Simulation modeling of breast cancer endocrine therapy duration by patient and tumor characteristics.

BACKGROUND: Extending endocrine therapy from 5 to 10 years is recommended for women with invasive estrogen receptor (ER)-positive breast cancers. We evaluated the benefits and harms of the five additional years of therapy. METHODS: An established Cancer Intervention and Surveillance Network (CISNET) model used a lifetime horizon with national and clinical trial data on treatment efficacy and adverse events and other-cause mortality among multiple birth cohorts of U.S. women ages 25-79 newly diagnosed with ER+, non-metastatic breast cancer. We assumed 100% use of therapy. Outcomes included life years (LYs), quality-adjusted life years (QALYs), and breast cancer mortality. Results were discounted at 3%. Sensitivity analyses tested a 15-year time horizon and alternative assumptions. RESULTS: Extending tamoxifen therapy duration among women ages 25-49 reduced the lifetime probability of breast cancer death from 11.9% to 9.3% (absolute difference 2.6%). This translates to a gain of 0.77 LYs (281 days)/woman (undiscounted). Adverse events reduce this gain to 0.44 QALYs and after discounting, gains are 0.20 QALYs (73 days)/woman. Extended aromatase inhibitor therapy in women 50-79 had small absolute benefits and gains were offset by adverse events (loss of 0.06 discounted QALYs). There were greater gains with extended endocrine therapy for women with node-positive versus negative cancers, but only women ages 25-49 and 50-59 had a net QALY gain. All gains were reduced with less than 100% treatment completion. CONCLUSION: The extension of endocrine therapy from 5 to 10 years modestly improved lifetime breast cancer outcomes, but in some women, treatment-related adverse events may outweigh benefits.

Development and Validation of a Simulation Model-Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions.

PURPOSE: There is a need for industry-independent decision tools that integrate clinicopathologic features, comorbidities, and genomic information for women with node-negative, invasive, hormone receptor-positive, human epidermal growth factor receptor-2-negative (early-stage) breast cancer. METHODS: We adapted an extant Cancer Intervention and Surveillance Modeling Network simulation model to estimate the 10-year risk of distant recurrence, breast cancer-specific mortality, other-cause mortality, and life-years gained with chemoendocrine versus endocrine therapy. We simulated outcomes for 1,512 unique patient subgroups based on all possible combinations of age, tumor size, grade, and comorbidity level; simulations were performed with and without 21-gene recurrence scores (RSs). Model inputs were derived from clinical trials, large US cohort studies, registry, and claims data. External validation was performed by comparing results to observed rates in two independent sources. We highlight results for one scenario where treatment choice may be uncertain. RESULTS: Chemoendocrine versus endocrine therapy in a 65-69-year-old woman with a small (≤ 2 cm), intermediate-grade tumor, and mild comorbidities provides a 1.3% absolute reduction in 10-year distant recurrence risk, with 0.23 life-years gained. With these tumor features, a woman like this will have a 28% probability of having an RS 16-20, 18% RS 21-25, and 11% RS 26+. If testing is done, and her RS is 16-20, chemoendocrine therapy reduces 10-year distant recurrence risk to 1%, with 0.20 life-years gained, a similar result as without testing. The absolute benefits would increase to 4.8%-5.5% if the RS was 26+. The model closely reproduced observed rates in both independent data sets. CONCLUSION: Our validated clinical decision tool is flexible, readily adaptable to include new therapies, and can support discussions about genomic testing and early breast cancer treatment.

Simulation of Chemotherapy Effects in Older Breast Cancer Patients With High Recurrence Scores.

BACKGROUND: Tumor genomic expression profile data are used to guide chemotherapy choice, but there are gaps in evidence for women aged 65 years and older. We estimate chemotherapy effects by age and comorbidity level among women with early-stage, hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancers and Oncotype DX scores of 26 or higher. METHODS: A discrete-time stochastic state transition simulation model synthesized data from population studies and clinical trials to estimate outcomes over a 25-year horizon for subgroups based on age (65-69, 70-74, 75-79, and 80-89 years) and comorbidity levels (no or low, moderate, severe). Outcomes were discounted at 3%, and included quality-adjusted life-years (QALYs), life-years, and breast cancer and other-cause mortality with chemoendocrine vs endocrine therapy. Sensitivity analysis tested the effect of varying uncertain parameters. RESULTS: Women aged 65-69 years with no or low comorbidity gained 0.16 QALYs with chemo-endocrine and reduced breast cancer mortality from 34.8% to 29.7%, for an absolute difference of 5.1%; this benefit was associated with a 12.8% rate of grade 3-4 toxicity. Women aged 65-69 years with no or low or moderate comorbidity levels, and women aged 70-74 years with no or low comorbidity had small chemotherapy benefits. All women aged 75 years and older experienced net losses in QALYs with chemo-endocrine therapy. The results were robust in sensitivity analyses. Chemotherapy had greater benefits as treatment effectiveness increased, but toxicity reduced the QALYs gained. CONCLUSION: Among women aged 65-89 years whose tumors indicate a high recurrence risk, only those aged 65-74 years with no or low or moderate comorbidity have small benefits from adding chemotherapy to endocrine therapy. Genomic expression profile testing (and chemotherapy use) should be reserved for women aged younger than 75 years without severe comorbidity.

Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model.

BACKGROUND: The Georgetown University-Albert Einstein College of Medicine breast cancer simulation model (Model GE) has evolved over time in structure and function to reflect advances in knowledge about breast cancer, improvements in early detection and treatment technology, and progress in computing resources. This article describes the model and provides examples of model applications. METHODS: The model is a discrete events microsimulation of single-life histories of women from multiple birth cohorts. Events are simulated in the absence of screening and treatment, and interventions are then applied to assess their impact on population breast cancer trends. The model accommodates differences in natural history associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) biomarkers, as well as conventional breast cancer risk factors. The approach for simulating breast cancer natural history is phenomenological, relying on dates, stage, and age of clinical and screen detection for a tumor molecular subtype without explicitly modeling tumor growth. The inputs to the model are regularly updated to reflect current practice. Numerous technical modifications, including the use of object-oriented programming (C++), and more efficient algorithms, along with hardware advances, have increased program efficiency permitting simulations of large samples. RESULTS: The model results consistently match key temporal trends in US breast cancer incidence and mortality. CONCLUSION: The model has been used in collaboration with other CISNET models to assess cancer control policies and will be applied to evaluate clinical trial design, recurrence risk, and polygenic risk-based screening.

Cost Effectiveness of Gene Expression Profile Testing in Community Practice.

Purpose Gene expression profile (GEP) testing can support chemotherapy decision making for patients with early-stage, estrogen receptor-positive, human epidermal growth factor 2-negative breast cancers. This study evaluated the cost effectiveness of one GEP test, Onco type DX (Genomic Health, Redwood City, CA), in community practice with test-eligible patients age 40 to 79 years. Methods A simulation model compared 25-year societal incremental costs and quality-adjusted life-years (QALYs) of community Onco type DX use from 2005 to 2012 versus usual care in the pretesting era (2000 to 2004). Inputs included Onco type DX and chemotherapy data from an integrated health care system and national and published data on Onco type DX accuracy, chemotherapy effectiveness, utilities, survival and recurrence, and Medicare and patient costs. Sensitivity analyses varied individual parameters; results were also estimated for ideal conditions (ie, 100% testing and adherence to test-suggested treatment, perfect test accuracy, considering test effects on reassurance or worry, and lowest costs). Results Twenty-four percent of test-eligible patients had Onco type DX testing. Testing was higher in younger patients and patients with stage I disease ( v stage IIA), and 75.3% and 10.2% of patients with high and low recurrence risk scores received chemotherapy, respectively. The cost-effectiveness ratio for testing ( v usual care) was $188,125 per QALY. Considering test effects on worry versus reassurance decreased the cost-effectiveness ratio to $58,431 per QALY. With perfect test accuracy, the cost-effectiveness ratio was $28,947 per QALY, and under ideal conditions, it was $39,496 per QALY. Conclusion GEP testing is likely to have a high cost-effectiveness ratio on the basis of community practice patterns. However, realistic variations in assumptions about key variables could result in GEP testing having cost-effectiveness ratios in the range of other accepted interventions. The differences in cost-effectiveness ratios on the basis of community versus ideal conditions underscore the importance of considering real-world implementation when assessing the new technology.

Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012.

Importance: Given recent advances in screening mammography and adjuvant therapy (treatment), quantifying their separate and combined effects on US breast cancer mortality reductions by molecular subtype could guide future decisions to reduce disease burden. Objective: To evaluate the contributions associated with screening and treatment to breast cancer mortality reductions by molecular subtype based on estrogen-receptor (ER) and human epidermal growth factor receptor 2 (ERBB2, formerly HER2 or HER2/neu). Design, Setting, and Participants: Six Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2012 using national data on plain-film and digital mammography patterns and performance, dissemination and efficacy of ER/ERBB2-specific treatment, and competing mortality. Multiple US birth cohorts were simulated. Exposures: Screening mammography and treatment. Main Outcomes and Measures: The models compared age-adjusted, overall, and ER/ERBB2-specific breast cancer mortality rates from 2000 to 2012 for women aged 30 to 79 years relative to the estimated mortality rate in the absence of screening and treatment (baseline rate); mortality reductions were apportioned to screening and treatment. Results: In 2000, the estimated reduction in overall breast cancer mortality rate was 37% (model range, 27%-42%) relative to the estimated baseline rate in 2000 of 64 deaths (model range, 56-73) per 100 000 women: 44% (model range, 35%-60%) of this reduction was associated with screening and 56% (model range, 40%-65%) with treatment. In 2012, the estimated reduction in overall breast cancer mortality rate was 49% (model range, 39%-58%) relative to the estimated baseline rate in 2012 of 63 deaths (model range, 54-73) per 100 000 women: 37% (model range, 26%-51%) of this reduction was associated with screening and 63% (model range, 49%-74%) with treatment. Of the 63% associated with treatment, 31% (model range, 22%-37%) was associated with chemotherapy, 27% (model range, 18%-36%) with hormone therapy, and 4% (model range, 1%-6%) with trastuzumab. The estimated relative contributions associated with screening vs treatment varied by molecular subtype: for ER+/ERBB2-, 36% (model range, 24%-50%) vs 64% (model range, 50%-76%); for ER+/ERBB2+, 31% (model range, 23%-41%) vs 69% (model range, 59%-77%); for ER-/ERBB2+, 40% (model range, 34%-47%) vs 60% (model range, 53%-66%); and for ER-/ERBB2-, 48% (model range, 38%-57%) vs 52% (model range, 44%-62%). Conclusions and Relevance: In this simulation modeling study that projected trends in breast cancer mortality rates among US women, decreases in overall breast cancer mortality from 2000 to 2012 were associated with advances in screening and in adjuvant therapy, although the associations varied by breast cancer molecular subtype.

Tailoring Breast Cancer Screening Intervals by Breast Density and Risk for Women Aged 50 Years or Older: Collaborative Modeling of Screening Outcomes.

Background: Biennial screening is generally recommended for average-risk women aged 50 to 74 years, but tailored screening may provide greater benefits. Objective: To estimate outcomes for various screening intervals after age 50 years based on breast density and risk for breast cancer. Design: Collaborative simulation modeling using national incidence, breast density, and screening performance data. Setting: United States. Patients: Women aged 50 years or older with various combinations of breast density and relative risk (RR) of 1.0, 1.3, 2.0, or 4.0. Intervention: Annual, biennial, or triennial digital mammography screening from ages 50 to 74 years (vs. no screening) and ages 65 to 74 years (vs. biennial digital mammography from ages 50 to 64 years). Measurements: Lifetime breast cancer deaths, life expectancy and quality-adjusted life-years (QALYs), false-positive mammograms, benign biopsy results, overdiagnosis, cost-effectiveness, and ratio of false-positive results to breast cancer deaths averted. Results: Screening benefits and overdiagnosis increase with breast density and RR. False-positive mammograms and benign results on biopsy decrease with increasing risk. Among women with fatty breasts or scattered fibroglandular density and an RR of 1.0 or 1.3, breast cancer deaths averted were similar for triennial versus biennial screening for both age groups (50 to 74 years, median of 3.4 to 5.1 vs. 4.1 to 6.5 deaths averted; 65 to 74 years, median of 1.5 to 2.1 vs. 1.8 to 2.6 deaths averted). Breast cancer deaths averted increased with annual versus biennial screening for women aged 50 to 74 years at all levels of breast density and an RR of 4.0, and those aged 65 to 74 years with heterogeneously or extremely dense breasts and an RR of 4.0. However, harms were almost 2-fold higher. Triennial screening for the average-risk subgroup and annual screening for the highest-risk subgroup cost less than $100 000 per QALY gained. Limitation: Models did not consider women younger than 50 years, those with an RR less than 1, or other imaging methods. Conclusion: Average-risk women with low breast density undergoing triennial screening and higher-risk women with high breast density receiving annual screening will maintain a similar or better balance of benefits and harms than average-risk women receiving biennial screening. Primary Funding Source: National Cancer Institute.