The contribution of breast cancer pathology to statistical models to predict mutation risk in BRCA carriers

• Published in: Familial cancer 2010-12, Vol.9 (4), p.545-553
• Main Authors: Vargas, Ana Cristina, Silva, Leonard Da, Lakhani, Sunil R.
• Format: Article
• Language: English
• Subjects: Biomedical and Life Sciences; Biomedicine; BRCA1 Protein - genetics; BRCA2 Protein - genetics; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Cancer Research; Epidemiology; Female; Genetic Predisposition to Disease; Germ-Line Mutation - genetics; Heterozygote; Human Genetics; Humans; Models, Statistical
• ISSN: 1389-9600; 1573-7292
• DOI: 10.1007/s10689-010-9362-5

BRCA 1 and BRCA 2 associated breast cancer comprises a small but important group of hereditary breast cancer. Testing for BRCA1 and BRCA2 has significant clinical and personal implications for the patients in terms of therapy and follow-up of individual family members. The sequencing of the genes is expensive and since the information derived may have a profound effect on the individual and family members, it is important that testing is done only when the risk of carrying a mutation is thought to be high. Over the last decade, researchers have developed a number of statistical models for predicting risk for harboring mutations in these genes and the risk of subsequently developing breast and ovarian cancer. These models usually take into account the type of tumor and age at occurrence as well as family history. Data from pathological analysis show that although breast tumours are heterogeneous, there are histological characteristics that are seen more frequently in carriers of BRCA1 germ line mutations compared to BRCA2 and sporadic breast cancers. A number of authors have suggested that the addition of pathological data to risk algorithms may improve the predictive power of these models and provide a more accurate way of identifying individuals who may benefit from testing. Here we review the pathology of familial breast cancer and assess the evidence to justify the use of pathology in refining risk assessment models.