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Bhatt R, van den Hout A, Antoniou AC, Shah M, Ficorella L, Steggall E, Easton DF, Pharoah PDP, Pashayan N. Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors. Cancer 2024; 130:1590-1599. [PMID: 38174903 PMCID: PMC7615824 DOI: 10.1002/cncr.35183] [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: 06/14/2023] [Revised: 11/08/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors. METHODS The authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states. RESULTS There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively. CONCLUSION In risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied.
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Affiliation(s)
- Rikesh Bhatt
- Department of Applied Health Research, University College London, London, UK
| | - Ardo van den Hout
- Department of Statistical Science, University College London, London, UK
| | - Antonis C. Antoniou
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mitul Shah
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Lorenzo Ficorella
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Douglas F. Easton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul D. P. Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California, USA
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London, UK
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Mars N, Kerminen S, Tamlander M, Pirinen M, Jakkula E, Aaltonen K, Meretoja T, Heinävaara S, Widén E, Ripatti S. Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women. J Clin Oncol 2024; 42:1477-1487. [PMID: 38422475 PMCID: PMC11095905 DOI: 10.1200/jco.23.00295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 11/24/2023] [Accepted: 12/20/2023] [Indexed: 03/02/2024] Open
Abstract
PURPOSE Family history (FH) and pathogenic variants (PVs) are used for guiding risk surveillance in selected high-risk women but little is known about their impact for breast cancer screening on population level. In addition, polygenic risk scores (PRSs) have been shown to efficiently stratify breast cancer risk through combining information about common genetic factors into one measure. METHODS In longitudinal real-life data, we evaluate PRS, FH, and PVs for stratified screening. Using FinnGen (N = 117,252), linked to the Mass Screening Registry for breast cancer (1992-2019; nationwide organized biennial screening for age 50-69 years), we assessed the screening performance of a breast cancer PRS and compared its performance with FH of breast cancer and PVs in moderate- (CHEK2)- to high-risk (PALB2) susceptibility genes. RESULTS Effect sizes for FH, PVs, and high PRS (>90th percentile) were comparable in screening-aged women, with similar implications for shifting age at screening onset. A high PRS identified women more likely to be diagnosed with breast cancer after a positive screening finding (positive predictive value [PPV], 39.5% [95% CI, 37.6 to 41.5]). Combinations of risk factors increased the PPVs up to 45% to 50%. A high PRS conferred an elevated risk of interval breast cancer (hazard ratio [HR], 2.78 [95% CI, 2.00 to 3.86] at age 50 years; HR, 2.48 [95% CI, 1.67 to 3.70] at age 60 years), and women with a low PRS (<10th percentile) had a low risk for both interval- and screen-detected breast cancers. CONCLUSION Using real-life screening data, this study demonstrates the effectiveness of a breast cancer PRS for risk stratification, alone and combined with FH and PVs. Further research is required to evaluate their impact in a prospective risk-stratified screening program, including cost-effectiveness.
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Affiliation(s)
- Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Sini Kerminen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Max Tamlander
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Eveliina Jakkula
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
| | - Kirsimari Aaltonen
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
| | - Tuomo Meretoja
- Breast Surgery Unit, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
- University of Helsinki, Helsinki, Finland
| | - Sirpa Heinävaara
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Finnish Cancer Registry, Cancer Society of Finland, Helsinki, Finland
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Public Health, University of Helsinki, Helsinki, Finland
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Jayasinghe D, Momin MM, Beckmann K, Hyppönen E, Benyamin B, Lee SH. Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models. Genet Epidemiol 2024; 48:85-100. [PMID: 38303123 DOI: 10.1002/gepi.22546] [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: 07/25/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 02/03/2024]
Abstract
The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.
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Affiliation(s)
- Dovini Jayasinghe
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
| | - Md Moksedul Momin
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
- Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, Bangladesh
| | - Kerri Beckmann
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
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Rodriguez J, Grassmann F, Xiao Q, Eriksson M, Mao X, Bajalica-Lagercrantz S, Hall P, Czene K. Investigation of Genetic Alterations Associated With Interval Breast Cancer. JAMA Oncol 2024; 10:372-379. [PMID: 38270937 PMCID: PMC10811589 DOI: 10.1001/jamaoncol.2023.6287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/16/2023] [Indexed: 01/26/2024]
Abstract
Importance Breast cancers (BCs) diagnosed between 2 screening examinations are called interval cancers (ICs), and they have worse clinicopathological characteristics and poorer prognosis than screen-detected cancers (SDCs). However, the association of rare germline genetic variants with IC have not been studied. Objective To evaluate whether rare germline deleterious protein-truncating variants (PTVs) can be applied to discriminate between IC and SDC while considering mammographic density. Design, Setting, and Participants This population-based genetic association study was based on women aged 40 to 76 years who were attending mammographic screening in Sweden. All women with a diagnosis of BC between January 2001 and January 2016 were included, together with age-matched controls. Patients with BC were followed up for survival until 2021. Statistical analysis was performed from September 2021 to December 2022. Exposure Germline PTVs in 34 BC susceptibility genes as analyzed by targeted sequencing. Main Outcomes and Measures Odds ratios (ORs) were used to compare IC with SDC using logistic regression. Hazard ratios were used to investigate BC-specific survival using Cox regression. Results All 4121 patients with BC (IC, n = 1229; SDC, n = 2892) were female, with a mean (SD) age of 55.5 (7.1) years. There were 5631 age-matched controls. The PTVs of the ATM, BRCA1, BRCA2, CHEK2, and PALB2 genes were more common in patients with IC compared with SDC (OR, 1.48; 95% CI, 1.06-2.05). This association was primarily influenced by BRCA1/2 and PALB2 variants. A family history of BC together with PTVs of any of these genes synergistically increased the probability of receiving a diagnosis of IC rather than SDC (OR, 3.95; 95% CI, 1.97-7.92). Furthermore, 10-year BC-specific survival revealed that if a patient received a diagnosis of an IC, carriers of PTVs in any of these 5 genes had significantly worse survival compared with patients not carrying any of them (hazard ratio, 2.04; 95% CI, 1.06-3.92). All of these associations were further pronounced in a subset of patients with IC who had a low mammographic density at prior screening examination. Conclusions and Relevance The results of this study may be helpful in future optimizations of screening programs that aim to lower mortality as well as the clinical treatment of patients with BC.
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Affiliation(s)
- Juan Rodriguez
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Health and Medical University, Potsdam, Germany
| | - Qingyang Xiao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Zhu P, Zhang Y, Chen Q, Qiu W, Chen M, Xue L, Lin M, Yang H. The interaction of diet, alcohol, genetic predisposition, and the risk of breast cancer: a cohort study from the UK Biobank. Eur J Nutr 2024; 63:343-356. [PMID: 37914956 PMCID: PMC10899287 DOI: 10.1007/s00394-023-03269-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 10/06/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND Dietary factors have consistently been associated with breast cancer risk. However, there is limited evidence regarding their associations in women with different genetic susceptibility to breast cancer, and their interaction with alcohol consumption is also not well understood. METHODS We analyzed data from 261,853 female participants in the UK Biobank. Multivariable adjusted Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations between dietary factors and breast cancer risk. Additionally, we assessed the interaction of dietary factors with alcohol consumption and polygenic risk score (PRS) for breast cancer. RESULTS A moderately higher risk of breast cancer was associated with the consumption of processed meat (HR = 1.10, 95% CI 1.03, 1.18, p-trend = 0.016). Higher intake of raw vegetables and fresh fruits, and adherence to a healthy dietary pattern were inversely associated with breast cancer risk [HR (95% CI):0.93 (0.88-0.99), 0.87 (0.81, 0.93) and 0.93 (0.86-1.00), p for trend: 0.025, < 0.001, and 0.041, respectively]. Furthermore, a borderline significant interaction was found between alcohol consumption and the intake of processed meat with regard to breast cancer risk (P for interaction = 0.065). No multiplicative interaction was observed between dietary factors and PRS. CONCLUSION Processed meat was positively associated with breast cancer risk, and vegetables, fruits, and healthy dietary patterns were negatively associated with breast cancer risk. We found no strong interaction of dietary factors with alcohol consumption and genetic predisposition for risk of breast cancer.
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Affiliation(s)
- Pingxiu Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Xuefu North Road 1, University Town, Fuzhou, 350122, China
| | - Yanyu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Xuefu North Road 1, University Town, Fuzhou, 350122, China
| | - Qianni Chen
- Department of Ultrasonography, Fuqing City Hospital Affiliated to Fujian Medical University, Fuqing, China
| | - Wenji Qiu
- School of Health Management, Fujian Medical University, Fuzhou, 350122, China
| | - Minhui Chen
- Department of Ultrasonography, Fuqing City Hospital Affiliated to Fujian Medical University, Fuqing, China
| | - Lihua Xue
- Department of Ultrasonography, Fuqing City Hospital Affiliated to Fujian Medical University, Fuqing, China
| | - Moufeng Lin
- No. 5 Hospital of Fuqing City, Fuzhou, 350319, China
| | - Haomin Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Xuefu North Road 1, University Town, Fuzhou, 350122, China.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden.
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Rotili A, Pesapane F, Signorelli G, Penco S, Nicosia L, Bozzini A, Meneghetti L, Zanzottera C, Mannucci S, Bonanni B, Cassano E. An Unenhanced Breast MRI Protocol Based on Diffusion-Weighted Imaging: A Retrospective Single-Center Study on High-Risk Population for Breast Cancer. Diagnostics (Basel) 2023; 13:1996. [PMID: 37370892 DOI: 10.3390/diagnostics13121996] [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: 03/01/2023] [Revised: 05/10/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
PURPOSE This study aimed to investigate the use of contrast-free magnetic resonance imaging (MRI) as an innovative screening method for detecting breast cancer in high-risk asymptomatic women. Specifically, the researchers evaluated the diagnostic performance of diffusion-weighted imaging (DWI) in this population. METHODS MR images from asymptomatic women, carriers of a germline mutation in either the BRCA1 or BRCA2 gene, collected in a single center from January 2019 to December 2021 were retrospectively evaluated. A radiologist with experience in breast imaging (R1) and a radiology resident (R2) independently evaluated DWI/ADC maps and, in case of doubts, T2-WI. The standard of reference was the pathological diagnosis through biopsy or surgery, or ≥1 year of clinical and radiological follow-up. Diagnostic performances were calculated for both readers with a 95% confidence interval (CI). The agreement was assessed using Cohen's kappa (κ) statistics. RESULTS Out of 313 women, 145 women were included (49.5 ± 12 years), totaling 344 breast MRIs with DWI/ADC maps. The per-exam cancer prevalence was 11/344 (3.2%). The sensitivity was 8/11 (73%; 95% CI: 46-99%) for R1 and 7/11 (64%; 95% CI: 35-92%) for R2. The specificity was 301/333 (90%; 95% CI: 87-94%) for both readers. The diagnostic accuracy was 90% for both readers. R1 recalled 40/344 exams (11.6%) and R2 recalled 39/344 exams (11.3%). Inter-reader reproducibility between readers was in moderate agreement (κ = 0.43). CONCLUSIONS In female carriers of a BRCA1/2 mutation, breast DWI supplemented with T2-WI allowed breast cancer detection with high sensitivity and specificity by a radiologist with extensive experience in breast imaging, which is comparable to other screening tests. The findings suggest that DWI and T2-WI have the potential to serve as a stand-alone method for unenhanced breast MRI screening in a selected population, opening up new perspectives for prospective trials.
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Affiliation(s)
- Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giulia Signorelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Cristina Zanzottera
- Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Sara Mannucci
- Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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Lopes Cardozo JM, Andrulis IL, Bojesen SE, Dörk T, Eccles DM, Fasching PA, Hooning MJ, Keeman R, Nevanlinna H, Rutgers EJ, Easton DF, Hall P, Pharoah PD, van 't Veer LJ, Schmidt MK. Associations of a Breast Cancer Polygenic Risk Score With Tumor Characteristics and Survival. J Clin Oncol 2023; 41:1849-1863. [PMID: 36689693 PMCID: PMC10082287 DOI: 10.1200/jco.22.01978] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/25/2022] [Accepted: 12/16/2022] [Indexed: 01/24/2023] Open
Abstract
PURPOSE A polygenic risk score (PRS) consisting of 313 common genetic variants (PRS313) is associated with risk of breast cancer and contralateral breast cancer. This study aimed to evaluate the association of the PRS313 with clinicopathologic characteristics of, and survival following, breast cancer. METHODS Women with invasive breast cancer were included, 98,397 of European ancestry and 12,920 of Asian ancestry, from the Breast Cancer Association Consortium (BCAC), and 683 women from the European MINDACT trial. Associations between PRS313 and clinicopathologic characteristics, including the 70-gene signature for MINDACT, were evaluated using logistic regression analyses. Associations of PRS313 (continuous, per standard deviation) with overall survival (OS) and breast cancer-specific survival (BCSS) were evaluated with Cox regression, adjusted for clinicopathologic characteristics and treatment. RESULTS The PRS313 was associated with more favorable tumor characteristics. In BCAC, increasing PRS313 was associated with lower grade, hormone receptor-positive status, and smaller tumor size. In MINDACT, PRS313 was associated with a low risk 70-gene signature. In European women from BCAC, higher PRS313 was associated with better OS and BCSS: hazard ratio (HR) 0.96 (95% CI, 0.94 to 0.97) and 0.96 (95% CI, 0.94 to 0.98), but the association disappeared after adjustment for clinicopathologic characteristics (and treatment): OS HR, 1.01 (95% CI, 0.98 to 1.05) and BCSS HR, 1.02 (95% CI, 0.98 to 1.07). The results in MINDACT and Asian women from BCAC were consistent. CONCLUSION An increased PRS313 is associated with favorable tumor characteristics, but is not independently associated with prognosis. Thus, PRS313 has no role in the clinical management of primary breast cancer at the time of diagnosis. Nevertheless, breast cancer mortality rates will be higher for women with higher PRS313 as increasing PRS313 is associated with an increased risk of disease. This information is crucial for modeling effective stratified screening programs.
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Affiliation(s)
- Josephine M.N. Lopes Cardozo
- Department of Surgery, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
- European Organisation for Research and Treatment of Cancer Headquarters, Brussels, Belgium
| | - Irene L. Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Stig E. Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Diana M. Eccles
- Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Peter A. Fasching
- Department of Gynecology and Obstetricss, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Maartje J. Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Emiel J.T. Rutgers
- Department of Surgery, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Paul D.P. Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Laura J. van 't Veer
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA
| | - Marjanka K. Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
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Breast cancer polygenic risk scores are associated with short-term risk of poor prognosis breast cancer. Breast Cancer Res Treat 2022; 196:389-398. [PMID: 36138293 DOI: 10.1007/s10549-022-06739-5] [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: 06/15/2022] [Accepted: 09/04/2022] [Indexed: 11/02/2022]
Abstract
PURPOSE Polygenic risk scores (PRS) for breast cancer may help guide screening decisions. However, few studies have examined whether PRS are associated with risk of short-term or poor prognosis breast cancers. The study purpose was to evaluate the association of the 313 SNP breast cancer PRS with 2-year risk of poor prognosis breast cancer. METHODS We evaluated the association of breast cancer PRS with breast cancer overall, ER + and ER- breast cancer, and poor prognosis breast cancer diagnosed within 2 years of a negative mammogram among a cohort of 3657 women using logistic regression adjusted for age, breast density, race/ethnicity, year of screening, and genetic ancestry principal components. Breast cancers were considered poor prognosis if they were metastatic, positive lymph nodes, ER/PR + HER2- and > 2 cm, ER/PR/HER2-, or HER2 + and > 1 cm. RESULTS Of the 308 breast cancers, 137 (44%) were poor prognosis. The overall breast cancer PRS was significantly associated with breast cancer diagnosis within 2 years (OR 1.39, 95% CI 1.23-1.57, p < 0.001). The breast cancer PRS was also associated specifically with diagnosis of poor prognosis disease (OR 1.24, 95% CI 1.03-1.49, p = 0.018), but was more strongly associated with good prognosis cancer (OR 1.52 95% CI 1.29-1.80 p = 3.60 × 10-7) The ER + PRS was significantly associated with ER/PR + breast cancer (OR 1.41, 95% CI 1.24-1.61, p < 0.001) and the ER- PRS was significantly associated with ER- breast cancer (OR 1.48, 95% CI 1.08-2.02, p = 0.015). CONCLUSION Breast cancer PRS was independently and significantly associated with diagnosis of both breast cancer overall and poor prognosis breast cancer within 2 years of a negative mammogram, suggesting PRS may help guide decisions about screening intervals and supplemental screening.
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9
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Athira K, Gopakumar G. Breast cancer stage prediction: a computational approach guided by transcriptome analysis. Mol Genet Genomics 2022; 297:1467-1479. [PMID: 35922530 DOI: 10.1007/s00438-022-01932-z] [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: 12/08/2020] [Accepted: 07/17/2022] [Indexed: 11/25/2022]
Abstract
Breast cancer is the second leading cancer among women in terms of mortality rate. In recent years, its incidence frequency has been continuously rising across the globe. In this context, the new therapeutic strategies to manage the deadly disease attracts tremendous research focus. However, finding new prognostic predictors to refine the selection of therapy for the various stages of breast cancer is an unattempted issue. Aberrant expression of genes at various stages of cancer progression can be studied to identify specific genes that play a critical role in cancer staging. Moreover, while many schemes for subtype prediction in breast cancer have been explored in the literature, stage-wise classification remains a challenge. These observations motivated the proposed two-phased method: stage-specific gene signature selection and stage classification. In the first phase, meta-analysis of gene expression data is conducted to identify stage-wise biomarkers that were then used in the second phase of cancer classification. From the analysis, 118, 12 and 4 genes respectively in stage I, stage II and stage III are determined as potential biomarkers. Pathway enrichment, gene network and literature analysis validate the significance of the identified genes in breast cancer. In this study, machine learning methods were combined with principal component and posterior probability analysis. Such a scheme offers a unique opportunity to build a meaningful model for predicting breast cancer staging. Among the machine learning models compared, Support Vector Machine (SVM) is found to perform the best for the selected datasets with an accuracy of 92.21% during test data evaluation. Perhaps, biomarker identification performed here for stage-specific cancer treatment would be a meaningful step towards predictive medicine. Significantly, the determination of correct cancer stage using the proposed 134 gene signature set can possibly act as potential target for breast cancer therapeutics.
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Affiliation(s)
- K Athira
- Department of Computer Science and Engineering, NIT Campus PO, National Institute of Technology Calicut, Calicut, Kerala, India.
| | - G Gopakumar
- Department of Computer Science and Engineering, NIT Campus PO, National Institute of Technology Calicut, Calicut, Kerala, India
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10
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Tang Y, You D, Yi H, Yang S, Zhao Y. IPRS: Leveraging Gene-Environment Interaction to Reconstruct Polygenic Risk Score. Front Genet 2022; 13:801397. [PMID: 35401709 PMCID: PMC8989431 DOI: 10.3389/fgene.2022.801397] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/08/2022] [Indexed: 12/30/2022] Open
Abstract
Background: Polygenic risk score (PRS) is widely regarded as a predictor of genetic susceptibility to disease, applied to individuals to predict the risk of disease occurrence. When the gene-environment (G×E) interaction is considered, the traditional PRS prediction model directly uses PRS to interact with the environment without considering the interactions between each variant and environment, which may lead to prediction performance and risk stratification of complex diseases are not promising. Methods: We developed a method called interaction PRS (iPRS), reconstructing PRS by leveraging G×E interactions. Two extensive simulations evaluated prediction performance, risk stratification, and calibration performance of the iPRS prediction model, and compared it with the traditional PRS prediction model. Real data analysis was performed using existing data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial study to predict genetic susceptibility, pack-years of smoking history, and G×E interactions in patients with lung cancer. Results: Two extensive simulations indicated iPRS prediction model could improve the prediction performance of disease risk, the accuracy of risk stratification, and clinical calibration performance compared with the traditional PRS prediction model, especially when antagonism accounted for the majority of the interaction. PLCO real data analysis also suggested that the iPRS prediction model was superior to the PRS prediction model in predictive effect (p = 0.0205). Conclusion: IPRS prediction model could have a good application prospect in predicting disease risk, optimizing the screening of high-risk populations, and improving the clinical benefits of preventive interventions among populations.
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Affiliation(s)
- Yingdan Tang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongfang You
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Honggang Yi
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Sheng Yang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
- *Correspondence: Sheng Yang, ; Yang Zhao,
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
- Center of Biomedical Big Data and the Laboratory of Biomedical Big Data, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Sheng Yang, ; Yang Zhao,
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11
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Park JH, Han HS, Lim SD, Kim WY, Park KS, Yoo YB, Lee SE, Kim WS. Fatty acid synthetase expression in triple-negative breast cancer. J Pathol Transl Med 2022; 56:73-80. [PMID: 35051326 PMCID: PMC8935000 DOI: 10.4132/jptm.2021.10.27] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/27/2021] [Indexed: 11/17/2022] Open
Abstract
Background Triple-negative breast cancer (TNBC) has a relatively poor prognosis. Research has identified potential metabolic targets, including fatty acid metabolism, in TNBC. The absence of effective target therapies for TNBC led to exploration of the role of fatty acid synthetase (FASN) as a potential target for TNBC therapy. Here, we analyzed the expression of FASN, a representative lipid metabolism–related protein, and investigated the association between FASN expression and Ki-67 and the programmed death ligand 1 (PD-L1) biomarkers in TNBC. Methods Immunohistochemical expression of FASN was analyzed in 166 patients with TNBC. For analytical purposes, patients with 0–1+ FASN staining were grouped as low-grade FASN and patients with 2–3+ FASN staining as high-grade FASN. Results FASN expression was observed in 47.1% of TNBC patients. Low and high expression of FASN was identified in 75.9% and 24.1%, respectively, and no statistically significant difference was found in T category, N category, American Joint Committee on Cancer stage, or recurrence rate between the low and high-FASN expression groups. Ki-67 proliferation level was significantly different between the low and high-FASN expression groups. FASN expression was significantly related to Ki-67 as the level increased. There was no significant difference in PD-L1 positivity between the low- and high-FASN expression groups. Conclusions We identified FASN expression in 166 TNBC patients. The Ki-67 proliferation index was positively correlated with FASN level, indicating higher proliferation activity as FASN increases. However, there was no statistical association with PD-L1 SP142, the currently FDA-approved assay, or FASN expression level.
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Affiliation(s)
- Jin Hee Park
- Department of Pathology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Hye Seung Han
- Department of Pathology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - So Dug Lim
- Department of Pathology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Wook Youn Kim
- Department of Pathology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Kyoung Sik Park
- Department of Surgery, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Young Bum Yoo
- Department of Surgery, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Seung Eun Lee
- Department of Pathology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Wan-Seop Kim
- Department of Pathology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
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12
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Ugalde-Morales E, Grassmann F, Humphreys K, Li J, Eriksson M, Tobin NP, Lindström LS, Vallon-Christersson J, Borg Å, Hall P, Czene K. Interval breast cancer is associated with interferon immune response. Eur J Cancer 2022; 162:194-205. [PMID: 35026490 DOI: 10.1016/j.ejca.2021.12.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND The aggressive nature of breast cancers detected between planned mammographic screens, so-called interval cancers, remains elusive. Here, we aim to characterise underlying molecular features of interval cancer. METHODS From 672 patients with invasive breast cancer, we analysed gene expression differences between 90 'true' interval cancer cases (i.e. women with low-dense breasts defined as per cent mammographic density <25%) and 310 screen-detected tumours while accounting for PAM50 subtypes and thus overall tumour aggressiveness. We computed an interval cancer gene expression profile (IC-Gx) in a total of 2270 breast tumours (regardless of interval cancer status) and tested for association with expression-based immune subtypes in breast cancer. In addition, we investigated the contribution of inherited and somatic genetic variants in distinct features of interval cancer. RESULTS We identified 8331 genes nominally associated with interval cancer (P-value < 0.05, fold-change > 1.5). Gene set enrichment analysis showed immune-related pathways as key processes altered in interval cancer. Our IC-Gx, based on 47 genes with the strongest associations (false discovery rate < 0.05), was found to be associated mainly with immune subtypes involving interferon response. We isolated an interaction network of interval cancer and interferon genes for which a significant load of somatic and germline variants in class I interferon genes was observed. CONCLUSION We identified novel molecular features of interval breast cancer highlighting interferon pathways as a potential target for prevention or treatment.
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Affiliation(s)
- Emilio Ugalde-Morales
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Health and Medical University, Potsdam, Germany
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Swedish EScience Research Centre (SeRC), Karolinska Institutet Stockholm, SE-17177, Sweden
| | - Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Human Genetics, Genome Institute of Singapore, Singapore, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nicholas P Tobin
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Linda S Lindström
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Johan Vallon-Christersson
- Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Medicon Village 404-A2 Lund, SE-22381, Sweden; Lund University Cancer Center Lund, SE-22381, Sweden; CREATE Health Strategic Centre for Translational Cancer Research, Lund University Lund, SE-22381, Sweden
| | - Åke Borg
- Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Medicon Village 404-A2 Lund, SE-22381, Sweden; Lund University Cancer Center Lund, SE-22381, Sweden; CREATE Health Strategic Centre for Translational Cancer Research, Lund University Lund, SE-22381, Sweden; Department of Clinical Sciences, SCIBLU Genomics, Lund University Lund, SE-22381, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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13
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Torabi Dalivandan S, Plummer J, Gayther SA. Risks and Function of Breast Cancer Susceptibility Alleles. Cancers (Basel) 2021; 13:3953. [PMID: 34439109 PMCID: PMC8393346 DOI: 10.3390/cancers13163953] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/30/2021] [Accepted: 07/31/2021] [Indexed: 12/22/2022] Open
Abstract
Family history remains one of the strongest risk factors for breast cancer. It is well established that women with a first-degree relative affected by breast cancer are twice as likely to develop the disease themselves. Twins studies indicate that this is most likely due to shared genetics rather than shared epidemiological/lifestyle risk factors. Linkage and targeted sequencing studies have shown that rare high- and moderate-penetrance germline variants in genes involved in the DNA damage response (DDR) including BRCA1, BRCA2, PALB2, ATM, and TP53 are responsible for a proportion of breast cancer cases. However, breast cancer is a heterogeneous disease, and there is now strong evidence that different risk alleles can predispose to different subtypes of breast cancer. Here, we review the associations between the different genes and subtype-specificity of breast cancer based on the most comprehensive genetic studies published. Genome-wide association studies (GWAS) have also been used to identify an additional hereditary component of breast cancer, and have identified hundreds of common, low-penetrance susceptibility alleles. The combination of these low penetrance risk variants, summed as a polygenic risk score (PRS), can identify individuals across the spectrum of disease risk. However, there remains a substantial bottleneck between the discovery of GWAS-risk variants and their contribution to tumorigenesis mainly because the majority of these variants map to the non-protein coding genome. A range of functional genomic approaches are needed to identify the causal risk variants and target susceptibility genes and establish their underlying role in disease biology. We discuss how the application of these multidisciplinary approaches to understand genetic risk for breast cancer can be used to identify individuals in the population that may benefit from clinical interventions including screening for early detection and prevention, and treatment strategies to reduce breast cancer-related mortalities.
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Affiliation(s)
| | | | - Simon A. Gayther
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA; (S.T.D.); (J.P.)
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14
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Adedokun B, Du Z, Gao G, Ahearn TU, Lunetta KL, Zirpoli G, Figueroa J, John EM, Bernstein L, Zheng W, Hu JJ, Ziegler RG, Nyante S, Bandera EV, Ingles SA, Press MF, Deming-Halverson SL, Rodriguez-Gil JL, Yao S, Ogundiran TO, Ojengbede O, Blot W, Troester MA, Nathanson KL, Hennis A, Nemesure B, Ambs S, Fiorica PN, Sucheston-Campbell LE, Bensen JT, Kushi LH, Torres-Mejia G, Hu D, Fejerman L, Bolla MK, Dennis J, Dunning AM, Easton DF, Michailidou K, Pharoah PDP, Wang Q, Sandler DP, Taylor JA, O'Brien KM, Kitahara CM, Falusi AG, Babalola C, Yarney J, Awuah B, Addai-Wiafe B, Chanock SJ, Olshan AF, Ambrosone CB, Conti DV, Ziv E, Olopade OI, Garcia-Closas M, Palmer JR, Haiman CA, Huo D. Cross-ancestry GWAS meta-analysis identifies six breast cancer loci in African and European ancestry women. Nat Commun 2021; 12:4198. [PMID: 34234117 PMCID: PMC8263739 DOI: 10.1038/s41467-021-24327-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 06/02/2021] [Indexed: 02/06/2023] Open
Abstract
Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P < 0.05 from African ancestry GWAS meta-analysis (9241 cases and 10193 controls), then meta-analyze with European ancestry GWAS data (122977 cases and 105974 controls) from the Breast Cancer Association Consortium. The approach identifies four loci for overall breast cancer risk [1p13.3, 5q31.1, 15q24 (two independent signals), and 15q26.3] and two loci for estrogen receptor-negative disease (1q41 and 7q11.23) at genome-wide significance. Four of the index single nucleotide polymorphisms (SNPs) lie within introns of genes (KCNK2, C5orf56, SCAMP2, and SIN3A) and the other index SNPs are located close to GSTM4, AMPD2, CASTOR2, and RP11-168G16.2. Here we present risk loci with consistent direction of associations in African and European descendants. The study suggests that replication across multiple ancestry populations can help improve the understanding of breast cancer genetics and identify causal variants.
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Affiliation(s)
- Babatunde Adedokun
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Zhaohui Du
- Department of Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Gary Zirpoli
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Jonine Figueroa
- Usher Institute and CRUK Edinburgh Centre, University of Edinburgh, Edinburgh, UK
| | - Esther M John
- Departments of Epidemiology & Population Health and of Medicine (Oncology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Leslie Bernstein
- Biomarkers of Early Detection and Prevention, Department of Population Sciences, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Jennifer J Hu
- Department of Public Health Sciences, University of Miami, Miami, FL, USA
| | - Regina G Ziegler
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Sarah Nyante
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Elisa V Bandera
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Sue A Ingles
- Department of Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael F Press
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sandra L Deming-Halverson
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Jorge L Rodriguez-Gil
- Genomics, Development and Disease Section, Genetic Disease Research Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Temidayo O Ogundiran
- Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oladosu Ojengbede
- Center for Population and Reproductive Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - William Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Melissa A Troester
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Katherine L Nathanson
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anselm Hennis
- University of the West Indies, Bridgetown, Barbados
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Peter N Fiorica
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Lara E Sucheston-Campbell
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Jeannette T Bensen
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Lawrence H Kushi
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Gabriela Torres-Mejia
- Center for Population Health Research, Instituto Nacional de Salud Publica, Cuernavaca, Mexico
| | - Donglei Hu
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Laura Fejerman
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Alison M Dunning
- 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
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Paul D P Pharoah
- 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
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Cari M Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Adeyinka G Falusi
- Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Oyo, Nigeria
| | - Chinedum Babalola
- Department of Pharmaceutical Chemistry, University of Ibadan, Ibadan, Oyo, Nigeria
| | | | | | | | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Andrew F Olshan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - David V Conti
- Department of Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Elad Ziv
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Olufunmilayo I Olopade
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, IL, USA
| | | | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Christopher A Haiman
- Department of Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Dezheng Huo
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, IL, USA.
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
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15
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Ugalde‐Morales E, Grassmann F, Humphreys K, Li J, Eriksson M, Tobin NP, Borg Å, Vallon‐Christersson J, Hall P, Czene K. Association between breast cancer risk and disease aggressiveness: Characterizing underlying gene expression patterns. Int J Cancer 2020; 148:884-894. [PMID: 32856720 PMCID: PMC7818270 DOI: 10.1002/ijc.33270] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 08/12/2020] [Accepted: 08/13/2020] [Indexed: 12/12/2022]
Abstract
The association between breast cancer risk defined by the Tyrer-Cuzick score (TC) and disease prognosis is not well established. Here, we investigated the relationship between 5-year TC and disease aggressiveness and then characterized underlying molecular processes. In a case-only study (n = 2474), we studied the association of TC with molecular subtypes and tumor characteristics. In a subset of patients (n = 672), we correlated gene expression to TC and computed a low-risk TC gene expression (TC-Gx) profile, that is, a profile expected to be negatively associated with risk, which we used to test for association with disease aggressiveness. We performed enrichment analysis to pinpoint molecular processes likely to be altered in low-risk tumors. A higher TC was found to be inversely associated with more aggressive surrogate molecular subtypes and tumor characteristics (P < .05) including Ki-67 proliferation status (P < 5 × 10-07 ). Our low-risk TC-Gx, based on the weighted sum of 37 expression values of genes strongly correlated with TC, was associated with basal-like (P < 5 × 10-13 ), HER2-enriched subtype (P < 5 × 10-07 ) and worse 10-year breast cancer-specific survival (log-rank P < 5 × 10-04 ). Associations between low-risk TC-Gx and more aggressive molecular subtypes were replicated in an independent cohort from The Cancer Genome Atlas database (n = 975). Gene expression that correlated with low TC was enriched in proliferation and oncogenic signaling pathways (FDR < 0.05). Moreover, higher proliferation was a key factor explaining the association with worse survival. Women who developed breast cancer despite having a low risk were diagnosed with more aggressive tumors and had a worse prognosis, most likely driven by increased proliferation. Our findings imply the need to establish risk factors associated with more aggressive breast cancer subtypes.
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Affiliation(s)
- Emilio Ugalde‐Morales
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Felix Grassmann
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
- Institute of Medical SciencesUniversity of AberdeenAberdeenUK
| | - Keith Humphreys
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
- Swedish eScience Research Centre (SeRC)Karolinska InstitutetStockholmSweden
| | - Jingmei Li
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
- Department of Human GeneticsGenome Institute of SingaporeSingaporeSingapore
- Department of Surgery, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore
| | - Mikael Eriksson
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Nicholas P. Tobin
- Department of Oncology‐PathologyKarolinska InstitutetStockholmSweden
| | - Åke Borg
- Department of Clinical Sciences, Division of Oncology and PathologyLund UniversityLundSweden
- Department of OncologyLund University Cancer CenterLundSweden
- CREATE Health Strategic Centre for Translational Cancer ResearchLund UniversityLundSweden
- Department of Clinical Sciences, SCIBLU GenomicsLund UniversityLundSweden
| | - Johan Vallon‐Christersson
- Department of Clinical Sciences, Division of Oncology and PathologyLund UniversityLundSweden
- Department of OncologyLund University Cancer CenterLundSweden
- CREATE Health Strategic Centre for Translational Cancer ResearchLund UniversityLundSweden
| | - Per Hall
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
- Department of OncologySödersjukhusetStockholmSweden
| | - Kamila Czene
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
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16
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Yanes T, McInerney-Leo AM, Law MH, Cummings S. The emerging field of polygenic risk scores and perspective for use in clinical care. Hum Mol Genet 2020; 29:R165-R176. [DOI: 10.1093/hmg/ddaa136] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/30/2020] [Accepted: 07/01/2020] [Indexed: 02/06/2023] Open
Abstract
Abstract
Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.
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Affiliation(s)
- Tatiane Yanes
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Aideen M McInerney-Leo
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Matthew H Law
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Herston QLD 4006, Australia
- Faculty of Health, School of Biomedical Sciences, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove QLD 4059, Australia
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17
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Yanes T, Young MA, Meiser B, James PA. Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field. Breast Cancer Res 2020; 22:21. [PMID: 32066492 PMCID: PMC7026946 DOI: 10.1186/s13058-020-01260-3] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 02/07/2020] [Indexed: 01/04/2023] Open
Abstract
Polygenic factors are estimated to account for an additional 18% of the familial relative risk of breast cancer, with those at the highest level of polygenic risk distribution having a least a twofold increased risk of the disease. Polygenic testing promises to revolutionize health services by providing personalized risk assessments to women at high-risk of breast cancer and within population breast screening programs. However, implementation of polygenic testing needs to be considered in light of its current limitations, such as limited risk prediction for women of non-European ancestry. This article aims to provide a comprehensive review of the evidence for polygenic breast cancer risk, including the discovery of variants associated with breast cancer at the genome-wide level of significance and the use of polygenic risk scores to estimate breast cancer risk. We also review the different applications of this technology including testing of women from high-risk breast cancer families with uninformative genetic testing results, as a moderator of monogenic risk, and for population screening programs. Finally, a potential framework for introducing testing for polygenic risk in familial cancer clinics and the potential challenges with implementing this technology in clinical practice are discussed.
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Affiliation(s)
- Tatiane Yanes
- Psychosocial Research Group, Prince of Wales Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia. .,The University of Queensland Diamantina Institute, Dermatology Research Centre, University of Queensland, Brisbane, QLD, 4102, Australia.
| | - Mary-Anne Young
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Bettina Meiser
- Psychosocial Research Group, Prince of Wales Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Paul A James
- Parkville Integrated Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
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18
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Reworking GWAS Data to Understand the Role of Nongenetic Factors in MS Etiopathogenesis. Genes (Basel) 2020; 11:genes11010097. [PMID: 31947683 PMCID: PMC7017269 DOI: 10.3390/genes11010097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 01/03/2020] [Accepted: 01/10/2020] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies have identified more than 200 multiple sclerosis (MS)-associated loci across the human genome over the last decade, suggesting complexity in the disease etiology. This complexity poses at least two challenges: the definition of an etiological model including the impact of nongenetic factors, and the clinical translation of genomic data that may be drivers for new druggable targets. We reviewed studies dealing with single genes of interest, to understand how MS-associated single nucleotide polymorphism (SNP) variants affect the expression and the function of those genes. We then surveyed studies on the bioinformatic reworking of genome-wide association studies (GWAS) data, with aggregate analyses of many GWAS loci, each contributing with a small effect to the overall disease predisposition. These investigations uncovered new information, especially when combined with nongenetic factors having possible roles in the disease etiology. In this context, the interactome approach, defined as “modules of genes whose products are known to physically interact with environmental or human factors with plausible relevance for MS pathogenesis”, will be reported in detail. For a future perspective, a polygenic risk score, defined as a cumulative risk derived from aggregating the contributions of many DNA variants associated with a complex trait, may be integrated with data on environmental factors affecting the disease risk or protection.
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19
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Nguyen TL, Li S, Dite GS, Aung YK, Evans CF, Trinh HN, Baglietto L, Stone J, Song YM, Sung J, English DR, Jenkins MA, Dugué PA, Milne RL, Southey MC, Giles GG, Pike MC, Hopper JL. Interval breast cancer risk associations with breast density, family history and breast tissue aging. Int J Cancer 2019; 147:375-382. [PMID: 31609476 PMCID: PMC7318124 DOI: 10.1002/ijc.32731] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/16/2019] [Accepted: 09/27/2019] [Indexed: 01/04/2023]
Abstract
Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case-control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first-degree family history by questionnaire, measured body mass index (BMI) and calculated age-adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age-adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69-0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts-an increased risk of developing an interval breast cancer.
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Affiliation(s)
- Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Ye K Aung
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Perth, WA, Australia
| | - Yun-Mi Song
- Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joohon Sung
- Department of Epidemiology School of Public Health, Seoul National University, Seoul, South Korea.,Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, 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
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, 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
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, 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
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
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20
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Interval breast cancer is associated with other types of tumors. Nat Commun 2019; 10:4648. [PMID: 31641120 PMCID: PMC6805891 DOI: 10.1038/s41467-019-12652-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 09/20/2019] [Indexed: 12/23/2022] Open
Abstract
Breast cancer (BC) patients diagnosed between two screenings (interval cancers) are more likely than screen-detected patients to carry rare deleterious mutations in cancer genes potentially leading to increased risk for other non-breast cancer (non-BC) tumors. In this study, we include 14,846 women diagnosed with BC of which 1,772 are interval and 13,074 screen-detected. Compared to women with screen-detected cancers, interval breast cancer patients are more likely to have a non-BC tumor before (Odds ratio (OR): 1.43 [1.19–1.70], P = 9.4 x 10−5) and after (OR: 1.28 [1.14–1.44], P = 4.70 x 10−5) breast cancer diagnosis, are more likely to report a family history of non-BC tumors and have a lower genetic risk score based on common variants for non-BC tumors. In conclusion, interval breast cancer is associated with other tumors and common cancer variants are unlikely to be responsible for this association. These findings could have implications for future screening and prevention programs. Interval cancer patients are more likely to carry rare gene mutations than screen-detected breast cancer patients. Here, the authors report that interval cancer patients are more likely cancer survivors and are at a greater risk of developing other non-breast tumors.
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21
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Lee JS, Kim HA, Cho SH, Lee HB, Park MH, Jeong J, Park HK, Oh M, Yi O. Five-Year Overall Survival of Interval Breast Cancers is Better than Non- Interval Cancers from Korean Breast Cancer Registry. Asian Pac J Cancer Prev 2019; 20:1717-1726. [PMID: 31244292 PMCID: PMC7021595 DOI: 10.31557/apjcp.2019.20.6.1717] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 04/05/2019] [Indexed: 11/25/2022] Open
Abstract
Objective: Interval breast cancer (IC) is a limitation of breast cancer screening. We investigated data from a large scaled breast cancer dataset of patients with breast cancer who underwent breast cancer screening in order to recapitulate the overall survival (OS) of patients with ICs compared to those with non-ICs. Methods: A total of 27,141 patients in the Korean breast cancer registry with breast cancer who had ever participated in biannual national breast cancer screening programs between 2009 and 2013 were enrolled. We compared the social, pregnancy-associated, and pathologic characteristics between the IC and non-IC groups and identified the significant prognostic factors for OS. Results: The proportion of ICs was 1.3% (370/27,141) in this study population. ICs were correlated with age 45-55 years at diagnosis, higher levels of education, early menopause (<50 years), hormone replacement therapy, specific provinces (Kangwon, Kyungnam, Jeju, and Dae-jeon), and family history of breast cancer. Low-to-intermediate nuclear grade, early stage (stage 0-I), and low Ki-67 level were also correlated with IC proportion. Non-ICs were associated with an increased risk of five-year mortality (hazard ratio [HR] 7.4; 95% confidence interval [CI]:1.85-29.66; p = 0.005) compared to ICs. Lymph node metastasis, residence (Kyung-nam province), low education status, high histologic grade, and asymptomatic cancers increased the HR of five-year OS. Conclusion: ICs occurred unequally in specific province and relatively high-educated women in Korea. They were also diagnosed with early-stage breast cancer with a favorable recurrence risk, and their outcome was better than those of patients with other breast cancers in breast cancer screening.
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Affiliation(s)
- Jung Sun Lee
- Department of Surgery, Haeundae Paik Hospital, College of Medicine, Inje University, Busan, Korea.
| | - Hyun-Ah Kim
- Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
| | - Se-Heon Cho
- Department of Surgery, College of Medicine, Dong- A University, Busan, Korea
| | - Han-Byoel Lee
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Min Ho Park
- Department of Surgery, Chonnam National University Hwasun Hospital, Chonnam, Korea
| | - Joon Jeong
- Department of Surgery, Gangnam Severance Hospital, Yonsei University Medical College, Seoul, Korea
| | - Heung Kyu Park
- Department of Surgery, Breast Cancer Center, Gachon University Gill Medical Center, Incheon, Korea
| | - Minkyung Oh
- Department of Pharmacology, Inje University College of medicine, Clinical Trial Center, Inje University Busan Paik Hospital, Busan, Korea
| | - Onvox Yi
- Department of Surgery, Dongnam Institution of Radiological and Medical Science, Busan, Korea
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22
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Chasioti D, Yan J, Nho K, Saykin AJ. Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases. Trends Genet 2019; 35:371-382. [PMID: 30922659 PMCID: PMC6475476 DOI: 10.1016/j.tig.2019.02.005] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 02/12/2019] [Accepted: 02/22/2019] [Indexed: 11/25/2022]
Abstract
Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies.
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Affiliation(s)
- Danai Chasioti
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Kwangsik Nho
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Andrew J Saykin
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
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23
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Cheasley D, Li N, Rowley SM, Elder K, Mann GB, Loi S, Savas P, Goode DL, Kader T, Zethoven M, Semple T, Fox SB, Pang JM, Byrne D, Devereux L, Nickson C, Procopio P, Lee G, Hughes S, Saunders H, Fujihara KM, Kuykhoven K, Connaughton J, James PA, Gorringe KL, Campbell IG. Molecular comparison of interval and screen-detected breast cancers. J Pathol 2019; 248:243-252. [PMID: 30746706 DOI: 10.1002/path.5251] [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: 11/27/2018] [Revised: 01/15/2019] [Accepted: 01/30/2019] [Indexed: 01/22/2023]
Abstract
Breast cancer (BC) diagnosed after a negative mammogram but prior to the next screening episode is termed an 'interval BC' (IBC). Understanding the molecular differences between IBC and screen-detected BCs (SDBC) could improve mammographic screening and management options. Therefore, we assessed both germline and somatic genomic aberrations in a prospective cohort. Utilising the Lifepool cohort of >54 000 women attending mammographic screening programs, 930 BC cases with screening status were identified (726 SDBC and 204 IBC). Clinico-pathological and family history information were recorded. Germline and tumour DNA were collected where available and sequenced for BC predisposition and driver gene mutations. Compared to SDBC, IBCs were significantly associated with a younger age at diagnosis and tumour characteristics associated with worse prognosis. Germline DNA assessment of BC cases that developed post-enrolment (276 SDBCs and 77 IBCs) for pathogenic mutations in 12 hereditary BC predisposition genes identified 8 carriers (2.27%). The germline mutation frequency was higher in IBC versus SDBC, although not statistically significant (3.90% versus 1.81%, p = 0.174). Comparing somatic genetic features of IBC and SDBC matched for grade, histological subtype and hormone receptor revealed no significant differences, with the exception of higher homologous recombination deficiency scores in IBC, and copy number changes on chromosome Xq in triple negative SDBCs. Our data demonstrates that while IBCs are clinically more aggressive than SDBC, when matched for confounding clinico-pathological features they do not represent a unique molecular class of invasive BC, but could be a consequence of timing of tumour initiation and mammographic screening. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Dane Cheasley
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Na Li
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Simone M Rowley
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Kenneth Elder
- Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia.,The Royal Melbourne and Royal Women's Hospitals, Parkville, Victoria, Australia.,The Edinburgh Breast Unit, Western General Hospital, Edinburgh, UK
| | - G Bruce Mann
- Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia.,The Royal Melbourne and Royal Women's Hospitals, Parkville, Victoria, Australia
| | - Sherene Loi
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.,Division of Clinical Medicine and Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Peter Savas
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.,Division of Clinical Medicine and Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - David L Goode
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Tanjina Kader
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Magnus Zethoven
- Bioinformatics Consulting Core, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Tim Semple
- Genomics Core, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Stephen B Fox
- Department of Pathology, Peter MacCallum Cancer Centre, and University of Melbourne, Melbourne, Victoria, Australia
| | - Jia-Min Pang
- Department of Pathology, Peter MacCallum Cancer Centre, and University of Melbourne, Melbourne, Victoria, Australia
| | - David Byrne
- Department of Pathology, Peter MacCallum Cancer Centre, and University of Melbourne, Melbourne, Victoria, Australia
| | - Lisa Devereux
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Lifepool, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Carolyn Nickson
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Pietro Procopio
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Grant Lee
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Siobhan Hughes
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Hugo Saunders
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Kenji M Fujihara
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Keilly Kuykhoven
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Jacquie Connaughton
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Paul A James
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.,Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Kylie L Gorringe
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.,Cancer Genetics and Genomics Program, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Ian G Campbell
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
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24
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Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A, Tyrer JP, Chen TH, Wang Q, Bolla MK, Yang X, Adank MA, Ahearn T, Aittomäki K, Allen J, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Auer PL, Auvinen P, Barrdahl M, Beane Freeman LE, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bernstein L, Blomqvist C, Bogdanova NV, Bojesen SE, Bonanni B, Børresen-Dale AL, Brauch H, Bremer M, Brenner H, Brentnall A, Brock IW, Brooks-Wilson A, Brucker SY, Brüning T, Burwinkel B, Campa D, Carter BD, Castelao JE, Chanock SJ, Chlebowski R, Christiansen H, Clarke CL, Collée JM, Cordina-Duverger E, Cornelissen S, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dos-Santos-Silva I, Dumont M, Durcan L, Dwek M, Eccles DM, Ekici AB, Eliassen AH, Ellberg C, Engel C, Eriksson M, Evans DG, Fasching PA, Figueroa J, Fletcher O, Flyger H, Försti A, Fritschi L, Gabrielson M, Gago-Dominguez M, Gapstur SM, García-Sáenz JA, Gaudet MM, Georgoulias V, Giles GG, Gilyazova IR, Glendon G, Goldberg MS, Goldgar DE, González-Neira A, Grenaker Alnæs GI, Grip M, Gronwald J, Grundy A, Guénel P, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hamann U, Hankinson SE, Harkness EF, Hart SN, He W, Hein A, Heyworth J, Hillemanns P, Hollestelle A, Hooning MJ, Hoover RN, Hopper JL, Howell A, Huang G, Humphreys K, Hunter DJ, Jakimovska M, Jakubowska A, Janni W, John EM, Johnson N, Jones ME, Jukkola-Vuorinen A, Jung A, Kaaks R, Kaczmarek K, Kataja V, Keeman R, Kerin MJ, Khusnutdinova E, Kiiski JI, Knight JA, Ko YD, Kosma VM, Koutros S, Kristensen VN, Krüger U, Kühl T, Lambrechts D, Le Marchand L, Lee E, Lejbkowicz F, Lilyquist J, Lindblom A, Lindström S, Lissowska J, Lo WY, Loibl S, Long J, Lubiński J, Lux MP, MacInnis RJ, Maishman T, Makalic E, Maleva Kostovska I, Mannermaa A, Manoukian S, Margolin S, Martens JWM, Martinez ME, Mavroudis D, McLean C, Meindl A, Menon U, Middha P, Miller N, Moreno F, Mulligan AM, Mulot C, Muñoz-Garzon VM, Neuhausen SL, Nevanlinna H, Neven P, Newman WG, Nielsen SF, Nordestgaard BG, Norman A, Offit K, Olson JE, Olsson H, Orr N, Pankratz VS, Park-Simon TW, Perez JIA, Pérez-Barrios C, Peterlongo P, Peto J, Pinchev M, Plaseska-Karanfilska D, Polley EC, Prentice R, Presneau N, Prokofyeva D, Purrington K, Pylkäs K, Rack B, Radice P, Rau-Murthy R, Rennert G, Rennert HS, Rhenius V, Robson M, Romero A, Ruddy KJ, Ruebner M, Saloustros E, Sandler DP, Sawyer EJ, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schumacher F, Schürmann P, Schwentner L, Scott C, Scott RJ, Seynaeve C, Shah M, Sherman ME, Shrubsole MJ, Shu XO, Slager S, Smeets A, Sohn C, Soucy P, Southey MC, Spinelli JJ, Stegmaier C, Stone J, Swerdlow AJ, Tamimi RM, Tapper WJ, Taylor JA, Terry MB, Thöne K, Tollenaar RAEM, Tomlinson I, Truong T, Tzardi M, Ulmer HU, Untch M, Vachon CM, van Veen EM, Vijai J, Weinberg CR, Wendt C, Whittemore AS, Wildiers H, Willett W, Winqvist R, Wolk A, Yang XR, Yannoukakos D, Zhang Y, Zheng W, Ziogas A, Dunning AM, Thompson DJ, Chenevix-Trench G, Chang-Claude J, Schmidt MK, Hall P, Milne RL, Pharoah PDP, Antoniou AC, Chatterjee N, Kraft P, García-Closas M, Simard J, Easton DF. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet 2019; 104:21-34. [PMID: 30554720 PMCID: PMC6323553 DOI: 10.1016/j.ajhg.2018.11.002] [Citation(s) in RCA: 583] [Impact Index Per Article: 116.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 11/03/2018] [Indexed: 12/29/2022] Open
Abstract
Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
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Affiliation(s)
- Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, 1683 Nicosia, Cyprus
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Laura Fachal
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Jonathan P Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Ting-Huei Chen
- Department of Mathematics and Statistics, Laval University, Québec City, QC G1V 0A6, Canada
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Muriel A Adank
- Family Cancer Clinic, the Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, 1066 CX, the Netherlands
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Kristiina Aittomäki
- Department of Clinical Genetics, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland
| | - Jamie Allen
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Hoda Anton-Culver
- Department of Epidemiology, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA 92617, USA
| | - Natalia N Antonenkova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk 223040, Belarus
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Kristan J Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Paul L Auer
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53205, USA
| | - Päivi Auvinen
- Cancer Center, Kuopio University Hospital, Kuopio 70210, Finland; Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio 70210, Finland; Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland
| | - Myrto Barrdahl
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Laura E Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Javier Benitez
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain; Biomedical Network on Rare Diseases (CIBERER), Madrid 28029, Spain
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa 450054, Russia
| | - Leslie Bernstein
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland; Department of Oncology, Örebro University Hospital, Örebro 70185, Sweden
| | - Natalia V Bogdanova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk 223040, Belarus; Department of Radiation Oncology, Hannover Medical School, Hannover 30625, Germany; Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics, IEO, European Institute of Oncology IRCCS, Milan 20141, Italy
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo 0450, Norway
| | - Hiltrud Brauch
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart 70376, Germany; University of Tübingen, Tübingen 72074, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Michael Bremer
- Department of Radiation Oncology, Hannover Medical School, Hannover 30625, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany
| | - Adam Brentnall
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London EC1M 6BQ, UK
| | - Ian W Brock
- Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK
| | - Angela Brooks-Wilson
- Genome Sciences Centre, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada; Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Sara Y Brucker
- Department of Gynecology and Obstetrics, University of Tübingen, Tübingen 72076, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum 44789, Germany
| | - Barbara Burwinkel
- Department of Obstetrics and Gynecology, University of Heidelberg, Heidelberg 69120, Germany; Molecular Epidemiology Group, C080, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Daniele Campa
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Department of Biology, University of Pisa, Pisa 56126, Italy
| | - Brian D Carter
- Epidemiology Research Program, American Cancer Society, Atlanta, GA 30303, USA
| | - Jose E Castelao
- Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo 36312, Spain
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Rowan Chlebowski
- Division of Medical Oncology and Hematology, University of California at Los Angeles, Los Angeles, CA 90024, USA
| | - Hans Christiansen
- Department of Radiation Oncology, Hannover Medical School, Hannover 30625, Germany
| | - Christine L Clarke
- Westmead Institute for Medical Research, University of Sydney, Sydney, NSW 2145, Australia
| | - J Margriet Collée
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam 3015 CN, the Netherlands
| | - Emilie Cordina-Duverger
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif 94805, France
| | - Sten Cornelissen
- Division of Molecular Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Angela Cox
- Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK
| | - Simon S Cross
- Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield S10 2TN, UK
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Martine Dumont
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Université Laval, Québec City, QC G1V 4G2, Canada
| | - Lorraine Durcan
- Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK; Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK
| | - Miriam Dwek
- School of Life Sciences, University of Westminster, London W1B 2HW, UK
| | - Diana M Eccles
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK
| | - Arif B Ekici
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
| | - Carolina Ellberg
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund 222 42, Sweden
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig 04107, Germany; LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig 04103, Germany
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - D Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9WL, UK; North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany; David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA; Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh EH16 4TJ, UK; Cancer Research UK Edinburgh Centre, Edinburgh EH4 2XR, UK
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW7 3RP, UK
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark
| | - Asta Försti
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Center for Primary Health Care Research, Clinical Research Center, Lund University, Malmö 205 02, Sweden
| | - Lin Fritschi
- School of Public Health, Curtin University, Perth, WA 6102, Australia
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela 15706, Spain; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, GA 30303, USA
| | - José A García-Sáenz
- Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid 28040, Spain
| | - Mia M Gaudet
- Epidemiology Research Program, American Cancer Society, Atlanta, GA 30303, USA
| | - Vassilios Georgoulias
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion 711 10, Greece
| | - Graham G Giles
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Irina R Gilyazova
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa 450054, Russia; Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa 450076, Russia
| | - Gord Glendon
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Mark S Goldberg
- Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada; Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, QC H4A 3J1, Canada
| | - David E Goldgar
- Department of Dermatology and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Anna González-Neira
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Grethe I Grenaker Alnæs
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway
| | - Mervi Grip
- Department of Surgery, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
| | - Jacek Gronwald
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland
| | - Anne Grundy
- Centre de Recherche du Centre Hospitalier de Université de Montréal (CHUM), Université de Montréal, Montréal, QC H2X 0A9, Canada
| | - Pascal Guénel
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif 94805, France
| | - Lothar Haeberle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - Eric Hahnen
- Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne 50937, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Niclas Håkansson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Susan E Hankinson
- Department of Biostatistics & Epidemiology, University of Massachusetts, Amherst, Amherst, MA 1003, USA
| | - Elaine F Harkness
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PT, UK; Nightingale Breast Screening Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester M23 9LT, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Steven N Hart
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - Alexander Hein
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - Jane Heyworth
- School of Population and Global Health, University of Western Australia, Perth, WA 6009, Australia
| | - Peter Hillemanns
- Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Antoinette Hollestelle
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam 3015 CN, the Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam 3015 CN, the Netherlands
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester M13 9PL, UK
| | - Guanmengqian Huang
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - David J Hunter
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Milena Jakimovska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov," Macedonian Academy of Sciences and Arts, Skopje 1000, Republic of Macedonia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland; Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin 71-252, Poland
| | - Wolfgang Janni
- Department of Gynecology and Obstetrics, University Hospital Ulm, Ulm 89075, Germany
| | - Esther M John
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Nichola Johnson
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW7 3RP, UK
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SM2 5NG, UK
| | - Arja Jukkola-Vuorinen
- Department of Oncology, Tampere University Hospital, Tampere, Finland Box 2000, 33521 Tampere, Finland
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Katarzyna Kaczmarek
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland
| | - Vesa Kataja
- Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland; Central Finland Health Care District, Jyväskylä Central Hospital, Jyväskylä 40620, Finland
| | - Renske Keeman
- Division of Molecular Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands
| | - Michael J Kerin
- Surgery, School of Medicine, National University of Ireland, Galway H91TK33, Ireland
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa 450054, Russia; Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa 450076, Russia
| | - Johanna I Kiiski
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland
| | - Julia A Knight
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5T 3L9, Canada; Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Yon-Dschun Ko
- Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn 53177, Germany
| | - Veli-Matti Kosma
- Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland; Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio 70210, Finland; Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio 70210, Finland
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Vessela N Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo 0450, Norway
| | - Ute Krüger
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund 222 42, Sweden
| | - Tabea Kühl
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, Leuven 3000, Belgium; Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven 3000, Belgium
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Eunjung Lee
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Flavio Lejbkowicz
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel
| | - Jenna Lilyquist
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, and Department of Clinical Genetics, Karolinska University Hospital, Stockholm 171 76, Sweden
| | - Sara Lindström
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98195, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M Sklodowska-Curie Cancer Center - Oncology Institute, Warsaw 02-034, Poland
| | - Wing-Yee Lo
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart 70376, Germany; University of Tübingen, Tübingen 72074, Germany
| | - Sibylle Loibl
- German Breast Group, GmbH, Neu Isenburg 63263, Germany
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland
| | - Michael P Lux
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - Robert J MacInnis
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Tom Maishman
- Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK; Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Ivana Maleva Kostovska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov," Macedonian Academy of Sciences and Arts, Skopje 1000, Republic of Macedonia
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland; Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio 70210, Finland; Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio 70210, Finland
| | - Siranoush Manoukian
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan 20133, Italy
| | - Sara Margolin
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm 118 83, Sweden; Department of Oncology, Södersjukhuset, Stockholm 118 83, Sweden
| | - John W M Martens
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam 3015 CN, the Netherlands
| | - Maria Elena Martinez
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA; Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA 92093, USA
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion 711 10, Greece
| | - Catriona McLean
- Anatomical Pathology, The Alfred Hospital, Melbourne, VIC 3004, Australia
| | - Alfons Meindl
- Department of Gynecology and Obstetrics, Ludwig Maximilian University of Munich, Munich 80336, Germany
| | - Usha Menon
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, University College London, London WC1V 6LJ, UK
| | - Pooja Middha
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Faculty of Medicine, University of Heidelberg, Heidelberg 69120, Germany
| | - Nicola Miller
- Surgery, School of Medicine, National University of Ireland, Galway H91TK33, Ireland
| | - Fernando Moreno
- Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid 28040, Spain
| | - Anna Marie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada; Laboratory Medicine Program, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Claire Mulot
- Université Paris Sorbonne Cité, INSERM UMR-S1147, Paris 75270, France
| | | | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland
| | - Patrick Neven
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven 3000, Belgium
| | - William G Newman
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9WL, UK; North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Sune F Nielsen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark
| | - Børge G Nordestgaard
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Aaron Norman
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Håkan Olsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund 222 42, Sweden
| | - Nick Orr
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast BT7 1NN, UK
| | - V Shane Pankratz
- University of New Mexico Health Sciences Center, University of New Mexico, Albuquerque, NM 87131, USA
| | | | - Jose I A Perez
- Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Oviedo 33012, Spain
| | - Clara Pérez-Barrios
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid 28222, Spain
| | - Paolo Peterlongo
- Genome Diagnostic Program, IFOM the FIRC (Italian Foundation for Cancer Research) Institute of Molecular Oncology, Milan 20139, Italy
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Mila Pinchev
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov," Macedonian Academy of Sciences and Arts, Skopje 1000, Republic of Macedonia
| | - Eric C Polley
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Ross Prentice
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Nadege Presneau
- School of Life Sciences, University of Westminster, London W1B 2HW, UK
| | - Darya Prokofyeva
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa 450076, Russia
| | - Kristen Purrington
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu 90220, Finland; Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu 90220, Finland
| | - Brigitte Rack
- Department of Gynecology and Obstetrics, University Hospital Ulm, Ulm 89075, Germany
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan 20133, Italy
| | - Rohini Rau-Murthy
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Gad Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel
| | - Hedy S Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel
| | - Valerie Rhenius
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Mark Robson
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid 28222, Spain
| | - Kathryn J Ruddy
- Department of Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthias Ruebner
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | | | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA
| | - Elinor J Sawyer
- Research Oncology, Guy's Hospital, King's College London, London SE1 9RT, UK
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Rita K Schmutzler
- Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne 50937, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany
| | - Andreas Schneeweiss
- National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg 69120, Germany
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SM2 5NG, UK
| | - Fredrick Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Peter Schürmann
- Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Lukas Schwentner
- Department of Gynecology and Obstetrics, University Hospital Ulm, Ulm 89075, Germany
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Rodney J Scott
- Division of Molecular Medicine, Pathology North, John Hunter Hospital, Newcastle, NSW 2305, Australia; Discipline of Medical Genetics, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, Callaghan, NSW 2308, Australia; Hunter Medical Research Institute, John Hunter Hospital, Newcastle, NSW 2305, Australia
| | - Caroline Seynaeve
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam 3015 CN, the Netherlands
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Mark E Sherman
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, FL 32224, USA
| | - Martha J Shrubsole
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Susan Slager
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Ann Smeets
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven 3000, Belgium
| | - Christof Sohn
- National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg 69120, Germany
| | - Penny Soucy
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Université Laval, Québec City, QC G1V 4G2, Canada
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - John J Spinelli
- Population Oncology, BC Cancer, Vancouver, BC V5Z 1G1, Canada; School of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | | | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; The Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Perth, WA 6000, Australia
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SM2 5NG, UK; Division of Breast Cancer Research, The Institute of Cancer Research, London SW7 3RP, UK
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
| | - William J Tapper
- Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA; Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Kathrin Thöne
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Ian Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 7BN, UK
| | - Thérèse Truong
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif 94805, France
| | - Maria Tzardi
- Department of Pathology, University Hospital of Heraklion, Heraklion 711 10, Greece
| | - Hans-Ulrich Ulmer
- Frauenklinik der Stadtklinik Baden-Baden, Baden-Baden 76532, Germany
| | - Michael Untch
- Department of Gynecology and Obstetrics, Helios Clinics Berlin-Buch, Berlin 13125, Germany
| | - Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Elke M van Veen
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9WL, UK; North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Joseph Vijai
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA
| | - Camilla Wendt
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm 118 83, Sweden; Department of Oncology, Södersjukhuset, Stockholm 118 83, Sweden
| | - Alice S Whittemore
- Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hans Wildiers
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven 3000, Belgium
| | - Walter Willett
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu 90220, Finland; Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu 90220, Finland
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden; Department of Surgical Sciences, Uppsala University, Uppsala 751 05, Sweden
| | - Xiaohong R Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research "Demokritos," Athens 15310, Greece
| | - Yan Zhang
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Argyrios Ziogas
- Department of Epidemiology, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA 92617, USA
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Marjanka K Schmidt
- Division of Molecular Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; Division of Psychosocial Research and Epidemiology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam 1066 CX, the Netherlands
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden; Department of Oncology, Södersjukhuset, Stockholm 118 83, Sweden
| | - Roger L Milne
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Nilanjan Chatterjee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA; Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD 21205, USA; Department of Oncology, School of Medicine, John Hopkins University, Baltimore, MD 21205, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Université Laval, Québec City, QC G1V 4G2, Canada
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
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Li J, Ugalde-Morales E, Wen WX, Decker B, Eriksson M, Torstensson A, Christensen HN, Dunning AM, Allen J, Luccarini C, Pooley KA, Simard J, Dorling L, Easton DF, Teo SH, Hall P, Czene K. Differential Burden of Rare and Common Variants on Tumor Characteristics, Survival, and Mode of Detection in Breast Cancer. Cancer Res 2018; 78:6329-6338. [PMID: 30385609 DOI: 10.1158/0008-5472.can-18-1018] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 07/25/2018] [Accepted: 09/26/2018] [Indexed: 11/16/2022]
Abstract
Genetic variants that increase breast cancer risk can be rare or common. This study tests whether the genetic risk stratification of breast cancer by rare and common variants in established loci can discriminate tumors with different biology, patient survival, and mode of detection. Multinomial logistic regression tested associations between genetic risk load [protein-truncating variant (PTV) carriership in 31 breast cancer predisposition genes-or polygenic risk score (PRS) using 162 single-nucleotide polymorphisms], tumor characteristics, and mode of detection (OR). Ten-year breast cancer-specific survival (HR) was estimated using Cox regression models. In this unselected cohort of 5,099 patients with breast cancer diagnosed in Sweden between 2001 and 2008, PTV carriers (n = 597) were younger and associated with more aggressive tumor phenotypes (ER-negative, large size, high grade, high proliferation, luminal B, and basal-like subtype) and worse outcome (HR, 1.65; 1.16-2.36) than noncarriers. After excluding 92 BRCA1/2 carriers, PTV carriership remained associated with high grade and worse survival (HR, 1.76; 1.21-2.56). In 5,007 BRCA1/2 noncarriers, higher PRS was associated with less aggressive tumor characteristics (ER-positive, PR-positive, small size, low grade, low proliferation, and luminal A subtype). Among patients with low mammographic density (<25%), non-BRCA1/2 PTV carriers were more often interval than screen-detected breast cancer (OR, 1.89; 1.12-3.21) than noncarriers. In contrast, higher PRS was associated with lower risk of interval compared with screen-detected cancer (OR, 0.77; 0.64-0.93) in women with low mammographic density. These findings suggest that rare and common breast cancer susceptibility loci are differentially associated with tumor characteristics, survival, and mode of detection.Significance: These findings offer the potential to improve screening practices for breast cancer by providing a deeper understanding of how risk variants affect disease progression and mode of detection. Cancer Res; 78(21); 6329-38. ©2018 AACR.
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Affiliation(s)
- Jingmei Li
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore.
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Emilio Ugalde-Morales
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei Xiong Wen
- Cancer Research Malaysia, Sime Darby Medical Centre, Selangor, Subang Jaya, Malaysia
| | - Brennan Decker
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Jamie Allen
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Karen A Pooley
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Canada Research Chair in Oncogenetics, Université Laval, Quebec City, Canada
| | - Leila Dorling
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Soo Hwang Teo
- Cancer Research Malaysia, Sime Darby Medical Centre, Selangor, Subang Jaya, Malaysia
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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26
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Kuhl CK. Abbreviated breast MRI for screening women with dense breast: the EA1141 trial. Br J Radiol 2018; 91:20170441. [PMID: 28749202 PMCID: PMC6350487 DOI: 10.1259/bjr.20170441] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 07/13/2017] [Accepted: 07/19/2017] [Indexed: 11/22/2022] Open
Abstract
Early diagnosis improves survival of females with breast cancer. Mammographic screening improves early diagnosis of breast cancer. And yet, there appears to be room for improvement. Major shortcomings of mammographic screening are overdiagnosis of prognostically unimportant cancer, as well as underdiagnosis of cancers that are indeed relevant. Failure to detect biologically relevant breast cancer with mammographic screening is driven not only by host-related factors, i.e. breast tissue density, but also by tumour-related factors: Biologically relevant cancers may exhibit imaging features that render them indistinguishable from normal or benign breast tissue on mammography. These cancers will then progress to become the advanced-stage interval cancers observed in females undergoing mammographic screening. Since breast cancer continues to represent a major cause of cancer death in females, the search for improved breast cancer screening method continues. Abbreviated breast MRI has been proposed for this purpose because it will greatly reduce the cost associated with this method, due to a greatly reduced magnet time (down to 3 min), but especially also due to a greatly abridged image interpretation time, i.e. radiologist reading time. This commentary reviews the current situation and presents the EA1141 trial designed to investigate the utility of abbreviated breast MRI for screening average-risk females with dense breast tissue.
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Affiliation(s)
- Christiane K Kuhl
- Department of Diagnostic and Interventional Radiology, University of Aachen, RWTH, Aachen, Germany
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van Beek EJR, Kuhl C, Anzai Y, Desmond P, Ehman RL, Gong Q, Gold G, Gulani V, Hall-Craggs M, Leiner T, Lim CCT, Pipe JG, Reeder S, Reinhold C, Smits M, Sodickson DK, Tempany C, Vargas HA, Wang M. Value of MRI in medicine: More than just another test? J Magn Reson Imaging 2018; 49:e14-e25. [PMID: 30145852 DOI: 10.1002/jmri.26211] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 05/16/2018] [Indexed: 02/06/2023] Open
Abstract
There is increasing scrutiny from healthcare organizations towards the utility and associated costs of imaging. MRI has traditionally been used as a high-end modality, and although shown extremely important for many types of clinical scenarios, it has been suggested as too expensive by some. This editorial will try and explain how value should be addressed and gives some insights and practical examples of how value of MRI can be increased. It requires a global effort to increase accessibility, value for money, and impact on patient management. We hope this editorial sheds some light and gives some indications of where the field may wish to address some of its research to proactively demonstrate the value of MRI. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;49:e14-e25.
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Affiliation(s)
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University of Aachen, Aachen, Germany
| | - Yoshimi Anzai
- Department of Radiology, University of Utah, Salt Lake City, Utah, USA
| | - Patricia Desmond
- Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia
| | - Richard L Ehman
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Garry Gold
- Department of Radiology, Engineering and Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Vikas Gulani
- Departments of Radiology, Urology and Biomedical Imaging, Case Western Reserve University, University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Margaret Hall-Craggs
- Department of Medical Imaging and Radiology, University College Hospital NHS Trust, London, UK
| | - Tim Leiner
- Department of Radiology and Nuclear Medicine, University Medical Centre, Utrecht, The Netherlands
| | - C C Tschoyoson Lim
- Department of Neuroradiology, National Neuroscience Institute and Duke NUS Medical School, Singapore, Singapore
| | - James G Pipe
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Scott Reeder
- Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine and Emergency Medicine, University of Madison, Madison, Wisconsin, USA
| | - Caroline Reinhold
- Department of Radiology, McGill University Health Center, Montreal, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Daniel K Sodickson
- Department of Radiology, New York University Langone Health, New York, New York, USA
| | - Clare Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - H Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, Henan, China
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28
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Eriksson L, Bergh J, Humphreys K, Wärnberg F, Törnberg S, Czene K. Time from breast cancer diagnosis to therapeutic surgery and breast cancer prognosis: A population-based cohort study. Int J Cancer 2018; 143:1093-1104. [PMID: 29603736 DOI: 10.1002/ijc.31411] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 03/07/2018] [Accepted: 03/22/2018] [Indexed: 11/12/2022]
Abstract
Theoretically, time from breast cancer diagnosis to therapeutic surgery should affect survival. However, it is unclear whether this holds true in a modern healthcare setting in which breast cancer surgery is carried out within weeks to months of diagnosis. This is a population- and register-based study of all women diagnosed with invasive breast cancer in the Stockholm-Gotland healthcare region in Sweden, 2001-2008, and who were initially operated. Follow-up of vital status ended 2014. 7,017 women were included in analysis. Our main outcome was overall survival. Main analyses were carried out using Cox proportional hazards models. We adjusted for likely confounders and stratified on mode of detection, tumor size and lymph node metastasis. We found that a longer interval between date of morphological diagnosis and therapeutic surgery was associated with a poorer prognosis. Assuming a linear association, the hazard rate of death from all causes increased by 1.011 (95% CI 1.006-1.017) per day. Comparing, for example, surgery 6 weeks after diagnosis to surgery 3 weeks after diagnosis, thereby confers a 1.26-fold increased hazard rate. The increase in hazard rate associated with surgical delay was strongest in women with largest tumors. Whilst there was a clear association between delays and survival in women without lymph node metastasis, the association may be attenuated in subgroups with increasing number of lymph node metastases. We found no evidence of an interaction between time to surgery and mode of detection. In conclusion, unwarranted delays to primary treatment of breast cancer should be avoided.
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Affiliation(s)
- Louise Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden.,Department of Oncology-Pathology, Cancer Center Karolinska, Department of Oncology, Radiumhemmet, Karolinska Institutet and Karolinska University Hospital, Stockholm, 17176, Sweden
| | - Jonas Bergh
- Department of Oncology-Pathology, Cancer Center Karolinska, Department of Oncology, Radiumhemmet, Karolinska Institutet and Karolinska University Hospital, Stockholm, 17176, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden
| | - Fredrik Wärnberg
- Department of Surgical Sciences, Uppsala University, Uppsala, 751 85, Sweden.,Department of Surgery, Uppsala Academic Hospital, Uppsala, 751 85, Sweden
| | - Sven Törnberg
- Department of Cancer Screening, Stockholm-Gotland Regional Cancer Centre, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden
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29
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Holm J, Eriksson L, Ploner A, Eriksson M, Rantalainen M, Li J, Hall P, Czene K. Assessment of Breast Cancer Risk Factors Reveals Subtype Heterogeneity. Cancer Res 2017; 77:3708-3717. [PMID: 28512241 DOI: 10.1158/0008-5472.can-16-2574] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 02/05/2017] [Accepted: 04/19/2017] [Indexed: 11/16/2022]
Abstract
Subtype heterogeneity for breast cancer risk factors has been suspected, potentially reflecting etiologic differences and implicating risk prediction. However, reports are conflicting regarding the presence of heterogeneity for many exposures. To examine subtype heterogeneity across known breast cancer risk factors, we conducted a case-control analysis of 2,632 breast cancers and 15,945 controls in Sweden. Molecular subtype was predicted from pathology record-derived IHC markers by a classifier trained on PAM50 subtyping. Multinomial logistic regression estimated separate ORs for each subtype by the exposures parity, age at first birth, breastfeeding, menarche, hormone replacement therapy use, somatotype at age 18, benign breast disease, mammographic density, polygenic risk score, family history of breast cancer, and BRCA mutations. We found clear subtype heterogeneity for genetic factors and breastfeeding. Polygenic risk score was associated with all subtypes except for the basal-like (Pheterogeneity < 0.0001). "Never breastfeeding" was associated with increased risk of basal-like subtype [OR 4.17; 95% confidence interval (CI) 1.89-9.21] compared with both nulliparity (reference) and breastfeeding. Breastfeeding was not associated with risk of HER2-overexpressing type, but protective for all other subtypes. The observed heterogeneity in risk of distinct breast cancer subtypes for germline variants supports heterogeneity in etiology and has implications for their use in risk prediction. The association between basal-like subtype and breastfeeding merits more research into potential causal mechanisms and confounders. Cancer Res; 77(13); 3708-17. ©2017 AACR.
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Affiliation(s)
- Johanna Holm
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden.
| | - Louise Eriksson
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden.,Department of Oncology and Pathology, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Alexander Ploner
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Jingmei Li
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Per Hall
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
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30
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Kuhl CK, Strobel K, Bieling H, Leutner C, Schild HH, Schrading S. Supplemental Breast MR Imaging Screening of Women with Average Risk of Breast Cancer. Radiology 2017; 283:361-370. [DOI: 10.1148/radiol.2016161444] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Christiane K. Kuhl
- From the Department of Diagnostic and Interventional Radiology (C.K.K., K.S., S.S.) and Section of Biostatistics, Department of Diagnostic and Interventional Radiology (H.B.), University of Aachen, RWTH, Pauwelsstr 30, 52074 Aachen, Germany; and Department of Radiology, University of Bonn, Bonn, Germany (C.L., H.H.S.)
| | - Kevin Strobel
- From the Department of Diagnostic and Interventional Radiology (C.K.K., K.S., S.S.) and Section of Biostatistics, Department of Diagnostic and Interventional Radiology (H.B.), University of Aachen, RWTH, Pauwelsstr 30, 52074 Aachen, Germany; and Department of Radiology, University of Bonn, Bonn, Germany (C.L., H.H.S.)
| | - Heribert Bieling
- From the Department of Diagnostic and Interventional Radiology (C.K.K., K.S., S.S.) and Section of Biostatistics, Department of Diagnostic and Interventional Radiology (H.B.), University of Aachen, RWTH, Pauwelsstr 30, 52074 Aachen, Germany; and Department of Radiology, University of Bonn, Bonn, Germany (C.L., H.H.S.)
| | - Claudia Leutner
- From the Department of Diagnostic and Interventional Radiology (C.K.K., K.S., S.S.) and Section of Biostatistics, Department of Diagnostic and Interventional Radiology (H.B.), University of Aachen, RWTH, Pauwelsstr 30, 52074 Aachen, Germany; and Department of Radiology, University of Bonn, Bonn, Germany (C.L., H.H.S.)
| | - Hans H. Schild
- From the Department of Diagnostic and Interventional Radiology (C.K.K., K.S., S.S.) and Section of Biostatistics, Department of Diagnostic and Interventional Radiology (H.B.), University of Aachen, RWTH, Pauwelsstr 30, 52074 Aachen, Germany; and Department of Radiology, University of Bonn, Bonn, Germany (C.L., H.H.S.)
| | - Simone Schrading
- From the Department of Diagnostic and Interventional Radiology (C.K.K., K.S., S.S.) and Section of Biostatistics, Department of Diagnostic and Interventional Radiology (H.B.), University of Aachen, RWTH, Pauwelsstr 30, 52074 Aachen, Germany; and Department of Radiology, University of Bonn, Bonn, Germany (C.L., H.H.S.)
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31
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Li J, Ivansson E, Klevebring D, Tobin NP, Lindström LS, Holm J, Prochazka G, Cristando C, Palmgren J, Törnberg S, Humphreys K, Hartman J, Frisell J, Rantalainen M, Lindberg J, Hall P, Bergh J, Grönberg H, Czene K. Molecular Differences between Screen-Detected and Interval Breast Cancers Are Largely Explained by PAM50 Subtypes. Clin Cancer Res 2016; 23:2584-2592. [DOI: 10.1158/1078-0432.ccr-16-0967] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 08/08/2016] [Accepted: 08/15/2016] [Indexed: 11/16/2022]
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32
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Breast cancer risk prediction using a clinical risk model and polygenic risk score. Breast Cancer Res Treat 2016; 159:513-25. [PMID: 27565998 DOI: 10.1007/s10549-016-3953-2] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 08/18/2016] [Indexed: 12/12/2022]
Abstract
Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95 % CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95 % CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95 % CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18 % of cases as high-risk (5-year risk ≥3 %), compared with 7 % using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.
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33
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Holm J, Li J, Darabi H, Eklund M, Eriksson M, Humphreys K, Hall P, Czene K. Associations of Breast Cancer Risk Prediction Tools With Tumor Characteristics and Metastasis. J Clin Oncol 2016; 34:251-8. [DOI: 10.1200/jco.2015.63.0624] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Purpose The association between established risk factors for breast cancer and subtypes or prognosis of the disease is not well known. We analyzed whether the Tyrer-Cuzick–predicted 10-year breast cancer risk score (TCRS), mammographic density (MD), and a 77-single nucleotide polymorphism polygenic risk score (PRS) were associated with breast cancer tumor prognosticators and risk of distant metastasis. Patients and Methods We used a case-only design in a population-based cohort of 5,500 Swedish patients with breast cancer. Logistic and multinomial logistic regression of outcomes, estrogen receptor (ER) status, lymph node involvement, tumor size, and grade was performed with TCRS, PRS, and percent MD as exposures. Cox proportional hazard models were used to estimate hazard ratios (HRs) of distant metastasis. Results Women at high risk for breast cancer based on PRS and/or TCRS were significantly more likely to be diagnosed with favorable prognosticators, such as ER-positive and low-grade tumors. In contrast, PRS weighted on ER-negative disease was associated with ER-negative tumors. When stratifying by age, the associations of TCRS with favorable prognosticators were restricted to women younger than age 50. Women scoring high in both TCRS and PRS had a lower risk of distant metastasis (HR, 0.69; 95% CI, 0.49 to 0.98). MD was not associated with any of the examined prognosticators. Conclusion Women at high risk for breast cancer based on genetic and lifestyle factors were significantly more likely to be diagnosed with breast cancers with a favorable prognosis. Better knowledge of subtype-specific risk factors could be vital for the success of prevention programs aimed at lowering mortality.
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Affiliation(s)
- Johanna Holm
- All authors: Karolinska Institutet, Stockholm, Sweden
| | - Jingmei Li
- All authors: Karolinska Institutet, Stockholm, Sweden
| | - Hatef Darabi
- All authors: Karolinska Institutet, Stockholm, Sweden
| | - Martin Eklund
- All authors: Karolinska Institutet, Stockholm, Sweden
| | | | | | - Per Hall
- All authors: Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- All authors: Karolinska Institutet, Stockholm, Sweden
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