My main role is to perform a broad range of analyses of molecular and clinical data on large cohorts, using bioinformatics tools (R Studio and Jupyter Notebook). The research aims at acquiring a comprehensive picture of breast cancer risk and prognosis, studying the association between inherited genetic factors and prognosis. Currently, women at high-risk of breast cancer are primarily identified on the basis of family history and mutation screening of the BRCA1 and BRCA2 genes. Research over the past decade has, however, identified many additional variants associated with breast cancer. These include rare variants in so-called non-BRCA1/2 breast cancer genes, which confer a range of risks from “moderate” to “high”. In addition, more than 300 common variants (SNPs) have been also identified using GWAS studies.
We have examined the combined impact of common and rare variants on tumor characteristics, breast cancer survival, and risk of interval breast cancer (an aggressive subgroup of breast cancer that develops within the time interval between screening examinations). Multinomial logistic regression was used to assess the associations between mutations in 35 known and suspected breast cancer predisposition genes included in commercial panels or polygenic risk score (PRS) using more than 300 SNPs, tumor characteristics and interval cancer status. Breast cancer-specific survival was estimated using Cox regression models.
In addition, we are also studying blood-bound molecular markers using multi-omics approaches and assessing the impact of these markers on the risk of aggressive breast cancer. The chosen molecular markers have been miRNA and DNA methylation, from plasma and blood samples respectively. For around 1,000 women, we will use high-throughput methods to measure a broad selection of multiple molecular markers from blood collected at baseline.