evaluarea riscului de purtator BRCA

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Breast Disease 27 (2006,2007) 5–20 5IOS Press

Assessing Breast Cancer Risk and BRCA1/2

Carrier Probability

Julie Culver∗, Katrina Lowstuter and Lauren BowlingDepartment of Clinical Cancer Genetics, City of Hope Comprehensive Cancer Center, Duarte, CA, USA

Abstract. By identifying individuals with an increased risk of breast cancer, health professionals can offer prevention strategies

tailored to individual risk levels. Such strategies may include early initiationof cancer screening, more frequent screening, targeted

therapeutic or behavioral interventions, or prophylactic surgery. In order to achieve clinical benefits with this approach, however,

risk assessment strategies and effective prevention measures must be available. In this article we review current knowledge about

cancer risk assessment for unaffected women and probability models for identifying individuals who are carriers of a mutation

in BRCA1 or BRCA2, the two genes most commonly implicated in hereditary breast cancer. We review BRCA1 and BRCA2

mutations in various ethnic populations and how this information factors into risk assessment. Additionally, we summarize the

current guidelines for when to make a referral to genetic services for risk assessment and evaluation.

Keywords: Breast carcinoma, BRCA1, BRCA2, cancer genetics, cancer risk assessment, probability models, hereditary breast

cancer

INTRODUCTION

Cancer risk assessment is practiced in clinics spe-

cializing in genetics as well as other health care set-

tings. We will describe many of the tools used by ge-

netic counselors and others to evaluate breast cancer

risk and determine the likelihood of hereditary breast

cancer caused by BRCA1 or BRCA2.

PEDIGREE-BASED RISK ASESSMENT

Breast cancer has long been known to “run in fam-

ilies.” Epidemiological studies have established fami-

ly history as a major risk factor for breast and ovarian

cancer. Relative risks associated with an affected first-

degree relative range from 2 to 4 (Table 1). Relative risk

increases with increasingnumbers of affected relatives,

∗Corresponding author: Julie Culver, MS, CGC, Department of Clinical Cancer Genetics, City of Hope Comprehensive Cancer Cen-ter, 1500 E. Duarte Rd. Mod 173, Duarte, CA 91010, USA. Tel.: +1626 256 8662; Fax: +1 626 930 5495; E-mail: [email protected].

greater biological closeness of affected relative(s), and

earlier age of onset of cancer.

Epidemiological studies provide evidence for two

general categories of risk based on familyhistory alone:

(1) moderate risk , typically associated with a family

history of breast cancer in a close relative (2) high risk ,

typically associated with a family history pattern of

breast cancer in two or more relatives, indicating the

inheritance of a highly penetrant breast cancer gene

mutation. Example pedigrees are shown in Fig. 1.

Features of moderate risk families include later ages

of onset of breast cancer (50 years and above), few rel-

atives affected, lack of ovarian cancer, and no evidence

of autosomal dominant transmission. It is likely that

a large proportion of this type of familial clustering of

breast cancer is due to the presence of genetic traits

that only modestly contribute to cancer risk. Similar

environmental exposures as well as gene-environment

interactions are also likely to account for some of the

risk associated with moderate family history.

High risk families are characterized by early age

of onset of breast cancer (less than 50 years), bilater-

al breast cancer, multiple affected individuals, ovarian

cancer at any age, male breast cancer at any age, and a

0888-6008/06,07/$17.00 2006,2007 – IOS Press and the authors. All rights reserved



6 J. Culver et al. / Assessing Breast Cancer Risk and BRCA1/2 Carrier Probability

Table 1Empiric Risk of Cancer Based on Family History

Cancer Family History Relative Risk

Breast First degree relative with breast cancerAll ages 2.1 (95% CI 2.0–2.2)Affected< 50 years 2.3 (95% CI 2.2–2.5)Affected50 years 1.8 (95% CI 1.6–2.0) [50]

Breast Sister with breast cancer at:20–29 years 4.68 (95% CI 0.92–11.36)30–39 years 3.28(95% CI 1.91–4.65)40–49 years 2.56 (95% CI 1.89–3.24)50–59 years 2.68 (95% CI 1.98–3.38)60–69 years 1.71 (95% CI 0.98–2.44) [64,66]

Breast Second degree relative with breast cancer 1.5 (95% CI 1.4–1.6) [50]

Ovarian Breast cancer in mother or sister before age 40 SIR 1.7 (95% CI 1.3–2.1) [46]

Ovarian Parent or sibling with breast cancer OR 1.6 (95% CI 1.3–2.0) [51]

(cancer mortality)

Ovarian First degree relative with ovarian cancer 3.1 (95% CI 2.6–3.7Daughter with ovarian cancer 1.1 (95% CI 0.8–1.6)Sister with ovarian cancer 3.8 (95% CI 2.0–5.1)Mother with ovarian cancer 6.0 (95% CI 3.0–11.9)More than one affected relative (first or second degree) 11.7 (95% CI 5.3–25.9) [59]

Ovarian Second degree relative with ovarian cancer 2.5 (95% CI 1.5–4.3) [59]

pattern of autosomal dominant transmission often ap-

pearing to “skip” males in the family. Families with

these characteristics are more likely to have a muta-

tion in a highly penetrant autosomal dominant cancer

susceptibility gene such as BRCA1 or BRCA2, com-

pared to moderate risk families. Ductal carcinoma insitu (DCIS) may be part of the phenotype of hereditary

breast cancer [32,35]. A population based study found

the rate of BRCA mutation detection is similar in DCIS

and invasive breast cancer cases [16].Of note, when breast cancer is seen in families with

additionaltypes of cancers, other hereditarysyndromes

(reviewed by Nusbaum et al. in this issue) may be the

cause of breast cancer in the family and should be con-

sidered. For example, breast, endometrial, and thyroid

cancers occurring in an autosomal dominant pattern

may be the result of an inherited PTEN mutation.

When evaluating small families, it is particularly im-portant to consider family structure and the number of

women surviving through later ages. A single case of a

womanwith early onset breast cancer whohas a limited

family history due to lack of female relatives or early

ageat death of femalerelatives mayindeed have a high-

er probability of carrying a BRCA1 or BRCA2 mutation

than a similarly affected woman from a large families

with many unaffected women surviving through later

ages. Among 204 single breast cancer cases before

age 55 who underwent genetic testing of BRCA1 and

BRCA2, family structure was a strong predictor of mu-

tation status (P = 0.009), with BRCA mutations iden-

tified in 17.3% of women with limited vs. 5.7% with

adequate family structure [71]. Some of the BRCA1/2

probabilitymodels, such as BRCAPRO, do take into ac-

count family structure, (see section below on this topic)

but others do not.

The rate of de novo (non-inherited) BRCA mutationsis thought to be negligible, however there are rare re-

ports of such mutations in the literature [61,69].

EMPIRIC MODELS OF BREAST CANCER

RISK ASSESSMENT

Family history data can be utilized in various models

for predicting individual breast cancer risk. The four

risk models to be reviewed in this section include the

Claus, Gail, Tyrer-Cuzick, and BRCAPRO models. A

comparison of risk estimates produced by these modelsis shown in Table 2; because these four risk models in-

corporate different risk factors, theysometimes provide

strikingly different risk estimates and may be utilized

to provide a range of risks in the clinical setting.

Claus (or CASH ) Model

The Claus model estimates the probability of an un-

affected woman developing breast cancer based on her

family history of breast cancer [17]. This model was

derived from empiric observations in the Cancer and

Steroid Hormone Study (CASH) [73]. Genetic mod-



J. Culver et al. / Assessing Breast Cancer Risk and BRCA1/2 Carrier Probability 7

Table 2Lifetime breast cancer risks for hypothetical patients, based on four risk models

Family History Claus1 [17] Tyrer- BRCAPRO3 Gail4 Notes on model limitationsCuzick 2 [67] [13,47] [29]

Case 1 40 year old woman– mother BC 35– maternal aunt BC 41

34% 24% 18% 19% Gail does not incorporatematernal aunt (SDR)

Case 2 40 year old woman– paternal aunt BC 28– paternal grandmother BC 39

23% 21% 18% 11% Gail does not incorporatepaternal (SDR) relatives

Case 3 40 year old woman– mother OC 55– maternal aunt BC 45– maternal grandmother BC 49

19% 25% 23% 11% Gail does not incorporateOC or SDRs; Claus doesnot incorporate OC

Case 4 40 year old woman of Ashkenazi Jewish

ancestry

– mother OC 55

– maternal aunt BC 45– maternal grandmother BC 49

19% 31% 30% 11% Same as Case 3; addition-ally, Gail and Claus do notincorporate AJ ancestry

BC= Breast cancer, OC = ovarian cancer, SDR = second-degree relative, AJ = Ashkenazi Jewish.1Claus model calculates breast cancer risk to age 79 years.2Tyrer-Cuzick model calculates lifetime breast cancer risk to an unspecified age. Other personal characteristics included in the model for eachcase were: age at menarche = 12, age at first birth = 28, height = 1.37 meters (5 feet, 4 inches), weight = 61 kg (134 lbs), woman has neverused HRT, no atypical hyperplasia or LCIS.3BRCAPRO calculates breast cancer risk to age 85 years.4Gail model calculates breast cancer risk to age 90. Other personal characteristics included in the Gail risk model for each case were: age atmenarche= 12, age at first birth = 28, breast biopsies = 0, race =White.

els were developed to fit the age-specific incidence of

breast cancer among first- and second- degree relatives

of 4730 Caucasian breast cancer cases and 4688 Cau-

casian controls, aged 20–54 years. Although the Clausmodel is based on an assumption that risk associat-

ed with a family history can be exclusively attributed

to rare autosomal dominant mutations with high pene-

trance, which is almost certainly incorrect, the results

of this model agree with observations concerning the

association of family history andbreast cancerrisk. For

any given unaffected female patient, the model incor-

porates up to two relatives affected with breast cancer

(first- or second- degree) and the decade of onset of

breast cancer for each relative. The model provides

risk estimates for each decade of the patient’s life up to

age 79. However, the Claus model does not incorpo-

rate family size, ethnic background, or other risk fac-

tors. Therefore, it may not be not suitable for women

with more than two affected relatives, as it may un-

derestimate risk. Furthermore, the Claus model does

not include incorporate relatives with ovarian cancer.

A separate Claus paper allows for the calculation of

breast cancer risk for women with a first-degree family

history of ovarian cancer [18].

Claus risk estimates are easily calculated using the

published tables in the original paper [17]. However,

one adjustment must be made using a formula on page

645 of the paper, which accounts for the patient’s cur-

rent age, to appropriately reduce her risk due to having

passed some of her years of breast cancer risk. For ex-

ample, in Table 2, Case 1, the 40 year old woman with a

34% lifetime risk of breast cancer should have her risk re-calculated if she ages and does not develop breast

cancer; at age 50 the Claus model would predict a 28%

lifetime risk. Another approach to making the adjust-

ment for current age is to use the software available to

calculate Claus risks on a Palm pilot [1], which makes

calculating these risks very simple. Also, the same ad-

justment to the Claus model is made by CancerGene

(BRCAPRO) program, discussed below (downloadable

from link in Table 3).

Tyrer-Cuzick Model

The Tyrer-Cuzick model [67] incorporates the prob-

ability of a BRCA1 or BRCA2 mutation, the likelihood

of a low penetrance gene mutation, and personal risk

factors. For an individual unaffected woman, family

history is used in conjunction with Bayes’ theorem to

produce the likelihood of a BRCA mutation. The asso-

ciated breastcancer risk is then calculated andmodified

to reflect the relative risk associated with the woman’s

personal risk factors. Personal risk factors included

are: current age, age at menarche, parity, age at first

livebirth, age at menopause, history of atypical hyper-

plasia or lobular carcinoma in situ, height, and body




Table 4BRCA1/BRCA2 carrier probability estimates for hypothetical patients, based on five models

Case Family History Modified Myriad BRCA- Tyrer- Manchester Notes on models

Couch1 PRO Cusick 2

1 40 year old women with BC3

– 3 older unaffected sisters4N/A 7% 2% N/A BRCA1 score= 3

BRCA2 score= 3Does not meet 10%threshold

– Couch is not designed to calcu-late probability for a single affectedindividual– BRCAPRO considers the unaf-fected sisters

2 50 year old women with BC at40 and OC at 50– Mother deceased at 40

accident– Mat aunt BC 35

88% 55% 88% N/A BRCA1 score= 15BRCA2 score= 12 if BRCA1 negativeMeets 10% threshold forboth genes

– BRCAPRO considers the moth-er’s early death– Risk estimates are high becauseof BC andOC in a single individual

3 51 year old women with BC3

– Mother BC 52– Mat aunt BC at 56

4% 3% 8% N/A BRCA1 score= 6BRCA2 score= 6Does not meet 10%

threshold

– BRCAPRO mayoverestimaterisk as unaffected relatives (if any)werenot entered

4 −35 year oldunaffected woman5

– Sister BC 40– Pat aunt OC 55

21% 12% 10% 4% – BRCA1 score= 15BRCA2 score= 12 if BRCA1 -negativeMeets 10% threshold forboth genes

– Risks are approximately doublefor the patient’s sister

5 −35 year old unaffectedwoman5 of AshkenaziJewish ancestry– Sister BC 40– Pat aunt OC 55

44% 27% 36% 25% Model does not apply toAJ population

– In comparison to Case 4, eachmodel shows a higher probabilitywith AJ ancestry.– Manchester does not apply to AJpopulation

BC= Breast cancer, OC = ovarian cancer, AJ = Ashkenazi Jewish.1Probabilities are derived from modifying Couch to include BRCA1 and BRCA2 probability of a mutation (see description of modification in thetext); Couch model is not used for Case 1 because the model does not apply to single cases.2Tyrer-Cuzick model cannot be used for Cases 1–3 because it is only applicable to unaffected patients. See Table 2, footnote 2 for personal

characteristics of Cases 4 and 5 included in Tyrer-Cuzick model.3Age of women equals age of diagnosis with cancer.4Ages of sisters entered into to BRCAPRO was 50, 55, and 60 years of age.5Risk is for the unaffected patient. For the Couch model, this represents 50% of the family risk calculated, modified to include BRCA2 asdiscussed in the text.

mass index (BMI). This is a statistical model basedon combining relative risks, and not an actual sampleof women. The model has been incorporated into acomputer program, which produces very user-friendlyoutputs of both the likelihood of the patient developingbreast cancer as well as the probability of the patientcarrying a BRCA mutation (Table 3).

BRCAPRO Model

The BRCAPRO model [13,47] provides risk esti-mates for breast and ovarian cancer based on the likeli-hood that a person carries a BRCA1 or 2 mutation. Us-ing a patient’s current age, cancer history, and familyhistory of breastand ovarian cancerin first- andsecond-degree relatives, the program uses Bayesian analysisto calculate the probability of a BRCA mutation, andfrom that probability, the risk of breast and ovarian can-cer. See detailed discussion of the BRCAPRO modelin the section on BRCA probability and related models,below.

Gail Model

The Gail model [29] estimates the probability of an

unaffected woman developing breast cancer over spec-

ified time intervals based on her age and personal risk

factors. It was developedusing data from a nested case-

control subset of the 284,780 women participating in

the Breast Cancer Detectionand Demonstration Project

(BCDDP) [11]. These were predominately Caucasian

women 35 to 79 years of age, receiving annual mam-

mography screening. The model includes risk factors

that were important predictors of risk in the BCDDP

and was derived from an unconditional logistic regres-

sion analysis. Risk factors (and their associated codes)

include: age [<50; 50], age at menarche [14; 12–

13; <12], age at first live birth [<20; 20–24; 25–29;

30; or nulliparous;], number of previous breast biop-

sies [0; 1; 2] and whether any biopsy found atypical

hyperplasia (yes, no), and number of first-degree rela-

tives (mother or sisters) with breast cancer [0; 1; 2].




(a)

Br, 42

d. 43

Br, 50; Br, 68

Ov, 53

d. 56

Br, 40 Br, 35

(b)

Br, 52

Br, 68

Fig.1. Example pedigrees illustrating ahigh and moderate risk breastcancer families.

The Gail model determines an odds ratio for a given

womanto developbreast cancer andcombines this with

baseline age-specific hazard rates and competing mor-

tality risks, resulting in an absolute risk of breast can-

cer over specified time intervals. Later, the National

Surgical Adjuvant Breast and Bowel Project’s Breast

Cancer Prevention Trial modified the Gail model to

incorporate race and include daughters as first-degree

relatives [25].

When using the Gail model for breast cancer risk

assessment, it is importantto consider the limitations of

this model. TheGail model is inadequate for evaluating

family history because it does not incorporate second-

degree relatives (including aunts, grandmothers, or any

paternal relatives) or the age of onset of breast cancer

in any relative. For example, Cases 2, 3, and 4 in

Table 2, theGail model does notcalculate increasedrisk

of breast cancer attributable to family history because

second-degreeand paternal relatives are not included in

the risk calculation. Therefore, we do not recommendits use for evaluating patients with a significant familyhistory of breast cancer. However, the Gail model is aneffective clinical tool in determining whether a patientmeets a minimum risk thresholdto be offered tamoxifenfor chemoprevention. This risk threshold was the entrycriteria of the BCDDP trial and was equal or greaterthan the risk of a an average 60-year old woman, whichis equivalent to a 5 year predictedrisk of breastcancer of at least 1.66% [25] For breast cancer risk estimation inclinical practice, the Gail model is most appropriate forwomen with affected first-degree relatives or womenwith a history of biopsies. Both Palm Pilot [3] and web

versions [2] are available, and the Gail model is alsoavailable in CancerGene (BRCAPRO), with web link shown in Table 3.

Validation studies have been performed on the origi-nal Gail model, which demonstrated that in some casesit failed to accurately predict cancer risk. Two stud-ies found the Gail model overpredicted the absoluterisk of breast cancer in women less than age 60 whodid not undergo annual mammography screening [14,57]. Additionally, the model tended to overpredict risk for women less than age 60 and underpredict risk forwomen over age 60 [14].

It is important to note that risk estimates for the same

woman using either the Claus and Gail models may notbe identical, in part based upon the differentparametersof the models. Indeed, when looking at large numbersof women with a family history, Gail estimates tend tobe higher than Claus estimates [39,40].

Although no risk assessment model is appropriatefor every patient, clinicians often choose one modelover another for different types of patients. Patientswith a significant family history of breast cancer insecond degree relativesshould notbe evaluated with theGail model (Table 2). However, patients with a biopsyhistory, especially a biopsy with atypical hyperplasia,may best be evaluated with the Gail model. The Gail

andClausmodels shouldbe used with caution if ovariancancer is present in the family. The Tyrer-Cuzick andBRCAPRO models can incorporate Ashkenazi Jewishancestry, while Gail and Claus cannot. Additionally,BRCAPRO can account for the size and structure of thepatient’s family and current age of family members. Inthe clinical setting, a patient can be provided with arange of risk estimates from the models that are deemedappropriate for her circumstance. Providing the rangewill also enable the patient to see that risk estimationis an imprecise science. Based on an assessment of the risk numbers provided, screening and preventionprograms can then be tailored individually.




BRCA 1/2 Probability and Related Models

There are numerous models now available to esti-

mate the probability of an individual having a mutation

in BRCA1 and BRCA2 genes. A few of these models

also predict individual cancer risks. (Table 3) Some of

these models were first developed around 1997, soon

after BRCA clinical genetic testing became available.

However, the field of mutation and cancer risk proba-

bility modeling continuesto evolve as evidenced by the

recent revision of the Couch model and publications of

new models such as BOADICEA.

Couch Model

The Couch model, published by Couch et al. in 1997,

is a widely used logistic regression model that predicts

the probability of a BRCA1 mutation in a given fami-

ly [19]. The purpose of Couch et al.’s original publica-

tion was to define the incidence of BRCA1 mutations in

women with breast cancer who were referred for breast

cancer risk assessment. Couch et al. gathered personal

cancer history, family history of cancer, and blood from

263 women with breast cancer seen for cancer risk as-

sessment between 1993 and 1995. The Couch model is

presented in a table format within the publication [19].

Themodelutilizes personal and familyhistory of breastand ovarian cancer in first and second-degree relatives

to estimate the mutation probability. The model also

considers Ashkenazi Jewish ancestry. An average age

of onset of breast cancer in the family is used to gen-

erate the mutation probability, (age of onset of ovarian

cancer is not included in the average age calculation).

Therisk provided is the family’s probability of a BRCA1

mutation and applies to all affected (diagnosed with

breast or ovarian cancer) family members. The proba-

bility of a mutation in unaffected first-degree relatives

of breast/ovarian cancer patients is half of the family’s

probability for carrying a mutation. For example, if a

Couch mutation probability is 10% for a family, then

the daughter of an affected individual in the family has

a 5% chance of having a BRCA1 mutation, as she has

a 50% chance of inheriting the mutated allele from her

mother. The model can be applied by using the table in

the original paper [19] and in the CancerGene program

(Table 3).

The limitations of the Couch model should be con-

sidered when being applied to a clinical setting. The

model does not account for other types of cancer as-

sociated with the BRCA genes aside from breast and

ovarian cancer and does not consider male breast can-

cer or bilateral female breast cancer. The model is

further limited as it predicts for BRCA1 mutation sta-tus only and the study population consisted mainly of

Caucasian women. In clinical practice, the Couch may

be modified to include BRCA2 mutation probability by

multiplying the estimated BRCA1 mutation probability

by a factor of 1.33. The modification by a factor of 1.33

accounts for the contribution of BRCA2 to the over-

all load of hereditary breast cancer due to BRCA1 and

BRCA2 mutations and is based on the published data

from the combined analysis for the original cohort [56].

Thesame research team from theUniversity of Penn-

sylvania Abramson Cancer Center that developed the

original Couch model has recently revised and updat-

ed the model, entitled “Penn II,” to predict for both

BRCA1 and BRCA2 mutations and to consider other

personal and family cancer history (Table 3). The mod-

el takes into account three generations of breast and

ovarian cancer (e.g. including cousins) as well as oth-

er BRCA-associated cancers including pancreatic and

male specific cancers (prostate and male breast cancer).

The development and validation paper for the Penn II

model is currently under review [58].

Myriad

The mutation prevalence tables published by Myri-ad Genetic Laboratories provide easily accessible risk

estimates for detecting a BRCA mutation. These tables

are based on methods published by Frank et al. [27].

The risk estimates presented were originally derived

from BRCA clinical test results over a three year period

from 10,000 individuals with a personal and/or family

history of breast and/or ovarian cancer. Of these in-

dividuals, 7,461 had full gene sequencing of BRCA1

and BRCA2 and 2,539 were screened only for the three

Ashkenazi Jewish founder mutations. Approximately

90% of individuals tested were women and ∼45% had

a personal history of breast cancer only. The recently

updated mutation prevalence tables, released on the in-

ternet by Myriad in March of 2006, (Table 3) are based

on clinical test results from ∼49,000 individuals who

had full gene sequencing and∼15,000 individuals who

were screened for the three Ashkenazi Jewish founder

mutations.

The benefits and limitations of the Myriad mutation

prevalence tables should be considered when provid-

ing mutation risk estimates in the clinical setting. The

greatest advantage of using these tables is that the risk

estimates are based on a large clinical sample and cat-

egorized by Ashkenazi Jewish versus non-Ashkenazi




Jewish ancestry. In addition, these tables are easy to

use and updated frequently. The tables are available fordownload on palm pilot (Table 3) and the risk numbers

are produced by the CancerGene program (Table 3).

However, the risk estimates presented in these tables

do not take into account: the specific age of onset of

breast cancer, the number of affected relatives, bilater-

al breast cancer, unaffected relatives, or other BRCA-

associated cancers. There is also no distinction be-

tween first and second degree relatives or maternal ver-

sus paternal affected relatives. The Ashkenazi Jewish

tables also include some women who had testing for

the three founder mutations because of a known muta-

tion in the family, and the numbers in the table could

be an overestimate. Furthermore, these risk estimates

are entirely dependent upon the personal and family

history information provided on test requisition forms

completed by the ordering clinician, which is subject

to errors and omissions by health care providers. In

summary, these tables are widely used in the clinical

setting to provide risk estimates prior to BRCA testing,

but the tables may under- or over-estimate the risk of

detecting a BRCA mutation in some families andshould

be interpreted with caution.

BRCAPRO

BRCAPRO is a mathematical model that predicts the

probability of a BRCA mutation [13,47]. The founda-

tion of this model uses Mendelian genetics and Bayes’

theorem to evaluate a family history of cancer for mu-

tation probability. Specifically this model predicts

the probability having a mutation in either gene, both

genes, or neither gene [12]. The model also estimates

breast and ovarian cancer risk, as described above. The

model incorporates all family members (up to second-

degree relatives), their history of breast and ovarian

cancer, bilateral breast cancer, male breast cancer, and

whether the family has Ashkenazi ancestry. Mutation

probabilities can be calculated for both affected and

unaffected individuals; however, cancer risk estimates

only apply to unaffected individuals. The model also

takes into account mutation status in the family (i.e., if

a family member has tested negative for BRCA muta-

tion).

The model can be downloaded for free as part of

CancerGene (Table 3) and is also available as part of

the Progeny pedigree software package [5]. Cancer-

Gene Version 4.3 accounts for whether a woman had an

oophorectomy. The output is easy to interpret; howev-

er, in order to obtain the most accurate mutation prob-

ability, the pedigree must be entered, which can be

time-consuming. Another limitation of this model isthe penetrance data used to derive the model were tak-

en mainly from Caucasian families therefore its use in

non-Caucasian families may be limited.

Validation studies were conducted in 2002 by com-

paring the estimated probability of carrying a BRCA

mutation as computed by BRCAPRO to actual genetic

test results. These studies found that BRCAPRO gives

an accurate measurement of the probability of a muta-

tion and therefore is a useful instrument in the counsel-

ing process [12].

Tyrer-Cuzick Model

The Tyrer-Cuzick breast cancer risk assessment

model (discussed in detail above) [67] also calculates

BRCA mutation probabilities. This model incorporates

first and second- degree gamily members with breast

and ovarian cancer and their ages of onset. However, a

disadvantage is that the model calculates the mutation

probability only for an unaffected individual, which

is usually not the ideal candidate for initiating testing

within a family. Software is available to calculate risks

(Table 3) and a user-friendly printout is produced.

Manchester Model

The Manchester Model is a scoring system that will

determine whether a family has 10% probability of

a mutation in either BRCA1 or BRCA2 [22] or a 20%

combined risk [23]. This model was developed in

Manchester, England using a population of 422 non-

Ashkenazi British individuals. Using this population

the authors designed a scoring system to determine

whether a family may have a deleterious BRCA1 or

BRCA2 mutation. The cancers included in the scoring

system are female breast cancer, male breast cancer,

ovarian cancer, pancreatic cancer, and prostate cancer.

Between 1 and 8 points are given for each cancer di-

agnosis, depending on type of cancer and age of onset.

Higher scores are given for earlier ages of onset, and

the decade of diagnosis is included in calculating the

score for breast cancer cases. For ovarian, male breast,

and pancreatic cancer cases, a distinction of diagnosis

before or after age 60 is made. Separate scores are cal-

culated for BRCA1 and BRCA2 and a total score of 10

for one lineage in a family is equivalent to a 10% prob-

ability of a mutation in that gene. For example, female

breast cancer diagnosed <30 years is given a score of

6 for BRCA1 and 5 for BRCA2; ovarian cancer <60




is given a score of 8 for BRCA1 and 5 for BRCA2 (if

BRCA1 has already been tested). Therefore, a familywith one case of breast cancer before age 30 and one

case of ovarian cancer before age 60 is given a total

BRCA1 score of 14anda BRCA2 score of 10 (if BRCA1

testing is negative). Thus testing is justified for this

family, with BRCA1 testing to be done prior to BRCA2

testing. Using this model, the gene with the higher

score (over 10) may be tested first; if no mutation is

found in that gene, scores may be subsequently adjust-

ed for the other gene. This model does not suggest that

mutation analysis is justified in an isolated breast or

ovarian cancer case at any age.

An important advantage of the Manchester model is

that the scoring system can be easily used in the clini-

cal setting and does not require the use of software or

the input of an entire pedigree into a computer. Ad-

ditionally, validation studies by the authors compared

the Manchester model against BRCAPRO, Couch, and

Myriad and found the Manchester model to outperform

the other models in discriminating families with a 10%

likelihood of a mutation [22].

An important limitation of this model is that it does

not calculate the exact probability of a mutation, but

rather distinguishes whether a family meets the 10%

or 20% probability cutoff or not. This model may not

be as useful in a clinic that uses a different probabilitycutoff or does not useany specific numerical probability

cutoff for offering BRCA testing. Also, the model is

not designed for use in Ashkenazi Jewish individuals.

BOADICEA

The BOADICEA model was developedby Antoniou

et al. in 2002 in order to predict the probability of a

BRCA1 or BRCA2 mutation and provide breast and

ovarian cancer risks [6–8]. The model was devel-

oped using complex segregation analysis of the occur-

rence of breast and ovarian cancer in two data sets

(population based series of 1484 breast cancer cases

and 156 multiple case families). Both data sets were

ascertained from probands with breast cancer, main-

ly from the United Kingdom. The model takes in-

to account that familial breast cancer is explained by

both BRCA1 and BRCA2 mutations as well as a poly-

genic component (reflecting the joint multiplicative ef-

fect of multiple genes of small effect on breast cancer

risk). Furthermore the model accounts for the possi-

bility of genetic modifiers, which may affect the pene-

trance of BRCA1 and BRCA2 mutations. The remain-

ing clusters of cancer in families not accounted for by

Table 5Selected examples of recurrent and founder mutations in the BRCA

genes

Population BRCA1 BRCA2

Ashkenazi Jewish 185delAG5382 ins C

6174delT

Icelandic 999del15British 6-kb dup exon 13

4184 del4Dutch 2804delAA

del exon 13del exon 22

Chinese 1081delGAfrican American 943ins10

1832del55296del4

Hispanic 185delAG

del exon 9–12French Canadian 4446C>T

2953del3 + C3768insA

8765delAG2816insA6085G>T6503delTT

the BRCA1 and BRCA2 genes are assigned to poly-

genic factors. In 2005, the model was found to ac-

curately predict the carrier probability in individuals

of French Canadian ancestry [6]. The researchers are

developing a web-based software interface, which en-

ables clinicians to enter pedigree information to deter-

mine probabilityinformation. Updates on this software

can be obtained by checking the BOADICEA web site(Table 3). One can also utilize the published tables [8]

to assess risk.

In clinical practice, using multiple BRCA probability

models is time consuming and therefore it may be best

to choose a model or two that best suit the patient. We

have indicated some of the benefits and limitations of

each model in Tables 3 and 4. Additionally, Table 4

shows the probabilityestimates producedby eachavail-

able model for various pedigrees. Of note BOADICIA

was not included in this table as the software needed to

use the model is not available at this time.

Here are some considerations of when to use the

published models discussed above. These comments

are based on our clinical practice and others may have

differing viewpoints. The Couch model should not be

used when there is only a single case of breast or ovar-

ian cancer in a family since the probability table in the

original article is based on families with multiple af-

fected individuals. In families with multiple affected

relatives, the Couch model estimates should be modi-

fied to include BRCA2 probabilities, as explained in the

section on the Couch model above. The Myriad model

is useful for both single cases and families; this mod-

el is extremely quick to calculate probability estimates




Table 6Published guidelines for referral to cancer genetics services

Non-Ashkenazi Jewish Ashkenazi Jewish

Affected National Comprehensive Cancer Network (United States)*BC 50y#*Two primary HBOC cancers in an individual (bilateral breast cancer andbreast and ovarian cancer)*Two breast primaries or breast and ovarian cancers in close relatives) on thesame side of the family*Clustering of breast cancer with male breast cancer, thyroid cancer, sarco-ma, adrenocortical carcinoma, endometrial cancer, pancreatic cancer, braintumors, dermatologic manifestations or leukemia/lymphoma on the same sidethe family*Member of a family with a known mutation in breast cancer susceptibilitygene*Male breast cancer*Clustering of ovarian cancer

National Comprehensive Cancer Network (United States)*Less stringent than for non-AshkenaziJewish

Unaffected United States Preventive Services Task Force*Two FDR with BC, with one diagnosed <50y*Combination of 3 FDRs or SDRs diagnosed with BC regardless of age*Combination of both BC and OC among FDR and SDR*FDR with bilateral BC*Combination of 2 or more FDR or SDR with OC*Single FDR or SDR having both BC and OC*Male BC in any relative

United States Preventive Services Task Force*Any FDR with BC or OC*Two SDR on the same side of the familywith BC or OC

National Institute for Health And ClinicalExcellence (United Kingdom)*Seek advice fromtertiary care contact aboutlevels of risk and appropriateness of referral

National Institute for Health And Clinical Excellence (United Kingdom)*Two FDR with BC, diagnosed before average age 50y*Three FDRs or SDRs, diagnosed before average age 60y*Four relatives diagnosed at any age (including at least 1 FDR)*Combination of one OC in any relative and:

– FDR or SDR with BC < 50

– Another OC– Two FDR or SDR with BC, diagnosed before average age 60y*Bilateral BC in a FDR diagnosed before average age 50y*Bilateral BC in a FDR or SDR and one FDR or SDR with BC diagnosed<60y*Male BC in any relative and:

– FDR or SDR with BC diagnosed <50y– Two FDRs or SDRs diagnosed with BC before average age 60y

Key BC= Breast cancer, OC = ovarian cancer, FDR = first-degree relative, SDR = second-degree relative, y= years.# Includes both invasive and ductal carcinoma in situ.

and we find it very useful in the clinic. BRCAPRO

model is often useful if a family is particularly large

or small because its Bayesian analysis considers family

size. Additionally, the model is useful if genetic testing

has been performed in the family and is negative, but

one wishes to calculate the probability of a mutation in

other family members. Finally, BRCAPRO is the only

published model currently available that incorporates

bilateral breast cancer.

The Tyrer-Cuzick model is only applicable to unaf-

fected women. A nice feature of this model is that it

provides both a breast cancer risk estimate and a BRCA

mutation probabilityestimate; however,when possible,

it is almost always most informative to test an affect-

ed family member first, so a probability estimate may

be needed for that individual, which the Tyrer-Cuzick

model does not calculate. The Manchester model is

useful in a setting of limited resources when a muta-

tion probability of 10% is used as a threshold for offer-

ing genetic testing. Finally, it is important to remem-

ber that none of the risk models was developed from

non-Caucasian populations.

When sharing these probability estimates with pa-

tients, providing a range of numbers may be helpful

to illustrate that the likelihood of a mutation may vary,

depending on the factors considered in their family. In

many cases, the probability of a mutation is not criti-

cal to the decision to undergo testing, because of how

critical the results of testing can be. Results will often

influence medical management, such as whether a pa-

tient should undergo an oophorectomy or more inten-

sive breast cancer surveillance.




Giving a patient probability estimates often sets an

expectation for the likelihood the test will be positive.A patient who has a very low probability of a mutation

who is insistent upon having genetic testing may be

convinced that testing is not worthwhile if the proba-

bility of finding a mutation is very low (additionally,

her insurance company may not cover the cost of test-

ing and seeing her probability estimate may help her

understand why that is the case.)

RISK ASSESSMENT IN SPECIFIC

POPULATIONS

In the overall population the incidence of BRCA

mutations varies in the literature from approximately

1/500–1/1100 [4]. Higher allele frequency of BRCA

mutations in specific populations is due to founder ef-

fect or recurrent mutations.1 The most well-known and

prevalent founder effect in BRCA genes occurs with-

in the Ashkenazi Jewish population. Approximately

95% of all hereditary breast cancer within the Ashke-

nazi Jewish population is attributed to three founder

mutations: 185delAG and 5385insC in BRCA1 and

6407delT in BRCA2. Approximately 1 in 40 (2.5%)

individuals of Ashkenazi Jewish ancestry is a carrier

of one of these three BRCA mutations [53]. This isdramatically higher than the frequency in the general

Caucasian population.

Other populations also have founder effects or recur-

rent mutations. For example within the Icelandic pop-

ulation a single BRCA2 mutation (999del5) accounts

for a high proportion of familial breast cancer [33,65].

Some founder mutations, such as the exon 13 duplica-

tion, have been identified across geographically diverse

populations who originate from a common background.

In 1999, a BRCA1 exon 13 duplication was identified

and found in one Portuguese family and three appar-

ently unrelated families of European ancestry from the

United States; via haplotype analysis, these families

appeared to be from a common ancestor. In 2000,

the Exon 13 duplication Group set out to estimate the

1Founder effect is defined as a high frequency of a mutated allelein a population, which was founded by a small group where memberof the group was a carrier of the mutated allele [43]. Individualswho carry the same founder mutation also share common markerswithin the gene or adjacent to the gene (same haplotype). Recurrentmutations are commonly seen mutations that do not segregate withthe same markers (i.e. have different haplotypes) among differentcarriers. These mutations are likely due to areas within the geneprone to mutation or ‘hot spots.’

geographic diversity and frequency of this mutation.

This group concluded that the Exon 13 duplication islikely a founder mutation in countries that have a his-

torical link to Great Britain. Another high frequency

mutation identified within the British population is the

BRCA1 4184del4 mutation. Because different haplo-

types have been identified with the 4184del4 mutation

it is unlikely to be a true founder mutation; most likely,

it is a highly recurrent mutation found in the United

Kingdom [24]. In 1997, Peelen et al. characterized the

2804delAA BRCA1 Dutch founder mutation as origi-

nating 32 generations ago in the Dutch population [48].

Within the Dutch population, large deletions of exon

13 and 22 in BRCA1 have also been characterized as

founder mutations [49]. Although there have been few

studies of BRCA mutation prevalence in Asian popula-

tions, a number of founder mutations have been iden-

tified to date. One of these mutations, the 1081delG

mutation in BRCA1, was described as a likely founder

mutation in China by Khoo et al. in 2002 [37].

Even within a single country, individuals of differ-

ent ethnic backgrounds have founder mutations. In

the United Sates, approximately 12% of the popula-

tion is of Hispanic ancestry. In a clinic based cohort

study of primarily Mexican Hispanic individuals seen

for breast cancer risk assessment, 4 out of 110 (3.6%)

had the BRCA1 185delAGmutation [72]. Interestingly,the 185delAG mutation found in these individuals had

the same haplotype as the Ashkenazi Jewish founder

haplotype. None of the four unrelated Hispanic pa-

tients with the 185delAG mutation had any knowledge

of Jewish ancestry. This may be the result of the Jewish

population in Spain during the Spanish Inquisition be-

ing forced to convert to Christianity or they would have

been expelled from the country. Many of the conver-

sos (Jews converted to Christianity) and crypto-Jews

(conversos who secretly practiced Judaism) may have

migrated to the United States carrying with them the

185delAG BRCA1 mutation. Also in 2005, Weitzel et

al. identified an apparent founder rearrangement mu-

tation within the Mexican Hispanic population involv-

ing a deletion of exons 9–12. This was identified in

three apparently unrelated families [70]. There are also

documentedfounder mutations identified in families of

African ancestry in America. For example the BRCA1

943ins10 was described in 1999 as being associated

with a single haplotype in five families of African an-

cestry from different geographic locations (three from

the United States, one from the Ivory coast and one

from the Bahamas) [41]. This mutation has been de-

scribed as an ancient founder mutation of West African




origin of a similar age to the 185delAG founder mu-

tation in the Ashkenazi Jewish population [45]. Therehave also been two other BRCA1 recurrent mutations

described in the literature in African American fami-

lies: BRCA1 5296del4 and BRCA1 1832del5 [30]. One

challenge in interrupting BRCA genetic test results in

individuals of African ancestry is the high rate of vari-

ants/polymorphism identified within African Ameri-

cans compared to Caucasian Americans [45]. Interpre-

tation of these variants of uncertain significant is cov-

ered in more detail in the Brown et al. article in this

issue.

In other parts of North America there are other pop-

ulations with a high rate of founder mutations. For

example there are seven founder mutations within the

BRCA genes which account for a significant proportion

of the hereditary breast and ovarian cancer in individ-

uals of French ancestry in Canada [62]. Of these sev-

en mutations two occur more frequently than the oth-

ers, BRCA1 4446C>T and BRCA2 8765delAG [15].

A complete list of the seven founder mutations can be

found in Table 5. These mutations are so common that

often individuals of French Canadian ancestry initiate

testing of the BRCA genes with a panel of the founder

mutations and if negative then consider reflex to com-

plete evaluation of the genes. This testing strategy is

comparable to that used for individuals of AshkenaziJewish ancestry.

Knowledge of the numerous recurrent and founder

mutations in BRCA1 and BRCA2 genes have important

implications for risk assessment and clinical care. In

countries such as Iceland where a single BRCA2 muta-

tion accounts for a large proportion of BRCA mutations,

it may be worthwhile to initiate testing with the com-

mon BRCA2 999del5 mutation first; if negative, testing

for the other BRCA mutations may be considered. In

individuals of British and Dutch ancestry, it is critical

to include rearrangement studies to evaluate for the re-

current exon 13 duplication in the British population

and the large deletions of exon 13 and 22 in the Dutch

population; these rearrangements cannot be detected

by full gene sequence analysis alone. For individuals

presenting with breast cancer who are of Ashkenazi

Jewish ancestry it is recommended to start genetic test-

ing with evaluation for the three founder mutations. If

the family is of high risk (i.e., highly suspicious for

a BRCA mutation such as a history of multiple cases

of breast and/or ovarian cancer), complete analysis of

BRCA genes may be warranted. For such families it

is worthwhile to continue with further genetic testing

to increase the negative predictive value of the test, as

approximately 5% of hereditary breast cancer in the

Ashkenazi Jewish population is caused by non-foundermutations [36,55]. When a founder mutation has been

identified in an Ashkenazi Jewish family, testing at-

risk family members should include evaluation for the

other two-founder mutation, as there have been numer-

ous reports of families segregating with more than one

founder mutation [28,38].

Furthermore, knowledge of these mutations helps to

estimate the likelihood of a BRCA mutation. For exam-

ple, in the Ashkenazi Jewish population, the likelihood

of being a carrier of a BRCA mutation is higher due to

the relatively high frequencyof the three founder muta-

tions and thereforethe thresholdof when to offer genet-

ic testing is lowerthan in the general Caucasian popula-

tion. Many of the mutation probability models detailed

previously consider Ashkenazi Jewish ancestry for

BRCA mutation probability calculations. Knowledge

of founder and recurrent mutations leads to more ac-

curate risk estimation, cost effective mutation-targeted

testing, and guides appropriate testing for when to offer

rearrangement analysis or other testing methodologies.

LOW PENETRANCE GENES

Germline mutations in high-penetrance breast cancersusceptibility genes (BRCA1, BRCA2, p53, and PTEN )

are rare in the general population and only account

for approximately 5–10% of all breast cancers [21,26,

44]. On the other hand, variants in low-penetrance

breast cancer susceptibility genes, which are relatively

common in the general population, are expected to ac-

count for the majority of breast cancers [34,44,52,60].

Variants in these genes may confer a modest increase

in breast cancer risk and are thought to interact with

both exogenous (diet and pollution) as well as endoge-

nous (hormonal) risk factors [52,54]. Numerous low-

penetrance genes have been identified including en-

zymes involved in DNA repair, cell signaling process-

es, detoxification of reactive oxygen species, as well as

metabolism of estrogen, carcinogens, and alcohol [21].

These genes have almost exclusively been studied in

Caucasianpopulations and limited informationis avail-

able on whether these low-penetrance genes contribute

to increased cancer risks in other populations. Al-

though some laboratories have recently started offering

clinical testing for some of these low-penetrancegenes,

clinical management based on test results of these genes

remains controversial. In addition, many of these tests

were developed for other genetic syndromes and were




not initially intended to screen for breast cancer risk

(i.e. ATM gene testing was originally developed for di-agnostic testing for ataxia-telangiectasia or CDKN2A

(p16 ) gene testing was originally developed for heredi-

tary melanoma). In addition, some commercial labora-

tories (for example, a lab called Oncovue) have begun

offering breast cancer risk genetic testing on various

low-penetrance genes as well as variants in single nu-

cleotide polymorphisms (SNPs), however, such testing

is generally considered premature and is not typically

practiced in cancer genetics clinics. A detailed review

of low penetrance genes is presented by Nusbaum et

al. in this issue.

CRITERIA FOR REFERRAL FOR GENETIC

COUNSELING AND TESTING

It is critical that individualswho areat risk forheredi-

tary cancer syndromes be identified as they may benefit

from increased surveillance to detect cancer at earlier

more treatable stages as well as preventative interven-

tion strategies. Dueto the myriadof differentpublished

diagnostic and referral criteria, it is often challenging

for healthcare providersto determinewho should be re-

ferred for genetic cancer risk assessment. Furthermore,

there are differences in guidelines for who shouldbe re-ferred for risk assessment versus who is appropriate for

genetic testing. Most referral guidelines are less strin-

gent than diagnostic/genetic testing guidelines, which

helps to ensure that all high-risk individuals are identi-

fied.

Below is a review of some of the published clinical

practice guidelines for referral to cancer genetics ser-

vices. Table 6 also provides a summary of some of the

criteria below. Additionally, in the United States, crite-

ria for genetic testing for BRCA1 and BRCA2 have been

established by third party payers (such as Kaiser, Medi-

care, Aetna, Blue Cross, etc.). These criteria change

frequently and can be accessed directly from these in-

stitutions.

NCCN The National Comprehensive Cancer Net-

work (NCCN)is a group of 20 UnitedStates cancercen-

ters designated by the United States National Cancer

Institute as comprehensive cancer centers. The NCCN

Genetic/Familial High Risk Assessment panel, which

consists of experts within the field from the NCCN

member organizations,has published guidelinesfor ge-

netic/familial risk assessment of breast and ovariancan-

cer [20], shown in Table 6. The guidelines include con-

sensus statements from the panel of experts of accepted

referral patterns. In general, the guidelines recommend

referral for cancer genetics services for early onsetbreast cancer or diagnosis of any age of ovarian cancer

and/or a family history concerning for hereditary beast

ovarian cancer syndrome. The guidelines also take into

account non-BRCA genes, which can cause hereditary

breast cancer such as PTEN and p53, by including fam-

ily history of thyroid cancer, sarcoma, adrenocortical

carcinoma, endometrial cancer, brain tumors, dermato-

logic manifestations or leukemia/lymphoma [20].

USPSTF The United States Preventive Services Task

Force published a recommendation statement for the

referral of unaffected women for genetic counseling

and evaluation for BRCA testing [68]. The recommen-

dation, published with supporting scientific evidence,

recommended whichwomen without a personal history

of breast or ovarian cancer should be referred. Guide-

lines are shown in Table 6.

NICE The National Institute for Health and Clinical

Excellence (NICE) of the United Kingdom published

guidelines to classify women at risk of familial breast

cancer. NICE is an independent organization respon-

sible for providing national guidance on the promo-

tion of good health and the prevention and treatment

of ill health. The NICE guidelines include primary,

secondary, and tertiary management for women with

a family history of breast cancer. Recommendationsfor referral to tertiary management (genetic counsel-

ing) are shown in Table 6, and are more stringent than

the USPSTF guidelines. The guidelines also include

recommendations for cancer surveillance in high risk

individuals [42].Hampel Hampel et al. published risk assessment

criteria designed to guide health care professionals in

identifying individuals who are appropriate for referral

to cancer genetics services [31]. Guidelines for referral

were created from review of published diagnostic cri-

teria for hereditary cancer syndromes. When the pub-

lished guidelines differed from each other, the authors

used expert opinion to develop their guidelines. The

goal of the criteria was to assist health care providersin

recognition of individuals who would benefit from risk

assessment and create uniformityin referral guidelines.

ASCO The American Society of Clinical Oncology

published a consensus statement in 1996 and an updat-

ed consensus statement in 2003 guiding when cancer

susceptibly genetic testing should be used. ASCO rec-

ommends that genetic cancer risk assessment and ge-

netic testing be offered to individuals when 1) there is a

personal or family history suggestive of a genetic can-

cer susceptibility syndrome 2) the genetic test can be




adequately interpreted 3) the results of genetic testing

will influence medical management. Initial guidelinesin 1996 suggested that a person with a 10% or greater

probability of having a mutation for hereditary cancer

syndrome should be offered genetic testing [10]. The

2003 guidelines were revised to reflect that for many of

the syndromes it is difficult to accurately predict muta-

tion probability or there is variance between the muta-

tion probability models; therefore, ASCO does not rec-

ommend a numerical threshold of when genetic testingshould be offered but instead states that expert clinical

judgment is more appropriate [9].NSGC The National Society for Genetic Counselors

recommends that a referral for cancer genetic risk

assessment and counseling should be considered forclients with personal or family history features sugges-

tive of familial or hereditary cancer and should not be

limited to just those individuals who are potential can-

didates for genetic testing. Individuals from high-risk

families may benefit from a detailed discussion about

hereditability of cancer in their families, appropriatecancer risk management strategies, and the option of

genetic testing [63].

In conclusion, the tools in this article will enable

health care providers to identify individuals with in-

creased breast cancer risk, estimate their risk, as well

as determine the probability that they may carry aBRCA1 or BRCA2 mutation. By finding individuals

with an increased risk of breast cancer, health profes-

sionals can offer prevention strategies to reducerisk as-

sociated with breast and ovarian cancer. Additionally,

the published guidelines summarized above will helpprovidersmake appropriatereferrals to genetic services

for further evaluation and genetic testing.

ACKNOWLEDGEMENTS

The authors thank Wylie Burke, MD, PhD for her

assistance in compiling the material in Tables 1 and 2,as well as the discussion of pedigree-based risk assess-

ment and the Claus model.

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