Developing National Guidance on Genetic Testingfor Breast Cancer Predisposition:The Role of Economic Evidence?

William Sullivan,1 D. Gareth Evans,2 William G. Newman,2 Simon C. Ramsden,3

Hans Scheffer,4 and Katherine Payne5

Advancements in genetic testing to identify predisposition for hereditary breast cancer (HBC) mean that it isimportant to understand the incremental costs and benefits of the new technologies compared with current testingstrategies. This study aimed to (1) identify and critically appraise existing economic evidence for BRCA1/2mutation testing for HBC and (2) establish whether economic evidence was used to inform national guidance inEngland and Wales. A telephone interview with diagnostic laboratories (n = 14) offering BRCA1/2 mutation testingidentified that 9 (64%) used Sanger DNA sequencing with multiplex ligation-dependent probe amplification andtwo offered next generation sequencing. A systematic review identified 15 economic studies that evaluated:genetic testing for HBC (5 studies); preventive management options for women at risk of HBC (8 studies); anddifferent laboratory approaches for BRCA1 testing (2 studies). These evaluations were not relevant to U.K.practice, and therefore the development of national guidance using a risk threshold to trigger BRCA1/2 testing hasnot been informed by existing economic evidence. The lack of economic evidence supporting the current riskthreshold for national guidance has implications for the efficient use of healthcare resources and the design ofeconomic evaluations of new technologies for BRCA1/2 testing.

Introduction

Historically, neither diagnostic nor genetic testshave been a focus for health technology assessment

(HTA), perhaps because of their perceived low impact onhealthcare resources. More recently, it has been acknowl-edged that diagnostics have substantial cost implications interms of subsequent use of resources and selection of treat-ment pathways, which may translate into improved patientoutcomes (LewinGroup, 2009). Decision-making bodies pro-ducing guidance to inform the allocation of healthcare re-sources have been advised to turn their attention to the HTAof diagnostics including genetic testing (House of Lords Sci-ence and Technology Committee, 2009).

Finite resources in the available funding and the necessaryassessment skills mean that conducting HTAs of all diagnosticand genetic tests is not possible. The initial focus should, there-fore, perhaps be tests that have a potentially large impact on theuse of healthcare resources. Genetic testing for breast cancer

predisposition is a good example of an intervention that willhave a sizeable impact because of the large volume of tests un-dertaken and also significant implications for subsequent patientpreventive treatment and care pathways. Breast cancer consti-tutes the most common form of cancer among women world-wide and research suggests that over 500,000 women in theUnited Kingdom are living with breast cancer today (CancerResearch UK, 2011). In 3% to 5% of cases, breast cancer is causedby high-penetrance inherited genetic mutations (Newman et al.,1988; Claus et al., 1991; Evans and Howell, 2007). Since 1996,testing for mutations in two high-penetrance predisposing genes,BRCA1 and BRCA2, has been clinically available in a number ofcountries, including the United Kingdom (Peters et al., 2005). Arecent audit suggests that over 4000 full gene BRCA screens havebeen performed annually in U.K. clinical laboratories in recentyears (Clinical Molecular Genetics Society, 2011). Mutations inthe hereditary breast cancer (HBC) genes BRCA1 and BRCA2account for the majority (65%–90%) of high-penetrance inheritedbreast cancers (Stratton and Rahman, 2008).

1School of Health and Related Medicine, University of Sheffield, Sheffield, United Kingdom.2Academic Unit of Medical Genetics, St. Mary’s Hospital, Manchester, United Kingdom.3Department of Genetic Medicine, St. Mary’s Hospital, Manchester, United Kingdom.4Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.5Health Sciences—Economics, School of Community Based Medicine, University of Manchester, Manchester, United Kingdom.

GENETIC TESTING AND MOLECULAR BIOMARKERSVolume 16, Number 6, 2012ª Mary Ann Liebert, Inc.Pp. 580–591DOI: 10.1089/gtmb.2011.0236

580

Sanger sequencing for BRCA1/2 (Sanger et al., 1977) used inconjunction with a technique to identify micro-deletions/duplications, for example, multiplex ligation-dependentprobe amplification (MLPA), is currently used in most mo-lecular diagnostic laboratories. Current U.K. cost estimates fora genetic test for BRCA1/2 using this method are around £600for the proband and £120 for a family member of a personalready identified as carrying a BRCA1/2 mutation. Recenttechnological developments have introduced next-generation(or highly parallel) sequencing (NGS), which has the potentialto increase the speed and volume of testing and also thenumber of genes tested simultaneously (Hutchison, 2007;MRC, 2009). This will potentially mean that laboratories couldincrease their capacity and offer more tests with faster turn-around times. In theory, laboratories could offer BRCA1/2testing to a higher proportion of women. Clinical scientists arealready developing methods that will move NGS from theresearch to the clinical laboratory environment (e.g., EU Fra-mework Seven Projects: ‘‘TECHGENE,’’ Project Reference223143; ‘‘EURO-GENE-SCAN,’’ Project Reference 223293;‘‘NMD-CHIP,’’ Project Reference 223026). To evaluate theincremental costs and benefits, and inform efficient resourceallocation, it is first necessary to understand the economicevidence currently available to support a genetic testing ser-vice using DNA Sanger sequencing technology for BRCA1/2mutations in the study populations and countries of interestfor a decision-maker. This is for two reasons: (i) it is necessaryto be clear whether current practice was informed by robustevidence that considered the cost and outcomes for a definedpatient population; (ii) identifying existing economic evidencewill potentially provide a useful starting point to design andpopulate an economic model evaluating a new technology forBRCA1/2 testing. Modeling methods enable researchers tocombine and analyze data from a number of sources and alsoprovide a summary of the extent of uncertainty in the currentevidence base (Drummond et al., 2005). Modeling methods area necessity if information is required for resource allocationdecisions about the incremental costs and benefits of a newhealthcare technology, such as innovations in diagnostics. Theaim of this current study was to identify, summarize, andcritically appraise current published economic evaluations ofgenetic testing for HBC predisposition to understand thecurrent level of economic evidence supporting U.K. guide-lines. A secondary aim was to assess the extent to whichSanger DNA sequencing is currently used in U.K. clinicallaboratories to test for BRCA1/2 mutations, in order to un-derstand whether this technique truly encapsulates currentstandard practice.

Materials and Methods

Two methods were used: telephone interviews and asystematic review of published literature and national guid-ance relevant to England and Wales (Scotland has a separatesystem for HTA and reimbursement).

Telephone interviews to establish currentBRCA1/2 testing practice

In September 2010, telephone interviews with the 14 U.K.diagnostic laboratories listed on the Clinical MolecularGenetics Society (CMGS) Web site (www.cmgs.org) as offer-ing a BRCA1/2 mutation testing service were undertaken to

understand current practice. The named contact person fromeach laboratory on the CMGS Web site was asked one ques-tion: What laboratory methods do you currently use to test forBRCA1/2 mutations?

Scope of the review

A systematic review, using Centre for Reviews and Dis-semination systematic review methods (Craig et al., 2007),aimed to identify all economic evaluations relevant to genetictesting for HBC. An economic evaluation was defined as ‘‘thecomparative analysis of alternative courses of action in termsof both their costs and consequences’’ (Drummond et al.,2005). A second pragmatic review, conducted using handsearching of key Web sites, was used to identify nationalguidance relevant to HBC in England and Wales.

Systematic review inclusion criteria

Figure 1 identifies four separate stages of the care pathwayfor which it is possible to undertake cost-effectiveness ana-lyses relevant to genetic testing in HBC and defines the scopeof the review (Stages 1, 2 and 3). Economic evaluations oftreatment options for HBC patients (Stage 4 of care) weredeemed beyond the scope of the review because as long asgenetic testing using a new technology for HBC does notidentify new genetic variants, a change in technology wouldnot affect treatment options. Electronic searches of MEDLINE(via Ovid), EMBASE (via Ovid), PsychINFO (via Ovid), andthe NHS Economic Evaluation Database (NHS EED; viawww.york.ac.uk) were run in February 2010. This search wassupplemented by hand searching key Web sites (NationalInstitute for Health Research Health Technology Assessmentmonographs [www.hta.ac.uk], NICE guidelines and tech-nology appraisals). The start date of 1996 was used becausethis was when BRCA1/2 testing became clinically available.The electronic search strategies combined terms to identifyeconomic evaluations, using the NHS EED search strategy(CRD, 2011), with terms relevant to HBC and genetic testing.The search strategies were modified for each database, andare available from the authors upon request. Commentary-type discussion articles and non-English-language publica-tions were excluded from the review. Two reviewers (W.S.and K.P.) screened titles, and potentially relevant articles wereretrieved in full and screened for inclusion. Included articleswere hand searched for potentially relevant references.

Data extraction and analysis

The primary focus was to critically appraise publishedeconomic evaluations of genetic testing for HBC predisposi-tion (see Stage 2 of Fig. 1). Data were extracted and summa-rized in tables and described in a narrative review, and allidentified studies were critically appraised for quality usingguidelines produced by the NHS Economic Evaluation Da-tabase (Craig et al., 2007) and good practice guidelines fordecision analytic modeling (Philips et al., 2006).

Results

The telephone survey identified that 9 (64%) of the U.K.diagnostic laboratories were offering BRCA1/2 mutationscreening using Sanger DNA testing in conjunction withMLPA.

ECONOMIC EVIDENCE ON GENETIC TESTING FOR BREAST CANCER PREDISPOSITION 581

One laboratory used confirmation sensitive capillary elec-trophoresis (Esteban-Cardenosa et al., 2004) with confirma-tion by Sanger sequencing, in conjunction with MLPA. Twodiagnostic laboratories were already using NGS technology(clonal sequencing with MLPA) to diagnose BRCA1/2 muta-tions. The remaining two laboratories outsourced theirBRCA1/2 test samples to other U.K. laboratories.

National clinical guidelines for the classification and careof women at risk of familial breast cancer were identified(McIntosh et al., 2004) and stated that patients are eligible forgenetic testing if they have at least a 20% chance of carrying aBRCA1/2 mutation. Economic evidence was not used to in-form these guidelines. No published HTA of BRCA1/2 testingwas found; one HTA monograph comparing strategies forreferral into the genetic service for familial breast cancer wasidentified, but the focus was on referral methods (Wilson et al.,2005).

Summary of identified evaluations

Fifteen economic evaluations were included in the finalreview (see Fig. 2). Two retrospective modeling economicevaluations of different protocols for performing genetictesting (Stage 1 of care) compared 20 combinations of labo-ratory techniques for detecting BRCA1 mutations (Sevillaet al., 2002, 2003). The studies met the criteria for an economicevaluation, but did not report a model structure. They didinclude clear descriptions of the key steps of a protocol for

BRCA1 testing and recognized the importance of consideringlaboratory capacity when evaluating the cost-effectiveness ofa clinical diagnostic service.

Eight economic evaluations (Grann et al., 1998, 2000; An-derson et al., 2006; Griebsch et al., 2006; Plevritis et al., 2006;Norman et al., 2007; Norum et al., 2008; Reis et al., 2009) fo-cused on the preventive strategies to use following detectionof predisposition to HBC (Stage 3 of care), of which three(Griebsch et al., 2006; Norman et al., 2007; Reis et al., 2009) wererelevant to U.K. practice. These eight studies can be broadlyseparated into (i) studies comparing surveillance strategiesalone such as magnetic resonance imaging (Griebsch et al.,2006; Plevritis et al., 2006; Norman et al., 2007) and X-raymammography (characterizing standard clinical practice)(Griebsch et al., 2006; Plevritis et al., 2006; Norman et al., 2007;Reis et al., 2009) at various intervals, and (ii) studies compar-ing more invasive preventive strategies such as bilateralrisk reducing mastectomy (RRM) (Grann et al., 1998, 2000;Anderson et al., 2006), bilateral risk reducing salpingo-oophorectomy (RRSPO) (Grann et al., 1998, 2000; Andersonet al., 2006; Norum et al., 2008), RRM and RRSPO combined(Grann et al., 1998, 2000; Anderson et al., 2006; Norum et al.,2008), chemoprevention (Grann et al., 2000; Anderson et al.,2006), and some form of surveillance (characterizing standardclinical practice) (Grann et al., 1998, 2000; Anderson et al., 2006;Norum et al., 2008). Only one of these eight studies (Griebschet al., 2006) clearly used prospective data to evaluate the cost-effectiveness of different surveillance programs, and the

FIG. 1. Key stages of the care pathway for BRCA1/2 testing and management of HBC predisposition. HBC, hereditarybreast cancer.

582 SULLIVAN ET AL.

majority (Grann et al., 1998, 2000; Anderson et al., 2006;Plevritis et al., 2006; Norman et al., 2007; Norum et al., 2008;Reis et al., 2009) were retrospective modeling studies. Oneeconomic evaluation (Reis et al., 2009) was effectively basedon a single study that collected observational data for theintervention, a surveillance program for people at risk ofbreast cancer, but used modeling assumptions concerningprevalence and recovery rates for breast cancer in the studypopulation.

Five studies (Grann et al., 1999; Heimdal et al., 1999; Tengsand Berry, 2000; Balmana et al., 2004; Holland et al., 2009) wereretrospective economic evaluations of a clinical genetic testingservice (Stage 2 of care). None were relevant to the UnitedKingdom. Table 1 provides a summary of these five studies.Table 2 shows the key parameters, and those included in eachStage 2 study. Table 2 illustrates the lack of comparability, interms of the model structure and key parameters, across thefive studies.

All five studies reported the genetic testing strategy to be anacceptably cost-effective use of healthcare resources in theirstated study populations. Two studies (Tengs and Berry, 2000;Holland et al., 2009) recognized that this conclusion could beaffected by changing the probability threshold for a mutationat which testing is deemed acceptable. In three of five studies(Grann et al., 1999; Tengs and Berry, 2000; Holland et al., 2009),the conclusion that a strategy involving genetic testing iscost-effective was qualified by some assumption concerninguptake of preventive measures among identified mutationcarriers. Two studies (Grann et al., 1999; Tengs and Berry,

2000) assumed 100% uptake of preventive surgery in womentesting mutation positive; another (Holland et al., 2009) in-corporated data on typical uptake rates of RRSPO and RRMinto their economic model.

One study (Holland et al., 2009) specifically aimed toidentify the threshold probability of BRCA mutation testingabove which it would be cost effective to test women forBRCA1/2 status, concluding that a threshold as low as 10% isjustified but that an even lower threshold would be cost-effective as long as a negative mutation test result leads to aminimum utility gain for the patient. Tengs and Berry (2000)also aimed to determine the level of hereditary risk at which itis cost effective to offer BRCA1/2 testing and concluded that athreshold of 10% probability of mutation (5% probability ofmutation in each gene) is cost-effective, but that testing indi-viduals at population risk of BRCA1/2 mutations would be aninefficient use of healthcare resources. However, another(Holland et al., 2009) considered only the benefits to the wo-men tested and not other family members. Both of thesestudies used a study population, costs, and outcomes thatwere relevant to a U.S. population only.

Quality assessment

The quality assessment focused on the five economicevaluations relevant to genetic testing (Stage 2 of care). Thequality of the reporting varied. One reason for this may be areflection of the word length restrictions imposed by journals.Three studies (Tengs and Berry, 2000; Balmana et al., 2004;

FIG. 2. Identification of eligible studies.

ECONOMIC EVIDENCE ON GENETIC TESTING FOR BREAST CANCER PREDISPOSITION 583

Holland et al., 2009) explicitly reported the model structureused. All of the studies reported the use of literature reviewsto identify data to populate the economic models, but nonereported sufficient details on the methods used to identify andassess the quality of identified studies. Therefore, it was dif-ficult to assess whether the most appropriate and unbiasedinput values, and ranges around these values, were used asmodel parameters. Four of five studies (Grann et al., 1999;Tengs and Berry, 2000; Balmana et al., 2004; Holland et al.,2009) report using expert opinion data where other datasources were not available but again none report the methodsused to elicit expert opinion and assessment of whether theseestimates of the input parameters are fair and unbiased wasnot possible.

The types of resources and costs included in the studiesvaried. The chosen study perspectives were not always re-ported explicitly (Heimdal et al., 1999; Balmana et al., 2004)and on two occasions (Tengs and Berry, 2000; Holland et al.,2009) the resources included were not consistent with thestated study perspective. The costs reported were not con-sistent across the five studies and included: genetic testing;genetic counseling; follow-up examinations and care; pre-ventive care; cancer care and terminal care. Sources of costdata were generally well reported across studies but in four ofthe five studies (Grann et al., 1999; Heimdal et al., 1999; Tengsand Berry, 2000; Holland et al., 2009) charges rather than unitcosts were used. Charges, set for reimbursement purposes, donot generally reflect the true cost of a procedure. There isevidence to suggest that charges for a medical procedure orcare is not by necessity equivalent to and is in fact likely to behigher than the sum of the unit costs of its constituent parts(Drummond et al., 2005). Cost data in these four studies wereeither informed solely by insurance reimbursement paymentsand drug cost records (Grann et al., 1999; Heimdal et al., 1999)or also relied on data from the literature (Tengs and Berry,2000; Holland et al., 2009), and one study (Holland et al., 2009)also used unit cost data in part to estimate the cost of care. Theremaining study (Balmana et al., 2004) used hospital data toestimate costs.

Current guidelines for economic evaluation of pharma-ceuticals in the United Kingdom (National Institute for Healthand Clinical Excellence, 2008) advocate the use of a generichealth-related quality of life measure to quantify patientbenefits. Three of the five identified economic evaluations of aclinical genetic testing service for HBC predisposition mea-sure outcomes in life years gained, which were estimatedfrom epidemiological survival data (Grann et al., 1999;Heimdal et al., 1999) and patient-specific hospital data (Bal-mana et al., 2004). Two of the studies attempted to estimatequality-adjusted life years (QALYs) to measure health out-comes. Holland et al. (2009) used utility estimates from a timetrade-off study designed to elicit preferences for cancer healthstates and prevention strategies by women: who had breastcancer (n = 21); who were at high risk of breast cancer (n = 28);or who had neither condition (n = 135). Tengs and Berry (2000)used utility estimates from a previously reported decisionanalytic model to weight the additional years of life with avalue (utility) to reflect the quality of the years gained, but didnot provide details on how the utility values were elicited.

Incremental analysis examines the additional costs ofone technology over another, usually current practice, andcompares these with the additional benefits delivered

(Drummond et al., 2005). It was not always clearly reportedhow the incremental analysis of costs and benefits was per-formed across the five studies. In Heimdal et al. (1999), it wasnot clear whether an incremental analysis has been under-taken. In two studies (Grann et al., 1999; Tengs and Berry,2000) it was unclear how the incremental analyses were per-formed from the total cost and outcomes data reported. Onlyone study (Grann et al., 1999) reported a measure of variationaround the base case incremental analysis.

Economic models use inputs (parameter values) assimi-lated from a variety of data sources. Sampling uncertainty is,therefore, inherent in economic evaluations and this impre-cision should be reflected around the input parameters ineconomic models (Drummond et al., 2005; Philips et al., 2006).All five evaluations performed some type of sensitivity anal-ysis focusing on parameter uncertainty and two evaluations(Grann et al., 1999; Holland et al., 2009) conducted probabi-listic sensitivity analysis as recommended by publishedguidance (National Institute for Health and Clinical Ex-cellence, 2008). There was no investigation into the impact ofother types of uncertainty that could be explored in an eco-nomic decision model (methodological, heterogeneity, andstructural). From the sensitivity analyses reported, the fol-lowing model-driving parameters were identified in all butone of the studies (Grann et al., 1999; Heimdal et al., 1999;Balmana et al., 2004; Tengs and Berry, 2000): penetrance ofBRCA1 and BRCA2 mutations in study populations andpenetrance of breast and ovarian cancer among mutationcarriers. One study reported that the efficacy of preventivestrategies taken up by patients testing positive for predis-posing mutations as an important driver of cost-effectiveness(Grann et al., 1999). Both studies measuring QALYs (Tengsand Berry, 2000; Holland et al., 2009) reported that their cost-effectiveness estimates were sensitive to the utility valuesused to derive QALYs. Participation in preventive strategieswas also a key driver of cost-effectiveness.

Discussion

This review identified 15 published economic evaluationsrelevant to interventions for predisposition for HBC, but 8 ofthese were evaluations of preventive strategies for womenpredisposed to HBC and did not directly consider the eco-nomic impact of genetic testing as an intervention. Twofurther studies were potentially promising because theywere purportedly evaluations of different genetic testingstrategies for BRCA1 testing but neither used sufficientlyrobust reporting of modeling methods to provide a usefulstarting point for evaluating a new genetic testing technol-ogy. Five economic evaluations of genetic testing for HBCpredisposition as a clinical service were identified, but nonewere relevant to the focus of economic evidence informing orsupporting national guidance for BRCA1/2 testing in Eng-land and Wales. The quality of reporting for all identifiedpublished economic evaluations was varied, and in mostinstances it was not possible to clearly identify both themodel structure and reliability of the model inputs used togenerate estimates of cost-effectiveness. This limits the extentto which a decision maker, representing a jurisdiction dif-ferent to the viewpoint of the published study, can use andinterpret whether the findings are relevant to local clinicalpractice.

584 SULLIVAN ET AL.

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ctic

eS

tud

yp

op

ula

tio

n:

No

tcl

earl

yre

po

rted

Met

ho

du

sed

toes

tim

ate

effe

ctiv

enes

s:P

rim

aril

yta

ken

fro

mo

ne

cen

ter;

sup

ple

men

ted

by

lite

ratu

rere

vie

wan

dex

per

tas

sum

pti

on

sP

rim

ary

ou

tco

me

mea

sure

:L

YG

Gen

etic

test

ing

;g

enet

icco

un

seli

ng

;cl

inic

alex

amin

atio

n;

foll

ow

-up

surv

eill

ance

Pri

ceY

ear:

1999

Co

st-e

ffec

tiv

enes

san

aly

sis

No

mo

del

stru

ctu

rere

po

rted

Tim

eh

ori

zon

:N

ot

rep

ort

ed

No

tex

pli

citl

yre

po

rted

Un

cert

ain

ty:

Det

erm

inis

tic

sen

siti

vit

yan

aly

sis

for

sele

cted

par

amet

ers

Effi

cacy

of

earl

yin

terv

enti

on

foll

ow

ing

ap

osi

tiv

ete

st,

the

inte

nsi

tyo

fsc

reen

ing

,an

dp

enet

ran

cean

dp

rev

alen

ceo

ffo

un

der

mu

tati

on

s

Ho

llan

det

al.

(200

9),

Un

ited

Sta

tes

Vie

wp

oin

t:S

oci

etal

Alt

ern

ativ

es:

(1)

No

gen

etic

test

ing

(sta

nd

ard

care

);(2

)G

enet

icte

stin

gfo

rB

RC

A1

/2m

uta

tio

ns

(fo

llo

wed

by

the

po

ssib

ilit

yo

fp

rev

enti

ve

surg

ery

ifte

stre

sult

po

siti

ve)

Stu

dy

po

pu

lati

on

:35

-yea

r-o

ldw

om

enw

ho

are

con

cern

edab

ou

th

avin

ga

BR

CA

1an

d/

or

BR

CA

2m

uta

tio

n

Met

ho

du

sed

toes

tim

ate

effe

ctiv

enes

s:L

iter

atu

rere

vie

wan

dex

per

tas

sum

pti

on

sP

rim

ary

ou

tco

me

mea

sure

:Q

AL

Ys

Val

uat

ion

of

hea

lth

ben

efits

:T

ime-

trad

eo

ff(p

rev

iou

sly

rep

ort

ed)

Gen

etic

test

ing

;h

ealt

hca

rew

hen

wel

l;te

rmin

alh

ealt

hca

re;

pre

ven

tiv

esu

rger

yan

dfo

llo

w-u

p;

can

cer

trea

tmen

tan

dca

reP

rice

Yea

r:20

06

Co

st-e

ffec

tiv

enes

san

aly

sis

Mar

ko

vm

od

el(1

-yea

rcy

cles

)T

ime

ho

rizo

n:

70y

ears

$900

0/Q

AL

YU

nce

rtai

nty

:P

SA

and

det

erm

inis

tic

on

e-w

ayse

nsi

tiv

ity

anal

ysi

s

Uti

lity

der

ived

fro

ma

neg

ativ

ete

stre

sult

Ten

gs

and

Ber

ry(2

000)

,U

nit

edS

tate

s

Vie

wp

oin

t:S

oci

etal

Alt

ern

ativ

es:

(1)

No

gen

etic

test

ing

(sta

nd

ard

care

);(2

)G

enet

icte

stin

gfo

rB

RC

A1

/2m

uta

tio

ns

(fo

llo

wed

by

pre

ven

tiv

est

rate

gy

fou

nd

toy

ield

the

mo

stQ

AL

Ys

inth

em

od

elif

test

resu

lts

are

po

siti

ve)

Stu

dy

po

pu

lati

on

:30

-yea

r-o

ldw

om

enin

the

Un

ites

Sta

tes,

wit

hv

ary

ing

risk

sfo

rH

BC

Met

ho

du

sed

toes

tim

ate

effe

ctiv

enes

s:L

iter

atu

rere

vie

wan

dex

per

tas

sum

pti

on

sP

rim

ary

ou

tco

me

mea

sure

:Q

AL

Ys

Val

uat

ion

of

hea

lth

ben

efits

:T

aken

fro

mp

rev

iou

sst

ud

ies

(met

ho

ds

no

tre

po

rted

)

Gen

etic

test

ing

;g

enet

icco

un

seli

ng

;p

rev

enti

ve

surg

ery

,an

dfo

llo

w-u

pca

re;

can

cer

trea

tmen

tan

dca

reP

rice

Yea

r:N

ot

rep

ort

ed

Co

st-e

ffec

tiv

enes

san

aly

sis

Mar

ko

vh

ealt

hst

ates

app

end

edto

ad

ecis

ion

tree

mo

del

(cy

cle

len

gth

no

tre

po

rted

)T

ime

ho

rizo

n:

No

tre

po

rted

$34,

000/

QA

LY

Un

cert

ain

ty:

Det

erm

inis

tic

sen

siti

vit

yan

aly

sis

for

sele

cted

par

amet

ers

Co

stan

dac

cura

cyo

fth

ete

st

HB

C,

her

edit

ary

bre

ast

can

cer;

QA

LY

,q

ual

ity

-ad

just

edli

fey

ear;

LY

G,

life

yea

rsg

ain

ed;

PS

A,

pro

bab

ilis

tic

sen

siti

vit

yan

aly

sis.

586

Ta

bl

e2.

Su

mm

ar

yo

fK

ey

Mo

de

lIn

pu

ts

an

dA

ssu

mp

tio

ns

Par

amet

erB

alm

ana

etal

.(2

00

4),

Sp

ain

Gra

nn

etal

.(1

99

9),

Un

ited

Sta

tes

Hei

md

alet

al.

(19

99

),N

orw

ayH

olla

nd

etal

.(2

00

9),

a

Un

ited

Sta

tes

Ten

gs

and

Ber

ry(2

00

0),

a

Un

ited

Sta

tes

Ty

pe

of

gen

etic

test

Fu

llse

qu

ence

anal

ysi

so

fB

RC

A1

/2:

PT

Tan

dS

SC

PT

est

for

thre

esp

ecifi

cm

uta

tio

ns

com

mo

nin

the

po

pu

lati

on

:18

5del

AG

and

5382

insC

inB

RC

A1

and

6174

del

Tin

VR

CA

2

Tes

tfo

rtw

osp

ecifi

c(t

ho

ug

hu

nsp

ecifi

ed)

mu

tati

on

sin

BR

CA

1

Fu

llse

qu

ence

anal

ysi

so

fB

RC

A1

/2F

ull

seq

uen

cean

aly

sis

of

BR

CA

1/2

Gen

etic

cou

nse

lin

gan

d/

or

oth

erg

enet

ics

serv

ice

incl

ud

ed

Yes

No

tex

pli

citl

yre

po

rted

Yes

No

tex

pli

citl

yre

po

rted

No

tex

pli

citl

yre

po

rted

Pre

ven

tiv

est

rate

gie

sin

clu

ded

An

nu

alC

lin

ical

bre

ast

exam

inat

ion

and

mam

mo

gra

ph

y

Bil

ater

alR

RS

PO

;B

ilat

eral

RR

M;

RR

Mp

lus

RR

SP

O;

Su

rvei

llan

ce(a

nn

ual

mam

mo

gra

m)

No

tin

clu

ded

RR

SP

O;

RR

M;

scre

enin

gac

cord

ing

tore

com

men

dat

ion

s(n

ot

exp

lici

tly

defi

ned

)

RR

SP

O;

RR

M;

RR

SP

Op

lus

RR

M;

‘‘no

pre

ven

tiv

em

easu

re’’

(no

tex

pli

citl

yd

efin

ed)

Pro

bab

ilit

yo

fb

ein

ga

mu

tati

on

carr

ier

No

tex

pli

citl

yre

po

rted

2.5%

inan

Ash

ken

azi

Jew

ish

po

pu

lati

on

0.6%

of

all

bre

ast

can

cers

(est

imat

edfr

om

ov

aria

nca

nce

rd

ata)

Var

ied

and

use

dfo

ran

aly

sis

(ran

ge

0%–2

0%)

Var

ied

and

use

dfo

ran

aly

sis;

BR

CA

1ra

ng

e0–

0.5;

BR

CA

2ra

ng

e0–

0.5;

Av

erag

ep

op

ula

tio

nri

skB

RC

A1

0.00

06;

Av

erag

ep

op

ula

tio

nri

skB

RC

A2

0.00

02T

est

Sen

siti

vit

yN

ot

rep

ort

ed98

%N

ot

rep

ort

ed99

%(9

5%–1

00%

ran

ge

exp

lore

din

sen

siti

vit

yan

aly

sis)

98%

Tes

tS

pec

ifici

tyN

ot

rep

ort

ed99

%N

ot

rep

ort

ed99

%(9

5%–1

00%

ran

ge

exp

lore

din

sen

siti

vit

yan

aly

sis)

99%

Tes

tco

st(p

rice

yea

r)e

1202

.02

for

gen

etic

stu

dy

of

ind

exca

sep

erfa

mil

y;

e11

26.8

8fo

rg

enet

icco

un

seli

ng

per

fam

ily

;(t

hen

calc

ula

tein

div

idu

alp

rice

sb

yes

tim

atin

gsi

xw

om

enp

erfa

mil

yre

ceiv

edth

esc

reen

ing

reco

mm

end

atio

ns

(pri

cey

ear

no

tre

po

rted

)

$450

gen

ete

stin

gfo

rth

ree

spec

ific

mu

tati

on

s;$3

00g

enet

icco

un

seli

ng

;F

ull

seq

uen

ceB

RC

A1

/2an

aly

sis

$240

0(p

rice

yea

r19

95)

e23

4,87

9fo

rte

stin

go

fp

rob

and

plu

s15

fam

ily

mem

ber

s;e

131,

221

gen

etic

cou

nse

lin

go

fp

rob

and

plu

s15

fam

ily

mem

ber

s;e

2250

full

gen

esc

reen

ing

BR

CA

1/2

(pri

cey

ear

1999

)

$254

2,ra

ng

e$1

301–

5421

(pri

cey

ear

2006

)$3

10p

rete

stco

un

seli

ng

;$2

580

firs

tfa

mil

ym

emb

erte

sted

(pri

cey

ear

no

tre

po

rted

)

Lif

etim

eri

sko

fb

reas

tca

nce

r58

.5%

30-y

ear-

old

BR

CA

1/2

mu

tati

on

carr

ier;

8.6%

30-y

ear-

old

no

nm

uta

tio

nca

rrie

r

56%

30-y

ear-

old

wo

men

wit

ho

ne

of

thre

esp

ecifi

cm

uta

tio

ns

50%

ov

er25

yea

rs(a

ge

35–6

0)fo

rsp

ecifi

cB

RC

A1

mu

tati

on

carr

iers

;18

.25%

ov

er25

yea

rs(a

ge

35–6

0)fo

rn

on

mu

tati

on

carr

iers

inth

ep

rog

ram

73.5

%o

ver

50y

ears

(ag

e30

–80

)B

RC

A1

/2m

uta

tio

nca

rrie

r;6.

8%o

ver

50y

ears

(ag

e30

–80)

gen

eral

po

pu

lati

on

BR

CA

1m

uta

tio

nca

rrie

rs0.

71;

BR

CA

2m

uta

tio

nca

rrie

rs0.

84;

no

nca

rrie

rs0.

126

(con

tin

ued

)

587

Ta

bl

e2.

(Co

nt

in

ue

d)

Par

amet

erB

alm

ana

etal

.(2

00

4),

Sp

ain

Gra

nn

etal

.(1

99

9),

Un

ited

Sta

tes

Hei

md

alet

al.

(19

99

),N

orw

ayH

olla

nd

etal

.(2

00

9),

a

Un

ited

Sta

tes

Ten

gs

and

Ber

ry(2

00

0),

a

Un

ited

Sta

tes

Lif

etim

eri

sko

fo

var

ian

can

cer

No

tre

po

rted

16%

30-y

ear-

old

wo

men

wit

ho

ne

of

thre

esp

ecifi

cm

uta

tio

ns

No

tin

clu

ded

27.8

%o

ver

50y

ears

(ag

e30

–80)

BR

CA

1/2

mu

tati

on

carr

ier;

1.8%

ov

er50

yea

rs(a

ge

30–8

0)g

ener

alp

op

ula

tio

n

BR

CA

1m

uta

tio

nca

rrie

rs0.

63;

BR

CA

2m

uta

tio

nca

rrie

rs0.

27;

No

nca

rrie

rs0.

0157

Uti

lity

val

ues

No

tin

clu

ded

No

tin

clu

ded

No

tin

clu

ded

Ag

e-sp

ecifi

cu

tili

tyat

35y

ears

wel

lst

ate,

0.92

(ran

ge

0.8–

1);

Dec

reas

ein

uti

lity

iny

ear

of

bre

ast

can

cer

dia

gn

osi

s,0.

2(r

ang

e0–

1);

Dec

reas

ein

uti

lity

iny

ear

of

ov

aria

nca

nce

rd

iag

no

sis,

0.29

(ran

ge

0–1)

;U

tili

tyin

firs

ty

ear

po

st-

RR

SP

O,

0.68

(ran

ge

0.5–

1);

Uti

lity

infi

rst

yea

rp

ost

-RR

M,

0.82

(ran

ge

0.5–

1)

Bre

ast

can

cer

0.89

;O

var

ian

can

cer

0.82

;B

reas

tan

dO

var

ian

Can

cer

0.82

;R

RM

0.86

;R

RS

PO

bef

ore

age

50w

ith

ho

rmo

ne

rep

lace

men

t0.

91;

RR

SP

Ob

efo

reag

e50

wit

ho

ut

ho

rmo

ne

rep

lace

men

t1;

RR

Mp

lus

RR

SP

O0.

86.

Up

tak

eo

fB

RC

A1

/2te

stin

gam

on

gfa

mil

ym

emb

ers

No

tre

po

rted

No

tre

po

rted

100%

No

tre

po

rted

No

tre

po

rted

Up

tak

eo

fp

rev

enti

ve

stra

teg

ies

No

tre

po

rted

No

tre

po

rted

No

tin

clu

ded

RR

Mat

35y

ears

,B

RC

A1

/2m

uta

tio

n0.

15(r

ang

e0.

03–0

.54)

;R

RS

PO

at35

yea

rs,

BR

CA

1/2

mu

tati

on

0.25

(ran

ge

0.13

–0.7

8)

Ass

um

ed10

0%o

fB

RC

A1

/2-

po

siti

ve

pat

ien

tsw

ou

ldo

pt

for

the

pre

ven

tiv

est

rate

gy

sho

wn

asm

ost

effe

ctiv

eb

ym

od

el:

RR

SP

Oat

age

30o

r50

.Im

pac

to

fp

rev

enti

ve

stra

teg

ies

No

tex

pli

citl

yre

po

rted

RR

SP

Ore

du

ced

risk

of

ov

aria

nca

nce

rb

y45

%;

RR

Mre

du

ced

risk

of

bre

ast

can

cer

by

90%

No

tin

clu

ded

RR

SP

O(b

efo

reag

e50

)re

du

ced

risk

of

bre

ast

can

cer

by

0.45

(ran

ge

0.25

–0.6

5);

RR

SP

Ore

du

ced

risk

of

ov

aria

nca

nce

rb

y0.

96(r

ang

e0.

9–0.

99);

RR

Mre

du

ced

risk

of

bre

ast

can

cer

by

0.9

(ran

ge

0.8–

0.99

);R

RM

no

red

uct

ion

inri

sko

fo

var

ian

can

cer

RR

SP

Ore

du

ced

risk

of

bre

ast

can

cer

by

61%

;R

RS

PO

red

uce

dri

sko

fo

var

ian

can

cer

by

46%

;R

RM

red

uce

dri

sko

fb

reas

tca

nce

rb

y91

%;

RR

Mn

ore

du

ctio

nin

risk

of

ov

aria

nca

nce

r;R

RS

PO

plu

sR

RM

red

uce

dri

sko

fb

reas

tca

nce

rb

y91

%;

RR

SP

Op

lus

RR

Mre

du

ced

risk

of

ov

aria

nca

nce

rb

y46

%

Wh

enst

ud

ies

hav

ere

po

rted

aco

stfo

rg

enet

icco

un

seli

ng

bu

tn

ot

men

tio

ned

itin

the

tex

tac

cou

nt

of

cost

s,th

isis

des

crib

edas

‘‘No

tex

pli

citl

yre

po

rted

.’’aH

oll

and

(200

9)an

dT

eng

san

dB

erry

(200

0)d

on

ot

exp

lici

tly

stat

e‘‘f

ull

seq

uen

cean

aly

sis,

’’b

ut

fro

mth

ete

xtin

each

itis

clea

rth

atth

eyar

ese

arch

ing

for

mu

tati

on

sac

ross

the

wh

ole

of

the

two

BR

CA

gen

es.

RR

M,

risk

red

uci

ng

mas

tect

om

y;

RR

SP

O,

risk

red

uci

ng

salp

ing

o-o

op

ho

rect

om

y;

PT

T,

pro

tein

tru

nca

tio

nte

st;

SS

CP

,si

ng

le-s

tran

dco

nfo

rmat

ion

po

lym

orp

his

m.

588

The telephone survey confirmed that Sanger DNA se-quencing followed by MLPA is used in the majority of U.K.diagnostic laboratories offering a BRCA1/2 mutation testingservice. These findings suggest that Sanger sequencing fol-lowed by MLPA is the legitimate choice for a standard prac-tice comparator in England and Wales. However, 2 of 14laboratories were using NGS technology at time of the survey.There is, therefore, some evidence that new genetic testingtechnologies are already being used for BRCA1/2 testing. Thissuggests that it is timely to think about generating economicevidence to support the use of NGS methods before they arewidely used in clinical practice.

BRCA1/2 testing is offered as part of a clinical geneticsservice and involves a defined care pathway for womensuspected to be predisposed to HBC and their family mem-bers. Not all women who have family members with breastcancer can be or should be referred and offered genetic testing.National guidelines specify who should be offered mutationtesting on the basis of a patients’ risk for carrying a mutationdetermined by the presence and age at diagnosis of cancer inother family members, using a risk assessment tool such as theManchester scoring system (Evans et al., 2004). Results fromgenetic testing may then impact the coordination of preven-tive care for patients. None of the economic evaluationsidentified considered the challenge of identifying variants ofuncertain significance (VUS), which are found in around 5%–6% of samples. These VUS results potentially have a negativeoutcome on individuals concerned as they create greater un-certainty. As such, thresholds may need to take into account abalance that reflects a greater chance of identifying a mean-ingful pathogenic mutation than a VUS. The findings fromthis review of published economic evaluations indicates thatdevelopment of national guidelines, as well as the current riskthreshold of carrying a BRCA1/2 mutation, was not informedby economic evidence. This lack of economic evidence sup-porting current clinical practice has implications for futureHTA of genetic testing strategies using new technologies. Thefirst key step of any evaluation is to identify current practiceand understand how a new intervention will change currentpractice. In the absence of published data, it is not clearwhether current practice, using Sanger DNA sequencing withMLPA combined with a risk threshold for testing set at 20%, isthe most effective or cost-effective use of healthcare resources.Existing national guidelines have been largely informedby pragmatic decision making by experts advising in guide-line development. The setting of the 20% threshold in 2004likely reflected the large backlog of untested samples andthe need to fully test samples that were previously only par-tially screened by techniques employed at the time of theguidelines.

Providing BRCA1/2 testing will also be constrained by theavailability of genetic counseling services in addition to lab-oratory capacity and test result turnaround time. Only two ofthe economic evaluations (Heimdal et al., 1999; Balmana et al.,2004) identified clearly considered genetic testing as an inte-gral part of a clinical genetics service with associated geneticcounseling. Furthermore, the nature of the genetic testing in-tervention differed in the key studies. One study (Heimdalet al., 1999) only considered testing for BRCA1 mutations,while two (Grann et al., 1999; Heimdal et al., 1999) used anintervention testing for specific mutations instead of full se-quence analysis. However, current U.K. clinical practice is to

fully sequence both BRCA1 and BRCA2 even though availableevidence would suggest that the probability of an individualcarrying a mutation in both genes is negligible.

Significant costs and benefits of genetic testing for HBCpredisposition are associated with events that occur after thetest, as a result of the test outcome. The same is true for a rangeof medical diagnostic interventions. It is therefore necessary tobe clear what current care pathways involve and how a ge-netic test will change such pathways. There is no publishedevidence that adequately describes current care pathways forgenetic testing for predisposition to HBC. Furthermore, whilethe QALY is now routinely used to measure benefits in theeconomic evaluation of pharmaceuticals, there is concern inthat such a measure cannot capture the full potential benefits,such as familial and nonhealth effects, which are relevant togenetic testing and diagnostics (Basu and Meltzer, 2005;Grosse et al., 2008).

More generally, challenges for HTA of genetic testing werealso identified when conducting this review. One key chal-lenge is how best to generate data on the resource use for carepathways affected by genetic testing and also how to quantifythe relative benefits to patients from different approaches togenetic testing in terms of life-years gained and QALYs. Thisis particularly problematic because of the lack of robust pro-spective data on the relative effectiveness of genetic tests thatis driven, in part, by the current regulatory processes for di-agnostics. This lack of data places reliance on the elicitation ofexpert opinion to generate effectiveness, utility, and cost data,but the elicitation process should also use robust and appro-priate methods (Sullivan and Payne, 2011). Diagnostic accu-racy is a surrogate outcome (Caro et al., 2010), but still anecessary component of an economic evaluation of NGS ge-netic testing; however, the sensitivity and specificity of cur-rent testing methods were not always explicitly reported.Current regulatory mechanisms do not encourage the pro-duction of robust evidence sufficient for populating economicmodels to inform decision making (Payne, 2009).

Policy implications

As decision-making bodies such as NICE move to evaluatediagnostic services alongside pharmaceuticals as part of thereimbursement package and technology in medical diagnos-tics continues to evolve rapidly, it is necessary that sufficienteconomic evidence is generated. This study has highlightedspecific challenges for the robust economic evaluation of newtechnological developments for diagnostic testing. The keychallenges are as follows: clearly describing current practiceand how the existing technology is used and understandinghow the new technology can change current practice, in thelaboratory and in terms of clinical services and subsequentcare pathways. The lack of robust economic data to supportnational guidance on genetic testing for HBC in England andWales is likely to be a reflection of a broader paucity of data topopulate economic models.

Conclusions

There is a lack of economic evidence to support the choiceof the current risk threshold for BRCA1/2 testing in Englandand Wales. The lack of economic evidence supporting thecurrent risk threshold for in national guidance has implica-tions for the efficient use of healthcare resources and the

ECONOMIC EVIDENCE ON GENETIC TESTING FOR BREAST CANCER PREDISPOSITION 589

design of economic evaluations of new technologies forBRCA1/2 testing. It is not clear, from currently available eco-nomic evidence, whether the most efficient use of healthcareresources is to improve the use and selection of women usingthe current DNA sequencing technology or to introduce a newNGS technology to improve laboratory capacity to expand thenumber of women tested for predisposition to HBC.

Acknowledgments

The authors would like to acknowledge the expertise andkind help of the following people, who helped us to under-stand cross-border differences in laboratory procedures andcare pathways relevant to genetic testing for breast cancerpredisposition:

Marion McAllister, Developmental Biomedicine, Uni-versity of Manchester; Gert Matthijs, Centre for Human Ge-netics, University Hospitals Leuven; Harry Cuppens, Centrefor Human Genetics, University Hospitals Leuven; GenevieveMichils, Centre for Human Genetics, University HospitalsLeuven; Frans Hogervorst, Diagnostic Oncology, Nether-lands Cancer Institute.

Disclosure Statement

The research leading to these results has received fundingfrom the European Community’s Seventh Framework Pro-gram FP7/2007-2013 under grant agreement no. 223143(Project acronym TECHGENE). The work of D.G.E., S.C.R.,and W.G.N. is supported by the NIHR Manchester Biomedi-cal Research Centre.

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Address correspondence to:Katherine Payne, Ph.D.

Department of Health Sciences—EconomicsSchool of Community Based Medicine

The University of Manchester4th Floor, University Place

Jean McFarlane Building, Oxford RoadManchester M13 9PL

United Kingdom

E-mail: katherine.payne@manchester.ac.uk

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