Meyer Anna¹, Braun Leon², Collins Emma³, Richter Paul⁴, Wagner Zoe⁵, Keller Max⁶, Schultz Ella⁷
ABSTRACT:
Immune checkpoint inhibitors (ICIs) have significantly improved survival outcomes in patients with advanced lung cancer and melanoma. However, therapeutic response remains heterogeneous, necessitating the identification of robust predictive biomarkers. PD-L1 expression, tumor mutational burden (TMB), and the presence of tumor-infiltrating lymphocytes (TILs) are among the most studied markers. Additionally, circulating immune-related biomarkers and gut microbiota composition are emerging as potential predictors. Genetic and epigenetic alterations, as well as transcriptomic signatures, are being integrated into predictive models. Understanding the interplay between the tumor microenvironment and host immunity is critical to guiding ICI therapy. Recent studies also highlight the relevance of peripheral blood immune profiling and microbiome-mediated metabolic pathways in modulating checkpoint blockade efficacy. Integration of multi-omic data and machine learning is accelerating biomarker discovery and validation. This review explores the current landscape, ongoing clinical trials, and future potential of predictive biomarkers for optimizing immunotherapy in clinical practice.