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.

The regression dilution with the FFQ is considerably greater than with the 7DD and also, for the nutrients considered, greater than would be inferred if validation studies were based solely on record or diary type instruments.

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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.

Summary Many studies have investigated the association between alterations in the p53 gene and clinical outcome of breast cancer, and most investigators have reported poorer overall and disease-free survival (as indicated by a relative hazard (RH) greater than one) in breast cancer cases with somatic mutations in p53. However, different studies have produced widely differing RH estimates, ranging from no risk (RH = 1) to a relative hazard of 23, and not all of these results have been statistically significant. We have therefore reviewed all the published studies that have investigated the association between somatic mutations in the p53 gene and breast cancer prognosis and used standard techniques of meta-analysis to combine the results of these studies to produce a more precise estimate of the prognostic significance of p53 mutations. Eleven studies investigated overall survival in a total of 2319 unselected cases. The RH estimates from these ranged from 1 to 23.4 with a combined RH estimate of 2.0 (confidence interval 1.7-2.5). Three studies investigated the role of p53 in node-negative patients and in these, the combined estimate of RH was 1.7 (1.2-2.3). For three studies of node-positive breast cancer the combined risk estimate was 2.6 (1.7-3.9). The inclusion of p53 mutation screening in large breast cancer clinical trials seems warranted in the light of these results. Analysis of large numbers of cases matched for stage and therapy will allow definitive clarification of the value of p53 mutational status in prognostication, and possibly choice of therapy.