From prediction to precision: Biomarker discovery and predictive modeling for personalized immune checkpoint blockade therapy.

Abstract

Immune checkpoint blockers have revolutionized cancer therapy, offering a promising treatment option for various malignancies. However, response rates vary widely across cancer types, and a significant proportion of patients do not benefit from this therapy. While numerous clinical and molecular biomarkers have been proposed to predict response at baseline, few offer sufficient accuracy and generalizability across tumor types. Advances in molecular profiling, particularly single-cell and spatial transcriptomics, now enable high-resolution analysis of the tumor microenvironment, revealing interactions between tumor and immune cells that drive therapy sensitivity, resistance, and immune-related adverse events. These technologies provide unprecedented insight into the cellular and molecular dynamics of tumors and their surrounding cells and could support the discovery of novel biomarkers and therapeutic targets. In this review, we discuss the current landscape of ICB biomarkers, the profiling techniques underlying their identification, and computational methods that drive biomarker discovery and integration across multiple modalities to predict therapy response and toxicity. Finally, we discuss how the incorporation of molecular profiling with predictive modeling holds great promise for advancing precision immunotherapy in the neoadjuvant setting. Ultimately, the goal is to deliver tailored treatment strategies that maximize response and minimize harmful side effects.