Data from Identification of a Quantitative MINT Locus Methylation Profile Predicting Local Regional Recurrence of Rectal Cancer

Abstract

Abstract Purpose: Risk assessment for locoregional disease recurrence would be highly valuable in preoperative treatment planning for patients undergoing primary rectal tumor resection. Epigenetic aberrations such as DNA methylation have been shown to be significant prognostic biomarkers of disease outcome. In this study, we evaluated the significance of a quantitative epigenetic multimarker panel analysis of primary tumors to predict local recurrence in rectal cancer patients from a retrospective multicenter clinical trial. Experimental Design: Primary tumors were studied from patients enrolled in the trial who underwent total mesorectal excision for rectal cancer (n = 325). Methylation levels of seven methylated-in-tumor (MINT) loci were assessed by absolute quantitative assessment of methylated alleles. Unsupervised random forest clustering of quantitative MINT methylation data was used to show subclassification into groups with matching methylation profiles. Results: Variable importance parameters [Gini-Index (GI)] of the clustering algorithm indicated MINT3 and MINT17 (GI, 20.2 and 20.7, respectively) to be informative for patient grouping compared with the other MINT loci (highest GI, 12.2). When using this two-biomarker panel, four different patient clusters were identified. One cluster containing 73% (184 of 251) of the patients was at significantly increased risk of local recurrence (hazard ratio, 10.23; 95% confidence interval, 1.38-75.91) in multivariate analysis, corrected for standard prognostic factors of rectal cancer. This group showed a significantly higher local recurrence probability than patients receiving preoperative radiation (P < 0.0001). Conclusion: Quantitative epigenetic subclassification of rectal cancers has clinical utility in distinguishing tumors with increased risk for local recurrence and may help tailor treatment regimens for locoregional control. Clin Cancer Res; 16(10); 2811–8. ©2010 AACR.

Authors 10

1. Michiel F.G. de Maat
   Affiliation as printed

   Department of Molecular Oncology, John Wayne Cancer Institute, Santa Monica, California; Departments of 2 Surgery,

2. Cornelis J.�H. van de Velde
   Affiliation as printed

   Department of Molecular On-cology, John Wayne Cancer Institute, 2200 Santa Monica Boulevard, Santa Monica, CA 90404

3. Anne Benard
   Affiliation as printed

   Department of Molecular Oncology, John Wayne Cancer Institute, Santa Monica, California; Departments of 2 Surgery,

4. Hein Putter

   Institute for Medical Informatics and Biostatistics

   Affiliation as printed

   Medical Statistics and Bioinformatics, and

5. Hans M. Morreau Aachen Pathology

   Leiden University Medical Center

   Affiliation as printed

   Pathology, Leiden University Medical Center, Leiden, the Netherlands;

6. J. Han van Krieken

   Radboud University Nijmegen · Radboud University Medical Center

   Affiliation as printed

   Department of Pathology, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands; and

7. Elma Meershoek Klein-Kranenbarg
   Affiliation as printed

   Department of Molecular On-cology, John Wayne Cancer Institute, 2200 Santa Monica Boulevard, Santa Monica, CA 90404

8. Eelco J. de Graaf

   IJsselland Ziekenhuis

   Affiliation as printed

   Department of Surgery, Ijsselland Hospital, Capelle a/d Ijssel, the Netherlands

9. Rob A.�E.�M. Tollenaar
   Affiliation as printed

   Department of Molecular On-cology, John Wayne Cancer Institute, 2200 Santa Monica Boulevard, Santa Monica, CA 90404

10. Dave S.�B. Hoon corresponding
    Affiliation as printed

    Department of Molecular Oncology, John Wayne Cancer Institute, Santa Monica, California; Departments of 2 Surgery,