alexa Value of Phenotypic and Single-Nucleotide Polymorphism Panel Markers in Predicting the Risk of Breast Cancer | OMICS International | Abstract
ISSN: 2157-7412

Journal of Genetic Syndromes & Gene Therapy
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Research Article

Value of Phenotypic and Single-Nucleotide Polymorphism Panel Markers in Predicting the Risk of Breast Cancer

Adam R Brentnall1*, D Gareth Evans2 and Jack Cuzick1

1Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK

2Genesis Breast Cancer Prevention Centre, University Hospital of South Manchester NHS Trust, Wythenshawe, Manchester M23 9LT, UK

*Corresponding Author:
Adam R Brentnall
Centre for Cancer Prevention
Wolfson Institute of Preventive Medicine
Queen Mary University of London
Charterhouse Square, London EC1M 6BQ, UK
Tel: +442078823531
Fax: +442078823890
E-mail: [email protected]

Received date: October 26, 2013; Accepted date:November 27, 2013; Published date: December 06, 2013

Citation: Brentnall AR, Evans DG, Cuzick J (2013) Value of Phenotypic and Single-Nucleotide Polymorphism Panel Markers in Predicting the Risk of Breast Cancer. J Genet Syndr Gene Ther 4:202. doi:10.4172/2157-7412.1000202

Copyright: © 2013 Brentnall AR, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The risk of breast cancer from a number of SNPs (Single-Nucleotide Polymorphisms) has recently been estimated singly by COGS (Collaborative Oncological Gene-Environment Study). We assessed how the predicted risk from a panel of SNPs would compare with classical phenotypic factors including age, family history and parity, and how much it might add to risk assessment. The analysis was based on prospective data from ten thousand women of routine screening age enrolled into the UK Predicting Risk of Breast Cancer at Screening (PROCAS) study, and computer simulation SNP scores. We found that the current panel of 67 SNPs was less able to identify high-risk women than classical phenotypic factors, but if they can be treated independently, then in combination a substantially increased predictive effect might be seen. The proportion of women in the PROCAS cohort with a 10- year risk of more than 8% increased from 0.5% using age and the SNP67 score; to 1.1% using the phenotypic factors in the Tyrer-Cuzick model; to 3.3% when combined.

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