Almars Amany I, Shaheen Sameerah, Ghouth Nahlah M, Abumansour Iman S, Khogeer Asim Abdulaziz, Alsulaimani Fayez, Basri Ahmed M, Elhawary Nasser A, Hasan Tabinda, Almohaimeed Hailah M
Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, P.O. Box 80324, 21589, Jeddah, Saudi Arabia.
Editome: Precision Gene Editing Research Group, King Fahad Medical Research Center, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
Clin Transl Oncol. 2025 Sep 6. doi: 10.1007/s12094-025-04051-4.
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.
食管癌（EC）是全球最严重的健康问题之一，在最致命的癌症类型中排名第七，在全球最常见的癌症类型中排名第十一。传统疗法，如手术、化疗和放疗，往往成效有限，尤其是在食管癌的晚期阶段，这促使人们寻求新颖且更有效的治疗策略。免疫疗法已成为一种有前景的选择；然而，患者反应的差异阻碍了其临床成效。这凸显了对预测性生物标志物的迫切需求，这些标志物能够识别最有可能从免疫治疗干预中获益的患者。基于生物标志物的患者分层可以改善治疗效果，避免不必要的暴露，并节省医疗资源。本综述探讨了用于预测食管癌免疫治疗反应的已确立和新兴生物标志物。我们通过将这些生物标志物分为四大类来进行讨论：（i）肿瘤相关生物标志物（程序性死亡受体配体1表达、肿瘤突变负荷和微卫星不稳定性），（ii）肿瘤免疫微环境相关生物标志物（肿瘤浸润淋巴细胞以及免疫细胞亚型和比例），（iii）血液生物标志物（循环肿瘤DNA、外泌体和可溶性蛋白），以及（iv）微生物群（口腔、食管和肠道微生物群）。此外，还讨论了生物标志物发现技术的进展，如高通量测序、多组学方法、人工智能和机器学习、单细胞分析以及液体活检，这些技术在优化生物标志物识别和临床应用方面的潜力。
