Development of predictive biomarkers for transarterial chemoembolization efficacy in hepatocellular carcinoma

Abstract

Transarterial chemoembolization (TACE) is widely acknowledged as the first-line therapeutic strategy for hepatocellular carcinoma (HCC), one of the most common malignant tumors of the liver. Despite its established efficacy, the responses of TACE are subject to significant variability due to the inherent tumor heterogeneity and patient-specific physiological and genetic factors. This creates a challenge in treatment standardization and demands a tailored approach for each patient. What’s more, multiple TACE sessions are often required, particularly for larger tumors. However, redundant repeated treatments without proper patient selection may lead to TACE resistance or liver function damage, potentially foreclosing other therapeutic options. This clinical landscape underscores the pressing need for developing precise and minimally invasive tools for predicting TACE efficacy. Biomarkers are emerging as particularly promising tools in this context. Defined as quantifiable variables, biomarkers can be objectively measured to reflect the biological impact of treatment or exposure, which is widely employed in disease diagnosis, monitoring, curative effect evaluation, prognosis prediction, and drug development. In this review, we delve into the current research progression on predictive biomarkers for TACE efficacy. These include standard laboratory tests, advanced imaging techniques, and emerging technologies such as liquid biopsy and artificial intelligence (AI). Laboratory assays may involve measuring liver function or cancer markers, while imaging studies can offer insights into tumor size and metabolic activity. Liquid biopsy captures circulating tumor DNA to provide real-time information, and AI applications have begun to offer more nuanced predictive analytics. Looking to the future, the incorporation of big data and multi-omics studies could revolutionize the field. These integrative analyses promise to refine the existing predictive models for TACE efficacy, enabling more personalized and effective treatment strategies for patients suffering from HCC. As we move forward, these advancements will undoubtedly have a profound impact on clinical decision-making processes, ultimately improving patient outcomes.