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Wu HC, Lai Y, Liao Y, Deyssenroth M, Miller GW, Santella RM, Terry MB. Plasma metabolomics profiles and breast cancer risk. Breast Cancer Res 2024; 26:141. [PMID: 39385226 PMCID: PMC11463119 DOI: 10.1186/s13058-024-01896-5] [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: 03/08/2024] [Accepted: 09/18/2024] [Indexed: 10/12/2024] Open
Abstract
BACKGROUND Breast cancer (BC) is the most common cancer in women and incidence rates are increasing; metabolomics may be a promising approach for identifying the drivers of the increasing trends that cannot be explained by changes in known BC risk factors. METHODS We conducted a nested case-control study (median followup 6.3 years) within the New York site of the Breast Cancer Family Registry (BCFR) (n = 40 cases and 70 age-matched controls). We conducted a metabolome-wide association study using untargeted metabolomics coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with high-resolution mass spectrometry (LC-HRMS) to identify BC-related metabolic features. RESULTS We found eight metabolic features associated with BC risk. For the four metabolites negatively associated with risk, the adjusted odds ratios (ORs) ranged from 0.31 (95% confidence interval (CI): 0.14, 0.66) (L-Histidine) to 0.65 (95% CI: 0.43, 0.98) (N-Acetylgalactosamine), and for the four metabolites positively associated with risk, ORs ranged from 1.61 (95% CI: 1.04, 2.51, (m/z: 101.5813, RT: 90.4, 1,3-dibutyl-1-nitrosourea, a potential carcinogen)) to 2.20 (95% CI: 1.15, 4.23) (11-cis-Eicosenic acid). These results were no longer statistically significant after adjusting for multiple comparisons. Adding the BC-related metabolic features to a model, including age, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk score improved the accuracy of BC prediction from an area under the curve (AUC) of 66% to 83%. CONCLUSIONS If replicated in larger prospective cohorts, these findings offer promising new ways to identify exposures related to BC and improve BC risk prediction.
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Affiliation(s)
- Hui-Chen Wu
- Department of Environmental Health Sciences, Mailman School of Public Health of Columbia University, New York, NY, 10032, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.
| | - Yunjia Lai
- Department of Environmental Health Sciences, Mailman School of Public Health of Columbia University, New York, NY, 10032, USA
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health of Columbia University, New York, NY, USA
| | - Maya Deyssenroth
- Department of Environmental Health Sciences, Mailman School of Public Health of Columbia University, New York, NY, 10032, USA
| | - Gary W Miller
- Department of Environmental Health Sciences, Mailman School of Public Health of Columbia University, New York, NY, 10032, USA
| | - Regina M Santella
- Department of Environmental Health Sciences, Mailman School of Public Health of Columbia University, New York, NY, 10032, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
| | - Mary Beth Terry
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health of Columbia University, New York, NY, USA
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Phillips KA, Kotsopoulos J, Domchek SM, Terry MB, Chamberlain JA, Bassett JK, Aeilts AM, Andrulis IL, Buys SS, Cui W, Daly MB, Eisen AF, Foulkes WD, Friedlander ML, Gronwald J, Hopper JL, John EM, Karlan BY, Kim RH, Kurian AW, Lubinski J, Metcalfe K, Nathanson KL, Singer CF, Southey MC, Symecko H, Tung N, Narod SA, Milne RL. Hormonal Contraception and Breast Cancer Risk for Carriers of Germline Mutations in BRCA1 and BRCA2. J Clin Oncol 2024:JCO2400176. [PMID: 39356978 DOI: 10.1200/jco.24.00176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 06/10/2024] [Accepted: 08/06/2024] [Indexed: 10/04/2024] Open
Abstract
PURPOSE It is uncertain whether, and to what extent, hormonal contraceptives increase breast cancer (BC) risk for germline BRCA1 or BRCA2 mutation carriers. METHODS Using pooled observational data from four prospective cohort studies, associations between hormonal contraceptive use and BC risk for unaffected female BRCA1 and BRCA2 mutation carriers were assessed using Cox regression. RESULTS Of 3,882 BRCA1 and 1,509 BRCA2 mutation carriers, 53% and 71%, respectively, had ever used hormonal contraceptives for at least 1 year (median cumulative duration of use, 4.8 and 5.7 years, respectively). Overall, 488 BRCA1 and 191 BRCA2 mutation carriers developed BC during median follow-up of 5.9 and 5.6 years, respectively. Although for BRCA1 mutation carriers, neither current nor past use of hormonal contraceptives for at least 1 year was statistically significantly associated with BC risk (hazard ratio [HR], 1.40 [95% CI, 0.94 to 2.08], P = .10 for current use; 1.16 [0.80 to 1.69], P = .4, 1.40 [0.99 to 1.97], P = .05, and 1.27 [0.98 to 1.63], P = .07 for past use 1-5, 6-10, and >10 years before, respectively), ever use was associated with increased risk (HR, 1.29 [95% CI, 1.04 to 1.60], P = .02). Furthermore, BC risk increased with longer cumulative duration of use, with an estimated proportional increase in risk of 3% (1%-5%, P = .002) for each additional year of use. For BRCA2 mutation carriers, there was no evidence that current or ever use was associated with increased BC risk (HR, 0.70 [95% CI, 0.33 to 1.47], P = .3 and 1.07 [0.73 to 1.57], P = .7, respectively). CONCLUSION Hormonal contraceptives were associated with increased BC risk for BRCA1 mutation carriers, especially if used for longer durations. Decisions about their use in women with BRCA1 mutations should carefully weigh the risks and benefits for each individual.
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Affiliation(s)
- Kelly-Anne Phillips
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Joanne Kotsopoulos
- Women's College Research Institute, Women's College Hospital, University of Toronto, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Susan M Domchek
- Basser Center for BRCA, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - James A Chamberlain
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Amber M Aeilts
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, Ohio
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Saundra S Buys
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Wanda Cui
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA
| | - Andrea F Eisen
- Odette Cancer Centre, Sunnybrook Health Sciences, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - William D Foulkes
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Michael L Friedlander
- Department of Medical Oncology, Prince of Wales and Royal Hospital for Women, Sydney, NSW, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Jacek Gronwald
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Esther M John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Beth Y Karlan
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA
| | - Raymond H Kim
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network and Sinai Health, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Allison W Kurian
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Jan Lubinski
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - Kelly Metcalfe
- Women's College Research Institute, Women's College Hospital, University of Toronto, Toronto, ON, Canada
- Bloomberg School of Nursing, University of Toronto, Toronto, ON, Canada
| | - Katherine L Nathanson
- Basser Center for BRCA, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Christian F Singer
- Department of Obstetrics and Gynecology and Center for Breast Health, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Heather Symecko
- Basser Center for BRCA, University of Pennsylvania, Philadelphia, PA
| | - Nadine Tung
- Beth Israel Deaconess Medical Center, Boston, MA
| | - Steven A Narod
- Women's College Research Institute, Women's College Hospital, University of Toronto, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
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3
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McDonald JA, Liao Y, Knight JA, John EM, Kurian AW, Daly M, Buys SS, Huang Y, Frost CJ, Andrulis IL, Colonna SV, Friedlander ML, Hopper JL, Chung WK, Genkinger JM, MacInnis RJ, Terry MB. Pregnancy-Related Factors and Breast Cancer Risk for Women Across a Range of Familial Risk. JAMA Netw Open 2024; 7:e2427441. [PMID: 39186276 DOI: 10.1001/jamanetworkopen.2024.27441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/27/2024] Open
Abstract
Importance Few studies have investigated whether the associations between pregnancy-related factors and breast cancer (BC) risk differ by underlying BC susceptibility. Evidence regarding variation in BC risk is critical to understanding BC causes and for developing effective risk-based screening guidelines. Objective To examine the association between pregnancy-related factors and BC risk, including modification by a of BC where scores are based on age and BC family history. Design, Setting, and Participants This cohort study included participants from the prospective Family Study Cohort (ProF-SC), which includes the 6 sites of the Breast Cancer Family Registry (US, Canada, and Australia) and the Kathleen Cuningham Foundation Consortium (Australia). Analyses were performed in a cohort of women enrolled from 1992 to 2011 without any personal history of BC who were followed up through 2017 with a median (range) follow-up of 10 (1-23) years. Data were analyzed from March 1992 to March 2017. Exposures Parity, number of full-term pregnancies (FTP), age at first FTP, years since last FTP, and breastfeeding. Main Outcomes and Measures BC diagnoses were obtained through self-report or report by a first-degree relative and confirmed through pathology and data linkages. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% CIs for each exposure, examining modification by PARS of BC. Differences were assessed by estrogen receptor (ER) subtype. Results The study included 17 274 women (mean [SD] age, 46.7 [15.1] years; 791 African American or Black participants [4.6%], 1399 Hispanic or Latinx participants [8.2%], and 13 790 White participants [80.7%]) with 943 prospectively ascertained BC cases. Compared with nulliparous women, BC risk was higher after a recent pregnancy for those women with higher PARS (last FTP 0-5 years HR for interaction, 1.53; 95% CI, 1.13-2.07; P for interaction < .001). Associations between other exposures were limited to ER-negative disease. ER-negative BC was positively associated with increasing PARS and increasing years since last FTP (P for interaction < .001) with higher risk for recent pregnancy vs nulliparous women (last FTP 0-5 years HR for interaction, 1.54; 95% CI, 1.03-2.31). ER-negative BC was positively associated with increasing PARS and being aged 20 years or older vs less than 20 years at first FTP (P for interaction = .002) and inversely associated with multiparity vs nulliparity (P for interaction = .01). Conclusions and Relevance In this cohort study of women with no prior BC diagnoses, associations between pregnancy-related factors and BC risk were modified by PARS, with greater associations observed for ER-negative BC.
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Affiliation(s)
| | - Yuyan Liao
- Columbia University Irving Medical Center, New York, New York
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Esther M John
- Stanford University School of Medicine, Stanford, California
| | | | - Mary Daly
- Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Saundra S Buys
- University of Utah Health Sciences Center, Salt Lake City
| | - Yun Huang
- Ministry of Education, Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine and School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Caren J Frost
- College of Social Work, The University of Utah, Salt Lake City
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Sarah V Colonna
- University of Utah Health Huntsman Cancer Institute, Salt Lake City
| | | | - John L Hopper
- Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Wendy K Chung
- Columbia University Irving Medical Center, New York, New York
| | | | - Robert J MacInnis
- Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Council Victoria, East Melbourne, Victoria, Australia
| | - Mary Beth Terry
- Columbia University Irving Medical Center, New York, New York
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4
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Hopper JL, Li S, MacInnis RJ, Dowty JG, Nguyen TL, Bui M, Dite GS, Esser VFC, Ye Z, Makalic E, Schmidt DF, Goudey B, Alpen K, Kapuscinski M, Win AK, Dugué PA, Milne RL, Jayasekara H, Brooks JD, Malta S, Calais-Ferreira L, Campbell AC, Young JT, Nguyen-Dumont T, Sung J, Giles GG, Buchanan D, Winship I, Terry MB, Southey MC, Jenkins MA. Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies. Genet Epidemiol 2024. [PMID: 38504141 DOI: 10.1002/gepi.22555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/29/2024] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Karen Alpen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Miroslaw Kapuscinski
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
- Genetic Medicine, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Harindra Jayasekara
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Jennifer D Brooks
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Sue Malta
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Lucas Calais-Ferreira
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Alexander C Campbell
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Jesse T Young
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Centre for Adolescent Health, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
- Justice Health Group, Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, South Korea
- Genome Medicine Institute, Seoul National University, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel Buchanan
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ingrid Winship
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
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Terry MB, Colditz GA. Epidemiology and Risk Factors for Breast Cancer: 21st Century Advances, Gaps to Address through Interdisciplinary Science. Cold Spring Harb Perspect Med 2023; 13:a041317. [PMID: 36781224 PMCID: PMC10513162 DOI: 10.1101/cshperspect.a041317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Research methods to study risk factors and prevention of breast cancer have evolved rapidly. We focus on advances from epidemiologic studies reported over the past two decades addressing scientific discoveries, as well as their clinical and public health translation for breast cancer risk reduction. In addition to reviewing methodology advances such as widespread assessment of mammographic density and Mendelian randomization, we summarize the recent evidence with a focus on the timing of exposure and windows of susceptibility. We summarize the implications of the new evidence for application in risk stratification models and clinical translation to focus prevention-maximizing benefits and minimizing harm. We conclude our review identifying research gaps. These include: pathways for the inverse association of vegetable intake and estrogen receptor (ER)-ve tumors, prepubertal and adolescent diet and risk, early life adiposity reducing lifelong risk, and gaps from changes in habits (e.g., vaping, binge drinking), and environmental exposures.
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Affiliation(s)
- Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, Chronic Disease Unit Leader, Department of Epidemiology, Herbert Irving Comprehensive Cancer Center, Associate Director, New York, New York 10032, USA
| | - Graham A Colditz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine and Alvin J. Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital in St Louis, St. Louis, Missouri 63110, USA
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6
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Bacon B, Repin M, Shuryak I, Wu HC, Santella RM, Terry MB, Brenner DJ, Turner HC. High-throughput measurement of double strand break global repair phenotype in peripheral blood mononuclear cells after long-term cryopreservation. Cytometry A 2023; 103:575-583. [PMID: 36823754 PMCID: PMC10680149 DOI: 10.1002/cyto.a.24725] [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: 08/18/2022] [Revised: 01/02/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023]
Abstract
Peripheral blood mononuclear cells (PBMCs) are a useful model for biochemical assays, particularly for etiological studies. We describe here a method for measuring DNA repair capacity (DRC) in archival cryogenically preserved PBMCs. To model DRC, we measured γ-H2AX repair kinetics in thawed PBMCs after irradiation with 3 Gy gamma rays. Time-dependent fluorescently labeled γ-H2AX levels were measured at five time points from 1 to 20 h, yielding an estimate of global DRC repair kinetics as well as a measure of unrepaired double strand breaks at 20 h. While γ-H2AX levels are traditionally measured by either microscopy or flow-cytometry, we developed a protocol for imaging flow cytometry (IFC) that combines the detailed information of microscopy with the statistical power of flow methods. The visual imaging component of the IFC allows for monitoring aspects such as cellular health and apoptosis as well as fluorescence localization of the γ-H2AX signal, which ensures the power and significance of this technique. Application of a machine-learning based image classification improved flow cytometry fluorescent measurements by identifying apoptotic cells unable to undergo DNA repair. We present here DRC repair parameters from 18 frozen archival PBMCs and 28 fresh blood samples collected from a demographically diverse cohort of women measured in a high-throughput IFC format. This thaw method and assay can be used alone or in conjunction with other assays to measure etiological phenotypes in cryogenic biobanks of PBMCs.
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Affiliation(s)
- Bezalel Bacon
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
| | - Mikhail Repin
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
| | - Igor Shuryak
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
| | - Hui-Chen Wu
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center
| | - Regina M. Santella
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center
| | - Mary Beth Terry
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center
- Department of Epidemiology, Mailman School of Public Health, Columbia University, Irving Medical Center, New York
| | - David J. Brenner
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
| | - Helen C. Turner
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
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7
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Okunola HL, Shuryak I, Repin M, Wu HC, Santella RM, Terry MB, Turner HC, Brenner DJ. Improved prediction of breast cancer risk based on phenotypic DNA damage repair capacity in peripheral blood B cells. RESEARCH SQUARE 2023:rs.3.rs-3093360. [PMID: 37461559 PMCID: PMC10350237 DOI: 10.21203/rs.3.rs-3093360/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Background Standard Breast Cancer (BC) risk prediction models based only on epidemiologic factors generally have quite poor performance, and there have been a number of risk scores proposed to improve them, such as AI-based mammographic information, polygenic risk scores and pathogenic variants. Even with these additions BC risk prediction performance is still at best moderate. In that decreased DNA repair capacity (DRC) is a major risk factor for development of cancer, we investigated the potential to improve BC risk prediction models by including a measured phenotypic DRC assay. Methods Using blood samples from the Breast Cancer Family Registry we assessed the performance of phenotypic markers of DRC in 46 matched pairs of individuals, one from each pair with BC (with blood drawn before BC diagnosis) and the other from controls matched by age and time since blood draw. We assessed DRC in thawed cryopreserved peripheral blood mononuclear cells (PBMCs) by measuring γ-H2AX yields (a marker for DNA double-strand breaks) at multiple times from 1 to 20 hrs after a radiation challenge. The studies were performed using surface markers to discriminate between different PBMC subtypes. Results The parameter F res , the residual damage signal in PBMC B cells at 20 hrs post challenge, was the strongest predictor of breast cancer with an AUC (Area Under receiver-operator Curve) of 0.89 [95% Confidence Interval: 0.84-0.93] and a BC status prediction accuracy of 0.80. To illustrate the combined use of a phenotypic predictor with standard BC predictors, we combined F res in B cells with age at blood draw, and found that the combination resulted in significantly greater BC predictive power (AUC of 0.97 [95% CI: 0.94-0.99]), an increase of 13 percentage points over age alone. Conclusions If replicated in larger studies, these results suggest that inclusion of a fingerstick-based phenotypic DRC blood test has the potential to markedly improve BC risk prediction.
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Affiliation(s)
| | | | | | - Hui-Chen Wu
- Columbia University Mailman School of Public Health
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8
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Kast K, John EM, Hopper JL, Andrieu N, Noguès C, Mouret-Fourme E, Lasset C, Fricker JP, Berthet P, Mari V, Salle L, Schmidt MK, Ausems MGEM, Garcia EBG, van de Beek I, Wevers MR, Evans DG, Tischkowitz M, Lalloo F, Cook J, Izatt L, Tripathi V, Snape K, Musgrave H, Sharif S, Murray J, Colonna SV, Andrulis IL, Daly MB, Southey MC, de la Hoya M, Osorio A, Foretova L, Berkova D, Gerdes AM, Olah E, Jakubowska A, Singer CF, Tan Y, Augustinsson A, Rantala J, Simard J, Schmutzler RK, Milne RL, Phillips KA, Terry MB, Goldgar D, van Leeuwen FE, Mooij TM, Antoniou AC, Easton DF, Rookus MA, Engel C. Associations of height, body mass index, and weight gain with breast cancer risk in carriers of a pathogenic variant in BRCA1 or BRCA2: the BRCA1 and BRCA2 Cohort Consortium. Breast Cancer Res 2023; 25:72. [PMID: 37340476 PMCID: PMC10280955 DOI: 10.1186/s13058-023-01673-w] [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: 12/16/2022] [Accepted: 06/10/2023] [Indexed: 06/22/2023] Open
Abstract
INTRODUCTION Height, body mass index (BMI), and weight gain are associated with breast cancer risk in the general population. It is unclear whether these associations also exist for carriers of pathogenic variants in the BRCA1 or BRCA2 genes. PATIENTS AND METHODS An international pooled cohort of 8091 BRCA1/2 variant carriers was used for retrospective and prospective analyses separately for premenopausal and postmenopausal women. Cox regression was used to estimate breast cancer risk associations with height, BMI, and weight change. RESULTS In the retrospective analysis, taller height was associated with risk of premenopausal breast cancer for BRCA2 variant carriers (HR 1.20 per 10 cm increase, 95% CI 1.04-1.38). Higher young-adult BMI was associated with lower premenopausal breast cancer risk for both BRCA1 (HR 0.75 per 5 kg/m2, 95% CI 0.66-0.84) and BRCA2 (HR 0.76, 95% CI 0.65-0.89) variant carriers in the retrospective analysis, with consistent, though not statistically significant, findings from the prospective analysis. In the prospective analysis, higher BMI and adult weight gain were associated with higher postmenopausal breast cancer risk for BRCA1 carriers (HR 1.20 per 5 kg/m2, 95% CI 1.02-1.42; and HR 1.10 per 5 kg weight gain, 95% CI 1.01-1.19, respectively). CONCLUSION Anthropometric measures are associated with breast cancer risk for BRCA1 and BRCA2 variant carriers, with relative risk estimates that are generally consistent with those for women from the general population.
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Affiliation(s)
- Karin Kast
- Center for Hereditary Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
| | - Esther M John
- Department of Epidemiology & Population Health and of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Nadine Andrieu
- INSERM U900, Paris, France
- Institut Curie, Paris, France
- Mines Paris Tech, Fontainebleau, France
- PSL Research University, Paris, France
| | - Catherine Noguès
- Aix Marseille Université, INSERM, IRD, SESSTIM, Marseille, France
- Département d'Anticipation et de Suivi Des Cancers, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France
| | | | | | | | | | | | - Lucie Salle
- Oncogénétique Poitou-Charentes, Niort, France
| | - Marjanka K Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute, Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Margreet G E M Ausems
- Department of Genetics, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Irma van de Beek
- Department of Human Genetics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Marijke R Wevers
- Department of Clinical Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - D Gareth Evans
- The Prevent Breast Cancer Research Unit, The Nightingale Centre, Manchester University NHS Foundation Trust, Manchester, UK
- Genomic Medicine, Division of Evolution and Genomic Sciences, The University of Manchester, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Manchester Breast Centre, Oglesby Cancer Research Centre, The Christie, University of Manchester, Manchester, UK
| | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Fiona Lalloo
- Clinical Genetics Service, Manchester Centre for Genomic Medicine, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Jackie Cook
- Sheffield Clinical Genetics Service, Sheffield Children's Hospital, Sheffield, UK
| | - Louise Izatt
- Department of Clinical Genetics, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Vishakha Tripathi
- Clinical Genetics Service, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Katie Snape
- Department of Clinical Genetics, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Hannah Musgrave
- Yorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Saba Sharif
- West Midlands Regional Genetics Service, Birmingham Women's Hospital Healthcare NHS Trust, Edgbaston, Birmingham, UK
| | - Jennie Murray
- Yorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- West Midlands Regional Genetics Service, Birmingham Women's Hospital Healthcare NHS Trust, Edgbaston, Birmingham, UK
- South East of Scotland Regional Genetics Service, Western General Hospital, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Sarah V Colonna
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at, Monash Health Monash University, Clayton, VIC, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | - Miguel de la Hoya
- Molecular Oncology Laboratory, Hospital Clínico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
| | - Ana Osorio
- Familial Cancer Clinical Unit, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO) and Spanish Network On Rare Diseases (CIBERER), Madrid, Spain
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Dita Berkova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Anne-Marie Gerdes
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Edith Olah
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Christian F Singer
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Yen Tan
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Annelie Augustinsson
- Department of Oncology, Clinical Sciences in Lund, Lund University Hospital, Lund, Sweden
| | | | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Quebec City, QC, Canada
| | - Rita K Schmutzler
- Center for Hereditary Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Kelly-Anne Phillips
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health and the Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - David Goldgar
- Department of Dermatology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Flora E van Leeuwen
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Thea M Mooij
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Matti A Rookus
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
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9
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Adherence to the 2020 American Cancer Society Guideline for Cancer Prevention and risk of breast cancer for women at increased familial and genetic risk in the Breast Cancer Family Registry: an evaluation of the weight, physical activity, and alcohol consumption recommendations. Breast Cancer Res Treat 2022; 194:673-682. [PMID: 35780210 DOI: 10.1007/s10549-022-06656-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 06/08/2022] [Indexed: 11/02/2022]
Abstract
PURPOSE The American Cancer Society (ACS) published an updated Guideline for Cancer Prevention (ACS Guideline) in 2020. Research suggests that adherence to the 2012 ACS Guideline might lower breast cancer risk, but there is limited evidence that this applies to women at increased familial and genetic risk of breast cancer. METHODS Using the Breast Cancer Family Registry (BCFR), a cohort enriched for increased familial and genetic risk of breast cancer, we examined adherence to three 2020 ACS Guideline recommendations (weight management (body mass index), physical activity, and alcohol consumption) with breast cancer risk in 9615 women. We used Cox proportional hazard regression modeling to calculate hazard ratios (HRs) and 95% confidence intervals (CI) overall and stratified by BRCA1 and BRCA2 pathogenic variant status, family history of breast cancer, menopausal status, and estrogen receptor-positive (ER +) breast cancer. RESULTS We observed 618 incident invasive or in situ breast cancers over a median 12.9 years. Compared with being adherent to none (n = 55 cancers), being adherent to any ACS recommendation (n = 563 cancers) was associated with a 27% lower breast cancer risk (HR = 0.73, 95% CI: 0.55-0.97). This was evident for women with a first-degree family history of breast cancer (HR = 0.68, 95% CI: 0.50-0.93), women without BRCA1 or BRCA2 pathogenic variants (HR = 0.71, 95% CI: 0.53-0.95), postmenopausal women (HR = 0.63, 95% CI: 0.44-0.89), and for risk of ER+ breast cancer (HR = 0.63, 95% CI: 0.40-0.98). DISCUSSION Adherence to the 2020 ACS Guideline recommendations for BMI, physical activity, and alcohol consumption could reduce breast cancer risk for postmenopausal women and women at increased familial risk.
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10
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Ye Z, Li S, Dite GS, Nguyen TL, MacInnis RJ, Andrulis IL, Buys SS, Daly MB, John EM, Kurian AW, Genkinger JM, Chung WK, Phillips KA, Thorne H, Thorne H, Winship IM, Milne RL, Dugué PA, Southey MC, Giles GG, Terry MB, Hopper JL. Weight is More Informative than Body Mass Index for Predicting Postmenopausal Breast Cancer Risk: Prospective Family Study Cohort (ProF-SC). Cancer Prev Res (Phila) 2022; 15:185-191. [PMID: 34965921 PMCID: PMC8977841 DOI: 10.1158/1940-6207.capr-21-0164] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/17/2021] [Accepted: 12/20/2021] [Indexed: 01/07/2023]
Abstract
We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (ΔAIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (ΔAIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (ΔAIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk. PREVENTION RELEVANCE Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Genetic Technologies Limited, Fitzroy, Victoria, Australia
| | - Tuong L. Nguyen
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
| | - Robert J. MacInnis
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Irene L. Andrulis
- Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Saundra S. Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, Utah
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Esther M. John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
| | - Allison W. Kurian
- Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, California
| | - Jeanine M. Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York City, New York
| | - Wendy K. Chung
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York City, New York
- Departments of Pediatrics and Medicine, Columbia University, New York City, New York
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Heather Thorne
- Research Department, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Heather Thorne
- Research Department, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | | | - Ingrid M. Winship
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Roger L. Milne
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The Melbourne Medical School, The University of Melbourne, Melbourne, Australia
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York City, New York
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
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11
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Niehoff NM, Terry MB, Bookwalter DB, Kaufman JD, O'Brien KM, Sandler DP, White AJ. Air Pollution and Breast Cancer: An Examination of Modification By Underlying Familial Breast Cancer Risk. Cancer Epidemiol Biomarkers Prev 2022; 31:422-429. [PMID: 34906967 PMCID: PMC8825697 DOI: 10.1158/1055-9965.epi-21-1140] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/18/2021] [Accepted: 12/03/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND An increased familial risk of breast cancer may be due to both shared genetics and environment. Women with a breast cancer family history may have a higher prevalence of breast cancer-related gene variants and thus increased susceptibility to environmental exposures. We evaluated whether air pollutant and breast cancer associations varied by familial risk. METHODS Sister Study participants living in the contiguous United States at enrollment (2003-2009; N = 48,453), all of whom had at least one first-degree relative with breast cancer, were followed for breast cancer. Annual NO2 and PM2.5 concentrations were estimated at the enrollment addresses. We predicted 1-year familial breast cancer risk using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). Using Cox regression, we estimated HRs and 95% confidence intervals (CI) for associations between each pollutant dichotomized at the median and breast cancer with interaction terms to examine modification by BOADICEA score. RESULTS NO2 was associated with a higher breast cancer risk among those with BOADICEA score >90th percentile (HR, 1.28; 95% CI, 1.05-1.56) but not among those with BOADICEA score ≤90th percentile (HR, 0.98; 95% CI, 0.90-1.06; P interaction = 0.01). In contrast to NO2, associations between PM2.5 and breast cancer did not vary between individuals with BOADICEA score >90th percentile and ≤90th percentile (P interaction = 0.26). CONCLUSIONS Our results provide additional evidence that air pollution may be implicated in breast cancer, particularly among women with a higher familial risk. IMPACT Women at higher underlying breast cancer risk may benefit more from interventions to reduce exposure to NO2.
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Affiliation(s)
- Nicole M Niehoff
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina.
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York and the Herbert Irving Comprehensive Cancer Center, New York, New York
| | | | - Joel D Kaufman
- Department of Environmental and Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, Seattle, Washington
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
| | - Alexandra J White
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
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12
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Kehm RD, MacInnis RJ, John EM, Liao Y, Kurian AW, Genkinger JM, Knight JA, Colonna SV, Chung WK, Milne R, Zeinomar N, Dite GS, Southey MC, Giles GG, McLachlan SA, Whitaker KD, Friedlander ML, Weideman PC, Glendon G, Nesci S, Phillips KA, Andrulis IL, Buys SS, Daly MB, Hopper JL, Terry MB. Recreational Physical Activity and Outcomes After Breast Cancer in Women at High Familial Risk. JNCI Cancer Spectr 2021; 5:pkab090. [PMID: 34950851 PMCID: PMC8692829 DOI: 10.1093/jncics/pkab090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/08/2021] [Accepted: 10/14/2021] [Indexed: 12/13/2022] Open
Abstract
Background Recreational physical activity (RPA) is associated with improved survival after breast cancer (BC) in average-risk women, but evidence is limited for women who are at increased familial risk because of a BC family history or BRCA1 and BRCA2 pathogenic variants (BRCA1/2 PVs). Methods We estimated associations of RPA (self-reported average hours per week within 3 years of BC diagnosis) with all-cause mortality and second BC events (recurrence or new primary) after first invasive BC in women in the Prospective Family Study Cohort (n = 4610, diagnosed 1993-2011, aged 22-79 years at diagnosis). We fitted Cox proportional hazards regression models adjusted for age at diagnosis, demographics, and lifestyle factors. We tested for multiplicative interactions (Wald test statistic for cross-product terms) and additive interactions (relative excess risk due to interaction) by age at diagnosis, body mass index, estrogen receptor status, stage at diagnosis, BRCA1/2 PVs, and familial risk score estimated from multigenerational pedigree data. Statistical tests were 2-sided. Results We observed 1212 deaths and 473 second BC events over a median follow-up from study enrollment of 11.0 and 10.5 years, respectively. After adjusting for covariates, RPA (any vs none) was associated with lower all-cause mortality of 16.1% (95% confidence interval [CI] = 2.4% to 27.9%) overall, 11.8% (95% CI = -3.6% to 24.9%) in women without BRCA1/2 PVs, and 47.5% (95% CI = 17.4% to 66.6%) in women with BRCA1/2 PVs (RPA*BRCA1/2 multiplicative interaction P = .005; relative excess risk due to interaction = 0.87, 95% CI = 0.01 to 1.74). RPA was not associated with risk of second BC events. Conclusion Findings support that RPA is associated with lower all-cause mortality in women with BC, particularly in women with BRCA1/2 PVs.
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Affiliation(s)
- Rebecca D Kehm
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Allison W Kurian
- Division of Medical Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeanine M Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Sarah V Colonna
- Division of Medical Oncology, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY, USA
| | - Roger Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Nur Zeinomar
- Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Sue-Anne McLachlan
- Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Medical Oncology, St Vincent’s Hospital, Fitzroy, Melbourne, Victoria, Australia
| | - Kristen D Whitaker
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Michael L Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Oncology, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Stephanie Nesci
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
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Etzioni R, Shen Y, Shih YCT. Identifying Preferred Breast Cancer Risk Predictors: A Holistic Perspective. J Natl Cancer Inst 2021; 113:660-661. [PMID: 33301010 DOI: 10.1093/jnci/djaa181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 11/02/2020] [Indexed: 02/06/2023] Open
Affiliation(s)
- Ruth Etzioni
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yu Shen
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ya-Chen Tina Shih
- Section of Cancer Economics and Policy, Department of Health Services Research, University of Texas MD Anderson Cancer Center, TX, USA
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14
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Choi YH, Terry MB, Daly MB, MacInnis RJ, Hopper JL, Colonna S, Buys SS, Andrulis IL, John EM, Kurian AW, Briollais L. Association of Risk-Reducing Salpingo-Oophorectomy With Breast Cancer Risk in Women With BRCA1 and BRCA2 Pathogenic Variants. JAMA Oncol 2021; 7:585-592. [PMID: 33630024 PMCID: PMC7907985 DOI: 10.1001/jamaoncol.2020.7995] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 12/01/2020] [Indexed: 11/14/2022]
Abstract
IMPORTANCE Women with pathogenic variants in BRCA1 and BRCA2 are at high risk of developing breast and ovarian cancers. They usually undergo intensive cancer surveillance and may also consider surgical interventions, such as risk-reducing mastectomy or risk-reducing salpingo-oophorectomy (RRSO). Risk-reducing salpingo-oophorectomy has been shown to reduce ovarian cancer risk, but its association with breast cancer risk is less clear. OBJECTIVE To assess the association of RRSO with the risk of breast cancer in women with BRCA1 and BRCA2 pathogenic variants. DESIGN, SETTING, AND PARTICIPANTS This case series included families enrolled in the Breast Cancer Family Registry between 1996 and 2000 that carried an inherited pathogenic variant in BRCA1 (498 families) or BRCA2 (378 families). A survival analysis approach was used that was designed specifically to assess the time-varying association of RRSO with breast cancer risk and accounting for other potential biases. Data were analyzed from August 2019 to November 2020. EXPOSURE Risk-reducing salpingo-oophorectomy. MAIN OUTCOMES AND MEASURES In all analyses, the primary end point was the time to a first primary breast cancer. RESULTS A total of 876 families were evaluated, including 498 with BRCA1 (2650 individuals; mean [SD] event age, 55.8 [19.1] years; 437 White probands [87.8%]) and 378 with BRCA2 (1925 individuals; mean [SD] event age, 57.0 [18.6] years; 299 White probands [79.1%]). Risk-reducing salpingo-oophorectomy was associated with a reduced risk of breast cancer for BRCA1 and BRCA2 pathogenic variant carriers within 5 years after surgery (hazard ratios [HRs], 0.28 [95% CI, 0.10-0.63] and 0.19 [95% CI, 0.06-0.71], respectively), whereas the corresponding HRs were weaker after 5 years postsurgery (HRs, 0.64 [95% CI, 0.38-0.97] and 0.99 [95% CI; 0.84-1.00], respectively). For BRCA1 and BRCA2 pathogenic variant carriers who underwent RRSO at age 40 years, the cause-specific cumulative risk of breast cancer was 49.7% (95% CI, 40.0-60.3) and 52.7% (95% CI, 47.9-58.7) by age 70 years, respectively, compared with 61.0% (95% CI, 56.7-66.0) and 54.0% (95% CI, 49.3-60.1), respectively, for women without RRSO. CONCLUSIONS AND RELEVANCE Although the primary indication for RRSO is the prevention of ovarian cancer, it is also critical to assess its association with breast cancer risk in order to guide clinical decision-making about RRSO use and timing. The results of this case series suggest a reduced risk of breast cancer associated with RRSO in the immediate 5 years after surgery in women carrying BRCA1 and BRCA2 pathogenic variants, and a longer-term association with cumulative breast cancer risk in women carrying BRCA1 pathogenic variants.
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Affiliation(s)
- Yun-Hee Choi
- Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada
| | - Mary Beth Terry
- Mailman School of Public Health, Columbia University, New York, New York
- Herbert Irving Comprehensive Cancer Center, Columbia Irving Medical Center, New York, New York
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Robert J. MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - John L. Hopper
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Sarah Colonna
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City
| | - Saundra S. Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City
| | - Irene L. Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Esther M. John
- Departments of Epidemiology & Population Health and of Medicine (Oncology), Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Allison W. Kurian
- Departments of Epidemiology & Population Health and of Medicine (Oncology), Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Laurent Briollais
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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15
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VTRNA2-1: Genetic Variation, Heritable Methylation and Disease Association. Int J Mol Sci 2021; 22:ijms22052535. [PMID: 33802562 PMCID: PMC7961504 DOI: 10.3390/ijms22052535] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 12/28/2022] Open
Abstract
VTRNA2-1 is a metastable epiallele with accumulating evidence that methylation at this region is heritable, modifiable and associated with disease including risk and progression of cancer. This study investigated the influence of genetic variation and other factors such as age and adult lifestyle on blood DNA methylation in this region. We first sequenced the VTRNA2-1 gene region in multiple-case breast cancer families in which VTRNA2-1 methylation was identified as heritable and associated with breast cancer risk. Methylation quantitative trait loci (mQTL) were investigated using a prospective cohort study (4500 participants with genotyping and methylation data). The cis-mQTL analysis (334 variants ± 50 kb of the most heritable CpG site) identified 43 variants associated with VTRNA2-1 methylation (p < 1.5 × 10−4); however, these explained little of the methylation variation (R2 < 0.5% for each of these variants). No genetic variants elsewhere in the genome were found to strongly influence VTRNA2-1 methylation. SNP-based heritability estimates were consistent with the mQTL findings (h2 = 0, 95%CI: −0.14 to 0.14). We found no evidence that age, sex, country of birth, smoking, body mass index, alcohol consumption or diet influenced blood DNA methylation at VTRNA2-1. Genetic factors and adult lifestyle play a minimal role in explaining methylation variability at the heritable VTRNA2-1 cluster.
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16
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Knight JA, Kehm RD, Schwartz L, Frost CJ, Chung WK, Colonna S, Keegan THM, Goldberg M, Houghton LC, Hanna D, Glendon G, Daly MB, Buys SS, Andrulis IL, John EM, Bradbury AR, Terry MB. Prepubertal Internalizing Symptoms and Timing of Puberty Onset in Girls. Am J Epidemiol 2021; 190:431-438. [PMID: 33057572 DOI: 10.1093/aje/kwaa223] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 10/02/2020] [Accepted: 10/09/2020] [Indexed: 11/12/2022] Open
Abstract
Stressful environments have been associated with earlier menarche. We hypothesized that anxiety, and possibly other internalizing symptoms, are also associated with earlier puberty in girls. The Lessons in Epidemiology and Genetics of Adult Cancer From Youth (LEGACY) Girls Study (2011-2016) included 1,040 girls aged 6-13 years at recruitment whose growth and development were assessed every 6 months. Prepubertal maternal reports of daughter's internalizing symptoms were available for breast onset (n = 447), pubic hair onset (n = 456), and menarche (n = 681). Using Cox proportional hazard regression, we estimated prospective hazard ratios and 95% confidence intervals for the relationship between 1 standard deviation of the percentiles of prepubertal anxiety, depression, and somatization symptoms and the timing of each pubertal outcome. Multivariable models included age, race/ethnicity, study center, maternal education, body mass index percentile, and family history of breast cancer. Additional models included maternal self-reported anxiety. A 1-standard deviation increase in maternally reported anxiety in girls at baseline was associated with earlier subsequent onset of breast (hazard ratio (HR) = 1.22, 95% confidence interval (CI): 1.09, 1.36) and pubic hair (HR = 1.15, 95% CI: 1.01, 1.30) development, but not menarche (HR = 0.94, 95% CI: 0.83, 1.07). The association of anxiety with earlier breast development persisted after adjustment for maternal anxiety. Increased anxiety in young girls may indicate risk for earlier pubertal onset.
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17
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MacInnis RJ, Knight JA, Chung WK, Milne RL, Whittemore AS, Buchsbaum R, Liao Y, Zeinomar N, Dite GS, Southey MC, Goldgar D, Giles GG, Kurian AW, Andrulis IL, John EM, Daly MB, Buys SS, Phillips KA, Hopper JL, Terry MB. Comparing 5-Year and Lifetime Risks of Breast Cancer using the Prospective Family Study Cohort. J Natl Cancer Inst 2020; 113:785-791. [PMID: 33301022 DOI: 10.1093/jnci/djaa178] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/06/2020] [Accepted: 10/13/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Clinical guidelines often use predicted lifetime risk from birth to define criteria for making decisions regarding breast cancer screening rather than thresholds based on absolute 5-year risk from current age. METHODS We used the Prospective Family Cohort Study of 14 657 women without breast cancer at baseline in which, during a median follow-up of 10 years, 482 women were diagnosed with invasive breast cancer. We examined the performances of the International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk models when using the alternative thresholds by comparing predictions based on 5-year risk with those based on lifetime risk from birth and remaining lifetime risk. All statistical tests were 2-sided. RESULTS Using IBIS, the areas under the receiver-operating characteristic curves were 0.66 (95% confidence interval = 0.63 to 0.68) and 0.56 (95% confidence interval = 0.54 to 0.59) for 5-year and lifetime risks, respectively (Pdiff < .001). For equivalent sensitivities, the 5-year incidence almost always had higher specificities than lifetime risk from birth. For women aged 20-39 years, 5-year risk performed better than lifetime risk from birth. For women aged 40 years or older, receiver-operating characteristic curves were similar for 5-year and lifetime IBIS risk from birth. Classifications based on remaining lifetime risk were inferior to 5-year risk estimates. Results were similar using BOADICEA. CONCLUSIONS Our analysis shows that risk stratification using clinical models will likely be more accurate when based on predicted 5-year risk compared with risks based on predicted lifetime and remaining lifetime, particularly for women aged 20-39 years.
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Affiliation(s)
- Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.,Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Alice S Whittemore
- Department of Health Research and Policy and of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Richard Buchsbaum
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - David Goldgar
- Department of Dermatology and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Allison W Kurian
- Department of Medicine and Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | | | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Department of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Esther M John
- Department of Epidemiology & Population Health and Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.,Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
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18
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Rosner B, Tamimi RM, Kraft P, Gao C, Mu Y, Scott C, Winham SJ, Vachon CM, Colditz GA. Simplified Breast Risk Tool Integrating Questionnaire Risk Factors, Mammographic Density, and Polygenic Risk Score: Development and Validation. Cancer Epidemiol Biomarkers Prev 2020; 30:600-607. [PMID: 33277321 DOI: 10.1158/1055-9965.epi-20-0900] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 09/01/2020] [Accepted: 12/01/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS). METHODS We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up. RESULTS In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: ORper SD = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, ORper SD = 1.37 (95% CI: 1.21-1.55) and QS, ORper SD = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls. CONCLUSION A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk. IMPACT This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.See related commentary by Tehranifar et al., p. 587.
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Affiliation(s)
- Bernard Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Division of Epidemiology, Population Health Sciences Department, Weill Cornell Medicine, New York, New York
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Chi Gao
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Yi Mu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Christopher Scott
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Stacey J Winham
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Celine M Vachon
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Graham A Colditz
- Alvin J. Siteman Cancer Center and Department of Surgery, Division of Public Health Sciences, School of Medicine, Washington University in St. Louis, St. Louis, Missouri
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19
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Monson KR, Goldberg M, Wu HC, Santella RM, Chung WK, Terry MB. Circulating growth factor concentrations and breast cancer risk: a nested case-control study of IGF-1, IGFBP-3, and breast cancer in a family-based cohort. Breast Cancer Res 2020; 22:109. [PMID: 33092613 PMCID: PMC7579807 DOI: 10.1186/s13058-020-01352-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/07/2020] [Indexed: 12/19/2022] Open
Abstract
Background Insulin-like growth factor 1 (IGF-1) and binding protein 3 (IGFBP-3) are associated with breast cancer in women at average risk of cancer. Less is known whether these biomarkers also predict risk in women with breast cancer family history. Methods We conducted a nested case-control study within the New York site of the Breast Cancer Family Registry (BCFR, n = 80 cases, 156 controls), a cohort enriched for breast cancer family history. Using conditional logistic regression, we estimated the association between IGF-1 and IGFBP-3 levels and breast cancer risk and examined whether this risk differed by predicted absolute breast cancer risk based on pedigree models. Results The overall association between IGF-1 or IGFBP-3 elevation (≥ median in controls) and breast cancer risk was elevated, but not statistically significant (IGF-1 OR = 1.37, 95% CI = 0.66–2.85; IGFBP-3 OR = 1.62, 95% CI = 0.81–3.24). Women with elevated predicted absolute 10-year risk ≥ 3.4% and elevated IGFBP-3 (≥ median) had more than a 3-fold increased risk compared to women with lower predicted absolute 10-year risk (< 3.4%) and low IGFBP-3 (OR = 3.47 95% CI = 1.04–11.6). Conclusions These data offer some support that the overall magnitude of the associations between IGF-1 and IGFBP3 seen in average risk cohorts may be similar in women enriched with a strong breast cancer family history.
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Affiliation(s)
- Kelsey R Monson
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, 10032, USA
| | - Mandy Goldberg
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, 10032, USA
| | - Hui-Chen Wu
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, 10032, USA.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, 1130 St. Nicholas Avenue, New York, NY, 10032, USA
| | - Regina M Santella
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, 10032, USA.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, 1130 St. Nicholas Avenue, New York, NY, 10032, USA
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, 1130 St. Nicholas Avenue, New York, NY, 10032, USA.,Department of Pediatrics, Columbia University, 622 West 168th Street, New York, NY, USA.,Department of Medicine, Columbia University Medical Center, 630 West 168th Street, New York, NY, USA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, 10032, USA. .,Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, 10032, USA. .,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, 1130 St. Nicholas Avenue, New York, NY, 10032, USA.
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20
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Houghton LC, Howland RE, Wei Y, Ma X, Kehm RD, Chung WK, Genkinger JM, Santella RM, Hartmann MF, Wudy SA, Terry MB. The Steroid Metabolome and Breast Cancer Risk in Women with a Family History of Breast Cancer: The Novel Role of Adrenal Androgens and Glucocorticoids. Cancer Epidemiol Biomarkers Prev 2020; 30:89-96. [PMID: 32998947 DOI: 10.1158/1055-9965.epi-20-0471] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/09/2020] [Accepted: 09/26/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND No study has comprehensively examined how the steroid metabolome is associated with breast cancer risk in women with familial risk. METHODS We examined 36 steroid metabolites across the spectrum of familial risk (5-year risk ranged from 0.14% to 23.8%) in pre- and postmenopausal women participating in the New York site of the Breast Cancer Family Registry (BCFR). We conducted a nested case-control study with 62 cases/124 controls individually matched on menopausal status, age, and race. We measured metabolites using GC-MS in urine samples collected at baseline before the onset of prospectively ascertained cases. We used conditional logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) per doubling in hormone levels. RESULTS The average proportion of total steroid metabolites in the study sample were glucocorticoids (61%), androgens (26%), progestogens (11%), and estrogens (2%). A doubling in glucocorticoids (aOR = 2.7; 95% CI = 1.3-5.3) and androgens (aOR = 1.6; 95% CI = 1.0-2.7) was associated with increased breast cancer risk. Specific glucocorticoids (THE, THF αTHF, 6β-OH-F, THA, and α-THB) were associated with 49% to 161% increased risk. Two androgen metabolites (AN and 11-OH-AN) were associated with 70% (aOR = 1.7; 95% CI = 1.1-2.7) and 90% (aOR = 1.9; 95% CI = 1.2-3.1) increased risk, respectively. One intermediate metabolite of a cortisol precursor (THS) was associated with 65% (OR = 1.65; 95% CI = 1.0-2.7) increased risk. E1 and E2 estrogens were associated with 20% and 27% decreased risk, respectively. CONCLUSIONS Results suggest that glucocorticoids and 11-oxygenated androgens are positively associated with breast cancer risk across the familial risk spectrum. IMPACT If replicated, our findings suggest great potential of including steroids into existing breast cancer risk assessment tools.
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Affiliation(s)
- Lauren C Houghton
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York. .,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York
| | - Renata E Howland
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
| | - Ying Wei
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York
| | - Xinran Ma
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
| | - Rebecca D Kehm
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York.,Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, New York
| | - Jeanine M Genkinger
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York
| | - Regina M Santella
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York.,Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York
| | - Michaela F Hartmann
- Steroid Research and Mass Spectrometry Unit, Laboratory for Translational Hormone Analytics in Pediatric Endocrinology, Division of Pediatric Endocrinology and Diabetology, Justus Liebig University, Giessen, Germany
| | - Stefan A Wudy
- Steroid Research and Mass Spectrometry Unit, Laboratory for Translational Hormone Analytics in Pediatric Endocrinology, Division of Pediatric Endocrinology and Diabetology, Justus Liebig University, Giessen, Germany
| | - Mary Beth Terry
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York
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21
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MacInnis RJ, Liao Y, Knight JA, Milne RL, Whittemore AS, Chung WK, Leoce N, Buchsbaum R, Zeinomar N, Dite GS, Southey MC, Goldgar D, Giles GG, McLachlan SA, Weideman PC, Nesci S, Friedlander ML, Glendon G, Andrulis IL, John EM, Daly MB, Buys SS, Phillips KA, Hopper JL, Terry MB. Considerations When Using Breast Cancer Risk Models for Women with Negative BRCA1/BRCA2 Mutation Results. J Natl Cancer Inst 2020; 112:418-422. [PMID: 31584660 DOI: 10.1093/jnci/djz194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 09/06/2019] [Accepted: 09/17/2019] [Indexed: 01/20/2023] Open
Abstract
The performance of breast cancer risk models for women with a family history but negative BRCA1 and/or BRCA2 mutation test results is uncertain. We calculated the cumulative 10-year invasive breast cancer risk at cohort entry for 14 657 unaffected women (96.1% had an affected relative) not known to carry BRCA1 or BRCA2 mutations at baseline using three pedigree-based models (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm, BRCAPRO, and International Breast Cancer Intervention Study). During follow-up, 482 women were diagnosed with invasive breast cancer. Mutation testing was conducted independent of incident cancers. All models underpredicted risk by 26.3%-56.7% for women who tested negative but whose relatives had not been tested (n = 1363; 63 breast cancers). Although replication studies with larger sample sizes are needed, until these models are recalibrated for women who test negative and have no relatives tested, caution should be used when considering changing the breast cancer risk management intensity of such women based on risk estimates from these models.
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Affiliation(s)
- Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Alice S Whittemore
- Departments of Health Research and Policy and Biomedical Data Science, Stanford University School of Medicine, Stanford
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York.,Departments of Pediatrics and Medicine, Columbia University, New York
| | - Nicole Leoce
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York
| | - Richard Buchsbaum
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York
| | - Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - David Goldgar
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Sue-Anne McLachlan
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Parkville, Victoria, Australia.,Department of Medical Oncology, St Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Stephanie Nesci
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Michael L Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Department of Medical Oncology, Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | | | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.,The Research Department, The Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT
| | - Kelly Anne Phillips
- Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York
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22
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Zeinomar N, Oskar S, Kehm RD, Sahebzeda S, Terry MB. Environmental exposures and breast cancer risk in the context of underlying susceptibility: A systematic review of the epidemiological literature. ENVIRONMENTAL RESEARCH 2020; 187:109346. [PMID: 32445942 PMCID: PMC7314105 DOI: 10.1016/j.envres.2020.109346] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 03/02/2020] [Accepted: 03/02/2020] [Indexed: 05/07/2023]
Abstract
BACKGROUND The evidence evaluating environmental chemical exposures (ECE) and breast cancer (BC) risk is heterogeneous which may stem in part as few studies measure ECE during key BC windows of susceptibility (WOS). Another possibility may be that most BC studies are skewed towards individuals at average risk, which may limit the ability to detect signals from ECE. OBJECTIVES We reviewed the literature on ECE and BC focusing on three types of studies or subgroup analyses based on higher absolute BC risk: BC family history (Type 1); early onset BC (Type 2); and/or genetic susceptibility (Type 3). METHODS We systematically searched the PubMed database to identify epidemiologic studies examining ECE and BC risk published through June 1, 2019. RESULTS We identified 100 publications in 56 unique epidemiologic studies. Of these 56 studies, only 2 (3.6%) were enriched with BC family history and only 11% of studies (6/56) were specifically enriched with early onset cases. 80% of the publications from these 8 enriched studies (Type 1: 8/10 publications; Type 2: 8/10 publications) supported a statistically significant association between ECE and BC risk including studies of PAH, indoor cooking, NO2, DDT; PCBs, PFOSA; metals; personal care products; and occupational exposure to industrial dyes. 74% of Type 3 publications (20/27) supported statistically significant associations for PAHs, traffic-related air pollution, PCBs, phthalates, and PFOSAs in subgroups of women with greater genetic susceptibility due to variants in carcinogen metabolism, DNA repair, oxidative stress, cellular apoptosis and tumor suppressor genes. DISCUSSION Studies enriched for women at higher BC risk through family history, younger age of onset and/or genetic susceptibility consistently support an association between an ECE and BC risk. In addition to measuring exposures during WOS, designing studies that are enriched with women at higher absolute risk are necessary to robustly measure the role of ECE on BC risk.
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Affiliation(s)
- Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Sabine Oskar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Rebecca D Kehm
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Shamin Sahebzeda
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
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23
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Terry MB, Liao Y, Hopper JL, MacInnis RJ. Performance of BCRAT in high-risk patients with breast cancer - Authors' reply. Lancet Oncol 2020; 20:e286. [PMID: 31162093 DOI: 10.1016/s1470-2045(19)30311-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/30/2019] [Indexed: 12/23/2022]
Affiliation(s)
- Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health Columbia University, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health Columbia University, New York, NY 10032, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, VIC, Australia
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24
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Romanescu RG, Green J, Andrulis IL, Bull SB. Gene-based and pathway-based testing for rare-variant association in affected sib pairs. Genet Epidemiol 2020; 44:368-381. [PMID: 32237178 PMCID: PMC7318298 DOI: 10.1002/gepi.22291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/28/2020] [Accepted: 03/06/2020] [Indexed: 12/04/2022]
Abstract
Next generation sequencing technologies have made it possible to investigate the role of rare variants (RVs) in disease etiology. Because RVs associated with disease susceptibility tend to be enriched in families with affected individuals, study designs based on affected sib pairs (ASP) can be more powerful than case-control studies. We construct tests of RV-set association in ASPs for single genomic regions as well as for multiple regions. Single-region tests can efficiently detect a gene region harboring susceptibility variants, while multiple-region extensions are meant to capture signals dispersed across a biological pathway, potentially as a result of locus heterogeneity. Within ascertained ASPs, the test statistics contrast the frequencies of duplicate rare alleles (usually appearing on a shared haplotype) against frequencies of a single rare allele copy (appearing on a nonshared haplotype); we call these allelic parity tests. Incorporation of minor allele frequency estimates from reference populations can markedly improve test efficiency. Under various genetic penetrance models, application of the tests in simulated ASP data sets demonstrates good type I error properties as well as power gains over approaches that regress ASP rare allele counts on sharing state, especially in small samples. We discuss robustness of the allelic parity methods to the presence of genetic linkage, misspecification of reference population allele frequencies, sequencing error and de novo mutations, and population stratification. As proof of principle, we apply single- and multiple-region tests in a motivating study data set consisting of whole exome sequencing of sisters ascertained with early onset breast cancer.
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Affiliation(s)
- Razvan G. Romanescu
- Lunenfeld‐Tanenbaum Research InstituteSinai Health SystemTorontoOntarioCanada
- Centre for Healthcare Innovation, Rady Faculty of Health ScienceUniversity of ManitobaWinnipegManitobaCanada
| | - Jessica Green
- Lunenfeld‐Tanenbaum Research InstituteSinai Health SystemTorontoOntarioCanada
| | - Irene L. Andrulis
- Lunenfeld‐Tanenbaum Research InstituteSinai Health SystemTorontoOntarioCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoOntarioCanada
| | - Shelley B. Bull
- Division of Biostatistics, Dalla Lana School of Public HealthUniversity of TorontoTorontoOntarioCanada
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25
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Terry MB, Daly MB, Phillips KA, Ma X, Zeinomar N, Leoce N, Dite GS, MacInnis RJ, Chung WK, Knight JA, Southey MC, Milne RL, Goldgar D, Giles GG, Weideman PC, Glendon G, Buchsbaum R, Andrulis IL, John EM, Buys SS, Hopper JL. Risk-Reducing Oophorectomy and Breast Cancer Risk Across the Spectrum of Familial Risk. J Natl Cancer Inst 2020; 111:331-334. [PMID: 30496449 PMCID: PMC6410936 DOI: 10.1093/jnci/djy182] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 08/02/2018] [Accepted: 09/07/2018] [Indexed: 02/06/2023] Open
Abstract
There remains debate about whether risk-reducing salpingo-oophorectomy (RRSO), which reduces ovarian cancer risk, also reduces breast cancer risk. We examined the association between RRSO and breast cancer risk using a prospective cohort of 17 917 women unaffected with breast cancer at baseline (7.2% known carriers of BRCA1 or BRCA2 mutations). During a median follow-up of 10.7 years, 1046 women were diagnosed with incident breast cancer. Modeling RRSO as a time-varying exposure, there was no association with breast cancer risk overall (hazard ratio [HR] = 1.04, 95% confidence interval [CI] = 0.87 to 1.24) or by tertiles of predicted absolute risk based on family history (HR = 0.68, 95% CI = 0.32 to 1.47, HR = 0.94, 95% CI = 0.70 to 1.26, and HR = 1.10, 95% CI = 0.88 to 1.39, for lowest, middle, and highest tertile of risk, respectively) or for BRCA1 and BRCA2 mutation carriers when examined separately. There was also no association after accounting for hormone therapy use after RRSO. These findings suggest that RRSO should not be considered efficacious for reducing breast cancer risk.
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Affiliation(s)
- Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA
| | - Kelly Anne Phillips
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Xinran Ma
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Nicole Leoce
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY.,Departments of Pediatrics and Medicine, Columbia University, New York, NY
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Melissa C Southey
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - David Goldgar
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT
| | - Graham G Giles
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,The Research Department, The Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Richard Buchsbaum
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
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26
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Li H, Terry MB, Antoniou AC, Phillips KA, Kast K, Mooij TM, Engel C, Noguès C, Stoppa-Lyonnet D, Lasset C, Berthet P, Mari V, Caron O, Barrowdale D, Frost D, Brewer C, Evans DG, Izatt L, Side L, Walker L, Tischkowitz M, Rogers MT, Porteous ME, Snape K, Meijers-Heijboer HEJ, Gille JJP, Blok MJ, Hoogerbrugge N, Daly MB, Andrulis IL, Buys SS, John EM, McLachlan SA, Friedlander M, Tan YY, Osorio A, Caldes T, Jakubowska A, Simard J, Singer CF, Olah E, Navratilova M, Foretova L, Gerdes AM, Roos-Blom MJ, Arver B, Olsson H, Schmutzler RK, Hopper JL, Milne RL, Easton DF, Van Leeuwen FE, Rookus MA, Andrieu N, Goldgar DE. Alcohol Consumption, Cigarette Smoking, and Risk of Breast Cancer for BRCA1 and BRCA2 Mutation Carriers: Results from The BRCA1 and BRCA2 Cohort Consortium. Cancer Epidemiol Biomarkers Prev 2020; 29:368-378. [PMID: 31792088 PMCID: PMC7611162 DOI: 10.1158/1055-9965.epi-19-0546] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/08/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Tobacco smoking and alcohol consumption have been intensively studied in the general population to assess their effects on the risk of breast cancer, but very few studies have examined these effects in BRCA1 and BRCA2 mutation carriers. Given the high breast cancer risk for mutation carriers and the importance of BRCA1 and BRCA2 in DNA repair, better evidence on the associations of these lifestyle factors with breast cancer risk is essential. METHODS Using a large international pooled cohort of BRCA1 and BRCA2 mutation carriers, we conducted retrospective (5,707 BRCA1 mutation carriers and 3,525 BRCA2 mutation carriers) and prospective (2,276 BRCA1 mutation carriers and 1,610 BRCA2 mutation carriers) analyses of alcohol and tobacco consumption using Cox proportional hazards models. RESULTS For both BRCA1 and BRCA2 mutation carriers, none of the smoking-related variables was associated with breast cancer risk, except smoking for more than 5 years before a first full-term pregnancy (FFTP) when compared with parous women who never smoked. For BRCA1 mutation carriers, the HR from retrospective analysis (HRR) was 1.19 [95% confidence interval (CI), 1.02-1.39] and the HR from prospective analysis (HRP) was 1.36 (95% CI, 0.99-1.87). For BRCA2 mutation carriers, smoking for more than 5 years before an FFTP showed an association of a similar magnitude, but the confidence limits were wider (HRR = 1.25; 95% CI, 1.01-1.55 and HRP = 1.30; 95% CI, 0.83-2.01). For both carrier groups, alcohol consumption was not associated with breast cancer risk. CONCLUSIONS The finding that smoking during the prereproductive years increases breast cancer risk for mutation carriers warrants further investigation. IMPACT This is the largest prospective study of BRCA mutation carriers to assess these important risk factors.
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Affiliation(s)
- Hongyan Li
- Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Mary Beth Terry
- Department of Epidemiology, Columbia University, New York, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, United Kingdom
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Victoria, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Karin Kast
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany
- German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thea M Mooij
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Germany
| | - Catherine Noguès
- Institut Paoli-Calmettes, Département d'Anticipation et de Suivi des Cancers, Oncogénétique Clinique and Aix Marseille Univ, INSERM, IRD, SESSTIM, Marseille, France
| | - Dominique Stoppa-Lyonnet
- Institut Curie, Service de Génétique Médicale, Paris, France
- Inserm, U830, Université Paris Descartes, Paris, France
| | - Christine Lasset
- Unité de prévention et Epidémiologie Génétique, Centre Léon Bérard - Lyon/UMR CNRS 5558, Université de Lyon - Lyon, France
| | - Pascaline Berthet
- Département de biopathologie, Oncogénétique clinique, Centre François Baclesse - Caen, France
| | - Veronique Mari
- CLCC Antoine Lacassagne, Département d'Hématologie - Oncologie médicale, Nice, France
| | - Olivier Caron
- Département de Médecine, Gustave Roussy Hôpital Universitaire - Villejuif, France
| | - Daniel Barrowdale
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, United Kingdom
| | - Debra Frost
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, United Kingdom
| | - Carole Brewer
- Department of Clinical Genetics, Royal Devon & Exeter Hospital, Exeter, United Kingdom
| | - D Gareth Evans
- Genomic Medicine, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Sciences Centre, Division of Evolution and Genomic Sciences, Manchester University, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
| | - Louise Izatt
- Clinical Genetics, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Lucy Side
- Wessex Clinical Genetics Service, The Princess Anne Hospital, Southampton, United Kingdom
| | - Lisa Walker
- Oxford Regional Genetics Service, Churchill Hospital, Oxford, United Kingdom
| | - Marc Tischkowitz
- University of Cambridge Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, and Cancer Research UK Cambridge Center, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Mark T Rogers
- All Wales Medical Genetics Services, University Hospital of Wales, Cardiff, United Kingdom
| | - Mary E Porteous
- South East of Scotland Regional Genetics Service, Western General Hospital, Edinburgh, United Kingdom
| | - Katie Snape
- Medical Genetics Unit, St. George's, University of London, London, United Kingdom
| | | | - Johan J P Gille
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, the Netherlands
| | - Marinus J Blok
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Nicoline Hoogerbrugge
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Irene L Andrulis
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Saundra S Buys
- Department of Medicine, University of Utah Health Sciences Center, Huntsman Cancer Institute, Salt Lake City, Utah
| | - Esther M John
- Stanford University School of Medicine, Department of Medicine, Division of Oncology, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Sue-Anne McLachlan
- Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Parkville, Victoria, Australia
- Department of Medical Oncology, St. Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Michael Friedlander
- Department of Medical Oncology, Prince of Wales Hospital, Randwick, New South Wales, Australia
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Yen Y Tan
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Ana Osorio
- Human Genetics Group, Spanish National Cancer Centre (CNIO) and Biomedical Network on Rare Diseases (CIBERER), Madrid, Spain
| | - Trinidad Caldes
- Molecular Oncology Laboratory, Hospital Clinico San Carlos, IdISSC, CIBERONC (ISCIII), Madrid, Spain
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Quebec City, Quebec, Canada
| | - Christian F Singer
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Edith Olah
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
| | - Marie Navratilova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Zluty kopec 7, Brno, Czech Republic
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Zluty kopec 7, Brno, Czech Republic
| | - Anne-Marie Gerdes
- The Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden
| | - Marie-José Roos-Blom
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Brita Arver
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Oncology, Lund University Hospital, Lund, Sweden
| | - Håkan Olsson
- Department of Oncology, Lund University Hospital, Lund, Sweden
| | - Rita K Schmutzler
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Victoria, Australia
- Division of Cancer Epidemiology and Intelligence, Cancer Council Victoria, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, United Kingdom
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, United Kingdom
| | - Flora E Van Leeuwen
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Matti A Rookus
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Nadine Andrieu
- INSERM, U900, Paris, France.
- Institut Curie, Paris, France
- Mines Paris Tech, Fontainebleau, France
- PSL Research University, Paris, France
| | - David E Goldgar
- Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah.
- Department of Dermatology, University of Utah School of Medicine, Salt Lake City, Utah
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Mavaddat N, Antoniou AC, Mooij TM, Hooning MJ, Heemskerk-Gerritsen BA, Noguès C, Gauthier-Villars M, Caron O, Gesta P, Pujol P, Lortholary A, Barrowdale D, Frost D, Evans DG, Izatt L, Adlard J, Eeles R, Brewer C, Tischkowitz M, Henderson A, Cook J, Eccles D, van Engelen K, Mourits MJE, Ausems MGEM, Koppert LB, Hopper JL, John EM, Chung WK, Andrulis IL, Daly MB, Buys SS, Benitez J, Caldes T, Jakubowska A, Simard J, Singer CF, Tan Y, Olah E, Navratilova M, Foretova L, Gerdes AM, Roos-Blom MJ, Van Leeuwen FE, Arver B, Olsson H, Schmutzler RK, Engel C, Kast K, Phillips KA, Terry MB, Milne RL, Goldgar DE, Rookus MA, Andrieu N, Easton DF. Risk-reducing salpingo-oophorectomy, natural menopause, and breast cancer risk: an international prospective cohort of BRCA1 and BRCA2 mutation carriers. Breast Cancer Res 2020; 22:8. [PMID: 31948486 PMCID: PMC6966793 DOI: 10.1186/s13058-020-1247-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 01/05/2020] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The effect of risk-reducing salpingo-oophorectomy (RRSO) on breast cancer risk for BRCA1 and BRCA2 mutation carriers is uncertain. Retrospective analyses have suggested a protective effect but may be substantially biased. Prospective studies have had limited power, particularly for BRCA2 mutation carriers. Further, previous studies have not considered the effect of RRSO in the context of natural menopause. METHODS A multi-centre prospective cohort of 2272 BRCA1 and 1605 BRCA2 mutation carriers was followed for a mean of 5.4 and 4.9 years, respectively; 426 women developed incident breast cancer. RRSO was modelled as a time-dependent covariate in Cox regression, and its effect assessed in premenopausal and postmenopausal women. RESULTS There was no association between RRSO and breast cancer for BRCA1 (HR = 1.23; 95% CI 0.94-1.61) or BRCA2 (HR = 0.88; 95% CI 0.62-1.24) mutation carriers. For BRCA2 mutation carriers, HRs were 0.68 (95% CI 0.40-1.15) and 1.07 (95% CI 0.69-1.64) for RRSO carried out before or after age 45 years, respectively. The HR for BRCA2 mutation carriers decreased with increasing time since RRSO (HR = 0.51; 95% CI 0.26-0.99 for 5 years or longer after RRSO). Estimates for premenopausal women were similar. CONCLUSION We found no evidence that RRSO reduces breast cancer risk for BRCA1 mutation carriers. A potentially beneficial effect for BRCA2 mutation carriers was observed, particularly after 5 years following RRSO. These results may inform counselling and management of carriers with respect to RRSO.
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Affiliation(s)
- Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
| | - Thea M. Mooij
- Department of Epidemiology, Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Maartje J. Hooning
- Department of Medical Oncology, Family Center Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | | | - Catherine Noguès
- DASC, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France
| | | | - Olivier Caron
- Département de Médecine Oncologique, Gustave Roussy Hôpital Universitaire, Villejuif, France
| | - Paul Gesta
- Centre Hospitalier, Service Régional d’Oncologie Génétique Poitou-Charentes, Niort, France
| | - Pascal Pujol
- Unité d’Oncogénétique, CHU Arnaud de Villeneuve, Montpellier, France
| | - Alain Lortholary
- Centre Catherine de Sienne, Service d’Oncologie Médicale, Nantes, France
| | - Daniel Barrowdale
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
| | - Debra Frost
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
| | - D. Gareth Evans
- Genomic Medicine, Manchester Academic Health Sciences Centre, Division of Evolution and Genomic Sciences, Manchester University, Central Manchester, University Hospitals NHS Foundation Trust, Manchester, UK
| | - Louise Izatt
- Clinical Genetics, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Julian Adlard
- Yorkshire Regional Genetics Service, Chapel Allerton Hospital and University of Leeds, Leeds, UK
| | - Ros Eeles
- Oncogenetics Team, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK
| | - Carole Brewer
- Department of Clinical Genetics, Royal Devon & Exeter Hospital, Exeter, UK
| | - Marc Tischkowitz
- Academic Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Alex Henderson
- Institute of Genetic Medicine, Centre for Life, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK
| | - Jackie Cook
- Sheffield Clinical Genetics Service, Sheffield Children’s Hospital, Sheffield, UK
| | - Diana Eccles
- University of Southampton Faculty of Medicine, Southampton University Hospitals NHS Trust, Southampton, UK
| | - Klaartje van Engelen
- Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Marian J. E. Mourits
- Department of Gynaecological Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Linetta B. Koppert
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010 Australia
| | - Esther M. John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY USA
| | - Irene L. Andrulis
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario Canada
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA USA
| | - Saundra S. Buys
- Department of Medicine, Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT USA
| | - kConFab Investigators
- Research Department, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
| | - Javier Benitez
- Human Genetics Group and Genotyping Unit, CEGEN, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Trinidad Caldes
- Molecular Oncology Laboratory, Hospital Clinico San Carlos, IdISSC, CIBERONC (ISCIII), Madrid, Spain
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, Quebec Canada
| | - Christian F. Singer
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, A 1090 Vienna, Austria
| | - Yen Tan
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, A 1090 Vienna, Austria
| | - Edith Olah
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
| | - Marie Navratilova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653 Brno, Czech Republic
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653 Brno, Czech Republic
| | - Anne-Marie Gerdes
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Marie-José Roos-Blom
- Department of Epidemiology, Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Flora E. Van Leeuwen
- Department of Epidemiology, Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Brita Arver
- The Department of Oncology and Pathology, Karolinska Institute, 171 76 Stockholm, Sweden
- Department of Oncology, Lund University Hospital, Lund, Sweden
| | - Håkan Olsson
- Department of Oncology, Lund University Hospital, Lund, Sweden
| | - Rita K. Schmutzler
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Karin Kast
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010 Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
- Department of Medical Oncology Peter MacCallum Cancer Centre, Locked Bag 1, A’Beckett St, East Melbourne, Victoria 8006 Australia
| | - Mary Beth Terry
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY USA
- Department of Epidemiology, Columbia University, New York, NY USA
| | - Roger L. Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010 Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria Australia
| | - David E. Goldgar
- Department of Dermatology, University of Utah School of Medicine, 30 North 1900 East, SOM 4B454, Salt Lake City, UT 841232 USA
| | - Matti A. Rookus
- Department of Epidemiology, Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
| | - Nadine Andrieu
- INSERM, U900, Paris, France
- Institut Curie, Paris, France
- Mines Paris Tech, Fontainebleau, France
- PSL Research University, Paris, France
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
| | - on behalf of IBCCS
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
- Department of Epidemiology, Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
- Department of Medical Oncology, Family Center Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- DASC, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France
- Institut Curie, Service de Génétique, Paris, France
- Département de Médecine Oncologique, Gustave Roussy Hôpital Universitaire, Villejuif, France
- Centre Hospitalier, Service Régional d’Oncologie Génétique Poitou-Charentes, Niort, France
- Unité d’Oncogénétique, CHU Arnaud de Villeneuve, Montpellier, France
- Centre Catherine de Sienne, Service d’Oncologie Médicale, Nantes, France
- Genomic Medicine, Manchester Academic Health Sciences Centre, Division of Evolution and Genomic Sciences, Manchester University, Central Manchester, University Hospitals NHS Foundation Trust, Manchester, UK
- Clinical Genetics, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
- Yorkshire Regional Genetics Service, Chapel Allerton Hospital and University of Leeds, Leeds, UK
- Oncogenetics Team, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK
- Department of Clinical Genetics, Royal Devon & Exeter Hospital, Exeter, UK
- Academic Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
- Institute of Genetic Medicine, Centre for Life, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK
- Sheffield Clinical Genetics Service, Sheffield Children’s Hospital, Sheffield, UK
- University of Southampton Faculty of Medicine, Southampton University Hospitals NHS Trust, Southampton, UK
- The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON), Coordinating Center: Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Gynaecological Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010 Australia
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY USA
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario Canada
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA USA
- Department of Medicine, Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT USA
- Research Department, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
- Human Genetics Group and Genotyping Unit, CEGEN, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Molecular Oncology Laboratory, Hospital Clinico San Carlos, IdISSC, CIBERONC (ISCIII), Madrid, Spain
- Department of Genetics and Pathology, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, Quebec Canada
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, A 1090 Vienna, Austria
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653 Brno, Czech Republic
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- The Department of Oncology and Pathology, Karolinska Institute, 171 76 Stockholm, Sweden
- Department of Oncology, Lund University Hospital, Lund, Sweden
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medical Oncology Peter MacCallum Cancer Centre, Locked Bag 1, A’Beckett St, East Melbourne, Victoria 8006 Australia
- Department of Epidemiology, Columbia University, New York, NY USA
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria Australia
- Department of Dermatology, University of Utah School of Medicine, 30 North 1900 East, SOM 4B454, Salt Lake City, UT 841232 USA
- INSERM, U900, Paris, France
- Institut Curie, Paris, France
- Mines Paris Tech, Fontainebleau, France
- PSL Research University, Paris, France
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
| | - kConFab
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
- Department of Epidemiology, Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
- Department of Medical Oncology, Family Center Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- DASC, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France
- Institut Curie, Service de Génétique, Paris, France
- Département de Médecine Oncologique, Gustave Roussy Hôpital Universitaire, Villejuif, France
- Centre Hospitalier, Service Régional d’Oncologie Génétique Poitou-Charentes, Niort, France
- Unité d’Oncogénétique, CHU Arnaud de Villeneuve, Montpellier, France
- Centre Catherine de Sienne, Service d’Oncologie Médicale, Nantes, France
- Genomic Medicine, Manchester Academic Health Sciences Centre, Division of Evolution and Genomic Sciences, Manchester University, Central Manchester, University Hospitals NHS Foundation Trust, Manchester, UK
- Clinical Genetics, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
- Yorkshire Regional Genetics Service, Chapel Allerton Hospital and University of Leeds, Leeds, UK
- Oncogenetics Team, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK
- Department of Clinical Genetics, Royal Devon & Exeter Hospital, Exeter, UK
- Academic Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
- Institute of Genetic Medicine, Centre for Life, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK
- Sheffield Clinical Genetics Service, Sheffield Children’s Hospital, Sheffield, UK
- University of Southampton Faculty of Medicine, Southampton University Hospitals NHS Trust, Southampton, UK
- The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON), Coordinating Center: Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Gynaecological Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010 Australia
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY USA
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario Canada
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA USA
- Department of Medicine, Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT USA
- Research Department, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
- Human Genetics Group and Genotyping Unit, CEGEN, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Molecular Oncology Laboratory, Hospital Clinico San Carlos, IdISSC, CIBERONC (ISCIII), Madrid, Spain
- Department of Genetics and Pathology, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, Quebec Canada
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, A 1090 Vienna, Austria
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653 Brno, Czech Republic
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- The Department of Oncology and Pathology, Karolinska Institute, 171 76 Stockholm, Sweden
- Department of Oncology, Lund University Hospital, Lund, Sweden
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medical Oncology Peter MacCallum Cancer Centre, Locked Bag 1, A’Beckett St, East Melbourne, Victoria 8006 Australia
- Department of Epidemiology, Columbia University, New York, NY USA
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria Australia
- Department of Dermatology, University of Utah School of Medicine, 30 North 1900 East, SOM 4B454, Salt Lake City, UT 841232 USA
- INSERM, U900, Paris, France
- Institut Curie, Paris, France
- Mines Paris Tech, Fontainebleau, France
- PSL Research University, Paris, France
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
| | - BCFR
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
- Department of Epidemiology, Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands
- Department of Medical Oncology, Family Center Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- DASC, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France
- Institut Curie, Service de Génétique, Paris, France
- Département de Médecine Oncologique, Gustave Roussy Hôpital Universitaire, Villejuif, France
- Centre Hospitalier, Service Régional d’Oncologie Génétique Poitou-Charentes, Niort, France
- Unité d’Oncogénétique, CHU Arnaud de Villeneuve, Montpellier, France
- Centre Catherine de Sienne, Service d’Oncologie Médicale, Nantes, France
- Genomic Medicine, Manchester Academic Health Sciences Centre, Division of Evolution and Genomic Sciences, Manchester University, Central Manchester, University Hospitals NHS Foundation Trust, Manchester, UK
- Clinical Genetics, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
- Yorkshire Regional Genetics Service, Chapel Allerton Hospital and University of Leeds, Leeds, UK
- Oncogenetics Team, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK
- Department of Clinical Genetics, Royal Devon & Exeter Hospital, Exeter, UK
- Academic Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
- Institute of Genetic Medicine, Centre for Life, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK
- Sheffield Clinical Genetics Service, Sheffield Children’s Hospital, Sheffield, UK
- University of Southampton Faculty of Medicine, Southampton University Hospitals NHS Trust, Southampton, UK
- The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON), Coordinating Center: Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Gynaecological Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010 Australia
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY USA
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario Canada
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA USA
- Department of Medicine, Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT USA
- Research Department, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
- Human Genetics Group and Genotyping Unit, CEGEN, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Molecular Oncology Laboratory, Hospital Clinico San Carlos, IdISSC, CIBERONC (ISCIII), Madrid, Spain
- Department of Genetics and Pathology, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Unii Lubelskiej 1, Szczecin, Poland
- Genomics Center, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, Quebec Canada
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, A 1090 Vienna, Austria
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653 Brno, Czech Republic
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- The Department of Oncology and Pathology, Karolinska Institute, 171 76 Stockholm, Sweden
- Department of Oncology, Lund University Hospital, Lund, Sweden
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medical Oncology Peter MacCallum Cancer Centre, Locked Bag 1, A’Beckett St, East Melbourne, Victoria 8006 Australia
- Department of Epidemiology, Columbia University, New York, NY USA
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria Australia
- Department of Dermatology, University of Utah School of Medicine, 30 North 1900 East, SOM 4B454, Salt Lake City, UT 841232 USA
- INSERM, U900, Paris, France
- Institut Curie, Paris, France
- Mines Paris Tech, Fontainebleau, France
- PSL Research University, Paris, France
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Research Laboratory, Worts Causeway, University of Cambridge, Cambridge, CBI 8RN UK
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28
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Kehm RD, Genkinger JM, MacInnis RJ, John EM, Phillips KA, Dite GS, Milne RL, Zeinomar N, Liao Y, Knight JA, Southey MC, Chung WK, Giles GG, McLachlan SA, Whitaker KD, Friedlander M, Weideman PC, Glendon G, Nesci S, Investigators KC, Andrulis IL, Buys SS, Daly MB, Hopper JL, Terry MB. Recreational Physical Activity Is Associated with Reduced Breast Cancer Risk in Adult Women at High Risk for Breast Cancer: A Cohort Study of Women Selected for Familial and Genetic Risk. Cancer Res 2020; 80:116-125. [PMID: 31578201 PMCID: PMC7236618 DOI: 10.1158/0008-5472.can-19-1847] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 08/13/2019] [Accepted: 09/23/2019] [Indexed: 12/14/2022]
Abstract
Although physical activity is associated with lower breast cancer risk for average-risk women, it is not known if this association applies to women at high familial/genetic risk. We examined the association of recreational physical activity (self-reported by questionnaire) with breast cancer risk using the Prospective Family Study Cohort, which is enriched with women who have a breast cancer family history (N = 15,550). We examined associations of adult and adolescent recreational physical activity (quintiles of age-adjusted total metabolic equivalents per week) with breast cancer risk using multivariable Cox proportional hazards regression, adjusted for demographics, lifestyle factors, and body mass index. We tested for multiplicative interactions of physical activity with predicted absolute breast cancer familial risk based on pedigree data and with BRCA1 and BRCA2 mutation status. Baseline recreational physical activity level in the highest four quintiles compared with the lowest quintile was associated with a 20% lower breast cancer risk (HR, 0.80; 95% confidence interval, 0.68-0.93). The association was not modified by familial risk or BRCA mutation status (P interactions >0.05). No overall association was found for adolescent recreational physical activity. Recreational physical activity in adulthood may lower breast cancer risk for women across the spectrum of familial risk. SIGNIFICANCE: These findings suggest that physical activity might reduce breast cancer risk by about 20% for women across the risk continuum, including women at higher-than-average risk due to their family history or genetic susceptibility.See related commentary by Niehoff et al., p. 23.
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Affiliation(s)
- Rebecca D Kehm
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Jeanine M Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Australia; Division of Cancer Medicine, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, Australia
| | - Wendy K Chung
- Department of Pediatrics and Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
| | - Sue-Anne McLachlan
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia; Department of Medical Oncology, St Vincent's Hospital, Melbourne, Australia
| | - Kristen D Whitaker
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Michael Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, Australia; Department of Medical Oncology, Prince of Wales Hospital, Sydney, Australia
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
| | - Stephanie Nesci
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - kConFab Investigators
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia; The Research Department, The Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada; Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, Utah
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York; Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York.
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29
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Phillips KA, Liao Y, Milne RL, MacInnis RJ, Collins IM, Buchsbaum R, Weideman PC, Bickerstaffe A, Nesci S, Chung WK, Southey MC, Knight JA, Whittemore AS, Dite GS, Goldgar D, Giles GG, Glendon G, Cuzick J, Antoniou AC, Andrulis IL, John EM, Daly MB, Buys SS, Hopper JL, Terry MB. Accuracy of Risk Estimates from the iPrevent Breast Cancer Risk Assessment and Management Tool. JNCI Cancer Spectr 2019; 3:pkz066. [PMID: 31853515 PMCID: PMC6901082 DOI: 10.1093/jncics/pkz066] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 07/14/2019] [Accepted: 08/20/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND iPrevent is an online breast cancer (BC) risk management decision support tool. It uses an internal switching algorithm, based on a woman's risk factor data, to estimate her absolute BC risk using either the International Breast Cancer Intervention Study (IBIS) version 7.02, or Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm version 3 models, and then provides tailored risk management information. This study assessed the accuracy of the 10-year risk estimates using prospective data. METHODS iPrevent-assigned 10-year invasive BC risk was calculated for 15 732 women aged 20-70 years and without BC at recruitment to the Prospective Family Study Cohort. Calibration, the ratio of the expected (E) number of BCs to the observed (O) number and discriminatory accuracy were assessed. RESULTS During the 10 years of follow-up, 619 women (3.9%) developed BC compared with 702 expected (E/O = 1.13; 95% confidence interval [CI] =1.05 to 1.23). For women younger than 50 years, 50 years and older, and BRCA1/2-mutation carriers and noncarriers, E/O was 1.04 (95% CI = 0.93 to 1.16), 1.24 (95% CI = 1.11 to 1.39), 1.13 (95% CI = 0.96 to 1.34), and 1.13 (95% CI = 1.04 to 1.24), respectively. The C-statistic was 0.70 (95% CI = 0.68 to 0.73) overall and 0.74 (95% CI = 0.71 to 0.77), 0.63 (95% CI = 0.59 to 0.66), 0.59 (95% CI = 0.53 to 0.64), and 0.65 (95% CI = 0.63 to 0.68), respectively, for the subgroups above. Applying the newer IBIS version 8.0b in the iPrevent switching algorithm improved calibration overall (E/O = 1.06, 95% CI = 0.98 to 1.15) and in all subgroups, without changing discriminatory accuracy. CONCLUSIONS For 10-year BC risk, iPrevent had good discriminatory accuracy overall and was well calibrated for women aged younger than 50 years. Calibration may be improved in the future by incorporating IBIS version 8.0b.
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Affiliation(s)
- Kelly-Anne Phillips
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yuyan Liao
- Department of Epidemiology, Columbia University Medical Center, New York, NY
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Ian M Collins
- School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - Richard Buchsbaum
- Department of Biostatistics, Columbia University Medical Center, New York, NY
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Adrian Bickerstaffe
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Stephanie Nesci
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Wendy K Chung
- Mailman School of Public Health, and Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY
| | - Melissa C Southey
- Genetic Epidemiology Laboratory, Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Alice S Whittemore
- Departments of Health Research and Policy and of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - David Goldgar
- Department of Dermatology and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Columbia University Medical Center, New York, NY
| | - for the kConFab Investigators
- Research Department, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia
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30
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Zeinomar N, Knight JA, Genkinger JM, Phillips KA, Daly MB, Milne RL, Dite GS, Kehm RD, Liao Y, Southey MC, Chung WK, Giles GG, McLachlan SA, Friedlander ML, Weideman PC, Glendon G, Nesci S, Andrulis IL, Buys SS, John EM, MacInnis RJ, Hopper JL, Terry MB. Alcohol consumption, cigarette smoking, and familial breast cancer risk: findings from the Prospective Family Study Cohort (ProF-SC). Breast Cancer Res 2019; 21:128. [PMID: 31779655 PMCID: PMC6883541 DOI: 10.1186/s13058-019-1213-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 10/15/2019] [Indexed: 12/20/2022] Open
Abstract
Background Alcohol consumption and cigarette smoking are associated with an increased risk of breast cancer (BC), but it is unclear whether these associations vary by a woman’s familial BC risk. Methods Using the Prospective Family Study Cohort, we evaluated associations between alcohol consumption, cigarette smoking, and BC risk. We used multivariable Cox proportional hazard models to estimate hazard ratios (HR) and 95% confidence intervals (CI). We examined whether associations were modified by familial risk profile (FRP), defined as the 1-year incidence of BC predicted by Breast Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), a pedigree-based algorithm. Results We observed 1009 incident BC cases in 17,435 women during a median follow-up of 10.4 years. We found no overall association of smoking or alcohol consumption with BC risk (current smokers compared with never smokers HR 1.02, 95% CI 0.85–1.23; consuming ≥ 7 drinks/week compared with non-regular drinkers HR 1.10, 95% CI 0.92–1.32), but we did observe differences in associations based on FRP and by estrogen receptor (ER) status. Women with lower FRP had an increased risk of ER-positive BC associated with consuming ≥ 7 drinks/week (compared to non-regular drinkers), whereas there was no association for women with higher FRP. For example, women at the 10th percentile of FRP (5-year BOADICEA = 0.15%) had an estimated HR of 1.46 (95% CI 1.07–1.99), whereas there was no association for women at the 90th percentile (5-year BOADICEA = 4.2%) (HR 1.07, 95% CI 0.80–1.44). While the associations with smoking were not modified by FRP, we observed a positive multiplicative interaction by FRP (pinteraction = 0.01) for smoking status in women who also consumed alcohol, but not in women who were non-regular drinkers. Conclusions Moderate alcohol intake was associated with increased BC risk, particularly for women with ER-positive BC, but only for those at lower predicted familial BC risk (5-year BOADICEA < 1.25). For women with a high FRP (5-year BOADICEA ≥ 6.5%) who also consumed alcohol, being a current smoker was associated with increased BC risk.
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Affiliation(s)
- Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W. 168th Street, Room 1611, New York, NY, 10032, USA
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Jeanine M Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W. 168th Street, Room 1611, New York, NY, 10032, USA.,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Rebecca D Kehm
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W. 168th Street, Room 1611, New York, NY, 10032, USA
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W. 168th Street, Room 1611, New York, NY, 10032, USA
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.,Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Sue-Anne McLachlan
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Parkville, Victoria, Australia.,Department of Medical Oncology, St Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Michael L Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Department of Medical Oncology, Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Stephanie Nesci
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | | | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT, USA
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W. 168th Street, Room 1611, New York, NY, 10032, USA. .,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
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31
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Terry MB, Zeinomar N. Response to Lee et al 2019: Essential to frame study implications within the context of prior findings from enriched cohorts for underlying familial risk of breast cancer. Occup Environ Med 2019; 76:592. [PMID: 31300563 DOI: 10.1136/oemed-2019-105936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 05/15/2019] [Indexed: 11/04/2022]
Affiliation(s)
- Mary Beth Terry
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA.,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, NY, New York, USA
| | - Nur Zeinomar
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA
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32
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Moran O, Eisen A, Demsky R, Blackmore K, Knight JA, Panchal S, Ginsburg O, Zbuk K, Yaffe M, Metcalfe KA, Narod SA, Kotsopoulos J. Predictors of mammographic density among women with a strong family history of breast cancer. BMC Cancer 2019; 19:631. [PMID: 31242899 PMCID: PMC6595553 DOI: 10.1186/s12885-019-5855-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 06/19/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Mammographic density is one of the strongest risk factors for breast cancer. In the general population, mammographic density can be modified by various exposures; whether this is true for women a strong family history is not known. Thus, we evaluated the association between reproductive, hormonal, and lifestyle risk factors and mammographic density among women with a strong family history of breast cancer but no BRCA1 or BRCA2 mutation. METHODS We included 97 premenopausal and 59 postmenopausal women (age range: 27-68 years). Risk factor data was extracted from the research questionnaire closest in time to the mammogram performed nearest to enrollment. The Cumulus software was used to measure percent density, dense area, and non-dense area for each mammogram. Multivariate generalized linear models were used to evaluate the relationships between breast cancer risk factors and measures of mammographic density, adjusting for relevant covariates. RESULTS Among premenopausal women, those who had two live births had a mean percent density of 28.8% vs. 41.6% among women who had one live birth (P=0.04). Women with a high body weight had a lower mean percent density compared to women with a low body weight among premenopausal (17.6% vs. 33.2%; P=0.0006) and postmenopausal women (8.7% vs. 14.7%; P=0.04). Among premenopausal women, those who smoked for 14 years or longer had a lower mean dense area compared to women who smoked for a shorter duration (25.3cm2 vs. 53.1cm2; P=0.002). Among postmenopausal women, former smokers had a higher mean percent density (19.5% vs. 10.8%; P=0.003) and dense area (26.9% vs. 16.4%; P=0.01) compared to never smokers. After applying the Bonferroni correction, the association between body weight and percent density among premenopausal women remained statistically significant. CONCLUSIONS In this cohort of women with a strong family history of breast cancer, body weight was associated with mammographic density. These findings suggest that mammographic density may explain the underlying relationship between some of these risk factors and breast cancer risk, and lend support for the inclusion of mammographic density into risk prediction models.
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Affiliation(s)
- Olivia Moran
- Women's College Research Institute, Women's College Hospital, 76 Grenville St., 6th Floor, Toronto, ON, Canada.,Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Andrea Eisen
- Toronto-Sunnybrook Regional Cancer Center, Toronto, ON, Canada
| | - Rochelle Demsky
- Division of Gynecologic Oncology, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | | | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Seema Panchal
- Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - Ophira Ginsburg
- Perlmutter Cancer Centre, Department of Population Health, NYU Langone Health, New York, NY, USA
| | - Kevin Zbuk
- Department of Oncology, McMaster University, Hamilton, ON, Canada
| | - Martin Yaffe
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Kelly A Metcalfe
- Women's College Research Institute, Women's College Hospital, 76 Grenville St., 6th Floor, Toronto, ON, Canada.,Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada
| | - Steven A Narod
- Women's College Research Institute, Women's College Hospital, 76 Grenville St., 6th Floor, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Joanne Kotsopoulos
- Women's College Research Institute, Women's College Hospital, 76 Grenville St., 6th Floor, Toronto, ON, Canada. .,Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada. .,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
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33
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Kehm RD, Hopper JL, John EM, Phillips KA, MacInnis RJ, Dite GS, Milne RL, Liao Y, Zeinomar N, Knight JA, Southey MC, Vahdat L, Kornhauser N, Cigler T, Chung WK, Giles GG, McLachlan SA, Friedlander ML, Weideman PC, Glendon G, Nesci S, Andrulis IL, Buys SS, Daly MB, Terry MB. Regular use of aspirin and other non-steroidal anti-inflammatory drugs and breast cancer risk for women at familial or genetic risk: a cohort study. Breast Cancer Res 2019; 21:52. [PMID: 30999962 PMCID: PMC6471793 DOI: 10.1186/s13058-019-1135-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/05/2019] [Indexed: 01/23/2023] Open
Abstract
Background The use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) has been associated with reduced breast cancer risk, but it is not known if this association extends to women at familial or genetic risk. We examined the association between regular NSAID use and breast cancer risk using a large cohort of women selected for breast cancer family history, including 1054 BRCA1 or BRCA2 mutation carriers. Methods We analyzed a prospective cohort (N = 5606) and a larger combined, retrospective and prospective, cohort (N = 8233) of women who were aged 18 to 79 years, enrolled before June 30, 2011, with follow-up questionnaire data on medication history. The prospective cohort was further restricted to women without breast cancer when medication history was asked by questionnaire. Women were recruited from seven study centers in the United States, Canada, and Australia. Associations were estimated using multivariable Cox proportional hazards regression models adjusted for demographics, lifestyle factors, family history, and other medication use. Women were classified as regular or non-regular users of aspirin, COX-2 inhibitors, ibuprofen and other NSAIDs, and acetaminophen (control) based on self-report at follow-up of ever using the medication for at least twice a week for ≥1 month prior to breast cancer diagnosis. The main outcome was incident invasive breast cancer, based on self- or relative-report (81% confirmed pathologically). Results From fully adjusted analyses, regular aspirin use was associated with a 39% and 37% reduced risk of breast cancer in the prospective (HR = 0.61; 95% CI = 0.33–1.14) and combined cohorts (HR = 0.63; 95% CI = 0.57–0.71), respectively. Regular use of COX-2 inhibitors was associated with a 61% and 71% reduced risk of breast cancer (prospective HR = 0.39; 95% CI = 0.15–0.97; combined HR = 0.29; 95% CI = 0.23–0.38). Other NSAIDs and acetaminophen were not associated with breast cancer risk in either cohort. Associations were not modified by familial risk, and consistent patterns were found by BRCA1 and BRCA2 carrier status, estrogen receptor status, and attained age. Conclusion Regular use of aspirin and COX-2 inhibitors might reduce breast cancer risk for women at familial or genetic risk. Electronic supplementary material The online version of this article (10.1186/s13058-019-1135-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rebecca D Kehm
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY, 10032, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, 780 Welch Road, Stanford, CA, 94304, USA
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, 3010, Australia.,Department of Medical Oncology, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC, 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, 3010, Australia.,Cancer Epidemiology, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC, 3004, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, 3010, Australia.,Cancer Epidemiology, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC, 3004, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY, 10032, USA
| | - Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY, 10032, USA
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Ave, Toronto, Ontario, M5G 1X5, Canada.,Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, Ontario, M5T3M7, Canada
| | - Melissa C Southey
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Linda Vahdat
- Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA.,C Anthony and Jean Whittingham Cancer Center, 34 Maple Street, Norwalk, CT, 06856, USA
| | - Naomi Kornhauser
- Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA
| | - Tessa Cigler
- Weill Cornell Medicine Breast Center, 428 E 72nd St, New York, NY, 10021, USA
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, 1150 St Nicholas Ave, New York, NY, 10032, USA.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, 1130 St Nicholas Ave, New York, NY, 10032, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, 3010, Australia.,Cancer Epidemiology, Cancer Council Victoria, 615 St Kilda Rd, Melbourne, VIC, 3004, Australia
| | - Sue-Anne McLachlan
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Parkville, VIC, 3010, Australia.,Department of Medical Oncology, St Vincent's Hospital, 41 Victoria St, Fitzroy, VIC, 3065, Australia
| | - Michael L Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia.,Department of Medical Oncology, Prince of Wales Hospital, Barker St, Randwick, NSW, 2031, Australia
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Ave, Toronto, Ontario, M5G 1X5, Canada
| | - Stephanie Nesci
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC, 3000, Australia
| | | | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 600 University Ave, Toronto, Ontario, M5G 1X5, Canada.,Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, 2000 Cir of Hope Dr, Salt Lake City, UT, 84103, USA
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, 333 Cottman Ave, Philadelphia, PA, 19111, USA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY, 10032, USA. .,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, 1130 St Nicholas Ave, New York, NY, 10032, USA.
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10-year performance of four models of breast cancer risk: a validation study. Lancet Oncol 2019; 20:504-517. [DOI: 10.1016/s1470-2045(18)30902-1] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 11/20/2018] [Accepted: 11/22/2018] [Indexed: 12/27/2022]
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John EM, Sangaramoorthy M, Koo J, Whittemore AS, West DW. Enrollment and biospecimen collection in a multiethnic family cohort: the Northern California site of the Breast Cancer Family Registry. Cancer Causes Control 2019; 30:395-408. [PMID: 30835011 DOI: 10.1007/s10552-019-01154-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 02/22/2019] [Indexed: 01/26/2023]
Abstract
PURPOSE Racial/ethnic minorities are often assumed to be less willing to participate in and provide biospecimens for biomedical research. We examined racial/ethnic differences in enrollment of women with breast cancer (probands) and their first-degree relatives in the Northern California site of the Breast Cancer Family Registry from 1996 to 2011. METHODS We evaluated participation in several study components, including biospecimen collection, for probands and relatives by race/ethnicity, cancer history, and other factors. RESULTS Of 4,780 eligible probands, 76% enrolled in the family registry by completing the family history and risk factor questionnaires and 68% also provided a blood or mouthwash sample. Enrollment was highest (81%) for non-Hispanic whites (NHWs) and intermediate (73-76%) for Hispanics, African Americans, and all Asian American subgroups, except Filipina women (66%). Of 4,279 eligible relatives, 77% enrolled in the family registry, and 65% also provided a biospecimen sample. Enrollment was highest for NHWs (87%) and lowest for Chinese (68%) and Filipinas (67%). Among those enrolled, biospecimen collection rates were similar for NHW, Hispanic, and African American women, both for probands (92-95%) and relatives (82-87%), but lower for some Asian-American subgroups (probands: 72-88%; relatives: 71-88%), foreign-born Asian Americans, and probands those who were more recent immigrants or had low English language proficiency. CONCLUSIONS These results show that racial/ethnic minority populations are willing to provide biospecimen samples for research, although some Asian American subgroups in particular may need more directed recruitment methods. To address long-standing and well-documented cancer health disparities, minority populations need equal opportunities to contribute to biomedical research.
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Affiliation(s)
- Esther M John
- Cancer Prevention Institute of California, Fremont, CA, 94358, USA. .,Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, 94304, USA. .,Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, 94304, USA. .,Stanford Cancer Institute, 780 Welch Road, Suite CJ250C, Stanford, CA, 94304-5769, USA.
| | | | - Jocelyn Koo
- Cancer Prevention Institute of California, Fremont, CA, 94358, USA.,Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Alice S Whittemore
- Department of Health Research and Policy, Stanford University of School of Medicine, Stanford, CA, 94305, USA.,Department of Biomedical Data Science, Stanford University of School of Medicine, 94305, Stanford, CA, USA
| | - Dee W West
- Cancer Prevention Institute of California, Fremont, CA, 94358, USA.,Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, 94304, USA.,Department of Health Research and Policy, Stanford University of School of Medicine, Stanford, CA, 94305, USA
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Zeinomar N, Phillips KA, Daly MB, Milne RL, Dite GS, MacInnis RJ, Liao Y, Kehm RD, Knight JA, Southey MC, Chung WK, Giles GG, McLachlan SA, Friedlander ML, Weideman PC, Glendon G, Nesci S, Andrulis IL, Buys SS, John EM, Hopper JL, Terry MB. Benign breast disease increases breast cancer risk independent of underlying familial risk profile: Findings from a Prospective Family Study Cohort. Int J Cancer 2019; 145:370-379. [PMID: 30725480 DOI: 10.1002/ijc.32112] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/28/2018] [Accepted: 12/12/2018] [Indexed: 12/30/2022]
Abstract
Benign breast disease (BBD) is an established breast cancer (BC) risk factor, but it is unclear whether the magnitude of the association applies to women at familial or genetic risk. This information is needed to improve BC risk assessment in clinical settings. Using the Prospective Family Study Cohort, we used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of BBD with BC risk. We also examined whether the association with BBD differed by underlying familial risk profile (FRP), calculated using absolute risk estimates from the Breast Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model. During 176,756 person-years of follow-up (median: 10.9 years, maximum: 23.7) of 17,154 women unaffected with BC at baseline, we observed 968 incident cases of BC. A total of 4,704 (27%) women reported a history of BBD diagnosis at baseline. A history of BBD was associated with a greater risk of BC: HR = 1.31 (95% CI: 1.14-1.50), and did not differ by underlying FRP, with HRs of 1.35 (95% CI: 1.11-1.65), 1.26 (95% CI: 1.00-1.60), and 1.40 (95% CI: 1.01-1.93), for categories of full-lifetime BOADICEA score <20%, 20 to <35%, ≥35%, respectively. There was no difference in the association for women with BRCA1 mutations (HR: 1.64; 95% CI: 1.04-2.58), women with BRCA2 mutations (HR: 1.34; 95% CI: 0.78-2.3) or for women without a known BRCA1 or BRCA2 mutation (HR: 1.31; 95% CI: 1.13-1.53) (pinteraction = 0.95). Women with a history of BBD have an increased risk of BC that is independent of, and multiplies, their underlying familial and genetic risk.
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Affiliation(s)
- Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, Australia.,Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Rebecca D Kehm
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Melissa C Southey
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY.,Department of Pediatrics and Medicine, Columbia University, New York, NY
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Sue-Anne McLachlan
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Parkville, VIC, Australia.,Department of Medical Oncology, St Vincent's Hospital, Fitzroy, VIC, Australia
| | - Michael L Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, NSW, Australia.,Department of Medical Oncology, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Stephanie Nesci
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
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- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.,The Research Department, The Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.,Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY
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Assessing patient readiness for personalized genomic medicine. J Community Genet 2019; 10:109-120. [PMID: 29804257 PMCID: PMC6325047 DOI: 10.1007/s12687-018-0365-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 04/18/2018] [Indexed: 12/30/2022] Open
Abstract
The Human Genome Project and the continuing advances in DNA sequencing technology have ushered in a new era in genomic medicine. Successful translation of genomic medicine into clinical care will require that providers of this information are aware of the level of understanding, attitudes, perceived risks, benefits, and concerns of their patients. We used a mixed methods approach to conduct in-depth interviews with participants in the NCI-funded Breast Cancer Family Registry (BCFR). Our goal was to gain a better understanding of attitudes towards different types and amounts of genomic information, current interest in pursuing genomic testing, and perceived risks and benefits. We interviewed 32 women from the six BCFR sites in the USA, Canada, and Australia. In this sample of women with a personal or family history of breast cancer, we found high acknowledgement of the potential of genetics/genomics to improve their own health and that of their family members through lifestyle changes or alterations in their medical management. Respondents were more familiar with cancer genetics than the genetics of other diseases. Concerns about the testing itself included a potential sense of loss of control over health, feelings of guilt on passing on a mutation to a child, loss of privacy and confidentiality, questions about the test accuracy, and the potential uncertainty of the significance of test results. These data provide important insights into attitudes about the introduction of increasingly complex genetic testing, to inform interventions to prepare individuals for the introduction of this new technology into their clinical care.
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Terry MB, Liao Y, Kast K, Antoniou AC, McDonald JA, Mooij TM, Engel C, Nogues C, Buecher B, Mari V, Moretta-Serra J, Gladieff L, Luporsi E, Barrowdale D, Frost D, Henderson A, Brewer C, Evans DG, Eccles D, Cook J, Ong KR, Izatt L, Ahmed M, Morrison PJ, Dommering CJ, Oosterwijk JC, Ausems MGEM, Kriege M, Buys SS, Andrulis IL, John EM, Daly M, Friedlander M, McLachlan SA, Osorio A, Caldes T, Jakubowska A, Simard J, Singer CF, Tan Y, Olah E, Navratilova M, Foretova L, Gerdes AM, Roos-Blom MJ, Arver B, Olsson H, Schmutzler RK, Hopper JL, van Leeuwen FE, Goldgar D, Milne RL, Easton DF, Rookus MA, Andrieu N. The Influence of Number and Timing of Pregnancies on Breast Cancer Risk for Women With BRCA1 or BRCA2 Mutations. JNCI Cancer Spectr 2018; 2:pky078. [PMID: 30873510 PMCID: PMC6405439 DOI: 10.1093/jncics/pky078] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 10/08/2018] [Accepted: 12/08/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Full-term pregnancy (FTP) is associated with a reduced breast cancer (BC) risk over time, but women are at increased BC risk in the immediate years following an FTP. No large prospective studies, however, have examined whether the number and timing of pregnancies are associated with BC risk for BRCA1 and BRCA2 mutation carriers. METHODS Using weighted and time-varying Cox proportional hazards models, we investigated whether reproductive events are associated with BC risk for mutation carriers using a retrospective cohort (5707 BRCA1 and 3525 BRCA2 mutation carriers) and a prospective cohort (2276 BRCA1 and 1610 BRCA2 mutation carriers), separately for each cohort and the combined prospective and retrospective cohort. RESULTS For BRCA1 mutation carriers, there was no overall association with parity compared with nulliparity (combined hazard ratio [HRc] = 0.99, 95% confidence interval [CI] = 0.83 to 1.18). Relative to being uniparous, an increased number of FTPs was associated with decreased BC risk (HRc = 0.79, 95% CI = 0.69 to 0.91; HRc = 0.70, 95% CI = 0.59 to 0.82; HRc = 0.50, 95% CI = 0.40 to 0.63, for 2, 3, and ≥4 FTPs, respectively, P trend < .0001) and increasing duration of breastfeeding was associated with decreased BC risk (combined cohort P trend = .0003). Relative to being nulliparous, uniparous BRCA1 mutation carriers were at increased BC risk in the prospective analysis (prospective hazard ration [HRp] = 1.69, 95% CI = 1.09 to 2.62). For BRCA2 mutation carriers, being parous was associated with a 30% increase in BC risk (HRc = 1.33, 95% CI = 1.05 to 1.69), and there was no apparent decrease in risk associated with multiparity except for having at least 4 FTPs vs. 1 FTP (HRc = 0.72, 95% CI = 0.54 to 0.98). CONCLUSIONS These findings suggest differential associations with parity between BRCA1 and BRCA2 mutation carriers with higher risk for uniparous BRCA1 carriers and parous BRCA2 carriers.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Nadine Andrieu
- Correspondence to: Nadine Andrieu, PhD, Cancer Genetic Epidemiology Team, INSERM Unit 900, Institut Curie, 26 rue d’Ulm, 75005 Paris, France (e-mail: )
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Hopper JL, Dite GS, MacInnis RJ, Liao Y, Zeinomar N, Knight JA, Southey MC, Milne RL, Chung WK, Giles GG, Genkinger JM, McLachlan SA, Friedlander ML, Antoniou AC, Weideman PC, Glendon G, Nesci S, Andrulis IL, Buys SS, Daly MB, John EM, Phillips KA, Terry MB. Age-specific breast cancer risk by body mass index and familial risk: prospective family study cohort (ProF-SC). Breast Cancer Res 2018; 20:132. [PMID: 30390716 PMCID: PMC6215632 DOI: 10.1186/s13058-018-1056-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 10/02/2018] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND The association between body mass index (BMI) and risk of breast cancer depends on time of life, but it is unknown whether this association depends on a woman's familial risk. METHODS We conducted a prospective study of a cohort enriched for familial risk consisting of 16,035 women from 6701 families in the Breast Cancer Family Registry and the Kathleen Cunningham Foundation Consortium for Research into Familial Breast Cancer followed for up to 20 years (mean 10.5 years). There were 896 incident breast cancers (mean age at diagnosis 55.7 years). We used Cox regression to model BMI risk associations as a function of menopausal status, age, and underlying familial risk based on pedigree data using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), all measured at baseline. RESULTS The strength and direction of the BMI risk association depended on baseline menopausal status (P < 0.001); after adjusting for menopausal status, the association did not depend on age at baseline (P = 0.6). In terms of absolute risk, the negative association with BMI for premenopausal women has a much smaller influence than the positive association with BMI for postmenopausal women. Women at higher familial risk have a much larger difference in absolute risk depending on their BMI than women at lower familial risk. CONCLUSIONS The greater a woman's familial risk, the greater the influence of BMI on her absolute postmenopausal breast cancer risk. Given that age-adjusted BMI is correlated across adulthood, maintaining a healthy weight throughout adult life is particularly important for women with a family history of breast cancer.
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Affiliation(s)
- John L. Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC Australia
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC Australia
| | - Robert J. MacInnis
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC Australia
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC Australia
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, 7th Floor, New York, NY USA
| | - Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, 7th Floor, New York, NY USA
| | - Julia A. Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
| | - Melissa C. Southey
- Department of Pathology, Genetic Epidemiology Laboratory, The University of Melbourne, Parkville, VIC Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, CA VIC 3168 USA
| | - Roger L. Milne
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC Australia
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC Australia
| | - Wendy K. Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY USA
- Departments of Pediatrics and Medicine, Columbia University, New York, NY USA
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC Australia
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC Australia
| | - Jeanine M. Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, 7th Floor, New York, NY USA
| | - Sue-Anne McLachlan
- Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Parkville, VIC Australia
- Department of Medical Oncology, St Vincent’s Hospital, Fitzroy, VIC Australia
| | - Michael L. Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, NSW Australia
- Department of Medical Oncology, Prince of Wales Hospital, Randwick, NSW Australia
| | - Antonis C. Antoniou
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Prue C. Weideman
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON Canada
| | - Stephanie Nesci
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - kConFab Investigators
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC Australia
- The Research Department, The Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - Irene L. Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON Canada
- Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON Canada
| | - Saundra S. Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, UT USA
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA USA
| | - Esther M. John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Kelly Anne Phillips
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, VIC Australia
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, 7th Floor, New York, NY USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY USA
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Carlton, VIC 3010, Australia
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Dougan MM, Li Y, Chu LW, Haile RW, Whittemore AS, Han SS, Moore SC, Sampson JN, Andrulis IL, John EM, Hsing AW. Metabolomic profiles in breast cancer:a pilot case-control study in the breast cancer family registry. BMC Cancer 2018; 18:532. [PMID: 29728083 PMCID: PMC5935968 DOI: 10.1186/s12885-018-4437-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 04/25/2018] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Metabolomics is emerging as an important tool for detecting differences between diseased and non-diseased individuals. However, prospective studies are limited. METHODS We examined the detectability, reliability, and distribution of metabolites measured in pre-diagnostic plasma samples in a pilot study of women enrolled in the Northern California site of the Breast Cancer Family Registry. The study included 45 cases diagnosed with breast cancer at least one year after the blood draw, and 45 controls. Controls were matched on age (within 5 years), family status, BRCA status, and menopausal status. Duplicate samples were included for reliability assessment. We used a liquid chromatography/gas chromatography mass spectrometer platform to measure metabolites. We calculated intraclass correlations (ICCs) among duplicate samples, and coefficients of variation (CVs) across metabolites. RESULTS Of the 661 named metabolites detected, 338 (51%) were found in all samples, and 490 (74%) in more than 80% of samples. The median ICC between duplicates was 0.96 (25th - 75th percentile: 0.82-0.99). We observed a greater than 20% case-control difference in 24 metabolites (p < 0.05), although these associations were not significant after adjusting for multiple comparisons. CONCLUSIONS These data show that assays are reproducible for many metabolites, there is a minimal laboratory variation for the same sample, and a large between-person variation. Despite small sample size, differences between cases and controls in some metabolites suggest that a well-powered large-scale study is likely to detect biological meaningful differences to provide a better understanding of breast cancer etiology.
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Affiliation(s)
- Marcelle M. Dougan
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
- San Jose State University, San Jose, CA USA
| | - Yuqing Li
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
- Cancer Prevention Institute of California, Fremont, California, USA
| | - Lisa W. Chu
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Robert W. Haile
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
- Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Alice S. Whittemore
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Summer S. Han
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | | | | | - Irene L. Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, University of Toronto, Toronto, ON Canada
| | - Esther M. John
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
- Cancer Prevention Institute of California, Fremont, California, USA
| | - Ann W. Hsing
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, USA
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Terry MB, Keegan THM, Houghton LC, Goldberg M, Andrulis IL, Daly MB, Buys SS, Wei Y, Whittemore AS, Protacio A, Bradbury AR, Chung WK, Knight JA, John EM. Pubertal development in girls by breast cancer family history: the LEGACY girls cohort. Breast Cancer Res 2017; 19:69. [PMID: 28595647 PMCID: PMC5465536 DOI: 10.1186/s13058-017-0849-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Accepted: 05/03/2017] [Indexed: 01/29/2023] Open
Abstract
Background Pubertal milestones, such as onset of breast development and menstruation, play an important role in breast cancer etiology. It is unclear if these milestones are different in girls with a first- or second-degree breast cancer family history (BCFH). Methods In the LEGACY Girls Study (n = 1040), we examined whether three mother/guardian-reported pubertal milestones (having reached Tanner Stage 2 or higher (T2+) for breast and pubic hair development, and having started menstruation) differed by BCFH. We also examined whether associations between body size and race/ethnicity and pubertal milestones were modified by BCFH. We used mother/guardian reports as the primary measure of pubertal milestones, but also conducted sensitivity analyses using clinical Tanner measurements available for a subcohort (n = 204). We analyzed cross-sectional baseline data with logistic regression models for the entire cohort, and longitudinal data with Weibull survival models for the subcohort of girls that were aged 5–7 years at baseline (n = 258). Results BCFH was modestly, but not statistically significantly, associated with Breast T2+ (odds ratio (OR) = 1.36, 95% confidence interval (CI) = 0.88–2.10), with a stronger association seen in the subcohort of girls with clinical breast Tanner staging (OR = 2.20, 95% CI = 0.91–5.32). In a longitudinal analysis of girls who were aged 5–7 years at baseline, BCFH was associated with a 50% increased rate of having early breast development (hazard ratio (HR) = 1.49, 95% CI = 1.0–2.21). This association increased to twofold in girls who were not overweight at baseline (HR = 2.04, 95% CI = 1.29–3.21). BCFH was not associated with pubic hair development and post-menarche status. The median interval between onset of breast development and menarche was longer for BCFH+ than BCFH– girls (2.3 versus 1.7 years), suggesting a slower developmental tempo for BCFH+ girls. Associations between pubertal milestones and body size and race/ethnicity were similar in girls with or without a BCFH. For example, weight was positively associated with Breast T2+ in both girls with (OR = 1.06 per 1 kg, 95% CI = 1.03–1.10) and without (OR = 1.14 per 1 kg, 95% CI = 1.04–1.24) a BCFH. Conclusions These results suggest that BCFH may be related to earlier breast development and slower pubertal tempo independent of body size and race/ethnicity. Electronic supplementary material The online version of this article (doi:10.1186/s13058-017-0849-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mary Beth Terry
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th Street, New York, NY, 10032, USA. .,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.
| | - Theresa H M Keegan
- Division of Hematology and Oncology, University of California (UC) Davis School of Medicine, and UC Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Lauren C Houghton
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th Street, New York, NY, 10032, USA
| | - Mandy Goldberg
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th Street, New York, NY, 10032, USA
| | - Irene L Andrulis
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Saundra S Buys
- Department of Medicine, University of Utah Health Sciences Center, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Ying Wei
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Alice S Whittemore
- Departments of Biomedical Data Sciences and Health Research and Policy, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Angeline Protacio
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th Street, New York, NY, 10032, USA
| | - Angela R Bradbury
- Departments of Medicine, Hematology/Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.,Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY, USA
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Esther M John
- Cancer Prevention Institute of California, Fremont, CA, USA.,Department of Health Research and Policy (Epidemiology), and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
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43
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Shen J, Liao Y, Hopper JL, Goldberg M, Santella RM, Terry MB. Dependence of cancer risk from environmental exposures on underlying genetic susceptibility: an illustration with polycyclic aromatic hydrocarbons and breast cancer. Br J Cancer 2017; 116:1229-1233. [PMID: 28350789 PMCID: PMC5418454 DOI: 10.1038/bjc.2017.81] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 02/16/2017] [Accepted: 03/03/2017] [Indexed: 12/11/2022] Open
Abstract
Background: Most studies of environmental risk factors and breast cancer are conducted using average risk cohorts. Methods: We examined the association between polycyclic aromatic hydrocarbon (PAH)-albumin adducts in bloods from baseline and breast cancer risk in a prospective nested case–control study (New York site of the BCFR, 80 cases and 156 controls). We estimated the 10-year absolute breast cancer risk by a risk model that uses pedigree information (BOADICEA) and evaluated whether the increased risk from PAH differed by absolute risk. Results: Women with detectable levels of PAH had a twofold association with breast cancer risk (odds ratio (OR)=2.04; 95% CI=1.06–3.93) relative to women with non-detectable levels. The association increased with higher levels of PAH (⩾median) and by a higher level of absolute breast cancer risk (10-year risk ⩾3.4%: OR=4.09, 95% CI=1.38–12.13). Conclusions: These results support that family-based cohorts can be an efficient way to examine gene–environment interactions.
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Affiliation(s)
- Jing Shen
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, VIC 3010, Australia
| | - Mandy Goldberg
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA
| | - Regina M Santella
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, 1130 St Nicholas Avenue, New York, NY 10032, USA
| | - Mary Beth Terry
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA.,Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA.,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, 1130 St Nicholas Avenue, New York, NY 10032, USA
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44
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Dite GS, MacInnis RJ, Bickerstaffe A, Dowty JG, Milne RL, Antoniou AC, Weideman P, Apicella C, Giles GG, Southey MC, Jenkins MA, Phillips KA, Win AK, Terry MB, Hopper JL. Testing for Gene-Environment Interactions Using a Prospective Family Cohort Design: Body Mass Index in Early and Later Adulthood and Risk of Breast Cancer. Am J Epidemiol 2017; 185:487-500. [PMID: 28399571 PMCID: PMC6158796 DOI: 10.1093/aje/kww241] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 08/01/2016] [Accepted: 08/04/2016] [Indexed: 12/11/2022] Open
Abstract
The ability to classify people according to their underlying genetic susceptibility to a disease is increasing with new knowledge, better family data, and more sophisticated risk prediction models, allowing for more effective prevention and screening. To do so, however, we need to know whether risk associations are the same for people with different genetic susceptibilities. To illustrate one way to estimate such gene-environment interactions, we used prospective data from 3 Australian family cancer cohort studies, 2 enriched for familial risk of breast cancer. There were 288 incident breast cancers in 9,126 participants from 3,222 families. We used Cox proportional hazards models to investigate whether associations of breast cancer with body mass index (BMI; weight (kg)/height (m)2) at age 18-21 years, BMI at baseline, and change in BMI differed according to genetic risk based on lifetime breast cancer risk from birth, as estimated by BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) software, adjusted for age at baseline data collection. Although no interactions were statistically significant, we have demonstrated the power with which gene-environment interactions can be investigated using a cohort enriched for persons with increased genetic risk and a continuous measure of genetic risk based on family history.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - John L. Hopper
- Correspondence to Prof. John L. Hopper, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie Street, Parkville, VIC 3010, Australia (e-mail: )
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45
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Li H, Feng B, Miron A, Chen X, Beesley J, Bimeh E, Barrowdale D, John EM, Daly MB, Andrulis IL, Buys SS, Kraft P, Thorne H, Chenevix-Trench G, Southey M, Antoniou AC, James PA, Terry MB, Phillips KA, Hopper JL, Mitchell G, Goldgar DE. Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab. Genet Med 2017; 19:30-35. [PMID: 27171545 PMCID: PMC5107177 DOI: 10.1038/gim.2016.43] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 02/23/2016] [Indexed: 12/28/2022] Open
Abstract
PURPOSE This study examined the utility of sets of single-nucleotide polymorphisms (SNPs) in familial but non-BRCA-associated breast cancer (BC). METHODS We derived a polygenic risk score (PRS) based on 24 known BC risk SNPs for 4,365 women from the Breast Cancer Family Registry and Kathleen Cuningham Consortium Foundation for Research into Familial Breast Cancer familial BC cohorts. We compared scores for women based on cancer status at baseline; 2,599 women unaffected at enrollment were followed-up for an average of 7.4 years. Cox proportional hazards regression was used to analyze the association of PRS with BC risk. The BOADICEA risk prediction algorithm was used to measure risk based on family history alone. RESULTS The mean PRS at baseline was 2.25 (SD, 0.35) for affected women and was 2.17 (SD, 0.35) for unaffected women from combined cohorts (P < 10-6). During follow-up, 205 BC cases occurred. The hazard ratios for continuous PRS (per SD) and upper versus lower quintiles were 1.38 (95% confidence interval: 1.22-1.56) and 3.18 (95% confidence interval: 1.84-5.23) respectively. Based on their PRS-based predicted risk, management for up to 23% of women could be altered. CONCLUSION Including BC-associated SNPs in risk assessment can provide more accurate risk prediction than family history alone and can influence recommendations for cancer screening and prevention modalities for high-risk women.Genet Med 19 1, 30-35.
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Affiliation(s)
- Hongyan Li
- Cancer Control and Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Bingjian Feng
- Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Alexander Miron
- Dana Farber Cancer Institute, Boston, MA, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xiaoqing Chen
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jonathan Beesley
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Emmanuella Bimeh
- Division of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Daniel Barrowdale
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Esther M. John
- Cancer Prevention Institute of California, Fremont, CA, USA
- Department of Health Research and Policy (Epidemiology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Irene L. Andrulis
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Saundra S. Buys
- Department of Medicine, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - kConFab investigators
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
| | - Heather Thorne
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
| | | | - Melissa Southey
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Australia
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul A. James
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
| | - Kelly-Anne Phillips
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia
| | - John L. Hopper
- Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Gillian Mitchell
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - David E. Goldgar
- Cancer Control and Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
- Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA
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46
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Terry MB, Bradbury A. Family-based Breast Cancer Prevention Efforts in Adolescence. Pediatrics 2016; 138:S78-S80. [PMID: 27940980 DOI: 10.1542/peds.2015-4268k] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/16/2016] [Indexed: 11/24/2022] Open
Affiliation(s)
- Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York; and
| | - Angela Bradbury
- Departments of Medicine and Hematology/Oncology and Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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47
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Collins IM, Bickerstaffe A, Ranaweera T, Maddumarachchi S, Keogh L, Emery J, Mann GB, Butow P, Weideman P, Steel E, Trainer A, Bressel M, Hopper JL, Cuzick J, Antoniou AC, Phillips KA. iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management. Breast Cancer Res Treat 2016; 156:171-82. [PMID: 26909793 PMCID: PMC4788692 DOI: 10.1007/s10549-016-3726-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Accepted: 02/16/2016] [Indexed: 01/04/2023]
Abstract
We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent(®) selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent(®) then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent(®), risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent(®), IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent(®) (i.e., IBIS or BOADICEA) with the programmed iPrevent(®) model choice algorithm was assessed. Estimated breast cancer risks from iPrevent(®) were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent(®) were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent(®) logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent(®) were 100 % appropriate. iPrevent(®) successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.
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Affiliation(s)
- Ian M Collins
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett St., Melbourne, VIC, Australia
- The Greater Green Triangle Clinical School, Deakin University School of Medicine, Warrnambool, Australia
| | - Adrian Bickerstaffe
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Thilina Ranaweera
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Sanjaya Maddumarachchi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Louise Keogh
- Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Jon Emery
- Department of General Practice, The University of Melbourne, Melbourne, Australia
| | - G Bruce Mann
- The Breast Service, Royal Melbourne and Royal Women's Hospital, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Phyllis Butow
- Centre for Medical Psychology and Evidence-based Decision-Making (CeMPED) and The Psycho-Oncology Cooperative Research Group (PoCoG), The University of Sydney, Sydney, Australia
| | - Prue Weideman
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett St., Melbourne, VIC, Australia
| | - Emma Steel
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett St., Melbourne, VIC, Australia
- Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Alison Trainer
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett St., Melbourne, VIC, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Australia
| | - Mathias Bressel
- Department of Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kelly-Anne Phillips
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett St., Melbourne, VIC, Australia.
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia.
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.
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