Prevalence of the HOXB13 G84E germline mutation in British men and correlation with prostate cancer risk, tumour characteristics and clinical outcomes.

BACKGROUND: A rare recurrent missense variant in HOXB13 (rs138213197/G84E) was recently reported to be associated with hereditary prostate cancer. Population-based studies have established that, since the frequency of this single-nucleotide polymorphism (SNP) varies between geographic regions, the associated proportion of prostate cancer (PrCa) risk contribution is also highly variable by country. PATIENTS AND METHODS: This is the largest comprehensive case-control study assessing the prevalence of the HOXB13 G84E variant to date and is the first in the UK population. We genotyped 8652 men diagnosed with PrCa within the UK Genetic Prostate Cancer Study (UKGPCS) and 5252 healthy men from the UK ProtecT study. RESULTS: HOXB13 G84E was identified in 0.5% of the healthy controls and 1.5% of the PrCa cases, and it was associated with a 2.93-fold increased risk of PrCa [95% confidence interval (CI) 1.94-4.59; P = 6.27 × 10(-8)]. The risk was even higher among men with family history of PrCa [odds ratio (OR) = 4.53, 95% CI 2.86-7.34; P = 3.1 × 10(-8)] and in young-onset PrCa (diagnosed up to the age of 55 years; OR = 3.11, 95% CI 1.98-5.00; P = 6.1 × 10(-7)). There was no significant association between Gleason Score, presenting prostate specific antigen, tumour-node-metastasis (TNM) stage or NCCN risk group and carrier status. HOXB13 G84E was not associated with overall or cancer-specific survival. We found that the polygenic PrCa risk score (PR score), calculated using the 71 known single-nucleotide polymorphisms (SNPs) associated with PrCa and the HOXB13 G84E variant act multiplicatively on PrCa risk. Based on the estimated prevalence and risk, this rare variant explains ∼1% of the familial risk of PrCa in the UK population. CONCLUSIONS: The clinical importance of HOXB13 G84E in PrCa management has not been established. This variant was found to have no effect on prognostic implications but could be used for stratifying screening, by identifying men at high risk. CLINICAL TRIALS NUMBERS: Prostate Testing for Cancer and Treatment (ProtecT): NCT02044172. UK GENETIC PROSTATE CANCER STUDY: Epidemiology and Molecular Genetics Studies (UKGPCS): NCT01737242.

Frequent germline deleterious mutations in DNA repair genes in familial prostate cancer cases are associated with advanced disease.

BACKGROUND: Prostate cancer (PrCa) is one of the most common diseases to affect men worldwide and among the leading causes of cancer-related death. The purpose of this study was to use second-generation sequencing technology to assess the frequency of deleterious mutations in 22 tumour suppressor genes in familial PrCa and estimate the relative risk of PrCa if these genes are mutated. METHODS: Germline DNA samples from 191 men with 3 or more cases of PrCa in their family were sequenced for 22 tumour suppressor genes using Agilent target enrichment and Illumina technology. Analysis for genetic variation was carried out by using a pipeline consisting of BWA, Genome Analysis Toolkit (GATK) and ANNOVAR. Clinical features were correlated with mutation status using standard statistical tests. Modified segregation analysis was used to determine the relative risk of PrCa conferred by the putative loss-of-function (LoF) mutations identified. RESULTS: We discovered 14 putative LoF mutations in 191 samples (7.3%) and these mutations were more frequently associated with nodal involvement, metastasis or T4 tumour stage (P=0.00164). Segregation analysis of probands with European ancestry estimated that LoF mutations in any of the studied genes confer a relative risk of PrCa of 1.94 (95% CI: 1.56-2.42). CONCLUSIONS: These findings show that LoF mutations in DNA repair pathway genes predispose to familial PrCa and advanced disease and therefore warrants further investigation. The clinical utility of these findings will become increasingly important as targeted screening and therapies become more widespread.

BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface.

BACKGROUND: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction model that is used to compute probabilities of carrying mutations in the high-risk breast and ovarian cancer susceptibility genes BRCA1 and BRCA2, and to estimate the future risks of developing breast or ovarian cancer. In this paper, we describe updates to the BOADICEA model that extend its capabilities, make it easier to use in a clinical setting and yield more accurate predictions. METHODS: We describe: (1) updates to the statistical model to include cancer incidences from multiple populations; (2) updates to the distributions of tumour pathology characteristics using new data on BRCA1 and BRCA2 mutation carriers and women with breast cancer from the general population; (3) improvements to the computational efficiency of the algorithm so that risk calculations now run substantially faster; and (4) updates to the model's web interface to accommodate these new features and to make it easier to use in a clinical setting. RESULTS: We present results derived using the updated model, and demonstrate that the changes have a significant impact on risk predictions. CONCLUSION: All updates have been implemented in a new version of the BOADICEA web interface that is now available for general use: http://ccge.medschl.cam.ac.uk/boadicea/.

Prospective validation of the breast cancer risk prediction model BOADICEA and a batch-mode version BOADICEACentre.

BACKGROUND: Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction algorithm that can be used to compute estimates of age-specific risk of breast cancer. It is uncertain whether BOADICEA performs adequately for populations outside the United Kingdom. METHODS: Using a batch mode version of BOADICEA that we developed (BOADICEACentre), we calculated the cumulative 10-year invasive breast cancer risk for 4176 Australian women of European ancestry unaffected at baseline from 1601 case and control families in the Australian Breast Cancer Family Registry. Based on 115 incident breast cancers, we investigated calibration, discrimination (using receiver-operating characteristic (ROC) curves) and accuracy at the individual level. RESULTS: The ratio of expected to observed number of breast cancers was 0.92 (95% confidence interval (CI) 0.76-1.10). The E/O ratios by subgroups of the participant's relationship to the index case and by the reported number of affected relatives ranged between 0.83 and 0.98 and all 95% CIs included 1.00. The area under the ROC curve was 0.70 (95% CI 0.66-0.75) and there was no evidence of systematic under- or over-dispersion (P=0.2). CONCLUSION: BOADICEA is well calibrated for Australian women, and had good discrimination and accuracy at the individual level.

Estimating single nucleotide polymorphism associations using pedigree data: applications to breast cancer.

BACKGROUND: Pedigrees with multiple genotyped family members have been underutilised in breast cancer (BC) genetic-association studies. We developed a pedigree-based analytical framework to characterise single-nucleotide polymorphism (SNP) associations with BC risk using data from 736 BC families ascertained through multiple affected individuals. On average, eight family members had been genotyped for 24 SNPs previously associated with BC. METHODS: Breast cancer incidence was modelled on the basis of SNP effects and residual polygenic effects. Relative risk (RR) estimates were obtained by maximising the retrospective likelihood (RL) of observing the family genotypes conditional on all disease phenotypes. Models were extended to assess parent-of-origin effects (POEs). RESULTS: Thirteen SNPs were significantly associated with BC under the pedigree RL approach. This approach yielded estimates consistent with those from large population-based studies. Logistic regression models ignoring pedigree structure generally gave larger RRs and association P-values. SNP rs3817198 in LSP1, previously shown to exhibit POE, yielded maternal and paternal RR estimates that were similar to those previously reported (paternal RR=1.12 (95% confidence interval (CI): 0.99-1.27), P=0.081, one-sided P=0.04; maternal RR=0.94 (95% CI: 0.84-1.06), P=0.33). No other SNP exhibited POE. CONCLUSION: Our pedigree-based methods provide a valuable and efficient tool for characterising genetic associations with BC risk or other diseases and can complement population-based studies.

Unravelling modifiers of breast and ovarian cancer risk for BRCA1 and BRCA2 mutation carriers: update on genetic modifiers.

Pathogenic mutations in the tumour suppressor genes BRCA1 and BRCA2 confer increased risks for breast and ovarian cancer and account for approximately 15% of the excess familial risk of breast cancer amongst first-degree relatives of patients with breast cancer. There is considerable evidence indicating that these risks vary by other genetic and environmental factors clustering in families. In the past few years, based on the availability of genome-wide association data and samples from large collaborative studies, several common alleles have been found to modify breast or ovarian cancer risk for BRCA1 and BRCA2 mutation carriers. These common alleles explain a small proportion of the genetic variability in breast or ovarian cancer risk for mutation carriers, suggesting more modifiers remain to be identified. We review the so far identified genetic modifiers of breast and ovarian cancer risk and consider the implications for risk prediction. BRCA1 and BRCA2 mutation carriers could be some of the first to benefit from clinical applications of common variants identified through genome-wide association studies. However, to be able to provide more individualized risk estimates, it will be important to understand how the associations vary with different tumour characteristics and their interactions with other genetic and environmental modifiers.

Genetic modifiers of cancer risk for BRCA1 and BRCA2 mutation carriers.

Germline mutations in BRCA1 and BRCA2 confer high risks of female breast and ovarian cancer. However, there is strong evidence that these risks are modified by other factors, including familial or genetic factors. Genome-wide association studies have identified several breast cancer genetic susceptibility variants in the general population that are also associated with breast cancer risk for mutation carriers. The patterns of association for these variants vary between BRCA1 and BRCA2 mutation carriers and this variation appears to be driven by their differential associations with breast cancer subtypes defined by estrogen receptor status. We review the latest evidence regarding genetic modifiers of cancer risk for female BRCA1 and BRCA2 mutation carriers emerging from candidate gene studies, variants found in genome-wide association studies (GWAS) to be associated with cancer risk in the general population and GWAS specifically in mutation carriers. We also discuss the implications of these findings for cancer risk prediction in these women. BRCA1 and BRCA2 mutation carriers could potentially be among the first groups of individuals for whom clinically applicable risk profiling could be developed using the common breast cancer susceptibility variants identified through GWAS.

The TP53 Arg72Pro and MDM2 309G>T polymorphisms are not associated with breast cancer risk in BRCA1 and BRCA2 mutation carriers.

BACKGROUND: The TP53 pathway, in which TP53 and its negative regulator MDM2 are the central elements, has an important role in carcinogenesis, particularly in BRCA1- and BRCA2-mediated carcinogenesis. A single nucleotide polymorphism (SNP) in the promoter region of MDM2 (309T>G, rs2279744) and a coding SNP of TP53 (Arg72Pro, rs1042522) have been shown to be of functional significance. METHODS: To investigate whether these SNPs modify breast cancer risk for BRCA1 and BRCA2 mutation carriers, we pooled genotype data on the TP53 Arg72Pro SNP in 7011 mutation carriers and on the MDM2 309T>G SNP in 2222 mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Data were analysed using a Cox proportional hazards model within a retrospective likelihood framework. RESULTS: No association was found between these SNPs and breast cancer risk for BRCA1 (TP53: per-allele hazard ratio (HR)=1.01, 95% confidence interval (CI): 0.93-1.10, P(trend)=0.77; MDM2: HR=0.96, 95%CI: 0.84-1.09, P(trend)=0.54) or for BRCA2 mutation carriers (TP53: HR=0.99, 95%CI: 0.87-1.12, P(trend)=0.83; MDM2: HR=0.98, 95%CI: 0.80-1.21, P(trend)=0.88). We also evaluated the potential combined effects of both SNPs on breast cancer risk, however, none of their combined genotypes showed any evidence of association. CONCLUSION: There was no evidence that TP53 Arg72Pro or MDM2 309T>G, either singly or in combination, influence breast cancer risk in BRCA1 or BRCA2 mutation carriers.

An evaluation of the polymorphisms Ins16bp and Arg72Pro in p53 as breast cancer risk modifiers in BRCA1 and BRCA2 mutation carriers.

The close functional relationship between p53 and the breast cancer susceptibility genes BRCA1 and BRCA2 has promoted the investigation of various polymorphisms in the p53 gene as possible risk modifiers in BRCA1/2 mutation carriers. Specifically, two polymorphisms in p53, c.97-147ins16bp and p.Arg72Pro have been analysed as putative breast cancer susceptibility variants, and it has been recently reported that a p53 haplotype combining the absence of the 16-bp insertion and the presence of proline at codon 72 (No Ins-72Pro) was associated with an earlier age at the onset of the first primary tumour in BRCA2 mutation carriers in the Spanish population. In this study, we have evaluated this association in a series of 2932 BRCA1/2 mutation carriers from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2.

Predicting the likelihood of carrying a BRCA1 or BRCA2 mutation: validation of BOADICEA, BRCAPRO, IBIS, Myriad and the Manchester scoring system using data from UK genetics clinics.

OBJECTIVES: Genetic testing for the breast and ovarian cancer susceptibility genes BRCA1 and BRCA2 has important implications for the clinical management of people found to carry a mutation. However, genetic testing is expensive and may be associated with adverse psychosocial effects. To provide a cost-efficient and clinically appropriate genetic counselling service, genetic testing should be targeted at those individuals most likely to carry pathogenic mutations. Several algorithms that predict the likelihood of carrying a BRCA1 or a BRCA2 mutation are currently used in clinical practice to identify such individuals. DESIGN: We evaluated the performance of the carrier prediction algorithms BOADICEA, BRCAPRO, IBIS, the Manchester scoring system and Myriad tables, using 1934 families seen in cancer genetics clinics in the UK in whom an index patient had been screened for BRCA1 and/or BRCA2 mutations. The models were evaluated for calibration, discrimination and accuracy of the predictions. RESULTS: Of the five algorithms, only BOADICEA predicted the overall observed number of mutations detected accurately (ie, was well calibrated). BOADICEA also provided the best discrimination, being significantly better (p<0.05) than all models except BRCAPRO (area under the receiver operating characteristic curve statistics: BOADICEA = 0.77, BRCAPRO = 0.76, IBIS = 0.74, Manchester = 0.75, Myriad = 0.72). All models underpredicted the number of BRCA1 and BRCA2 mutations in the low estimated risk category. CONCLUSIONS: Carrier prediction algorithms provide a rational basis for counselling individuals likely to carry BRCA1 or BRCA2 mutations. Their widespread use would improve equity of access and the cost-effectiveness of genetic testing.

The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions.

Multiple genetic loci confer susceptibility to breast and ovarian cancers. We have previously developed a model (BOADICEA) under which susceptibility to breast cancer is explained by mutations in BRCA1 and BRCA2, as well as by the joint multiplicative effects of many genes (polygenic component). We have now updated BOADICEA using additional family data from two UK population-based studies of breast cancer and family data from BRCA1 and BRCA2 carriers identified by 22 population-based studies of breast or ovarian cancer. The combined data set includes 2785 families (301 BRCA1 positive and 236 BRCA2 positive). Incidences were smoothed using locally weighted regression techniques to avoid large variations between adjacent intervals. A birth cohort effect on the cancer risks was implemented, whereby each individual was assumed to develop cancer according to calendar period-specific incidences. The fitted model predicts that the average breast cancer risks in carriers increase in more recent birth cohorts. For example, the average cumulative breast cancer risk to age 70 years among BRCA1 carriers is 50% for women born in 1920-1929 and 58% among women born after 1950. The model was further extended to take into account the risks of male breast, prostate and pancreatic cancer, and to allow for the risk of multiple cancers. BOADICEA can be used to predict carrier probabilities and cancer risks to individuals with any family history, and has been implemented in a user-friendly Web-based program (http://www.srl.cam.ac.uk/genepi/boadicea/boadicea_home.html).

Models of genetic susceptibility to breast cancer.

One of the most important risk factors for breast cancer is family history of the disease, indicating that genetic factors are important determinants of breast cancer risk. A number of breast cancer susceptibility genes have been identified, the most important being BRCA1 and BRCA2. However, it is estimated that all the currently known breast cancer susceptibility genes accounts for less than 25% of the familial aggregation of breast cancer. In this paper, we review the evidence for other breast cancer susceptibility genes arising from twin studies, pedigree analysis and studies of phenotypes associated with breast cancer, and the progress towards finding other breast cancer susceptibility genes through linkage and association studies. Taken together, the available evidence indicates that susceptibility to breast cancer is mediated through variants in many genes, each conferring a moderate risk of the disease. Such a model of susceptibility has implications for both risk prediction and for future gene identification studies.

Breast and ovarian cancer risks to carriers of the BRCA1 5382insC and 185delAG and BRCA2 6174delT mutations: a combined analysis of 22 population based studies.

A recent report estimated the breast cancer risks in carriers of the three Ashkenazi founder mutations to be higher than previously published estimates derived from population based studies. In an attempt to confirm this, the breast and ovarian cancer risks associated with the three Ashkenazi founder mutations were estimated using families included in a previous meta-analysis of populatrion based studies. The estimated breast cancer risks for each of the founder BRCA1 and BRCA2 mutations were similar to the corresponding estimates based on all BRCA1 or BRCA2 mutations in the meta-analysis. These estimates appear to be consistent with the observed prevalence of the mutations in the Ashkenazi Jewish population.

The BOADICEA model of genetic susceptibility to breast and ovarian cancer.

Several genes conferring susceptibility to breast and ovarian cancer, notably BRCA1 and BRCA2, have been identified. The majority of the familial aggregation of breast cancer is, however, not explained by these genes. We have previously derived, using segregation analysis, a susceptibility model (BOADICEA, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) in which susceptibility to these genes is explained by mutations in BRCA1 and BRCA2 together with a polygenic component reflecting the joint multiplicative effect of multiple genes of small effect on breast cancer risk. Here, we consider the predictions made by this model. The overall familial risks of breast cancer predicted by this model are close to those observed in epidemiological studies. The predicted prevalences of BRCA1 and BRCA2 mutations among unselected cases of breast and ovarian cancer are also consistent with observations from population-based studies. These predictions are closer to the observed values than those obtained using the Claus model and BRCAPRO. The predicted mutation probabilities and cancer risks in individuals with a family history (FH) can differ markedly from those predicted by other models. We conclude that this model provides a rational basis for risk assessment in individuals with a FH of breast or ovarian cancer.

A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes.

In computing the probability that a woman is a BRCA1 or BRCA2 carrier for genetic counselling purposes, it is important to allow for the fact that other breast cancer susceptibility genes may exist. We used data from both a population based series of breast cancer cases and high risk families in the UK, with information on BRCA1 and BRCA2 mutation status, to investigate the genetic models that can best explain familial breast cancer outside BRCA1 and BRCA2 families. We also evaluated the evidence for risk modifiers in BRCA1 and BRCA2 carriers. We estimated the simultaneous effects of BRCA1, BRCA2, a third hypothetical gene 'BRCA3', and a polygenic effect using segregation analysis. The hypergeometric polygenic model was used to approximate polygenic inheritance and the effect of risk modifiers. BRCA1 and BRCA2 could not explain all the observed familial clustering. The best fitting model for the residual familial breast cancer was the polygenic, although a model with a single recessive allele produced a similar fit. There was also significant evidence for a modifying effect of other genes on the risks of breast cancer in BRCA1 and BRCA2 mutation carriers. Under this model, the frequency of BRCA1 was estimated to be 0.051% (95% CI: 0.021-0.125%) and of BRCA2 0.068% (95% CI: 0.033-0.141%). The breast cancer risk by age 70 years, based on the average incidence over all modifiers was estimated to be 35.3% for BRCA1 and 50.3% for BRCA2. The corresponding ovarian cancer risks were 25.9% for BRCA1 and 9.1% for BRCA2. The findings suggest that several common, low penetrance genes with multiplicative effects on risk may account for the residual non-BRCA1/2 familial aggregation of breast cancer. The modifying effect may explain the previously reported differences between population based estimates for BRCA1/2 penetrance and estimates based on high-risk families.

Evidence for further breast cancer susceptibility genes in addition to BRCA1 and BRCA2 in a population-based study.

We used data from a population based series of breast cancer patients to investigate the genetic models that can best explain familial breast cancer not due to the BRCA1 and BRCA2 genes. The data set consisted of 1,484 women diagnosed with breast cancer under age 55 registered in the East Anglia Cancer registry between 1991-1996. Blood samples taken from the patients were analysed for mutations in BRCA1 and BRCA2. The genetic models were constructed using information on breast and ovarian cancer history in first-degree relatives and on the mutation status of the index patients. We estimated the simultaneous effects of BRCA1, BRCA2, a third hypothetical gene BRCA3, and a polygenic effect. The models were assessed by likelihood comparisons and by comparison of the observed numbers of mutations and affected relatives with the predicted numbers. BRCA1 and BRCA2 could not explain all the familial clustering of breast cancer. The best-fitting single gene model for BRCA3 was a recessive model with a disease allele frequency 24% and penetrance 42% by age 70. However, a polygenic model gave a similarly good fit. The estimated population frequencies for BRCA1 and BRCA2 mutations were similar under both recessive and polygenic models, 0.024 and 0.041%, respectively. A dominant model for BRCA3 gave a somewhat worse fit, although the difference was not significant. The mixed recessive model was identical to the recessive model and the mixed dominant very similar to the polygenic model. The BRCA3 genetic models were robust to the BRCA1 and BRCA2 penetrance assumptions. The overall fit of all models was improved when the known effects of parity on breast and ovarian cancer risks were included in the model-in this case a polygenic model fits best. These findings suggest that a number of common, low-penetrance genes with additive effects may account for the residual non-BRCA1/2 familial aggregation of breast cancer, but Mendelian inheritance of an autosomal recessive allele cannot be ruled out.

After BRCA1 and BRCA2-what next? Multifactorial segregation analyses of three-generation, population-based Australian families affected by female breast cancer.

Mutations in BRCA1 and BRCA2 that cause a dominantly inherited high risk of female breast cancer seem to explain only a small proportion of the aggregation of the disease. To study the possible additional genetic components, we conducted single-locus and two-locus segregation analyses, with and without a polygenic background, using three-generation families ascertained through 858 women with breast cancer diagnosed at age <40 years, ascertained through population cancer registries in Melbourne and Sydney, Australia. Extensive testing for deleterious mutations in BRCA1 and BRCA2, to date, has identified 34 carriers. Our analysis suggested that, after other possible unmeasured familial factors are adjusted for and the known BRCA1 and BRCA2 mutation carriers are excluded, there appears to be a residual dominantly inherited risk of female breast cancer in addition to that derived from mutations in BRCA1 and BRCA2. This study also suggests that there is a substantial recessively inherited risk of early-onset breast cancer. According to the best-fitting model, after excluding known carriers of mutations in BRCA1 and BRCA2, about 1/250 (95% confidence interval [CI] 1/500 to 1/125) women have a recessive risk of 86% (95% CI 69%-100%) by age 50 years and of almost 100% by age 60 years. Possible reasons that our study has implicated a novel strong recessive effect include our inclusion of data on lineal aunts and grandmothers, study of families ascertained through women with early-onset breast cancer, allowance for multiple familial factors in the analysis, and removal of families for whom the cause (i.e., BRCA1 or BRCA2) is known. Our findings may have implications for attempts to identify new breast cancer-susceptibility genes.

Risk models for familial ovarian and breast cancer.

We investigated risk models for the inherited susceptibility of breast and ovarian cancer, using data from both high-risk families and a population based series of ovarian cancer. The first data set consisted of 112 families containing two or more relatives with epithelial ovarian cancer. BRCA1 and BRCA2 germline mutations were detected in 50% of these families. The second study involved 374 ovarian cancer cases, unselected for family history, who had DNA samples analyzed for BRCA1 mutations. Twelve women were found to be carriers. We constructed genetic models for ovarian and breast cancer using the computer program MENDEL. In the first study, we modeled the effects of BRCA1 and BRCA2 simultaneously and allowed for a third gene predisposing to ovarian cancer. None of the models fitted gave significant evidence for a third gene. Population frequencies of BRCA1 and BRCA2 mutations were estimated to be 0. 00128 and 0.00172, respectively. Our results suggest that BRCA1 and BRCA2 may be sufficient to explain the majority of familial ovarian cancer and that families without mutations can be explained by sensitivity of mutation testing and chance clusters of sporadic cases. Using data on the families of the 12 mutation carriers in the second study, we estimated age-specific ovarian and breast cancer risks for BRCA1 mutation carriers. Under the best-fitting model, the cumulative ovarian cancer risk was 66% by age 70, and the corresponding breast cancer risk was 45%. The high penetrance estimate for ovarian cancer, compared with other studies, suggests that modifying genetic or environmental factors may be important determinants of risk.

The contribution of germline BRCA1 and BRCA2 mutations to familial ovarian cancer: no evidence for other ovarian cancer-susceptibility genes.

To establish the contribution of germline BRCA1 and BRCA2 mutations to familial ovarian cancer, we have analyzed both genes in DNA samples obtained from an affected individual in each of 112 families containing at least two cases of epithelial ovarian cancer. Germline mutations were found in 43% of the families; BRCA1 mutations were approximately four times more common than BRCA2 mutations. The extent of family history of ovarian and breast cancers was strongly predictive of BRCA1-mutation status. Segregation analysis suggests that a combination of chance clustering of sporadic cases and insensitivity of mutation detection may account for the remaining families; however, the contribution of other genes cannot be excluded. We discuss the implications for genetic testing and clinical management of familial ovarian cancer arising from the data presented in these studies.