Dynamic programmed death-ligand 1 and multimodal biomarkers in gastric cancer immunotherapy

Abstract

Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) and programmed death-1 (PD-1) are promising therapies for advanced gastric cancer (GC). However, challenges remain in identifying the best biomarkers to guide anti-PD-L1/PD-1 therapy. There are two competing paradigms for selecting immunotherapy biomarkers in clinical practice. One favors static biomarkers, such as Epstein-Barr virus positivity, microsatellite instability status, and the PD-L1 combined positive score, due to their track record in clinical trials. The other emerging view prefers dynamic tumor biomarkers, such as circulating tumor DNA, exosomal PD-L1, and immune cell profiling, to better reveal therapy-induced immune changes. This article discusses three main questions for real-time assessment of anti-PD-L1/PD-1 therapy in advanced GC: (1) What complementary approaches can overcome static PD-L1 assessment limitations; (2) How dynamic tumor biomarkers could be used in clinical practice to improve monitoring and patient selection; and (3) How multimodal artificial intelligence could support personalized immunotherapy. These strategies may improve patient selection and enhance the clinical impact of immunotherapy in advanced GC.

Keywords: Gastric cancer; Programmed death-ligand 1 expression; Tumor biomarkers; Artificial intelligence; Immune checkpoint inhibitors

Core Tip: Emerging non-invasive, real-time biomarkers such as serial exosomal programmed death-ligand 1 (PD-L1), circulating tumor DNA, and circulating immune cells may help overcome the limitations of static PD-L1 assessment, thereby improving the efficacy of anti-programmed death-1/PD-L1 therapy in advanced gastric cancer. Validation of rapid, cost-effective, and point-of-care dynamic tumor biomarkers is essential before their integration into routine clinical practice. Moreover, integrating histopathological and radiological findings with standard tumor biomarker data through artificial intelligence offers the potential to overcome the main limitations of circulating tumor DNA next-generation sequencing and PD-L1 immunohistochemistry assessment.