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Urine cell-based DNA methylation classifier for monitoring bladder cancer
BACKGROUND: Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). METHODS: V...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975622/ https://www.ncbi.nlm.nih.gov/pubmed/29854012 http://dx.doi.org/10.1186/s13148-018-0496-x |
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author | van der Heijden, Antoine G. Mengual, Lourdes Ingelmo-Torres, Mercedes Lozano, Juan J. van Rijt-van de Westerlo, Cindy C. M. Baixauli, Montserrat Geavlete, Bogdan Moldoveanud, Cristian Ene, Cosmin Dinney, Colin P. Czerniak, Bogdan Schalken, Jack A. Kiemeney, Lambertus A. L. M. Ribal, Maria J. Witjes, J. Alfred Alcaraz, Antonio |
author_facet | van der Heijden, Antoine G. Mengual, Lourdes Ingelmo-Torres, Mercedes Lozano, Juan J. van Rijt-van de Westerlo, Cindy C. M. Baixauli, Montserrat Geavlete, Bogdan Moldoveanud, Cristian Ene, Cosmin Dinney, Colin P. Czerniak, Bogdan Schalken, Jack A. Kiemeney, Lambertus A. L. M. Ribal, Maria J. Witjes, J. Alfred Alcaraz, Antonio |
author_sort | van der Heijden, Antoine G. |
collection | PubMed |
description | BACKGROUND: Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). METHODS: Voided urine samples (N = 725) from BC patients, controls, and PFBC were prospectively collected in four centers. Finally, 626 urine samples were available for analysis. DNA was extracted from the urinary cells and bisulfite modificated, and methylation status was analyzed using pyrosequencing. Cytology was available from a subset of patients (N = 399). In the discovery phase, seven selected genes from the literature (CDH13, CFTR, NID2, SALL3, TMEFF2, TWIST1, and VIM2) were studied in 111 BC and 57 control samples. This training set was used to develop a gene classifier by logistic regression and was validated in 458 PFBC samples (173 with recurrence). RESULTS: A three-gene methylation classifier containing CFTR, SALL3, and TWIST1 was developed in the training set (AUC 0.874). The classifier achieved an AUC of 0.741 in the validation series. Cytology results were available for 308 samples from the validation set. Cytology achieved AUC 0.696 whereas the classifier in this subset of patients reached an AUC 0.768. Combining the methylation classifier with cytology results achieved an AUC 0.86 in the validation set, with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively. CONCLUSIONS: The combination of the three-gene methylation classifier and cytology results has high sensitivity and high negative predictive value in a real clinical scenario (PFBC). The proposed classifier is a useful test for predicting BC recurrence and decrease the number of cystoscopies in the follow-up of BC patients. If only patients with a positive combined classifier result would be cystoscopied, 36% of all cystoscopies can be prevented. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13148-018-0496-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5975622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59756222018-05-31 Urine cell-based DNA methylation classifier for monitoring bladder cancer van der Heijden, Antoine G. Mengual, Lourdes Ingelmo-Torres, Mercedes Lozano, Juan J. van Rijt-van de Westerlo, Cindy C. M. Baixauli, Montserrat Geavlete, Bogdan Moldoveanud, Cristian Ene, Cosmin Dinney, Colin P. Czerniak, Bogdan Schalken, Jack A. Kiemeney, Lambertus A. L. M. Ribal, Maria J. Witjes, J. Alfred Alcaraz, Antonio Clin Epigenetics Research BACKGROUND: Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). METHODS: Voided urine samples (N = 725) from BC patients, controls, and PFBC were prospectively collected in four centers. Finally, 626 urine samples were available for analysis. DNA was extracted from the urinary cells and bisulfite modificated, and methylation status was analyzed using pyrosequencing. Cytology was available from a subset of patients (N = 399). In the discovery phase, seven selected genes from the literature (CDH13, CFTR, NID2, SALL3, TMEFF2, TWIST1, and VIM2) were studied in 111 BC and 57 control samples. This training set was used to develop a gene classifier by logistic regression and was validated in 458 PFBC samples (173 with recurrence). RESULTS: A three-gene methylation classifier containing CFTR, SALL3, and TWIST1 was developed in the training set (AUC 0.874). The classifier achieved an AUC of 0.741 in the validation series. Cytology results were available for 308 samples from the validation set. Cytology achieved AUC 0.696 whereas the classifier in this subset of patients reached an AUC 0.768. Combining the methylation classifier with cytology results achieved an AUC 0.86 in the validation set, with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively. CONCLUSIONS: The combination of the three-gene methylation classifier and cytology results has high sensitivity and high negative predictive value in a real clinical scenario (PFBC). The proposed classifier is a useful test for predicting BC recurrence and decrease the number of cystoscopies in the follow-up of BC patients. If only patients with a positive combined classifier result would be cystoscopied, 36% of all cystoscopies can be prevented. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13148-018-0496-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-30 /pmc/articles/PMC5975622/ /pubmed/29854012 http://dx.doi.org/10.1186/s13148-018-0496-x Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research van der Heijden, Antoine G. Mengual, Lourdes Ingelmo-Torres, Mercedes Lozano, Juan J. van Rijt-van de Westerlo, Cindy C. M. Baixauli, Montserrat Geavlete, Bogdan Moldoveanud, Cristian Ene, Cosmin Dinney, Colin P. Czerniak, Bogdan Schalken, Jack A. Kiemeney, Lambertus A. L. M. Ribal, Maria J. Witjes, J. Alfred Alcaraz, Antonio Urine cell-based DNA methylation classifier for monitoring bladder cancer |
title | Urine cell-based DNA methylation classifier for monitoring bladder cancer |
title_full | Urine cell-based DNA methylation classifier for monitoring bladder cancer |
title_fullStr | Urine cell-based DNA methylation classifier for monitoring bladder cancer |
title_full_unstemmed | Urine cell-based DNA methylation classifier for monitoring bladder cancer |
title_short | Urine cell-based DNA methylation classifier for monitoring bladder cancer |
title_sort | urine cell-based dna methylation classifier for monitoring bladder cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975622/ https://www.ncbi.nlm.nih.gov/pubmed/29854012 http://dx.doi.org/10.1186/s13148-018-0496-x |
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