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Centre for Genetic Epidemiology, The University of Melbourne, Carlton, Victoria 3053, Australia
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Abstract |
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 Top Abstract IntroductionMethodsResultsDiscussionReferences |
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Introduction |
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However, recent population-based studies have challenged this paradigm.
As summarized in Table 1
, most incident cases of early-onset breast cancer in women with a germ
line mutation in BRCA1 or BRCA2 do not have a
first- or second-degree relative with breast cancer
(5–8)
. Even for women of Ashkenazi Jewish descent, for
whom the probability of carrying a mutation is more than 2% and the
risk of breast cancer in carriers is in excess of 50%
(9)
, the majority of community-sampled cases found to be a
mutation carrier do not have an affected first degree relative
(10)
. In other words, the majority of these
"hereditary" cases are "sporadic," in that they do not have a
family history of breast cancer.
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, these studies have shown that even for cases whose first- or
second-degree relative has breast cancer, only about 1 in 15 carried a
germ line mutation in BRCA1 or BRCA2. This
proportion increased to about 1 in 10 if two relatives were affected,
and to 1 in 3 if more than two relatives were affected. In the context
of Ashkenazi Jewish women, the probability of carrying a mutation in
cases with a family history is typically low unless the case was
diagnosed at a young age and there was either more than one relative
affected or at least one relative affected before age 50
(11)
. That is, the great majority of "familial breast
cancers" are not hereditary, where "familial" means having a
family history (12)
.
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The purpose of this investigation was to conduct simulation studies to try to understand these issues. In particular, we have calculated the conditional probability that a case (proband) is a mutation carrier given the number of her affected relatives, and the percentages of cases, and of mutation-carrying cases, by family history, for genetic models that may be appropriate for mutations in BRCA1 and BRCA2 in Ashkenazi and non-Ashkenazi populations. The size and structure of families were based on the population-based ABCFS. Breast cancer incidence rates in Australia are as high as in other Western countries.
For simplicity, we have ignored sources of familial aggregation other
than dominant inheritance of high-risk mutations in autosomal loci. We
have also restricted attention to breast cancer in females only.
Although an increased risk of ovarian cancer in women who carry a
mutation in BRCA1 or BRCA2 has been observed in
the BCLC families (14, 15)
, population-based studies
typically have found few cases of ovarian cancer in the families
ascertained through breast cancer in a mutation carrier. Similarly, an
increased risk of male breast cancer in BRCA2 mutation
carriers is evident from the BCLC families (16)
, but male
breast cancer is not a feature of population-based families with a
BRCA2 mutation. In the population-based studies listed in
Table 1
, there were only 4 cases of ovarian cancer reported in the
first- and second-degree relatives of the 66 mutation-carrying case
probands (i.e., 6% had a family history of ovarian
cancer) and no cases of male breast cancer.
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Methods |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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). For each sisterhood in the second and third generation, the number of
females was assumed to be distributed as an independent Poisson
variable with mean 1.25, based on census data from the Australian
Bureau of Statistics (18)
. Each pedigree had to
have at least one affected female member in the third generation,
called the case proband, as indicated by the solid symbol in Fig. 1
. If
two or more females in the third generation were affected, one was
randomly chosen to be the case proband.
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Following Li and Thompson (19)
, age at death
(td) was simulated according to the
density function
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td 100 and
ß = 15, so as to give a median age of death of 81 years,
consistent with Australian female population data (20)
.
The minimum of calendar age and age at death was taken as the censored
age tc =
min(tb,td),
where min stands for minimum.
Genetic Model.
Although there are two known autosomal loci, BRCA1 and
BRCA2, associated with breast cancer, the probability that
an individual inherits a mutation in either gene is small. For
simplicity, we have ignored the very rare possibility that more than
one mutation is segregating in a family. In effect, we assumed a single
autosomal locus model, with two alleles A and a,
where a represents a mutation in either BRCA1 or
BRCA2 and has allele frequency p.
Mating is considered to have been at random with respect to these loci, and Hardy-Weinberg equilibrium is presumed to have existed. Therefore, the genotype of each grandparent can be represented as AA, Aa, or aa with probabilities (1 - p)2, 2p(1 - p), and p2, respectively, independent of that of other grandparents in the first generation. For individuals in the second and third generations, their genotypes were generated by randomly and independently sampling one allele from the mother and one from the father.
This genotype-assigning process was first carried out for the grandparents. We assumed that a marriage has occurred in the second generation. If there was more than one daughter of the maternal grandparents and more than one son of the paternal grandparents, one daughter and one son were chosen at random to mate. The process was then repeated, assigning genotypes to members in the second and third generations.
The values of p chosen for simulations were 0.001, 0.005, and 0.01. The former two cover the range that is thought to be applicable for BRCA1 and BRCA2 mutations combined in a general Western population (21–25) , and the latter for the combined three founder mutations in people of Ashkenazi Jewish descent (9) .
For females with genotype AA, the hazard function
(i.e., the conditional probability of disease in the next
age interval, given being alive at the current age t,
measured in years) was assumed to be:
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= 4.21 and 
= 9.95 x
10-10. That is, the age at onset was assumed to
follow a Weibull distribution, with parameters consistent with breast
cancer incidence rates derived from Australian cancer registries
(26)
. The cumulative risk was 6% to age 70.
Because we are considering hereditary cases to be those with a germ
line mutation in BRCA1 or BRCA2, we assumed
dominant inheritance. For females with genotypes Aa or
aa, the hazard function was
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The disease status of each female relative of a case proband was
derived from the population hazard, her genotype-specific HR, and her
censored age (tc), according to the
formula:
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g = for genotype AA and HR
for genotype Aa or aa, where Pr stands for
probability. All of the male relatives were presumed to be unaffected
because this simulation study was focused on female breast cancer only. |
Results |
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Proportion of Probands by Family History.
Fig. 2
shows that, in all situations, the majority of case probands (60–80%)
did not have a family history (i.e., had no affected first-
or second-degree relatives). The proportion of case probands without a
family history decreased as the HR, or the allele frequency, increased.
In absolute terms, however, the effect of these genetic parameters on
the probability of not having a family history was not large. The
continuous curves in Fig. 2
were fitted using logistic regression.
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, the percentage of case probands with one affected relative was fairly
stable at 
20–30%. The proportion with two affected relatives
ranged between 2 and 7%, and the proportion with more than two
affected relatives was between 0.3 and 1.6%. These percentages
increased as the HR, or the allele frequency, increased. For
p = 0.001, all of the proportions were weakly dependent
on HR, in terms of both absolute and relative changes. For example, the
proportion with no affected relatives varied only between 72 and 78%,
whereas the proportion with two or more affected relatives varied
between 0.3 and 0.6%. For p = 0.01, the proportion
with no affected relatives varied from 62 to 75%, and the proportion
with two or more affected relatives from 0.3 to 1.6%. That is, the
effect of HR on the probability of n affected relatives
increased as p increased and, in absolute and proportional
terms, increased as n increased.
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Prevalence of Mutation Carriers among Case Probands by Family
History.
Fig. 3
shows the prevalence of mutation carriers among case probands
classified by their family history, where the continuous curve
represents a fitted logistic regression model that included linear and
quadratic terms for HR. The prevalence increased as the allele
frequency, extent of family history, or HR increased. For
p = 0.001, the prevalence of mutation carriers among
case probands without a family history was between 1 and 2%. For
p = 0.01, the prevalence was between 8 and 14%. The
prevalence of mutation carriers among probands with more than two
affected relatives ranged from 6 to 50% for p = 0.001,
and from 40 to 90% for p = 0.01.
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When p = 0.005 and HR was between 5 and 10, the prevalence of mutation carriers among case probands was below 5% for cases with no family history. As the number of affected relatives increased from one to two and to more than two, the prevalence increased from between 8 and 15%, to between 15 and 30% and to between 20 and 50%, respectively. That is, each affected female relative increased the risk of being a mutation carrier by about 2- to 3-fold (11, 28) .
Percentage of Mutation Carriers among Case Probands by the Number
of Affected Relatives.
Shown in Table 4
are the percentage of mutation carriers among case probands by the
number of affected relatives, for each fixed allele frequency and HR.
We see that the percentage of mutation-carrying probands without a
family history decreased as the HR increased. This percentage declined,
from 62% when HR = 5 and p = 0.001, to 30% when
HR = 20 and p = 0.01. When HR 10, more
than 50% of mutation carriers among case probands had no family
history for 0.001 p 0.01.
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of Hopper et al. (27)
summarized
findings from mutation testing in 400 cases of early-onset breast
cancer by a "tower" with a broad base representing sporadic cases
(i.e., cases with no family history). Familial cases (those
who had at least one affected first- or second-degree female relative)
were represented above the base, with the different categories
of family history labeled on the left-hand side of the figure. The
tower became progressively narrower as the number of affected relatives
increased. The sizes of the sporadic and familial boxes were
proportional to the breakdown of cases by family history in the
population-based sample. Hereditary cases (i.e., cases with
a BRCA1 or BRCA2 mutation) were represented by a
vertical core through the middle of the rectangles, with a fixed width
of one unit. Therefore, as the width of the column of the tower
decreased with increasing numbers of affected relatives, the proportion
of hereditary cases for each category—written in terms of "1 in
x" in the right-hand half of each box—also increased with
increasing family history. The percentage on the right-hand side of the
figure represents the proportion of all of the mutation carriers among
the case probands, broken down for each category of family history.
Fig. 4
shows the simulated tower for HR = 10 and p =
0.005, the genetic parameters compatible with the Australian
population-based data on which Figure 1
of Hopper et al.
(27)
was based. The relative sizes of the boxes were based
on the percentages of case probands by the number of affected relatives
given in bold for HR = 10 and p = 0.005 in Table 3
. The percentages of mutation carriers among case probands for each
category of family history listed on the right-hand side are derived
from the numbers given in bold font in Table 4
. The proportions of
carriers among case probands for each category of family history,
written as "1 in x," are derived from Fig. 3, p
= 0.005 and HR = 10. Using the same approach,
towers for other scenarios can be drawn (not shown).
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shows, therefore, that the majority of hereditary cases
(i.e., 53%) had no family history, and that only 2% of
hereditary case probands had more than two affected relatives, and only
11% had two or more affected. Comparison with Table 1
shows that the
observed proportion with no affected relatives (typically 50% or more
and on average 64%) is similar to that predicted by the model with
HR = 10. Where the model appears not to fit the observed data well
is in the smallest category of two or more affected relatives, into
which 6 (9%) of the 66 mutation-carrying case families were
classified, compared with the model’s prediction of just 1.3 (2%).
That is, there appear to be too many mutation-carrying families with
more than two affected female first- or second-degree relatives than
would be expected if the HR were 10 and independent of age.
In addition, Fig. 4
shows that the probability that a case carries a
mutation approximately doubles or triples for each additional affected
relative. This is similar to the effect being observed empirically,
both for population-based studies (see Table 2
) and for studies of
women of Ashkenazi Jewish descent (10, 11, 28)
.
In contrast, using the values HR = 20 and p =
0.005 appropriate for the multiple-case families of the BCLC, Table 4
shows that only 29% did not have a family history, inconsistent with
the observations listed in Table 1
. The model also predicts that at the
other end of the family history distribution, 5% would have at least
two affected relatives, and that 25% would have two or more affected
relatives, more comparable with the observed 11 (17%) of the
population-based families in Table 1
.
When we conducted simulations restricting to only first-degree
relatives and used the parameters appropriate for the combined
Ashkenazi founder mutations (HR = 15, p = 0.01),
we found that about 65% of case carriers did not have an affected
first-degree relative, and 32% had one affected relative, close to
that observed from the community-based Ashkenazi study (see
Table 1
).
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Discussion |
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is
similar to that derived from the ABCFS (27)
, although it
perhaps varies in the extreme category from that suggested by the
pooling of the population-based studies in Table 2
.
Whereas the population-based estimate of HR = 10 best describes
the mutation status of the majority of cases, for whom either no
relative or one relative is affected (base of the tower), it
appears to underestimate the proportion with two or more affected
relatives (top of the tower). In contrast, the BCLC-based
estimate of HR = 20 appears to describe the mutation status of
cases with multiple affected relatives better but gives poor
predictions for the majority of cases. That is, for the vast majority
of mutation carriers, the observed data may be best described by a
model with HR = 10 or even less [when one considers the
proportion of mutation carriers among cases with no family history (see
Table 2
)], but there may also be a small subset for whom HR = 20
is appropriate. It is tempting, therefore, to suggest that there is
heterogeneity of risk. This could be attributable to there being
classes of mutations associated with different risks, or that there are
other familial risk "triggers" (environmental or genetic) that
modify the risk in mutation carriers. Clearly, larger numbers of
population-sampled case carriers, and further simulations on models
that allow for heterogeneity and age-dependence of risk, are needed to
clarify this issue.
These simulation studies have confirmed that in the population setting,
even for HR = 20, family history of breast cancer is not a strong
predictor of mutation status, which is consistent with the observations
of population-based studies (7)
. Each affected relative
increased the risk of being a mutation carrier by 2- to 3-fold
(11, 28)
. The probability of being a mutation carrier was
generally low, except in families with extreme histories of breast
cancer, such as those deliberately ascertained for gene-hunting by the
BCLC.
Why Are the Majority of Hereditary Cases of Early-onset Breast
Cancer Sporadic?
First, there may not be many female mutation carriers in the family.
Their probability of developing breast cancer is not necessarily high,
especially when their age is taken into consideration. It now appears
that the average increased risk of breast cancer attributable to
mutations that cause early-onset breast cancer in the population is
about 10-fold, and, hence, the lifetime risk is less than 50%.
Consequently, more than one-half of female mutation carriers in a
family are unlikely to be affected, especially if they are not old.
Furthermore, on average, only one in every two female first-degree
relatives of a mutation carrier is a carrier herself, and mutations can
be passed down through the paternal line(s) so there may be no female
carriers in the family.
The second issue is the potential for under-reporting of affected
relatives in mutation carrying cases found by the population-based
studies. The population-based studies listed in Table 1
, however, have
made major efforts to record the family histories of the few case
carriers that they have identified, and they have even systematically
sought an interview with first- and second-degree relatives as in the
ABCFS. Nevertheless, there are some families for whom there is little
knowledge about the disease status of relatives, especially for
second-degree relatives.
The third issue is de novo mutations, which would increase the number of cases without a family history. However there is little evidence that this is a common occurrence for BRCA1 and BRCA2 (as it is for the APC gene). One instance of a de novo mutation in BRCA1 has been identified by the ABCFS (29) , and we are aware of one other anecdotal report.
The fourth factor that may affect the interpretation of our results is the sensitivity of testing for BRCA1 and BRCA2 mutations which, in practice, has yet to reach 100%. Some mutation carriers may not have been detected, and, although this would result in a lower prevalence of mutation carriers among case probands, the breakdown by family history would be altered only if the mutations detected were associated with a different risk of disease than those not detected.
The fifth factor is the risk of ovarian cancer in mutation-carrying
relatives. Because of the poor prognosis in those who develop ovarian
cancer, the probability that such carriers would also
develop breast cancer would be low. Contrary to the experience of the
BCLC, which had specifically over-sampled "breast-ovary" families,
ovarian cancers are not a common feature of BRCA1 or
BRCA2 mutation-carrying families as ascertained through a
population-sampled case. Only 6% of such families listed in
Table 1
have been observed to have a family history of ovarian cancer.
That is, whereas ovarian cancer may be a predictor of the presence of a
BRCA1 or even a BRCA2 mutation, especially when
it occurs in a family with multiple-cases of breast cancer, it is not a
typical feature of mutation-carrying families in the population.
Family size is another potentially important variable. In this study, the mean number of female siblings in the second and third generation was assumed to be 1.25. However, when we increased this to 1.8, the percentage of mutation carriers who did not have a family history only went from 53 to 59%, when p = 0.005 and HR = 10.
Although the findings presented in this report match recent population-based studies reasonably well, caution should be exercised when interpreting our results. We have assumed that HR at all ages is a constant, which represents an averaged genetic relative risk. To study the effects that a heterogeneity of risk between the young and the old would have on the family history distribution, further simulations should allow for HR to depend on a woman’s age.
We assessed the reliability of the percentages in Table 3
and Table 4
by calculating approximate SEs and confidence intervals based on
the analytical variance estimate from the binomial distribution
(30)
. For example, in Table 3
, 72.9% of case probands did
not have a family history, and 0.4% had more than two affected female
relatives, when p = 0.005 and HR = 10. Because
these estimates came from 4000 simulated families, their SEs were
approximately 0.7% and 0.1%, respectively. Accordingly, the
approximate 95% confidence intervals were from 71.5 to 74.3% and from
0.2 to 0.6%, respectively. Similarly, in Table 4
, 52.7 and 2.1% of
case probands who were mutation carriers had no family history and had
more than two affected female relatives, respectively. Because these
estimates came from 2916 (4000 x 72.9%) and 16 (4000 x
2.1%) families, the 95% confidence intervals were 50.9–54.5%
and 0–9.2%, respectively.
These simulations have shown that the findings of the population-based studies are not inconsistent with a genetic model for BRCA1 and BRCA2 mutations. They are reassuring, because early conference presentations and submitted manuscripts that were based on these findings (i.e., Refs. 31 and 7 , respectively) were met by considerable skepticism. Both the observed and predicted towers have a large and high base, and the great majority of case carriers have no affected relative or only one. Therefore, the multiple-case families who present to cancer genetics clinics, represented by the box at the top of the tower, constitute only a small proportion of all mutation carriers. It is, therefore, likely that only a small percentage of all mutation carriers will ever be detected by current mutation detection, which is almost exclusively limited to the so-called high-risk families. Although a small percent of women with breast cancer may carry a mutation in BRCA1 or BRCA2, the prospect of detecting more than a small percentage of them is not realistic given the current cost and acceptability of mutation testing. The population-based perspective of breast cancer genetics is quite different from that provided by the earlier studies, which focused on families with multiple cases of breast cancer.
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Footnotes |
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1 This work is supported by the National Health
and Medical Research Council of Australia and by NIH Grant U01-69638. 
2 To whom requests for reprints should be
addressed, at The University of Melbourne, Centre for Genetic
Epidemiology, 200 Berkeley Street, Carlton, Victoria 3053, Australia.
E-mail: j.hopper{at}gpph.unimelb.edu.au
3 The abbreviations used are: BCLC, Breast Cancer
Linkage Consortium; ABCFS, Australian Breast Cancer Family Study;
HR, hazard ratio.
Received 1/17/00; revised 5/10/00; accepted 5/23/00.
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References |
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