Update on Epstein-Barr virus and gastric cancer (Review)

Shinozaki‐Ushiku, Aya; Kunita, Akiko; Fukayama, Masashi

doi:10.3892/ijo.2015.2856

Cited by 257 publications

(275 citation statements)

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“…In our cohort, we found a lower incidence of EBV gastric cancers (5%) compared with the the Cancer Genome Atlas 6 (8.8%) and other publications (2-20%). 9 However, another study showed a similar frequency of 5.1%. 19 The mechanism of EBV carcinogenesis is a genome-wide DNA methylation.…”

Section: Discussionmentioning

confidence: 90%

“…6,23,24,28,29 This trend was also recorded in the Asian Cancer Research Group data set. 7 It has been suggested that the profound global DNA effects are responsible for the distinctive clinicopathologic features of EBV gastric cancers 20 including male predominance 9,30 and proximal location. 31 Notably, the purported younger age has not been confirmed by recent analysis.…”

Section: Discussionmentioning

confidence: 99%

“…9,10 Use of immunohistochemistry for mismatch repair proteins has a high sensitivity, specificity, and positive and negative predictive value for deficiency of the mismatch repair system. 11 Additionally, the predominant mechanism of microsatellite instable in gastric cancer is promoter hypermethylation of MLH1 rather than mutations.…”

Section: Rationale For Biomarker Evaluationmentioning

confidence: 99%

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A protein and mRNA expression-based classification of gastric cancer

et al. 2016

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The overall survival of gastric carcinoma patients remains poor despite improved control over known risk factors and surveillance. This highlights the need for new classifications, driven towards identification of potential therapeutic targets. Using sophisticated molecular technologies and analysis, three groups recently provided genetic and epigenetic molecular classifications of gastric cancer (The Cancer Genome Atlas, 'Singapore-Duke' study, and Asian Cancer Research Group). Suggested by these classifications, here, we examined the expression of 14 biomarkers in a cohort of 146 gastric adenocarcinomas and performed unsupervised hierarchical clustering analysis using less expensive and widely available immunohistochemistry and in situ hybridization. Ultimately, we identified five groups of gastric cancers based on Epstein-Barr virus (EBV) positivity, microsatellite instability, aberrant E-cadherin, and p53 expression; the remaining cases constituted a group characterized by normal p53 expression. In addition, the five categories correspond to the reported molecular subgroups by virtue of clinicopathologic features. Furthermore, evaluation between these clusters and survival using the Cox proportional hazards model showed a trend for superior survival in the EBV and microsatellite-instable related adenocarcinomas. In conclusion, we offer as a proposal a simplified algorithm that is able to reproduce the recently proposed molecular subgroups of gastric adenocarcinoma, using immunohistochemical and in situ hybridization techniques. Gastric carcinoma is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. Despite better control over known risk factors and development of new chemotherapeutic and targeted agents, in the United States, the 5-year overall survival for gastric cancer patients is only 29%. 1 With regard to patient stratification, although several morphologic classifications have been developed, one broadly used system is the Lauren classification, which divides gastric adenocarcinoma into intestinal and diffuse types. 2 The simple two-tiered classification gives a general understanding of the histogenesis and biology of gastric cancer, and has been particularly helpful in evaluating the epidemiologic data. Another widely used system is the WHO classification, which is based on more precise histologic patterns. It is an all-inclusive system, which recognizes all the rare subtypes that were not identified in the Lauren classification. 3 Nevertheless, its clinical utility is doubtful as there is little difference in outcomes between the distinct histological subgroups.Following the successful Trastuzumab for Gastric Cancer (ToGA) trial, the only validated predictive biomarker for personalized therapy of gastric cancer is limited to human epidermal growth factor receptor (Her2) protein expression. 4 Recently, anti-vascular endothelial growth factor receptor 2 (VEGFR2) antibody (ramucirumab) has been FDA approved, and anti-epidermal growth factor receptor (EGFR) an...

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Rationale For Biomarker Evaluationmentioning

confidence: 99%

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A protein and mRNA expression-based classification of gastric cancer

et al. 2016

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“…Besides, the infiltrated T cells produced much more IFNg in EBV-high-load GC other than EBV-low or negative GC, 39 therefore it is likely that EBVnGC is tend to acquire immune resistance through the upregulated IFNg production. In addition, EBVaGC is also characterized by a high frequency of gene mutation, such as PIK3CA (10.3%»80%), ARID1A (47%»55%), and AKT2 (38%), 14 tending to produce tumor neo-antigen. Studies confirmed that PD-1/PD-L1 and CTLA-4 inhibitor obtain better therapeutic efficacy in the tumors with the presence of a high mutation load, such as in malignant melanoma and non-small cell lung cancer.…”

Section: E1249558-10mentioning

confidence: 99%

“…Upon ligation with PD-L1 and PD-L2, PD-1 suppresses downstream PI3K-Akt signaling, which inhibits T cell proliferation. 14 Prior studies of lymphoma malignancies suggested EBV-associated upregulation of PD-L1 on the cell surface, which is IFNg-mediated, and thus inhibits killing of infected cells by cytotoxic T cells expressing PD-1. 15 Therefore, we hypothesis that the function of EBV-specific CTLs may be affected by the acquired immune resistance indicated by the upregulation of PD-1 on CTLs and we assumed that blocking the interaction between PD-1 and PD-L1/L2 could augment the antitumor immune responses in EBVaGC.…”

Section: Introductionmentioning

confidence: 99%

CRISPR-Cas9-mediated disruption of PD-1 on human T cells for adoptive cellular therapies of EBV positive gastric cancer

Zou

Chen

et al. 2016

OncoImmunology

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The successful use of immune cell checkpoint inhibitors PD-1 and PD-L1, over the past 5 y has raised the concern of using immunotherapy to treat various cancers. Epstein-Barr virus-associated gastric cancer (EBVaGC) exhibits high infiltration of lymphocytes and high amplification of immune-related genes including PD-L1 as distinguished from Epstein-Barr virus-non-associated gastric cancer (EBVnGC). Here, we presume that this PD-1/PD-L1 pathway may hinder the efficacy of adoptive T cell therapy toward EBVaGC. These studies reveal possibility of generating PD-1-disrupted CTL by CRISPR-Cas9 system and demonstrate enhanced immune response of these PD-1-disrupted CTLs to the EBV-LMP2A antigen and superior cytotoxicity to the EBV-positive gastric cancer cell. In addition, when combined with low-dose radiotherapy, these PD-1-disrupted CTLs mediated an impressive antitumor effect in a xenograft mouse model of EBVaGC. Taken together, these studies illustrate PD-1/PD-L1-mediated immune tolerance of EBVaGC and provide a new strategy for targeting immune checkpoints to break the tolerance for the T cell-based adoptive therapy.

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Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm

et al. 2022

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ImportanceEpstein-Barr virus (EBV)–associated gastric cancer (EBV-GC) is 1 of 4 molecular subtypes of GC and is confirmed by an expensive molecular test, EBV-encoded small RNA in situ hybridization. EBV-GC has 2 histologic characteristics, lymphoid stroma and lace-like tumor pattern, but projecting EBV-GC at biopsy is difficult even for experienced pathologists.ObjectiveTo develop and validate a deep learning algorithm to predict EBV status from pathology images of GC biopsy.Design, Setting, and ParticipantsThis diagnostic study developed a deep learning classifier to predict EBV-GC using image patches of tissue microarray (TMA) and whole slide images (WSIs) of GC and applied it to GC biopsy specimens from GCs diagnosed at Kangbuk Samsung Hospital between 2011 and 2020. For a quantitative evaluation and EBV-GC prediction on biopsy specimens, the area of each class and the fraction in total tissue or tumor area were calculated. Data were analyzed from March 5, 2021, to February 10, 2022.Main Outcomes and MeasuresEvaluation metrics of predictive model performance were assessed on accuracy, recall, precision, F1 score, area under the receiver operating characteristic curve (AUC), and κ coefficient.ResultsThis study included 137 184 image patches from 16 TMAs (708 tissue cores), 24 WSIs, and 286 biopsy images of GC. The classifier was able to classify EBV-GC image patches from TMAs and WSIs with 94.70% accuracy, 0.936 recall, 0.938 precision, 0.937 F1 score, and 0.909 κ coefficient. The classifier was used for predicting and measuring the area and fraction of EBV-GC on biopsy tissue specimens. A 10% cutoff value for the predicted fraction of EBV-GC to tissue (EBV-GC/tissue area) produced the best prediction results in EBV-GC biopsy specimens and showed the highest AUC value (0.8723; 95% CI, 0.7560-0.9501). That cutoff also obtained high sensitivity (0.895) and moderate specificity (0.745) compared with experienced pathologist sensitivity (0.842) and specificity (0.854) when using the presence of lymphoid stroma and a lace-like pattern as diagnostic criteria. On prediction maps, EBV-GCs with lace-like pattern and lymphoid stroma showed the same prediction results as EBV-GC, but cases lacking these histologic features revealed heterogeneous prediction results of EBV-GC and non–EBV-GC areas.Conclusions and RelevanceThis study showed the feasibility of EBV-GC prediction using a deep learning algorithm, even in biopsy samples. Use of such an image-based classifier before a confirmatory molecular test will reduce costs and tissue waste.

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Update on Epstein-Barr virus and gastric cancer (Review)

Cited by 257 publications

References 123 publications

A protein and mRNA expression-based classification of gastric cancer

A protein and mRNA expression-based classification of gastric cancer

CRISPR-Cas9-mediated disruption of PD-1 on human T cells for adoptive cellular therapies of EBV positive gastric cancer

Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm

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