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Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis
BACKGROUND: The sensitivity and specificity of minimal residual disease detected by circulating tumor DNA profiling (ctDNA MRD) in lung cancer, with particular attention to the distinction between landmark strategy and surveillance strategy, for predicting relapse in lung cancer patients after defin...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176776/ https://www.ncbi.nlm.nih.gov/pubmed/37173789 http://dx.doi.org/10.1186/s12916-023-02849-z |
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author | Zhong, Ran Gao, Rui Fu, Wenhai Li, Caichen Huo, Zhenyu Gao, Yuewen Lu, Yi Li, Feng Ge, Fan Tu, Hengjia You, Zhixuan He, Jianxing Liang, Wenhua |
author_facet | Zhong, Ran Gao, Rui Fu, Wenhai Li, Caichen Huo, Zhenyu Gao, Yuewen Lu, Yi Li, Feng Ge, Fan Tu, Hengjia You, Zhixuan He, Jianxing Liang, Wenhua |
author_sort | Zhong, Ran |
collection | PubMed |
description | BACKGROUND: The sensitivity and specificity of minimal residual disease detected by circulating tumor DNA profiling (ctDNA MRD) in lung cancer, with particular attention to the distinction between landmark strategy and surveillance strategy, for predicting relapse in lung cancer patients after definitive therapy has yet to be determined. METHODS: The prognostic value of ctDNA MRD by landmark strategy and surveillance strategy was evaluated in a large cohort of patients with lung cancer who received definitive therapy using a systemic literature review and meta-analysis. Recurrence status stratified by ctDNA MRD result (positive or negative) was extracted as the clinical endpoint. We calculated the area under the summary receiver operating characteristic curves, and pooled sensitivities and specificities. Subgroup analyses were conducted based on histological type and stage of lung cancer, types of definitive therapy, and ctDNA MRD detection methods (detection technology and strategy such as tumor-informed or tumor-agnostic). RESULTS: This systematic review and meta-analysis of 16 unique studies includes 1251 patients with lung cancer treated with definitive therapy. The specificity of ctDNA MRD in predicting recurrence is high (0.86–0.95) with moderate sensitivity (0.41–0.76), whether shortly after treatment or during the surveillance. The landmark strategy appears to be more specific but less sensitive than the surveillance strategy. CONCLUSIONS: Our study suggests that ctDNA MRD is a relatively promising biomarker for relapse prediction among lung cancer patients after definitive therapy, with a high specificity but suboptimal sensitivity, whether in landmark strategy or surveillance strategy. Although surveillance ctDNA MRD analysis decreases specificity compared with the landmark strategy, the decrease is minimal compared to the increase in sensitivity for relapse prediction of lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-023-02849-z. |
format | Online Article Text |
id | pubmed-10176776 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101767762023-05-13 Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis Zhong, Ran Gao, Rui Fu, Wenhai Li, Caichen Huo, Zhenyu Gao, Yuewen Lu, Yi Li, Feng Ge, Fan Tu, Hengjia You, Zhixuan He, Jianxing Liang, Wenhua BMC Med Research Article BACKGROUND: The sensitivity and specificity of minimal residual disease detected by circulating tumor DNA profiling (ctDNA MRD) in lung cancer, with particular attention to the distinction between landmark strategy and surveillance strategy, for predicting relapse in lung cancer patients after definitive therapy has yet to be determined. METHODS: The prognostic value of ctDNA MRD by landmark strategy and surveillance strategy was evaluated in a large cohort of patients with lung cancer who received definitive therapy using a systemic literature review and meta-analysis. Recurrence status stratified by ctDNA MRD result (positive or negative) was extracted as the clinical endpoint. We calculated the area under the summary receiver operating characteristic curves, and pooled sensitivities and specificities. Subgroup analyses were conducted based on histological type and stage of lung cancer, types of definitive therapy, and ctDNA MRD detection methods (detection technology and strategy such as tumor-informed or tumor-agnostic). RESULTS: This systematic review and meta-analysis of 16 unique studies includes 1251 patients with lung cancer treated with definitive therapy. The specificity of ctDNA MRD in predicting recurrence is high (0.86–0.95) with moderate sensitivity (0.41–0.76), whether shortly after treatment or during the surveillance. The landmark strategy appears to be more specific but less sensitive than the surveillance strategy. CONCLUSIONS: Our study suggests that ctDNA MRD is a relatively promising biomarker for relapse prediction among lung cancer patients after definitive therapy, with a high specificity but suboptimal sensitivity, whether in landmark strategy or surveillance strategy. Although surveillance ctDNA MRD analysis decreases specificity compared with the landmark strategy, the decrease is minimal compared to the increase in sensitivity for relapse prediction of lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-023-02849-z. BioMed Central 2023-05-12 /pmc/articles/PMC10176776/ /pubmed/37173789 http://dx.doi.org/10.1186/s12916-023-02849-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Zhong, Ran Gao, Rui Fu, Wenhai Li, Caichen Huo, Zhenyu Gao, Yuewen Lu, Yi Li, Feng Ge, Fan Tu, Hengjia You, Zhixuan He, Jianxing Liang, Wenhua Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis |
title | Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis |
title_full | Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis |
title_fullStr | Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis |
title_full_unstemmed | Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis |
title_short | Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis |
title_sort | accuracy of minimal residual disease detection by circulating tumor dna profiling in lung cancer: a meta-analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176776/ https://www.ncbi.nlm.nih.gov/pubmed/37173789 http://dx.doi.org/10.1186/s12916-023-02849-z |
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