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Schreurs MAC, Ramón Y Cajal T, Adank MA, Collée JM, Hollestelle A, van Rooij J, Schmidt MK, Hooning MJ. The benefit of adding polygenic risk scores, lifestyle factors, and breast density to family history and genetic status for breast cancer risk and surveillance classification of unaffected women from germline CHEK2 c.1100delC families. Breast 2024; 73:103611. [PMID: 38039887 PMCID: PMC10730863 DOI: 10.1016/j.breast.2023.103611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023] Open
Abstract
To determine the changes in surveillance category by adding a polygenic risk score based on 311 breast cancer (BC)-associated variants (PRS311), questionnaire-based risk factors and breast density on personalized BC risk in unaffected women from Dutch CHEK2 c.1100delC families. In total, 117 unaffected women (58 heterozygotes and 59 non-carriers) from CHEK2 families were included. Blood-derived DNA samples were genotyped with the GSAMDv3-array to determine PRS311. Lifetime BC risk was calculated in CanRisk, which uses data from the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). Women, were categorized into three surveillance groups. The surveillance advice was reclassified in 37.9 % of heterozygotes and 32.2 % of non-carriers after adding PRS311. Including questionnaire-based risk factors resulted in an additional change in 20.0 % of heterozygotes and 13.2 % of non-carriers; and a subanalysis showed that adding breast density on top shifted another 17.9 % of heterozygotes and 33.3 % of non-carriers. Overall, the majority of heterozygotes were reclassified to a less intensive surveillance, while non-carriers would require intensified surveillance. The addition of PRS311, questionnaire-based risk factors and breast density to family history resulted in a more personalized BC surveillance advice in CHEK2-families, which may lead to more efficient use of surveillance.
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Affiliation(s)
- Maartje A C Schreurs
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Teresa Ramón Y Cajal
- Familial Cancer Clinic, Medical Oncology Service, Hospital Sant Pau, Barcelona, Spain
| | - Muriel A Adank
- Department of Clinical Genetics, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - J Margriet Collée
- Department of Clinical Genetics, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | | | - Jeroen van Rooij
- Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
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Zirpoli GR, Pfeiffer RM, Bertrand KA, Huo D, Lunetta KL, Palmer JR. Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women. Breast Cancer Res 2024; 26:2. [PMID: 38167144 PMCID: PMC10763003 DOI: 10.1186/s13058-023-01748-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). METHODS Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. RESULTS AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644). CONCLUSIONS This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.
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Affiliation(s)
- Gary R Zirpoli
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Ruth M Pfeiffer
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
- Division of Cancer Epidemiology and Biostatistics, National Cancer Institute, Bethesda, USA.
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University, Boston, MA, USA
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago, Chicago, IL, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA.
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
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Oblak T, Škerl P, Narang BJ, Blagus R, Krajc M, Novaković S, Žgajnar J. Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence. Breast 2023; 72:103590. [PMID: 37857130 PMCID: PMC10587756 DOI: 10.1016/j.breast.2023.103590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 10/21/2023] Open
Abstract
GOALS To determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40-49, in a Central European population with BC incidence below EU average. METHODS 502 women aged 40-49 years at the time of BC diagnosis completed a questionnaire on BC risk factors (as per Tyrer-Cuzick algorithm) with data known at age 40 and before BC diagnosis. Blood samples were collected for DNA isolation. 250 DNA samples from healthy women aged 50 served as a control cohort. 18 BC-associated SNPs were genotyped in both groups and PRS18 was calculated. The predictive power of PRS18 to detect BC was evaluated using a ROC curve. 10-year BC risk was calculated using the Tyrer-Cuzick algorithm adapted to the Slovenian incidence rate (S-IBIS): first based on questionnaire-based risk factors and, second, including PRS18. RESULTS The AUC for PRS18 was 0.613 (95 % CI 0.570-0.657). 83.3 % of women were classified at above-average risk for BC with S-IBIS without PRS18 and 80.7 % when PRS18 was included. CONCLUSION BC risk prediction models and SNPs panels should not be automatically used in clinical practice in different populations without prior population-based validation. In our population the addition of an 18SNPs PRS to questionnaire-based risk factors in the Tyrer-Cuzick algorithm in general did not improve BC risk stratification, however, some improvements were observed at higher BC risk scores and could be valuable in distinguishing women at intermediate and high risk of BC.
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Affiliation(s)
- Tjaša Oblak
- Department of Surgical Oncology, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia; Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia.
| | - Petra Škerl
- Department of Molecular Diagnostics, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
| | - Benjamin J Narang
- Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia; Department of Automatics, Jožef Stefan Institute, Biocybernetics and Robotics, Jamova cesta 39, Ljubljana, Slovenia; Faculty of Sport, University of Ljubljana, Gortanova 22, Ljubljana, Slovenia.
| | - Rok Blagus
- Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia; Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000, Koper, Slovenia.
| | - Mateja Krajc
- Cancer Genetics Clinic, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
| | - Srdjan Novaković
- Department of Molecular Diagnostics, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
| | - Janez Žgajnar
- Department of Surgical Oncology, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
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Mbuya-Bienge C, Pashayan N, Kazemali CD, Lapointe J, Simard J, Nabi H. A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population. Cancers (Basel) 2023; 15:5380. [PMID: 38001640 PMCID: PMC10670420 DOI: 10.3390/cancers15225380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/26/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.
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Affiliation(s)
- Cynthia Mbuya-Bienge
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London WC1E 6BT, UK;
| | - Cornelia D. Kazemali
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Julie Lapointe
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Jacques Simard
- Endocrinology and Nephology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada;
- Department of Molecular Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Hermann Nabi
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
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Ho PJ, Lim EH, Hartman M, Wong FY, Li J. Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank. Genet Med 2023; 25:100917. [PMID: 37334786 DOI: 10.1016/j.gim.2023.100917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening. METHODS We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk. RESULTS In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability. CONCLUSION Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.
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Affiliation(s)
- Peh Joo Ho
- Laboratory of Women's Health and Genetics, Genome Institute of Singapore, A∗STAR Research Entities, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Elaine H Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Jingmei Li
- Laboratory of Women's Health and Genetics, Genome Institute of Singapore, A∗STAR Research Entities, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Monticciolo DL, Newell MS, Moy L, Lee CS, Destounis SV. Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR. J Am Coll Radiol 2023; 20:902-914. [PMID: 37150275 DOI: 10.1016/j.jacr.2023.04.002] [Citation(s) in RCA: 60] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/26/2023] [Accepted: 04/06/2023] [Indexed: 05/09/2023]
Abstract
Early detection decreases breast cancer death. The ACR recommends annual screening beginning at age 40 for women of average risk and earlier and/or more intensive screening for women at higher-than-average risk. For most women at higher-than-average risk, the supplemental screening method of choice is breast MRI. Women with genetics-based increased risk, those with a calculated lifetime risk of 20% or more, and those exposed to chest radiation at young ages are recommended to undergo MRI surveillance starting at ages 25 to 30 and annual mammography (with a variable starting age between 25 and 40, depending on the type of risk). Mutation carriers can delay mammographic screening until age 40 if annual screening breast MRI is performed as recommended. Women diagnosed with breast cancer before age 50 or with personal histories of breast cancer and dense breasts should undergo annual supplemental breast MRI. Others with personal histories, and those with atypia at biopsy, should strongly consider MRI screening, especially if other risk factors are present. For women with dense breasts who desire supplemental screening, breast MRI is recommended. For those who qualify for but cannot undergo breast MRI, contrast-enhanced mammography or ultrasound could be considered. All women should undergo risk assessment by age 25, especially Black women and women of Ashkenazi Jewish heritage, so that those at higher-than-average risk can be identified and appropriate screening initiated.
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Affiliation(s)
- Debra L Monticciolo
- Division Chief, Breast Imaging, Massachusetts General Hospital, Boston, Massachusetts.
| | - Mary S Newell
- Interim Division Chief, Breast Imaging, Emory University, Atlanta, Georgia
| | - Linda Moy
- Associate Chair for Faculty Mentoring, New York University Grossman School of Medicine, New York, New York; Editor-in-Chief, Radiology
| | - Cindy S Lee
- New York University Grossman School of Medicine, New York, New York
| | - Stamatia V Destounis
- Elizabeth Wende Breast Care, Rochester, New York; Chair, ACR Commission on Breast Imaging
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Thanh Thi Ngoc Nguyen, Nguyen THN, Phan HN, Nguyen HT. Seven-Single Nucleotide Polymorphism Polygenic Risk Score for Breast Cancer Risk Prediction in a Vietnamese Population. CYTOL GENET+ 2022. [DOI: 10.3103/s0095452722040065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Wang L, Desai H, Verma SS, Le A, Hausler R, Verma A, Judy R, Doucette A, Gabriel PE, Nathanson KL, Damrauer SM, Mowery DL, Ritchie MD, Kember RL, Maxwell KN. Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank. Genet Med 2022; 24:601-609. [PMID: 34906489 PMCID: PMC9680700 DOI: 10.1016/j.gim.2021.10.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/09/2021] [Accepted: 10/22/2021] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Genome-wide association studies have identified hundreds of single nucleotide variations (formerly single nucleotide polymorphisms) associated with several cancers, but the predictive ability of polygenic risk scores (PRSs) is unclear, especially among non-Whites. METHODS PRSs were derived from genome-wide significant single-nucleotide variations for 15 cancers in 20,079 individuals in an academic biobank. We evaluated the improvement in discriminatory accuracy by including cancer-specific PRS in patients of genetically-determined African and European ancestry. RESULTS Among the individuals of European genetic ancestry, PRSs for breast, colon, melanoma, and prostate were significantly associated with their respective cancers. Among the individuals of African genetic ancestry, PRSs for breast, colon, prostate, and thyroid were significantly associated with their respective cancers. The area under the curve of the model consisting of age, sex, and principal components was 0.621 to 0.710, and it increased by 1% to 4% with the inclusion of PRS in individuals of European genetic ancestry. In individuals of African genetic ancestry, area under the curve was overall higher in the model without the PRS (0.723-0.810) but increased by <1% with the inclusion of PRS for most cancers. CONCLUSION PRS moderately increased the ability to discriminate the cancer status in individuals of European but not African ancestry. Further large-scale studies are needed to identify ancestry-specific genetic factors in non-White populations to incorporate PRS into cancer risk assessment.
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Affiliation(s)
- Louise Wang
- Division of Gastroenterology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Heena Desai
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Shefali S Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anh Le
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ryan Hausler
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Renae Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Abigail Doucette
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
| | - Peter E Gabriel
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA; Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Katherine L Nathanson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA; Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Corporal Michael J. Crescenz VA Medical Center, U.S. Department of Veterans Affairs, Philadelphia, PA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Rachel L Kember
- Corporal Michael J. Crescenz VA Medical Center, U.S. Department of Veterans Affairs, Philadelphia, PA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kara N Maxwell
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA; Corporal Michael J. Crescenz VA Medical Center, U.S. Department of Veterans Affairs, Philadelphia, PA.
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Saghatchian M, Abehsera M, Yamgnane A, Geyl C, Gauthier E, Hélin V, Bazire M, Villoing-Gaudé L, Reyes C, Gentien D, Golmard L, Stoppa-Lyonnet D. Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: results from a dedicated risk clinic. Breast Cancer Res Treat 2022; 192:375-383. [PMID: 34994879 PMCID: PMC8739506 DOI: 10.1007/s10549-021-06445-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/08/2021] [Indexed: 11/02/2022]
Abstract
PURPOSE A personalized approach to prevention and early detection based on known risk factors should contribute to early diagnosis and treatment of breast cancer. We initiated a risk assessment clinic for all women wishing to undergo an individual breast cancer risk assessment. METHODS Women underwent a complete breast cancer assessment including a questionnaire, mammogram with evaluation of breast density, collection of saliva sample, consultation with a radiologist, and a breast cancer specialist. Women aged 40 or older, with 0 or 1 first-degree relative with breast cancer diagnosed after the age of 40 were eligible for risk assessment using MammoRisk, a machine learning-based tool that provides an individual 5-year estimated risk of developing breast cancer based on the patient's clinical data and breast density, with or without polygenic risk scores (PRSs). DNA was extracted from saliva samples for genotyping of 76 single-nucleotide polymorphisms. The individual risk was communicated to the patient, with individualized screening and prevention recommendations. RESULTS A total of 290 women underwent breast cancer assessment, among which 196 women (68%) were eligible for risk assessment using MammoRisk (median age 52, range 40-72). When PRS was added to MammoRisk, 40% (n = 78) of patients were assigned a different risk category, with 28% (n = 55) of patients changing from intermediate to moderate or high risk. CONCLUSION Individual risk assessment is feasible in the general population. Screening recommendations could be given based on individual risk. The use of PRS changed the risk score and screening recommendations in 40% of women.
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Affiliation(s)
- Mahasti Saghatchian
- American Hospital of Paris, Neuilly-sur-Seine, France. .,Paris-Descartes University, Paris, France.
| | - Marc Abehsera
- American Hospital of Paris, Neuilly-sur-Seine, France
| | | | - Caroline Geyl
- American Hospital of Paris, Neuilly-sur-Seine, France
| | | | | | | | | | | | | | - Lisa Golmard
- INSERM U830 D.R.U.M. Team, Institut Curie Hospital, Paris-University, Paris, France
| | - Dominique Stoppa-Lyonnet
- Institut Curie, Paris, France.,INSERM U830 D.R.U.M. Team, Institut Curie Hospital, Paris-University, Paris, France
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10
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Olsen M, Fischer K, Bossuyt PM, Goetghebeur E. Evaluating the prognostic performance of a polygenic risk score for breast cancer risk stratification. BMC Cancer 2021; 21:1351. [PMID: 34930164 PMCID: PMC8691010 DOI: 10.1186/s12885-021-08937-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 10/29/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) could potentially improve breast cancer screening recommendations. Before a PRS can be considered for implementation, it needs rigorous evaluation, using performance measures that can inform about its future clinical value. OBJECTIVES To evaluate the prognostic performance of a regression model with a previously developed, prevalence-based PRS and age as predictors for breast cancer incidence in women from the Estonian biobank (EstBB) cohort; to compare it to the performance of a model including age only. METHODS We analyzed data on 30,312 women from the EstBB cohort. They entered the cohort between 2002 and 2011, were between 20 and 89 years, without a history of breast cancer, and with full 5-year follow-up by 2015. We examined PRS and other potential risk factors as possible predictors in Cox regression models for breast cancer incidence. With 10-fold cross-validation we estimated 3- and 5-year breast cancer incidence predicted by age alone and by PRS plus age, fitting models on 90% of the data. Calibration, discrimination, and reclassification were calculated on the left-out folds to express prognostic performance. RESULTS A total of 101 (3.33‰) and 185 (6.1‰) incident breast cancers were observed within 3 and 5 years, respectively. For women in a defined screening age of 50-62 years, the ratio of observed vs PRS-age modelled 3-year incidence was 0.86 for women in the 75-85% PRS-group, 1.34 for the 85-95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year incidence, this was respectively 0.94, 1.15, and 1.08. Yet the number of breast cancer events was relatively low in each PRS-subgroup. For all women, the model's AUC was 0.720 (95% CI: 0.675-0.765) for 3-year and 0.704 (95% CI: 0.670-0.737) for 5-year follow-up, respectively, just 0.022 and 0.023 higher than for the model with age alone. Using a 1% risk prediction threshold, the 3-year NRI for the PRS-age model was 0.09, and 0.05 for 5 years. CONCLUSION The model including PRS had modest incremental performance over one based on age only. A larger, independent study is needed to assess whether and how the PRS can meaningfully contribute to age, for developing more efficient screening strategies.
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Affiliation(s)
- Maria Olsen
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands.
| | - Krista Fischer
- Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, 51009, Tartu, Estonia.,Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Patrick M Bossuyt
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands
| | - Els Goetghebeur
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Institute for Continuing Education Center for Statistics, Campus Sterre, S9, Krijgslaan 281, 9000, Ghent, Belgium
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11
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Wang Y, Zhu M, Ma H, Shen H. Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention. MEDICAL REVIEW (BERLIN, GERMANY) 2021; 1:129-149. [PMID: 37724297 PMCID: PMC10471106 DOI: 10.1515/mr-2021-0025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/13/2021] [Indexed: 09/20/2023]
Abstract
Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.
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Affiliation(s)
- Yuzhuo Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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12
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UK Women's Views of the Concepts of Personalised Breast Cancer Risk Assessment and Risk-Stratified Breast Screening: A Qualitative Interview Study. Cancers (Basel) 2021; 13:cancers13225813. [PMID: 34830965 PMCID: PMC8616436 DOI: 10.3390/cancers13225813] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/12/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022] Open
Abstract
Simple Summary Risk-based breast screening will involve tailoring the amount of screening to women’s level of risk. Therefore, women at high-risk may be offered more frequent screening and over a longer period of time than those at low risk for whom less screening may be recommended. As this will involve considerable changes to the NHS Breast Screening Programme, it is important to explore what women in the UK think and feel about this approach. Analysis of in-depth interviews revealed that some women would find both high and low-risk screening options acceptable whereas others were resistant to the prospect of reduced screening if they were assessed as low-risk. We also found that the idea of risk-based screening had little influence on the attitudes of women who were already sceptical about breast screening. These findings highlight the communication challenges that will be faced by those introducing risk-based screening and suggest a need for tailored support and advice. Abstract Any introduction of risk-stratification within the NHS Breast Screening Programme needs to be considered acceptable by women. We conducted interviews to explore women’s attitudes to personalised risk assessment and risk-stratified breast screening. Twenty-five UK women were purposively sampled by screening experience and socioeconomic background. Interview transcripts were qualitatively analysed using Framework Analysis. Women expressed positive intentions for personal risk assessment and willingness to receive risk feedback to provide reassurance and certainty. Women responded to risk-stratified screening scenarios in three ways: ‘Overall acceptors’ considered both high- and low-risk options acceptable as a reasonable allocation of resources to clinical need, yet acceptability was subject to specified conditions including accuracy of risk estimates and availability of support throughout the screening pathway. Others who thought ‘more is better’ only supported high-risk scenarios where increased screening was proposed. ‘Screening sceptics’ found low-risk scenarios more aligned to their screening values than high-risk screening options. Consideration of screening recommendations for other risk groups had more influence on women’s responses than screening-related harms. These findings demonstrate high, but not universal, acceptability. Support and guidance, tailored to screening values and preferences, may be required by women at all levels of risk.
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13
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Huang Y, Wang H, Lyu Z, Dai H, Liu P, Zhu Y, Song F, Chen K. Development and evaluation of the screening performance of a low-cost high-risk screening strategy for breast cancer. Cancer Biol Med 2021; 19:j.issn.2095-3941.2020.0758. [PMID: 34570443 PMCID: PMC9500221 DOI: 10.20892/j.issn.2095-3941.2020.0758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 04/06/2021] [Indexed: 01/31/2023] Open
Abstract
OBJECTIVE To develop and evaluate the screening performance of a low-cost high-risk screening strategy for breast cancer in low resource areas. METHODS Based on the Multi-modality Independent Screening Trial, 6 questionnaire-based risk factors of breast cancer (age at menarche, age at menopause, age at first live birth, oral contraceptive, obesity, family history of breast cancer) were used to determine the women with high risk of breast cancer. The screening performance of clinical breast examination (CBE), breast ultrasonography (BUS), and mammography (MAM) were calculated and compared to determine the optimal screening method for these high risk women. RESULTS A total of 94 breast cancers were detected among 31,720 asymptomatic Chinese women aged 45-65 years. Due to significantly higher detection rates (DRs) and suitable coverage of the population, high risk women were defined as those with any of 6 risk factors. Among high risk women, the DR for BUS [3.09/1,000 (33/10,694)] was similar to that for MAM [3.18/1,000 (34/10,696)], while it was significantly higher than that for the CBE [1.73/1,000 (19/10,959), P = 0.002]. Compared with MAM, BUS showed significantly higher specificity [98.64% (10,501/10,646) vs. 98.06% (10,443/10,650), P = 0.001], but no significant differences in sensitivity [68.75% (33/48) vs. 73.91% (34/46)], positive prediction values [18.54% (33/178) vs. 14.11% (34/241)], and negative prediction values [99.86% (10,501/10,516) vs. 99.89% (10,443/10,455)]. Further analyses showed no significant difference in the percentages of early stage breast cancer [53.57% (15/28) vs. 50.00% (15/30)], lymph node involvement [22.73% (5/22) vs. 28.00% (7/25)], and tumor size ≥ 2 cm [37.04% (10/27) vs. 29.03% (9/31)] between BUS and MAM. Subgroup analyses stratified by breast densities or age at enrollment showed similar results. CONCLUSIONS The low-cost high-risk screening strategy based on 6 questionnaire-based risk factors was an easy-to-use method to identify women with high risk of breast cancer. Moreover, BUS and MAM had comparable screening performances among high risk women.
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Affiliation(s)
- Yubei Huang
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Huan Wang
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Zhangyan Lyu
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Hongji Dai
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Peifang Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Ying Zhu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Fengju Song
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Kexin Chen
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
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14
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Azam S, Eriksson M, Sjölander A, Gabrielson M, Hellgren R, Czene K, Hall P. Mammographic microcalcifications and risk of breast cancer. Br J Cancer 2021; 125:759-765. [PMID: 34127810 PMCID: PMC8405644 DOI: 10.1038/s41416-021-01459-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/18/2021] [Accepted: 06/02/2021] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Mammographic microcalcifications are considered early signs of breast cancer (BC). We examined the association between microcalcification clusters and the risk of overall and subtype-specific BC. Furthermore, we studied how mammographic density (MD) influences the association between microcalcification clusters and BC risk. METHODS We used a prospective cohort (n = 53,273) of Swedish women with comprehensive information on BC risk factors and mammograms. The total number of microcalcification clusters and MD were measured using a computer-aided detection system and the STRATUS method, respectively. Cox regressions and logistic regressions were used to analyse the data. RESULTS Overall, 676 women were diagnosed with BC. Women with ≥3 microcalcification clusters had a hazard ratio [HR] of 2.17 (95% confidence interval [CI] = 1.57-3.01) compared to women with no clusters. The estimated risk was more pronounced in premenopausal women (HR = 2.93; 95% CI = 1.67-5.16). For postmenopausal women, microcalcification clusters and MD had a similar influence on BC risk. No interaction was observed between microcalcification clusters and MD. Microcalcification clusters were significantly associated with in situ breast cancer (odds ratio: 2.03; 95% CI = 1.13-3.63). CONCLUSIONS Microcalcification clusters are an independent risk factor for BC, with a higher estimated risk in premenopausal women. In postmenopausal women, microcalcification clusters have a similar association with BC as baseline MD.
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Affiliation(s)
- Shadi Azam
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Arvid Sjölander
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Roxanna Hellgren
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.,Department of Mammography, South General Hospital, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.,Department of Oncology, South General Hospital, Stockholm, Sweden
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15
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Shih YCT, Dong W, Xu Y, Etzioni R, Shen Y. Incorporating Baseline Breast Density When Screening Women at Average Risk for Breast Cancer : A Cost-Effectiveness Analysis. Ann Intern Med 2021; 174:602-612. [PMID: 33556275 PMCID: PMC8171124 DOI: 10.7326/m20-2912] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Breast density classification is largely determined by mammography, making the timing of the first screening mammogram clinically important. OBJECTIVE To evaluate the cost-effectiveness of breast cancer screening strategies that are stratified by breast density. DESIGN Microsimulation model to generate the natural history of breast cancer for women with and those without dense breasts and assessment of the cost-effectiveness of strategies tailored to breast density and nontailored strategies. DATA SOURCES Model parameters from the literature; statistical modeling; and analysis of Surveillance, Epidemiology, and End Results-Medicare data. TARGET POPULATION Women aged 40 years or older. TIME HORIZON Lifetime. PERSPECTIVE Societal. INTERVENTION No screening; biennial or triennial mammography from age 50 to 75 years; annual mammography from age 50 to 75 years for women with dense breasts at age 50 years and biennial or triennial mammography from age 50 to 75 years for those without dense breasts at age 50 years; and annual mammography at age 40 to 75 years for women with dense breasts at age 40 years and biennial or triennial mammography at age 50 to 75 years for those without dense breasts at age 40 years. OUTCOME MEASURES Lifetime costs and quality-adjusted life-years (QALYs), discounted at 3% annually. RESULTS OF BASE-CASE ANALYSIS Baseline screening at age 40 years followed by annual screening at age 40 to 75 years for women with dense breasts and biennial screening at age 50 to 75 years for women without dense breasts was effective and cost-effective, yielding an incremental cost-effectiveness ratio of $36 200 per QALY versus the biennial strategy at age 50 to 75 years. RESULTS OF SENSITIVITY ANALYSIS At a societal willingness-to-pay threshold of $100 000 per QALY, the probability that the density-stratified strategy at age 40 years was optimal was 56% compared with 6 other strategies. LIMITATION Findings may not be generalizable outside the United States. CONCLUSION The study findings advocate for breast density-stratified screening with baseline mammography at age 40 years. PRIMARY FUNDING SOURCE National Cancer Institute.
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Affiliation(s)
- Ya-Chen Tina Shih
- The University of Texas MD Anderson Cancer Center, Houston, Texas (Y.T.S., W.D., Y.X., Y.S.)
| | - Wenli Dong
- The University of Texas MD Anderson Cancer Center, Houston, Texas (Y.T.S., W.D., Y.X., Y.S.)
| | - Ying Xu
- The University of Texas MD Anderson Cancer Center, Houston, Texas (Y.T.S., W.D., Y.X., Y.S.)
| | - Ruth Etzioni
- Fred Hutchinson Cancer Center, Seattle, Washington (R.E.)
| | - Yu Shen
- The University of Texas MD Anderson Cancer Center, Houston, Texas (Y.T.S., W.D., Y.X., Y.S.)
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16
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Rostami S, Rafei A, Damghanian M, Khakbazan Z, Maleki F, Zendehdel K. Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women. IRANIAN JOURNAL OF PUBLIC HEALTH 2021; 49:2205-2213. [PMID: 33708742 PMCID: PMC7917489 DOI: 10.18502/ijph.v49i11.4739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background: The Gail model is the most well-known tool for breast cancer risk assessment worldwide. Although it was validated in various Western populations, inconsistent results were reported from Asian populations. We used data from a large case-control study and evaluated the discriminatory accuracy of the Gail model for breast cancer risk assessment among the Iranian female population. Methods: We used data from 942 breast cancer patients and 975 healthy controls at the Cancer Institute of Iran, Tehran, Iran, in 2016. We refitted the Gail model to our case-control data (the IR-Gail model). We compared the discriminatory power of the IR-Gail with the original Gail model, using ROC curve analyses and estimation of the area under the ROC curve (AUC). Results: Except for the history of biopsies that showed an extremely high relative risk (OR=9.1), the observed ORs were similar to the estimates observed in Gail’s study. Incidence rates of breast cancer were extremely lower in Iran than in the USA, leading to a lower average absolute risk among the Iranian population (2.78, ±SD 2.45). The AUC was significantly improved after refitting the model, but it remained modest (0.636 vs. 0.627, ΔAUC = 0.009, bootstrapped P=0.008). We reported that the cut-point of 1.67 suggested in the Gail study did not discriminate between breast cancer patients and controls among the Iranian female population. Conclusion: Although the coefficients from the local study improved the discriminatory accuracy of the model, it remained modest. Cohort studies are warranted to evaluate the validity of the model for Iranian women.
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Affiliation(s)
- Sahar Rostami
- Department of Reproductive Health and Midwifery, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran.,Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Rafei
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Damghanian
- Nursing and Midwifery Care Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Zohreh Khakbazan
- Nursing and Midwifery Care Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Maleki
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Social Determinants of Health Research Center, Urmia University of Medical Sciences, Urmia, Iran
| | - Kazem Zendehdel
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.,Breast Disease Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
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17
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Li R, Chen Y, Moore JH. Integration of genetic and clinical information to improve imputation of data missing from electronic health records. J Am Med Inform Assoc 2021; 26:1056-1063. [PMID: 31329892 DOI: 10.1093/jamia/ocz041] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 03/12/2019] [Accepted: 03/18/2019] [Indexed: 01/29/2023] Open
Abstract
OBJECTIVE Clinical data of patients' measurements and treatment history stored in electronic health record (EHR) systems are starting to be mined for better treatment options and disease associations. A primary challenge associated with utilizing EHR data is the considerable amount of missing data. Failure to address this issue can introduce significant bias in EHR-based research. Currently, imputation methods rely on correlations among the structured phenotype variables in the EHR. However, genetic studies have shown that many EHR-based phenotypes have a heritable component, suggesting that measured genetic variants might be useful for imputing missing data. In this article, we developed a computational model that incorporates patients' genetic information to perform EHR data imputation. MATERIALS AND METHODS We used the individual single nucleotide polymorphism's association with phenotype variables in the EHR as input to construct a genetic risk score that quantifies the genetic contribution to the phenotype. Multiple approaches to constructing the genetic risk score were evaluated for optimal performance. The genetic score, along with phenotype correlation, is then used as a predictor to impute the missing values. RESULTS To demonstrate the method performance, we applied our model to impute missing cardiovascular related measurements including low-density lipoprotein, heart failure, and aortic aneurysm disease in the electronic Medical Records and Genomics data. The integration method improved imputation's area-under-the-curve for binary phenotypes and decreased root-mean-square error for continuous phenotypes. CONCLUSION Compared with standard imputation approaches, incorporating genetic information offers a novel approach that can utilize more of the EHR data for better performance in missing data imputation.
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Affiliation(s)
- Ruowang Li
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yong Chen
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Evidence-based Practice, The University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Applied Mathematics & Computational Science, Penn Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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18
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Vilmun BM, Vejborg I, Lynge E, Lillholm M, Nielsen M, Nielsen MB, Carlsen JF. Impact of adding breast density to breast cancer risk models: A systematic review. Eur J Radiol 2020; 127:109019. [DOI: 10.1016/j.ejrad.2020.109019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 01/19/2023]
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19
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Wood ME, Farina NH, Ahern TP, Cuke ME, Stein JL, Stein GS, Lian JB. Towards a more precise and individualized assessment of breast cancer risk. Aging (Albany NY) 2020; 11:1305-1316. [PMID: 30787204 PMCID: PMC6402518 DOI: 10.18632/aging.101803] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 01/24/2019] [Indexed: 02/07/2023]
Abstract
Many clinically based models are available for breast cancer risk assessment; however, these models are not particularly useful at the individual level, despite being designed with that intent. There is, therefore, a significant need for improved, precise individualized risk assessment. In this Research Perspective, we highlight commonly used clinical risk assessment models and recent scientific advances to individualize risk assessment using precision biomarkers. Genome-wide association studies have identified >100 single nucleotide polymorphisms (SNPs) associated with breast cancer risk, and polygenic risk scores (PRS) have been developed by several groups using this information. The ability of a PRS to improve risk assessment is promising; however, validation in both genetically and ethnically diverse populations is needed. Additionally, novel classes of biomarkers, such as microRNAs, may capture clinically relevant information based on epigenetic regulation of gene expression. Our group has recently identified a circulating-microRNA signature predictive of long-term breast cancer in a prospective cohort of high-risk women. While progress has been made, the importance of accurate risk assessment cannot be understated. Precision risk assessment will identify those women at greatest risk of developing breast cancer, thus avoiding overtreatment of women at average risk and identifying the most appropriate candidates for chemoprevention or surgical prevention.
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Affiliation(s)
- Marie E Wood
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Division of Hematology and Oncology, The Robert Larner MD College of Medicine, University of Vermont Medical Center, Burlington, VT 05405, USA
| | - Nicholas H Farina
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Thomas P Ahern
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Surgery, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Melissa E Cuke
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Division of Hematology and Oncology, The Robert Larner MD College of Medicine, University of Vermont Medical Center, Burlington, VT 05405, USA
| | - Janet L Stein
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Gary S Stein
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Surgery, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Jane B Lian
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA.,Department of Biochemistry, and The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT 05405, USA
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20
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Rainey L, van der Waal D, Jervaeus A, Donnelly LS, Evans DG, Hammarström M, Hall P, Wengström Y, Broeders MJM. European women's perceptions of the implementation and organisation of risk-based breast cancer screening and prevention: a qualitative study. BMC Cancer 2020; 20:247. [PMID: 32209062 PMCID: PMC7092605 DOI: 10.1186/s12885-020-06745-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 03/12/2020] [Indexed: 01/28/2023] Open
Abstract
Background Increased knowledge of breast cancer risk factors has meant that we are currently exploring risk-based screening, i.e. determining screening strategies based on women’s varying levels of risk. This also enables risk management through primary prevention strategies, e.g. a lifestyle programme or risk-reducing medication. However, future implementation of risk-based screening and prevention will warrant significant changes in current practice and policy. The present study explores women’s perceptions of the implementation and organisation of risk-based breast cancer screening and prevention to optimise acceptability and uptake. Methods A total of 143 women eligible for breast cancer screening in the Netherlands, the United Kingdom, and Sweden participated in focus group discussions. The focus group discussions were transcribed verbatim and the qualitative data was analysed using thematic analysis. Results Women from all three countries generally agreed on the overall proceedings, e.g. a risk assessment after which the risk estimate is communicated via letter (for below average and average risk) or consultation (for moderate and high risk). However, discrepancies in information needs, preferred risk communication format and risk counselling professional were identified between countries. Additionally, a need to educate healthcare professionals on all aspects of the risk-based screening and prevention programme was established. Conclusion Women’s insights identified the need for country-specific standardised protocols regarding the assessment and communication of risk, and the provision of heterogeneous screening and prevention recommendations, monitoring the principle of solidarity in healthcare policy.
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Affiliation(s)
- Linda Rainey
- Radboud Institute for Health Sciences, Radboud university medical center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Daniëlle van der Waal
- Radboud Institute for Health Sciences, Radboud university medical center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Anna Jervaeus
- Department of Neurobiology, Care Sciences and Society, Division of Nursing, Karolinska Institutet, Alfred, Nobels allé 23, 23300, 14183, Huddinge, Sweden
| | - Louise S Donnelly
- Prevent Breast Cancer Research Unit, The Nightingale Centre, Manchester University NHS Foundation Trust, Southmoor Road, Manchester, M23 9LT, UK
| | - D Gareth Evans
- Prevent Breast Cancer Research Unit, The Nightingale Centre, Manchester University NHS Foundation Trust, Southmoor Road, Manchester, M23 9LT, UK.,Genomic Medicine, Division of Evolution and Genomic Sciences, Manchester Academic Health Sciences Centre, Manchester University NHS Foundation Trust, Manchester, M13 9WL, UK.,The Christie NHS Foundation Trust, Withington, Manchester, M20 4BX, UK
| | - Mattias Hammarström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 171 77, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 171 77, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Sjukhusbacken 10, 118 83, Stockholm, Sweden
| | - Yvonne Wengström
- Department of Neurobiology, Care Sciences and Society, Division of Nursing, Karolinska Institutet, Alfred, Nobels allé 23, 23300, 14183, Huddinge, Sweden.,Theme Cancer, Karolinska University Hospital, Alfred Nobels allé 23, 23300, 14183, Huddinge, Sweden
| | - Mireille J M Broeders
- Radboud Institute for Health Sciences, Radboud university medical center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.,Dutch Expert Centre for Screening, PO Box 6873, 6503 GJ, Nijmegen, The Netherlands
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21
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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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22
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Gabrielson M, Ubhayasekera KA, Acharya SR, Franko MA, Eriksson M, Bergquist J, Czene K, Hall P. Inclusion of Endogenous Plasma Dehydroepiandrosterone Sulfate and Mammographic Density in Risk Prediction Models for Breast Cancer. Cancer Epidemiol Biomarkers Prev 2020; 29:574-581. [PMID: 31948996 DOI: 10.1158/1055-9965.epi-19-1120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 11/06/2019] [Accepted: 01/10/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Endogenous hormones and mammographic density are risk factors for breast cancer. Joint analyses of the two may improve the ability to identify high-risk women. METHODS This study within the KARMA cohort included prediagnostic measures of plasma hormone levels of dehydroepiandrosterone (DHEA), its sulfate (DHEAS), and mammographic density in 629 cases and 1,223 controls, not using menopausal hormones. We evaluated the area under the receiver-operating curve (AUC) for risk of breast cancer by adding DHEA, DHEAS, and mammographic density to the Gail or Tyrer-Cuzick 5-year risk scores or the CAD2Y 2-year risk score. RESULTS DHEAS and percentage density were independently and positively associated with breast cancer risk (P = 0.007 and P < 0.001, respectively) for postmenopausal, but not premenopausal, women. No significant association was seen for DHEA. In postmenopausal women, those in the highest tertiles of both DHEAS and density were at greatest risk of breast cancer (OR, 3.5; 95% confidence interval, 1.9-6.3) compared with the lowest tertiles. Adding DHEAS significantly improved the AUC for the Gail (+2.1 units, P = 0.008) and Tyrer-Cuzick (+1.3 units, P = 0.007) risk models. Adding DHEAS to the Gail and Tyrer-Cuzick models already including mammographic density further increased the AUC by 1.2 units (P = 0.006) and 0.4 units (P = 0.007), respectively, compared with only including density. CONCLUSIONS DHEAS and mammographic density are independent risk factors for breast cancer and improve risk discrimination for postmenopausal breast cancer. IMPACT Combining DHEAS and mammographic density could help identify women at high risk who may benefit from individualized breast cancer screening and/or preventive measures among postmenopausal women.
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Affiliation(s)
- Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Kumari A Ubhayasekera
- Analytical Chemistry and Neurochemistry, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
| | - Santosh R Acharya
- Analytical Chemistry and Neurochemistry, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
| | - Mikael Andersson Franko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jonas Bergquist
- Analytical Chemistry and Neurochemistry, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
| | - Kamila Czene
- 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, South General Hospital, Stockholm, Sweden
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23
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Stark GF, Hart GR, Nartowt BJ, Deng J. Predicting breast cancer risk using personal health data and machine learning models. PLoS One 2019; 14:e0226765. [PMID: 31881042 PMCID: PMC6934281 DOI: 10.1371/journal.pone.0226765] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 12/03/2019] [Indexed: 12/23/2022] Open
Abstract
Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and additional personal health data relevant to breast cancer risk. For both sets of inputs, six machine learning models were trained and evaluated on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data set. The area under the receiver operating characteristic curve metric quantified each model’s performance. Since this data set has a small percentage of positive breast cancer cases, we also reported sensitivity, specificity, and precision. We used Delong tests (p < 0.05) to compare the testing data set performance of each machine learning model to that of the Breast Cancer Risk Prediction Tool (BCRAT), an implementation of the Gail model. None of the machine learning models with only BCRAT inputs were significantly stronger than the BCRAT. However, the logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs were all significantly stronger than the BCRAT. These results suggest that relative to the BCRAT, additional easy-to-obtain personal health inputs can improve five-year breast cancer risk prediction. Our models could be used as non-invasive and cost-effective risk stratification tools to increase early breast cancer detection and prevention, motivating both immediate actions like screening and long-term preventative measures such as hormone replacement therapy and chemoprevention.
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Affiliation(s)
- Gigi F. Stark
- Department of Therapeutic Radiology, Yale University, New Haven, CT, United States of America
| | - Gregory R. Hart
- Department of Therapeutic Radiology, Yale University, New Haven, CT, United States of America
| | - Bradley J. Nartowt
- Department of Therapeutic Radiology, Yale University, New Haven, CT, United States of America
| | - Jun Deng
- Department of Therapeutic Radiology, Yale University, New Haven, CT, United States of America
- * E-mail:
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24
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Patron J, Serra-Cayuela A, Han B, Li C, Wishart DS. Assessing the performance of genome-wide association studies for predicting disease risk. PLoS One 2019; 14:e0220215. [PMID: 31805043 PMCID: PMC6894795 DOI: 10.1371/journal.pone.0220215] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 11/01/2019] [Indexed: 12/24/2022] Open
Abstract
To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.
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Affiliation(s)
- Jonas Patron
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | | | - Beomsoo Han
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | - Carin Li
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | - David Scott Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
- Department of Computing Science, University of Alberta, Edmonton, Canada
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25
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Zhang Z, Bien J, Mori M, Jindal S, Bergan R. A way forward for cancer prevention therapy: personalized risk assessment. Oncotarget 2019; 10:6898-6912. [PMID: 31839883 PMCID: PMC6901339 DOI: 10.18632/oncotarget.27365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 11/19/2019] [Indexed: 12/17/2022] Open
Abstract
Cancer is characterized by genetic and molecular aberrations whose number and complexity increase dramatically as cells progress along the spectrum of carcinogenesis. The pharmacologic application of agents in the context of a lower burden of dysregulated cellular processes constitutes an efficient strategy to enhance therapeutic efficacy, and underlies the rationale for using cancer prevention agents in high-risk populations. A longstanding barrier to implementing this strategy is that the risk in the general population is low for any given cancer, many people would have to be treated in order to benefit a few. Therefore, identifying and treating high-risk individuals will improve the risk: benefit ratio. Currently, risk is defined by considering a relatively low number of factors. A strategy that considers multiple factors has the ability to define a much-higher-risk cohort than the general population. This article will review the rationale for evaluating multiple risk factors so as to identify individuals at highest risk. It will use breast and lung cancer as examples, will describe currently available risk assessment tools, and will discuss ongoing efforts to expand the impact of this approach. The high potential of this strategy to provide a way forward for developing cancer prevention therapy will be highlighted.
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Affiliation(s)
- Zhenzhen Zhang
- Division of Hematology/Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeffrey Bien
- Division of Oncology, Stanford University, Palo Alto, California, USA
| | - Motomi Mori
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA.,OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, Oregon, USA
| | - Sonali Jindal
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, Oregon, USA
| | - Raymond Bergan
- Division of Hematology/Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
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26
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Willingness to decrease mammogram frequency among women at low risk for hereditary breast cancer. Sci Rep 2019; 9:9599. [PMID: 31270367 PMCID: PMC6610104 DOI: 10.1038/s41598-019-45967-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 06/20/2019] [Indexed: 01/02/2023] Open
Abstract
This study aimed to assess women's willingness to alter mammogram frequency based on their low risk for HBOC, and to examine if cognitive and emotional factors are associated with women's inclination to decrease mammogram frequency. We conducted an online survey with women (N = 124) who were unlikely to have a BRCA mutation and at average population risk for breast cancer based on family history. Most women were either white (50%) or African American (38%) and were 50 years or older (74%). One-third of women (32%) were willing to decrease mammogram frequency (as consistent with the USPSTF guideline), 42% reported being unwilling and 26% were unsure. Multivariate logistic regression showed that feeling worried about breast cancer (Adjust OR = 0.33, p = 0.01), greater genetic risk knowledge (Adjust OR = 0.74, p = 0.047), and more frequent past mammogram screening (Adjust OR = 0.13, p = 0.001) were associated with being less willing to decrease screening frequency. Findings suggest that emerging genomics-informed medical guidelines may not be accepted by many patients when the recommendations go against what is considered standard practice. Further study of the interplay between emotion- and cognition-based processing of the HBOC screen result will be important for strategizing communication interventions aimed at realizing the potential of precision public health.
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27
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Kim JY, Kang D, Nam SJ, Kim SW, Lee JE, Yu JH, Lee SK, Im YH, Ahn JS, Guallar E, Cho J, Park YH. Clinical Features and Outcomes of Invasive Breast Cancer: Age-Specific Analysis of a Modern Hospital-Based Registry. J Glob Oncol 2019; 5:1-9. [PMID: 31260394 PMCID: PMC6613670 DOI: 10.1200/jgo.19.00034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/23/2019] [Indexed: 12/18/2022] Open
Abstract
PURPOSE We evaluated the clinical features and outcomes of invasive breast cancer (BC) among different age groups by analyzing a modern BC registry including subtypes and treatment information. METHODS This was a retrospective cohort study of 6,405 women aged 18 years or older with pathologically confirmed stage I, II, or III BC who underwent curative surgery followed by adjuvant therapy at a university-based hospital in Seoul, South Korea, between January 2003 and December 2011. The study end point was all-cause mortality. We used Cox proportional hazards models and hazard ratios (HRs) with 95% CIs calculated after adjusting for age, body mass index, stage, subtype, and treatment, including type of surgery and use of chemotherapy, radiation therapy, hormone therapy, and targeted therapy. RESULTS During 36,360 person-years of follow-up (median follow-up: 5.45 years; interquartile range, 4.3-7.1), 256 deaths were reported (mortality rate, 7.0/1,000 person-years). The adjusted HR for all-cause mortality was higher in patients older than 40 years (HR, 2.03; 95% CI, 1.44 to 2.87) and older than 60 years (HR, 2.35; 95% CI, 1.63 to 3.39) than in patients aged 40 to 49 years. Across age groups, advanced stage at diagnosis, luminal type as well as triple-negative BC, and not receiving adjuvant treatment were associated with increased risk of mortality. CONCLUSION A strong J-shaped relationship was observed between age and mortality, indicating worse clinical outcomes in young and old patients. This study suggested a possible benefit of personalized BC screening examination and precise and active treatment strategies to reduce BC-related mortality.
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Affiliation(s)
- Ji-Yeon Kim
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Danbee Kang
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seok Jin Nam
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seok Won Kim
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeong Eon Lee
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jong Han Yu
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Se Kyung Lee
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Young-Hyuck Im
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jin Seok Ahn
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Eliseo Guallar
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Juhee Cho
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Yeon Hee Park
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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28
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Clendenen TV, Ge W, Koenig KL, Afanasyeva Y, Agnoli C, Brinton LA, Darvishian F, Dorgan JF, Eliassen AH, Falk RT, Hallmans G, Hankinson SE, Hoffman-Bolton J, Key TJ, Krogh V, Nichols HB, Sandler DP, Schoemaker MJ, Sluss PM, Sund M, Swerdlow AJ, Visvanathan K, Zeleniuch-Jacquotte A, Liu M. Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model. Breast Cancer Res 2019; 21:42. [PMID: 30890167 PMCID: PMC6425605 DOI: 10.1186/s13058-019-1126-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 03/05/2019] [Indexed: 12/28/2022] Open
Abstract
Background Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35–50. Methods In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers. Results The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer. Conclusions AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35–50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history. Electronic supplementary material The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tess V Clendenen
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Wenzhen Ge
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Karen L Koenig
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Yelena Afanasyeva
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy
| | - Louise A Brinton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Farbod Darvishian
- Department of Pathology, New York University School of Medicine, New York, NY, USA.,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA
| | - Joanne F Dorgan
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Roni T Falk
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Göran Hallmans
- Department of Biobank Research, Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Susan E Hankinson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA
| | - Judith Hoffman-Bolton
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy
| | - Hazel B Nichols
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.,Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Patrick M Sluss
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Malin Sund
- Department of Surgery, Umeå University Hospital, Umeå, Sweden
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Anne Zeleniuch-Jacquotte
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA.,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA
| | - Mengling Liu
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA. .,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA.
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29
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Fung SM, Wong XY, Lee SX, Miao H, Hartman M, Wee HL. Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis. Cancer Epidemiol Biomarkers Prev 2018; 28:506-521. [DOI: 10.1158/1055-9965.epi-18-0810] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/30/2018] [Accepted: 12/03/2018] [Indexed: 11/16/2022] Open
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30
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Feld SI, Woo KM, Alexandridis R, Wu Y, Liu J, Peissig P, Onitilo AA, Cox J, Page CD, Burnside ES. Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2018; 2018:1253-1262. [PMID: 30815167 PMCID: PMC6371301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The predictive capability of combining demographic risk factors, germline genetic variants, and mammogram abnormality features for breast cancer risk prediction is poorly understood. We evaluated the predictive performance of combinations of demographic risk factors, high risk single nucleotide polymorphisms (SNPs), and mammography features for women recommended for breast biopsy in a retrospective case-control study (n = 768) with four logistic regression models. The AUC of the baseline demographic features model was 0.580. Both genetic variants and mammography abnormality features augmented the performance of the baseline model: demographics + SNP (AUC =0.668), demographics + mammography (AUC =0.702). Finally, we found that the demographics + SNP + mammography model (AUC = 0.753) had the greatest predictive power, with a significant performance improvement over the other models. The combination of demographic risk factors, genetic variants and imaging features improves breast cancer risk prediction over prior methods utilizing only a subset of these features.
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Affiliation(s)
- Shara I Feld
- University of Wisconsin Department of Radiology, Madison, WI
| | - Kaitlin M Woo
- University of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI
| | - Roxana Alexandridis
- University of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI
| | - Yirong Wu
- University of Wisconsin Department of Radiology, Madison, WI
| | - Jie Liu
- University of Washington Department of Genome Sciences, Seattle, WA
| | - Peggy Peissig
- Marshfield Clinic Research Institute, Marshfield, WI
| | - Adedayo A Onitilo
- Marshfield Clinic Research Institute, Marshfield, WI
- Marshfield Clinic Weston Center Department of Hematology/Oncology, Weston, WI
| | - Jennifer Cox
- University of Wisconsin Department of Radiology, Madison, WI
- University of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI
- University of Washington Department of Genome Sciences, Seattle, WA
- Marshfield Clinic Research Institute, Marshfield, WI
- Marshfield Clinic Weston Center Department of Hematology/Oncology, Weston, WI
| | - C David Page
- University of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI
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Gabrielson M, Ubhayasekera K, Ek B, Andersson Franko M, Eriksson M, Czene K, Bergquist J, Hall P. Inclusion of Plasma Prolactin Levels in Current Risk Prediction Models of Premenopausal and Postmenopausal Breast Cancer. JNCI Cancer Spectr 2018; 2:pky055. [PMID: 31360875 PMCID: PMC6649752 DOI: 10.1093/jncics/pky055] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 08/31/2018] [Accepted: 10/08/2018] [Indexed: 01/07/2023] Open
Abstract
Background Circulating plasma prolactin is associated with breast cancer risk and may improve our ability to identify high-risk women. Mammographic density is a strong risk factor for breast cancer, but the association with prolactin is unclear. We studied the association between breast cancer, established breast cancer risk factors and plasma prolactin, and improvement of risk prediction by adding prolactin. Methods We conducted a nested case-control study including 721 breast cancer patients and 1400 age-matched controls. Plasma prolactin levels were assayed using immunoassay and mammographic density measured by STRATUS. Odds ratios (ORs) were calculated by multivariable adjusted logistic regression, and improvement in the area under the curve for the risk of breast cancer by adding prolactin to established risk models. Statistical tests were two-sided. Results In multivariable adjusted analyses, prolactin was associated with risk of premenopausal (OR, top vs bottom quintile = 1.9; 1.88 (95% confidence interval [CI] = 1.08 to 3.26) but not with postmenopausal breast cancer. In postmenopausal cases prolactin increased by 10.6% per cBIRADS category (Ptrend = .03). In combined analyses of prolactin and mammographic density, ORs for women in the highest vs lowest tertile of both was 3.2 (95% CI = 1.3 to 7.7) for premenopausal women and 2.44 (95% CI = 1.44 to 4.14) for postmenopausal women. Adding prolactin to current risk models improved the area under the curve of the Gail model (+2.4 units, P = .02), Tyrer-Cuzick model (+3.8, P = .02), and the CAD2Y model (+1.7, P = .008) in premenopausal women. Conclusion Circulating plasma prolactin and mammographic density appear independently associated with breast cancer risk among premenopausal women, and prolactin may improve risk prediction by current risk models.
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Affiliation(s)
- Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kumari Ubhayasekera
- Analytical Chemistry and Neurochemistry, Department of Chemistry, Uppsala University, Uppsala, Sweden
| | - Bo Ek
- Analytical Chemistry and Neurochemistry, Department of Chemistry, Uppsala University, Uppsala, Sweden
| | - Mikael Andersson Franko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jonas Bergquist
- Analytical Chemistry and Neurochemistry, Department of Chemistry, Uppsala University, Uppsala, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, South General Hospital, Stockholm, Sweden
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Pashayan N, Morris S, Gilbert FJ, Pharoah PDP. Cost-effectiveness and Benefit-to-Harm Ratio of Risk-Stratified Screening for Breast Cancer: A Life-Table Model. JAMA Oncol 2018; 4:1504-1510. [PMID: 29978189 PMCID: PMC6230256 DOI: 10.1001/jamaoncol.2018.1901] [Citation(s) in RCA: 174] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 04/02/2018] [Indexed: 01/02/2023]
Abstract
Importance The age-based or "one-size-fits-all" breast screening approach does not take into account the individual variation in risk. Mammography screening reduces death from breast cancer at the cost of overdiagnosis. Identifying risk-stratified screening strategies with a more favorable ratio of overdiagnoses to breast cancer deaths prevented would improve the quality of life of women and save resources. Objective To assess the benefit-to-harm ratio and the cost-effectiveness of risk-stratified breast screening programs compared with a standard age-based screening program and no screening. Design, Setting, and Population A life-table model was created of a hypothetical cohort of 364 500 women in the United Kingdom, aged 50 years, with follow-up to age 85 years, using (1) findings of the Independent UK Panel on Breast Cancer Screening and (2) risk distribution based on polygenic risk profile. The analysis was undertaken from the National Health Service perspective. Interventions The modeled interventions were (1) no screening, (2) age-based screening (mammography screening every 3 years from age 50 to 69 years), and (3) risk-stratified screening (a proportion of women aged 50 years with a risk score greater than a threshold risk were offered screening every 3 years until age 69 years) considering each percentile of the risk distribution. All analyses took place between July 2016 and September 2017. Main Outcomes and Measures Overdiagnoses, breast cancer deaths averted, quality-adjusted life-years (QALYs) gained, costs in British pounds, and net monetary benefit (NMB). Probabilistic sensitivity analyses were used to assess uncertainty around parameter estimates. Future costs and benefits were discounted at 3.5% per year. Results The risk-stratified analysis of this life-table model included a hypothetical cohort of 364 500 women followed up from age 50 to 85 years. As the risk threshold was lowered, the incremental cost of the program increased linearly, compared with no screening, with no additional QALYs gained below 35th percentile risk threshold. Of the 3 screening scenarios, the risk-stratified scenario with risk threshold at the 70th percentile had the highest NMB, at a willingness to pay of £20 000 (US $26 800) per QALY gained, with a 72% probability of being cost-effective. Compared with age-based screening, risk-stratified screening at the 32nd percentile vs 70th percentile risk threshold would cost £20 066 (US $26 888) vs £537 985 (US $720 900) less, would have 26.7% vs 71.4% fewer overdiagnoses, and would avert 2.9% vs 9.6% fewer breast cancer deaths, respectively. Conclusions and Relevance Not offering breast cancer screening to women at lower risk could improve the cost-effectiveness of the screening program, reduce overdiagnosis, and maintain the benefits of screening.
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Affiliation(s)
- Nora Pashayan
- Department of Applied Health Research, University College London, London, England
| | - Steve Morris
- Department of Applied Health Research, University College London, London, England
| | - Fiona J. Gilbert
- Department of Radiology, Cambridge Biomedical Campus, University of Cambridge, Cambridge, England
| | - Paul D. P. Pharoah
- Departments of Oncology and Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, England
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Ma R, Zhai X, Zhu X, Zhang L. LINC01585 functions as a regulator of gene expression by the CAMP/CREB signaling pathway in breast cancer. Gene 2018; 684:139-148. [PMID: 30366079 DOI: 10.1016/j.gene.2018.10.063] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 10/18/2018] [Accepted: 10/22/2018] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Breast cancer is the leading cause of cancer death among women. Nowadays, long non-coding RNAs (lncRNAs) have been identified and emerged as critical bio-markers in breast cancer tumorigenesis and progression. However, only a handful of lncRNAs which are implicated in BC have been characterized. The underlying molecular mechanisms are still largely unknown. METHODS In this study, we explored 12 nominated lncRNAs at breast cancer susceptibility loci identified by genome-wide association studies to contribute to the risk and effects of breast cancer. We then analyzed these lncRNAs in a total of 132 pairs of breast cancer tissues and surrounding non-tumor tissues from southern China population. RESULTS Here, we report a novel lncRNA, LINC01585, is aberrantly down regulated during breast cancer (BC). Next, to explore the molecular mechanisms underlying the biological activity of LINC01585, we identified LINC01585 binding protein by RNA pull-down experiments. Functionally, we found that LINC01585 overexpression inhibited breast cancer proliferation and growth by prototypical experiments. Mechanistically, LINC01585 was located in nuclear and binding with NONO protein. Interestingly, when LINC01585 was down-expressed, NONO separated from LINC01585 and then interacted with CRTC. The complex promotes CAMP/CREB target gene transcription and thus promotes the growth of breast cancer. CONCLUSIONS A series of discoveries suggest to us that LINC01585 has a potential value in anti-carcinoma therapy and deserves further investigation.
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Affiliation(s)
- Rui Ma
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou 215004, China
| | - Xiaoming Zhai
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou 215004, China
| | - Xun Zhu
- Department of General Surgery, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou 215004, China
| | - Liyuan Zhang
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou 215004, China.
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Al-Ajmi K, Lophatananon A, Yuille M, Ollier W, Muir KR. Review of non-clinical risk models to aid prevention of breast cancer. Cancer Causes Control 2018; 29:967-986. [PMID: 30178398 PMCID: PMC6182451 DOI: 10.1007/s10552-018-1072-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 08/10/2018] [Indexed: 12/29/2022]
Abstract
A disease risk model is a statistical method which assesses the probability that an individual will develop one or more diseases within a stated period of time. Such models take into account the presence or absence of specific epidemiological risk factors associated with the disease and thereby potentially identify individuals at higher risk. Such models are currently used clinically to identify people at higher risk, including identifying women who are at increased risk of developing breast cancer. Many genetic and non-genetic breast cancer risk models have been developed previously. We have evaluated existing non-genetic/non-clinical models for breast cancer that incorporate modifiable risk factors. This review focuses on risk models that can be used by women themselves in the community in the absence of clinical risk factors characterization. The inclusion of modifiable factors in these models means that they can be used to improve primary prevention and health education pertinent for breast cancer. Literature searches were conducted using PubMed, ScienceDirect and the Cochrane Database of Systematic Reviews. Fourteen studies were eligible for review with sample sizes ranging from 654 to 248,407 participants. All models reviewed had acceptable calibration measures, with expected/observed (E/O) ratios ranging from 0.79 to 1.17. However, discrimination measures were variable across studies with concordance statistics (C-statistics) ranging from 0.56 to 0.89. We conclude that breast cancer risk models that include modifiable risk factors have been well calibrated but have less ability to discriminate. The latter may be a consequence of the omission of some significant risk factors in the models or from applying models to studies with limited sample sizes. More importantly, external validation is missing for most of the models. Generalization across models is also problematic as some variables may not be considered applicable to some populations and each model performance is conditioned by particular population characteristics. In conclusion, it is clear that there is still a need to develop a more reliable model for estimating breast cancer risk which has a good calibration, ability to accurately discriminate high risk and with better generalizability across populations.
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Affiliation(s)
- Kawthar Al-Ajmi
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Martin Yuille
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - William Ollier
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
| | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, The University of Manchester, Manchester, M139 PL UK
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Wang N, Wang Z, Wang C, Fu X, Yu G, Yue Z, Liu T, Zhang H, Li L, Chen M, Wang H, Niu G, Liu D, Zhang M, Xu Y, Zhang Y, Li J, Li Z, You J, Chu T, Li F, Liu D, Liu H, Zhang F. Prediction of leprosy in the Chinese population based on a weighted genetic risk score. PLoS Negl Trop Dis 2018; 12:e0006789. [PMID: 30231057 PMCID: PMC6166985 DOI: 10.1371/journal.pntd.0006789] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 10/01/2018] [Accepted: 08/26/2018] [Indexed: 01/03/2023] Open
Abstract
Genome wide association studies (GWASs) have revealed multiple genetic variants associated with leprosy in the Chinese population. The aim of our study was to utilize the genetic variants to construct a risk prediction model through a weighted genetic risk score (GRS) in a Chinese set and to further assess the performance of the model in identifying higher-risk contact individuals in an independent set. The highest prediction accuracy, with an area under the curve (AUC) of 0.743 (95% confidence interval (CI): 0.729-0.757), was achieved with a GRS encompassing 25 GWAS variants in a discovery set that included 2,144 people affected by leprosy and 2,671 controls. Individuals in the high-risk group, based on genetic factors (GRS > 28.06), have a 24.65 higher odds ratio (OR) for developing leprosy relative to those in the low-risk group (GRS≤18.17). The model was then applied to a validation set consisting of 1,385 people affected by leprosy and 7,541 individuals in contact with leprosy, which yielded a discriminatory ability with an AUC of 0.707 (95% CI: 0.691-0.723). When a GRS cut-off value of 22.38 was selected with the optimal sensitivity and specificity, it was found that 39.31% of high risk contact individuals should be screened in order to detect leprosy in 64.9% of those people affected by leprosy. In summary, we developed and validated a risk model for the prediction of leprosy that showed good discrimination capabilities, which may help physicians in the identification of patients coming into contact with leprosy and are at a higher-risk of developing this condition.
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Affiliation(s)
- Na Wang
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Zhenzhen Wang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Chuan Wang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Xi'an Fu
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Gongqi Yu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Zhenhua Yue
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Tingting Liu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Huimin Zhang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Lulu Li
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Mingfei Chen
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Honglei Wang
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Guiye Niu
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Dan Liu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Mingkai Zhang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Yuanyuan Xu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Yan Zhang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Jinghui Li
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Zhen Li
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Jiabao You
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Tongsheng Chu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Furong Li
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Dianchang Liu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Hong Liu
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
- * E-mail: (HL); (FZ)
| | - Furen Zhang
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
- * E-mail: (HL); (FZ)
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Zhang X, Rice M, Tworoger SS, Rosner BA, Eliassen AH, Tamimi RM, Joshi AD, Lindstrom S, Qian J, Colditz GA, Willett WC, Kraft P, Hankinson SE. Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case-control study. PLoS Med 2018; 15:e1002644. [PMID: 30180161 PMCID: PMC6122802 DOI: 10.1371/journal.pmed.1002644] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 07/25/2018] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND No prior study to our knowledge has examined the joint contribution of a polygenic risk score (PRS), mammographic density (MD), and postmenopausal endogenous hormone levels-all well-confirmed risk factors for invasive breast cancer-to existing breast cancer risk prediction models. METHODS AND FINDINGS We conducted a nested case-control study within the prospective Nurses' Health Study and Nurses' Health Study II including 4,006 cases and 7,874 controls ages 34-70 years up to 1 June 2010. We added a breast cancer PRS using 67 single nucleotide polymorphisms, MD, and circulating testosterone, estrone sulfate, and prolactin levels to existing risk models. We calculated area under the curve (AUC), controlling for age and stratified by menopausal status, for the 5-year absolute risk of invasive breast cancer. We estimated the population distribution of 5-year predicted risks for models with and without biomarkers. For the Gail model, the AUC improved (p-values < 0.001) from 55.9 to 64.1 (8.2 units) in premenopausal women (Gail + PRS + MD), from 55.5 to 66.0 (10.5 units) in postmenopausal women not using hormone therapy (HT) (Gail + PRS + MD + all hormones), and from 58.0 to 64.9 (6.9 units) in postmenopausal women using HT (Gail + PRS + MD + prolactin). For the Rosner-Colditz model, the corresponding AUCs improved (p-values < 0.001) by 5.7, 6.2, and 6.5 units. For estrogen-receptor-positive tumors, among postmenopausal women not using HT, the AUCs improved (p-values < 0.001) by 14.3 units for the Gail model and 7.3 units for the Rosner-Colditz model. Additionally, the percentage of 50-year-old women predicted to be at more than twice 5-year average risk (≥2.27%) was 0.2% for the Gail model alone and 6.6% for the Gail + PRS + MD + all hormones model. Limitations of our study included the limited racial/ethnic diversity of our cohort, and that general population exposure distributions were unavailable for some risk factors. CONCLUSIONS In this study, the addition of PRS, MD, and endogenous hormones substantially improved existing breast cancer risk prediction models. Further studies will be needed to confirm these findings and to determine whether improved risk prediction models have practical value in identifying women at higher risk who would most benefit from chemoprevention, screening, and other risk-reducing strategies.
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Affiliation(s)
- Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
| | - Megan Rice
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Shelley S. Tworoger
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America
| | - Bernard A. Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - A. Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Rulla M. Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Amit D. Joshi
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Sara Lindstrom
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Jing Qian
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, Massachusetts, United States of America
| | - Graham A. Colditz
- Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Walter C. Willett
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Susan E. Hankinson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, Massachusetts, United States of America
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Smilg JS. Are you dense? The implications and imaging of the dense breast. SA J Radiol 2018; 22:1356. [PMID: 31754514 PMCID: PMC6837771 DOI: 10.4102/sajr.v22i2.1356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 06/18/2018] [Indexed: 11/21/2022] Open
Abstract
Mammography relies on a visual interpretation of imaging results that is often confounded by dense breast tissue. Dense tissue affects the ability and accuracy with which the radiologist is able to detect cancer. Dense tissue may mask the presence of a breast cancer, and breast density is well recognised as an independent risk factor for the development of breast cancer. In the dense breast, detected cancers tend to be larger, more often lymph node positive and of a higher stage than those diagnosed in fatty tissue. The incidence of tumour multifocality and multicentricity is higher, decreasing the chances for breast conserving treatment. The literature convincingly supports the use of supplemental imaging modalities in women who present with increased breast density. There are clear advantages and disadvantages to each set of diagnostic imaging tests. However, there is no simple, cost-effective solution for women with dense breasts to obtain a definitive detection status through imaging. Suggestions are put forward as to what supplemental imaging choices should be included for the imaging of the dense breast with reference to the current South African setting. Use of supplemental screening modalities should be tailored to individual risk assessment. In a resource-constrained environment, international recommendations may need to be adjusted.
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Affiliation(s)
- Jacqueline S Smilg
- Evolutionary Studies Institute, University of the Witwatersrand, South Africa.,Department of Radiation Sciences, University of the Witwatersrand, South Africa.,Department of Diagnostic Radiology, Charlotte Maxeke Johannesburg Academic Hospital, Johannesburg, South Africa
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38
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Wunderle M, Olmes G, Nabieva N, Häberle L, Jud SM, Hein A, Rauh C, Hack CC, Erber R, Ekici AB, Hoyer J, Vasileiou G, Kraus C, Reis A, Hartmann A, Schulz-Wendtland R, Lux MP, Beckmann MW, Fasching PA. Risk, Prediction and Prevention of Hereditary Breast Cancer - Large-Scale Genomic Studies in Times of Big and Smart Data. Geburtshilfe Frauenheilkd 2018; 78:481-492. [PMID: 29880983 PMCID: PMC5986564 DOI: 10.1055/a-0603-4350] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 04/09/2018] [Accepted: 04/09/2018] [Indexed: 12/24/2022] Open
Abstract
Over the last two decades genetic testing for mutations in
BRCA1
and
BRCA2
has become standard of care for women and men who are at familial risk for breast or ovarian cancer. Currently, genetic testing more often also includes so-called panel genes, which are assumed to be moderate-risk genes for breast cancer. Recently, new large-scale studies provided more information about the risk estimation of those genes. The utilization of information on panel genes with regard to their association with the individual breast cancer risk might become part of future clinical practice. Furthermore, large efforts have been made to understand the influence of common genetic variants with a low impact on breast cancer risk. For this purpose, almost 450 000 individuals have been genotyped for almost 500 000 genetic variants in the OncoArray project. Based on first results it can be assumed that – together with previously identified common variants – more than 170 breast cancer risk single nucleotide polymorphisms can explain up to 18% of familial breast cancer risk. The knowledge about genetic and non-genetic risk factors and its implementation in clinical practice could especially be of use for individualized prevention. This includes an individualized risk prediction as well as the individualized selection of screening methods regarding imaging and possible lifestyle interventions. The aim of this review is to summarize the most recent developments in this area and to provide an overview on breast cancer risk genes, risk prediction models and their utilization for the individual patient.
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Affiliation(s)
- Marius Wunderle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Gregor Olmes
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Naiba Nabieva
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Lothar Häberle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany.,Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Sebastian M Jud
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Alexander Hein
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Claudia Rauh
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Carolin C Hack
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Juliane Hoyer
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Georgia Vasileiou
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Cornelia Kraus
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - André Reis
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Rüdiger Schulz-Wendtland
- Institute of Diagnostic Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Michael P Lux
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
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39
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Feld SI, Fan J, Yuan M, Wu Y, Woo KM, Alexandridis R, Burnside ES. Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:81-90. [PMID: 29888046 PMCID: PMC5961791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
While screening and treatment have sharply reduced breast cancer mortality in the past 50 years, more targeted diagnostic testing may improve the accuracy and efficiency of care. Our retrospective, age-matched, case-control study evaluated the differential value of mammography and genetic variants to predict breast cancer depending on patient age. We developed predictive models using logistic regression with group lasso comparing (1) diagnostic mammography findings, (2) selected genetic variants, and (3) a combination of both. For women older than 60, mammography features were most predictive of breast cancer risk (imaging AUC = 0.74, genetic variants AUC = 0.54, combined AUC = 0.71). For women younger than 60 there is additional benefit to obtaining genetic testing (imaging AUC = 0.69, genetic variants AUC = 0.70, combined AUC = 0.72). In summary, genetic testing supplements mammography in younger women while mammography appears sufficient in older women for breast cancer risk prediction.
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Affiliation(s)
- Shara I Feld
- University of Wisconsin Department of Radiology, Madison, WI
| | - Jun Fan
- Hong Kong Baptist University Department of Mathematics, Hong Kong, China
| | - Ming Yuan
- Columbia University Department of Statistics, New York, NY
| | - Yirong Wu
- University of Wisconsin Department of Radiology, Madison, WI
| | - Kaitlin M Woo
- University of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI
| | - Roxana Alexandridis
- University of Wisconsin Department of Biostatistics and Medical Informatics, Madison, WI
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40
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Borgquist S, Hall P, Lipkus I, Garber JE. Towards Prevention of Breast Cancer: What Are the Clinical Challenges? Cancer Prev Res (Phila) 2018; 11:255-264. [PMID: 29661853 DOI: 10.1158/1940-6207.capr-16-0254] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 12/28/2016] [Accepted: 02/21/2018] [Indexed: 11/16/2022]
Abstract
The dramatic increase in breast cancer incidence compels a paradigm shift in our preventive efforts. There are several barriers to overcome before prevention becomes an established part of breast cancer management. The objective of this review is to identify the clinical challenges for improved breast cancer prevention and discuss current knowledge on breast cancer risk assessment methods, risk communication, ethics, and interventional efforts with the aim of covering the aspects relevant for a breast cancer prevention trial. Herein, the following five areas are discussed: (i) Adequate tools for identification of women at high risk of breast cancer suggestively entitled Prevent! Online. (ii) Consensus on the definition of high risk, which is regarded as mandatory for all risk communication and potential prophylactic interventions. (iii) Risk perception and communication regarding risk information. (iv) Potential ethical concerns relevant for future breast cancer prevention programs. (v) Risk-reducing programs involving multileveled prevention depending on identified risk. Taken together, devoted efforts from both policy makers and health care providers are warranted to improve risk assessment and risk counseling in women at risk for breast cancer to optimize the prevention of breast cancer. Cancer Prev Res; 11(5); 255-64. ©2018 AACR.
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Affiliation(s)
- Signe Borgquist
- Lund University, Department of Oncology and Pathology, Skåne University Hospital, Lund, Sweden. .,Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Isaac Lipkus
- Duke University School of Nursing, Durham, North Carolina
| | - Judy E Garber
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
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41
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Dudbridge F, Pashayan N, Yang J. Predictive accuracy of combined genetic and environmental risk scores. Genet Epidemiol 2018; 42:4-19. [PMID: 29178508 PMCID: PMC5847122 DOI: 10.1002/gepi.22092] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 07/19/2017] [Accepted: 09/27/2017] [Indexed: 01/19/2023]
Abstract
The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.
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Affiliation(s)
- Frank Dudbridge
- Department of Health SciencesUniversity of LeicesterLeicesterUnited Kingdom
- Department of Non‐Communicable Disease EpidemiologyLondon School of Hygiene and Tropical MedicineLondonUnited Kingdom
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUnited Kingdom
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - Nora Pashayan
- Department of Applied Health ResearchUniversity College LondonLondonUnited Kingdom
| | - Jian Yang
- Institute for Molecular BioscienceUniversity of QueenslandBrisbaneQueenslandAustralia
- Queensland Brain InstituteUniversity of QueenslandBrisbaneQueenslandAustralia
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42
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Spjuth O, Karlsson A, Clements M, Humphreys K, Ivansson E, Dowling J, Eklund M, Jauhiainen A, Czene K, Grönberg H, Sparén P, Wiklund F, Cheddad A, Pálsdóttir Þ, Rantalainen M, Abrahamsson L, Laure E, Litton JE, Palmgren J. E-Science technologies in a workflow for personalized medicine using cancer screening as a case study. J Am Med Inform Assoc 2018; 24:950-957. [PMID: 28444384 PMCID: PMC7651972 DOI: 10.1093/jamia/ocx038] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 03/17/2017] [Indexed: 12/25/2022] Open
Abstract
Objective We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings. Materials and Methods We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences. Results The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform. Discussion and Conclusion E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.
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Affiliation(s)
- Ola Spjuth
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala Universitet, Uppsala, Sweden
| | - Andreas Karlsson
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Mark Clements
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Emma Ivansson
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Jim Dowling
- School of Information and Communication Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Alexandra Jauhiainen
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Early Clinical Biometrics, AstraZeneca AB R&D, Gothenburg, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Pär Sparén
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Abbas Cheddad
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Department of Computer Science and Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
| | - Þorgerður Pálsdóttir
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Nordic Information for Action e-Science Center, Stockholm, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Linda Abrahamsson
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Erwin Laure
- School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jan-Eric Litton
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium, Graz, Austria
| | - Juni Palmgren
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
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43
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Wong XY, Groothuis-Oudshoorn CG, Tan CS, van Til JA, Hartman M, Chong KJ, IJzerman MJ, Wee HL. Women's preferences, willingness-to-pay, and predicted uptake for single-nucleotide polymorphism gene testing to guide personalized breast cancer screening strategies: a discrete choice experiment. Patient Prefer Adherence 2018; 12:1837-1852. [PMID: 30271127 PMCID: PMC6154732 DOI: 10.2147/ppa.s171348] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Single-nucleotide polymorphism (SNP) gene test is a potential tool for improving the accuracy of breast cancer risk prediction. We seek to measure women's preferences and marginal willingness-to-pay (mWTP) for this new technology. MATERIALS AND METHODS We administered a discrete choice experiment (DCE) to English-speaking Singaporean women aged 40-69 years without any history of breast cancer, enrolled via door-to-door recruitment with quota sampling by age and ethnicity. DCE attributes comprise: 1) sample type (buccal swab and dried blood spot), 2) person conducting pretest discussion (specialist doctor, non-specialist doctor, and nurse educator), 3) test location (private family clinic, public primary-care clinic, and hospital), and 4) out-of-pocket cost (S$50, S$175, and S$300). Mixed logit model was used to estimate the effect of attribute levels on women's preferences and mWTP. Interactions between significant attributes and respondent characteristics were investigated. Predicted uptake rates for various gene testing scenarios were studied. RESULTS A total of 300 women aged 52.6±7.6 years completed the survey (100 Chinese, Malay, and Indian women, respectively). Sample type (P=0.046), person conducting pretest discussion, and out-of-pocket cost (P<0.001) are significantly associated with going for SNP gene testing. Women with higher income and education levels are more willing to pay higher prices for the test. Preferences in terms of mWTP across ethnic groups appear similar, but Chinese women have greater preference heterogeneity for the attributes. Predicted uptake for a feasible scenario consisting of buccal swab, pretest discussion with nurse educator at the hospital costing S$50 is 60.5%. Only 3.3% of women always opted out of the SNP gene test in real life. Reasons include high cost, poor awareness, and indifference toward test results. CONCLUSION SNP gene testing may be tailored according to individual preferences to encourage uptake. Future research should focus on outcomes and cost-effectiveness of personalized breast cancer screening using SNP gene testing.
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Affiliation(s)
- Xin Yi Wong
- Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Republic of Singapore,
| | - Catharina Gm Groothuis-Oudshoorn
- Department of Health Technology and Services Research, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Chuen Seng Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Republic of Singapore,
| | - Janine A van Til
- Department of Health Technology and Services Research, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Republic of Singapore,
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Kok Joon Chong
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Maarten J IJzerman
- Department of Health Technology and Services Research, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, The Netherlands
- Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
- Victorian Comprehensive Cancer Centre, Melbourne, Australia
| | - Hwee-Lin Wee
- Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Republic of Singapore,
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Republic of Singapore,
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44
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Farina NH, Ramsey JE, Cuke ME, Ahern TP, Shirley DJ, Stein JL, Stein GS, Lian JB, Wood ME. Development of a predictive miRNA signature for breast cancer risk among high-risk women. Oncotarget 2017; 8:112170-112183. [PMID: 29348816 PMCID: PMC5762501 DOI: 10.18632/oncotarget.22750] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 10/13/2017] [Indexed: 12/24/2022] Open
Abstract
Significant limitations exist in our ability to predict breast cancer risk at the individual level. Circulating microRNAs (C-miRNAs) have emerged as measurable biomarkers (liquid biopsies) for cancer detection. We evaluated the ability of C-miRNAs to identify women most likely to develop breast cancer by profiling miRNA from serum obtained long before diagnosis. 24 breast cancer cases and controls (matched for risk and age) were identified from women enrolled in the High-Risk Breast Program at the UVM Cancer Center. Isolated RNA from serum was profiled for over 2500 human miRNAs. The miRNA expression data were input into a stepwise linear regression model to discover a multivariable miRNA signature that predicts long-term risk of breast cancer. 25 candidate miRNAs were identified that individually classified cases and controls based on statistical methodologies. A refined 6-miRNA risk-signature was discovered following regression modeling that distinguishes cases and controls (AUC0.896, CI 0.804-0.988) in this cohort. A functional relationship between miRNAs that cluster together when cases are contrasted against controls was suggested and confirmed by pathway analyses. The discovered 6 miRNA risk-signature can discriminate high-risk women who ultimately develop breast cancer from those who remain cancer-free, improving current risk assessment models. Future studies will focus on functional analysis of the miRNAs in this signature and testing in larger cohorts. We propose that the combined signature is highly significant for predicting cancer risk, and worthy of further screening in larger, independent clinical cohorts.
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Affiliation(s)
- Nicholas H Farina
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Department of Biochemistry, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
| | - Jon E Ramsey
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Department of Biochemistry, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
| | - Melissa E Cuke
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Division of Hematology and Oncology, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
| | - Thomas P Ahern
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Department of Biochemistry, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Department of Surgery, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
| | - David J Shirley
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Department of Microbiology and Molecular Genetics, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
| | - Janet L Stein
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Department of Biochemistry, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
| | - Gary S Stein
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Department of Biochemistry, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Division of Hematology and Oncology, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
| | - Jane B Lian
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Department of Biochemistry, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
| | - Marie E Wood
- University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA.,Division of Hematology and Oncology, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA
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45
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Giordano L, Gallo F, Petracci E, Chiorino G, Segnan N. The ANDROMEDA prospective cohort study: predictive value of combined criteria to tailor breast cancer screening and new opportunities from circulating markers: study protocol. BMC Cancer 2017; 17:785. [PMID: 29166878 PMCID: PMC5700540 DOI: 10.1186/s12885-017-3784-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 11/13/2017] [Indexed: 11/10/2022] Open
Abstract
Background In recent years growing interest has been posed on alternative ways to screen women for breast cancer involving different imaging techniques or adjusting screening interval by breast cancer risk estimates. A new research area is studying circulating microRNAs as molecular biomarkers potentially useful for non invasive early detection together with the analysis of single-nucleotide polymorphisms (SNPs). The Andromeda study is a prospective cohort study on women attending breast cancer screening in a northern Italian area. The aims of the study are: 1) to define appropriate women risk-based stratifications for personalized screening considering different factors (reproductive, family and biopsy history, breast density, lifestyle habits); 2) to evaluate the diagnostic accuracy of selected circulating microRNAs in a case-control study nested within the above mentioned cohort. Methods About 21,000 women aged 46–67 years compliant to screening mammography are expected to be enrolled. At enrolment, information on well-known breast cancer risk factors and life-styles habits are collected through self-admistered questionnaires. Information on breast density and anthropometric measurements (height, weight, body composition, and waist circumference) are recorded. In addition, women are requested to provide a blood sample for serum, plasma and buffy-coat storing for subsequent molecular analyses within the nested case-control study. This investigation will be performed on approximately 233 cases (screen-detected) and 699 matched controls to evaluate SNPs and circulating microRNAs. The whole study will last three years and the cohort will be followed up for ten years to observe the onset of new breast cancer cases. Discussion Nowadays women undergo the same screening protocol, independently of their breast density and their individual risk to develop breast cancer. New criteria to better stratify women in risk groups could enable the screening strategies to target high-risk women while reducing interventions in those at low-risk. In this frame the present study will contribute in identifying the feasibility and impact of implementing personalized breast cancer screening. Trial registration NCT02618538 (retrospectively registered on 27–11-2015.) Electronic supplementary material The online version of this article (10.1186/s12885-017-3784-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Livia Giordano
- Centre for Cancer Prevention (CPO Piemonte), Unit of Epidemiology and Screening, AOU Città della Salute e della Scienza of Turin, Via Cavour 31, 10123, Turin, Italy.
| | - Federica Gallo
- Centre for Cancer Prevention (CPO Piemonte), Unit of Epidemiology and Screening, AOU Città della Salute e della Scienza of Turin, Via Cavour 31, 10123, Turin, Italy
| | - Elisabetta Petracci
- Unity of Biostatistics and Clinical Trials, Istituto Scientifico Romagnolo per lo Studio e Cura dei Tumori, IRCCS, Meldola, Italy
| | | | - Nereo Segnan
- Centre for Cancer Prevention (CPO Piemonte), Unit of Epidemiology and Screening, AOU Città della Salute e della Scienza of Turin, Via Cavour 31, 10123, Turin, Italy
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46
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Wong XY, Chong KJ, van Til JA, Wee HL. A qualitative study on Singaporean women's views towards breast cancer screening and Single Nucleotide Polymorphisms (SNPs) gene testing to guide personalised screening strategies. BMC Cancer 2017; 17:776. [PMID: 29162038 PMCID: PMC5697412 DOI: 10.1186/s12885-017-3781-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 11/13/2017] [Indexed: 12/17/2022] Open
Abstract
Background Breast cancer is the top cancer by incidence and mortality in Singaporean women. Mammography is by far its best screening tool, but current recommended age and interval may not yield the most benefit. Recent studies have demonstrated the potential of single nucleotide polymorphisms (SNPs) to improve discriminatory accuracy of breast cancer risk assessment models. This study was conducted to understand Singaporean women’s views towards breast cancer screening and SNPs gene testing to guide personalised screening strategies. Methods Focus group discussions were conducted among English-speaking women (n = 27) between 40 to 65 years old, both current and lapsed mammogram users. Women were divided into four groups based on age and mammogram usage. Discussions about breast cancer and screening experience, as well as perception and attitude towards SNPs gene testing were conducted by an experienced moderator. Women were also asked for factors that will influence their uptake of the test. Transcripts were analysed using thematic analysis to captured similarities and differences in views expressed. Results Barriers to repeat mammogram attendance include laziness to make appointment and painful and uncomfortable screening process. However, the underlying reason may be low perceived susceptibility to breast cancer. Facilitators to repeat mammogram attendance include ease of making appointment and timely reminders. Women were generally receptive towards SNPs gene testing, but required information on accuracy, cost, invasiveness, and side effects before they decide whether to go for it. Other factors include waiting time for results and frequency interval. On average, women gave a rating of 7.5 (range 5 to 10) when asked how likely they will go for the test. Conclusion Addressing concerns such as pain and discomfort during mammogram, providing timely reminders and debunking breast cancer myths can help to improve screening uptake. Women demonstrated a spectrum of responses towards a novel test like SNPs gene testing, but need more information to make an informed decision. Future public health education on predictive genetic testing should adequately address both benefits and risks. Findings from this study is used to inform a discrete choice experiment to empirically quantify women preferences and willingness-to-pay for SNPs gene testing. Electronic supplementary material The online version of this article (10.1186/s12885-017-3781-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xin Yi Wong
- Department of Pharmacy, Faculty of Science, National University of Singapore, Block S4A Level 3, 18 Science Drive 4, Singapore, 117543, Republic of Singapore
| | - Kok Joon Chong
- Department of Planning and Development, Regional Health System Planning Office, National University Health System, 1E Kent Ridge Road, Singapore, 119228, Republic of Singapore
| | - Janine A van Til
- Department of Health Technology & Services Research, School for Management & Governance, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Hwee Lin Wee
- Department of Pharmacy, Faculty of Science, National University of Singapore, Block S4A Level 3, 18 Science Drive 4, Singapore, 117543, Republic of Singapore. .,Saw Swee Hock School of Public Health, National University of Singapore, 12 Science Drive 2, #10-01, Singapore, 117549, Republic of Singapore.
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47
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Esserman LJ. The WISDOM Study: breaking the deadlock in the breast cancer screening debate. NPJ Breast Cancer 2017; 3:34. [PMID: 28944288 PMCID: PMC5597574 DOI: 10.1038/s41523-017-0035-5] [Citation(s) in RCA: 166] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 05/09/2017] [Accepted: 07/11/2017] [Indexed: 11/17/2022] Open
Abstract
There are few medical issues that have generated as much controversy as screening for breast cancer. In science, controversy often stimulates innovation; however, the intensely divisive debate over mammographic screening has had the opposite effect and has stifled progress. The same two questions—whether it is better to screen annually or bi-annually, and whether women are best served by beginning screening at 40 or some later age—have been debated for 20 years, based on data generated three to four decades ago. The controversy has continued largely because our current approach to screening assumes all women have the same risk for the same type of breast cancer. In fact, we now know that cancers vary tremendously in terms of timing of onset, rate of growth, and probability of metastasis. In an era of personalized medicine, we have the opportunity to investigate tailored screening based on a woman’s specific risk for a specific tumor type, generating new data that can inform best practices rather than to continue the rancorous debate. It is time to move from debate to wisdom by asking new questions and generating new knowledge. The WISDOM Study (Women Informed to Screen Depending On Measures of risk) is a pragmatic, adaptive, randomized clinical trial comparing a comprehensive risk-based, or personalized approach to traditional annual breast cancer screening. The multicenter trial will enroll 100,000 women, powered for a primary endpoint of non-inferiority with respect to the number of late stage cancers detected. The trial will determine whether screening based on personalized risk is as safe, less morbid, preferred by women, will facilitate prevention for those most likely to benefit, and adapt as we learn who is at risk for what kind of cancer. Funded by the Patient Centered Outcomes Research Institute, WISDOM is the product of a multi-year stakeholder engagement process that has brought together consumers, advocates, primary care physicians, specialists, policy makers, technology companies and payers to help break the deadlock in this debate and advance towards a new, dynamic approach to breast cancer screening.
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Affiliation(s)
- Laura J Esserman
- Department of Surgery and Radiology, University of California, San Francisco, CA USA
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48
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Rice MS, Rosner BA, Tamimi RM. Percent mammographic density prediction: development of a model in the nurses' health studies. Cancer Causes Control 2017; 28:677-684. [PMID: 28478536 DOI: 10.1007/s10552-017-0898-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 04/22/2017] [Indexed: 11/28/2022]
Abstract
PURPOSE To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses' Health Studies' nested breast cancer case-control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk. METHODS Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r 2 in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk. RESULTS In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase = 1.09 for both). CONCLUSION These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.
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Affiliation(s)
- Megan S Rice
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Bartlett 9, Boston, MA, 02114, USA.
| | - Bernard A Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02114, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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49
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Petridou E, Kibiro M, Gladwell C, Malcolm P, Toms A, Juette A, Borga M, Dahlqvist Leinhard O, Romu T, Kasmai B, Denton E. Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation. Clin Radiol 2017; 72:565-572. [PMID: 28363661 DOI: 10.1016/j.crad.2017.02.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 10/28/2016] [Accepted: 02/09/2017] [Indexed: 11/29/2022]
Abstract
AIM To compare magnetic resonance imaging (MRI)-derived breast density measurements using automatic segmentation algorithms with radiologist estimations using the Breast Imaging Reporting and Data Systems (BI-RADS) density classification. MATERIALS AND METHODS Forty women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fat-water separated MRI images derived from a two-point Dixon technique, phase-sensitive reconstruction, and atlas-based segmentation were obtained before and after intravenous contrast medium administration. Breast density was assessed using software from Advanced MR Analytics (AMRA), Linköping, Sweden, with results compared to the widely used four-quartile quantitative BI-RADS scale. RESULTS The proportion of glandular tissue in the breast on MRI was derived from the AMRA sequence. The mean unenhanced breast density was 0.31±0.22 (mean±SD; left) and 0.29±0.21 (right). Mean breast density on post-contrast images was 0.32±0.19 (left) and 0.32±0.2 (right). There was "almost perfect" correlation between pre- and post-contrast breast density quantification: Spearman's correlation rho=0.98 (95% confidence intervals [CI]: 0.97-0.99; left) and rho=0.99 (95% CI: 0.98-0.99; right). The 95% limits of agreement were -0.11-0.08 (left) and -0.08-0.03 (right). Interobserver reliability for BI-RADS was "substantial": weighted Kappa k=0.8 (95% CI: 0.74-0.87). The Spearman correlation coefficient between BI-RADS and MRI breast density was rho=0.73 (95% CI: 0.60-0.82; left) and rho=0.75 (95% CI: 0.63-0.83; right) which was also "substantial". CONCLUSION The AMRA sequence provides a fully automated, reproducible, objective assessment of fibroglandular breast tissue proportion that correlates well with mammographic assessment of breast density with the added advantage of avoidance of ionising radiation.
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Affiliation(s)
- E Petridou
- Department of Radiology, Norfolk and Norwich University Hospital, UK
| | - M Kibiro
- Department of Radiology, Norfolk and Norwich University Hospital, UK
| | - C Gladwell
- Department of Radiology, Norfolk and Norwich University Hospital, UK
| | - P Malcolm
- Department of Radiology, Norfolk and Norwich University Hospital, UK.
| | - A Toms
- Department of Radiology, Norfolk and Norwich University Hospital, UK
| | - A Juette
- Department of Radiology, Norfolk and Norwich University Hospital, UK
| | - M Borga
- Centre for Medical Image Science and Visualisation, Linköping University, Sweden; Department of Biomedical Engineering, Linköping University, Sweden; Advanced MR Analytics AB, Teknikringen 7, Linköping, Sweden
| | - O Dahlqvist Leinhard
- Centre for Medical Image Science and Visualisation, Linköping University, Sweden; Department of Medical and Health Sciences, Linköping University, Sweden; Advanced MR Analytics AB, Teknikringen 7, Linköping, Sweden
| | - T Romu
- Centre for Medical Image Science and Visualisation, Linköping University, Sweden; Department of Biomedical Engineering, Linköping University, Sweden
| | - B Kasmai
- Department of Radiology, Norfolk and Norwich University Hospital, UK
| | - E Denton
- Department of Radiology, Norfolk and Norwich University Hospital, UK
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50
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Eriksson M, Czene K, Pawitan Y, Leifland K, Darabi H, Hall P. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res 2017; 19:29. [PMID: 28288659 PMCID: PMC5348894 DOI: 10.1186/s13058-017-0820-y] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 03/01/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Most mammography screening programs are not individualized. To efficiently screen for breast cancer, the individual risk of the disease should be determined. We describe a model that could be used at most mammography screening units without adding substantial cost. METHODS The study was based on the Karma cohort, which included 70,877 participants. Mammograms were collected up to 3 years following the baseline mammogram. A prediction protocol was developed using mammographic density, computer-aided detection of microcalcifications and masses, use of hormone replacement therapy (HRT), family history of breast cancer, menopausal status, age, and body mass index. Relative risks were calculated using conditional logistic regression. Absolute risks were calculated using the iCARE protocol. RESULTS Comparing women at highest and lowest mammographic density yielded a fivefold higher risk of breast cancer for women at highest density. When adding microcalcifications and masses to the model, high-risk women had a nearly ninefold higher risk of breast cancer than those at lowest risk. In the full model, taking HRT use, family history of breast cancer, and menopausal status into consideration, the AUC reached 0.71. CONCLUSIONS Measures of mammographic features and information on HRT use, family history of breast cancer, and menopausal status enabled early identification of women within the mammography screening program at such a high risk of breast cancer that additional examinations are warranted. In contrast, women at low risk could probably be screened less intensively.
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Affiliation(s)
- Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden.
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden
| | - Karin Leifland
- Department of Radiology, South General Hospital, 118 83, Stockholm, Sweden
| | - Hatef Darabi
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden.,Department of Oncology, South General Hospital, 118 83, Stockholm, Sweden
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