CELL INTELLIGENCE · 肿瘤细胞治疗研究

Esophageal cancer (EC) is one of the most serious health issues around the world, ranking seventh among the most lethal types of cancer and eleventh among the most common types of cancer worldwide. Traditional therapies-such as surgery, chemotherapy, and radiation therapy-often yield limited success, especially in the advanced stages of EC, prompting the pursuit of novel and more effective treatment strategies. Immunotherapy has emerged as a promising option; nonetheless, its clinical success is hindered by variable patient responses. This underscores the urgent need for predictive biomarkers that can identify patients most likely to benefit from immunotherapeutic interventions. Biomarker-based patient stratification can improve treatment outcomes, prevent unnecessary exposures, and conserve healthcare resources. This review explores established and emerging biomarkers for predicting response to immunotherapy in EC. We discuss these biomarkers by categorizing them into four major groups: (i) tumor-related biomarkers (PD-L1 expression, tumor mutational burden, and microsatellite instability), (ii) tumor-immune microenvironment-related biomarkers (tumor-infiltrating lymphocytes and immune cell subtypes and ratios), (iii) blood-based biomarkers (circulating tumor DNA, exosomes, and soluble proteins), and (iv) microbiomes (oral, esophageal, and gut microbiomes). In addition, Advancements in biomarker discovery technologies such as high-throughput sequencing, multi-omics approaches, artificial intelligence and machine learning, single-cell analysis, and liquid biopsy are also discussed for their potential to refine biomarker identification and clinical application.