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Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer

In locally advanced rectal cancer a preoperative predictive biomarker is necessary to adjust treatment specifically for those patients expected to suffer relapse. We applied whole genome methylation CpG island array analyses to an initial set of patients (n=11) to identify differentially methylated...

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Autores principales: Gaedcke, Jochen, Leha, Andreas, Claus, Rainer, Weichenhan, Dieter, Jung, Klaus, Kitz, Julia, Grade, Marian, Wolff, Hendrik A., Jo, Peter, Doyen, Jérôme, Gérard, Jean-Pierre, Johnsen, Steven A., Plass, Christoph, Beißbarth, Tim, Ghadimi, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226671/
https://www.ncbi.nlm.nih.gov/pubmed/25261372
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author Gaedcke, Jochen
Leha, Andreas
Claus, Rainer
Weichenhan, Dieter
Jung, Klaus
Kitz, Julia
Grade, Marian
Wolff, Hendrik A.
Jo, Peter
Doyen, Jérôme
Gérard, Jean-Pierre
Johnsen, Steven A.
Plass, Christoph
Beißbarth, Tim
Ghadimi, Michael
author_facet Gaedcke, Jochen
Leha, Andreas
Claus, Rainer
Weichenhan, Dieter
Jung, Klaus
Kitz, Julia
Grade, Marian
Wolff, Hendrik A.
Jo, Peter
Doyen, Jérôme
Gérard, Jean-Pierre
Johnsen, Steven A.
Plass, Christoph
Beißbarth, Tim
Ghadimi, Michael
author_sort Gaedcke, Jochen
collection PubMed
description In locally advanced rectal cancer a preoperative predictive biomarker is necessary to adjust treatment specifically for those patients expected to suffer relapse. We applied whole genome methylation CpG island array analyses to an initial set of patients (n=11) to identify differentially methylated regions (DMRs) that separate a good from a bad prognosis group. Using a quantitative high-resolution approach, candidate DMRs were first validated in a set of 61 patients (test set) and then confirmed DMRs were further validated in additional independent patient cohorts (n=71, n=42). We identified twenty highly discriminative DMRs and validated them in the test set using the MassARRAY technique. Ten DMRs could be confirmed which allowed separation into prognosis groups (p=0.0207, HR=4.09). The classifier was validated in two additional cohorts (n=71, p=0.0345, HR=3.57 and n=42, p=0.0113, HR=3.78). Interestingly, six of the ten DMRs represented regions close to the transcriptional start sites of genes which are also marked by the Polycomb Repressor Complex component EZH2. In conclusion we present a classifier comprising 10 DMRs which predicts patient prognosis with a high degree of accuracy. These data may now help to discriminate between patients that may respond better to standard treatments from those that may require alternative modalities.
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spelling pubmed-42266712014-11-17 Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer Gaedcke, Jochen Leha, Andreas Claus, Rainer Weichenhan, Dieter Jung, Klaus Kitz, Julia Grade, Marian Wolff, Hendrik A. Jo, Peter Doyen, Jérôme Gérard, Jean-Pierre Johnsen, Steven A. Plass, Christoph Beißbarth, Tim Ghadimi, Michael Oncotarget Clinical Research Paper In locally advanced rectal cancer a preoperative predictive biomarker is necessary to adjust treatment specifically for those patients expected to suffer relapse. We applied whole genome methylation CpG island array analyses to an initial set of patients (n=11) to identify differentially methylated regions (DMRs) that separate a good from a bad prognosis group. Using a quantitative high-resolution approach, candidate DMRs were first validated in a set of 61 patients (test set) and then confirmed DMRs were further validated in additional independent patient cohorts (n=71, n=42). We identified twenty highly discriminative DMRs and validated them in the test set using the MassARRAY technique. Ten DMRs could be confirmed which allowed separation into prognosis groups (p=0.0207, HR=4.09). The classifier was validated in two additional cohorts (n=71, p=0.0345, HR=3.57 and n=42, p=0.0113, HR=3.78). Interestingly, six of the ten DMRs represented regions close to the transcriptional start sites of genes which are also marked by the Polycomb Repressor Complex component EZH2. In conclusion we present a classifier comprising 10 DMRs which predicts patient prognosis with a high degree of accuracy. These data may now help to discriminate between patients that may respond better to standard treatments from those that may require alternative modalities. Impact Journals LLC 2014-10-27 /pmc/articles/PMC4226671/ /pubmed/25261372 Text en Copyright: © 2014 Gaedcke et al. http://creativecommons.org/licenses/by/2.5/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
spellingShingle Clinical Research Paper
Gaedcke, Jochen
Leha, Andreas
Claus, Rainer
Weichenhan, Dieter
Jung, Klaus
Kitz, Julia
Grade, Marian
Wolff, Hendrik A.
Jo, Peter
Doyen, Jérôme
Gérard, Jean-Pierre
Johnsen, Steven A.
Plass, Christoph
Beißbarth, Tim
Ghadimi, Michael
Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer
title Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer
title_full Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer
title_fullStr Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer
title_full_unstemmed Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer
title_short Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer
title_sort identification of a dna methylation signature to predict disease-free survival in locally advanced rectal cancer
topic Clinical Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226671/
https://www.ncbi.nlm.nih.gov/pubmed/25261372
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