Biomarkers in Immune Checkpoint Inhibitor Cancer Treatment

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy by offering durable responses for specific patient populations. The heterogeneity in results among individuals underscores the vital need for reliable predictive biomarkers to guide therapeutic strategies. This review aims to elucidate the current landscape of biomarkers in ICI therapy, focusing on their capability to predict patient responses and guide personalized cancer treatment. We examine both existing and emerging biomarkers—such as programmed death-ligand 1 (PD-L1) expression, tumor mutational burden, circulating tumor DNA, gene expression profiles, and the neutrophil-to-lymphocyte ratio—which provide critical insights into patient classification, therapeutic response monitoring, and identification of resistance mechanisms. Despite their potential, considerable challenges remain, including tumor heterogeneity, variability in biomarker testing, and the complexity and dynamic nature of biomarkers across different tumor types. Progress in multi-biomarker approaches and the use of artificial intelligence demonstrate potential for enhancing prediction precision and facilitating more personalized immunotherapy treatments. Additionally, this review summarizes completed and ongoing clinical trials investigating the effect of predictive biomarkers in directing ICI-based cancer treatments and their ability to tailor therapeutic strategies. In conclusion, the review provides an overview of the role of predictive biomarkers in response to ICI therapy.