Models Of Breast Cancer Susceptibility

Several models have been developed to derive estimates of risk to women with a family history of breast cancer or to estimate the probability of carrying a mutation in the BRCA1 or BRCA2 gene. These models can be broadly categorized as "empirical models" and "genetic models." Empirical models are based summarily on measures of family history, such as the number of affected relatives and other risk factors. Perhaps the most widely used model of this kind is the Gail model, which incorporates a variety of breast cancer risk factors in addition to the number of affected relatives (31). Such a model is useful in the general population context, for example, in selecting women for prevention trials but is less useful in high-risk families where the nuances of the family history cannot be captured well.

Genetic models seek to model the familial aggregation of the disease in terms of the effects of specific genes or other familial risk factors. These models are developed from population-based studies of pedigrees using the statistical technique of segregation analysis. One best most well-known model of this kind is that of Claus et al. (32), based on an analysis of the Cancer and Steroid Hormone (CASH) study. This model postulated a single major gene, with an allele frequency of 0.3%, conferring a breast cancer risk of about 80% by age 80. This study found no evidence of any additional polygenic component, so that all the familial aggregation of breast cancer could be explained by a single gene. According to this model, approximately 5% of breast cancer cases would be attributable to this postulated gene. (The model is thus largely responsible for spawning the misleading statements that "about 5% of breast cancer is hereditary.") Other segregation analyses have also been conducted, some of which found other models of disease susceptibility, including models with a recessive component (33). However, the CASH model became widely accepted and used in genetic counseling, perhaps in part because it conformed to the general impression that high-risk families appeared to be dominant, and because it provided a straightforward way of classifying individual risk. And, to an extent, the model was vindicated by the identification of the BRCA1 and BRCA2 genes.

In reality, however, the situation is more complex. Mutations in the BRCA1 and BRCA2 genes do confer high risks of breast cancer comparable to those suggested by the model, but they do not explain all the familial aggregation of the disease. Since the identification of BRCA1 and BRCA2, more recent models have sought to model the effects of these genes. One of the most widely used is called BRCAPRO (34,35). It models the effects of BRCA1 and BRCA2 mutations and can therefore be used to obtain mutation carrier probabilities and age-specific cancer risks. However, it does not account for the effects of other genes and therefore tends to overpredict carrier probabilities. From population-based series and high-risk families from the United Kingdom, Antoniou et al. (36) found that the best fitting model was one incorporating the effects of BRCA1, BRCA2, and a polygenic component, the effect of many additional genes of small effect. This model, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), has been shown to model accurately the familial risks of breast cancer and the prevalence of BRCA1 and BRCA2 mutations in population-based series (36). An alternative model was devised by Tyrer et al. (37), which incorporates the effects of BRCA1, BRCA2, and a third major gene.

There are additional reasons for believing that the polygenic model is reasonable. The frequency of BRCA1 and BRCA2 in breast cancer families is strongly dependent on the degree of family history, so that the majority of families with a strong family history (for example, six or more cases) harbor a mutation in one of these genes. This suggests strongly that most other breast cancer genes will confer lower risks. In addition, further genetic linkage studies in multiple case families have not found evidence of any further susceptibility loci, suggesting that if other high-risk susceptibility loci do exist, the alleles are likely to be rare (15,38). The recent identification of some low-penetrance breast cancer loci is further confirmation that susceptibility to breast cancer does have a substantial polygenic component.

The polygenic model has quite different implications to single-gene models like that of Claus et al. (32). Although the latter model classifies everyone as either low or high risk, the polygenic model implies a virtually continuous distribution of risk, such that an individuals' risk is determined by the combination of high-risk alleles that they carry. Under this model, a much higher fraction of breast cancer cases can occur in "high-risk" individuals. Using this model, for example, Pharoah et al. (39) have estimated that about half of all breast cancer cases occur in women in the top 12% of the risk distribution.

Another prediction of the polygenic BOADICEA model is that the "polygenes" will also alter the risk in BRCA1 and BRCA2 carriers. Evidence in support of this is provided by the fact that estimates of the risk of breast cancer BRCA1/2 carriers have generally been higher than those estimated from studies of unselected breast cancer cases (40,41).

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