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PLoS One. 2015 Dec 2;10(12):e0143178. doi: 10.1371/journal.pone.0143178. eCollection 2015.

Intra-Gene DNA Methylation Variability Is a Clinically Independent Prognostic Marker in Women's Cancers.

Author information

1
Department of Women's Cancer, Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, United Kingdom.
2
Deparment of Mathematics, University College London, London, United Kingdom.
3
CoMPLEX, University College London, London, United Kingdom.
4
Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, United States of America.
5
Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, United States of America.
6
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States of America.
7
Department of Medical Oncology, Erasmus MC-Cancer Center, Rotterdam, The Netherlands.
8
Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway.
9
Department of Pathology, Oslo University Hospital, Norwegian Radium Hospital, University of Oslo, Faculty of Medicine, Institute of Clinical Medicine, Oslo, Norway.
10
Department of Gynaecological Oncology, Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway.
11
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology and Leuven Cancer Institute, University Hospitals Leuven, Katholieke Universiteit Leuven, Leuven, Belgium.

Abstract

We introduce a novel per-gene measure of intra-gene DNA methylation variability (IGV) based on the Illumina Infinium HumanMethylation450 platform, which is prognostic independently of well-known predictors of clinical outcome. Using IGV, we derive a robust gene-panel prognostic signature for ovarian cancer (OC, n = 221), which validates in two independent data sets from Mayo Clinic (n = 198) and TCGA (n = 358), with significance of p = 0.004 in both sets. The OC prognostic signature gene-panel is comprised of four gene groups, which represent distinct biological processes. We show the IGV measurements of these gene groups are most likely a reflection of a mixture of intra-tumour heterogeneity and transcription factor (TF) binding/activity. IGV can be used to predict clinical outcome in patients individually, providing a surrogate read-out of hard-to-measure disease processes.

PMID:
26629914
PMCID:
PMC4667934
DOI:
10.1371/journal.pone.0143178
[Indexed for MEDLINE]
Free PMC Article

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