Silje Nord's project groupMaking sense of GWAS data
Breast Cancer is a complex disease characterized by the interaction of numerous somatically acquired genetic lesions, influenced by underlying germline genetic variants and the epigenome.
Breast Cancer (BC), the most common cancer in females (WHO), is a heterogeneous disease where prognosis depends greatly on subtype and time of detection. Recent Genome Wide Association Studies (GWAS) have revealed common variants as risk factors for developing sporadic breast cancer (Michailidou et al., Nat Gen. 2013), partly through their influence on expression (Nordgard et al., Breast Cancer Res. 2007). The common denominator for these variants is their limited influence on risk and high allele frequency in the general population. Given the essence of early detection for breast cancer outcome, one may, by determining their functional effect, screen individuals at risk, i.e. those harboring known risk alleles, for tumor-phenotype associated gene expression pattern. Alternatively, this information may be used to identify candidates for preventive target therapy. The functional knowledge can also enable tailored treatment of patients depended on the risk allele(s) they carry. Additionally, this information may also be used to identify high from low risk patients in individuals with the same clinical and histopathological classification. By identifying patients carrying variants associated with poor outcome, one may, by reducing overtreatment, limit the long-term side effects such as chronic fatigue, cognitive problems, heart failure and leukemia.
Our HSØ (South-Eastern Norway Regional Health Authority) founded project will systematically dissect all putative effects of risk variants in the same cohort(s). This provides us with the means to estimate each SNPs individual influence on the various explanatory models. We have, through our participation in the international Breast Cancer Association Consortium (BCAC), access to BC risk fine mapping data from more than 100.000 patients. With this translational project we hope, through functional characterization, to take the outcome of BC genome wide analysis one step closer to the clinic, both preventive and curative.
|Figure. Genomic location of breast cancer risk loci in Caucasians (BCAC).|
Our group has also nourished multiple pet projects aimed at developing novel models describing biological phenomenon, including an algorithm to call germline genotypes from tumor DNA, a model describing the methylation signature of a gene and how to assess the co-occurrence of copy number and methylation events in a tumor, and the impact on the cancer transcriptome.