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Familial Cancer
Published in: Familial Cancer 4/2010

01-12-2010

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

Authors: Ana Cristina Vargas, Leonard Da Silva, Sunil R. Lakhani

Published in: Familial Cancer | Issue 4/2010

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Abstract

BRCA1 and BRCA2 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.
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Metadata
Title
The contribution of breast cancer pathology to statistical models to predict mutation risk in BRCA carriers
Authors
Ana Cristina Vargas
Leonard Da Silva
Sunil R. Lakhani
Publication date
01-12-2010
Publisher
Springer Netherlands
Published in
Familial Cancer / Issue 4/2010
Print ISSN: 1389-9600
Electronic ISSN: 1573-7292
DOI
https://doi.org/10.1007/s10689-010-9362-5

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