Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy

Abstract

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment modality, offering promising outcomes for various malignancies. However, the efficacy of ICIs varies among patients, highlighting the essential need of accurate predictive biomarkers. This review synthesizes the current understanding of biomarkers for ICI therapy, and discusses the clinical utility and limitations of these biomarkers in predicting treatment outcomes. It discusses three US Food and Drug Administration (FDA)-approved biomarkers, programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), and explores other potential biomarkers, including tumor immune microenvironment (TIME)-related signatures, human leukocyte antigen (HLA) diversity, non-invasive biomarkers such as circulating tumor DNA (ctDNA), and combination biomarker strategies. The review also addresses multivariable predictive models integrating multiple features of patients, tumors, and TIME, which could be a promising approach to enhance predictive accuracy. The existing challenges are also pointed out, such as the tumor heterogeneity, the inconstant nature of TIME, nonuniformed thresholds and standardization approaches. The review concludes by emphasizing the importance of biomarker research in realizing the potential of personalized immunotherapy, with the goal of improving patient selection, treatment strategies, and overall outcomes in cancer treatment.

Keywords: MSI; PD-L1; TMB; emerging biomarkers; immune checkpoint inhibitors; immunotherapy.

Figure 1

Potential predictive biomarkers in predicting the response to immunotherapy. According to the clinical utility, biomarkers can be divided into FDA-approved biomarkers and emerging biomarkers. FDA-approved biomarkers include PD-L1 expression, TMB, and MSI/dMMR. Emerging biomarkers have many categories, and the most representative ones are TIME-related signatures, non-invasive biomarkers, HLA, and multivariable predictive models. PD-L1, programmed cell death ligand 1; TMB, tumor mutational burden; MSI, microsatellite instability; dMMR, deficiency of DNA mismatch repair; TIME, tumor immune microenvironment; HLA, human leukocyte antigen.