytotoxic chemotherapy and programmed cell death-1/programmed cell death ligand-1 (PD-1/PD-L1) inhibitors has become the established standard of care in first-line settings. In the context of gastric cancer, potential biomarkers such as PD-L1 expression, Epstein-Barr virus, microsatellite instability, and tumor mutational burden are being considered for immunotherapy. Recently, promising results have been reported in studies on anti-Claudin18.2 and fibroblast growth factor receptor 2 treatments. Currently, many ongoing trials are aimed at identifying potential targets using novel approaches. Further investigations will be conducted to enhance the progress of these therapies, addressing challenges such as primary and acquired resistance, tumor heterogeneity, and clonal evolution. We believe that these efforts will improve patient prognoses. Herein, we discuss the current evidence of potential targets for systemic treatment, clinical considerations, and future perspectives.