Classification of rare BRCA1 and BRCA2 sequence variants in breast cancer families

Background
Mutations in the BRCA1 and BRCA2 genes are thought to be responsible for about 30% of breast cancers in multiple-case families. Routine diagnostic BRCA1 and BRCA2 gene screening is thus commonplace for individuals from high-risk breast-ovarian families. However, in addition to obviously pathogenic truncating mutations, approximately 10-15% of all non-polymorphic sequence variants identified are individually rare variants of unknown clinical significance e.g. intronic changes or predicted missense alterations. Such unclassified variants (UVs) create considerable difficulties for counselling and clinical management of patients. Some variants may be classified as high-risk using multifactorial likelihood analysis, which estimates odds of causality using data on co-occurrence of the UV with pathogenic mutations in the same gene, co-segregation of the UV with affected status, amino acid conservation and physicochemical properties, and tumour features. Splicing and protein assays are also helpful in assessing the biological relevance of specific variants.

Aim
To use statistical and laboratory methods to assess the clinical relevance of rare BRCA1 and BRCA2 sequence variants identified in multi-case breast cancer families, identified in Australia or through the international consortium ENIGMA.

Approach
This project will assess the effect of the potential mutation on gene/protein function using a variety of molecular biological assays and bioinformatic predictions of rare BRCA1 and BRCA2 variants of unknown clinical significance, and family and case-control data where available. Techniques used will be primarily RNA analyses using LCLs and/or constructs, sequencing, pedigree analysis and simple statistical analyses.

Outcomes
Analysis of specific UVs will provide evidence regarding their pathogenicity for translation in the clinical setting. Comparison of assay results with risk will form the foundation for improving bioinformatic prediction tools and incorporating predictions and/or biological assay results in statistical models of risk prediction.


http://www.qimrberghofer.edu.au/page/Lab/Mol_Cancer_Epi/