Letter to the Editor: Integrating immune-related metrics with personalize first-line immunochemotherapy in human epidermal growth factor receptor 2-negative advanced gastric cancer

Abstract

Yao et al recently published a study in the World Journal of Gastrointestinal Oncology, which presented a timely prediction model using nomogram combined with immune, molecular and host-inflammation metrics, including programmed death ligand 1, microsatellite instability status, tumor-node-metastasis stage, tumor differentiation, neutrophil-to-lymphocyte ratio and the C-reactive protein-albumin-lymphocyte index to forcast in 200 human epidermal growth factor receptor 2 negative advanced gastric cancer patients treated with firstline sintilimab plus chemotherapy. Through internal validation, the models have showed promising discrimination with C-index of 0.78-0.82 offering a potentially practical tool for individualized risk stratification. This article commends the authors for integrating tumor biology and host systemic status into a single clinical instrument as well as highlighting routinely measurable inflammationnutrition metrics. At the same time, we raise methodological and implementation issues that merit attention before routine clinical use, including the need for external and prospective validation, risks of selection and treatmentspecific bias, assay heterogeneity for programmed death ligand 1 and microsatellite instability, potential overfitting with relatively small sample size, and the absence of tumor microenvironment or dynamic biomarkers. We propose concrete next steps to accelerate responsible translation by sharing the model with external validation across different programmed death 1 agents and populations, comprehensively assessing added value versus standard staging with decision curve and costeffectiveness analyses, and exploring incorporation into adaptive clinical trials and longitudinal monitoring.

Keywords: Gastric cancer; Programmed death ligand 1 inhibitor; Predictive model; Neutrophil-to-lymphocyte ratio; C-reactive protein-albumin-lymphocyte index; Efficacy

Core Tip: This study developed and validated a novel survival prediction model for advanced human epidermal growth factor receptor 2 negative gastric cancer patients receiving immunotherapy combined with chemotherapy. A retrospective analysis of 200 patients identified programmed death ligand 1 expression, microsatellite instability, tumor-node-metastasis stage, tumor differentiation, neutrophil-to-lymphocyte ratio, and C-reactive protein-albumin-lymphocyte index as independent prognostic factors. Based on these factors, nomogram models for progression-free survival and overall survival were constructed and validated using the concordance index and area under the curve. The results showed good discrimination ability in both the training and validation sets, indicating that the model effectively predicts patient survival outcomes and provides strong support for personalized treatment decisions.