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.

[1]  E. Feuer,et al.  Changing patterns in breast cancer incidence trends. , 2006, Journal of the National Cancer Institute. Monographs.

[2]  C. Begg,et al.  The effect of changes in tumor size on breast carcinoma survival in the U.S.: 1975–1999 , 2005, Cancer.

[3]  C. Lehman,et al.  Identifying women with dense breasts at high risk for interval cancer: a cohort study. , 2015, Annals of internal medicine.

[4]  Oguzhan Alagoz,et al.  Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography. , 2014, Journal of the National Cancer Institute.

[5]  Oguzhan Alagoz,et al.  The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update , 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

[6]  M. Zelen,et al.  The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update , 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

[7]  Kathleen A Cronin,et al.  US incidence of breast cancer subtypes defined by joint hormone receptor and HER2 status. , 2014, Journal of the National Cancer Institute.

[8]  T. Holford,et al.  The Contribution of Mammography Screening to Breast Cancer Incidence Trends in the United States: An Updated Age–Period–Cohort Model , 2015, Cancer Epidemiology, Biomarkers & Prevention.

[9]  E. Winer,et al.  Systemic therapy for patients with advanced human epidermal growth factor receptor 2-positive breast cancer: American Society of Clinical Oncology clinical practice guideline. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  E. Feuer,et al.  Trends in use of adjuvant multi-agent chemotherapy and tamoxifen for breast cancer in the United States: 1975-1999. , 2002, Journal of the National Cancer Institute.

[11]  E. Feuer,et al.  Modeling the dissemination of mammography in the United States , 2005, Cancer Causes & Control.

[12]  D. Berry,et al.  Breast Cancer Working Group of the Cancer Intervention and Surveillance Modeling Network. Effects of mammography screening under different screening schedules: Model estimates of potential benefits and harms (Annals of Internal Medicine (2009) 151, (738-747)) , 2010 .

[13]  Marvin Zelen,et al.  Effects of Mammography Screening Under Different Screening Schedules: Model Estimates of Potential Benefits and Harms , 2009 .

[14]  D. Berry,et al.  A Bayesian Simulation Model for Breast Cancer Screening, Incidence, Treatment, and Mortality , 2017, Medical decision making : an international journal of the Society for Medical Decision Making.

[15]  S. Giordano Breast Cancer Mortality Trends in the United States According to Estrogen Receptor Status and Age at Diagnosis , 2008 .

[16]  Ping Sun,et al.  Differences in breast cancer stage at diagnosis and cancer-specific survival by race and ethnicity in the United States. , 2015, JAMA.

[17]  R. Peto,et al.  Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. , 2012, Lancet.

[18]  E. Feuer,et al.  Additional common inputs for analyzing impact of adjuvant therapy and mammography on U.S. mortality. , 2006, Journal of the National Cancer Institute. Monographs.

[19]  D. Cutter,et al.  Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100 000 women in 123 randomised trials , 2012, The Lancet.

[20]  D. Berry,et al.  Effect of screening and adjuvant therapy on mortality from breast cancer , 2005 .

[21]  S. Plevritis,et al.  A Molecular Subtype–Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010 , 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

[22]  Oguzhan Alagoz,et al.  Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality. , 2014, Journal of the National Cancer Institute.

[23]  Jeroen J. van den Broek,et al.  Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia , 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

[24]  Angela Mariotto,et al.  Cancer Intervention and Surveillance Modeling Network (CISNET) , 2001, Journal of Investigative Medicine.

[25]  E. Feuer,et al.  Dissemination of adjuvant multiagent chemotherapy and tamoxifen for breast cancer in the United States using estrogen receptor information: 1975-1999. , 2006, Journal of the National Cancer Institute. Monographs.

[26]  Clyde B Schechter,et al.  Structure, Function, and Applications of the Georgetown–Einstein (GE) Breast Cancer Simulation Model , 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

[27]  V. Beral,et al.  Breast cancer mortality rates are levelling off or beginning to decline in many western countries: analysis of time trends, age-cohort and age-period models of breast cancer mortality in 20 countries. , 1996, British Journal of Cancer.

[28]  D. Berry,et al.  Effect of screening and adjuvant therapy on mortality from breast cancer. , 2006, The New England journal of medicine.

[29]  Constance D Lehman,et al.  Digital Breast Tomosynthesis and the Challenges of Implementing an Emerging Breast Cancer Screening Technology Into Clinical Practice. , 2016, Journal of the American College of Radiology : JACR.

[30]  Emily F Conant,et al.  Breast cancer screening using tomosynthesis in combination with digital mammography. , 2014, JAMA.

[31]  Margo J. Anderson,et al.  The Census , 1921, The Hospital.

[32]  C. D'Orsi,et al.  Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening , 2006 .

[33]  Mike Clarke,et al.  UK and USA breast cancer deaths down 25% in year 2000 at ages 20–69 years , 2000, The Lancet.

[34]  C. Lehman,et al.  Comparative Effectiveness of Digital Versus Film-Screen Mammography in Community Practice in the United States , 2011, Annals of Internal Medicine.

[35]  S. Plevritis,et al.  Estimating Breast Cancer Survival by Molecular Subtype in the Absence of Screening and Adjuvant Treatment , 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

[36]  S. Plevritis,et al.  Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling , 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

[37]  P. Rosenberg,et al.  Estrogen Receptor Status and the Future Burden of Invasive and In Situ Breast Cancers in the United States. , 2015, Journal of the National Cancer Institute.

[38]  A. Trentham-Dietz,et al.  Contribution of Breast Cancer to Overall Mortality for US Women , 2018, Medical decision making : an international journal of the Society for Medical Decision Making.

[39]  Mitchell H Gail,et al.  Improvements in US Breast Cancer Survival and Proportion Explained by Tumor Size and Estrogen-Receptor Status. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.