Amany I Almars, Sameerah Shaheen, Nahlah M Ghouth, Iman S Abumansour, Asim Abdulaziz Khogeer, Fayez Alsulaimani, Ahmed M Basri, Nasser A Elhawary, Tabinda Hasan, Hailah M Almohaimeed
{"title":"Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.","authors":"Amany I Almars, Sameerah Shaheen, Nahlah M Ghouth, Iman S Abumansour, Asim Abdulaziz Khogeer, Fayez Alsulaimani, Ahmed M Basri, Nasser A Elhawary, Tabinda Hasan, Hailah M Almohaimeed","doi":"10.1007/s12094-025-04051-4","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50685,"journal":{"name":"Clinical & Translational Oncology","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical & Translational Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12094-025-04051-4","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Clinical and Translational Oncology is an international journal devoted to fostering interaction between experimental and clinical oncology. It covers all aspects of research on cancer, from the more basic discoveries dealing with both cell and molecular biology of tumour cells, to the most advanced clinical assays of conventional and new drugs. In addition, the journal has a strong commitment to facilitating the transfer of knowledge from the basic laboratory to the clinical practice, with the publication of educational series devoted to closing the gap between molecular and clinical oncologists. Molecular biology of tumours, identification of new targets for cancer therapy, and new technologies for research and treatment of cancer are the major themes covered by the educational series. Full research articles on a broad spectrum of subjects, including the molecular and cellular bases of disease, aetiology, pathophysiology, pathology, epidemiology, clinical features, and the diagnosis, prognosis and treatment of cancer, will be considered for publication.