Abstract
The incidence of cancer is continuing to rise and risk-tailored early diagnostic and/or primary prevention strategies are urgently required. The ideal risk-predictive test should: integrate the effects of both genetic and nongenetic factors and aim to capture these effects using an approach that is both biologically stable and technically reproducible; derive a score from easily accessible biological samples that acts as a surrogate for the organ in question; and enable the effectiveness of risk-reducing measures to be monitored. Substantial evidence has accumulated suggesting that the epigenome and, in particular, DNA methylation-based tests meet all of these requirements. However, the development and implementation of DNA methylation-based risk-prediction tests poses considerable challenges. In particular, the cell type specificity of DNA methylation and the extensive cellular heterogeneity of the easily accessible surrogate cells that might contain information relevant to less accessible tissues necessitates the use of novel methods in order to account for these confounding issues. Furthermore, the engagement of the scientific community with health-care professionals, policymakers and the public is required in order to identify and address the organizational, ethical, legal, social and economic challenges associated with the routine use of epigenetic testing.
Highlights
Cancer is a leading cause of mortality worldwide, accounting for 14.1 million new cases and 8.2 million deaths in 20121
Analogous to the genome-wide association studies (GWAS)[124] we propose that a minimum of 100,000 CpGs per individual are analysed in order to apply the term “epigenome-wide”
The first large study provided a direct link between DNA methylation (DNAme) of the oestrogen-receptor interacting ZNF217 gene, serum oestrogen receptor alpha bioactivity and breast cancer risk[135]. These data and the majority of data referenced have been generated based on the analysis of biological material derived from prevalent cases; this comes with several challenges as outlined in the following example: The first study analysing a larger number of CpGs - approximately 25,000 CpGs (i.e. Illumina’s 27k methylation array) - was conducted in blood from ovarian cancer patients and non-cancer control women[136] and concluded that the timing of sample collection for DNAme analysis and adjustment for sample cell-type composition is essential for valid interpretation of results
Summary
Cancer is a leading cause of mortality worldwide, accounting for 14.1 million new cases and 8.2 million deaths in 20121. The first large study (sample size larger than 1000 cases and controls) provided a direct link between DNAme of the oestrogen-receptor interacting ZNF217 gene, serum oestrogen receptor alpha bioactivity and breast cancer risk[135] These data and the majority of data referenced (apart from those referenced in Table 3) have been generated based on the analysis of biological material (i.e. surrogate tissue) derived from prevalent (i.e. already existing) cases; this comes with several challenges as outlined in the following example: The first study analysing a larger number of CpGs - approximately 25,000 CpGs (i.e. Illumina’s 27k methylation array) - was conducted in blood from ovarian cancer patients and non-cancer control women[136] and concluded that the timing of sample collection for DNAme analysis and adjustment for sample cell-type composition is essential for valid interpretation of results (see chapter “Tissue specificity of the epigenome” for more details). Once evaluated by a RHS design, new screening tests – if found to be superior to the old policy – could be immediately implemented since the programme has already been part of the testing phase
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