Cost Effectiveness of Gene Expression Profiling for Early Stage Breast Cancer

Original Article
Cost Effectiveness of Gene Expression Profiling for Early Stage
Breast Cancer
A Decision-Analytic Model
Mo Yang, MS1,2; Suja Rajan, PhD1,2; and Amalia M. Issa, PhD, MPH1,2,3
BACKGROUND: Gene expression profiling (GEP) is being used increasingly for risk stratification to identify women with lymph nodenegative, estrogen receptor-positive, early stage breast cancer who are most likely to benefit from adjuvant chemotherapy. The
authors of this report evaluated the cost effectiveness of recurrence score-guided treatment using 2 commercially available GEP
tests, Oncotype DX (Genomic Health, Redwood City, Calif) and MammaPrint (Agendia Inc., Irvine, Calif), from a third-party payer’s
perspective. METHODS: A 10-year Markov model was developed to compare the costs and quality-adjusted life-years (QALYs) of
treatment decisions guided by either Oncotype DX or MammaPrint in a hypothetical cohort of women with early stage, lymph nodenegative, estrogen receptor-positive breast cancer who may experience recurrence. Outcomes included no recurrence, recurrence,
and death. The costs considered included gene test costs, the costs of adjuvant chemotherapy and other chemotherapy (including
premedication, oncology visits, and monitoring for adverse events), the cost of treating recurrence, costs associated with the treatment of adverse events, and end-of-life care costs. RESULTS: The model demonstrated that the patients who received the Oncotype
DX test to guide treatment spent $27,882 (in US dollars) and gained 7.364 QALYs, whereas patients who received the MammaPrint
test to guide treatment spent $21,598 and gained 7.461 QALYs. Sensitivity analyses demonstrated that the results were robust to
changes in all parameters. CONCLUSIONS: The model suggested that MammaPrint is a more cost-effective GEP test compared with
Oncotype DX at a threshold willingness-to-pay of $50,000 per QALY. Because Oncotype DX is the most frequently used GEP in clinical practice in the United States, the authors concluded that the current findings have implications for health policy, particularly
C 2012 American Cancer Society.
health insurance reimbursement decisions. Cancer 2012;118:5163–70. V
KEYWORDS: gene-expression profiling, breast cancer, Markov modeling, cost-effectiveness, health economics.
INTRODUCTION
Breast cancer is the second most common malignancy and the second most common cause of cancer death for women in
the United States. Breast cancer accounts for an estimated 209,060 newly diagnosed cases annually.1 A key challenge for
patients with breast cancer and their health care providers while making treatment decisions relates to the decision to use
or forego adjuvant chemotherapy.2,3 The argument for the use of adjuvant chemotherapy is the potential to reduce the
risk of recurrence and mortality for patients with breast cancer. However, there is variation in the degree of benefit from
adjuvant therapy, and the benefit is questionable for patients who have estrogen receptor-positive, or lymph node-negative, or tamoxifen-responsive disease.4-6 Chemotherapy also is expensive for payers and may produce significant toxicities,
such as myelosuppression and permanent ovarian failure, which may have a substantial impact on quality of life.4
Several gene expression profiling (GEP) assays are currently on the market for use as genomic diagnostics to provide
prognostic or risk information. The potential value of these genomic diagnostics lies in their claims to measure gene
expression levels of a patient’s tumor and to produce a ‘‘numerical score’’ depicting risk for disease recurrence. The estimation of disease recurrence helps identify patients who are likely to benefit from chemotherapy and, thus, assists providers
in making decisions regarding the administration of chemotherapy. However, these tests differ from one another in several
ways, including the presentation of risk recurrence data.
To date, Oncotype DX (Genomic Health, Redwood City, Calif), a 21-gene profile assay that uses real-time (RT) polymerase chain reaction (PCR) for expression analysis of paraffin-embedded tissue, remains the most frequently used GEP
in clinical practice in the United States. Oncotype DX uses a recurrence score (RS), which places patients in 3 categories:
high risk, intermediate risk, and low risk. The putative test objective is to tailor treatment to the individual patient so that
Corresponding author: Amalia M. Issa, PhD, MPH, Program in Personalized Medicine and Targeted Therapeutics and Department of Health Policy and Public
Health, University of the Sciences, 600 S. 43rd Street, Box 22, Philadelphia, PA 19104; Fax (215) 596-7614; [email protected]
1
Program in Personalized Medicine and Targeted Therapeutics, University of the Sciences, Philadelphia, Pennsylvania; 2Department of Clinical Sciences and
Administration, College of Pharmacy, University of Houston, Houston, Texas; 3Department of Health Policy and Public Health, University of the Sciences, Philadelphia, Pennsylvania.
DOI: 10.1002/cncr.27443, Received: September 18, 2011; Revised: November 23, 2011; Accepted: December 6, 2011, Published online February 22, 2012 in
Wiley Online Library (wileyonlinelibrary.com)
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Original Article
patients with a low risk of recurrence who do not need
chemotherapy can avoid it, which will enhance patient
quality of life and save money for payers.
MammaPrint (Agendia Inc., Irvine, Calif), a 70gene profile microarray assay that was developed and is
used more frequently in Europe, reports results in a binary
mode: either high risk or low risk for recurrence. MammaPrint uses fresh-frozen tumor tissues, which are available more frequently from procedures done in Europe,
whereas Oncotype DX uses tissues preserved in paraffin, a
typical procedure in the United States.
Two trials with the expressed purpose of examining
efficacy and providing level I evidence of Oncotype DX
and MammaPrint, the Trial Assigning Individualized
Options for Treatment (TAILORx) and the Microarray
in Lymph node-Negative Disease may Avoid Chemotherapy (MINDACT) trial, respectively, currently are
ongoing.7-9 However, full results from those trials are not
yet available, and decisions regarding quality of life and
costs need to be made in the interim.
Although previous economic analyses have been
conducted on GEP assays,10-17 to our knowledge, only 1
study examined MammaPrint,13,17 and we are not aware
of any studies that have compared the cost-effectiveness of
these 2 commercially available GEPs directly. The use of
GEP may have significant effects on patient outcomes,
medical services use, and costs. Much of the discussion
surrounding health care reform before and after the enactment of the Patient Protection and Affordable Care Act18
is centered on ways to create higher quality, more efficient
care and to reduce rising health care expenditures. Thus,
assessing the outcomes, use, and costs associated with
these genomic diagnostic tests is critical for future clinical
decision making. The objective of the current study was
to evaluate the cost effectiveness of treatment decisions
using Oncotype DX compared with treatment decisions
using MammaPrint from a third-party payer’s
perspective.
MATERIALS AND METHODS
Model Description
A 10-year Markov model was developed using Tree-Age
(TreeAge Software, Williamstown, Mass) to evaluate the
costs and quality-adjusted life-years (QALYs) associated
with using 2 GEP-guided treatment strategies: Oncotype
DX and MammaPrint. Patients were distributed into 3
mutually exclusive health states: no recurrence (diseasefree survival), recurrence, and death. The study was conducted from a third-party payer’s perspective.
5164
The model assumed a hypothetical cohort of 1000
patients with lymph node-negative, estrogen receptorpositive breast cancer and simulated events for receiving
either of the 2 GEP tests (Fig. 1). All patients started with
a risk classification state based on an assessment by Adjuvant! Online (Adjuvant, Inc., San Antonio, Tex). Subsequently, patients were reclassified into risk categories
based on the 2 GEP tests using reclassification probabilities from the literature.15,17,19-22 We compared each of
the 2 GEP tests with Adjuvant! Online, because both
strategies had been compared previously for analytic validation purposes with Adjuvant! Online in earlier studies.17,19-21 In the first strategy, patients were offered
Oncotype DX, and their risk was reclassified using the RS.
For the second strategy, patient risk was reclassified
according to MammaPrint.
We assumed that each patient could only have 1 recurrence. Once a patient enters a health state, the patient
will not progress to a better health state. A total simulated
time horizon of 10 years was conducted with 1 year considered as a cycle. During each Markov cycle, the patients
may remain disease-free or develop recurrence. Once
patients develop recurrence, they remain in the recurrence
state until they die from breast cancer. Costs and QALYs
are discounted at 3% in the base case. Patients may experience no, minor, major, or fatal toxicity from chemotherapy. We also assumed that 90% of patients who were at
high risk according to both Adjuvant! Online and Oncotype DX received chemotherapy, whereas 90% of patients
who were at low risk according to both Adjuvant! Online
and Oncotype DX did not receive chemotherapy. For
patients who experienced a conflicting result between Adjuvant! Online and Oncotype DX, 50% of the subpopulation received chemotherapy. The same assumption was
applied to MammaPrint.
Probability of Risk Classification
Data on risk classification using Adjuvant! Online and
GEP tests were derived from the literature (Table 1).15,22
For Strategy 1, in total, 668 patients were classified as either low risk or high risk using Adjuvant! Online, and
they were reclassified as low risk or high/intermediate risk
using the RS. The intermediate-risk group was combined
with the high-risk group in the RS category. Of the 354
patients (52.99%) at low risk according to Adjuvant!
Online, 216 (61.02%) were reclassified as low risk, and
138 (39%) were reclassified as high risk using the RS. Of
the 314 patients (47.01%) at high risk according to Adjuvant! Online, 122 (38.85%) were reclassified as low risk,
and 192 (61.15%) were reclassified as high risk using the
RS. For Strategy 2, in total, 302 patients were classified as
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October 15, 2012
Cost-Effectiveness of GEP Tests/Yang et al
Figure 1. The analytical decision model assumed patients with lymph node-negative, estrogen receptive-positive (þ) breast cancer for the Oncotype DX (21-gene signature) and MammaPrint (70-gene signature) gene expression profiling tests, and simulated
events for 2 strategies. All patients started in the risk classification state in which they underwent risk assessment by Adjuvant!
Online (Adjuvant, Inc., San Antonio, Tex). In the first strategy, patients were offered Oncotype DX, and their risk was reclassified
using the recurrence score. For the second strategy, patients’ risk was reclassified according to MammaPrint.
either low risk or high risk using Adjuvant! Online, and
they were reclassified as low risk or high risk using MammaPrint.18 Of the 80 patients (26.58%) at low risk
according to Adjuvant! Online, 52 (65%) were reclassified
as low risk, and 28 (35%) were reclassified as high risk
using MammaPrint. Of the 222 patients (73.51%) at
high risk according to Adjuvant! Online, 59 (26.58%)
were reclassified as low risk, and 163 (73.42%) were
reclassified as high risk using MammaPrint.
Risk of Recurrence and Death
Model inputs for transition probabilities based on recurrence rates and death rates for different risk classifications
were obtained from the literature (Table 2).10,15-18,23 On
the basis of a study by Hornberger et al,10 a 15% relative
risk reduction in the recurrence rate because of chemotherapy was applied to patients classified as high risk by
both Oncotype DX RS and MammaPrint.10,24,25 A 45%
relative risk reduction in the recurrence rate because of
chemotherapy was applied to patients classified as low risk
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October 15, 2012
by both Oncotype DX RS and MammaPrint.10,24,25 We
assumed that the transition probability of death was constant. The probabilities of different toxicities after chemotherapy were obtained from the literature.26
Utility
The utility used to adjust survival for quality of life ranges
from 0 to 1, with 0 representing death and 1 representing
perfect health (Table 2). Utilities for toxicity from chemotherapy, recurrence-free states, and recurrence states were
obtained from the peer-reviewed literature, including a
systematic review of cost-utility assessment in oncology
and a review of health-related quality-of-life estimates.27,29 The QALYs were calculated by the length of
time spent in a health state multiplied by the utility of that
state.
Costs
Unit costs were obtained from various sources. All costs
are reported in 2009 US dollars. Costs from a different
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Original Article
Table 1. Risk Reclassification and Recurrence Rates for the Oncotype DX and MammaPrint Gene Expression Profiling Tests
Adjuvant! Onlinea
Oncotype DXb
Comparator
Risk Group
No. of Patients
in Risk Group (%)
10-Year Risk
of Recurrence, %
Risk
Group
No. of Patients in
Risk Group (%)
10-Year Risk
of Recurrence, %
References
Low
354 (52.99)
8.40
Low
216 (61.02)
5.60
Tsoi 201015;
Marchionni 200822
High
314 (47.01)
22.20
Int/high
Low
Int/high
138 (38.98)
122 (38.85)
192 (61.15)
12.85
8.90
30.71
Comparator
Risk Group
Adjuvant! Online
No. of Patients 10-Year Risk
in Risk Group
of Recurrence,
(%)
%
Risk
Group
MammaPrintc
No. of Patients 10-Year Risk
in Risk Group
of Recurrence,
(%)
%
Low
80 (26.49)
8.40
Low
52 (65)
5.60
High
222 (73.51)
22.20
High
Low
High
28 (35)
59 (26.58)
163 (73.42)
12.85
8.90
30.71
References
Tsoi 201015;
Buyse 200619;
Marchionni 200822
Abbreviations: Int/high, intermediate/high.
a
Adjuvant! Online; Adjuvant, Inc., San Antonio, Tex.
b
Oncotype DX; Genomic Health, Redwood City, Calif.
c
MammaPrint; Agendia Inc., Irvine, Calif.
Table 2. Transition Probabilities and Utilities Used in Modeling the 2 Gene Expression Profiling Strategies
Variable
Base Case
Range Tested in
Sensitivity Analysis
0.60
0.05
0.005
0.48-0.72
0.04-0.06
0.004-0.006
0.036
0.012
0.036
0.013
0.027-0.045
0.009-0.015
0.027-0.045
0.010-0,016
1.00
0.98
0.75
1.00
0.80
0.70
0
0.75-1.00
0.74-1.00
0.56-0.94
—
0.60-1.00
0.53-0.88
—
Reference
Transition probability
Hillner & Smith 199126
Probability of toxicity
Minor
Major
Fatal
Buyse 200619
Death rates: Adjuvant! Online/GEP
High/high
High/low
Low/high
Low/low
Utility
No chemotherapy
No recurrence with/without chemotherapy
Recurrence with/without chemotherapy
No toxicity from chemotherapy
Minor toxicity from chemotherapy
Major toxicity from chemotherapy
Death
Assumption
Earle 200027
Earle 200027
Assumption
Gold 199628
Gold 199628
Assumption
Abbreviations: GEP: gene expression profiling.
base year were inflated by 3% per year. The cost of Oncotype DX was based on the list price of $3975, whereas the
cost of MammaPrint was $4200.
Costs of chemotherapy included costs for chemotherapy medications and administration. We assumed a
threshold willingness to pay for breast cancer of
$50,000, and this was increased to $100,000 for sensi5166
tivity analyses. Costs that were considered in the model
are listed in Table 3.
Outcomes
The model generated estimates of the health care costs
and QALYs, incremental cost per QALY gained, and
incremental cost-effectiveness ratio (ICER). The ICER
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Cost-Effectiveness of GEP Tests/Yang et al
Table 3. Costs Used in Modeling the 2 Gene Expression Profiling Strategies
Variable
Base Case, $
Range Tested in
Sensitivity Analysis, $
3975
4200
19,618
2981-4969
3150-5250
14,714-24,523
Reference
Slodkowska 200930
GEP tests
Oncotype DX
MammaPrint
Adjuvant chemotherapy
Hornberger 200510
Elkin 200439
Other chemotherapy costs
Premedication, per cycle
Oncology visit, per cycle
Monitoring for side-effects, per y
Recurrence
15
44
668
10,837
11-18
33-55
501-835
8128-13,546
2709
18,061
45,153
34,778
2032-3386
13,546-22,576
33,865-56,441
26,084-43,473
Hillner & Smith 199126
Hillner & Smith 199126
Treatment of side effects
Minor
Major
Fatal
End-of-life
Abbreviations: GEP: gene expression profiling.
Table 4. Incremental Cost-Effectiveness Ratio Results for the Base Case
Diagnostic
Strategy
Costs, $
Incremental, $
QALYs
Incremental QALYs
C/E, $/QALYs
ICER, $/QALYs
MammaPrint
Oncotype DX
21,598
27,882
6284
7.461
7.364
0.097
2895
3786
Dominated
Abbreviations: C/E, cost-effectiveness; ICER, incremental cost-effectiveness ratio; QALYs, quality-adjusted life-years.
estimated the differences in cumulative costs divided by
the differences in QALYs between the Oncotype DX and
MammaPrint strategies.
Sensitivity Analyses
One-way sensitivity analyses were performed on assumptions, probabilities, and costs while holding the other variables fixed to assess the robustness of the Markov model.
A probabilistic analysis using Monte Carlo simulation was
carried out to determine the effect of uncertainty in all
variables on the cost-utility ratio for the cohort. We
applied the simulation 1000 times to ensure the reliability
of the model. To quantify the uncertainty captured by the
sensitivity analysis, we used 1000 Monte Carlo simulations to perform t tests and establish whether the costs and
effectiveness associated with MammaPrint and Oncotype
DX were statistically different from one another. This
helped establish the statistical robustness of our sensitivity
analysis.
RESULTS
Base-Case Analysis
The model shows that patients for whom Oncotype DX
was used to guide treatment spent US $27,882 and gained
7.364 QALYs, whereas patients for whom MammaPrint
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October 15, 2012
was used to guide treatment spent US $21,598 and gained
7.461 QALYs (Table 4). The results suggest that MammaPrint is a more cost-effective test than Oncotype DX at
a threshold willingness to pay of $50,000 per QALY.
Sensitivity Analyses
Sensitivity analyses were performed using an incremental
cost-effectiveness (ICE) scatter plot between Oncotype
DX versus MammaPrint with 1000 Monte Carlo simulation trials using MammaPrint as the reference category
(Fig. 2). In this plot, each trial point represents a comparison of the incremental costs and effectiveness of MammaPrint and Oncotype DX using simultaneous and
randomly assigned values from the probability, costs and
outcome distributions of the base-case parameter estimates. The ICE scatter plot diagram was divided into 4
quadrants. A trial point in Quadrant I indicates that
Oncotype DX is more costly and more effective than
MammaPrint. A trial point in Quadrant II indicates that
Oncotype DX is more costly and less effective than MammaPrint, thereby indicating that MammaPrint the dominant strategy. A trial point in Quadrant III suggests that
Oncotype DX is less costly and less effective than MammaPrint. A trial point in Quadrant IV suggests that Oncotype DX is less costly and more effective than
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Original Article
Figure 2. This is an incremental cost-effectiveness scatter
plot of the 2 gene expression profiling tests: Oncotype DX
(Genomic Health, Redwood City, Calif) versus MammaPrint
(Agendia Inc., Irvine, Calif). QALY indicates quality-adjusted
life-year.
MammaPrint, thereby indicating that Oncotype DX is the
dominant strategy. Figure 2 indicates that 82% of our trial
points are in Quadrant II, indicating that MammaPrint is
the dominant strategy. T tests for means performed using
the 1000 Monte Carlo simulation values provided the following results:
1. Costs of Oncotype DX and MammaPrint are statistically different with P values < .01; the mean
cost of Oncotype DX is $27,882 with a standard
error of $1455, and the mean cost of Mamma
Print is $21,598 with a standard error of $1246.
2. The effectiveness of Oncotype DX and Mamma
Print are statistically different with P values < .01;
the mean effectiveness of Oncotype DX is 7.36
with a standard error of 0.07, and the mean effectiveness of MammaPrint is 7.46 with a standard
error of 0.07.
One-way sensitivity analyses also were performed by
varying the values of the probabilities, utilities, and costs.
MammaPrint was identified as beneficial and cost effective using alternative assumptions, probabilities, and cost
variables that were measured in the sensitivity analysis. In
the univariate sensitivity analyses, the results were robust
to changes in all parameters.
DISCUSSION
We used a decision-analytic model to estimate the costs,
QALYs, and incremental cost-effectiveness of 2 commercially available GEPs: Oncotype DX and MammaPrint,
which are used to guide treatment decisions about adju5168
vant chemotherapy in early stage breast cancer. To the
best of our knowledge, this study is the first of its kind to
compare the cost effectiveness of Oncotype DX directly
with that of MammaPrint. In the base-case analysis and in
the probabilistic sensitivity analysis of recurrence rates
under the 2 testing strategies, the MammaPrint test dominated the Oncotype DX test. Because Oncotype DX is the
most frequently used GEP in clinical practice in the
United States, our finding has implications for health policy, particularly health insurance reimbursement
decisions.
It has been estimated that the willingness to pay for
better health outcomes in the United States is approximately $50,000 per QALY gained, and it is well known
that cancer therapeutics have relatively high ICERs.31-34
Thus, although both Oncotype DX and MammaPrint are
costly and have high ICERs, it is reasonable to presume a
willingness to pay for testing strategies that are likely to
yield reduced expenditures for payers, health systems,
patients, and society in the long term.
This study has several limitations. Like all decisionanalytic models, our cost-effectiveness analysis relies on
key assumptions. The available data for inputs into our
model were limited; therefore, we combined the 21-gene
signature, RS-guided, high-risk and intermediate-risk
groups into 1 group (called high risk). We did not specify
recurrence as local or regional recurrence, a secondary primary, or a contralateral breast cancer because of the limited availability of data. We opted to model 1 relapse per
patient, which is the common assumption.35-37 Thus,
there may be an underestimate of relapse costs if certain
patients developed more than 1 relapse (and approximately 30% of patients with breast cancer do develop
more than 1 relapse). Finally, our analysis reflects a thirdparty payer perspective from the United States that may
not be applicable in other health care systems.
In summary, we modeled 2 commercially available
GEP tests, Oncotype DX and MammaPrint, both of
which are aimed at providing information based on
genetic characteristics of breast cancer tumors to aid in
guiding treatment decisions about adjuvant therapy. Our
analysis suggests that MammaPrint is the more cost-effective (dominant) testing strategy for guiding treatment
decisions for adjuvant therapy in early stage breast cancer.
Oncotype DX uses an RS that places patients into 3
risk categories: high, intermediate, and low. On the basis
of available population-level data to date, patients who
receive intermediate risk scores of 11 to 25 have a breast
cancer recurrence rate between 7% and 16%. The effects
of adjuvant chemotherapy combined with hormone
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Cost-Effectiveness of GEP Tests/Yang et al
therapy in patients who receive an intermediate score
(approximately 44% of patients with lymph node-negative, hormone-positive breast cancer) are unknown. Furthermore, oncologists report that an intermediate score is
ambiguous for informed decision making.38 Therefore,
the use of MammaPrint as a prognostic assessment tool is
not only cost effective, it also appears to circumvent the
ambiguity in the Oncotype DX RS results.
New genomic diagnostics and personalized medicine applications are increasingly entering the market,
and this broader availability of diagnostics has major
implications for health care delivery. Oncotype DX and
MammaPrint are the first of a new wave of technologies
designed to provide prognostic information that can
directly impact treatment decisions and, as a consequence, use and cost effectiveness in health care delivery
systems.
Considering the increasing costs of cancer therapeutics and companion diagnostics in oncology, our current
study provides a valuable and timely contribution to the
literature and to policy makers. Our analysis may assist
payers and health care providers in selecting the optimal
GEP strategy until further level I evidence is available
from the TAILOR-X and MINDACT trials.39
FUNDING SOURCES
This work was supported by a grant from InHealth, the Institute
for Health Technology Studies (principal investigator: Amalia
M. Issa, PhD, MPH).
CONFLICT OF INTEREST DISCLOSURES
The authors made no disclosures.
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