The potential of circulating tumor DNA methylation analysis for the early detection and management of ovarian cancer.

BACKGROUND: Despite a myriad of attempts in the last three decades to diagnose ovarian cancer (OC) earlier, this clinical aim still remains a significant challenge. Aberrant methylation patterns of linked CpGs analyzed in DNA fragments shed by cancers into the bloodstream (i.e. cell-free DNA) can provide highly specific signals indicating cancer presence. METHODS: We analyzed 699 cancerous and non-cancerous tissues using a methylation array or reduced representation bisulfite sequencing to discover the most specific OC methylation patterns. A three-DNA-methylation-serum-marker panel was developed using targeted ultra-high coverage bisulfite sequencing in 151 women and validated in 250 women with various conditions, particularly in those associated with high CA125 levels (endometriosis and other benign pelvic masses), serial samples from 25 patients undergoing neoadjuvant chemotherapy, and a nested case control study of 172 UKCTOCS control arm participants which included serum samples up to two years before OC diagnosis. RESULTS: The cell-free DNA amount and average fragment size in the serum samples was up to ten times higher than average published values (based on samples that were immediately processed) due to leakage of DNA from white blood cells owing to delayed time to serum separation. Despite this, the marker panel discriminated high grade serous OC patients from healthy women or patients with a benign pelvic mass with specificity/sensitivity of 90.7% (95% confidence interval [CI] = 84.3-94.8%) and 41.4% (95% CI = 24.1-60.9%), respectively. Levels of all three markers plummeted after exposure to chemotherapy and correctly identified 78% and 86% responders and non-responders (Fisher's exact test, p = 0.04), respectively, which was superior to a CA125 cut-off of 35 IU/mL (20% and 75%). 57.9% (95% CI 34.0-78.9%) of women who developed OC within two years of sample collection were identified with a specificity of 88.1% (95% CI = 77.3-94.3%). Sensitivity and specificity improved further when specifically analyzing CA125 negative samples only (63.6% and 87.5%, respectively). CONCLUSIONS: Our data suggest that DNA methylation patterns in cell-free DNA have the potential to detect a proportion of OCs up to two years in advance of diagnosis and may potentially guide personalized treatment. The prospective use of novel collection vials, which stabilize blood cells and reduce background DNA contamination in serum/plasma samples, will facilitate clinical implementation of liquid biopsy analyses.

DNA methylation markers for early detection of women's cancer: promise and challenges.

Breast, ovarian and endometrial cancers cause significant morbidity and mortality. Despite the presence of existing screening, diagnostic and treatment modalities, they continue to pose considerable unsolved challenges. Overdiagnosis is a growing problem in breast cancer screening and neither screening nor early diagnosis of ovarian or endometrial cancer is currently possible. Moreover, treatment of the diversity of these cancers presenting in the clinic is not sufficiently personalized at present. Recent technological advances, including reduced representation bisulfite sequencing, methylation arrays, digital PCR, next-generation sequencing and advanced statistical data analysis, enable the analysis of methylation patterns in cell-free tumor DNA in serum/plasma. Ongoing work is bringing these methods together for the analysis of samples from large clinical trials, which have been collected well in advance of cancer diagnosis. These efforts pave the way for the development of a noninvasive method that would enable us to overcome existing challenges to personalized medicine.

A collaborative approach to develop a multi-omics data analytics platform for translational research.

The integration and analysis of large datasets in translational research has become an increasingly challenging problem. We propose a collaborative approach to integrate established data management platforms with existing analytical systems to fill the hole in the value chain between data collection and data exploitation. Our proposal in particular ensures data security and provides support for widely distributed teams of researchers. As a successful example for such an approach, we describe the implementation of a unified single platform that combines capabilities of the knowledge management platform tranSMART and the data analysis system Genedata Analyst™. The combined end-to-end platform helps to quickly find, enter, integrate, analyze, extract, and share patient- and drug-related data in the context of translational R&D projects.

Quantitative DNA methylation profiling on a high-density oligonucleotide microarray.

Recently, the analysis and functional elucidation of CpG island methylation has become a focus area of genomic research. Deviations from the normal parental imprinting pattern have been shown to cause developmental defects associated with serious symptoms. Aberrant DNA methylation of tumor suppressor and other functional genes, especially when found in 5' untranslated regions and early exons, has been associated with tumorigenesis. In the context of applying DNA methylation analysis for the molecular characterization of cancer and other diseases, standardized protocols enabling parallel genome-wide methylation profiling of numerous samples are required. DNA methylation profiling is described using a CpG island microarray representing more than 50,000 CpG-rich DNA fragments. Fragments were selected to represent the vast majority of known 5'-untranslated regions as well as the first exons of thousands of genes. Measurement probes were designed to represent these fragments were displayed on an Affymetrix custom array. A modified procedure for differential methylation hybridization (DMH) is described for methylation enrichment. Application of a novel signal normalization concept enables accurate and reproducible measurements using a single fluorescence channel. The use of defined calibrator material allows quantification of DNA methylation patterns by DMH in a massively parallel fashion.

Comparative DNA methylation analysis in normal and tumour tissues and in cancer cell lines using differential methylation hybridisation.

Immortalized human cancer cell lines are widely used as tools and model systems in cancer research but their authenticity with regard to primary tissues remains a matter of debate. We have used differential methylation hybridisation to obtain comparative methylation profiles from normal and tumour tissues of lung and colon, and permanent cancer cell lines originally derived from these tissues. Average methylation differences only larger than 25% between sample groups were considered for the profiles and with this criterion approximately 1000 probesets, around 2% of the sites represented on the array, indicated differential methylation between normal lung and primary lung cancer tissue, and approximately 700 probesets between normal colon and primary colon cancer tissue. Both hyper- and hypomethylation was found to differentiate normal tissue from cancer tissue. The profiles obtained from these tissue comparisons were found to correspond largely to those from the corresponding cancer cell lines, indicating that the cell lines represent the methylation pattern of the primary tissue rather well. Moreover, the cancer specific profiles were found to be very similar for the two tumour types studied. Tissue specific differential methylation between lung and colon tissues, in contrast, was found to be preserved to a larger extent only in the malignant tissue, but was not preserved well in the cancer cell lines studied. Overall, our data therefore provide further evidence that permanent cell lines are good model systems for cancer specific methylation patterns, but deviate with regard to tissue-specific methylation.

Evolutionary analysis of Arabidopsis, cyanobacterial, and chloroplast genomes reveals plastid phylogeny and thousands of cyanobacterial genes in the nucleus.

Chloroplasts were once free-living cyanobacteria that became endosymbionts, but the genomes of contemporary plastids encode only approximately 5-10% as many genes as those of their free-living cousins, indicating that many genes were either lost from plastids or transferred to the nucleus during the course of plant evolution. Previous estimates have suggested that between 800 and perhaps as many as 2,000 genes in the Arabidopsis genome might come from cyanobacteria, but genome-wide phylogenetic surveys that could provide direct estimates of this number are lacking. We compared 24,990 proteins encoded in the Arabidopsis genome to the proteins from three cyanobacterial genomes, 16 other prokaryotic reference genomes, and yeast. Of 9,368 Arabidopsis proteins sufficiently conserved for primary sequence comparison, 866 detected homologues only among cyanobacteria and 834 other branched with cyanobacterial homologues in phylogenetic trees. Extrapolating from these conserved proteins to the whole genome, the data suggest that approximately 4,500 of Arabidopsis protein-coding genes ( approximately 18% of the total) were acquired from the cyanobacterial ancestor of plastids. These proteins encompass all functional classes, and the majority of them are targeted to cell compartments other than the chloroplast. Analysis of 15 sequenced chloroplast genomes revealed 117 nuclear-encoded proteins that are also still present in at least one chloroplast genome. A phylogeny of chloroplast genomes inferred from 41 proteins and 8,303 amino acids sites indicates that at least two independent secondary endosymbiotic events have occurred involving red algae and that amino acid composition bias in chloroplast proteins strongly affects plastid genome phylogeny.