Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes

CDH1 loss promotes diffuse-type gastric cancer tumorigenesis via epigenetic reprogramming and immune evasion

Diffuse-type gastric adenocarcinoma (DGAC) is a deadly cancer often diagnosed late and resistant to treatment. While hereditary DGAC is linked to CDH1 gene mutations, causing E-Cadherin loss, its role in sporadic DGAC is unclear. We discovered CDH1 inactivation in a subset of DGAC patient tumors. Analyzing single-cell transcriptomes in malignant ascites, we identified two DGAC subtypes: DGAC1 (CDH1 loss) and DGAC2 (lacking immune response). DGAC1 displayed distinct molecular signatures, activated DGAC-related pathways, and an abundance of exhausted T cells in ascites. Genetically engineered murine gastric organoids showed that Cdh1 knock-out (KO), KrasG12D, Trp53 KO (EKP) accelerates tumorigenesis with immune evasion compared to KrasG12D, Trp53 KO (KP). We also identified EZH2 as a key mediator promoting CDH1 loss-associated DGAC tumorigenesis. These findings highlight DGAC's molecular diversity and potential for personalized treatment in CDH1-inactivated patients.

Learning Individual Survival Models from PanCancer Whole Transcriptome Data

PURPOSE

Personalized medicine attempts to predict survival time for each patient, based on their individual tumor molecular profile. We investigate whether our survival learner in combination with a dimension reduction method can produce useful survival estimates for a variety of patients with cancer.

EXPERIMENTAL DESIGN

This article provides a method that learns a model for predicting the survival time for individual patients with cancer from the PanCancer Atlas: given the (16,335 dimensional) gene expression profiles from 10,173 patients, each having one of 33 cancers, this method uses unsupervised nonnegative matrix factorization (NMF) to reexpress the gene expression data for each patient in terms of 100 learned NMF factors. It then feeds these 100 factors into the Multi-Task Logistic Regression (MTLR) learner to produce cancer-specific models for each of 20 cancers (with >50 uncensored instances); this produces "individual survival distributions" (ISD), which provide survival probabilities at each future time for each individual patient, which provides a patient's risk score and estimated survival time.

RESULTS

Our NMF-MTLR concordance indices outperformed the VAECox benchmark by 14.9% overall. We achieved optimal survival prediction using pan-cancer NMF in combination with cancer-specific MTLR models. We provide biological interpretation of the NMF model and clinical implications of ISDs for prognosis and therapeutic response prediction.

CONCLUSIONS

NMF-MTLR provides many benefits over other models: superior model discrimination, superior calibration, meaningful survival time estimates, and accurate probabilistic estimates of survival over time for each individual patient. We advocate for the adoption of these cancer survival models in clinical and research settings.

Definition of a Novel Immunogenic Cell Death-Relevant Gene Signature Associated with Immune Landscape in Gastric Cancer

Immunogenic cell death (ICD) induces anti-tumor immunity and aids in dismantling the immunosuppressive immune microenvironment (TME), which belongs to a type of regulated cell death. The differentiation of gastric cancer (GC) subtypes and the discovery of prognostic biomarkers are crucial for its treatment because GC is a disease that is both highly heterogeneous and aggressive. However, although the induction of ICD in tumor cells is associated with a favorable prognosis, the exact mechanism of its role in GC remains unclear. Transcriptome profiling data and clinical data of GC patients were retrieved from The Cancer Genome Atlas (TCGA) database. Herein, patients were classified with the consensus clustering algorithm, and the associated biological functions and immune microenvironment infiltration were explored based on the expression of ICD-associated genes. A risk score signature consisting of 11 ICD-related genes was established via the least absolute shrinkage and selection operator regression (LASSO) method. We have retrieved similar studies in recent years and compared them with our study using the time-dependent receiver operating characteristic (ROC) curves. Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were performed to explore the association between the signature and tumor microenvironment (TME). Two distinct subtypes associated with ICD in GC were identified, each with a different prognosis. The ICD-high expression subtype was associated with higher immune cell infiltration and a better prognosis. The ICD-related gene signature containing 11 genes (CGB5, Z84468.1, APOA5, EPHA8, CLEC18C, TLR7, MUC7, MUC15, CTLA4, CALB2, and UGT2B28), could independently and accurately predict the prognosis of GC. In this study, an ICD-based classification was conducted to assist in the diagnosis and personalized therapy for GC. The ICD-related genes risk score model was established to predict prognosis.

Exploring the molecular characteristics of the malignant potential of gastric adenocarcinoma with enteroblastic differentiation

AIMS

Gastric adenocarcinoma with enteroblastic differentiation (GAED) is a rare subset of alpha-fetoprotein (AFP)-producing carcinomas with poor prognosis. However, the molecular features associated with the malignant potential of GEAD remain partially elucidated.

METHODS AND RESULTS

In this study, the relationship between clinicopathological parameters and aggressive biological behaviour was analysed in 37 patients with GAED. The results showed that GAED tended to infiltrate the deep layer of the gastric wall and possessed more frequent vascular invasion than conventional gastric adenocarcinoma (CGA) (P < 0.001). All distant metastases were observed in the GAED group, not the CGA group (P < 0.001). High HER2 expression was found in nearly 24.32% of the informative cases, and none showed EBV-encoded RNA positivity or deficient mismatch repair. The most frequently mutated gene in GAED was p53. Programmed cell death-ligand 1 (PD-L1) immunostaining revealed 13 patients with a combined positive score (CPS) ≥ 5 (65%, 13 of 20). Thus, based on these molecular markers (immunostaining, in situ hybridisation and mutation analysis), GAED may be classified as a unique subgroup of the chromosomal instability subtype with HER2+ /EBV- /MSS/TP53+ /PD-L1+ . Next-generation sequencing analyses showed that mutations in the TOPI, ELOA and NOTCH3 genes were found only in GAED, and abnormally expressed genes in GAED were significantly enriched in hepatocellular carcinoma-, gland development-, and gastric cancer-related pathways.

CONCLUSION

The HER2+ /EBV- /MSS/TP53+ /PD-L1+ profile and hepatocellular carcinoma-related pathways may be significant in the malignant potential of GAED. In addition to anti-HER2 therapy, immune check-point inhibitors may be an effective treatment option for patients with GAED.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis

The dynamic changes of key biological characteristics from gastric low-grade intraepithelial neoplasia (LGIN) to high-grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC) are still unclear, which greatly affect the accurate diagnosis and treatment of EGC and prognosis evaluation of gastric cancer (GC). In this study, bioinformatics methods/tools are applied to quantitatively analyze molecular characteristics evolution of GC progression, and a prognosis model is constructed. This study finds that some dysregulated differentially expressed mRNAs (DEmRNAs) in the LGIN stage may continue to promote the occurrence and development of EGC. Among the LGIN, HGIN, and EGC stages, there are differences and relevance in the transcription expression patterns of DEmRNAs, and the activation related to immune cells is very different. The biological functions continuously changed during the progression from LGIN to HGIN to EGC. The COX model constructed based on the three EGC-related DEmRNAs has GC prognostic risk prediction ability. The evolution of biological characteristics during the development of EGC mined by the authors provides new insight into understanding the molecular mechanism of EGC occurrence and development. The three-gene prognostic risk model provides a new method for assisting GC clinical treatment decisions.

MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection

Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation.

Dynamic single-cell mapping unveils Epstein‒Barr virus-imprinted T-cell exhaustion and on-treatment response

Epstein‒Barr virus (EBV)-associated gastric cancer (GC) manifests an intriguing immunotherapy response. However, the cellular basis for EBV-imprinted tumour immunity and on-treatment response remains undefined. This study aimed to finely characterize the dynamic tumour immune contexture of human EBV (+) GC treated with immunochemotherapy by longitudinal scRNA-seq and paired scTCR/BCR-seq. EBV (+) GC exhibits an inflamed-immune phenotype with increased T-cell and B-cell infiltration. Immunochemotherapy triggers clonal revival and reinvigoration of effector T cells which step to determine treatment response. Typically, an antigen-specific ISG-15+CD8+ T-cell population is highly enriched in EBV (+) GC patients, which represents a transitory exhaustion state. Importantly, baseline intratumoural ISG-15+CD8+ T cells predict immunotherapy responsiveness among GC patients. Re-emerged clonotypes of pre-existing ISG-15+CD8+ T cells could be found after treatment, which gives rise to a CXCL13-expressing effector population in responsive EBV (+) tumours. However, LAG-3 retention may render the ISG-15+CD8+ T cells into a terminal exhaustion state in non-responsive EBV (+) tumours. In accordance, anti-LAG-3 therapy could effectively reduce tumour burden in refractory EBV (+) GC patients. Our results delineate a distinct implication of EBV-imprinted on-treatment T-cell immunity in GC, which could be leveraged to optimize the rational design of precision immunotherapy.

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer

OBJECTIVE

To construct and validate a prediction model based on dual-layer detector spectral CT (DLCT) and clinico-radiologic features to predict the microsatellite instability (MSI) status of gastric cancer (GC) and to explore the relationship between the prediction results and patient prognosis.

METHODS

A total of 264 GC patients who underwent preoperative DLCT examination were randomly allocated into the training set (n = 187) and validation set (n = 80). Clinico-radiologic features and DLCT parameters were used to build the clinical and DLCT model through multivariate logistic regression analysis. A combined DLCT parameter (CDLCT) was constructed to predict MSI. A combined prediction model was constructed using multivariate logistic regression analysis by integrating the significant clinico-radiologic features and CDLCT. The Kaplan-Meier survival analysis was used to explore the prognostic significant of the prediction results of the combined model.

RESULTS

In this study, there were 70 (26.52%) MSI-high (MSI-H) GC patients. Tumor location and CT_N staging were independent risk factors for MSI-H. In the validation set, the area under the curve (AUC) of the clinical model and DLCT model for predicting MSI status was 0.721 and 0.837, respectively. The combined model achieved a high prediction efficacy in the validation set, with AUC, sensitivity, and specificity of 0.879, 78.95%, and 75.4%, respectively. Survival analysis demonstrated that the combined model could stratify GC patients according to recurrence-free survival (p = 0.010).

CONCLUSION

The combined model provides an efficient tool for predicting the MSI status of GC noninvasively and tumor recurrence risk stratification after surgery.

CRITICAL RELEVANCE STATEMENT

MSI is an important molecular subtype in gastric cancer (GC). But MSI can only be evaluated using biopsy or postoperative tumor tissues. Our study developed a combined model based on DLCT which could effectively predict MSI preoperatively. Our result also showed that the combined model could stratify patients according to recurrence-free survival. It may be valuable for clinicians in choosing appropriate treatment strategies to avoid tumor recurrence and predicting clinical prognosis in GC.

KEY POINTS

• Tumor location and CT_N staging were independent predictors for MSI-H in GC. • Quantitative DLCT parameters showed potential in predicting MSI status in GC. • The combined model integrating clinico-radiologic features and CDLCT could improve the predictive performance. • The prediction results could stratify the risk of tumor recurrence after surgery.

Transcriptome profiling of patient-derived tumor xenografts suggests novel extracellular matrix-related signatures for gastric cancer prognosis prediction

BACKGROUND

A major obstacle to the development of personalized therapies for gastric cancer (GC) is the prevalent heterogeneity at the intra-tumor, intra-patient, and inter-patient levels. Although the pathological stage and histological subtype diagnosis can approximately predict prognosis, GC heterogeneity is rarely considered. The extracellular matrix (ECM), a major component of the tumor microenvironment (TME), extensively interacts with tumor and immune cells, providing a possible proxy to investigate GC heterogeneity. However, ECM consists of numerous protein components, and there are no suitable models to screen ECM-related genes contributing to tumor growth and prognosis. We constructed patient-derived tumor xenograft (PDTX) models to obtain robust ECM-related transcriptomic signatures to improve GC prognosis prediction and therapy design.

METHODS

One hundred twenty two primary GC tumor tissues were collected to construct PDTX models. The tumorigenesis rate and its relationship with GC prognosis were investigated. Transcriptome profiling was performed for PDTX-originating tumors, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to extract prognostic ECM signatures and establish PDTX tumorigenicity-related gene (PTG) scores. The predictive ability of the PTG score was validated using two independent cohorts. Finally, we combined PTG score, age, and pathological stage information to establish a robust nomogram for GC prognosis prediction.

RESULTS

We found that PDTX tumorigenicity indicated a poor prognosis in patients with GC, even at the same pathological stage. Transcriptome profiling of PDTX-originating GC tissues and corresponding normal controls identified 383 differentially expressed genes, with enrichment of ECM-related genes. A robust prognosis prediction model using the PTG score showed robust performance in two validation cohorts. A high PTG score was associated with elevated M2 polarized macrophage and cancer-associated fibroblast infiltration. Finally, combining the PTG score with age and TNM stage resulted in a more effective prognostic model than age or TNM stage alone.

CONCLUSIONS

We found that ECM-related signatures may contribute to PDTX tumorigenesis and indicate a poor prognosis in GC. A feasible survival prediction model was built based on the PTG score, which was associated with immune cell infiltration. Together with patient ages and pathological TNM stages, PTG score could be a new approach for GC prognosis prediction.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

Clinically conserved genomic subtypes of gastric adenocarcinoma

Gastric adenocarcinoma (GAC) is a lethal disease characterized by genomic and clinical heterogeneity. By integrating 8 previously established genomic signatures for GAC subtypes, we identified 6 clinically and molecularly distinct genomic consensus subtypes (CGSs). CGS1 have the poorest prognosis, very high stem cell characteristics, and high IGF1 expression, but low genomic alterations. CGS2 is enriched with canonical epithelial gene expression. CGS3 and CGS4 have high copy number alterations and low immune reactivity. However, CGS3 and CGS4 differ in that CGS3 has high HER2 activation, while CGS4 has high SALL4 and KRAS activation. CGS5 has the high mutation burden and moderately high immune reactivity that are characteristic of microsatellite instable tumors. Most CGS6 tumors are positive for Epstein Barr virus and show extremely high levels of methylation and high immune reactivity. In a systematic analysis of genomic and proteomic data, we estimated the potential response rate of each consensus subtype to standard and experimental treatments such as radiation therapy, targeted therapy, and immunotherapy. Interestingly, CGS3 was significantly associated with a benefit from chemoradiation therapy owing to its high basal level of ferroptosis. In addition, we also identified potential therapeutic targets for each consensus subtype. Thus, the consensus subtypes produced a robust classification and provide for additional characterizations for subtype-based customized interventions.

SEC23A confers ER stress resistance in gastric cancer by forming the ER stress-SEC23A-autophagy negative feedback loop

BACKGROUND

Sec23 homolog A (SEC23A), a core component of coat protein complex II (COPII), has been reported to be involved in several cancers. However, the role of SEC23A in gastric cancer remains unclear.

METHODS

The expression of SEC23A in gastric cancer was analyzed by using qRT-PCR, western blotting and IHC staining. The role of SEC23A in ER stress resistance was explored by functional experiments in vitro and vivo. The occupation of STAT3 on the SEC23A promoter region was verified by luciferase reporter plasmids and CHIP assay. The interaction between SEC23A and ANXA2 was identified by Co-IP and mass spectrometry analysis.

RESULTS

We demonstrated that SEC23A was upregulated in gastric cancer and predicted poor prognosis in patients with gastric cancer. Mechanistically, SEC23A was transcriptional upregulated by ER stress-induced pY705-STAT3. Highly expressed SEC23A promoted autophagy by regulating the cellular localization of ANXA2. The SEC23A-ANXA2-autophay axis, in turn, protected gastric cancer cells from ER stress-induced apoptosis. Furthermore, we identified SEC23A attenuated 5-FU therapeutic effectiveness in gastric cancer cells through autophagy-mediated ER stress relief.

CONCLUSION

We reveal an ER stress-SEC23A-autophagy negative feedback loop that enhances the ability of gastric cancer cells to resist the adverse survival environments. These results identify SEC23A as a promising molecular target for potential therapeutic intervention and prognostic prediction in patients with gastric cancer.

The epigenetic modifier lysine methyltransferase 2C is frequently mutated in gastric remnant carcinoma

Gastric remnant carcinoma (GRC), which occurs in the stomach after partial gastrectomy, is a rare and aggressive form of gastric adenocarcinoma (GAC). Comprehensive profiling of genomic mutations in GRC could provide the basis for elucidating the origin and characteristics of this cancer. Herein, whole-exome sequencing (WES) was performed on 36 matched tumor-normal samples from patients with GRC and identified recurrent mutations in epigenetic modifiers, notably KMT2C, ARID1A, NSD1, and KMT2D, in 61.11% of cases. Mutational signature analysis revealed a low frequency of microsatellite instability (MSI) in GRC, which was further identified by MSIsensor, MSI-polymerase chain reaction, and immunohistochemistry analysis. Comparative analysis demonstrated that GRC had a distinct mutation spectrum compared to that of GAC in The Cancer Genome Atlas samples, with a significantly higher mutation rate of KMT2C. Targeted deep sequencing (Target-seq) of an additional 25 paired tumor-normal samples verified the high mutation frequency (48%) of KMT2C in GRC. KMT2C mutations correlated with poor overall survival in both WES and Target-seq cohorts and were independent prognosticators in GRC. In addition, KMT2C mutations were positively correlated with favorable outcomes in immune checkpoint inhibitor-treated pan-cancer patients and associated with higher intratumoral CD3+ , CD8+ tumor-infiltrating lymphocyte counts, and PD-L1 expression in GRC samples (p = 0.018, 0.092, 0.047, 0.010, and 0.034, respectively). Our dataset provides a platform for information and knowledge mining of the genomic characteristics of GRC and helps to frame new therapeutic approaches for this disease.

Single-cell Profiling Uncovers a Muc4-Expressing Metaplastic Gastric Cell Type Sustained by Helicobacter pylori-driven Inflammation

Mechanisms for Helicobacter pylori (Hp)-driven stomach cancer are not fully understood. In a transgenic mouse model of gastric preneoplasia, concomitant Hp infection and induction of constitutively active KRAS (Hp+KRAS+) alters metaplasia phenotypes and elicits greater inflammation than either perturbation alone. Gastric single-cell RNA sequencing showed that Hp+KRAS+ mice had a large population of metaplastic pit cells that expressed the intestinal mucin Muc4 and the growth factor amphiregulin. Flow cytometry and IHC-based immune profiling revealed that metaplastic pit cells were associated with macrophage and T-cell inflammation. Accordingly, expansion of metaplastic pit cells was prevented by gastric immunosuppression and reversed by antibiotic eradication of Hp. Finally, MUC4 expression was significantly associated with proliferation in human gastric cancer samples. These studies identify an Hp-associated metaplastic pit cell lineage, also found in human gastric cancer tissues, whose expansion is driven by Hp-dependent inflammation.

Significance

Using a mouse model, we have delineated metaplastic pit cells as a precancerous cell type whose expansion requires Hp-driven inflammation. In humans, metaplastic pit cells show enhanced proliferation as well as enrichment in precancer and early cancer tissues, highlighting an early step in the gastric metaplasia to cancer cascade.

A consolidated working classification of gastric cancer for histopathologists (Review)

Gastric cancer (GC) remains a disease with poor prognosis despite increasing availability of more effective targeted treatment. This may be in part due to the difficulty in selecting patients for appropriate treatment. Conventional taxonomic classifications of GC are ill-suited to make full use of recent advances in personalised therapy. In the past decade a number of molecular classifications have been proposed to address this; however, to date, there has been little implementation in the diagnostic routine. The lack of harmonisation between these classifications, the complexity and unavailability of some of the tests required plus the demands on time and resources, all contribute to poor uptake in the diagnostic routine. In the present study, these classifications were reviewed and an inclusive working classification that includes their main points, focuses on prognosis and treatment options and can be delivered using four on-slide tests (in situ hybridization for Epstein-Barr encoding region and immunohistochemistry for mismatch repair, E-cadherin and p53) is proposed. These tests can be performed on paraffin-embedded tissue and could be available in the majority of histopathology laboratories. The proposed classification also includes reflex testing for specific biomarkers relevant to treatment selection.

Comprehensive molecular phenotyping of ARID1A-deficient gastric cancer reveals pervasive epigenomic reprogramming and therapeutic opportunities

OBJECTIVE

Gastric cancer (GC) is a leading cause of cancer mortality, with ARID1A being the second most frequently mutated driver gene in GC. We sought to decipher ARID1A-specific GC regulatory networks and examine therapeutic vulnerabilities arising from ARID1A loss.

DESIGN

Genomic profiling of GC patients including a Singapore cohort (>200 patients) was performed to derive mutational signatures of ARID1A inactivation across molecular subtypes. Single-cell transcriptomic profiles of ARID1A-mutated GCs were analysed to examine tumour microenvironmental changes arising from ARID1A loss. Genome-wide ARID1A binding and chromatin profiles (H3K27ac, H3K4me3, H3K4me1, ATAC-seq) were generated to identify gastric-specific epigenetic landscapes regulated by ARID1A. Distinct cancer hallmarks of ARID1A-mutated GCs were converged at the genomic, single-cell and epigenomic level, and targeted by pharmacological inhibition.

RESULTS

We observed prevalent ARID1A inactivation across GC molecular subtypes, with distinct mutational signatures and linked to a NFKB-driven proinflammatory tumour microenvironment. ARID1A-depletion caused loss of H3K27ac activation signals at ARID1A-occupied distal enhancers, but unexpectedly gain of H3K27ac at ARID1A-occupied promoters in genes such as NFKB1 and NFKB2. Promoter activation in ARID1A-mutated GCs was associated with enhanced gene expression, increased BRD4 binding, and reduced HDAC1 and CTCF occupancy. Combined targeting of promoter activation and tumour inflammation via bromodomain and NFKB inhibitors confirmed therapeutic synergy specific to ARID1A-genomic status.

CONCLUSION

Our results suggest a therapeutic strategy for ARID1A-mutated GCs targeting both tumour-intrinsic (BRD4-assocatiated promoter activation) and extrinsic (NFKB immunomodulation) cancer phenotypes.

Identifying mitophagy-related genes as prognostic biomarkers and therapeutic targets of gastric carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data

Gastric carcinoma (GC) is the fourth leading cause of cancer-related mortality worldwide. Patients with advanced GC tend to have poor prognoses and shortened survival. Finding novel predictive biomarkers for GC prognosis is an urgent need. Mitophagy is the selection degradation of damaged mitochondria to maintain cellular homeostasis, which has been shown to play both pro- and anti-tumor effects. This study combined single-cell sequencing data and transcriptomics to screen mitophagy-related genes (MRGs) associated with GC progression and analyze their clinical values. Reverse transcription-quantitative PCR (RT-qPCR) and immunochemistry (IHC) further verified gene expression profiles. A total of 18 DE-MRGs were identified after taking an intersection of single-cell sequencing data and MRGs. Cells with a higher MRG score were mainly distributed in the epithelial cell cluster. Cell-to-cell communications among epithelial cells with other cell types were significantly upregulated. We established and validated a reliable nomogram model based on DE-MRGs (GABARAPL2 and CDC37) and traditional clinicopathological parameters. GABARAPL2 and CDC37 displayed different immune infiltration states. Given the significant correlation between hub genes and immune checkpoints, targeting MRGs in GC may supplement more benefits to patients who received immunotherapy. In conclusion, GABARAPL2 and CDC37 may be prognostic biomarkers and candidate therapeutic targets of GC.

Impact of programmed death-ligand 1 (PD-L1) positivity on clinical and molecular features of patients with metastatic gastric cancer

BACKGROUND

Programmed death-ligand 1 (PD-L1) is an important screening biomarker to select patients with gastric cancer (GC) for optimized treatment, including immune checkpoint inhibitors (ICI).

METHODS

In this single-institution retrospective cohort study, patients with metastatic GC with available PD-L1 results between October 2019 and September 2021 were identified by reviewing their electronic medical records. Genomic data were obtained from the Samsung Medical Center Clinical Sequencing Platform.

RESULTS

Among the 399 patients, 276 (69%) had a PD-L1 combined positive score (CPS) ≥1, 155 (39%) had a CPS between 1 and 5, and 121 (30%) had a CPS ≥5. Of the 121 patients with CPS ≥5, 28 (23%) had a known etiology for "inflamed tumor," with Epstein-Barr virus (EBV) positivity (N = 11) or high tumor mutational burden (TMB) (N = 17), which included microsatellite instability (MSI) (N = 9). PD-L1 CPS ≥5 was observed in 11/11 (100%) patients with EBV positivity, 9/12 (75%) patients with MSI, and 17/33 (52%) patients with high TMB. For the 108 patients who received ICI therapy, CPS ≥5 was the only predictor significantly associated with survival in multivariable analyses, including TMB, MSI, or EBV. Objective response rate (ORR) was 49% in patients with CPS ≥5, 30% in patients with 1 ≤ CPS <5, and 19% in patients with CPS <1. Among the 31 responders to ICI therapy, 27 (87%) had a CPS of ≥1. Mutations in TET2, IRS2, DOT1L, PTPRT, and LRP1B were associated with a higher ORR (63%-100%), whereas MDC1 mutations were associated with a low ORR (22%).

CONCLUSIONS

PD-L1 expression is an independent and sensitive biomarker for ICI therapy. Considering its significant association with several gene alterations, including PIK3CA mutations and MET amplification, combining ICI therapy with other targeted agents may be a promising therapeutic strategy for GC.

Genomic and evolutionary characteristics of metastatic gastric cancer by routes

BACKGROUND

In gastric cancer (GC) patients, metastatic progression through the lymphatic, hematogenous, peritoneal, and ovarian routes, is the ultimate cause of death. However, the genomic and evolutionary characteristics of metastatic GC have not been widely evaluated.

METHODS

Whole-exome sequencing data were analyzed for 99 primary and paired metastatic gastric cancers from 15 patients who underwent gastrectomy and metastasectomy.

RESULTS

Hematogenous metastatic tumors were associated with increased chromosomal instability and de novo gain/amplification in cancer driver genes, whereas peritoneal/ovarian metastasis was linked to sustained chromosomal stability and de novo somatic mutations in driver genes. The genomic distance of the hematogenous and peritoneal metastatic tumors was found to be closer to the primary tumors than lymph node (LN) metastasis, while ovarian metastasis was closer to LN and peritoneal metastasis than the primary tumor. Two migration patterns for metastatic GCs were identified; branched and diaspora. Both molecular subtypes of the metastatic tumors, rather than the primary tumor, and their migration patterns were related to patient survival.

CONCLUSIONS

Genomic characteristics of metastatic gastric cancer is distinctive by routes and associated with patients' prognosis along with genomic evolution pattenrs, indicating that both primary and metastatic gastric cancers require genomic evaluation.

Epigenetic Modification-Associated Molecular Classification of Gastric Cancer

Epigenetic modification is involved in tumorigenesis and cancer progression. We developed an epigenetic modification-associated molecular classification of gastric cancer (GC) to identify signature genes that accurately predict prognosis and the efficacy of immunotherapy. Least absolute shrinkage and selection operator and multivariate Cox regression analysis were conducted to develop an epigenetic modification-associated molecular classification. We investigated the significance of PIP4P2, an independent prognostic factor of the classification system, in predicting the prognosis and immunotherapy efficacy of patients with GC. The epigenetic modification-associated molecular classification was highly associated with the clinicopathological characteristics of patients and the existing classification of GC. PIP4P2 was highly expressed in GC tissue and tumor-associated macrophages. High PIP4P2 expression in GC tissue-induced tumor progression by activating PI3K/AKT signal transduction had a negative impact on immunotherapy efficacy. High expression of PIP4P2 in macrophages was correlated with poor prognosis in patients with GC. PIP4P2 is an independent unfavorable prognostic factor of epigenetic modification-associated molecular classification, is involved in tumorigenic progression, and is essential for assessing the prognosis and immunotherapy efficacy of GC.

Biomarkers for Predicting Response to Personalized Immunotherapy in Gastric Cancer

Despite advances in diagnostic imaging, surgical techniques, and systemic therapy, gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Unfortunately, molecular heterogeneity and, consequently, acquired resistance in GC are the major causes of failure in the development of biomarker-guided targeted therapies. However, by showing promising survival benefits in some studies, the recent emergence of immunotherapy in GC has had a significant impact on treatment-selectable procedures. Immune checkpoint inhibitors (ICIs), widely indicated in the treatment of several malignancies, target inhibitory receptors on T lymphocytes, including the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis and cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and release effector T-cells from negative feedback signals. In this article, we review currently available predictive biomarkers (including PD-L1, microsatellite instability, Epstein-Barr virus, and tumor mutational burden) that affect the ICI treatment response, focusing on PD-L1 expression. We further briefly describe other potential biomarkers or mechanisms for predicting the response to ICIs in GC. This review may facilitate the expansion of the understanding of biomarkers for predicting the response to ICIs and help select the appropriate therapeutic approaches for patients with GC.

Reduced DEFA5 Expression and STAT3 Activation Underlie the Submucosal Invasion of Early Gastric Cancers

INTRODUCTION

Submucosal invasion is a core hallmark of early gastric cancer (EGC) with poor prognosis. However, the molecular mechanism of the progression from intramucosal gastric cancer (IMGC) to early submucosal-invasive gastric cancer (SMGC) is not fully understood. The objective of this study was to identify genes and pathways involved in the submucosal invasion in EGC using comprehensive gene expression analysis.

METHODS

Gene expression profiling was performed for eight cases of IMGC and eight cases of early SMGC with submucosal invasion ≥500 μm. To validate the findings of gene expression analysis and to examine the gene expression pattern in tissues, immunohistochemical (IHC) staining was performed for 50 cases of IMGC and SMGC each.

RESULTS

Gene expression analysis demonstrated that the expression levels of small intestine-specific genes were significantly decreased in SMGC. Among them, defensin alpha 5 (DEFA5) was the most downregulated gene in SMGC, which was further validated in SMGC tissues by IHC staining. Gene set enrichment analysis showed a strong association between SMGC, the JAK-STAT signaling pathway, and the upregulation of STAT3-activating cytokines. The expression of phosphorylated STAT3 was significant in the nucleus of tumor cells in SMGC tissues but not in areas expressing DEFA5.

CONCLUSION

The results of this study strongly suggest that the downregulation of DEFA5 and the activation of STAT3 play a significant role in the submucosal invasion of EGC.

The Present and Future of Neoadjuvant and Adjuvant Therapy for Locally Advanced Gastric Cancer

Gastric cancer is a highly prevalent and lethal disease worldwide. Given the insidious nature of the presenting symptoms, patients are frequently diagnosed with advanced, unresectable disease. However, many patients will present with locally advanced gastric cancer (LAGC), which is often defined as the primary tumor extending beyond the muscularis propria (cT3-T4) or having nodal metastases (cN+) disease and without distant metastases (cM0). LAGC is typically treated with surgical resection and perioperative chemotherapy. The treatment of LAGC remains a challenge, given the heterogeneity of this disease, and the optimal multimodal treatment regimen may be different for different LAGC subtypes. However, many promising treatments are on the horizon based on knowledge of molecular subtypes and key biomarkers of LAGC, such as microsatellite instability, HER2, Claudin 18.2, FGFR2, and PD-L1. This review will expand upon the discussion of current standard neoadjuvant and adjuvant therapies for LAGC and explore the ongoing and future clinical trials for novel therapies, with information obtained from searches in PubMed and ClinicalTrials.gov.

Evolution of immune and stromal cell states and ecotypes during gastric adenocarcinoma progression

Understanding tumor microenvironment (TME) reprogramming in gastric adenocarcinoma (GAC) progression may uncover novel therapeutic targets. Here, we performed single-cell profiling of precancerous lesions, localized and metastatic GACs, identifying alterations in TME cell states and compositions as GAC progresses. Abundant IgA+ plasma cells exist in the premalignant microenvironment, whereas immunosuppressive myeloid and stromal subsets dominate late-stage GACs. We identified six TME ecotypes (EC1-6). EC1 is exclusive to blood, while EC4, EC5, and EC2 are highly enriched in uninvolved tissues, premalignant lesions, and metastases, respectively. EC3 and EC6, two distinct ecotypes in primary GACs, associate with histopathological and genomic characteristics, and survival outcomes. Extensive stromal remodeling occurs in GAC progression. High SDC2 expression in cancer-associated fibroblasts (CAFs) is linked to aggressive phenotypes and poor survival, and SDC2 overexpression in CAFs contributes to tumor growth. Our study provides a high-resolution GAC TME atlas and underscores potential targets for further investigation.

Predicting response to immunotherapy in gastric cancer via assessing perineural invasion-mediated inflammation in tumor microenvironment

BACKGROUND

The perineural invasion (PNI)-mediated inflammation of the tumor microenvironment (TME) varies among gastric cancer (GC) patients and exhibits a close relationship with prognosis and immunotherapy. Assessing the neuroinflammation of TME is important in predicting the response to immunotherapy in GC patients.

METHODS

Fifteen independent cohorts were enrolled in this study. An inflammatory score was developed and validated in GC. Based on PNI-related prognostic inflammatory signatures, patients were divided into Clusters A and B using unsupervised clustering. The characteristics of clusters and the potential regulatory mechanism of key genes were verified by RT-PCR, western-blot, immunohistochemistry and immunofluorescence in cell and tumor tissue samples.The neuroinflammation infiltration (NII) scoring system was developed based on principal component analysis (PCA) and visualized in a nomogram together with other clinical characteristics.

RESULTS

Inflammatory scores were higher in GC patients with PNI compared with those without PNI (P < 0.001). NII.clusterB patients with PNI had abundant immune cell infiltration in the TME but worse prognosis compared with patients in the NII.clusterA patients with PNI and non-PNI subgroups. Higher immune checkpoint expression was noted in NII.clusterB-PNI. VCAM1 is a specific signature of NII.clusterB-PNI, which regulates PD-L1 expression by affecting the phosphorylation of STAT3 in GC cells. Patients with PNI and high NII scores may benefit from immunotherapy. Patients with low nomogram scores had a better prognosis than those with high nomogram scores.

CONCLUSIONS

Inflammation mediated by PNI is one of the results of tumor-nerve crosstalk, but its impact on the tumor immune microenvironment is complex. Assessing the inflammation features of PNI is a potential method in predicting the response of immunotherapy effectively.

Immune regulation in gastric adenocarcinoma is linked with therapeutic efficacy and improved recovery

Adenocarcinomas are one of the most common histological types of gastric cancer. It has been ranked fifth among common cancers and is the third among death causing cancers worldwide. The high mortality rate among patients with gastric cancer is because of its silent evolution, genetic heterogeneity, high resistance to chemotherapy as well as unavailability of highly effective therapeutic strategy. Until now a number of several treatment strategies have been developed and are being practiced such as surgery, chemotherapy, radio therapy, and immunotherapy, however, further developments are required to improve the treatment responses and reduce the side effects. Therefore, novel personal therapeutic strategies based on immunological responses should be developed by targeting different check points and key immune players. Targeting macrophages and related molecular elements can be useful to achieve these goals. In this minireview, we discuss the available treatment options, molecular underpinnings and immunological regulations associated with gastric adenocarcinoma. We further describe the possible check points and immunological targets that can be used to develop novel therapeutic options.

Assessment of the reliability of MSI status and dMMR proteins deficiency screening on endoscopic biopsy material in esophagus and gastric adenocarcinoma

BACKGROUND

Microsatellite instability (MSI) is a negative predictive factor for neoadjuvant chemotherapy in resectable oesogastric adenocarcinoma and a crucial determinant for immunotherapy. We aimed to evaluate reliability of dMMR/MSI status screening performed on preoperative endoscopic biopsies.

METHODS

Paired pathological samples from biopsies and surgical specimen of oesogastric adenocarcinoma were retrospectively collected between 2009 and 2019. We compared dMMR status obtained by immunohistochemistry (IHC) and MSI status by PCR. dMMR/MSI status on surgical specimen was considered as reference.

RESULTS

PCR and IHC were conclusive on biopsies respectively for 53 (96.4%) and 47 (85.5%) of the 55 patients enrolled. IHC was not contributive for 1 surgical specimen. A third reading of IHC was carried out for 3 biopsies. MSI status was observed in 7 (12.5%) surgical specimens. When analyses were contributive, sensitivity and specificity of biopsies for dMMR/MSI were respectively 85% and 98% for PCR vs. 86% and 98% for IHC. Concordance rate between biopsies and surgical specimen was 96.2% for PCR and 97.8% for IHC.

CONCLUSIONS

Endoscopic biopsies are a suitable source of tissue for dMMR/MSI status determination in oesogastric adenocarcinoma which should be routinely performed at diagnosis to better adapt neoadjuvant treatment.

MINIABSTRACT

By comparison of dMMR phenotype obtained by immunohistochemistry and MSI status by PCR between match-paired samples of oesogastric cancer's endoscopic biopsies and surgical specimen, we observed that biopsies are a suitable source of tissue for dMMR/MSI status determination.

Mutational Signatures in Gastric Cancer and Their Clinical Implications

Gastric cancer is characterised by high inter- and intratumour heterogeneity. The majority of patients are older than 65 years and the global burden of this disease is increasing due to the aging of the population. The disease is usually diagnosed at advanced stages, which is a consequence of nonspecific symptoms. Few improvements have been made at the level of noninvasive molecular diagnosis of sporadic gastric cancer, and therefore the mortality rate remains high. A new field of mutational signatures has emerged in the past decade with advances in the genome sequencing technology. These distinct mutational patterns in the genome, caused by exogenous and endogenous mutational processes, can be associated with tumour aetiology and disease progression, and could provide novel perception on the treatment possibilities. This review assesses the mutational signatures found in gastric cancer and summarises their potential for use in clinical setting as diagnostic or prognostic biomarkers. Associated treatment options and biomarkers already implemented in clinical use are discussed, together with those that are still being explored or are in clinical studies.

KRAS activation in gastric cancer stem-like cells promotes tumor angiogenesis and metastasis

Our previous work showed that KRAS activation in gastric cancer cells leads to activation of an epithelial-to-mesenchymal transition (EMT) program and generation of cancer stem-like cells (CSCs). Here we analyze how this KRAS activation in gastric CSCs promotes tumor angiogenesis and metastasis. Gastric cancer CSCs were found to secrete pro-angiogenic factors such as vascular endothelial growth factor A (VEGF-A), and inhibition of KRAS markedly reduced secretion of these factors. In a genetically engineered mouse model, gastric tumorigenesis was markedly attenuated when both KRAS and VEGF-A signaling were blocked. In orthotropic implant and experimental metastasis models, silencing of KRAS and VEGF-A using shRNA in gastric CSCs abrogated primary tumor formation, lymph node metastasis, and lung metastasis far greater than individual silencing of KRAS or VEGF-A. Analysis of gastric cancer patient samples using RNA sequencing revealed a clear association between high expression of the gastric CSC marker CD44 and expression of both KRAS and VEGF-A, and high CD44 and VEGF-A expression predicted worse overall survival. In conclusion, KRAS activation in gastric CSCs enhances secretion of pro-angiogenic factors and promotes tumor progression and metastasis.

CHSY3 promotes proliferation and migration in gastric cancer and is associated with immune infiltration

BACKGROUND

The glycosyltransferase CHSY3 is a CHSY family member, yet its importance in the context of gastric cancer development remains incompletely understood. The present study was thus developed to explore the mechanistic importance of CHSY3 as a regulator of gastric cancer.

METHODS

Expression of CHSY3 was verified by TCGA, GEO and HPA databases. Kaplan-Meier curve, ROC, univariate cox, multivariate cox, and nomogram models were used to verify the prognostic impact and predictive value of CHSY3. KEGG and GO methods were used to identify signaling pathways associated with CHSY3. TIDE and IPS scores were used to assess the immunotherapeutic value of CHSY3. WGCNA, Cytoscape constructs PPI networks and random forest models to identify key Hub genes. Finally, qRT-PCR and immunohistochemical staining were performed to verify CHSY3 expression in clinical specimens. The ability of CHSY3 to regulate tumor was further assessed by CCK-8 assay and cloning assay, EDU assay, migration assay, invasion assay, and xenograft tumor model analysis.

RESULTS

The expression of CHSY3 was discovered to be abnormally upregulated in GC tissues through TCGA, GEO, and HPA databases, and the expression of CHSY3 was associated with poor prognosis in GC patients. Correlation analysis and Cox regression analysis revealed higher CHSY3 expression in higher T staging, an independent prognostic factor for GC. Moreover, elevated expression of CHSY3 was found to reduce the benefit of immunotherapy as assessed by the TIDE score and IPS score. Then, utilizing WGCNA, the PPI network constructed by Cytoscape, and random forest model, the Hub genes of COL5A2, POSTN, COL1A1, and FN1 associated with immunity were screened. Finally, the expression of CHSY3 in GC tissues was verified by qRT-PCR and immunohistochemical staining. Moreover, the expression of CHSY3 was further demonstrated by in vivo and in vitro experiments to promote the proliferation, migration, and invasive ability of GC.

CONCLUSIONS

The results of this study suggest that CHSY3 is an important regulator of gastric cancer progression, highlighting its promise as a therapeutic target for gastric cancer.

Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma

We aimed to characterize the stomach adenocarcinoma (STAD) microbiota and its clinical value using an integrated analysis of the microbiome and transcriptome. Microbiome and transcriptome data were downloaded from the Cancer Microbiome Atlas and the Cancer Genome Atlas databases. We identified nine differentially abundant microbial genera, including Helicobacter, Mycobacterium, and Streptococcus, which clustered patients into three subtypes with different survival rates. In total, 74 prognostic genes were screened from 925 feature genes of the subtypes, among which five genes were identified for prognostic model construction, including NTN5, MPV17L, MPLKIP, SIGLEC5, and SPAG16. The prognostic model could stratify patients into different risk groups. The high-risk group was associated with poor overall survival. A nomogram established using the prognostic risk score could accurately predict the 1, 3, and 5 year overall survival probabilities. The high-risk group had a higher proportion of histological grade 3 and recurrence samples. Immune infiltration analysis showed that samples in the high-risk group had a higher abundance of infiltrating neutrophils. The Notch signaling pathway activity showed a significant difference between the high- and low-risk groups. In conclusion, a prognostic model based on five feature genes of microbial subtypes could predict the overall survival for patients with STAD.

HER2-Positive Gastric Cancer: The Role of Immunotherapy and Novel Therapeutic Strategies

Gastric cancer is an aggressive disease with increasing global incidence in recent years. Human epidermal growth receptor 2 (HER2) is overexpressed in approximately 10-20% of gastric cancers. The implementation of targeted therapy against HER2 as part of the standard of care treatment in metastatic disease has improved the prognosis of this subset of patients. However, gastric cancer still has high mortality rates and urgently requires new treatment strategies. The combination of immunotherapy with HER2-targeted therapies has shown synergistic effects in preclinical models, this being the rationale behind exploring this combination in clinical trials in locally advanced and metastatic settings. Additionally, the irruption of antibody-drug conjugates and other novel HER2-targeted agents has led to the development of numerous clinical trials showing promising results. This review presents the molecular mechanisms supporting the use of HER2-targeted drugs in combination with immunotherapy and provides an overview of the therapeutic scenario of HER2-positive disease. We focus on the role of immunotherapy but also summarize emerging therapies and combinations under clinical research that may change the standard treatment in HER-2 positive disease in the future.

Novel Biomarkers of Gastric Cancer: Current Research and Future Perspectives

Gastric cancer is a heterogeneous disease with diverse histological and genomic subtypes, making it difficult to demonstrate treatment efficacy in clinical trials. However, recent efforts have been made to identify molecular biomarkers with prognostic and predictive implications to better understand the broad heterogeneity of gastric cancer and develop effective targeted therapies for it. HER2 overexpression, HER2/neu amplification, MSI-H, and PD-L1+ are predictive biomarkers in gastric cancer, and a growing number of clinical trials based on novel biomarkers have demonstrated the efficacy of targeted therapies alone or in combination with conventional chemotherapy. Enrichment design clinical trials of targeted therapies against FGFR2b and claudin 18.2 have demonstrated efficacy in unresectable advanced gastric cancer. Nonetheless, it is essential to continuously validate promising molecular biomarkers and introduce them into clinical practice to optimize treatment selection and improve patient outcomes. In this review, we focused on established (PD-L1, HER2, MSI) and emerging biomarkers (FGFR2, CLDN18.2) in gastric cancer, their clinical significance, detection methods, limitations, and molecular agents that target these biomarkers.

Immune escape and resistance to immunotherapy in mismatch repair deficient tumors

Up to 30% of colorectal, endometrial and gastric cancers have a deficiency in mismatch repair (MMR) protein expression due to either germline or epigenetic inactivation. Patients with Lynch Syndrome who inherit an inactive MMR allele have an up to 80% risk for developing a mismatch repair deficient (MMRd) cancer. Due to an inability to repair DNA, MMRd tumors present with genomic instability in microsatellite regions (MS). Tumors with high MS instability (MSI-H) are characterized by an increased frequency of insertion/deletions (indels) that can encode novel neoantigens if they occur in coding regions. The high tumor antigen burden for MMRd cancers is accompanied by an inflamed tumor microenvironment (TME) that contributes to the clinical effectiveness of anti-PD-1 therapy in this patient population. However, between 40 and 70% of MMRd cancer patients do not respond to treatment with PD-1 blockade, suggesting that tumor-intrinsic and -extrinsic resistance mechanisms may affect the success of checkpoint blockade. Immune evasion mechanisms that occur during early tumorigenesis and persist through cancer development may provide a window into resistance pathways that limit the effectiveness of anti-PD-1 therapy. Here, we review the mechanisms of immune escape in MMRd tumors during development and checkpoint blockade treatment, including T cell dysregulation and myeloid cell-mediated immunosuppression in the TME. Finally, we discuss the development of new therapeutic approaches to tackle resistance in MMRd tumors, including cancer vaccines, therapies targeting immunosuppressive myeloid programs, and immune checkpoint combination strategies.

Development of a Novel Anti-CD44 Variant 8 Monoclonal Antibody C44Mab-94 against Gastric Carcinomas

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. GC with peritoneal metastasis exhibits a poor prognosis due to the lack of effective therapy. A comprehensive analysis of malignant ascites identified the genomic alterations and significant amplifications of cancer driver genes, including CD44. CD44 and its splicing variants are overexpressed in tumors, and play crucial roles in the acquisition of invasiveness, stemness, and resistance to treatments. Therefore, the development of CD44-targeted monoclonal antibodies (mAbs) is important for GC diagnosis and therapy. In this study, we immunized mice with CD44v3-10-overexpressed PANC-1 cells and established several dozens of clones that produce anti-CD44v3-10 mAbs. One of the clones (C₄₄Mab-94; IgG₁, kappa) recognized the variant-8-encoded region and peptide, indicating that C₄₄Mab-94 is a specific mAb for CD44v8. Furthermore, C₄₄Mab-94 could recognize CHO/CD44v3-10 cells, oral squamous cell carcinoma cell line (HSC-3), or GC cell lines (MKN45 and NUGC-4) in flow cytometric analyses. C₄₄Mab-94 could detect the exogenous CD44v3-10 and endogenous CD44v8 in western blotting and stained the formalin-fixed paraffin-embedded gastric cancer cells. These results indicate that C₄₄Mab-94 is useful for detecting CD44v8 in a variety of experimental methods and is expected to become usefully applied to GC diagnosis and therapy.

Early stage gastric adenocarcinoma: clinical and molecular landscapes

Gastric adenocarcinoma, even when diagnosed at an early (localized) disease stage, poses a major health-care burden with cure rates that remain unsatisfactorily low, particularly in Western countries. This lack of progress reflects, among other aspects, the impracticality of early diagnosis, considerable variations in therapeutic approaches that is partly based on regional preferences, and the ingrained heterogeneity of gastric adenocarcinoma cells and their associated tumour microenvironment (TME). Clinical trials have long applied empirical interventions with the assumption that all early stage gastric adenocarcinomas are alike. Despite certain successes, the shortcomings of these approaches can potentially be overcome by targeting the specific molecular subsets of gastric adenocarcinomas identified by genomic and/or multi-omics analyses, including microsatellite instability-high, Epstein-Barr virus-induced, DNA damage repair-deficient, HER2-positive and PD-L1-high subtypes. Future approaches, including the availability of sophisticated vaccines, novel antibody technologies, agents targeting TME components (including fibroblasts, macrophages, cytokines or chemokines, and T cells) and novel immune checkpoint inhibitors, supported by improved tissue-based and blood-based diagnostic assays, seem promising. In this Review, we highlight current knowledge of the molecular and cellular biology of gastric adenocarcinomas, summarize the current approaches to clinical management of the disease, and consider the role of novel management and/or treatment strategies.

Artificial Intelligence in the Pathology of Gastric Cancer

Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

Proteomic profiling of gastric cancer with peritoneal metastasis identifies a protein signature associated with immune microenvironment and patient outcome

BACKGROUND

Peritoneal metastasis (PM) frequently occurs in patients with gastric cancer (GC) and is a major cause of mortality. Risk stratification for PM can optimize decision making in GC treatment.

METHODS

A total of 25 GC patients (13 with synchronous, 6 with metachronous PM and 6 PM-free) were included in this study. Quantitative proteomics by high-depth tandem mass tags labeling and whole-exome sequencing were conducted in primary GC and PM samples. Proteomic signature and prognostic model were established by machine learning algorithms in PM and PM-free GC, then validated in two external cohorts. Tumor-infiltrating immune cells in GC were analyzed by CIBERSORT.

RESULTS

Heterogeneity between paired primary and PM samples was observed at both genomic and proteomic levels. Compared to primary GC, proteome of PM samples was enriched in RNA binding and extracellular exosomes. 641 differently expressed proteins (DEPs) between primary GC of PM group and PM-free group were screened, which were enriched in extracellular exosome and cell adhesion pathways. Subsequently, a ten-protein signature was derived based on DEPs by machine learning. This signature was significantly associated with patient prognosis in internal cohort and two external proteomic datasets of diffuse and mixed type GC. Tumor-infiltrating immune cell analysis showed that the signature was associated with immune microenvironment of GC.

CONCLUSIONS

We characterized proteomic features that were informative for PM progression of GC. A protein signature associated with immune microenvironment and patient outcome was derived, and it could guide risk stratification and individualized treatment.

Molecular profile of poorly cohesive gastric carcinoma with special reference to survival

BACKGROUND

Patients with poorly cohesive gastric carcinoma (PCC) are known to have poor survival. However, detailed molecular biology of PCC has not been elucidated, except for mutations in CDH1 and RHOA. Additionally, the molecular profiles of signet-ring cell carcinoma (SRC) have not been fully investigated. We aimed to investigate the association between molecular profiles and survival in PCC and PCC subtypes.

METHODS

The present study included 455 patients with gastric adenocarcinoma underwent radical gastrectomy. Whole-exome sequencing and gene expression profiling were conducted. Patients were classified according to the WHO classification as PCC or non-PCC, with PCC being further classified into SRC, combined, and PCC not-otherwise-specified (NOS). Clinicopathological factors and survival were compared with molecular profiles.

RESULTS

Of the patients, 159 were classified with PCC, while 296 were classified with non-PCC. Among PCC, 44 were classified with SRC, 64 with combined, and 51 with PCC-NOS. Mutations in CDH1 and RHOA were remarkably more frequent in PCC than in non-PCC. PCC had worse overall survival (OS) and disease-specific survival (DSS) compared to non-PCC. For PCC, the SRC group had good OS and DSS, whereas PCC-NOS classification with CDH1 mutations was associated with extremely poor survival. In the PCC-NOS and combined groups, patients with mutations in the extracellular domain 1 of CDH1 had poor survival.

CONCLUSIONS

Our findings suggest that PCC has poorer survival than non-PCC. Accumulation of CDH1 and RHOA mutations are unique profiles in PCC. Among PCC, CDH1 mutations may play a crucial role in the survival of non-SRC PCC.

Immunotherapy for advanced gastric cancer

Gastric cancer (GC) is believed to be the fifth most common cancer and the third most common cause of death worldwide. Treatment techniques include radiation, chemotherapy, gastrectomy, and targeted treatments are often employed. Some hopeful results from the development of GC immunotherapy have already changed treatment approaches. Along with previous combination medicines, new immunotherapies have been developed that target distinct molecules. Despite ongoing studies into the current therapeutic options and significant improvements in this field, the prognosis for the ailment is poor. Since there are few treatment options and a delay in detection, the illness actually advances, spreads, and metastasizes. The bulk of immunotherapies in use today rely on cytotoxic immune cells, monoclonal antibodies, and gene-transferred vaccines. Immune checkpoint inhibitors have become more popular. In this review, we sought to examine the viewpoint and development of several immunotherapy treatment modalities for advanced GC, as well as the clinical results thus far reported. Additionally, we outlined tumor immune escape and tumor immunosurveillance.

Identification of a six-gene signature to predict survival and immunotherapy effectiveness of gastric cancer

Background

Gastric cancer (GC) ranks as the fifth most prevalent malignancy and the second leading cause of oncologic mortality globally. Despite staging guidelines and standard treatment protocols, significant heterogeneity exists in patient survival and response to therapy for GC. Thus, an increasing number of research have examined prognostic models recently for screening high-risk GC patients.

Methods

We studied DEGs between GC tissues and adjacent non-tumor tissues in GEO and TCGA datasets. Then the candidate DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of DEGs. We used the ROC curve, Kaplan-Meier curve, and risk score plot to evaluate the signature's performance and prognostic power. ESTIMATE, xCell, and TIDE algorithm were used to explore the relationship between the risk score and immune landscape relationship. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.

Results

There were 3211 DEGs in TCGA, 2371 DEGs in GSE54129, 627 DEGs in GSE66229, and 329 DEGs in GSE64951 selected as candidate genes and intersected with to obtain DEGs. In total, the 208 DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of 6 DEGs. External validation showed favorable predictive efficacy. We studied interaction between risk models, immunoscores, and immune cell infiltrate based on six-gene signature. The high-risk group exhibited significantly elevated ESTIMATE score, immunescore, and stromal score relative to low-risk group. The proportions of CD4⁺ memory T cells, CD8⁺ naive T cells, common lymphoid progenitor, plasmacytoid dentritic cell, gamma delta T cell, and B cell plasma were significantly enriched in low-risk group. According to TIDE, the TIDE scores, exclusion scores and dysfunction scores for low-risk group were lower than those for high-risk group. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.

Conclusion

In conclusion, we discovered a 6 gene signature to forecast GC patients' OS. This risk signature proves to be a valuable clinical predictive tool for guiding clinical practice.

The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study

Purpose

To establish and validate a machine learning based radiomics model for detection of perineural invasion (PNI) in gastric cancer (GC).

Methods

This retrospective study included a total of 955 patients with GC selected from two centers; they were separated into training (n=603), internal testing (n=259), and external testing (n=93) sets. Radiomic features were derived from three phases of contrast-enhanced computed tomography (CECT) scan images. Seven machine learning (ML) algorithms including least absolute shrinkage and selection operator (LASSO), naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was constructed by aggregating the radiomic signatures and important clinicopathological characteristics. The predictive ability of the radiomic model was then assessed with receiver operating characteristic (ROC) and calibration curve analyses in all three sets.

Results

The PNI rates for the training, internal testing, and external testing sets were 22.1, 22.8, and 36.6%, respectively. LASSO algorithm was selected for signature establishment. The radiomics signature, consisting of 8 robust features, revealed good discrimination accuracy for the PNI in all three sets (training set: AUC = 0.86; internal testing set: AUC = 0.82; external testing set: AUC = 0.78). The risk of PNI was significantly associated with higher radiomics scores. A combined model that integrated radiomics and T stage demonstrated enhanced accuracy and excellent calibration in all three sets (training set: AUC = 0.89; internal testing set: AUC = 0.84; external testing set: AUC = 0.82).

Conclusion

The suggested radiomics model exhibited satisfactory prediction performance for the PNI in GC.

LINE-1 repression in Epstein-Barr virus-associated gastric cancer through viral-host genome interaction

Long INterspersed Element 1 (LINE-1 or L1) acts as a major remodeling force in genome regulation and evolution. Accumulating evidence shows that virus infection impacts L1 expression, potentially impacting host antiviral response and diseases. The underlying regulation mechanism is unclear. Epstein-Barr virus (EBV), a double-stranded DNA virus linked to B-cell and epithelial malignancies, is known to have viral-host genome interaction, resulting in transcriptional rewiring in EBV-associated gastric cancer (EBVaGC). By analyzing publicly available datasets from the Gene Expression Omnibus (GEO), we found that EBVaGC has L1 transcriptional repression compared with EBV-negative gastric cancer (EBVnGC). More specifically, retrotransposition-associated young and full-length L1s (FL-L1s) were among the most repressed L1s. Epigenetic alterations, especially increased H3K9me3, were observed on FL-L1s. H3K9me3 deposition was potentially attributed to increased TASOR expression, a key component of the human silencing hub (HUSH) complex for H3K9 trimethylation. The 4C- and HiC-seq data indicated that the viral DNA interacted in the proximity of the TASOR enhancer, strengthening the loop formation between the TASOR enhancer and its promoter. These results indicated that EBV infection is associated with increased H3K9me3 deposition, leading to L1 repression. This study uncovers a regulation mechanism of L1 expression by chromatin topology remodeling associated with viral-host genome interaction in EBVaGC.

A comparative study of gastric adenocarcinoma HER2 IHC phenotype and mass spectrometry-based quantification

Introduction

Gastric cancer is a highly heterogeneous malignant tumor of the digestive system. Anti-HER2 treatment can inhibit downstream signaling pathways and improve clinical treatment and outcomes in patients with HER2 protein overexpression. Currently, two standard methods for evaluating HER2 expression status are immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). However, these low-throughput assays often produce discordant or equivocal results.

Methods

In this study, we presented a new HER2 protein detection method based on mass spectrometry selected reaction monitoring (MS-SRM) and validated the method. We conducted a retrospective study on 118 formalin-fixed paraffin-embedded (FFPE) tissues from patients with advanced gastric adenocarcinoma in northern China, and we compared the MS-SRM results with those from IHC and correlated them with FISH.

Results

We established and validated the upper and lower detection limits (300-700 amol/μg) for abnormal HER2 protein expression in advanced gastric cancer. We also found that, among samples with mixed Lauren subtypes, those with a high level of HER2 expression had typical intestinal type features in pathology.

Discussion

This study demonstrated that the MS-SRM method can overcome the limitations and deficiencies of IHC, directly quantify the expression of HER2 protein in tumor cells and be used as a supplement to IHC. It has the potential to be used as a companion diagnosis for new drugs used to treat advanced gastric cancer. Large-scale clinical validation is required.

A Novel Gene List Identifies Tumors with a Stromal-Mesenchymal Phenotype and Worse Prognosis in Gastric Cancer

BACKGROUND

Molecular biomarkers that predict disease progression can help identify tumor subtypes and shape treatment plans. In this study, we aimed to identify robust biomarkers of prognosis in gastric cancer based on transcriptomic data obtained from primary gastric tumors.

METHODS

Microarray, RNA sequencing, and single-cell RNA sequencing-based gene expression data from gastric tumors were obtained from public databases. Freshly frozen gastric tumors (n = 42) and matched FFPE (formalin-fixed, paraffin-embedded) (n = 40) tissues from a Turkish gastric cancer cohort were used for quantitative real-time PCR and immunohistochemistry-based assessments of gene expression, respectively.

RESULTS

A novel list of 20 prognostic genes was identified and used for the classification of gastric tumors into two major tumor subgroups with differential stromal gene expression ("Stromal-UP" (SU) and "Stromal-DOWN" (SD)). The SU group had a more mesenchymal profile with an enrichment of extracellular matrix-related gene sets and a poor prognosis compared to the SD group. Expression of the genes within the signature correlated with the expression of mesenchymal markers ex vivo. A higher stromal content in FFPE tissues was associated with shorter overall survival.

CONCLUSIONS

A stroma-rich, mesenchymal subgroup among gastric tumors identifies an unfavorable clinical outcome in all cohorts tested.

A thirty-three gene-based signature predicts lymph node metastasis and prognosis in patients with gastric cancer

Recently, several studies have indicated the great potential of gene expression signature of the primary tumor in predicting lymph node metastasis; however, few current gene biomarkers can predict lymph node status and prognosis in gastric cancer (GC). Thus, we used the RNA-seq data from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes between pathological lymph node-negative (pN0) and positive (pN+) patients and to establish a gene signature that could predict lymph node metastasis. Meanwhile, the robustness of identified gene signatures was validated in an independent dataset Asian Cancer Research Group (n = 300). In this study, our thirty-three gene-based signature was highly correlated with lymph node metastasis and could successfully discriminate pN + patients in the training set (Area under the receiver operating characteristic curve = 0.951). Moreover, Disease-free survival (P = 0.0029) and overall survival (P = 0.026) were significantly worse in high-risk compared with low-risk patients overall and when confined to pN0 patients only (P < 0.0001). Of note, this gene signature also proved useful in predicting lymph node status and survival in the validation cohort. The present study suggests a thirty-three gene-based signature that could effectively predict lymph node metastasis and prognosis in GC.

SOX9 Modulates the Transformation of Gastric Stem Cells Through Biased Symmetric Cell Division

BACKGROUND & AIMS

Transformation of stem/progenitor cells has been associated with tumorigenesis in multiple tissues, but stem cells in the stomach have been hard to localize. We therefore aimed to use a combination of several markers to better target oncogenes to gastric stem cells and understand their behavior in the initial stages of gastric tumorigenesis.

METHODS

Mouse models of gastric metaplasia and cancer by targeting stem/progenitor cells were generated and analyzed with techniques including reanalysis of single-cell RNA sequencing and immunostaining. Gastric cancer cell organoids were genetically manipulated with clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) for functional studies. Cell division was determined by bromodeoxyuridine-chasing assay and the assessment of the orientation of the mitotic spindles. Gastric tissues from patients were examined by histopathology and immunostaining.

RESULTS

Oncogenic insults lead to expansion of SOX9+ progenitor cells in the mouse stomach. Genetic lineage tracing and organoid culture studies show that SOX9+ gastric epithelial cells overlap with SOX2+ progenitors and include stem cells that can self-renew and differentiate to generate all gastric epithelial cells. Moreover, oncogenic targeting of SOX9+SOX2+ cells leads to invasive gastric cancer in our novel mouse model (Sox2-CreERT;Sox9-loxp(66)-rtTA-T2A-Flpo-IRES-loxp(71);Kras(Frt-STOP-Frt-G12D);P53R172H), which combines Cre-loxp and Flippase-Frt genetic recombination systems. Sox9 deletion impedes the expansion of gastric progenitor cells and blocks neoplasia after Kras activation. Although Sox9 is not required for maintaining tissue homeostasis where asymmetric division predominates, loss of Sox9 in the setting of Kras activation leads to reduced symmetric cell division and effectively attenuates the Kras-dependent expansion of stem/progenitor cells. Similarly, Sox9 deletion in gastric cancer organoids reduces symmetric cell division, organoid number, and organoid size. In patients with gastric cancer, high levels of SOX9 are associated with recurrence and poor prognosis.

CONCLUSION

SOX9 marks gastric stem cells and modulates biased symmetric cell division, which appears to be required for the malignant transformation of gastric stem cells.

Ubiquitin-proteasome system reveals clinical subtypes with distinct molecular characteristics and prognoses in gastric cancer

Modulators in ubiquitin-proteasome system (UPS) has been implicated in regulating cancer-related genes, immune responses, and oncogenesis. However, the global UPS expression pattern and its role in gastric cancer (GC) pathology remain elusive. Herein, we integrated the modulators in UPS and dissected their associations with tumor microenvironment (TME), therapeutic response and prognosis in GC. Ten eligible GC cohorts (n = 2161) were collected in this comprehensive analysis. Unsupervised clustering based on expression profile of ubiquitination regulators was performed to identify distinct expression pattern. Then, pathway activation, and TME characteristics and prognosis were explored for patients in each pattern. Finally, a UPS scoring system in GC, termed UPSGC, is developed for individualized quantification of UPS expression pattern. Two prognosis-distinctive UPS expression patterns were identified and validated. Multiple interdependent characteristics were found in each pattern. Patients in the pattern with poor prognosis were found with activation of EMT, TNFα/NF-κB and IL6/JAK/STAT3 signaling, and more infiltration of immunosuppressive M2 macrophages and Th2 cells in TME. And another pattern was characterized by upregulation of angiogenesis, Notch and Wnt-β/catenin signaling, as well as enrichment of microvessels in TME. Based on the UPSGC system, two pattern-related clinical subtypes were identified. Finally, the UPSGC subtypes were validated as robust biomarker to predict patient's therapeutic responses and survival outcomes. In conclusion, this study proposes two previously unexplored UPS expression patterns in GC, in which patients have distinct survival outcomes and molecular characteristics. The findings provide new evidences to support the clinical relevance of ubiquitination with personalized therapy.

Integrative single-cell multiomics analyses dissect molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer

Human gastric cancer is a highly lethal disease, but the underlying multiomic molecular signatures remain largely unclear. Here, we performed multi-regional sampling, parallel single-cell multiomics sequencing and integrated analyses of human gastric cancer. We identified common transcriptomic alterations of gastric cancer cells, such as aberrant down-regulation of genes associated with normal stomach function and up-regulation of KRT7, PI3, S100A4, etc. Surprisingly, aberrant and prevalent up-regulation of genes highly expressed in normal colorectal epithelial cells were also identified in cancer cells, which may be partially regulated by promoter chromatin accessibility and DNA methylation levels. We revealed the single-cell DNA methylome landscape of gastric cancer, and identified candidate DNA methylation biomarkers, such as hypermethylated promoters of TMEM240 and HAGLROS, and hypomethylated promoters of TRPM2-AS and HRH1. Additionally, the relationships between genetic lineages, DNA methylation and transcriptomic clusters were systematically revealed at single-cell level. We showed that DNA methylation heterogeneities were mainly among different genetic lineages of cancer cells. Moreover, we found that DNA methylation levels of cancer cells with poorer differentiation states tend to be higher than those of cancer cells with better differentiation states in the primary tumor within the same patient, although still lower than in normal gastric epithelial cells. Cancer cells with poorer differentiation states also prevalently down-regulated MUC1 expression and immune-related pathways, and had poor infiltration of CD8⁺ T cells. Our study dissected the molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer using integrative single-cell multiomics analyses.

Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in gastric cancer

Metabolic reprogramming has been defined as a key hall mark of human tumors. However, metabolic heterogeneity in gastric cancer has not been elucidated. Here we separated the TCGA-STAD dataset into two metabolic subtypes. The differences between subtypes were elaborated in terms of transcriptomics, genomics, tumor-infiltrating cells, and single-cell resolution. We found that metabolic subtype 1 is predominantly characterized by low metabolism, high immune cell infiltration. Subtype 2 is mainly characterized by high metabolism and low immune cell infiltration. From single-cell resolution, we found that the high metabolism of subtype 2 is dominated by epithelial cells. Not only epithelial cells, but also various immune cells and stromal cells showed high metabolism in subtype 2 and low metabolism in subtype 1. Our study established a classification of gastric cancer metabolic subtypes and explored the differences between subtypes from multiple dimensions, especially the single-cell resolution.