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Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients

Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available. Methods: To address this unmet medical need, we developed a novel gene...

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Autores principales: Guo, Jinan, Zhang, Xuhui, Xia, Taolin, Johnson, Heather, Feng, Xiaoyan, Simoulis, Athanasios, Wu, Alan H. B., Li, Fei, Tan, Wanlong, Johnson, Allan, Dizeyi, Nishtman, Abrahamsson, Per-Anders, Kenner, Lukas, Xiao, Kefeng, Zhang, Heqiu, Chen, Lingwu, Zou, Chang, Persson, Jenny L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8476767/
https://www.ncbi.nlm.nih.gov/pubmed/34595190
http://dx.doi.org/10.3389/fmed.2021.721554
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author Guo, Jinan
Zhang, Xuhui
Xia, Taolin
Johnson, Heather
Feng, Xiaoyan
Simoulis, Athanasios
Wu, Alan H. B.
Li, Fei
Tan, Wanlong
Johnson, Allan
Dizeyi, Nishtman
Abrahamsson, Per-Anders
Kenner, Lukas
Xiao, Kefeng
Zhang, Heqiu
Chen, Lingwu
Zou, Chang
Persson, Jenny L.
author_facet Guo, Jinan
Zhang, Xuhui
Xia, Taolin
Johnson, Heather
Feng, Xiaoyan
Simoulis, Athanasios
Wu, Alan H. B.
Li, Fei
Tan, Wanlong
Johnson, Allan
Dizeyi, Nishtman
Abrahamsson, Per-Anders
Kenner, Lukas
Xiao, Kefeng
Zhang, Heqiu
Chen, Lingwu
Zou, Chang
Persson, Jenny L.
author_sort Guo, Jinan
collection PubMed
description Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available. Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups. Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892–0.942) in the retrospective cohort (n = 520), AUC of 0.959 (95% CI 0.935–0.983) in the prospective cohort (n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort (n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1–3 cancer subgroups. Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance.
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spelling pubmed-84767672021-09-29 Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients Guo, Jinan Zhang, Xuhui Xia, Taolin Johnson, Heather Feng, Xiaoyan Simoulis, Athanasios Wu, Alan H. B. Li, Fei Tan, Wanlong Johnson, Allan Dizeyi, Nishtman Abrahamsson, Per-Anders Kenner, Lukas Xiao, Kefeng Zhang, Heqiu Chen, Lingwu Zou, Chang Persson, Jenny L. Front Med (Lausanne) Medicine Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available. Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups. Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892–0.942) in the retrospective cohort (n = 520), AUC of 0.959 (95% CI 0.935–0.983) in the prospective cohort (n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort (n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1–3 cancer subgroups. Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance. Frontiers Media S.A. 2021-09-14 /pmc/articles/PMC8476767/ /pubmed/34595190 http://dx.doi.org/10.3389/fmed.2021.721554 Text en Copyright © 2021 Guo, Zhang, Xia, Johnson, Feng, Simoulis, Wu, Li, Tan, Johnson, Dizeyi, Abrahamsson, Kenner, Xiao, Zhang, Chen, Zou and Persson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Guo, Jinan
Zhang, Xuhui
Xia, Taolin
Johnson, Heather
Feng, Xiaoyan
Simoulis, Athanasios
Wu, Alan H. B.
Li, Fei
Tan, Wanlong
Johnson, Allan
Dizeyi, Nishtman
Abrahamsson, Per-Anders
Kenner, Lukas
Xiao, Kefeng
Zhang, Heqiu
Chen, Lingwu
Zou, Chang
Persson, Jenny L.
Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients
title Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients
title_full Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients
title_fullStr Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients
title_full_unstemmed Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients
title_short Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients
title_sort non-invasive urine test for molecular classification of clinical significance in newly diagnosed prostate cancer patients
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8476767/
https://www.ncbi.nlm.nih.gov/pubmed/34595190
http://dx.doi.org/10.3389/fmed.2021.721554
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