A novel biomarker associated with EBV infection improves response prediction of immunotherapy in gastric cancer.

BACKGROUND: Novel biomarkers are required in gastric cancer (GC) treated by immunotherapy. Epstein-Barr virus (EBV) infection induces an immune-active tumor microenvironment, while its association with immunotherapy response is still controversial. Genes underlying EBV infection may determine the response heterogeneity of EBV + GC. Thus, we screened hub genes associated with EBV infection to predict the response to immunotherapy in GC. METHODS: Prognostic hub genes associated with EBV infection were screened using multi-omic data of GC. EBV + GC cells were established and confirmed by EBV-encoded small RNA in situ hybridization (EBER-ISH). Immunohistochemistry (IHC) staining of the hub genes was conducted in GC samples with EBER-ISH assay. Infiltrating immune cells were stained using immunofluorescence. RESULTS: CHAF1A was identified as a hub gene in EBV + GC, and its expression was an independent predictor of overall survival (OS). EBV infection up-regulated CHAF1A expression which also predicted EBV infection well. CHAF1A expression also predicted microsatellite instability (MSI) and a high tumor mutation burden (TMB). The combined score (CS) of CHAF1A expression with MSI or TMB further improved prognostic stratification. CHAF1A IHC score positively correlated with the infiltration of NK cells and macrophages M1. CHAF1A expression alone could predict the immunotherapy response, but its CS with EBV infection, MSI, TMB, or PD-L1 expression showed better effects and improved response stratification based on current biomarkers. CONCLUSIONS: CHAF1A could be a novel biomarker for immunotherapy of GC, with the potential to improve the efficacy of existing biomarkers.

Association of genes in hereditary metabolic diseases with diagnosis, prognosis, and treatment outcomes in gastric cancer.

Background: Aberrant metabolism is a major hallmark of cancers and hereditary diseases. Genes associated with inborn metabolic errors may also play roles in cancer development. This study evaluated the overall impact of these genes on gastric cancer (GC). Methods: In total, 162 genes involved in 203 hereditary metabolic diseases were identified in the Human Phenotype Ontology database. Clinical and multi-omic data were acquired from the GC cohort of the Affiliated Hospital of Jiangsu University and other published cohorts. A 4-gene and 32-gene signature was established for diagnosis and prognosis or therapeutic prediction, respectively, and corresponding abnormal metabolism scores (AMscores) were calculated. Results: The diagnostic AMscore showed high sensitivity (0.88-1.00) and specificity (0.89-1.00) to distinguish between GC and paired normal tissues, with area under the receiver operating characteristic curve (AUC) ranging from 0.911 to 1.000 in four GC cohorts. The prognostic or predictive AMscore was an independent predictor of overall survival (OS) in five GC cohorts and a predictor of the OS and disease-free survival benefit of postoperative chemotherapy or chemoradiotherapy in one GC cohort with such data. The AMscore adversely impacts immune biomarkers, including tumor mutation burden, tumor neoantigen burden, microsatellite instability, programmed death-ligand 1 protein expression, tumor microenvironment score, T cell receptor clonality, and immune cell infiltration detected by multiplex immunofluorescence staining. The AUC of the AMscore for predicting immunotherapy response ranging from 0.780 to 0.964 in four cohorts involving GC, urothelial cancer, melanoma, and lung cancer. The objective response rates in the low and high AMscore subgroups were 78.6% and 3.2%, 40.4% and 7%, 52.6% and 0%, and 72.7% and 0%, respectively (all p<0.001). In cohorts with survival data, a high AMscore was hazardous for OS or progression-free survival, with hazard ratios ranged from 5.79 to 108.59 (all p<0.001). Importantly, the AMscore significantly improved the prediction of current immune biomarkers for both response and survival, thus redefining the advantaged and disadvantaged immunotherapy populations. Conclusions: Signatures based on genes associated with hereditary metabolic diseases and their corresponding scores could be used to guide the diagnosis and treatment of GC. Therefore, further validation is required.