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Wolfson EA, Schonberg MA, Eliassen AH, Bertrand KA, Shvetsov YB, Rosner BA, Palmer JR, LaCroix AZ, Chlebowski RT, Nelson RA, Ngo LH. Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older. J Natl Cancer Inst 2024; 116:81-96. [PMID: 37676833 PMCID: PMC10777669 DOI: 10.1093/jnci/djad188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/24/2023] [Accepted: 08/29/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models. METHODS We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics. RESULTS When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC. CONCLUSIONS Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.
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Affiliation(s)
- Emily A Wolfson
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mara A Schonberg
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Yurii B Shvetsov
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Bernard A Rosner
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | | | - Rebecca A Nelson
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
| | - Long H Ngo
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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2
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Zirpoli GR, Pfeiffer RM, Bertrand KA, Huo D, Lunetta KL, Palmer JR. Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women. Breast Cancer Res 2024; 26:2. [PMID: 38167144 PMCID: PMC10763003 DOI: 10.1186/s13058-023-01748-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). METHODS Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. RESULTS AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644). CONCLUSIONS This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.
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Affiliation(s)
- Gary R Zirpoli
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Ruth M Pfeiffer
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
- Division of Cancer Epidemiology and Biostatistics, National Cancer Institute, Bethesda, USA.
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University, Boston, MA, USA
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago, Chicago, IL, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA.
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
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3
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Schonberg MA, Wolfson EA, Eliassen AH, Bertrand KA, Shvetsov YB, Rosner BA, Palmer JR, Ngo LH. A model for predicting both breast cancer risk and non-breast cancer death among women > 55 years old. Breast Cancer Res 2023; 25:8. [PMID: 36694222 PMCID: PMC9872276 DOI: 10.1186/s13058-023-01605-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 01/16/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Guidelines recommend shared decision making (SDM) for mammography screening for women ≥ 75 and not screening women with < 10-year life expectancy. High-quality SDM requires consideration of women's breast cancer (BC) risk, life expectancy, and values but is hard to implement because no models simultaneously estimate older women's individualized BC risk and life expectancy. METHODS Using competing risk regression and data from 83,330 women > 55 years who completed the 2004 Nurses' Health Study (NHS) questionnaire, we developed (in 2/3 of the cohort, n = 55,533) a model to predict 10-year non-breast cancer (BC) death. We considered 60 mortality risk factors and used best-subsets regression, the Akaike information criterion, and c-index, to identify the best-fitting model. We examined model performance in the remaining 1/3 of the NHS cohort (n = 27,777) and among 17,380 Black Women's Health Study (BWHS) participants, ≥ 55 years, who completed the 2009 questionnaire. We then included the identified mortality predictors in a previously developed competing risk BC prediction model and examined model performance for predicting BC risk. RESULTS Mean age of NHS development cohort participants was 70.1 years (± 7.0); over 10 years, 3.1% developed BC, 0.3% died of BC, and 20.1% died of other causes; NHS validation cohort participants were similar. BWHS participants were younger (mean age 63.7 years [± 6.7]); over 10-years 3.1% developed BC, 0.4% died of BC, and 11.1% died of other causes. The final non-BC death prediction model included 21 variables (age; body mass index [BMI]; physical function [3 measures]; comorbidities [12]; alcohol; smoking; age at menopause; and mammography use). The final BC prediction model included age, BMI, alcohol and hormone use, family history, age at menopause, age at first birth/parity, and breast biopsy history. When risk factor regression coefficients were applied in the validation cohorts, the c-index for predicting 10-year non-BC death was 0.790 (0.784-0.796) in NHS and 0.768 (0.757-0.780) in BWHS; for predicting 5-year BC risk, the c-index was 0.612 (0.538-0.641) in NHS and 0.573 (0.536-0.611) in BWHS. CONCLUSIONS We developed and validated a novel competing-risk model that predicts 10-year non-BC death and 5-year BC risk. Model risk estimates may help inform SDM around mammography screening.
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Affiliation(s)
- Mara A Schonberg
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | - Emily A Wolfson
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Kimberly A Bertrand
- Slone Epidemiology Center, Boston University, Boston University School of Medicine, Boston, MA, USA
| | - Yurii B Shvetsov
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Manoa, HI, USA
| | - Bernard A Rosner
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston University School of Medicine, Boston, MA, USA
| | - Long H Ngo
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
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4
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Jantzen R, Payette Y, de Malliard T, Labbé C, Noisel N, Broët P. Validation of breast cancer risk assessment tools on a French-Canadian population-based cohort. BMJ Open 2021; 11:e045078. [PMID: 33846154 PMCID: PMC8047995 DOI: 10.1136/bmjopen-2020-045078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
OBJECTIVES Evaluate the accuracy of the Breast Cancer Risk Assessment Tool (BCRAT), International Breast Cancer Intervention Study risk evaluation tool (IBIS), Polygenic Risk Scores (PRS) and combined scores (BCRAT+PRS and IBIS +PRS) to predict the occurrence of invasive breast cancers at 5 years in a French-Canadian population. DESIGN Population-based cohort study. SETTING We used the population-based cohort CARTaGENE, composed of 43 037 Quebec residents aged between 40 and 69 years and broadly representative of the population recorded on the Quebec administrative health insurance registries. PARTICIPANTS 10 200 women recruited in 2009-2010 were included for validating BCRAT and IBIS and 4555 with genetic information for validating the PRS and combined scores. OUTCOME MEASURES We computed the absolute risks of breast cancer at 5 years using BCRAT, IBIS, four published PRS and combined models. We reported the overall calibration performance, goodness-of-fit test and discriminatory accuracy. RESULTS 131 (1.28%) women developed a breast cancer at 5 years for validating BCRAT and IBIS and 58 (1.27%) for validating PRS and combined scores. Median follow-up was 5 years. BCRAT and IBIS had an overall expected-to-observed ratio of 1.01 (0.85-1.19) and 1.02 (0.86-1.21) but with significant differences when partitioning by risk groups (p<0.05). IBIS' c-index was significantly higher than BCRAT (63.42 (59.35-67.49) vs 58.63 (54.05-63.21), p=0.013). PRS scores had a global calibration around 0.82, with a CI including one, and non-significant goodness-of-fit tests. PRS' c-indexes were non-significantly higher than BCRAT and IBIS, the highest being 64.43 (58.23-70.63). Combined models did not improve the results. CONCLUSIONS In this French-Canadian population-based cohort, BCRAT and IBIS have good mean calibration that could be improved for risk subgroups, and modest discriminatory accuracy. Despite this modest discriminatory power, these tools can be of interest for primary care physicians for delivering a personalised message to their high-risk patients, regarding screening and lifestyle counselling.
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Affiliation(s)
- Rodolphe Jantzen
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
| | - Yves Payette
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
| | | | - Catherine Labbé
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
| | - Nolwenn Noisel
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
| | - Philippe Broët
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
- CESP, INSERM, University Paris-Saclay, Villejuif, France
- Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
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5
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Wood ME, Farina NH, Ahern TP, Cuke ME, Stein JL, Stein GS, Lian JB. Towards a more precise and individualized assessment of breast cancer risk. Aging (Albany NY) 2020; 11:1305-1316. [PMID: 30787204 PMCID: PMC6402518 DOI: 10.18632/aging.101803] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 01/24/2019] [Indexed: 02/07/2023]
Abstract
Many clinically based models are available for breast cancer risk assessment; however, these models are not particularly useful at the individual level, despite being designed with that intent. There is, therefore, a significant need for improved, precise individualized risk assessment. In this Research Perspective, we highlight commonly used clinical risk assessment models and recent scientific advances to individualize risk assessment using precision biomarkers. Genome-wide association studies have identified >100 single nucleotide polymorphisms (SNPs) associated with breast cancer risk, and polygenic risk scores (PRS) have been developed by several groups using this information. The ability of a PRS to improve risk assessment is promising; however, validation in both genetically and ethnically diverse populations is needed. Additionally, novel classes of biomarkers, such as microRNAs, may capture clinically relevant information based on epigenetic regulation of gene expression. Our group has recently identified a circulating-microRNA signature predictive of long-term breast cancer in a prospective cohort of high-risk women. While progress has been made, the importance of accurate risk assessment cannot be understated. Precision risk assessment will identify those women at greatest risk of developing breast cancer, thus avoiding overtreatment of women at average risk and identifying the most appropriate candidates for chemoprevention or surgical prevention.
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Affiliation(s)
- Marie E Wood
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Division of Hematology and Oncology, The Robert Larner MD College of Medicine, University of Vermont Medical Center, Burlington, VT 05405, USA
| | - Nicholas H Farina
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Thomas P Ahern
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Surgery, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Melissa E Cuke
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Division of Hematology and Oncology, The Robert Larner MD College of Medicine, University of Vermont Medical Center, Burlington, VT 05405, USA
| | - Janet L Stein
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Gary S Stein
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Surgery, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Jane B Lian
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
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6
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Lopez AM, Hudson L, Vanderford NL, Vanderpool R, Griggs J, Schonberg M. Epidemiology and Implementation of Cancer Prevention in Disparate Populations and Settings. Am Soc Clin Oncol Educ Book 2019; 39:50-60. [PMID: 31099623 DOI: 10.1200/edbk_238965] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Successful cancer prevention strategies must be tailored to support usability. In this article, we will focus on cancer prevention strategies in populations that differ by race and ethnicity, place and location, sexual orientation and gender identity, and age by providing examples of effective approaches. An individual may belong to none of these categories, to all of these categories, or to some. This intersectionality of belonging characterizes individuals and shapes their experiences. Even within a category, broad diversity exists. Effective cancer prevention strategies comprehensively engage the community at multiple levels of influence and may effectively include lay health workers and faith-based cancer education interventions. Health system efforts that integrate cancer health with other health promotion activities show promise. At the individual physician level, culturally literate approaches have demonstrated success. For example, when discussing cancer screening tests with older adults, clinicians should indicate whether any data suggest that the screening test improves quality or quantity of life and the lag time to benefit from the screening test. This will allow older adults to make an informed cancer screening decision based on a realistic understanding of the potential benefits and risks and their values and preferences. Addressing individual and health system bias remains a challenge. Quality improvement strategies can address gaps in quality of care with respect to timeliness of care, coordination of care, and patient experience. The time is ripe for research on effective and interdisciplinary prevention strategies that harness expertise from preventive medicine, behavioral medicine, implementation science, e-health, telemedicine, and other diverse fields of health promotion.
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Affiliation(s)
- Ana Maria Lopez
- 1 Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA
| | - Lauren Hudson
- 2 University of Kentucky Markey Cancer Center, Lexington, KY
| | | | | | | | - Mara Schonberg
- 4 Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
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7
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Açıkgöz A, Çımrın D, Ergör G. Meme, prostat, kolorektal ve akciğer kanserlerinde çevresel risk faktörleri ve risk düzeylerinin belirlenmesi: olgu-kontrol çalışması. CUKUROVA MEDICAL JOURNAL 2018. [DOI: 10.17826/cumj.345233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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8
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Wunderle M, Olmes G, Nabieva N, Häberle L, Jud SM, Hein A, Rauh C, Hack CC, Erber R, Ekici AB, Hoyer J, Vasileiou G, Kraus C, Reis A, Hartmann A, Schulz-Wendtland R, Lux MP, Beckmann MW, Fasching PA. Risk, Prediction and Prevention of Hereditary Breast Cancer - Large-Scale Genomic Studies in Times of Big and Smart Data. Geburtshilfe Frauenheilkd 2018; 78:481-492. [PMID: 29880983 PMCID: PMC5986564 DOI: 10.1055/a-0603-4350] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 04/09/2018] [Accepted: 04/09/2018] [Indexed: 12/24/2022] Open
Abstract
Over the last two decades genetic testing for mutations in
BRCA1
and
BRCA2
has become standard of care for women and men who are at familial risk for breast or ovarian cancer. Currently, genetic testing more often also includes so-called panel genes, which are assumed to be moderate-risk genes for breast cancer. Recently, new large-scale studies provided more information about the risk estimation of those genes. The utilization of information on panel genes with regard to their association with the individual breast cancer risk might become part of future clinical practice. Furthermore, large efforts have been made to understand the influence of common genetic variants with a low impact on breast cancer risk. For this purpose, almost 450 000 individuals have been genotyped for almost 500 000 genetic variants in the OncoArray project. Based on first results it can be assumed that – together with previously identified common variants – more than 170 breast cancer risk single nucleotide polymorphisms can explain up to 18% of familial breast cancer risk. The knowledge about genetic and non-genetic risk factors and its implementation in clinical practice could especially be of use for individualized prevention. This includes an individualized risk prediction as well as the individualized selection of screening methods regarding imaging and possible lifestyle interventions. The aim of this review is to summarize the most recent developments in this area and to provide an overview on breast cancer risk genes, risk prediction models and their utilization for the individual patient.
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Affiliation(s)
- Marius Wunderle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Gregor Olmes
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Naiba Nabieva
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Lothar Häberle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany.,Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Sebastian M Jud
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Alexander Hein
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Claudia Rauh
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Carolin C Hack
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Juliane Hoyer
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Georgia Vasileiou
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Cornelia Kraus
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - André Reis
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Rüdiger Schulz-Wendtland
- Institute of Diagnostic Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Michael P Lux
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
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Braithwaite D, Miglioretti DL, Zhu W, Demb J, Trentham-Dietz A, Sprague B, Tice JA, Onega T, Henderson LM, Buist DSM, Ziv E, Walter LC, Kerlikowske K. Family History and Breast Cancer Risk Among Older Women in the Breast Cancer Surveillance Consortium Cohort. JAMA Intern Med 2018; 178:494-501. [PMID: 29435563 PMCID: PMC5876845 DOI: 10.1001/jamainternmed.2017.8642] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
IMPORTANCE First-degree family history is a strong risk factor for breast cancer, but controversy exists about the magnitude of the association among older women. OBJECTIVE To determine whether first-degree family history is associated with increased risk of breast cancer among older women, and identify whether the association varies by breast density. DESIGN, SETTING, AND PARTICIPANTS Prospective cohort study between 1996 and 2012 from 7 Breast Cancer Surveillance Consortium (BCSC) registries located in New Hampshire, North Carolina, San Francisco Bay area, western Washington state, New Mexico, Colorado, and Vermont. During a mean (SD) follow-up of 6.3 (3.2) years, 10 929 invasive breast cancers were diagnosed in a cohort of 403 268 women 65 years and older with data from 472 220 mammography examinations. We estimated the 5-year cumulative incidence of invasive breast cancer by first-degree family history, breast density, and age groups. Cox proportional hazards models were fit to estimate the association of first-degree family history with risk of invasive breast cancer (after adjustment for breast density, BCSC registry, race/ethnicity, body mass index, postmenopausal hormone therapy use, and benign breast disease for age groups 65 to 74 years and 75 years and older, separately). Data analyses were performed between June 2016 and June 2017. EXPOSURE First-degree family history of breast cancer. MAIN OUTCOMES AND MEASURES Incident breast cancer. RESULTS In 403 268 women 65 years and older, first-degree family history was associated with an increased risk of breast cancer among women ages 65 to 74 years (hazard ratio [HR], 1.48; 95% CI, 1.35-1.61) and 75 years and older (HR, 1.44; 95% CI, 1.28-1.62). Estimates were similar for women 65 to 74 years with first-degree relative's diagnosis age younger than 50 years (HR, 1.47; 95% CI, 1.25-1.73) vs 50 years and older (HR, 1.33; 95% CI, 1.17-1.51) and for women ages 75 years and older with the relative's diagnosis age younger than 50 years (HR, 1.31; 95% CI, 1.05-1.63) vs 50 years and older (HR, 1.55; 95% CI, 1.33-1.81). Among women ages 65 to 74 years, the risk associated with first-degree family history was highest among those with fatty breasts (HR, 1.67; 95% CI, 1.27-2.21), whereas in women 75 years and older the risk associated with family history was highest among those with dense breasts (HR, 1.55; 95% CI, 1.29-1.87). CONCLUSIONS AND RELEVANCE First-degree family history was associated with increased risk of invasive breast cancer in all subgroups of older women irrespective of a relative's age at diagnosis.
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Affiliation(s)
- Dejana Braithwaite
- Department of Oncology, Georgetown University Medical Center, Washington, DC
| | - Diana L Miglioretti
- Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, California.,Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Weiwei Zhu
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Joshua Demb
- Department of Oncology, Georgetown University Medical Center, Washington, DC
| | - Amy Trentham-Dietz
- Department of Population Health Sciences, Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison
| | - Brian Sprague
- Department of Surgery, University of Vermont College of Medicine, Burlington
| | - Jeffrey A Tice
- Department of Medicine, University of California, San Francisco
| | - Tracy Onega
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Louise M Henderson
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill
| | - Diana S M Buist
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco
| | - Louise C Walter
- Department of Medicine, University of California, San Francisco
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Wang X, Huang Y, Li L, Dai H, Song F, Chen K. Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis. Breast Cancer Res 2018; 20:18. [PMID: 29534738 PMCID: PMC5850919 DOI: 10.1186/s13058-018-0947-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 02/26/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA). METHODS Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive. RESULTS Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively. CONCLUSIONS The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses. TRIAL REGISTRATION PROSPERO CRD42016047215 .
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Affiliation(s)
- Xin Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Yubei Huang
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Lian Li
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
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Schonberg MA. Decision-Making Regarding Mammography Screening for Older Women. J Am Geriatr Soc 2016; 64:2413-2418. [PMID: 27917463 DOI: 10.1111/jgs.14503] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The population is aging, and breast cancer incidence increases with age, peaking between the ages of 75 and 79. However, it is not known whether mammography screening helps women aged 75 and older live longer because they have not been included in randomized controlled trials evaluating mammography screening. Guidelines recommend that older women with less than a 10-year life expectancy not be screened because it takes approximately 10 years before a screen-detected breast cancer may affect an older woman's survival. Guidelines recommend that clinicians discuss the benefits and risks of screening with women aged 75 and older with a life expectancy of 10 years or longer to help them elicit their values and preferences. It is estimated that two of 1,000 women who continue to be screened every other year from age 70 to 79 may avoid breast cancer death, but 12% to 27% of these women will experience a false-positive test, and 10% to 20% of women who experience a false-positive test will undergo a breast biopsy. In addition, approximately 30% of screen-detected cancers would not otherwise have shown up in an older woman's lifetime, yet nearly all older women undergo treatment for these breast cancers, and the risks of treatment increase with age. To inform decision-making, tools are available to estimate life expectancy and to educate older women about the benefits and harms of mammography screening. Guides are also available to help clinicians discuss stopping screening with older women with less than a 10-year life expectancy. Ideally, screening decisions would consider an older woman's life expectancy, breast cancer risk, and her values and preferences.
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Affiliation(s)
- Mara A Schonberg
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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Schonberg MA, Li VW, Eliassen AH, Davis RB, LaCroix AZ, McCarthy EP, Rosner BA, Chlebowski RT, Hankinson SE, Marcantonio ER, Ngo LH. Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk. Breast Cancer Res Treat 2016; 160:547-562. [PMID: 27770283 PMCID: PMC5093031 DOI: 10.1007/s10549-016-4020-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 10/13/2016] [Indexed: 12/12/2022]
Abstract
PURPOSE Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. METHODS We included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). RESULTS Within 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]). CONCLUSIONS We developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.
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Affiliation(s)
- Mara A Schonberg
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Beth Israel Deaconess Medical Center, 1309 Beacon, Office 219, Brookline, MA, 02446, USA.
| | - Vicky W Li
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Roger B Davis
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Andrea Z LaCroix
- Division of Epidemiology, Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Ellen P McCarthy
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Bernard A Rosner
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rowan T Chlebowski
- Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan E Hankinson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts, 713 North Pleasant Street, Amherst, MA, USA
| | - Edward R Marcantonio
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Long H Ngo
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
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