A Risk‐Benefit Framework for Genetic Tests
Genetic Tests
David L. Veenstra, PharmD, PhD
David L. Veenstra, PharmD, PhD
Pharmaceutical Outcomes Research and Policy Program
y
g
Institute for Public Health Genetics
University of Washington
Seattle WA
Seattle, WA
G
Genomics is Exceptional
i i E
ti
l
1.
1
2.
3.
4.
Ease and speed of market access
Ease
and speed of market access
Evidence levels
Volume of tests
Costs dropping
pp g
– Just last year, genetic testing for warfarin therapy (3 SNPs) was available for $550. – Currently, available for $175
Currently available for $175
– Personal Genome Service® from 23andMe provides data on 580,000 SNPs for $999 $399.
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U l
Unclear Evidence Requirements
E id
R
i
t
• The
The dichotomy of perspectives is exemplified by a recent Wall dichotomy of perspectives is exemplified by a recent Wall
Street Journal article on the FDA’s decision to add pharmacogenomic information to the warfarin label:
– "It would be irresponsible and potentially harmful to suggest that testing be used, or even mentioned, in the label," said University of Washington professor Ann Wittkowsky in an interview before the g
p
y
FDA's decision. "It is fascinating science, but it is not yet ready for prime time."
– LLarry Lesko, director of the clinical pharmacology office at the FDA, L k di
f h li i l h
l
ffi
h FDA
says the agency has "substantial" evidence to support the new label
and hopes it will improve safety by informing doctors.
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Translational Challenge of the Translational
Challenge of the
Genome‐Wide Era
• How do we estimate the clinical risk‐benefit tradeoffs of genetic tests in a timely manner?
tradeoffs of genetic tests in a timely manner?
– To speed the development and use of beneficial tests
– To avoid the use of tests that may lead to harm
• Can we identify an evidence framework that y
meets stakeholders’ needs? Veenstra
4
Ch ll
Challenge of EBM
f EBM
• Traditional evidence‐based processes rely on direct evidence of clinical utility. • For example, as stated in a recent evidence report for g
gene expression profiling in breast cancer:
p
p
g
“… clinical utility…can only be assessed in the context of randomized clinical trials”
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F
Formal Risk‐Benefit Analysis (RBA)
l Ri k B
fit A l i (RBA)
• Recent
Recent interest in the use of formal risk‐
interest in the use of formal risk
benefit analysis in regulatory decision making for drugs and biologics (IOM panel; ki f d
d bi l i ( O
l
FDA consideration) Veenstra
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Historical Timeline on Drug Safety g
y
“Crisis”
– January 30, 2007—FDA announces 41 initiatives on drug safety in response to IOM report
One FDA initiative: “Developing and incorporating new quantitative tools in the assessment of risk and
benefit…
‐‐ June 2007—Avandia (rosiglitazone), diabetes drug controversy – November 2008 FDA‐PhRMA‐BIO Working Conference; Next Steps Working Group formed
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Obj ti
Objective
To help decision makers think more clearly To
help decision makers think more clearly
about the magnitude and uncertainty of the potential clinical benefits and harms of i l li i l b
fi
dh
f
genetic tests
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Components of Risk‐Benefit Components
of Risk‐Benefit
Framework:
1. Decision Modeling
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D ii A l i
Decision Analysis
• Systematic
– Consider all available data
– Evaluate alternative options
Evaluate alternative options
• Formal
– Reproducible
p
– Transparent
• Quantitative
– Probabilities of events
– Value of events
– Uncertainty evaluated
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Decision Modeling for g
Genetic Tests
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Components of Risk‐Benefit Components
of Risk‐Benefit
Framework:
2. Defining Clinical Utility
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Cli i l O t
Clinical Outcomes
• Benefit
– Absolute risk reduction
– NNT
• Harm
– Absolute risk increase
– NNH
• Clinical Utilityy
– (Benefits) – (Harms)
– Difference in (Quantity x Quality) of life Veenstra
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Q lit Adj t d Lif Y
Quality‐Adjusted Life‐Years (QALY)
(QALY)
•
•
•
•
A measure of overall clinical outcomes
A
measure of overall clinical outcomes
QALY = life expectancy x quality of life (QoL)
QoL estimated on a scale of 0 to 1
More specifically, health‐related QoL
p
y
– physical functioning
– social functioning
social functioning
– emotional functioning
– mental
mental functioning
functioning
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Components of Risk‐Benefit Components
of Risk‐Benefit
Framework:
3. Decision Matrix
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Ri k B
Risk‐Benefit Categorization Matrix
fit C t
i ti M t i
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Ch ll
Challenges
• USPSTF methods report
USPSTF methods report
“Although formal decision analyses … have been proposed as an objective method to weigh benefits and harms, … such analyses can be complex and opaque and may rely
such analyses can be complex and opaque and … may rely on various assumptions, each of which may have substantial uncertainty.”
Sawaya et al, Ann Int Med 2007
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Example 1:
W f i Ph
Warfarin Pharmacogenomic i
Testingg
• Does use of genetic testing improve patient outcomes?
• Decrease bleeding risk, Increase clotting risk?
– Balance of benefit vs. risk
– Net benefit
• Level of evidence
– Scientific and clinical plausibility?
– Comparative data?
– Definiti
Definitive RCT?
e RCT?
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Di t E id
Direct Evidence: A Clinical Trial?
A Cli i l T i l?
• Identify a comparator and setting
Identify a comparator and setting
– Standard of care or clinical algorithm?
• RCT measuring bleeds would be large
RCT meas ring bleeds o ld be large
– 5,000 – 10,000 patients would be required
• NHLBI
NHLBI‐sponsored RCT being initiated in ~2,000 d RCT b i i i i d i 2 000
patients, primarily in (academic) anticoagulation clinics
li i
– primary outcome: TTR in first month
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S h
Schematic of Decision Model
ti f D i i M d l
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RCT: Test vs No Test N=200
RCT: Test vs. No‐Test, N=200
Wild-types:
yp
 time
in-range
Anderson et al, Circulation 2007
Meckley et al, CDC NOPHG 10th Anniv. 2008
VKOR
O variants:
a a s
little change
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CYP2C9 variants:
trade-off
22
Warfarin Risk‐Benefit Analysis y
Results
• In a population of 10,000 people, testing would reduce the number of bleeds by 17, but increase the number of thromboembolic events by 3. • Life expectancy increased by 0.005 year
• Quality‐adjusted life‐years (QALYs) increased by 0.004 (1‐2 days)
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U
Uncertainty in Results
t i t i R lt
• Inputs varied using simulation techniques
– Results of repeated simulations stored and p
evaluated
• Range of QALYs
Range of QALYs
– Increase of 0.02 (1 week) to decrease of 0.01 (3 days)
y)
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Where Might We Put g
Warfarin PGx?
?
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Example 2:
p
Tamoxifen and Breast Cancer Treatment
• Tamoxifen
Tamoxifen is widely used for the treatment and prevention of is widely used for the treatment and prevention of
estrogen receptor (ER)‐positive breast cancer. • CYP2D6 is the major enzyme that ‘activates’ tamoxifen to j
y
endoxifen, the major contributor to tamoxifen’s anti‐
estrogenic effect.
• Recent studies have found that women with CYP2D6
Recent studies have found that women with CYP2D6 poor poor
metabolizer genotype may have higher cancer recurrence rates than women with no variant alleles.
• Pharmacogenetic screening for CYP2D6 variants could be a useful approach to help clinicians identify women that would be
be better candidates for alternative therapies.
better candidates for alternative therapies
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FDA L b l Ch
FDA Label Change?
?
• On
On October 18, 2006, an FDA Advisory October 18 2006 an FDA Advisory
Subcommittee reviewed findings to date
• The consensus was the label should be updated to The consensus was the label should be updated to
reflect the fact that postmenopausal candidates for tamoxifen who are CYP2D6 poor metabolizers are at increased risk for breast cancer recurrence. • The FDA declined to act on the Advisory Committee's recommendation
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BCBS TEC A
BCBS TEC Assessment
t
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Q
Quantitative Modeling
tit ti M d li
• Punglia et al developed 2‐state Markov model
Punglia et al developed 2 state Markov model
• Projected 5‐yr disease recurrence
– 5‐year disease‐free survival of tamoxifen‐treated patients with no mutations (83.9%) that was similar to that for g
genotypically unselected patients who were treated with yp
y
p
aromatase inhibitors (84.0%). – With stronger genetic association estimates, disease‐free survival with tamoxifen exceeded that with aromatase inhibitors in wildtype/wildtype patients.
Punglia, Weeks. JNCI 2008
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T T t
To Test…
“We
We believe that the current evidence is already believe that the current evidence is already
strong enough to warrant an ethical obligation for physicians to inform their patients
for physicians to inform their…patients about…CYP2D6 genetic testing”
Gurwitz, Newman. JNCI 2008
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…or Not to Test…
N tt T t
“Because
Because of this uncertainty, and despite the of this uncertainty and despite the
Punglia et al. model, we do not recommend routine CYP2D6 genotyping for all patients
routine CYP2D6 genotyping for all patients who are considering tamoxifen”
Hayes, Flockhart. JNCI 2008
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…That Is the Question
Th t I th Q ti
“The
The medical community is frequently torn medical community is frequently torn
between the rapid acceptance of a new, exciting and potentially helpful strategy and
exciting, and potentially helpful strategy and the possibility that after more extensive and careful investigation the risks associated with
careful investigation, the risks associated with its use might not outweigh the benefits.”
Hayes, Flockhart. JNCI 2008
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Risk‐Benefit Analysis of CYP2D6 Testing and Tamoxifen Treatment
Tamoxifen Treatment
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D t A
Data, Assumptions
ti
• PM
PM defined as people heterozygous or homozygous d fi d
l h t
h
for variant allele *4 • HR for recurrence risk PM vs. EM: 1.91 (Goetz 2007)
HR for recurrence risk PM vs EM: 1 91 (Goetz 2007)
• No adjuvant tx. effect once patients are off 5‐yr therapy
• Post‐tx relapse risk from EBCTG years 5‐15
• 2003 US mortality statistics
2003 US mortality statistics
• Mean age 64yo (ATAC trial)
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M d l V lid ti
Model Validation
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Results
Table 1. treatment options by 2D6 testing
QALY
2D6 testing
no testing
2D6 testing overall
12.191
EM- rx: tamoxifen
12.211
PM- rx: anastrozole
12.147
tamoxifen
11.951
anastrozole
12.147
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I
Impact of PM/EM Recurrence Hazard Ratio
t f PM/EM R
H
d R ti
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Where Does Tamoxifen Testing g
Belong?
?
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Li it ti
Limitations of Formal RBA
fF
l RBA
• Stakeholder acceptance
p
– transparency
– use of ‘QALY’
– uncertainty
• Non‐clinical impacts challenging – ‘personal utility’
• willingness to pay
• discrete choice experiments
discrete choice experiments
• Nuancing the ‘I’ recommendation
– evidence development
p
– targeted use Veenstra
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Summary
• Assessing clinical utility of genetic tests is challenging
• Lots of data, little information
• Quantitative risk‐benefit assessment may be useful to inform policy decisions
be useful to inform policy decisions
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A k
Acknowledgements
l d
t
•
•
University of Washington
University of Washington
–
–
–
–
–
–
–
–
–
–
Intermountain Healthcare
– Marc Williams
– Jim Gudgeon
– Jeff Anderson
Wylie Burke
Scott Ramsey
Lou Garrison
Lou Garrison
Rick Carlson
Lisa Meckley
Ann Wittkowsky
Ann Wittkowsky
Allan Rettie
Mark Rieder
Josh Carlson
Josh Carlson
Josh Roth
•
CDC
– Scott Grosse
– NOPHG Seed Fundingg
– U18 GD000005‐01 (GAPP Initiative)
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