Prevalence of <i>BRCA1</i> and <i>BRCA2</i> germline mutations in young breast cancer patients: A population‐based study

Sanjosé, Sílvia de; Léoné, Mélanie; Berez, V.; Izquierdo, Àngel; Font, Rebeca; Brunet, Joan; Louat, Thierry; Vilardell, L.; Borrás, Joan; Viladiu, P.; Bosch, F. Xavier; Lenoir, Gilbert; Sinilnikova, Olga M.

doi:10.1002/ijc.11271

Cited by 96 publications

(61 citation statements)

References 33 publications

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“…In case no family member fulfils one of the 'additional criteria', the selection is dependent on the Frank, Gilpin or Evans score. The prevalence of BRCA1 and BRCA2 mutations among all women diagnosed with an invasive breast cancer or ductal carcinoma in situ is similar, and varies between 0.4 -2.6% and 1.4 -2.4%, respectively (Peto et al, 1999;Sanjose et al, 2003;Claus et al, 2005). However, both in our settings as well as in the literature, the families known to carry a BRCA1 mutation outnumber those with a BRCA2 mutation by far (Newman et al, 1998;Syrjakoski et al, 2000;Claus et al, 2005).…”

Section: Discussionsupporting

confidence: 60%

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

et al. 2006

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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 X16% or Evans2 X12 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. British Journal of Cancer (2006) Identification of families at high risk of hereditary breast and/or ovarian cancer contributes to the prevention and early detection of breast and ovarian malignancies. Therefore, genetic testing is offered to women with an increased risk of hereditary breast or ovarian cancer based on familial clustering of breast and/or ovarian cancer, particularly in case of early onset or if breast cancer occurs in a male. Selection criteria to test for BRCA mutations vary. Because BRCA testing is laborious and expensive, as well as associated with medical, psychological and social consequences for the patient, careful patient selection is required before testing. To obtain optimal ascertainment, many risk assessment models and prior probability models have been developed and evaluated (de la Hoya et al, 2003;Domchek et al, 2003). Four such models, the Claus, Gilpin, Frank and Evans model (Claus et al, 1998;Gilpin et al, 2000;Frank et al, 2002;Evans et al, 2004) are empirically derived scoring systems, easy to apply in daily practice with the use of a pencil and a paper and easy to understand for both counsellor and patient.With the Claus tables the probability of developing breast cancer can be determined, but not the likelihood of detecting a BRCA mutation (as in prior probability models). These tables are based on series of unselected women with breast cancer. As this model does not account for breast cancer in more than two family members, the presence of ovarian cancer, male breast cancer or bilateral breast cancer, it underestimates the cancer risk in many families and cannot be used solely as a prediction model in the clinic.The Frank model, developed by Myriad Genetics, is an empirical model correlating the prevalence of BRCA mutations with personal and family history of breast and ovarian can...

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Section: Discussionsupporting

confidence: 60%

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

et al. 2006

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“…[36][37][38][39] We also found high familial aggregation of breast cancer at younger ages consistent with reports of higher prevalence of mutations in the BRCA1/BRCA2 genes in young patients and a high probability of the same gene mutations in their sisters. 40,41 Examining the changing risk with time since the index person's diagnosis, we found the pattern varied by cancer site: for colorectal and breast cancer, the risk profile for siblings was approximately constant for up to 20 years while for melanoma there was evidence of a small decline in risk in the first 5 years, and for prostate cancer a sharp decline. To further investigate the decreasing pattern for prostate cancer we investigated the incidence in case and control siblings during the first 2 years after the index diagnosis, stratifying by whether the diagnosis was before/after 1997, when PSA testing was widely available in Sweden.…”

Section: Epidemiologymentioning

confidence: 93%

Patterns of changing cancer risks with time since diagnosis of a sibling

Lee

Czene

Rebora

et al. 2014

Intl Journal of Cancer

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Family history is a well-known risk factor for many cancers. However, it is important to know if/how the familial risk of cancer changes over time. For each of four major cancers (colorectal, breast, prostate and melanoma), we identified siblings of cancer patients (case siblings) and siblings of matched cancer-free controls sampled from Swedish population-based registers. Effects of age and time since diagnosis on sibling risks were examined using Poisson regression and presented graphically as smoothed hazard ratios (HRs). Screening effects were investigated by comparing hazards before/after the introduction of mammography for breast cancer and prostate-specific antigen (PSA) testing for prostate cancer. Case siblings had higher cancer incidence than control siblings for all cancers at all ages, with overall incidence rate ratios (IRRs) of 2.41 (95% confidence interval 2.14-2.71) for colorectal cancer, 2.37 (2.24-2.52) for breast cancer, 3.69 (3.46-3.93) for prostate cancer and 3.20 (2.72-3.76) for melanoma. Risks were highest in siblings who were young when the first cancer was diagnosed in the family, with siblings aged 30-40 having IRR 9.05 (3.03-27.00) for colorectal cancer and 4.30 (2.87-6.45) for breast cancer. Smoothed HRs remained fairly constant for up to 20 years except for prostate cancer, where the HR decreased steeply during the first few years. After introduction of PSA testing, men had higher incidence of prostate cancer shortly after diagnosis in a brother, but no such screening effect was found for breast cancer. Our findings can help inform the screening and counseling of family members of cancer patients.Family history is long recognized as an important risk factor for cancer and has also been found to be associated with cancer prognosis. One of the main, but simple, ways to begin to understand cancer etiology is by identifying the involvement of heritable genes through investigation of familial aggregation. There is a large body of published research providing estimates of increased risk for various cancer sites in different types of relatives. [1][2][3][4] However, in addition to the estimates of overall or lifetime increased risk in family members, it is important to know if/how this risk changes over time. To address this question, different time scales need to be considered when studying the relatives at risk: current age, age at the time of diagnosis of the first cancer in the family, elapsed time from the cancer diagnosis and calendar time.Age is the strongest risk factor for cancer onset and many studies show both the age of relatives and the age of the cancer patient at diagnosis to be associated with the family cancer risk. 5,6 Young age at onset of cancer has been shown to have high heritability and so is an important factor in familial risk. 7,8 However, few studies have investigated in detail how this risk changes with time since the first (index) cancer diagnosis in a family. 9 Such risk profiles would not only provide insight into underlying etiology but could also be used to ...

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“…Population-based data indicate that approximately 10% of the breast cancers in women younger than 40 years are related to a BRCA1 or BRCA2 mutation [35].Although women who carry BRCA mutations have a very high risk of developing breast cancer, there is no convincing evidence that their risk of developing local relapse after BCT is significantly different from the risk in non-carriers [36][37][38]. The risk of developing a contralateral breast cancer is, on the other hand, much higher than in non-carriers [36,38,37].…”

mentioning

confidence: 99%

Are breast conservation and mastectomy equally effective in the treatment of young women with early breast cancer? Long-term results of a population-based cohort of 1,451 patients aged ≤40 years

Sangen

Wiel

Poortmans

et al. 2010

Breast Cancer Res Treat

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(95% CI, 26.7-39.1) to 16.1% (95% CI, 9.1-23.1), (P=.0007). In conclusion, local tumor control in young patients with early-stage breast cancer is worse after BCT than after mastectomy. Adjuvant systemic therapy significantly improves local control following BCT and also for that reason it should be considered for most patients ≤40years. Long-term follow-up is highly recommended for young patients after BCT, because even with systemic treatment an annual recurrence risk of 1% remains up to 15 years after treatment.Van der Sangen, et al.4

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Prevalence of BRCA1 and BRCA2 germline mutations in young breast cancer patients: A population‐based study

Cited by 96 publications

References 33 publications

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

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

Patterns of changing cancer risks with time since diagnosis of a sibling

Are breast conservation and mastectomy equally effective in the treatment of young women with early breast cancer? Long-term results of a population-based cohort of 1,451 patients aged ≤40 years

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