Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer

The ability of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model to predict BRCA1 and BRCA2 mutations and breast cancer incidence in women with a family history of breast cancer was evaluated. Observed mutations in 263 screened families were compared to retrospective predictions. Similarly, observed breast cancers in 640 women were compared to retrospective predictions of breast cancer incidence. The ratios of observed to expected number of BRCA1- , BRCA2- and BRCA(1 or 2) mutations were 1.43 (95% CI 1.05–1.90), 0.63 (95% CI 0.34–1.08), and 1.12 (95% CI 0.86–1.44), showing a significant underestimation of BRCA1 mutations. Discrimination between carriers and non-carriers as measured by area under the receiver operating characteristic (ROC) curve was 0.83 (95% CI 0.76–0.88). The ratio of observed to expected number of invasive breast cancers was 1.41 (0.91–2.08). The corresponding area under the ROC curve for prediction of invasive breast cancer at individual level was 0.62 (95% CI 0.52–0.73). In conclusion, the BOADICEA model can predict the total prevalence of BRCA(1 or 2) mutations and the incidence of invasive breast cancers. The mutation probability as generated by BOADICEA can be used clinically as a guideline for screening, and thus decrease the proportion of negative mutation analyses. Likewise, individual breast cancer risks can be used for selecting women whose risk of breast cancer indicates follow-up. Application of local mutation frequencies of BRCA1 and BRCA2 could improve the ability to distinguish between the two genes

A family history of breast cancer will not predict female early onset breast cancer in a population-based setting

ABSTRACT: BACKGROUND: An increased risk of breast cancer for relatives of breast cancer patients has been demonstrated in many studies, and having a relative diagnosed with breast cancer at an early age is an indication for breast cancer screening. This indication has been derived from estimates based on data from cancer-prone families or from BRCA1/2 mutation families, and might be biased because BRCA1/2 mutations explain only a small proportion of the familial clustering of breast cancer. The aim of the current study was to determine the predictive value of a family history of cancer with regard to early onset of female breast cancer in a population based setting. METHODS: An unselected sample of 1,987 women with and without breast cancer was studied with regard to the age of diagnosis of breast cancer. RESULTS: The risk of early-onset breast cancer was increased when there were: (1) at least 2 cases of female breast cancer in first-degree relatives (yes/no; HR at age 30: 3.09; 95% CI: 128-7.44), (2) at least 2 cases of female breast cancer in first or second-degree relatives under the age of 50 (yes/no; HR at age 30: 3.36; 95% CI: 1.12-10.08), (3) at least 1 case of female breast cancer under the age of 40 in a first- or second-degree relative (yes/no; HR at age 30: 2.06; 95% CI: 0.83-5.12) and (4) any case of bilateral breast cancer (yes/no; HR at age 30: 3.47; 95%: 1.33-9.05). The positive predictive value of having 2 or more of these characteristics was 13% for breast cancer before the age of 70, 11% for breast cancer before the age of 50, and 1% for breast cancer before the age of 30. CONCLUSION: Applying family history related criteria in an unselected population could result in the screening of many women who will not develop breast cancer at an early age

Optimal selection for BRCA1 and BRCA2 mutation testing using a combination of ' easy to apply ' probability models

To establish an efficient, reliable and easy to apply risk assessment tool to select families with breast and/or ovarian cancer patients for BRCA mutation testing, using available probability models. In a retrospective study of 263 families with breast and/or ovarian cancer patients, the utility of the Frank (Myriad), Gilpin (family history assessment tool) and Evans (Manchester) model was analysed, to select 49 BRCA mutation-positive families. For various cutoff levels and combinations, the sensitivity and specificity were calculated and compared. The best combinations were subsequently validated in additional sets of families. Comparable sensitivity and specificity were obtained with the Gilpin and Evans models. They appeared to be complementary to the Frank model. To obtain an optimal sensitivity, five ‘additional criteria' were introduced that are specific for the selection of small or uninformative families. The optimal selection is made by the combination ‘Frank ⩾16% or Evans2 ⩾12 or one of five additional criteria'. The efficiency of the selection of families for mutation testing of BRCA1 and BRCA2 can be optimised by using a combination of available easy to apply risk assessment models

Is the incidence of meningiomas underestimated? A regional survey

We assessed the undercount of meningiomas in a population-based cancer registry. A comprehensive material was formed by compiling hospital sources with the Finnish Cancer Registry database. The completeness of each source ranged 62–69%. The corrected age-standardised meningioma incidence was 2.9/100 000 for men and 13.0/100 000 for women, a third higher than the cancer registry figures

The pathology of familial breast cancer: The pre-BRCA1/BRCA2 era - historical perspectives

A proportion of breast carcinomas develop as a result of a genetic predispostion to the disease. Prior to cloning of the BRCA1 and BRCA2 genes a limited number of studies were carried out to identify specific histopathological characteristics of hereditary breast cancer. These studies are the subject of this review. The main finding was the association of the (atypical) medullary type of breast cancer with a family history; the most important caveat being that medullary breast cancer is found more frequently in young patients. In view of the frequent bilateral occurrence of lobular cancer, this histologic type is also likely to be associated with a predisposing genetic defect. Future investigations will have to test this hypothesis. In addition to mutations in the BRCA1 and BRCA2 genes, there are as yet unidentified genetic defects predisposing to breast cancer development, and histopathology may well help in identifying these genes in the future

The pathology of familial breast cancer: Morphological aspects

A small proportion of breast cancers are due to a heritable predisposition. Recently, two predisposition genes, BRCA1 and BRCA2, have been identified and cloned. The morphological features of tumours from patients harbouring mutations in the BRCA1 and BRCA2 genes differ from each other and from sporadic breast cancers. Both are of higher grade than are sporadic cases. An excess of medullary/atypical medullary carcinoma has been reported in patients with BRCA1 mutations. Multifactorial analysis, however, shows that the only features independently associated with BRCA1 mutations are a high mitotic count, pushing tumour margins and a lymphocytic infiltrate. For BRCA2 mutation, an association with tubular/lobular carcinoma has been suggested, but not substantiated in a larger Breast Cancer Linkage Consortium study. In multifactorial analysis, the independent features were a lack of tubule formation and pushing tumour margins only. The morphological analysis has implications for clinical management of patients

Mood instability, mental illness and suicidal ideas : results from a household survey

Purpose: There is weak and inconsistent evidence that mood instability (MI) is associated with depression, post traumatic stress disorder (PTSD) and suicidality although the basis of this is unclear. Our objectives were first to test whether there is an association between depression and PTSD, and MI and secondly whether MI exerts an independent effect on suicidal thinking over and above that explained by common mental disorders. Methods: We used data from the Adult Psychiatric Morbidity Survey 2007 (N = 7,131). Chi-square tests were used to examine associations between depression and PTSD, and MI, followed by regression modelling to examine associations between MI and depression, and with PTSD. Multiple logistic regression analyses were used to assess the independent effect of MI on suicidal thinking, after adjustment for demographic factors and the effects of common mental disorder diagnoses. Results: There are high rates of MI in depression and PTSD and the presence of MI increases the odds of depression by 10.66 [95 % confidence interval (CI) 7.51–15.13] and PTSD by 8.69 (95 % CI 5.90–12.79), respectively, after adjusting for other factors. Mood instability independently explained suicidal thinking, multiplying the odds by nearly five (odds ratio 4.82; 95 % CI 3.39–6.85), and was individually by some way the most important single factor in explaining suicidal thoughts. Conclusions: MI is strongly associated with depression and PTSD. In people with common mental disorders MI is clinically significant as it acts as an additional factor exacerbating the risk of suicidal thinking. It is important to enquire about MI as part of clinical assessment and treatment studies are required