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: 3.53). 12/2011; 6(12):e28141. DOI: 10.1371/journal.pone.0028141
Source: PubMed

ABSTRACT 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.

Download full-text


Available from: Ite A Laird-Offringa, Jul 02, 2015
  • [Show abstract] [Hide abstract]
    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.
    Epigenomics 06/2012; 4(3):325-41. DOI:10.2217/epi.12.21
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A biomarker is a molecular target analyzed in a qualitative or quantitative manner to detect and diagnose the presence of a disease, to predict the outcome and the response to a specific treatment allowing personalized tailoring of patient management. Biomarkers can belong to different types of biochemical molecules such as proteins, DNA, RNA or lipids, whereby protein biomarkers have been the most extensively studied and used, notably in blood-based protein quantification tests or immunohistochemistry. The rise of interest in epigenetic mechanisms has allowed the identification of a new type of biomarker, DNA methylation, which is of great potential for many applications. This stable and heritable covalent modification mostly affects cytosines in the context of a CpG dinucleotide in humans. It can be detected and quantified by a number of technologies including genome-wide screening methods as well as locus- or gene-specific high-resolution analysis in different types of samples such as frozen tissues and FFPE samples, but also in body fluids such as urine, plasma, and serum obtained through non-invasive procedures. In some cases, DNA methylation based biomarkers have proven to be more specific and sensitive than commonly used protein biomarkers, which could clearly justify their use in clinics. However, very few of them are at the moment used in clinics and even less commercial tests are currently available. The objective of this review is to discuss the advantages of DNA methylation as a biomarker, the practical considerations for their development, and their use in disease detection, prediction of outcome or treatment response, through multiple examples mainly focusing on cancer, but also to evoke their potential for complex diseases and prenatal diagnostics.
    Biochimie 07/2012; 94(11):2314-37. DOI:10.1016/j.biochi.2012.07.014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: So far, investigators have found numerous tumor suppressor genes (TSGs) and oncogenes (OCGs) that control cell proliferation and apoptosis during cancer development. Furthermore, TSGs and OCGs may act as modulators of transcription factors (TFs) to influence gene regulation. A comprehensive investigation of TSGs, OCGs, TFs, and their joint target genes at the network level may provide a deeper understanding of the post-translational modulation of TSGs and OCGs to TF gene regulation. In this study, we developed a novel computational framework for identifying target genes of TSGs and OCGs using TFs as bridges through the integration of protein-protein interactions and gene expression data. We applied this pipeline to ovarian cancer and constructed a three-layer regulatory network. In the network, the top layer was comprised of modulators (TSGs and OCGs), the middle layer included TFs, and the bottom layer contained target genes. Based on regulatory relationships in the network, we compiled TSG and OCG profiles and performed clustering analyses. Interestingly, we found TSGs and OCGs formed two distinct branches. The genes in the TSG branch were significantly enriched in DNA damage and repair, regulating macromolecule metabolism, cell cycle and apoptosis, while the genes in the OCG branch were significantly enriched in the ErbB signaling pathway. Remarkably, their specific targets showed a reversed functional enrichment in terms of apoptosis and the ErbB signaling pathway: the target genes regulated by OCGs only were enriched in anti-apoptosis and the target genes regulated by TSGs only were enriched in the ErbB signaling pathway. This study provides the first comprehensive investigation of the interplay of TSGs and OCGs in a regulatory network modulated by TFs. Our application in ovarian cancer revealed distinct regulatory patterns of TSGs and OCGs, suggesting a competitive regulatory mechanism acting upon apoptosis and the ErbB signaling pathway through their specific target genes.
    PLoS ONE 08/2012; 7(8):e44175. DOI:10.1371/journal.pone.0044175