Efficacy and predictive biomarkers of immunotherapy in Epstein-Barr virus-associated gastric cancer

Background Epstein-Barr virus (EBV)-associated gastric cancer (GC) (EBVaGC) is a distinct molecular subtype of GC with a favorable prognosis. However, the exact effects and potential mechanisms of EBV infection on immune checkpoint blockade (ICB) efficacy in GC remain to be clarified. Additionally, EBV-encoded RNA (EBER) in situ hybridization (ISH), the traditional method to detect EBV, could cause false-positive/false-negative results and not allow for characterizing other molecular biomarkers recommended by standard treatment guidelines for GC. Herein, we sought to investigate the efficacy and potential biomarkers of ICB in EBVaGC identified by next-generation sequencing (NGS). Design An NGS-based algorithm for detecting EBV was established and validated using two independent GC cohorts (124 in the training cohort and 76 in the validation cohort). The value of EBV infection for predicting ICB efficacy was evaluated among 95 patients with advanced or metastatic GC receiving ICB. The molecular predictive biomarkers for ICB efficacy were identified to improve the prediction accuracy of ICB efficacy in 22 patients with EBVaGC. Results Compared with orthogonal assay (EBER-ISH) results, the NGS-based algorithm achieved high performance with a sensitivity of 95.7% (22/23) and a specificity of 100% (53/53). EBV status was identified as an independent predictive factor for overall survival and progression-free survival in patients with DNA mismatch repair proficient (pMMR) GC following ICB. Moreover, the patients with EBV+/pMMR and EBV-/MMR deficient (dMMR) had comparable and favorable survival following ICB. Twenty-two patients with EBV+/pMMR achieved an objective response rate of 54.5% (12/22) on immunotherapy. Patients with EBVaGC with a high cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) level were less responsive to anti-programmed death-1/ligand 1 (PD-1/L1) monotherapy, and the combination of anti-CTLA-4 plus anti-PD-1/L1 checkpoint blockade benefited patients with EBVaGC more than anti-PD-1/L1 monotherapy with a trend close to significance (p=0.074). There were nearly significant differences in tumor mutational burden (TMB) level and SMARCA4 mutation frequency between the ICB response and non-response group. Conclusions We developed an efficient NGS-based EBV detection strategy, and this strategy-identified EBV infection was as effective as dMMR in predicting ICB efficacy in GC. Additionally, we identified CTLA-4, TMB, and SMARCA4 mutation as potential predictive biomarkers of ICB efficacy in EBVaGC, which might better inform ICB treatment for EBVaGC.

Automated summary

In this study, an efficient next-generation sequencing (NGS) based algorithm was developed and validated for detecting Epstein-Barr virus (EBV) infection in gastric cancer (GC). EBV infection was found to be an independent predictive factor for immune checkpoint blockade (ICB) efficacy in GC, and CTLA-4, tumor mutational burden (TMB), and SMARCA4 mutation were identified as potential predictive biomarkers for ICB efficacy in EBV-associated GC (EBVaGC).