The risk of breast cancer in BRCA1 and BRCA2 mutation carriers has been examined in many studies, but relatively little attention has been paid to the degree to which the risk may vary among carriers.
To determine the extent to which risks for BRCA1 and BRCA2 carriers vary with respect to observable and unobservable characteristics.
Design, Setting, and Participants
Probands were identified from a population-based, case-control study (Women’s Environmental Cancer and Radiation Epidemiology [WECARE]) of asynchronous contralateral breast cancer conducted during the period of January 2000 to July 2004. Participants previously diagnosed with contralateral breast cancer or unilateral breast cancer were genotyped for mutations in BRCA1 and BRCA2. All participants had their initial breast cancer diagnosed during the period of January 1985 to December 2000,
before the age of 55 years.
Main Outcome Measure Incidence of breast cancer in first-degree female relatives of the probands was examined and compared on the basis of proband characteristics and on the basis of variation between families.
Among the 1394 participants with unilateral breast cancer, 73
(5.2%) were identified as carriers of deleterious mutations (42 with BRCA1 and 31 with BRCA2).
Among the 704 participants with contralateral breast cancer, 108 (15.3%)
were identified as carriers of deleterious mutations (67 with BRCA1 and 41 with BRCA2).
Among relatives of carriers, risk was significantly associated with younger age at diagnosis in the proband (P = .04),
and there was a trend toward higher risk for relatives of contralateral breast cancer vs unilateral breast cancer participants (odds ratio,
1.4 [95% confidence interval, 0.8-2.4]; P = .28).
In addition, there were significant differences in risk between carrier families after adjusting for these observed characteristics.
There exists broad variation in breast cancer risk among carriers of BRCA1 and BRCA2 mutations.
The magnitude of the risk of breast cancer in carriers of mutations in BRCA1 or BRCA2 is critical for guiding decisions concerning cancer prevention options.
Many previous studies have reported on the cumulative risk to various ages (penetrance) of breast cancer in carriers. The recent literature primarily has involved studies of breast cancer incidence in the relatives of probands identified without consideration of family history. This literature has included studies of self-selected volunteers,1
but there appears to be some degree of consensus that the most reliable approach is to use population-based ascertainment.2-8 Most of this literature has been focused on the magnitude of the risk,
with relatively little attention being paid to the degree by which risk may vary among carriers.
Population-based studies to date have used incident cases from existing case-control investigations as probands. Estimates of risk based on studies of incident cases are inevitably inflated if there exists risk variation among carriers caused by additional, possibly unknown, genetic variants that influence risk.9-11
The thesis that substantial variation in the risk of breast cancer exists due to unknown genetic factors has become well established,12
and this has been supported by statistical modeling of disease aggregation,13
and theoretical models to explain the aggregation.14
However, although there is little doubt that other genes influence the risk of breast cancer, such as relatively rare mutations in TP53,15
and possibly CHEK216
as well as common low penetrance variants in genes that remain unidentified at this point, there is little direct evidence that variation in risk exists among BRCA1 and BRCA2 mutation carriers specifically.
In this article, we report the results of an investigation that provides direct evidence about risk variation among carriers. We use the information on lifetime risk of breast cancer in first-degree relatives of breast cancer patients (probands) who were identified as BRCA1 or BRCA2 carriers in the Women's Environmental Cancer and Radiation Epidemiology (WECARE)
a population-based case-control study. The WECARE Study is novel in that it involved recruitment of cases of asynchronous contralateral breast cancer and matched controls who had experienced a prior unilateral breast cancer. If there is no appreciable risk variation among BRCA1 or BRCA2 mutation carriers, then we would expect the risk estimates in relatives of carriers with contralateral breast cancer to be similar to the estimates from relatives of carriers with unilateral breast cancer. Likewise, evidence of risk variation may be demonstrated by significant differences in risk in groups of carrier families distinguished by any factors that are plausibly associated with risk, such as age at diagnosis of the proband.
The WECARE Study is a population-based, cancer registry–based,
nested case-control study of contralateral breast cancer. Participant recruitment was completed in 2004. The design has been described in detail in a previous article,18 but the essential features are as follows. Participants were identified and interviewed through 5 population-based cancer registries, 4 in the United States (covering Iowa, the Orange County and San Diego regions of California, Los Angeles County, California, and 3 counties in the Seattle, Washington area) and 1 covering all of Denmark.
All participants had a diagnosis of a first invasive breast cancer between January 1985 and December 2000. The cancer had to have occurred prior to the age of 55 years without evidence of spread beyond the regional lymph nodes at diagnosis. Individuals also had only breast cancer prior to the second primary cancer diagnosis for participants with contralateral breast cancer and to the corresponding matching date for unilateral breast cancer controls. Participants also had to be alive at the time of contact, able to complete the interview,
and provide a blood sample. Participants were eligible if they had an in situ or invasive diagnosis of a contralateral breast cancer at least 1 year after the first primary breast cancer diagnosis, and if they resided in the same reporting area for both diagnoses.
Control participants were selected randomly from the pool of available breast cancer patients in the cohort after matching individually on the basis of year of birth (5-year strata), year of diagnosis (4-year strata), registry, and race. Controls also were countermatched in a ratio of 2 to each contralateral breast cancer case on the basis of whether or not they had received radiotherapy treatment as recorded in the cancer registry.19 The study was reviewed and approved by local institutional review boards at each of these registry sites, and all biological samples and data were obtained after the participants provided informed consent.
Recruitment took place during the period from January 2000 to July 2004. A total of 998 women with contralateral breast cancer were eligible and were approached for inclusion in the study, and 708 of these women (71%) agreed to participate. Of the 2112 women who were selected as potential unilateral breast cancer participants, 1399
(66%) agreed to participate. The nonparticipants were similar to the participants with respect to age and calendar year of diagnosis and radiotherapy treatment of the initial primary breast cancer. Successful genotyping was accomplished in 704 participants with contralateral breast cancer and 1394 participants with unilateral breast cancer,
and these 2098 individuals represent the probands for the analyses in this article.
All participants were interviewed by telephone using a structured questionnaire. They were questioned about the breast cancer incidence in each of their first- and second-degree relatives. For each relative,
the interviewer ascertained the age at diagnosis of breast cancer,
the vital status, and the dates of death (if relevant) of the relatives.
For the purposes of this article, only the information on female first-degree relatives was used to restrict the analysis to relatives for which the data are most likely to have high accuracy.20,21
Coding and flanking intronic regions were screened for mutations or polymorphic variants by denaturing high-performance liquid chromatography (DHPLC). BRCA1 (GenBank No. U14680) was covered by 30 PCR amplicons, while 41 amplicons were used for BRCA2 (GenBank No. NM_000059). The majority of fragments were run at more than 1 DHPLC elution temperature condition for increased sensitivity.
A few fragments were screened by direct sequencing because of complex melting profiles unsuitable for DHPLC or because of the presence of multiple common variants and combinations thereof that made interpretation of chromatograms difficult. With the exception of the prevalent polymorphic variants (occurring in >10% of samples) with distinguishable chromatograms,
all variant DHPLC results (extra, shoulder, widened, or shifted peaks)
were followed up by direct sequencing of the appropriate amplicons.
Three laboratories performed the screening using fixed sets of primers and DHPLC protocols. Consistency in screening between and within laboratories was ensured via a laboratory quality-control plan including (1) blinded screening of an initial set of 21 positive controls by all laboratories; (2) initial screening of the same randomly selected 21 samples by all laboratories; (3) rescreening by 1 laboratory of a randomly selected 10% sample of all cases screened at each of the participating laboratories; and (4) blinded rescreening of a random 10% sample of each laboratory's own sample by that same laboratory.22
The analyses are focused exclusively on those sequence variants that are considered to have a deleterious effect based on current evidence. Specifically, the following sequence variant categories were classified as deleterious: (1) changes known or predicted to truncate protein production including all frameshift and nonsense variants with the exception of BRCA2 K3326X and other variants located 3′ thereof; (2) splice site mutations occurring within 2 base pair of an intron/exon boundary or shown to result in aberrant splicing; and (3) missense changes that have been demonstrated to have a deleterious effect on, for example, the function of the BRCA1 ring finger and BRCT domains.
The classification of missense changes of unknown clinical significance is an ongoing challenge in the field and we recognize that a small portion of the numerous missense changes identified and scored as unclassified variants may actually be deleterious. Our approach to classifying mutations as deleterious is comparable with that used in the clinical care sector and it is compatible with classifications used by the Breast Cancer Information Core (http://research.nhgri.nih.gov/projects/bic/). No attempt was made to screen for larger genomic deletions or duplications. Thus, some deleterious mutations may have escaped detection due to technical reasons or location in a region not covered by the current methodological approach.
All data analyses involve the incidence rates of breast cancer in the identified first-degree biological relatives of the probands (parents, full siblings, and children). These rates exclude the proband,
although the analyses involve subgroups defined by characteristics of the proband. Person-years at risk of breast cancer were determined for each relative up to the age at diagnosis of breast cancer, if diagnosed, age at death, or current age at the time the proband was interviewed.
To examine risk variation among carriers on the basis of characteristics of the proband and to construct formal statistical tests for its presence,
we conducted Poisson regression analyses of the incidences of breast cancer in family members of carrier probands. In these analyses, the periods at risk were grouped into 10-year age intervals and stratified on the basis of the relationship of the relative to the proband (mother,
sister, daughter). Various characteristics of the proband, such as contralateral breast cancer vs unilateral breast cancer status, age at diagnosis, and geographic site of recruitment (United States vs Denmark), also were included as covariates.
The analyses also adjusted for the location of the individual mutation on the BRCA1 or BRCA2 genes. Mutations on BRCA1 were grouped into 3 regions: nucleotides 1 to 2400 (47 probands), nucleotides 2401
to 4184 (36 probands), and nucleotides 4185 and above (26 probands).
The BRCA2 mutations were classified as within the ovarian cancer cluster region (22 probands with nucleotides 3059-6629)
or not (50 probands). These classifications are consistent with the meta-analysis by Antoniou et al.23 To account for residual variation in risk between carriers in these analyses,
a random effect was included for each family in which the random effects were assumed to conform to a normal distribution. In this method,
each family is assumed to have a distinct risk. The estimated variance of these random effects was then evaluated for departure from 0 to test for the presence of unexplained risk variation. This analysis was performed using Stata software version 7.0 (StataCorp, College Station, Texas).
The cumulative incidences of breast cancer to various ages in relatives of carriers and in relatives of noncarriers were calculated using the Kaplan-Meier method. The penetrance (imputed cumulative risk in a defined population of mutation carriers) was calculated by the kin-cohort method proposed by Chatterjee and Wacholder.24 In our analyses, the penetrance was calculated in several populations defined by the observed risk factors. Conceptually,
the method calculates the penetrance as double the rate observed in the first-degree relatives of carriers (because approximately half of these will be carriers), with an adjustment for the baseline incidence rate in relatives of noncarrier probands.
As an approximate benchmark for evaluating the estimated penetrance curves, a population cumulative incidence curve was constructed to reflect the population incidence of breast cancer. Reported age-specific rates from the Surveillance, Epidemiology, and End Results registries were used for this purpose, weighted to account for the calendar periods in which the individual relatives were at risk for breast cancer.
For calendar periods prior to 1975, we have used the 1975 rates, which are the earliest rates reported in the Surveillance, Epidemiology,
and End Results registries. Statistical tests were considered to be significant if the P value was less than .05.
Mutation screening of all coding exons and flanking intronic regions of BRCA1 and BRCA2 resulted in the identification of 470 unique sequence variants, among which a total of 113 unique deleterious mutations were identified, 57 located in BRCA1 and 56 in BRCA2.
Of the 113 unique deleterious mutations, 73 consisted of small frameshift deletions or insertions predicted to cause protein truncation, 26
were nonsense mutations, and 7 were splice-site mutations. Seven missense mutations were defined as deleterious, including C44S and C61G in the BRCA1 RING domain, R1699W, A1708E, G1738E and M1775R in the BRCA1 BRCT domains, as well as M1I, disrupting the translation initiation codon of BRCA2.
Among the 1394 unilateral breast cancer participants, 73 (5.2%)
were carriers of deleterious mutations (42 with BRCA1 and 31 with BRCA2) (Table 1). Among the 704 participants with contralateral breast cancer, 108 (15.3%) were carriers of deleterious mutations (67 with BRCA1 and 41 with BRCA2).
Data were reported for 598 first-degree female relatives of these 181 carrier probands (350 in BRCA1 families and 248 in BRCA2 families), among whom 103
breast cancers were reported (61 in BRCA1 families and 42 in BRCA2 families). For the 1917 noncarrier probands, there were 525 relatives with breast cancer. In total, 628
breast cancers were reported among the 7156 first-degree female relatives.
The crude familial aggregation that forms the basis for our analyses is displayed for descriptive purposes in Table 2. These frequencies do not reflect the varying numbers of relatives in each family and the ages of the relatives. The majority (75%) of the probands had no evidence of breast cancer in their first-degree relatives. This is true even for BRCA1 (58%) and BRCA2 (58%)
carriers. Of the relatively few families that demonstrate very strong familial aggregation (≥3 first-degree female relatives in addition to the proband), 8 of 10 occurred in probands with contralateral breast cancer, and 3 of these 10 occurred in carriers.
The results of our analyses of risk variation are presented in Table 3. Analyses are presented in a joint analysis of all carrier families and then separately for the relatives of BRCA1 carriers and BRCA2 carriers. All 3 analyses are multivariate analyses and include all of the factors listed in the table. There is a statistically significant trend for higher risks in relatives of women carriers diagnosed with breast cancer at younger ages (P = .04). Risks also are noticeably higher in relatives of contralateral breast cancer vs unilateral breast cancer probands, although this comparison is not statistically significant (odds ratio [OR], 1.4; 95% confidence interval [CI], 0.8-2.4; P = .28). The magnitudes of the trends are replicated broadly in the separate analyses of BRCA1 and BRCA2 carriers. For the analysis involving BRCA1, there is no evidence that risk is affected by location of the mutation on the gene (P = .99), while for BRCA2,
significantly higher breast cancer risks are evident for mutations outside of the ovarian cancer cluster region (P = .03).
For BRCA1, sisters (P = .07)
and daughters (P = .03) appear to be at a higher risk than mothers. There is no apparent difference in overall risk for BRCA1 vs BRCA2 mutations (OR, 1.1 [95% CI, 0.6-1.8]; data not shown). There is strong evidence of residual between-family variation in risk, even after adjusting for contralateral breast cancer vs unilateral breast cancer status, proband age at diagnosis, and mutation location. This is evidenced by the statistically significant tests of residual between-family variation in all 3 analyses (P = .004
overall, P = .04 for BRCA1, and P = .03 for BRCA2).
The estimated cumulative risks of breast cancer are displayed in the Figure. The curves indicate that relatives of either BRCA1 or BRCA2 mutation carriers have a substantially greater risk than relatives of noncarriers, and that relatives of case (contralateral breast cancer) probands have higher risk than relatives of control (unilateral breast cancer) probands, regardless of carrier status.
Both of these differences are statistically significant in a Poisson regression analysis, similar in structure to Table 3, but which includes the families of all noncarrier and carrier probands (BRCA1 vs noncarriers:
OR, 2.4 [95% CI, 1.7-3.5]; BRCA2 vs noncarriers:
OR, 2.6 [95% CI, 1.7-4.0]; contralateral breast cancer vs unilateral breast cancer: OR, 1.7 [95% CI, 1.4-2.0]).
Penetrance estimates in mutation carriers are imputed from these curves (Table 4). From relatives of unilateral breast cancer probands, the penetrance is estimated to be 20% by age 50 years, increasing to 40% by age 70 years, and 50% by age 80 years. The corresponding penetrance estimates from relatives of contralateral breast cancer probands are 32% by age 50 years, 51%
by age 70 years, and 57% by age 80 years. Table 4 also displays the penetrance estimates obtained separately from relatives of carriers diagnosed in distinctive age ranges.
The quantitative impact on the penetrance of the observed between-family residual risk variation can be interpreted as follows. Table 4 shows that the average risk to age 70 years in a first-degree relative of a unilateral breast cancer proband is 40%. Our random-effects analysis demonstrates that the actual risks in individual carrier families may be much higher or much lower than this average value. In fact, assuming a constant risk of breast cancer from age 30 years to age 70 years in carriers, our random-effects variance of 0.90 (Table 3) implies that carriers in carrier families at the upper 95th percentile of the risk distribution have a risk to age 70 years of 92% rather than 40%, while carriers at the lower fifth percentile have risks similar to the population risk of breast cancer.
Our study is one of the largest individual population-based family studies to date to address the breast cancer risks in BRCA1 and BRCA2 carriers,
comprising 181 carrier probands, with a total of 103 breast cancers reported in the 598 first-degree female relatives of these probands.
We examined variation of risk between carrier families by determining whether distinct risk profiles can be identified when carrier families are sorted by observed characteristics of the probands. We observed a statistically significant trend of increasing risk with decreasing age at diagnosis of the proband (P = .04).
Furthermore, there is strong evidence of residual variation in risk between carrier families due to unobserved risk factors on the basis of a statistically significant random-effects variance, even after accounting for observable proband characteristics (P = .004).
We observe that risks in relatives of contralateral breast cancer probands are higher than risks in relatives of unilateral breast cancer probands (P < .001), although this comparison is not statistically significant when conducted solely in the carrier families (P = .28).
These trends are consistent with the hypothesis that risks to BRCA1 or BRCA2 mutation carriers vary substantially due to the presence of additional unknown risk factors for breast cancer, which are more prevalent in the families of women diagnosed at a younger age, and in the families of women with contralateral breast cancer. These unknown factors, which could include variants in candidate genes such as ATM or CHEK2 or other unknown genes, may ultimately explain the strong familial clustering in the families exhibiting multiple cases of breast cancer, the preponderance of which are not linked to either BRCA1 or BRCA2 mutations.
Our results complement recent studies that examined risk variation in carriers on the basis of factors such as parity,25-27
age at first live birth,25-27
and mammographic density.28
Although the results from those studies are not fully consistent, they suggest that the relative risks conferred by these risk factors in carriers may be similar to the relative risks in noncarriers. In a recent study, Chen and Parmigiani29
have examined between-study heterogeneity of BRCA1 and BRCA2 risks,
but we emphasize that our analyses address between-family risk variation.
Our results underscore the conclusion that there is no single risk associated with BRCA1 or BRCA2 carrier status. On the contrary, risks for carriers vary substantially based on observable factors, such as the characteristics of the affected relatives (probands in our case) examined in this study, host factors such as the preceding ones, and other undetermined factors.
An alternative explanation for the observed risk variation is the possibility that individual variants in the BRCA1 and BRCA2 genes lead to substantially different breast cancer risks.5,30
In our study, we adjusted for potential within-gene effects of this nature by classifying the variants broadly using their position on the gene.
We observed no trend for the location of BRCA1 mutations,
but mutations in BRCA2 outside the ovarian cancer cluster region were shown to have substantially elevated breast cancer risk compared with mutations within it. However, our sensitivity for exploring variations at the level of the individual mutation is low due to the low frequencies of occurrence of individual variants.
Regardless of whether risk variation within BRCA1 and BRCA2 contributes meaningfully to the overall risk variation observed, it seems likely that other genetic factors play a major role. This conclusion is supported by a recent detailed review of studies that addressed this issue.31
Also, recently published genome-wide association studies suggest elevated breast cancer risk at several candidate loci.32,33
Furthermore, the fact that the preponderance of familial clustering occurs in families in which the proband is not a BRCA1 or BRCA2 carrier, a phenomenon discussed in depth in an earlier investigation by Cui and Hopper,34
also points to the existence of unexplained risk variation in the entire sample. It is also possible that some of the risk variation is due to environmental or lifestyle factors that aggregate in families,
such as age at first birth, and that also act as modifiers of risk in carriers, although a genetic explanation is more plausible.35
The overall penetrance estimates for BRCA1 or BRCA2 mutation carriers from our study are consistent with the literature on this topic from other population-based studies,
but are at the low end of a very broad range that has been reported.
The most comprehensive study of this type is the pooled analysis of 22 studies by Antoniou et al.23
These authors derive a risk to age 70 years of 65% in BRCA1 carriers and 45% in BRCA2 carriers. Their analysis included hospital-based as well as population-based studies, many of which used probands with early onset breast cancer (similar to our study), and some with ovarian cancer or male breast cancer probands.
Although our study is population-based, the sampling of probands was unusual, and this could affect the results through unforeseen selection effects. Probands were selected if they were alive and eligible during the recruitment period between 2000 and 2004, if they had a diagnosis of breast cancer from 1985 onward (2 diagnoses for contralateral breast cancer probands), and if they had no prior cancer other than breast cancer. This corresponds to a single ascertainment family-based design,36
and it could lead to overestimates of risk if families were inadvertently ascertained twice. We attempted to compare, in an algorithmic fashion, the family information of all pairs of probands, but this search revealed only 1 pair of sisters among the probands, confirmed on follow-up. Thus, we believe that double counting of members is not a concern requiring statistical adjustment.37
The ascertainment of probands who have survived sufficiently long to be eligible for the study could lead to a selection bias if some of the heritable factors affecting cancer risk also affect prognosis,
but we have no way to test this assumption. Our recruitment of women with a relatively young age at diagnosis is likely to have led to generally higher risk estimates from their relatives than would be expected in a study involving women of unrestricted age at diagnosis.
Interestingly, we observed a significantly higher risk in sisters of probands than in mothers. There is no obvious explanation for this finding, although it is consistent with a previous meta-analysis.38 This trend also was observed when we analyzed noncarrier probands (data not shown). Finally, our analysis is based on first-degree relatives, and so information on risk factors of these relatives is unavailable, except for age.
Our results imply that the risk of breast cancer in carriers who might be identified at random in the population without evidence of familial breast cancer may be even lower than the 40% at age 70
years that we estimate from families of probands with unilateral breast cancer. It is reasonable to infer that the reduction in risk from the estimates in contralateral breast cancer probands to the estimates in unilateral breast cancer probands may be mirrored in a corresponding reduction if we were able to measure risk in carriers identified from unselected population disease-free controls. Although population-based screening for these mutations is not recommended at this time, it is possible that in the future, as technology advances and genotyping costs are reduced, widespread genetic screening for important risk factors for breast cancer and other diseases may become routine, and will likely serve as the foundation for tailored risk reduction interventions.
For this reason, accurate estimation of the risks conferred in the population and identification of important sources of variation in these risks constitute important scientific goals with significant implications for the clinical management of female carriers of BRCA1 or BRCA2 mutations.
Corresponding Author: Colin B. Begg,
PhD, Memorial Sloan-Kettering Cancer Center, 307 E 63rd St, New York,
NY 10021 (firstname.lastname@example.org).
Author Contributions: Drs Begg and Sima had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Begg, Haile,
Borg, L. Bernstein, Olsen, Anton-Culver, J. Bernstein.
Acquisition of data: Borg, Malone,
Concannon, Langholz, L. Bernstein, Olsen, Lynch, Anton-Culver, Liang,
Analysis and interpretation of data:
Begg, Haile, Borg, Thomas, L. Bernstein, Capanu, Liang, Hummer, Sima,
Drafting of the manuscript: Begg, Hummer,
Critical revision of the manuscript for important intellectual content: Haile, Borg, Malone, Concannon, Thomas,
Langholz, L. Bernstein, Olsen, Lynch, Capanu, Liang, J. Bernstein.
Statistical analysis: Begg, Thomas,
Langholz, Capanu, Hummer, Sima.
Obtained funding: Haile, Malone, Concannon,
L. Bernstein, Olsen, J. Bernstein.
Administrative, technical, or material support:
Haile, Borg, L. Bernstein, Lynch, Liang, J. Bernstein.
Study supervision: Begg, J. Bernstein.
Financial Disclosures: None reported.
Funding/Support: The study was supported by awards CA097397, CA083178, and CA098438 from the National Cancer Institute.
Role of the Sponsor: The National Cancer Institute had no role in the design and conduct of the study, the collection, management, analysis, and interpretation of the data,
or in the preparation, review, and approval of the manuscript, other than with respect to the review of the grant application at the intiation of the project.
WECARE Study Collaborative Group:Coordinating Centers: Memorial Sloan-Kettering Cancer Center (New York, New York): Jonine L. Bernstein, PhD (WECARE Study primary investigator), Xiaolin Liang, MD, MS (informatics specialist),
Abigail Wolitzer, MSPH (project director); National Cancer Institute (Bethesda, Md): Daniela Seminara, PhD, MPH (program officer). Laboratories: University of Virginia (Charlotte):
Patrick Concannon, PhD (primary investigator), Sharon Teraoka, PhD (laboratory director), Eric R. Olson (laboratory manager); University of Southern California (Los Angeles): Robert W. Haile, DrPH (primary investigator), Anh T. Diep (laboratory director), Shanyan Xue (project manager), Evgenia Ter-Karapetova (biospecimen project manager), Nianmin Zhou (laboratory manager), Andre Hernandez (biospecimen data manager);
University of Lund (Lund, Sweden): Åke Borg, PhD, Therese Sandberg (laboratory manager), Lina Tellhed (laboratory manager); Memorial Sloan-Kettering Cancer Center (New York, New York): Irene Orlow, PhD (laboratory director, biorepository). Data Collection Centers: University of Southern California (Los Angeles): Leslie Bernstein, PhD (primary investigator), Laura Donnelly-Allen (project manager); Danish Cancer Society (Copenhagen, Denmark): Jørgen H. Olsen, MD, DMSc (primary investigator), Lene Mellemkjær,
PhD, MSc (project manager); University of Iowa (Iowa City): Charles F. Lynch, MD, PhD (primary investigator), Jeanne DeWall, MA (project manager); Fred Hutchinson Cancer Research Center (Seattle, Washington):
Kathleen E. Malone, PhD (primary investigator), Noemi Epstein (project manager); University of California (Irvine): Hoda Anton-Culver, PhD (primary investigator), Joan Largent, PhD, MPH (project manager). Radiation Measurement: University of Texas, MD Anderson Cancer Center (Houston): Marilyn Stovall, PhD (primary investigator),
Susan Smith, MPH (quality assurance dosimetry supervisor); New York University (New York): Roy E. Shore, PhD, DrPH (epidemiologist); International Epidemiology Institute (Rockville, Maryland) and Vanderbilt University (Nashville, Tennessee): John D. Boice Jr, ScD (consultant). Biostatistics Core: University of Southern California (Los Angeles): Bryan M. Langholz, PhD, Duncan C. Thomas, PhD; Memorial Sloan-Kettering Cancer Center (New York, NY): Colin Begg, PhD, Marinela Capanu, PhD; University of Southern Maine (Portland): W. Douglas Thompson,
PhD (primary investigator). External Advisors:
Stanford University (Palo Alto, Calif): Alice Whittemore, PhD.
Struewing JP, Hartge P, Wacholder S.
The risk of cancer associated with specific mutations of BRCA1
among Ashkenazi Jews.
N Engl J Med
. 1997;336(20):1401-14089145676Google ScholarCrossref
Anglian Breast Cancer Study Group.
Prevalence and penetrance of BRCA1
mutations in a population-based series of breast cancer cases.
Br J Cancer
. 2000;83(10):1301-130811044354Google ScholarCrossref
Antoniou AC, Gayther SA, Stratton JF, Ponder BA, Easton DF. Risk models for familial ovarian and breast cancer. Genet Epidemiol
. 2000;18(2):173-19010642429Google ScholarCrossref
Hopper JL, Southey MC, Dite GS.
Population-based estimate of the average age-specific cumulative risk of breast cancer for a defined set of protein-truncating mutations in BRCA1
and BRCA2. Cancer Epidemiol Biomarkers Prev
. 1999;8(9):741-74710498392Google Scholar
Risch HA, McLaughlin JR, Cole DE.
Prevalence and penetrance of germline BRCA1
mutations in a population series of 649 women with ovarian cancer.
Am J Hum Genet
. 2001;68(3):700-71011179017Google ScholarCrossref
Risch HA, McLaughlin JR, Cole DE.
mutation frequencies and cancer penetrances: a kin-cohort study in Ontario, Canada.
J Natl Cancer Inst
. 2006;98(23):1694-170617148771Google ScholarCrossref
Thorlacius S, Struewing JP, Hartge P.
Population-based study of risk of breast cancer in carriers of BRCA2
. 1998;352(9137):1337-13399802270Google ScholarCrossref
Warner E, Foulkes W, Goodwin P.
Prevalence and penetrance of BRCA1
gene mutations in unselected Ashkenazi Jewish women with breast cancer.
J Natl Cancer Inst
. 1999;91(14):1241-124710413426Google ScholarCrossref
Begg CB. On the use of familial aggregation in population-based case probands for calculating penetrance. J Natl Cancer Inst
. 2002;94(16):1221-122612189225Google ScholarCrossref
Gong G, Whittemore AS. Optimal designs for estimating penetrance of rare mutations of disease-susceptibility genes. Genet Epidemiol
. 2003;24(3):173-18012652521Google ScholarCrossref
Whittemore AS, Gong G. On the use of familial aggregation in population-based case probands for calculating penetrance. J Natl Cancer Inst
. 2003;95(1):76-7712509408Google ScholarCrossref
Ponder BA, Antoniou A, Dunning A, Easton DF, Pharoah PD. Polygenic inherited predisposition to breast cancer. Cold Spring Harb Symp Quant Biol
. 2005;70:35-4116869736Google ScholarCrossref
Antoniou AC, Pharoah PD, McMullan G.
A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2
and other genes.
Br J Cancer
. 2002;86(1):76-8311857015Google ScholarCrossref
Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BAJ. Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet
. 2002;31(1):33-3611984562Google ScholarCrossref
Garber JE, Goldstein AM, Kantor AF, Dreyfus MG, Fraumeni JF, Li FP. Follow-up study of twenty-four families with Li-Fraumeni syndrome. Cancer Res
. 1991;51(22):6094-60971933872Google Scholar
Meijers-Heijboer H, van den Ouweland A, Klijn J.
Low penetrance susceptibility to breast cancer due to CHEK2(*)1100delC in non-carriers of BRCA1
. 2002;31(1):55-5911967536Google ScholarCrossref
Bernstein JL, Langholz B, Haile RW.
et al. Study design: evaluating gene-environment interactions in the etiology of breast cancer: the WECARE study. Breast Cancer Res
. 2004;6(3):R199-R21415084244Google ScholarCrossref
Langholz B, Goldstein L. Risk set sampling in epidemiologic cohort studies. Stat Sci
. 1996;11(1):35-53Google ScholarCrossref
Ziogas A, Anton-Culver H. Validation of family history data in cancer family registries. Am J Prev Med
. 2003;24(2):190-19812568826Google ScholarCrossref
Bondy ML, Strom SS, Colopy MW, Brown BW, Strong LC. Accuracy of family history of cancer obtained through interview with relatives of patients with childhood sarcoma. J Clin Epidemiol
. 1994;47(1):89-968283198Google ScholarCrossref
Bernstein JL, Teraoka S, Haile RW.
Designing and implementing quality control for multi-center screening of mutations in the ATM
gene among women with breast cancer.
. 2003;21(5):542-55012673797Google ScholarCrossref
Antoniou A, Pharoah PD, Narod S.
Average risks of breast and ovarian cancer associated with BRCA1
mutations detected in case series unselected for family history: a combined analysis of 22 studies.
Am J Hum Genet
. 2003;72(5):1117-113012677558Google ScholarCrossref
Chatterjee N, Wacholder S. A marginal likelihood approach for estimating penetrance from kin-cohort designs. Biometrics
. 2001;57(1):245-25211252606Google ScholarCrossref
Andrieu N, Goldgar DE, Easton DF.
Pregnancies, breast feeding, and breast cancer risk in the International BRCA1
/2 Carrier Cohort Study.
J Natl Cancer Inst
. 2006;98(8):535-54416622123Google ScholarCrossref
Antoniou AC, Shenton A, Maher ER.
Parity and breast cancer risk among BRCA1
Breast Cancer Res
. 2006;8(6):R7217187672Google ScholarCrossref
Cullinane CA, Lubinski J, Neuhausen SL.
Effect of pregnancy as a risk factor for breast cancer in BRCA1
Int J Cancer
. 2005;117(6):988-99115986445Google ScholarCrossref
Mitchell G, Antoniou AC, Warren R.
Mammographic density and breast cancer risk in BRCA1
. 2006;66(3):1866-187216452249Google ScholarCrossref
Chen S, Parmigiani G.
Meta-analysis of BRCA1
J Clin Oncol
. 2007;25(11):1329-133317416853Google ScholarCrossref
Thompson D, Easton DF.
Variation in BRCA1
cancer risks by mutation position.
Cancer Epidemiol Biomarkers Prev
. 2002;11(4):329-33711927492Google Scholar
Antoniou AC, Easton DF. Models of genetic susceptibility to breast cancer. Oncogene
. 2006;25(43):5898-590616998504Google ScholarCrossref
Hunter DJ, Kraft P, Jacobs KB.
A genome-wide association study identifies alleles in FGFR2
associated with risk of sporadic post-menopausal breast cancer.
. 2007;39(7):870-87417529973Google ScholarCrossref
Easton DF, Pooley KA, Dunning AM.
et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature
. 2007;447(7148):1087-109317529967Google ScholarCrossref
Cui J, Hopper JL. Why are the majority of hereditary cases of early-onset breast cancer sporadic? a simulation study. Cancer Epidemiol Biomarkers Prev
. 2000;9(8):805-81210952097Google Scholar
Khoury MJ, Beaty TH, Liang KY. Can familial aggregation of disease be explained by familial aggregation of environmental risk factors? Am J Epidemiol
. 1988;127(3):674-6833341366Google Scholar
Thomas DC. Statistical Methods in Genetic Epidemiology. New York, NY: Oxford University Press; 2004:137-138
Langholz B, Ziogas A, Thomas DC, Faucett C, Huberman M, Goldstein L. Ascertainment bias in rate ratio estimation from case-sibling control studies of variable age-at-onset diseases. Biometrics
. 1999;55(4):1129-113611315058Google ScholarCrossref
Pharoah PDP, Day NE, Duffy S, Easton DF, Ponder BAJ. Family history and the risk of breast cancer: a systematic review and meta-analysis. Int J Cancer
. 1997;71(5):800-8099180149Google ScholarCrossref