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Breast cancer is the most common malignant disease in women, with 1 out of 12 women suffering from the disease during her life. According to twin studies, breast cancer is the result of both genetic (one-third) and environmental factors (two thirds). Familial breast cancer accounts for about 10 % of total breast cancer. Mutations in the high-penetrance genes BRCA1, BRCA2 and TP53, which are involved in the pathogenesis of hereditary forms of breast cancer, constitute < 5 % of all breast cancers and about 25 % of familial breast cancer. Due to the polygenetic model of inherited breast cancer, unfavourable combinations of genetic risk variants in low-penetrance genes mainly contribute to inherited breast cancer. The majority of these cancer susceptibility genes remains to be discovered. The identification of genetic risk factors will help to understand breast cancer carcinogenesis and may provide clues for diagnostic, preventive and therapeutic strategies.
Candidate gene and whole genome approach
We apply both the candidate gene and the whole genome approach as these two approaches are complementing each other. Applying the whole genome approach, it is possible to identify genes and gene variants that nobody has ever connected with cancer, although there is the problem of multiple testing that will provoke a lot of false positives, whereas the hypothesis-driven candidate gene/ candidate SNP approach allows to detect also rather rare variants mediating a mean to high risk.
Candidate gene and candidate SNP approach
Genetic risk factors are identified by a hypothesis-driven candidate gene approach focussing especially on in silico and literature-evaluated candidate SNPs.
Candidate genes are selected by focussing on important pathways und networks in carcinogenesis like the apoptosis - cell cycle network. Expression profile data indicating early up- and downregulated genes in breast cancer development as well as somatic mutation data will be considered. Beside the candidate gene approach, the analysis will focus on candidate SNPs that might be functionally relevant. Literature searches are performed combined with an in silico approach to select functional relevant candidate SNPs.
Whole genome approach
In addition, we perform a whole genome SNP chip analysis enabling an unbiased search for genetic factors that influence breast cancer risk using the 500k Affymetix SNP array. This will be done in collaboration with the members of the German Consortium for Hereditary Breast and Ovarian cancer and the Department of Molecular Genetic Epidemiology headed by Prof. Kari Hemminki.
Functional characterisation of identified risk variants
Functional analysis of identified genetic risk factors become more and more important for the scientific impact of association studies.
Promoter variants and haplotypes that have shown to be associated with cancer risk are cloned into a reporter gene vector and analysed for intrinsic differences in their ability to drive transcription in a reporter gene assay. The assays are being performed in different cell types. Gene variants altering in splice variant profile or leading to aberrant splicing are analysed at the level of RNA. Alterations in the splice variant profile are analysed by TaqMan real time PCR quantifying the different splice variants in the respective genotype carriers. Aberrant splicing is identified by sequencing of aberrant splice products of the respective gene variant carriers. Gene variants of transcription factors are analysed by comparing the expression profiles of cells transfected either with the wild type or the SNP variant.