Breast cancer remains the second leading cause of cancer morbidity and mortality among women in the US. New discoveries have resulted in the widely accepted view that breast cancer is a heterogeneous disease with molecularly distinguishable morphological subtypes. This awareness is driving the development of new paradigms for the prevention, early detection and clinical management of breast cancer. However, there are very limited data on the population effects of these novel cancer control approaches. Population modeling is a unique comparative effectiveness paradigm to fill this gap by translating advances from the laboratory and clinical trials to understanding their net effects on US breast cancer mortality. The CISNET Breast Working Group has collaborated over the past nine years to apply independent population models to evaluate cancer control practices and use results to inform clinical and public health guidelines. This proposal leverages the investment in these models and provides the continuity and cohesion of this highly productive group. The modeling groups include Dana Farber (D). Erasmus MC (E), Georgetown-Einstein (G), MD Anderson (M), Stanford (S) and Wisconsin-Harvard (W). For this application, we will extend our work by modeling populations of women with varying risk factors (e.g., breast density, HRT) for the development of specific molecular subtypes of breast cancer (based on ER and HER2).
Our specific aims are to use these adapted models to: 1) compare the impact of observed practice patterns to the benefits and harms of targeting new screening and adjuvant therapy modalities based on risk factors and molecular subtypes;2) explore the impact of improving access to new services;3) conduct value-of information-like analyses to evaluate the relationship between performance characteristics of a new screening test (e.g. blood based biomarker) and its impact on breast cancer mortality, utilization of treatments and over-diagnosis;and 4) communicate results to end-users using a web-based platform. This work will advance the field of modeling by explicitly capturing molecular attributes of breast cancer, and in so doing, build a robust capacity to inform debates about """"""""best practices"""""""" for cancer control interventions.

Public Health Relevance

Breast cancer is the 2 leading cause of cancer death in US women. New discoveries are re-defining best practices to reduce mortality. However, few of these novel strategies have been fully tested for effectiveness in reducing overall breast cancer deaths in the general population. Moreover, many women remain without access to even current standard cancer-related services. We will use population modeling to fill gaps in knowledge by translating research results from trials to their net effects on US breast cancer mortality rates.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA152958-05
Application #
8739610
Study Section
Special Emphasis Panel (ZCA1-SRLB-4 (M1))
Program Officer
Stedman, Margaret R
Project Start
2010-09-01
Project End
2015-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
5
Fiscal Year
2014
Total Cost
$1,314,797
Indirect Cost
$125,834
Name
Georgetown University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
049515844
City
Washington
State
DC
Country
United States
Zip Code
20057
Ray, G Thomas; Mandelblatt, Jeanne; Habel, Laurel A et al. (2016) Breast cancer multigene testing trends and impact on chemotherapy use. Am J Manag Care 22:e153-60
Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K et al. (2016) Using Active Learning for Speeding up Calibration in Simulation Models. Med Decis Making 36:581-93
Himes, Deborah O; Clayton, Margaret F; Donaldson, Gary W et al. (2016) Breast Cancer Risk Perceptions among Relatives of Women with Uninformative Negative BRCA1/2 Test Results: The Moderating Effect of the Amount of Shared Information. J Genet Couns 25:258-69
Chang, Yaojen; Near, Aimee M; Butler, Karin M et al. (2016) Economic Evaluation Alongside a Clinical Trial of Telephone Versus In-Person Genetic Counseling for BRCA1/2 Mutations in Geographically Underserved Areas. J Oncol Pract 12:59, e1-13
Trentham-Dietz, Amy; Kerlikowske, Karla; Stout, Natasha K et al. (2016) Tailoring Breast Cancer Screening Intervals by Breast Density and Risk for Women Aged 50 Years or Older: Collaborative Modeling of Screening Outcomes. Ann Intern Med 165:700-712
Miglioretti, Diana L; Lange, Jane; van den Broek, Jeroen J et al. (2016) Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling Study. Ann Intern Med 164:205-14
Huang, Xuelin; Yan, Fangrong; Ning, Jing et al. (2016) A two-stage approach for dynamic prediction of time-to-event distributions. Stat Med 35:2167-82
Wen, Sijin; Huang, Xuelin; Frankowski, Ralph F et al. (2016) A Bayesian multivariate joint frailty model for disease recurrences and survival. Stat Med 35:4794-4812
Mandelblatt, Jeanne S; Stout, Natasha K; Schechter, Clyde B et al. (2016) Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies. Ann Intern Med 164:215-25
Chang, Yaojen; Gallon, Lorenzo; Shetty, Kirti et al. (2015) Simulation modeling of the impact of proposed new simultaneous liver and kidney transplantation policies. Transplantation 99:424-30

Showing the most recent 10 out of 59 publications