Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

被引:68
作者
Choudhury, Parichoy Pal [1 ]
Wilcox, Amber N. [3 ]
Brook, Mark N. [4 ]
Zhang, Yan [1 ]
Ahearn, Thomas [3 ]
Orr, Nick [6 ]
Coulson, Penny [4 ]
Schoemaker, Minouk J. [4 ]
Jones, Michael E. [4 ]
Gail, Mitchell H. [3 ]
Swerdlow, Anthony J. [4 ,5 ]
Chatterjee, Nilanjan [1 ,2 ]
Garcia-Closas, Montserrat [3 ]
机构
[1] Johns Hopkins Univ, Bloomberg Sch Publ Hlth, Dept Biostat, 615 North Wolfe St,Room E3612, Baltimore, MD 21205 USA
[2] Johns Hopkins Univ, Dept Oncol, Sch Med, Baltimore, MD 21205 USA
[3] NCI, Div Canc Epidemiol & Genet, Bethesda, MD 20892 USA
[4] Inst Canc Res, Div Genet & Epidemiol, London, England
[5] Inst Canc Res, Div Breast Canc Res, London, England
[6] Queens Univ Belfast, Ctr Canc Res & Cell Biol, Belfast, Antrim, North Ireland
来源
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE | 2020年 / 112卷 / 03期
基金
欧盟地平线“2020”; 美国国家卫生研究院;
关键词
POLYGENIC RISK; GAIL MODEL; WHITE WOMEN; DENSITY; PERFORMANCE; ASSOCIATION; POPULATION; SNPS;
D O I
10.1093/jnci/djz113
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. Methods: Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74years. Risk projections in a target population of US white non-Hispanic women age 50-70years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). Results: The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50years, and iCARE-BPC3 (E/O=1.00, 95% CI = 0.93 to 1.09) for women 50years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. Conclusions: iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.
引用
收藏
页码:278 / 285
页数:8
相关论文
共 66 条
[1]   Reproductive Factors and Mammographic Density: Associations Among 24,840 Women and Comparison of Studies Using Digitized Film-Screen Mammography and Full-Field Digital Mammography [J].
Alexeeff, Stacey E. ;
Odo, Nnaemeka U. ;
McBride, Russell ;
McGuire, Valerie ;
Achacoso, Ninah ;
Rothstein, Joseph H. ;
Lipson, Jafi A. ;
Liang, Rhea Y. ;
Acton, Luana ;
Yaffe, Martin J. ;
Whittemore, Alice S. ;
Rubin, Daniel L. ;
Sieh, Weiva ;
Habel, Laurel A. .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2019, 188 (06) :1144-1154
[2]   SNPs and breast cancer risk prediction for African American and Hispanic women [J].
Allman, Richard ;
Dite, Gillian S. ;
Hopper, John L. ;
Gordon, Ora ;
Starlard-Davenport, Athena ;
Chlebowski, Rowan ;
Kooperberg, Charles .
BREAST CANCER RESEARCH AND TREATMENT, 2015, 154 (03) :583-589
[3]   Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme [J].
Amir, E ;
Evans, DG ;
Shenton, A ;
Lalloo, F ;
Moran, A ;
Boggis, C ;
Wilson, M ;
Howell, A .
JOURNAL OF MEDICAL GENETICS, 2003, 40 (11) :807-814
[4]  
[Anonymous], 2016, FINAL RECOMMENDATION
[5]  
[Anonymous], NCCN Clinical Practice Guidelines in Oncology: Breast Cancer
[6]   Risk prediction models of breast cancer: a systematic review of model performances [J].
Anothaisintawee, Thunyarat ;
Teerawattananon, Yot ;
Wiratkapun, Chollathip ;
Kasamesup, Vijj ;
Thakkinstian, Ammarin .
BREAST CANCER RESEARCH AND TREATMENT, 2012, 133 (01) :1-10
[7]   An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain [J].
Arrospide, Arantzazu ;
Forne, Carles ;
Rue, Montse ;
Tora, Nuria ;
Mar, Javier ;
Bare, Marisa .
BMC CANCER, 2013, 13
[8]   Evaluating breast cancer risk projections for Hispanic women [J].
Banegas, Matthew P. ;
Gail, Mitchell H. ;
LaCroix, Andrea ;
Thompson, Beti ;
Martinez, Maria Elena ;
Wactawski-Wende, Jean ;
John, Esther M. ;
Hubbell, F. Allan ;
Yasmeen, Shagufta ;
Katki, Hormuzd A. .
BREAST CANCER RESEARCH AND TREATMENT, 2012, 132 (01) :347-353
[9]   Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort [J].
Brentnall, Adam R. ;
Harkness, Elaine F. ;
Astley, Susan M. ;
Donnelly, Louise S. ;
Stavrinos, Paula ;
Sampson, Sarah ;
Fox, Lynne ;
Sergeant, Jamie C. ;
Harvie, Michelle N. ;
Wilson, Mary ;
Beetles, Ursula ;
Gadde, Soujanya ;
Lim, Yit ;
Jain, Anil ;
Bundred, Sara ;
Barr, Nicola ;
Reece, Valerie ;
Howell, Anthony ;
Cuzick, Jack ;
Evans, D. Gareth R. .
BREAST CANCER RESEARCH, 2015, 17
[10]  
Brentnall AR, 2018, JAMA ONCOLOGY, V4, P9