Genome-Scale Screen for DNA Methylation-Based Detection Markers for Ovarian Cancer

Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America.
PLoS ONE (Impact Factor: 3.23). 12/2011; 6(12):e28141. DOI: 10.1371/journal.pone.0028141
Source: PubMed


The identification of sensitive biomarkers for the detection of ovarian cancer is of high clinical relevance for early detection and/or monitoring of disease recurrence. We developed a systematic multi-step biomarker discovery and verification strategy to identify candidate DNA methylation markers for the blood-based detection of ovarian cancer.
We used the Illumina Infinium platform to analyze the DNA methylation status of 27,578 CpG sites in 41 ovarian tumors. We employed a marker selection strategy that emphasized sensitivity by requiring consistency of methylation across tumors, while achieving specificity by excluding markers with methylation in control leukocyte or serum DNA. Our verification strategy involved testing the ability of identified markers to monitor disease burden in serially collected serum samples from ovarian cancer patients who had undergone surgical tumor resection compared to CA-125 levels. We identified one marker, IFFO1 promoter methylation (IFFO1-M), that is frequently methylated in ovarian tumors and that is rarely detected in the blood of normal controls. When tested in 127 serially collected sera from ovarian cancer patients, IFFO1-M showed post-resection kinetics significantly correlated with serum CA-125 measurements in six out of 16 patients.
We implemented an effective marker screening and verification strategy, leading to the identification of IFFO1-M as a blood-based candidate marker for sensitive detection of ovarian cancer. Serum levels of IFFO1-M displayed post-resection kinetics consistent with a reflection of disease burden. We anticipate that IFFO1-M and other candidate markers emerging from this marker development pipeline may provide disease detection capabilities that complement existing biomarkers.

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Available from: Ite A Laird-Offringa
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    • "Hypermethylation of tumor suppressor genes linked to transcriptional silencing, global DNA demethylation associated with genome rearrangements and instability, and recently reported promoter hypomethylation linked to activation of oncogenes and prometastatic genes are hallmarks of nearly all types of cancer [2], [3], [4], [5]. DNA hypermethylation of tumor suppressor genes has been shown to have diagnostic potential in several cancers [6], [7], [8], however our recent unraveling of the broad scope of hypomethylation in liver cancer suggested that potential biomarkers might be found in hypomethylated genes that play a critical role in driving cancer and cancer metastasis [4]. Identifying reliable biomarkers of HCC is of particular importance since the late onset of clinical symptoms accounts for a late diagnosis and high mortality rate. "
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    ABSTRACT: Poor prognosis of hepatocellular carcinoma (HCC) associated with late diagnosis necessitates the development of early diagnostic biomarkers. We have previously delineated the landscape of DNA methylation in HCC patients unraveling the importance of promoter hypomethylation in activation of cancer- and metastasis-driving genes. The purpose of the present study was to test the feasibility that genes that are hypomethylated in HCC could serve as candidate diagnostic markers. We use high resolution melting analysis (HRM) as a simple translatable PCR-based method to define methylation states in clinical samples. We tested seven regions selected from the shortlist of genes hypomethylated in HCC and showed that HRM analysis of several of them distinguishes methylation states in liver cancer specimens from normal adjacent liver and chronic hepatitis in the Shanghai area. Such regions were identified within promoters of neuronal membrane glycoprotein M6-B (GPM6B) and melanoma antigen family A12 (MAGEA12) genes. Differences in HRM in the immunoglobulin superfamily Fc receptor (FCRL1) separated invasive tumors from less invasive HCC. The identified biomarkers differentiated HCC from chronic hepatitis in another set of samples from Dhaka. Although the main thrust in DNA methylation diagnostics in cancer is on hypermethylated genes, our study for the first time illustrates the potential use of hypomethylated genes as markers for solid tumors. After further validation in a larger cohort, the identified DNA hypomethylated regions can become important candidate biomarkers for liver cancer diagnosis and prognosis, especially in populations with high risk for HCC development.
    Full-text · Article · Aug 2013 · PLoS ONE
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    • "Methylati on changes are involved in a series of gene repression events, including tumor suppressor genes such as p16 ink4a , p21, p53 and RASSF1A [3] [4] [5]. DNA methylation has also been used as a novel prognost ic marker and drug target [6] [7] [8] [9]. However, it is unclear whether disruption of DNA methylation leads to tumorigenesis. "
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    ABSTRACT: Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium(®) Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement. We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing. We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as 'anchors' to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users. We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures.
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