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The American Journal of Managed Care December 2010
Step-Up Care Improves Impairment in Uncontrolled Asthma: An Administrative Data Study
Robert S. Zeiger, MD, PhD; Michael Schatz, MD, MS; Qiaowu Li, MS; Feng Zhang, MS; Anna S. Purdum, PharmD, MS; and Wansu Chen, MS
Comparison of Healthcare Utilization Among Patients Treated With Alcoholism Medications
Tami L. Mark, PhD; Leslie B. Montejano, MA; Henry R. Kranzler, MD; Mady Chalk, PhD; and David R. Gastfriend, MD
Resource Utilization With Insulin Pump Therapy for Type 2 Diabetes Mellitus
Peter M. Lynch, MPH; Aylin Altan Riedel, PhD; Navendu Samant, MS; Ying Fan, MS; Tim Peoples, MA; Jenifer Levinson; and Scott W. Lee, MD
Medication Adherence: The Search for Interventions That Work
Justin Gatwood, MPH; and Steven R. Erickson, PharmD
Part D Coverage Gap and Adherence to Diabetes Medications
Qian Gu, PhD; Feng Zeng, PhD; Bimal V. Patel, PharmD, MS; and Louis C. Tripoli, MD
Modes of Delivery for Interventions to Improve Cardiovascular Medication Adherence
Sarah L. Cutrona, MD, MPH; Niteesh K. Choudhry, MD, PhD; Michael A. Fischer, MD, MPH; Amber Servi, BA; Joshua N. Liberman, PhD; Troyen A. Brennan, MD, JD; and William H. Shrank, MD, MSHS
Can Electronic Health Records Help Improve Patients' Understanding of Medications?
Jennifer R. Webb, MA; Joseph Feinglass, PhD; Gregory Makoul, PhD; Cheryl L. Wilkes, MD; Daniel P. Dunham, MD, MPH; David W. Baker, MD, MPH; and Michael S. Wolf, PhD, MPH
"Under the Radar": Nurse Practitioner Prescribers and Pharmaceutical Industry Promotions
Elissa C. Ladd, PhD, RN, FNP-BC; Diane Feeney Mahoney, PhD, APRN, BC, FGSA, FAAN; and Srinivas Emani, PhD
Prescribing by Physician Assistants and Nurse Practitioners
Roderick S. Hooker, PhD
Nurse Practitioner Comanagement for Patients in an Academic Geriatric Practice
David A. Ganz, MD, PhD; Brandon K. Koretz, MD; Julia K. Bail, RN, MSN, GNP-C; Heather E. McCreath, PhD; Neil S. Wenger, MD, MPH; Carol P. Roth, RN, MPH; and David B. Reuben, MD
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Cost-Effectiveness of 70-Gene MammaPrint Signature in Node-Negative Breast Cancer
Er Chen, MPP; Kuo Bianchini Tong, MS; and Jennifer L. Malin, MD, PhD

Cost-Effectiveness of 70-Gene MammaPrint Signature in Node-Negative Breast Cancer

Er Chen, MPP; Kuo Bianchini Tong, MS; and Jennifer L. Malin, MD, PhD

Targeting chemotherapy with 70-gene MammaPrint signature in patients 60 years or younger with node-negative breast cancer is likely to be cost-effective.

Objective: To evaluate the cost-effectiveness of 70-gene MammaPrint signature (Agendia Inc, Huntington Beach, CA) vs Adjuvant! Online software (AS) ( in patients 60 years or younger with early-stage breast cancer.


Study Design: Cost-effectiveness and cost-utility analyses from a US payer perspective.


Methods: A Markov model with 3 health states was constructed. In the base case model, risk classification and patient outcomes were based on a 70-gene signature validation study. Efficacy of chemotherapy was derived from a published meta-analysis of clinical trials. An alternative model using data from AS and from the Surveillance, Epidemiology and End Results registry was built to examine the external validity of the base case model. The incremental benefits, costs, and cost-effectiveness of treatment guided by 70-gene signature were calculated.


Results: In the base case model, 70-gene signature reclassified 29% of patients and spared 10% of patients from chemotherapy. Compared with the AS strategy, the 70-gene signature strategy was associated with $1440 higher total cost per patient and with 0.14 additional life-year or 0.15 additional quality-adjusted life-year. Overall, the incremental cost-effectiveness ratios were approximately $10,000 per life-year or quality-adjusted life-year in the base case model and $700 in the alternative model. The model results were sensitive to estrogen receptor status, the proportion of patients classified as high risk vs low risk, and the overall survival in each risk group.


Conclusion: A 70-gene signature is likely to be a cost-effective strategy to guide adjuvant chemotherapy treatment in younger patients with early-stage breast cancer.

(Am J Manag Care. 2010;16(12):e333-e342)

A 70-gene MammaPrint signature (Agendia Inc, Huntington Beach, CA) is commercially available and is being integrated into clinical practice.


  • Results of this modeling analysis suggest that treatment guided by 70-gene signature may be associated with a decrease in chemotherapy use and an increase in life expectancy when applied appropriately.


  • However, model predictions are highly sensitive to the range of uncertainty in the clinical variables.
Breast cancer is a heterogeneous disease. Most routine clinical care for breast cancer depends on conventional clinicopathologic prognostic factors (eg, TNM, stage, and comorbidity), prognostic or predictive biomarkers (eg, estrogen receptor [ER], progesterone receptor, human epidermal growth factor receptor 2 [HER2], and grade), and clinical guidelines (eg, St Gallen International Expert Consensus, National Cancer Comprehensive Network (NCCN), and National Cancer Institute). Breast cancers with similar  clinicopathologic characteristics may have strikingly different outcomes. The “one size fits all” approach may prompt ineffective use of therapy, causing unnecessary toxic effects, delaying alternative treatments, and wasting economic resources. Gene expression profiling using DNA microarray measures the expression levels of large numbers of genes simultaneously to study the effects of certain treatments, diseases, and developmental stages on gene expression. A DNA microarray test could influence clinical care based on the individual molecular profile.1 A 70-gene MammaPrint signature (Agendia Inc, Huntington Beach, CA) measures 70 risk profile mRNAs and 536 quality and reference mRNAs to predict the likelihood of distant metastases for earlystage breast cancer (ESBC).2 It is the first assay to be cleared by the US Food and Drug Administration (FDA) using its new in vitro diagnostic multivariate index assay guidance.

A 70-gene signature was initially developed to predict the risk of developing distant metastases in 5 years for node-negative patients younger than 55 years.3 Validation studies2,4,5 demonstrated the prognostic value of 70-gene signature independent of clinical risk classification. In a prospective multicenter study6 of 427 patients younger than 61 years, the use of 70-gene signature altered adjuvant treatment recommendations in 37% of patients, sparing 20% of patients from chemotherapy. In addition, 70-gene signature demonstrates clinical value in accurately selecting postmenopausal women for adjuvant chemotherapy and recently received FDA clearance for use among older women.7,8

Determining the extent to which 70-gene signature may influence clinical treatment decisions and ultimately outcomes may best be accomplished by prospective studies of prognosis and prediction of chemotherapy response; however, such studies take many years to complete.9 In awaiting that information, decision makers need to evaluate the  economic and clinical trade-offs of the test, as well as factors that would influence its appropriate use.

Adjuvant! Online software ( (AS), a Web-based tool that calculates individualized 10-year survival probabilities and predicts benefit of adjuvant systemic therapy, is the most widely used prognostic tool to help inform clinicians and patients in decision making about therapeutic options. Risk estimates in AS were based on 10-year observed overall survival for women with ESBC in the Surveillance, Epidemiology and End Results (SEER) registry in the United States and were independently validated with the British Columbia Breast Cancer Outcomes Unit database and a large cohort of Dutch patient series.10,11 The objectives of our study were (1) to estimate the incremental benefits, costs, and cost-effectiveness of 70-gene signature–guided treatments vs AS-guided treatments using a decision analytic model, (2) to identify factors that  contribute to the cost-effectiveness of 70-gene signature, and (3) to determine patient groups in which the use of 70-gene signature is optimal.


Model Structure

A decision analytic model from a US payer perspective was developed. Prognosis of a hypothetical cohort of women with ESBC was provided via 70-gene signature or AS to determine whether they were at high risk or low risk for distant metastases, on which the treatment was based. Because this evaluation critically depends on the quality of evidence related to the performance of 70-gene signature, the population assessed in this study was consistent with the FDA-cleared indication at the time of the analysis, namely, patients 60 years or younger with ER-independent, T1 or T2, lymph node–negative tumors. Because most US patients with HER2-positive tumors receive trastuzumab-containing chemotherapy, these patients were excluded from our evaluation.

After surgery, patients were triaged to different therapies depending on risk profile indicated by 70-gene signature or AS. The following 4 treatment scenarios were included: (1) chemotherapy plus endocrine therapy for ER-positive and high-risk patients, (2) chemotherapy alone for ER-negative and high-risk patients, (3) endocrine therapy alone for ER-positive and low-risk patients, and (4) no adjuvant therapy for ER-negative and low-risk patients (Figure 1). After risk evaluation and adjuvant treatment, patients were evaluated through a Markov process.

The Markov model contained the following 3 mutually exclusive health states designed to simulate the transition of patients with ESBC after adjuvant treatment: (1) no recurrence, (2) death from cancer, and (3) death from other causes. All patients started in the no recurrence state. Patients might experience local, contralateral, distant recurrence, or metastatic progression before dying of cancer. Patients who did not die of cancer had a constant probability of dying of other causes based on the risk for similar patients with breast cancer. Events of interest were modeled according to patients’ transitions from one state to another in 1-year intervals. The Markov process was stopped when more than 99% of patients  were in the state of death (Figure 2).

Data Sources

Risk Classification and Survival for the Base Case Model and the Alternative Model. Evaluated were 2 distinct patient populations, namely, a 70-gene signature validation population  (the base case model) and patients with ESBC in the SEER registry (alternative model). In the base case model, risk classification and 10-year overall survival were estimated from the results of a 70-gene signature validation study described by Buyse and colleagues.4 In that study, tumor samples were collected from 302 ESBC patients 60 years or younger with T1 or T2 lymph node–negative tumors who did not receive any adjuvant chemotherapy. Patients were assigned to high-risk and low-risk groups based on 70-gene signature and AS classifications. Patients were followed up for a median of 13.6 years to evaluate the risk of distant metastases, disease-free survival, and overall survival in each risk group.4

The study by Buyse et al4 may not be representative of the US ESBC population. For example, it did not include any ER-negative patients who were clinically classified as low risk, implying a “high risk” population. Recognizing this potential limitation, an alternative model was built using data from patients with breast cancer who were included in the SEER registry, were aged between 20 and 60 years, had T1 or T2 lymph node–negative tumors, and underwent primary surgery. The SEER registry data were used to model risk    classification among clinically classified patients. Based on the median age at diagnosis,12 to be conservative, the overall survival was estimated by AS in the alternative model    based on a 50-year-old woman with comorbidities that were average for her age. As specific data were unavailable for 70-gene signature, its risk classification result was  extrapolated from the study by Buyse et al, assuming the same rate of cross-classification between low-risk and high-risk groups relative to AS. While a full validation is possible only with prospective studies for both prognosis and prediction of chemotherapy response, application of the test results among the SEER population provides an opportunity to evaluate the likely cost-effectiveness of 70-gene signature in a realworld population if ongoing studies confirm early findings of the utility of the test.

Risk Reduction Associated With Chemotherapy. The effect of adjuvant chemotherapy on overall survival was based on a meta-analysis13 of randomized trials. The proportional risk reductions for all-cause death associated with adjuvant chemotherapy were 26% among patients with ER-positive cancer (compared with those receiving tamoxifen citrate only) and 32% among patients with ER-negative cancer.

The clinical variables used in the base case model are given in Table 1. A comparison of clinical variables used in the base case model vs the alternative model is given in Table 2.

Resource Use and Costs

Values for resource use and cost were obtained from the literature. Evaluated were the cost of risk classification, adjuvant endocrine therapy, adjuvant chemotherapy, administration, treatment-related toxic effects, and breast cancer surveillance. For patients who died of cancer, a 1-time cost of treating local recurrence or distant recurrence, as well as the cost of terminal care for cancer-related death, was included. For patients whose death was unrelated to cancer, the cost of terminal care for patients without cancer was added.

The price of 70-gene signature was obtained from Agendia Inc. The cost of caring for patients receiving adjuvant chemotherapy was estimated from a population-based study17 of women younger than 63 years with newly diagnosed breast cancer. Using insurance claims, Hassett et al18 estimated an incremental expenditure of $35,964 ($31,134 in 2006 US  dollars) attributable to chemotherapy use, which included payments for chemotherapy medications, hospitalizations or emergency department visits for chemotherapy-related serious adverse events, hospitalization and emergency department visits for all causes, and ambulatory encounters and prescriptions. The study included patients receiving alkylating agents (58%), anthracyclines (51%), taxanes (25%), and antimetabolites (18%). Annual tamoxifen cost was used as the cost of endocrine therapy. The costs of caring for patients who did not develop recurrence and for patients who died of cancer were derived from a retrospective analysis of patients with ESBC identified from a large integrated tumor registry.19

All costs were calculated in 2007 US dollars. Costs incurred beyond the first year were discounted at 3% in the base case model and varied from 0% to 6% in the sensitivity analyses.20

Quality of Life and Utility

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