Clinical Research Papers:
Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer
Metrics: PDF 1209 views | HTML 942 views | ?
Jochen Gaedcke1*, Andreas Leha2*, Rainer Claus3,4, Dieter Weichenhan3, Klaus Jung2, Julia Kitz5, Marian Grade1, Hendrik A Wolff6, Peter Jo1, Jérôme Doyen7, Jean-Pierre Gérard7, Steven A. Johnsen1, Christoph Plass3, Tim Beißbarth2, Michael Ghadimi1
1 Department of General and Visceral Surgery, University Medical Center Goettingen, Goettingen, Germany
2 Department of Medical Statistics, University Medical Center Goettingen, Goettingen, Germany
3 Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), Heidelberg, Germany
4 Dept. of Medicine, Div. Hematology/Oncology, University of Freiburg Medical Center, Freiburg, Germany (present address)
5 Department of Pathology, University Medical Center Goettingen, Goettingen, Germany
6 Department of Radiation Oncology, University Medical Center Goettingen, Goettingen, Germany
7 Radiotherapy Department, Cyclotron Biomédical, Centre Antoine-Lacassagne Nice, France
* Both authors contributed equally
Dr. Jochen Gaedcke, e-mail: firstname.lastname@example.org
Received: August 03, 2014 Accepted: August 11, 2014 Published: August 19, 2014
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.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.