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Developing new biomarkers of colorectal neoplasia: Quantification of aberrant CpG island methylation in human faecal DNA

Project Code: N12009

30/12/2005

Institute of Food Research Enterprises Ltd
Johnson, I ;
University of Newcastle
Mathers, P

Methylation of CpG-islands in the promoter regions of functionally expressed genes leads to aberrant gene silencing; this has been shown to make a major contribution to the genetic dysfunction associated with the emergence of colorectal cancer. Methylation of genes such as MLH1 and APC is known to occur during the adenoma-carcinoma sequence, at both early and late stages of carcinogenesis, but it also occurs as an age-related field effect in morphologically normal mucosa. This is of great potential interest because, unlike somatic mutation, methylation can affect large numbers of epithelial cells simultaneously, potentially giving rise to field abnormalities such as hyperproliferation, failure of DNA repair and suppression of apoptosis. The aim of this three year project was to test the hypothesis that gene-specific CpG-island methylation can be used as a biomarker of susceptibility to colorectal cancer, which may be sensitive to dietary and other environmental factors. We applied both established and newly developed analytical techniques to mucosal biopsies, and to stool samples, obtained from a large number of human volunteers, with widely differing ages, and in patients with and without colorectal neoplasia.

A total of 80 volunteers were recruited from the endoscopy clinic of the Wansbeck Hospital, Northumberland. Nineteen had sporadic polyps at the time of recruitment, two were being followed up because of previous polyps and one was diagnosed with familial adenomatous polyposis. Two patients were diagnosed with cancer and seven were being followed up because of previous cancer. Forty nine of the patients were classified as “normal” in that no evidence of neoplasia was detected at endoscopy. A total of 35 patients presenting for surgery for colorectal cancer were recruited and provided samples of tumour, normal mucosa and stool. We also recruited a total of 217 healthy volunteers, who donated stool samples from which DNA was extracted. Stool and tissue samples from the patients were analysed using Methylation-Specific PCR (MSP), which is sensitive but non-quantitative, and Combined Restriction Analysis (COBRA), which is quantitative but less sensitive. The study confirmed the feasibility of using human DNA residues from stool samples to detect and quantify aberrant methylation of the promoter regions of several genes known to be involved in the development of colorectal cancer. Of the genes studied, HPP1 proved to be of particular interest because of the relatively high levels of methylation detectable in stool, and because the extent of methylation detected varied with clinical status in a statistically significant manner. Crucially, HPP1 methylation can be detected and quantified in the stool from healthy patients, as well as patients with polyps or colorectal cancer, and there are significantly higher methylation levels in the stool of patients with neoplasia. Thus HPP1 is a strong candidate for use as a faecal methylation marker both to detect the presence of disease, and to identify healthy individuals at increased risk of disease.

To pursue our major aim of analysing a series of new genes in stool samples from a large series of healthy volunteers, we chose a novel set of target genes by reference to the literature. A total of 11 genes ( HPP1, P16, ESR-1, APC, hMLH1, P14, MINT31, SFRP2, Ecad, N33 and MYOD) were analysed in approximately 170 individual stool samples, using Quantitative Methylation Specific PCR (QMSP), a method newly developed by us to overcome the problems of sensitivity and reproducibility associated with COBRA. Varying levels of methylation were detected in stool samples for all of the chosen genes. Relatively high and statistically significant correlations in the levels of methylation for some, but not all genes in the data set, were observed. Principal components analysis was used to simplify the data-set and reveal underlying relationships between genes. Some individuals had uniformly low levels of methylation whereas others showed substantial methylation for all the genes studied.

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