Research Progress on Postoperative Minimal/Molecular Residual Disease Detection in Lung Cancer. Issue 2 (31st March 2022)

• Record Type: Journal Article
• Authors: Wu, Manqi; Shen, Haifeng; Wang, Ziyang; Kanu, Nnennaya; Chen, Kezhong
• Other Names: Wang Jun guestEditor.; Li Xiao guestEditor.
• Is Part Of: Chronic diseases and translational medicine. Volume 8:Issue 2(2022)
• Page Start: 83
• Page End: 90
• Publication Date: 2022-03-31
• Subjects: circulating tumor DNA -- liquid biopsy -- lung cancer -- minimal/molecular residual disease; Chronic diseases -- Periodicals; Medicine -- Research -- Periodicals; Chronic diseases; Medicine -- Research; Chronic Disease; Translational Medical Research; Periodicals; Electronic journals; Fulltext; Internet Resources; Periodicals; Periodical; 616.044
• DOI: 10.1002/cdt3.10
• Languages: English
• ISSNs: 2095-882X

Abstract

Abstract: Lung cancer is the leading cause of cancer‐related deaths worldwide. Approximately 10%–50% of patients experience relapse after radical surgery, which may be attributed to the persistence of minimal/molecular residual disease (MRD). Circulating tumor DNA (ctDNA), a common liquid biopsy approach, has been demonstrated to have significant clinical merit. In this study, we review the evidence supporting the use of ctDNA for MRD detection and discuss the potential clinical applications of postoperative MRD detection, including monitoring recurrence, guiding adjuvant treatment, and driving clinical trials in lung cancer. We will also discuss the problems that prevent the routine application of ctDNA MRD detection. Multi‐analyte methods and identification of specific genetic and molecular alterations, especially methylation, are effective detection strategies and show considerable prospects for future development. Interventional prospective studies based on ctDNA detection are needed to determine whether the application of postoperative MRD detection can improve the clinical outcomes of lung cancer patients, and the accuracy, sensitivity, specificity, and robustness of different detection methods still require optimization and refinement.