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Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables

SIMPLE SUMMARY: Active surveillance (AS) has evolved as an alternative to radical treatment for potentially indolent prostate cancer. However, current selection criteria for entering AS are suboptimal, and a significant percentage of patients discontinue AS because of disease reclassification. Hence...

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Autores principales: Gandellini, Paolo, Ciniselli, Chiara Maura, Rancati, Tiziana, Marenghi, Cristina, Doldi, Valentina, El Bezawy, Rihan, Lecchi, Mara, Claps, Melanie, Catanzaro, Mario, Avuzzi, Barbara, Campi, Elisa, Colecchia, Maurizio, Badenchini, Fabio, Verderio, Paolo, Valdagni, Riccardo, Zaffaroni, Nadia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157371/
https://www.ncbi.nlm.nih.gov/pubmed/34069838
http://dx.doi.org/10.3390/cancers13102433
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author Gandellini, Paolo
Ciniselli, Chiara Maura
Rancati, Tiziana
Marenghi, Cristina
Doldi, Valentina
El Bezawy, Rihan
Lecchi, Mara
Claps, Melanie
Catanzaro, Mario
Avuzzi, Barbara
Campi, Elisa
Colecchia, Maurizio
Badenchini, Fabio
Verderio, Paolo
Valdagni, Riccardo
Zaffaroni, Nadia
author_facet Gandellini, Paolo
Ciniselli, Chiara Maura
Rancati, Tiziana
Marenghi, Cristina
Doldi, Valentina
El Bezawy, Rihan
Lecchi, Mara
Claps, Melanie
Catanzaro, Mario
Avuzzi, Barbara
Campi, Elisa
Colecchia, Maurizio
Badenchini, Fabio
Verderio, Paolo
Valdagni, Riccardo
Zaffaroni, Nadia
author_sort Gandellini, Paolo
collection PubMed
description SIMPLE SUMMARY: Active surveillance (AS) has evolved as an alternative to radical treatment for potentially indolent prostate cancer. However, current selection criteria for entering AS are suboptimal, and a significant percentage of patients discontinue AS because of disease reclassification. Hence, there is an unmet need for novel biomarkers for the accurate identification of high-risk PCa and the unequivocal classification of indolent disease. Circulating biomarkers, including microRNAs identified through liquid biopsies, represent a valuable approach to improve on currently available clinicopathological risk-stratification tools. In an attempt to identify specific microRNA signatures as potential circulating biomarkers, the authors performed an unprecedented analysis of the global microRNA profile in plasma samples from AS patients and identified and validated a three-microRNA signature able to predict patient reclassification. The addition of the three-microRNA signature was able to improve the performance of currently available clinicopathological variables, thus showing potential for the refinement of AS patients’ selection. ABSTRACT: Active surveillance (AS) has evolved as a strategy alternative to radical treatments for very low risk and low-risk prostate cancer (PCa). However, current criteria for selecting AS patients are still suboptimal. Here, we performed an unprecedented analysis of the circulating miRNome to investigate whether specific miRNAs associated with disease reclassification can provide risk refinement to standard clinicopathological features for improving patient selection. The global miRNA expression profiles were assessed in plasma samples prospectively collected at baseline from 386 patients on AS included in three independent mono-institutional cohorts (training, testing and validation sets). A three-miRNA signature (miR-511-5p, miR-598-3p and miR-199a-5p) was found to predict reclassification in all patient cohorts (training set: AUC 0.74, 95% CI 0.60–0.87, testing set: AUC 0.65, 95% CI 0.51–0.80, validation set: AUC 0.68, 95% CI 0.56–0.80). Importantly, the addition of the three-miRNA signature improved the performance of the clinical model including clinicopathological variables only (AUC 0.70, 95% CI 0.61–0.78 vs. 0.76, 95% CI 0.68–0.84). Overall, we trained, tested and validated a three-miRNA signature which, combined with selected clinicopathological variables, may represent a promising biomarker to improve on currently available clinicopathological risk stratification tools for a better selection of truly indolent PCa patients suitable for AS.
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spelling pubmed-81573712021-05-28 Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables Gandellini, Paolo Ciniselli, Chiara Maura Rancati, Tiziana Marenghi, Cristina Doldi, Valentina El Bezawy, Rihan Lecchi, Mara Claps, Melanie Catanzaro, Mario Avuzzi, Barbara Campi, Elisa Colecchia, Maurizio Badenchini, Fabio Verderio, Paolo Valdagni, Riccardo Zaffaroni, Nadia Cancers (Basel) Article SIMPLE SUMMARY: Active surveillance (AS) has evolved as an alternative to radical treatment for potentially indolent prostate cancer. However, current selection criteria for entering AS are suboptimal, and a significant percentage of patients discontinue AS because of disease reclassification. Hence, there is an unmet need for novel biomarkers for the accurate identification of high-risk PCa and the unequivocal classification of indolent disease. Circulating biomarkers, including microRNAs identified through liquid biopsies, represent a valuable approach to improve on currently available clinicopathological risk-stratification tools. In an attempt to identify specific microRNA signatures as potential circulating biomarkers, the authors performed an unprecedented analysis of the global microRNA profile in plasma samples from AS patients and identified and validated a three-microRNA signature able to predict patient reclassification. The addition of the three-microRNA signature was able to improve the performance of currently available clinicopathological variables, thus showing potential for the refinement of AS patients’ selection. ABSTRACT: Active surveillance (AS) has evolved as a strategy alternative to radical treatments for very low risk and low-risk prostate cancer (PCa). However, current criteria for selecting AS patients are still suboptimal. Here, we performed an unprecedented analysis of the circulating miRNome to investigate whether specific miRNAs associated with disease reclassification can provide risk refinement to standard clinicopathological features for improving patient selection. The global miRNA expression profiles were assessed in plasma samples prospectively collected at baseline from 386 patients on AS included in three independent mono-institutional cohorts (training, testing and validation sets). A three-miRNA signature (miR-511-5p, miR-598-3p and miR-199a-5p) was found to predict reclassification in all patient cohorts (training set: AUC 0.74, 95% CI 0.60–0.87, testing set: AUC 0.65, 95% CI 0.51–0.80, validation set: AUC 0.68, 95% CI 0.56–0.80). Importantly, the addition of the three-miRNA signature improved the performance of the clinical model including clinicopathological variables only (AUC 0.70, 95% CI 0.61–0.78 vs. 0.76, 95% CI 0.68–0.84). Overall, we trained, tested and validated a three-miRNA signature which, combined with selected clinicopathological variables, may represent a promising biomarker to improve on currently available clinicopathological risk stratification tools for a better selection of truly indolent PCa patients suitable for AS. MDPI 2021-05-18 /pmc/articles/PMC8157371/ /pubmed/34069838 http://dx.doi.org/10.3390/cancers13102433 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gandellini, Paolo
Ciniselli, Chiara Maura
Rancati, Tiziana
Marenghi, Cristina
Doldi, Valentina
El Bezawy, Rihan
Lecchi, Mara
Claps, Melanie
Catanzaro, Mario
Avuzzi, Barbara
Campi, Elisa
Colecchia, Maurizio
Badenchini, Fabio
Verderio, Paolo
Valdagni, Riccardo
Zaffaroni, Nadia
Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables
title Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables
title_full Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables
title_fullStr Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables
title_full_unstemmed Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables
title_short Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables
title_sort prediction of grade reclassification of prostate cancer patients on active surveillance through the combination of a three-mirna signature and selected clinical variables
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157371/
https://www.ncbi.nlm.nih.gov/pubmed/34069838
http://dx.doi.org/10.3390/cancers13102433
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