4.6 Article

Characterization of genomic alterations and neoantigens and analysis of immune infiltration identified therapeutic and prognostic biomarkers in adenocarcinoma at the gastroesophageal junction

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FRONTIERS IN ONCOLOGY
卷 12, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2022.941868

关键词

adenocarcinoma at the gastroesophageal junction; genome and transcriptome; tumor neoantigens; CD8+T cells; therapeutic biomarkers; prognostic prediction

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资金

  1. National Key Research and Development Program of China
  2. National Science Fund for Distinguished Young Scholars
  3. Medical and Health Technology Innovation Project of Chinese Academy of Medical Sciences
  4. Beijing Outstanding Young Scientist Program
  5. [2016YFC1302700]
  6. [81725015]
  7. [2016-I2M-4-002]
  8. [2019-I2M-2-001]
  9. [BJJWZYJH01201910023027]

向作者/读者索取更多资源

This study investigates the genomic alterations and neoantigen characteristics of adenocarcinoma at the gastroesophageal junction (ACGEJ), identifying mutation signatures, driver genes, and CD8+ T-cell infiltration-related genes. A risk model is developed to predict patient survival. The findings provide potential immunotherapeutic targets and prognostic biomarkers for ACGEJ treatment.
ObjectivesAdenocarcinoma at the gastroesophageal junction (ACGEJ) refers to a malignant tumor that occurs at the esophagogastric junction. Despite some progress in targeted therapies for HER2, FGFR2, EGFR, MET, Claudin 18.2 and immune checkpoints in ACGEJ tumors, the 5-year survival rate of patients remains poor. Thus, it is urgent to explore genomic alterations and neoantigen characteristics of tumors and identify CD8+ T-cell infiltration-associated genes to find potential therapeutic targets and develop a risk model to predict ACGEJ patients' overall survival (OS). MethodsWhole-exome sequencing (WES) was performed on 55 paired samples from Chinese ACGEJ patients. Somatic mutations and copy number variations were detected by Strelka2 and FACETS, respectively. SigProfiler and SciClone were employed to decipher the mutation signature and clonal structure of each sample, respectively. Neoantigens were predicted using the MuPeXI pipeline. RNA sequencing (RNA-seq) data of ACGEJ samples from our previous studies and The Cancer Genome Atlas (TCGA) were used to identify genes significantly associated with CD8+ T-cell infiltration by weighted gene coexpression network analysis (WGCNA). To construct a risk model, we conducted LASSO and univariate and multivariate Cox regression analyses. ResultsRecurrent MAP2K7, RNF43 and RHOA mutations were found in ACGEJ tumors. The COSMIC signature SBS17 was associated with ACGEJ progression. CCNE1 and VEGFA were identified as putative CNV driver genes. PI3KCA and TP53 mutations conferred selective advantages to cancer cells. The Chinese ACGEJ patient neoantigen landscape was revealed for the first time, and 58 potential neoantigens common to TSNAdb and IEDB were identified. Compared with Siewert type II samples, Siewert type III samples had significant enrichment of the SBS17 signature, a lower TNFRSF14 copy number, a higher proportion of samples with complex clonal architecture and a higher neoantigen load. We identified 10 important CD8+ T-cell infiltration-related Hub genes (CCL5, CD2, CST7, GVINP1, GZMK, IL2RB, IKZF3, PLA2G2D, P2RY10 and ZAP70) as potential therapeutic targets from the RNA-seq data. Seven CD8+ T-cell infiltration-related genes (ADAM28, ASPH, CAMK2N1, F2R, STAP1, TP53INP2, ZC3H3) were selected to construct a prognostic model. Patients classified as high risk based on this model had significantly worse OS than low-risk patients, which was replicated in the TCGA-ACGEJ cohort. ConclusionsThis study provides new neoantigen-based immunotherapeutic targets for ACGEJ treatment and effective disease prognosis biomarkers.

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