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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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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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Roberts E, van Veen EM, Byers H, Barnett-Griness O, Gronich N, Lejbkowicz F, Pinchev M, Smith MJ, Howell A, Newman WG, Woodward ER, Harkness EF, Brentnall AR, Cuzick J, Rennert G, Howell SJ, Evans DG. Breast cancer polygenic risk scores derived in White European populations are not calibrated for women of Ashkenazi Jewish descent. Genet Med 2023; 25:100846. [PMID: 37061873 DOI: 10.1016/j.gim.2023.100846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 04/17/2023] Open
Abstract
PURPOSE Polygenic risk scores (PRSs) are a major component of accurate breast cancer (BC) risk prediction but require ethnicity-specific calibration. Ashkenazi Jewish (AJ) population is assumed to be of White European (WE) origin in some commercially available PRSs despite differing effect allele frequencies (EAFs). We conducted a case-control study of WE and AJ women from the Predicting Risk of Cancer at Screening Study. The Breast Cancer in Northern Israel Study provided a separate AJ population-based case-control validation series. METHODS All women underwent Illumina OncoArray single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]) analysis. Two PRSs were assessed, SNV142 and SNV78. A total of 221 of 2243 WE women (discovery: cases = 111; controls = 110; validation: cases = 651; controls = 1772) and 221 AJ women (cases = 121; controls = 110) were included from the UK study; the Israeli series consisted of 2045 AJ women (cases = 1331; controls = 714). EAFs were obtained from the Genome Aggregation Database. RESULTS In the UK study, the mean SNV142 PRS demonstrated good calibration and discrimination in WE population, with mean PRS of 1.33 (95% CI 1.18-1.48) in cases and 1.01 (95% CI 0.89-1.13) in controls. In AJ women from Manchester, the mean PRS of 1.54 (1.38-1.70) in cases and 1.20 (1.08-1.32) in controls demonstrated good discrimination but overestimation of BC relative risk. After adjusting for EAFs for the AJ population, mean risk was corrected (mean SNV142 PRS cases = 1.30 [95% CI 1.16-1.44] and controls = 1.02 [95% CI 0.92-1.12]). This was recapitulated in the larger Israeli data set with good discrimination (area under the curve = 0.632 [95% CI 0.607-0.657] for SNV142). CONCLUSION AJ women should not be given BC relative risk predictions based on PRSs calibrated to EAFs from the WE population. PRSs need to be recalibrated using AJ-derived EAFs. A simple recalibration using the mean PRS adjustment ratio likely performs well.
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Affiliation(s)
- Eleanor Roberts
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Elke M van Veen
- Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Helen Byers
- Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Ofra Barnett-Griness
- Department of Community Medicine and Epidemiology, Carmel Medical Center, Haifa, Israel
| | - Naomi Gronich
- Department of Community Medicine and Epidemiology, Carmel Medical Center, Haifa, Israel; The Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Flavio Lejbkowicz
- Department of Community Medicine and Epidemiology, Carmel Medical Center, Haifa, Israel
| | - Mila Pinchev
- Department of Community Medicine and Epidemiology, Carmel Medical Center, Haifa, Israel
| | - Miriam J Smith
- Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Anthony Howell
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The Christie Hospital, Manchester, United Kingdom
| | - William G Newman
- Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Emma R Woodward
- Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Elaine F Harkness
- Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Division of Informatics, Imaging & Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Adam R Brentnall
- Queen Mary University of London, Centre for Cancer Prevention, Wolfson Institute of Population Health, Charterhouse Square, London, United Kingdom
| | - Jack Cuzick
- Queen Mary University of London, Centre for Cancer Prevention, Wolfson Institute of Population Health, Charterhouse Square, London, United Kingdom
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Carmel Medical Center, Haifa, Israel; The Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Sacha J Howell
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The Christie Hospital, Manchester, United Kingdom
| | - D Gareth Evans
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Manchester Breast Centre, Manchester Cancer Research Centre, The Christie Hospital, Manchester, United Kingdom.
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5
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Chiorino G, Petracci E, Sehovic E, Gregnanin I, Camussi E, Mello-Grand M, Ostano P, Riggi E, Vergini V, Russo A, Berrino E, Ortale A, Garena F, Venesio T, Gallo F, Favettini E, Frigerio A, Matullo G, Segnan N, Giordano L. Plasma microRNA ratios associated with breast cancer detection in a nested case-control study from a mammography screening cohort. Sci Rep 2023; 13:12040. [PMID: 37491482 PMCID: PMC10368693 DOI: 10.1038/s41598-023-38886-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 07/17/2023] [Indexed: 07/27/2023] Open
Abstract
Mammographic breast cancer screening is effective in reducing breast cancer mortality. Nevertheless, several limitations are known. Therefore, developing an alternative or complementary non-invasive tool capable of increasing the accuracy of the screening process is highly desirable. The objective of this study was to identify circulating microRNA (miRs) ratios associated with BC in women attending mammography screening. A nested case-control study was conducted within the ANDROMEDA cohort (women of age 46-67 attending BC screening). Pre-diagnostic plasma samples, information on life-styles and common BC risk factors were collected. Small-RNA sequencing was carried out on plasma samples from 65 cases and 66 controls. miR ratios associated with BC were selected by two-sample Wilcoxon test and lasso logistic regression. Subsequent assessment by RT-qPCR of the miRs contained in the selected miR ratios was carried out as a platform validation. To identify the most promising biomarkers, penalised logistic regression was further applied to candidate miR ratios alone, or in combination with non-molecular factors. Small-RNA sequencing yielded 20 candidate miR ratios associated with BC, which were further assessed by RT-qPCR. In the resulting model, penalised logistic regression selected seven miR ratios (miR-199a-3p_let-7a-5p, miR-26b-5p_miR-142-5p, let-7b-5p_miR-19b-3p, miR-101-3p_miR-19b-3p, miR-93-5p_miR-19b-3p, let-7a-5p_miR-22-3p and miR-21-5p_miR-23a-3p), together with body mass index (BMI), menopausal status (MS), the interaction term BMI * MS, life-style score and breast density. The ROC AUC of the model was 0.79 with a sensitivity and specificity of 71.9% and 76.6%, respectively. We identified biomarkers potentially useful for BC screening measured through a widespread and low-cost technique. This is the first study reporting circulating miRs for BC detection in a screening setting. Validation in a wider sample is warranted.Trial registration: The Andromeda prospective cohort study protocol was retrospectively registered on 27-11-2015 (NCT02618538).
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Affiliation(s)
- Giovanna Chiorino
- Cancer Genomics Lab, Fondazione Edo ed Elvo Tempia, Via Malta 3, 13900, Biella, Italy
| | - Elisabetta Petracci
- Unit of Biostatistics and Clinical Trials, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Emir Sehovic
- Cancer Genomics Lab, Fondazione Edo ed Elvo Tempia, Via Malta 3, 13900, Biella, Italy.
- Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy.
| | - Ilaria Gregnanin
- Cancer Genomics Lab, Fondazione Edo ed Elvo Tempia, Via Malta 3, 13900, Biella, Italy
| | - Elisa Camussi
- SSD Epidemiologia Screening, CPO-AOU Città della Salute e della Scienza di Torino, Via Camillo Benso Di Cavour 31, 10123, Turin, Italy
| | - Maurizia Mello-Grand
- Cancer Genomics Lab, Fondazione Edo ed Elvo Tempia, Via Malta 3, 13900, Biella, Italy
| | - Paola Ostano
- Cancer Genomics Lab, Fondazione Edo ed Elvo Tempia, Via Malta 3, 13900, Biella, Italy
| | - Emilia Riggi
- SSD Epidemiologia Screening, CPO-AOU Città della Salute e della Scienza di Torino, Via Camillo Benso Di Cavour 31, 10123, Turin, Italy
| | - Viviana Vergini
- SSD Epidemiologia Screening, CPO-AOU Città della Salute e della Scienza di Torino, Via Camillo Benso Di Cavour 31, 10123, Turin, Italy
| | - Alessia Russo
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Enrico Berrino
- Department of Medical Sciences, University of Turin, Turin, Italy
- Pathology Unit, Candiolo Cancer Institute, FPO IRCCS, Candiolo, Italy
| | - Andrea Ortale
- SSD Epidemiologia Screening, CPO-AOU Città della Salute e della Scienza di Torino, Via Camillo Benso Di Cavour 31, 10123, Turin, Italy
| | - Francesca Garena
- SSD Epidemiologia Screening, CPO-AOU Città della Salute e della Scienza di Torino, Via Camillo Benso Di Cavour 31, 10123, Turin, Italy
| | - Tiziana Venesio
- Pathology Unit, Candiolo Cancer Institute, FPO IRCCS, Candiolo, Italy
| | - Federica Gallo
- Epidemiology Unit, Staff Health Direction, Local Health Authority 1 of Cuneo, Cuneo, Italy
| | | | - Alfonso Frigerio
- SSD Epidemiologia Screening, CPO-AOU Città della Salute e della Scienza di Torino, Via Camillo Benso Di Cavour 31, 10123, Turin, Italy
| | - Giuseppe Matullo
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Nereo Segnan
- SSD Epidemiologia Screening, CPO-AOU Città della Salute e della Scienza di Torino, Via Camillo Benso Di Cavour 31, 10123, Turin, Italy.
| | - Livia Giordano
- SSD Epidemiologia Screening, CPO-AOU Città della Salute e della Scienza di Torino, Via Camillo Benso Di Cavour 31, 10123, Turin, Italy
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Roberts E, Howell S, Evans DG. Polygenic risk scores and breast cancer risk prediction. Breast 2023; 67:71-77. [PMID: 36646003 PMCID: PMC9982311 DOI: 10.1016/j.breast.2023.01.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Polygenic Risk Scores (PRS) are a major component of accurate breast cancer risk prediction and have the potential to improve screening and prevention strategies. PRS combine the risk from Single nucleotide polymorphisms (SNPs) associated with breast cancer in Genome Wide Association Studies (GWAS) and explain over 30% of breast cancer heritability. When incorporated into risk models, the more personalised risk assessment derived from PRS, help identify women at higher risk of breast cancer development and enables the implementation of stratified screening and prevention approaches. This review describes the role of PRS in breast cancer risk prediction including the development of PRS and their clinical application. We have also examined the role of PRS within more well-established risk prediction models which incorporate known classic risk factors and discuss the interaction of PRS with these factors and their capacity to predict breast cancer subtypes. Before PRS can be implemented on a population-wide scale, there are several challenges that must be addressed. Perhaps the most pressing of these is the use of PRS in women of non-White European origin, where PRS have been shown to have attenuated risk prediction both in discrimination and calibration. We discuss progress in developing and applying PRS in non-white European populations. PRS represent a significant advance in breast cancer risk prediction and their further development will undoubtedly enhance personalisation.
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Affiliation(s)
- Eleanor Roberts
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Sacha Howell
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK; Manchester Breast Centre, Manchester Cancer Research Centre, The Christie Hospital, Manchester, UK
| | - D Gareth Evans
- Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, UK; Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK; Manchester Breast Centre, Manchester Cancer Research Centre, The Christie Hospital, Manchester, UK.
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7
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Zheng Y, Dong X, Li J, Qin C, Xu Y, Wang F, Cao W, Xia C, Yu Y, Zhao L, Wu Z, Luo Z, Chen W, Li N, He J. Use of Breast Cancer Risk Factors to Identify Risk-Adapted Starting Age of Screening in China. JAMA Netw Open 2022; 5:e2241441. [PMID: 36355372 PMCID: PMC9650608 DOI: 10.1001/jamanetworkopen.2022.41441] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/14/2022] [Indexed: 11/11/2022] Open
Abstract
Importance Although current guidelines highlight the need for earlier screening in women at increased risk of breast cancer in China, data on risk-adapted starting ages of screening are limited. Objective To explore the risk-adapted starting age of breast cancer screening in China, with comprehensive consideration of breast cancer risk factors. Design, Setting, and Participants A multicenter community-based cohort study was conducted under the framework of the Cancer Screening Program in Urban China. Data were collected from January 1, 2013, to December 31, 2018, for unscreened community-dwelling women aged 40 to 74 years without a history of cancer, kidney dysfunction, or severe heart, brain, or lung disease. Data analysis was performed from October 1, 2021, to August 16, 2022. Exposures Baseline characteristics associated with breast cancer, including first-degree family history of breast cancer, benign breast disease, breastfeeding, age at menarche, and body mass index. Main Outcomes and Measures Outcomes included breast cancer diagnosis and age at diagnosis. Risk-adapted starting age of screening was defined as the age at which women with different levels of breast cancer risk attained a 10-year cumulative risk level similar to women aged 50 years in the general population. Results Of the 1 549 988 women enrolled in this study, 3895 had breast cancer (median follow-up, 4.47 [IQR, 3.16-6.35] years). Participants were divided into different risk groups according to breast cancer risk scores (driven by risk factors including first-degree family history of breast cancer, benign breast disease, breastfeeding, age at menarche, and body mass index). Using the 10-year cumulative risk of breast cancer at age 50 years in the general population as a benchmark (2.65% [95% CI, 2.50%-2.76%]), the optimal starting age of screening for women with high, medium, or low risk of breast cancer was identified as 43, 48, or after 55 years, respectively. An online calculator was developed to calculate an individual's optimal starting age of screening. Conclusions and Relevance This study identifies the risk-adapted starting age of breast cancer screening based on the principle of equal management of equal risks, which may inform updates of current screening guidelines.
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Affiliation(s)
- Yadi Zheng
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuesi Dong
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Qin
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongjie Xu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fei Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Cao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Changfa Xia
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yiwen Yu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liang Zhao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zheng Wu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zilin Luo
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wanqing Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Siltari A, Lönnerbro R, Pang K, Shiranov K, Asiimwe A, Evans-Axelsson S, Franks B, Kiran A, Murtola TJ, Schalken J, Steinbeisser C, Bjartell A, Auvinen A, Smith E, N'Dow J, Plass K, Ribal M, Mottet N, Moris L, Lardas M, Van den Broeck T, Willemse PP, Gandaglia G, Campi R, Greco I, Gacci M, Serni S, Briganti A, Crosti D, Meoni M, Garzonio R, Bangma R, Roobol M, Remmers S, Tilki D, Visakorpi T, Talala K, Tammela T, van Hemelrijck M, Bayer K, Lejeune S, Taxiarchopoulou G, van Diggelen F, Senthilkumar K, Schutte S, Byrne S, Fialho L, Cardone A, Gono P, De Vetter M, Ceke K, De Meulder B, Auffray C, Balaur IA, Taibi N, Power S, Kermani NZ, van Bochove K, Cavelaars M, Moinat M, Voss E, Bernini C, Horgan D, Fullwood L, Holtorf M, Lancet D, Bernstein G, Omar I, MacLennan S, Maclennan S, Healey J, Huber J, Wirth M, Froehner M, Brenner B, Borkowetz A, Thomas C, Horn F, Reiche K, Kreux M, Josefsson A, Tandefekt DG, Hugosson J, Huisman H, Hofmacher T, Lindgren P, Andersson E, Fridhammar A, Vizcaya D, Verholen F, Zong J, Butler-Ransohoff JE, Williamson T, Chandrawansa K, Dlamini D, waldeck R, Molnar M, Bruno A, Herrera R, Jiang S, Nevedomskaya E, Fatoba S, Constantinovici N, Maass M, Torremante P, Voss M, Devecseri Z, Cuperus G, Abott T, Dau C, Papineni K, Wang-Silvanto J, Hass S, Snijder R, Doye V, Wang X, Garnham A, Lambrecht M, Wolfinger R, Rogiers S, Servan A, Lefresne F, Caseriego J, Samir M, Lawson J, Pacoe K, Robinson P, Jaton B, Bakkard D, Turunen H, Kilkku O, Pohjanjousi P, Voima O, Nevalaita L, Reich C, Araujo S, Longden-Chapman E, Burke D, Agapow P, Derkits S, Licour M, McCrea C, Payne S, Yong A, Thompson L, Lujan F, Bussmann M, Köhler I. How well do polygenic risk scores identify men at high risk for prostate cancer? Systematic review and meta-analysis. Clin Genitourin Cancer 2022; 21:316.e1-316.e11. [PMID: 36243664 DOI: 10.1016/j.clgc.2022.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Genome-wide association studies have revealed over 200 genetic susceptibility loci for prostate cancer (PCa). By combining them, polygenic risk scores (PRS) can be generated to predict risk of PCa. We summarize the published evidence and conduct meta-analyses of PRS as a predictor of PCa risk in Caucasian men. PATIENTS AND METHODS Data were extracted from 59 studies, with 16 studies including 17 separate analyses used in the main meta-analysis with a total of 20,786 cases and 69,106 controls identified through a systematic search of ten databases. Random effects meta-analysis was used to obtain pooled estimates of area under the receiver-operating characteristic curve (AUC). Meta-regression was used to assess the impact of number of single-nucleotide polymorphisms (SNPs) incorporated in PRS on AUC. Heterogeneity is expressed as I2 scores. Publication bias was evaluated using funnel plots and Egger tests. RESULTS The ability of PRS to identify men with PCa was modest (pooled AUC 0.63, 95% CI 0.62-0.64) with moderate consistency (I2 64%). Combining PRS with clinical variables increased the pooled AUC to 0.74 (0.68-0.81). Meta-regression showed only negligible increase in AUC for adding incremental SNPs. Despite moderate heterogeneity, publication bias was not evident. CONCLUSION Typically, PRS accuracy is comparable to PSA or family history with a pooled AUC value 0.63 indicating mediocre performance for PRS alone.
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Mohamed A, Fakhry S, Basha T. Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1440-1443. [PMID: 36086431 DOI: 10.1109/embc48229.2022.9872011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breast density was proven as a reliable risk factor. Deep learning models can learn subtle cues in the mammogram images. CNN models were recently shown to improve the risk discrimination in full-field mammograms. This study aims to improve risk prediction models using bilateral analysis. Bilateral analysis is the process of comparing two breasts to verify presence of anomalies. We developed a Siamese neural network to leverage the bilateral information and asymmetries between the two mammograms of the same patient. We tested our model on 271 patients and compared the results of our Siamese model against the traditional unilateral CNN model. Our results showed AUCs of 0.75 and 0.70 respectively (p = 0.0056). The Siamese model also exhibits higher sensitivity, specificity, precision, and false positive rate with values of 0.68, 0.69, 0.71, 0.31 respectively. While the CNN values were 0.61, 0.66, 0.67, 0.34 respectively. We merged both models by two techniques using pre-trained weights and weighted voting ensemble. The merging technique boosted the AUC to 0.78. The results suggest that bilateral analysis can significantly improve the risk discrimination.
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Gynecologic Cancer Risk and Genetics: Informing an Ideal Model of Gynecologic Cancer Prevention. Curr Oncol 2022; 29:4632-4646. [PMID: 35877228 PMCID: PMC9322111 DOI: 10.3390/curroncol29070368] [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: 03/04/2022] [Revised: 06/09/2022] [Accepted: 06/22/2022] [Indexed: 11/17/2022] Open
Abstract
Individuals with proven hereditary cancer syndrome (HCS) such as BRCA1 and BRCA2 have elevated rates of ovarian, breast, and other cancers. If these high-risk people can be identified before a cancer is diagnosed, risk-reducing interventions are highly effective and can be lifesaving. Despite this evidence, the vast majority of Canadians with HCS are unaware of their risk. In response to this unmet opportunity for prevention, the British Columbia Gynecologic Cancer Initiative convened a research summit “Gynecologic Cancer Prevention: Thinking Big, Thinking Differently” in Vancouver, Canada on 26 November 2021. The aim of the conference was to explore how hereditary cancer prevention via population-based genetic testing could decrease morbidity and mortality from gynecologic cancer. The summit invited local, national, and international experts to (1) discuss how genetic testing could be more broadly implemented in a Canadian system, (2) identify key research priorities in this topic and (3) outline the core essential elements required for such a program to be successful. This report summarizes the findings from this research summit, describes the current state of hereditary genetic programs in Canada, and outlines incremental steps that can be taken to improve prevention for high-risk Canadians now while developing an organized population-based hereditary cancer strategy.
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El Hadi C, Ayoub G, Bachir Y, Haykal M, Jalkh N, Kourie HR. Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer. Expert Rev Mol Diagn 2022; 22:427-438. [PMID: 35400274 DOI: 10.1080/14737159.2022.2065195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer. AREAS COVERED The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress. EXPERT OPINION In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.
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Affiliation(s)
| | - George Ayoub
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Yara Bachir
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Michèle Haykal
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Nadine Jalkh
- Medical Genetics Unit, Technology and Health division, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Hampig Raphael Kourie
- Department of Hematology-Oncology, Hotel Dieu de France University Hospital, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
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Hou C, Xu B, Hao Y, Yang D, Song H, Li J. Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women. BMC Cancer 2022; 22:374. [PMID: 35395775 PMCID: PMC8991589 DOI: 10.1186/s12885-022-09425-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/15/2022] [Indexed: 02/08/2023] Open
Abstract
Background Studies investigating breast cancer polygenic risk score (PRS) in Chinese women are scarce. The objectives of this study were to develop and validate PRSs that could be used to stratify risk for overall and subtype-specific breast cancer in Chinese women, and to evaluate the performance of a newly proposed Artificial Neural Network (ANN) based approach for PRS construction. Methods The PRSs were constructed using the dataset from a genome-wide association study (GWAS) and validated in an independent case-control study. Three approaches, including repeated logistic regression (RLR), logistic ridge regression (LRR) and ANN based approach, were used to build the PRSs for overall and subtype-specific breast cancer based on 24 selected single nucleotide polymorphisms (SNPs). Predictive performance and calibration of the PRSs were evaluated unadjusted and adjusted for Gail-2 model 5-year risk or classical breast cancer risk factors. Results The primary PRSANN and PRSLRR both showed modest predictive ability for overall breast cancer (odds ratio per interquartile range increase of the PRS in controls [IQ-OR] 1.76 vs 1.58; area under the receiver operator characteristic curve [AUC] 0.601 vs 0.598) and remained to be predictive after adjustment. Although estrogen receptor negative (ER−) breast cancer was poorly predicted by the primary PRSs, the ER− PRSs trained solely on ER− breast cancer cases saw a substantial improvement in predictions of ER− breast cancer. Conclusions The 24 SNPs based PRSs can provide additional risk information to help breast cancer risk stratification in the general population of China. The newly proposed ANN approach for PRS construction has potential to replace the traditional approaches, but more studies are needed to validate and investigate its performance. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09425-3.
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Affiliation(s)
- Can Hou
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610047, Sichuan, China.,Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Bin Xu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China
| | - Yu Hao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China
| | - Daowen Yang
- Robot Perception and Control Joint Lab, Sichuan University & Aisono, Chengdu, China
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610047, Sichuan, China. .,Med-X Center for Informatics, Sichuan University, Chengdu, China.
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China.
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Clift AK, Dodwell D, Lord S, Petrou S, Brady SM, Collins GS, Hippisley-Cox J. The current status of risk-stratified breast screening. Br J Cancer 2022; 126:533-550. [PMID: 34703006 PMCID: PMC8854575 DOI: 10.1038/s41416-021-01550-3] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/25/2021] [Accepted: 09/14/2021] [Indexed: 12/23/2022] Open
Abstract
Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.
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Affiliation(s)
- Ash Kieran Clift
- Cancer Research UK Oxford Centre, Department of Oncology, University of Oxford, Oxford, UK.
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - David Dodwell
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Simon Lord
- Department of Oncology, University of Oxford, Oxford, UK
| | - Stavros Petrou
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Julia Hippisley-Cox
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24. Prostate Cancer Prostatic Dis 2022; 25:229-237. [PMID: 34127801 PMCID: PMC8669040 DOI: 10.1038/s41391-021-00403-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/07/2021] [Accepted: 05/27/2021] [Indexed: 12/02/2022]
Abstract
BACKGROUND We previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ. MATERIALS AND METHODS Genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC. RESULTS CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings. CONCLUSION We identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.
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Hipp LE, Hulswit BB, Milliron KJ. Clinical Tools and Counseling Considerations for Breast Cancer Risk Assessment and Evaluation for Hereditary Cancer Risk. Best Pract Res Clin Obstet Gynaecol 2022; 82:12-29. [DOI: 10.1016/j.bpobgyn.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 11/28/2022]
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Cross B, Turner R, Pirmohamed M. Polygenic risk scores: An overview from bench to bedside for personalised medicine. Front Genet 2022; 13:1000667. [PMID: 36437929 PMCID: PMC9692112 DOI: 10.3389/fgene.2022.1000667] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.
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Affiliation(s)
- Benjamin Cross
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Richard Turner
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
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Hendrix N, Gulati R, Jiao B, Kader AK, Ryan ST, Etzioni R. Clarifying the Trade-Offs of Risk-Stratified Screening for Prostate Cancer: A Cost-Effectiveness Study. Am J Epidemiol 2021; 190:2064-2074. [PMID: 34023874 DOI: 10.1093/aje/kwab155] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 12/18/2022] Open
Abstract
Cancer risk prediction is necessary for precision early detection, which matches screening intensity to risk. However, practical steps for translating risk predictions to risk-stratified screening policies are not well established. We used a validated population prostate-cancer model to simulate the outcomes of strategies that increase intensity for men at high risk and reduce intensity for men at low risk. We defined risk by the Prompt Prostate Genetic Score (PGS) (Stratify Genomics, San Diego, California), a germline genetic test. We first recalibrated the model to reflect the disease incidence observed within risk strata using data from a large prevention trial where some participants were tested with Prompt PGS. We then simulated risk-stratified strategies in a population with the same risk distribution as the trial and evaluated the cost-effectiveness of risk-stratified screening versus universal (risk-agnostic) screening. Prompt PGS risk-adapted screening was more cost-effective when universal screening was conservative. Risk-stratified strategies improved outcomes at a cost of less than $100,000 per quality-adjusted life year compared with biennial screening starting at age 55 years, but risk stratification was not cost-effective compared with biennial screening starting at age 45. Heterogeneity of risk and fraction of the population within each stratum were also important determinants of cost-effectiveness.
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18
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Gao C, Polley EC, Hart SN, Huang H, Hu C, Gnanaolivu R, Lilyquist J, Boddicker NJ, Na J, Ambrosone CB, Auer PL, Bernstein L, Burnside ES, Eliassen AH, Gaudet MM, Haiman C, Hunter DJ, Jacobs EJ, John EM, Lindström S, Ma H, Neuhausen SL, Newcomb PA, O'Brien KM, Olson JE, Ong IM, Patel AV, Palmer JR, Sandler DP, Tamimi R, Taylor JA, Teras LR, Trentham-Dietz A, Vachon CM, Weinberg CR, Yao S, Weitzel JN, Goldgar DE, Domchek SM, Nathanson KL, Couch FJ, Kraft P. Risk of Breast Cancer Among Carriers of Pathogenic Variants in Breast Cancer Predisposition Genes Varies by Polygenic Risk Score. J Clin Oncol 2021; 39:2564-2573. [PMID: 34101481 PMCID: PMC8330969 DOI: 10.1200/jco.20.01992] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 02/19/2021] [Accepted: 04/20/2021] [Indexed: 12/11/2022] Open
Abstract
PURPOSE This study assessed the joint association of pathogenic variants (PVs) in breast cancer (BC) predisposition genes and polygenic risk scores (PRS) with BC in the general population. METHODS A total of 26,798 non-Hispanic white BC cases and 26,127 controls from predominately population-based studies in the Cancer Risk Estimates Related to Susceptibility consortium were evaluated for PVs in BRCA1, BRCA2, ATM, CHEK2, PALB2, BARD1, BRIP1, CDH1, and NF1. PRS based on 105 common variants were created using effect estimates from BC genome-wide association studies; the performance of an overall BC PRS and estrogen receptor-specific PRS were evaluated. The odds of BC based on the PVs and PRS were estimated using penalized logistic regression. The results were combined with age-specific incidence rates to estimate 5-year and lifetime absolute risks of BC across percentiles of PRS by PV status and first-degree family history of BC. RESULTS The estimated lifetime risks of BC among general-population noncarriers, based on 10th and 90th percentiles of PRS, were 9.1%-23.9% and 6.7%-18.2% for women with or without first-degree relatives with BC, respectively. Taking PRS into account, more than 95% of BRCA1, BRCA2, and PALB2 carriers had > 20% lifetime risks of BC, whereas, respectively, 52.5% and 69.7% of ATM and CHEK2 carriers without first-degree relatives with BC, and 78.8% and 89.9% of those with a first-degree relative with BC had > 20% risk. CONCLUSION PRS facilitates personalization of BC risk among carriers of PVs in predisposition genes. Incorporating PRS into BC risk estimation may help identify > 30% of CHEK2 and nearly half of ATM carriers below the 20% lifetime risk threshold, suggesting the addition of PRS may prevent overscreening and enable more personalized risk management approaches.
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Affiliation(s)
- Chi Gao
- Harvard T.H. Chan School of Public Health, Boston, MA
| | | | | | - Hongyan Huang
- Harvard T.H. Chan School of Public Health, Boston, MA
| | | | | | | | | | - Jie Na
- Mayo Clinic, Rochester, MN
| | | | - Paul L. Auer
- UWM Joseph J. Zilber School of Public Health, Milwaukee, WI
| | | | | | - A. Heather Eliassen
- Harvard T.H. Chan School of Public Health, Boston, MA
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Mia M. Gaudet
- Department of Population Science, American Cancer Society, Atlanta, GA
| | - Christopher Haiman
- Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - David J. Hunter
- Harvard T.H. Chan School of Public Health, Boston, MA
- University of Oxford, Oxford, United Kingdom
| | - Eric J. Jacobs
- Department of Population Science, American Cancer Society, Atlanta, GA
| | | | - Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, WA
- Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Huiyan Ma
- Beckman Research Institute of City of Hope, Duarte, CA
| | | | - Polly A. Newcomb
- Department of Epidemiology, University of Washington, Seattle, WA
- Fred Hutchinson Cancer Research Center, Seattle, WA
| | | | | | | | - Alpa V. Patel
- Department of Population Science, American Cancer Society, Atlanta, GA
| | - Julie R. Palmer
- Boston University School of Medicine and Slone Epidemiology Center, Boston, MA
| | - Dale P. Sandler
- National Institute of Environmental Health Sciences, Durham, NC
| | - Rulla Tamimi
- Population Health Sciences Department, Weill Cornell Medicine, New York, NY
| | - Jack A. Taylor
- National Institute of Environmental Health Sciences, Durham, NC
| | - Lauren R. Teras
- Department of Population Science, American Cancer Society, Atlanta, GA
| | | | | | | | - Song Yao
- Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | | | | | - Susan M. Domchek
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | | | | | - Peter Kraft
- Harvard T.H. Chan School of Public Health, Boston, MA
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Borde J, Ernst C, Wappenschmidt B, Niederacher D, Weber-Lassalle K, Schmidt G, Hauke J, Quante AS, Weber-Lassalle N, Horváth J, Pohl-Rescigno E, Arnold N, Rump A, Gehrig A, Hentschel J, Faust U, Dutrannoy V, Meindl A, Kuzyakova M, Wang-Gohrke S, Weber BHF, Sutter C, Volk AE, Giannakopoulou O, Lee A, Engel C, Schmidt MK, Antoniou AC, Schmutzler RK, Kuchenbaecker K, Hahnen E. Performance of Breast Cancer Polygenic Risk Scores in 760 Female CHEK2 Germline Mutation Carriers. J Natl Cancer Inst 2021; 113:893-899. [PMID: 33372680 PMCID: PMC8246885 DOI: 10.1093/jnci/djaa203] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 09/17/2020] [Accepted: 12/08/2020] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Genome-wide association studies suggest that the combined effects of breast cancer (BC)-associated single nucleotide polymorphisms (SNPs) can improve BC risk stratification using polygenic risk scores (PRSs). The performance of PRSs in genome-wide association studies-independent clinical cohorts is poorly studied in individuals carrying mutations in moderately penetrant BC predisposition genes such as CHEK2. METHODS A total of 760 female CHEK2 mutation carriers were included; 561 women were affected with BC, of whom 74 developed metachronous contralateral BC (mCBC). For PRS calculations, 2 SNP sets covering 77 (SNP set 1, developed for BC risk stratification in women unselected for their BRCA1/2 germline mutation status) and 88 (SNP set 2, developed for BC risk stratification in female BRCA1/2 mutation carriers) BC-associated SNPs were used. All statistical tests were 2-sided. RESULTS Both SNP sets provided concordant PRS results at the individual level (r = 0.91, P < 2.20 × 10-16). Weighted cohort Cox regression analyses revealed statistically significant associations of PRSs with the risk for first BC. For SNP set 1, a hazard ratio of 1.71 per SD of the PRS was observed (95% confidence interval = 1.36 to 2.15, P = 3.87 × 10-6). PRSs identify a subgroup of CHEK2 mutation carriers with a predicted lifetime risk for first BC that exceeds the surveillance thresholds defined by international guidelines. Association of PRS with mCBC was examined via Cox regression analysis (SNP set 1 hazard ratio = 1.23, 95% confidence interval = 0.86 to 1.78, P = .26). CONCLUSIONS PRSs may be used to personalize risk-adapted preventive measures for women with CHEK2 mutations. Larger studies are required to assess the role of PRSs in mCBC predisposition.
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Affiliation(s)
- Julika Borde
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Corinna Ernst
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Barbara Wappenschmidt
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Dieter Niederacher
- Department of Gynecology and Obstetrics, University Hospital Duesseldorf, Heinrich-Heine University Duesseldorf, Duesseldorf, Germany
| | - Konstantin Weber-Lassalle
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Gunnar Schmidt
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Jan Hauke
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Anne S Quante
- Department of Gynecology and Obstetrics, Technical University Munich, University Hospital Rechts der Isar, Munich, Germany
| | - Nana Weber-Lassalle
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Judit Horváth
- Institute for Human Genetics, University Hospital Muenster, Muenster, Germany
| | - Esther Pohl-Rescigno
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Norbert Arnold
- Institute of Clinical Molecular Biology, Department of Gynaecology and Obstetrics, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel, Germany
| | - Andreas Rump
- Institute for Clinical Genetics, Technische Universitaet Dresden, Dresden, Germany
| | - Andrea Gehrig
- Institute of Human Genetics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Julia Hentschel
- Institute of Human Genetics, University of Leipzig Hospitals and Clinics, Leipzig, Germany
| | - Ulrike Faust
- Institute of Medical Genetics and Applied Genomics, University Hospital Tuebingen, Tuebingen, Germany
| | - Véronique Dutrannoy
- Institute of Medical and Human Genetics, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Alfons Meindl
- Department of Gynecology and Obstetrics, Ludwig-Maximilians-University Munich, University Hospital Munich, Munich, Germany
| | - Maria Kuzyakova
- Institute of Human Genetics, University Medical Center, Georg August University, Goettingen, Germany
| | - Shan Wang-Gohrke
- Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
| | - Bernhard H. F Weber
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
- Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany
| | - Christian Sutter
- Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany
| | - Alexander E Volk
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Olga Giannakopoulou
- Division of Psychiatry, University College London, London, UK
- UCL Genetics Institute, University College London, London, UK
| | - Andrew Lee
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Marjanka K Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Antonis C Antoniou
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Rita K Schmutzler
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, London, UK
- UCL Genetics Institute, University College London, London, UK
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
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20
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Karunamuni RA, Huynh-Le MP, Fan CC, Thompson W, Eeles RA, Kote-Jarai Z, Muir K, Lophatananon A, Schleutker J, Pashayan N, Batra J, Grönberg H, Walsh EI, Turner EL, Lane A, Martin RM, Neal DE, Donovan JL, Hamdy FC, Nordestgaard BG, Tangen CM, MacInnis RJ, Wolk A, Albanes D, Haiman CA, Travis RC, Stanford JL, Mucci LA, West CML, Nielsen SF, Kibel AS, Wiklund F, Cussenot O, Berndt SI, Koutros S, Sørensen KD, Cybulski C, Grindedal EM, Park JY, Ingles SA, Maier C, Hamilton RJ, Rosenstein BS, Vega A, Kogevinas M, Penney KL, Teixeira MR, Brenner H, John EM, Kaneva R, Logothetis CJ, Neuhausen SL, Razack A, Newcomb LF, Gamulin M, Usmani N, Claessens F, Gago-Dominguez M, Townsend PA, Roobol MJ, Zheng W, Mills IG, Andreassen OA, Dale AM, Seibert TM. Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer. Prostate Cancer Prostatic Dis 2021; 24:532-541. [PMID: 33420416 PMCID: PMC8157993 DOI: 10.1038/s41391-020-00311-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/10/2020] [Accepted: 12/04/2020] [Indexed: 01/29/2023]
Abstract
BACKGROUND Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). MATERIALS AND METHOD 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. RESULTS 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. CONCLUSIONS Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
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Affiliation(s)
- Roshan A Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
| | | | - Chun C Fan
- Center for Human Development, University of California San Diego, La Jolla, CA, USA
| | - Wesley Thompson
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Rosalind A Eeles
- The Institute of Cancer Research, London, SM2 5NG, UK
- Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | | | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
- Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Johanna Schleutker
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, PO Box 52, 20521, Turku, Finland
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London, WC1E 7HB, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, 4059, Australia
- Translational Research Institute, Brisbane, QLD, 4102, Australia
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Eleanor I Walsh
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Emma L Turner
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Athene Lane
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Richard M Martin
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - David E Neal
- Nuffield Department of Surgical Sciences, University of Oxford, Room 6603, Level 6, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
- Department of Oncology, University of Cambridge, Box 279, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK
| | - Jenny L Donovan
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Room 6603, Level 6, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
- Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC, 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
| | - Alicja Wolk
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, 75185, Uppsala, Sweden
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Catharine M L West
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, The Christie Hospital NHS Foundation Trust, Manchester, M13 9PL, UK
| | - Sune F Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
| | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Olivier Cussenot
- Sorbonne Universite, GRC n°5, AP-HP, Tenon Hospital, 4 rue de la Chine, F-75020, Paris, France
- CeRePP, Tenon Hospital, F-75020, Paris, France
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Karina Dalsgaard Sørensen
- Department of Molecular Medicine, Aarhus University Hospital, Palle Juul-Jensen Boulevard 99, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, DK-8200, Aarhus, Denmark
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115, Szczecin, Poland
| | - Eli Marie Grindedal
- Department of Medical Genetics, Oslo University Hospital, 0424, Oslo, Norway
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA
| | - Sue A Ingles
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
| | - Christiane Maier
- Humangenetik Tuebingen, Paul-Ehrlich-Str 23, D-72076, Tuebingen, Germany
| | - Robert J Hamilton
- Dept. of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON, M5G 2M9, Canada
- Dept. of Surgery (Urology), University of Toronto, Toronto, ON, Canada
| | - Barry S Rosenstein
- Department of Radiation Oncology and Department of Genetics and Genomic Sciences, Box 1236, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029-5674, USA
| | - Ana Vega
- Fundación Pública Galega Medicina Xenómica, Santiago de Compostela, 15706, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, 15706, Spain
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Santiago De Compostela, Spain
| | - Manolis Kogevinas
- ISGlobal, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Kathryn L Penney
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, 02184, USA
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), 4200-072, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313, Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), 4200-072, Porto, Portugal
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Esther M John
- Departments of Epidemiology & Population Health and of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Radka Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, 2 Zdrave Str., 1431, Sofia, Bulgaria
| | - Christopher J Logothetis
- The University of Texas M. D. Anderson Cancer Center, Department of Genitourinary Medical Oncology, 1515 Holcombe Blvd., Houston, TX, 77030, USA
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of the City of Hope, 1500 East Duarte Road, Duarte, CA, 91010, USA
| | - Azad Razack
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Lisa F Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA
- Department of Urology, University of Washington, 1959 NE Pacific Street, Box 356510, Seattle, WA, 98195, USA
| | - Marija Gamulin
- Division of Medical Oncology, Urogenital Unit, Department of Oncology, University Hospital Centre Zagreb, University of Zagreb, School of Medicine, 10000, Zagreb, Croatia
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, 11560 University Avenue, Edmonton, AB, T6G 1Z2, Canada
- Division of Radiation Oncology, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB, T6G 1Z2, Canada
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, KU, Leuven, BE-3000, Belgium
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, Servicio Galego de Saúde, SERGAS, 15706, Santiago de Compostela, Spain
- University of California San Diego, Moores Cancer Center, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, 92093-0012, USA
| | - Paul A Townsend
- Division of Cancer Sciences, Manchester Cancer Research Centre, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Health Innovation Manchester, Univeristy of Manchester, M13 9WL, Manchester, UK
| | - Monique J Roobol
- Department of Urology, Erasmus University Medical Center, 3015 CE, Rotterdam, The Netherlands
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 800, Nashville, TN, 37232, USA
| | - Ian G Mills
- Center for Cancer Research and Cell Biology, Queen's University of Belfast, Belfast, UK
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Tyler M Seibert
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
- Department of Radiology, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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21
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Sud A, Turnbull C, Houlston R. Will polygenic risk scores for cancer ever be clinically useful? NPJ Precis Oncol 2021; 5:40. [PMID: 34021222 PMCID: PMC8139954 DOI: 10.1038/s41698-021-00176-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/05/2021] [Indexed: 02/07/2023] Open
Affiliation(s)
- Amit Sud
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
| | - Clare Turnbull
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Richard Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
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22
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Allemailem KS, Almatroudi A, Alrumaihi F, Makki Almansour N, Aldakheel FM, Rather RA, Afroze D, Rah B. Single nucleotide polymorphisms (SNPs) in prostate cancer: its implications in diagnostics and therapeutics. Am J Transl Res 2021; 13:3868-3889. [PMID: 34017579 PMCID: PMC8129253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 03/09/2021] [Indexed: 06/12/2023]
Abstract
Prostate cancer is one of the most frequently diagnosed malignancies in developed countries and approximately 248,530 new cases of prostate cancer are likely to be diagnosed in the United States in 2021. During the late 1990s and 2000s, the prostate cancer-related death rate has decreased by 4% per year on average because of advancements in prostate-specific antigen (PSA) testing. However, the non-specificity of PSA to distinguish between benign and malignant forms of cancer is a major concern in the management of prostate cancer. Despite other risk factors in the pathogenesis of prostate cancer, recent advancement in molecular genetics suggests that genetic heredity plays a crucial role in prostate carcinogenesis. Approximately, 60% of heritability and more than 100 well-recognized single-nucleotide-polymorphisms (SNPs) have been found to be associated with prostate cancer and constitute a major risk factor in the development of prostate cancer. Recent findings revealed that a low to moderate effect on the progression of prostate cancer of individual SNPs was observed compared to a strong progressive effect when SNPs were in combination. Here, in this review, we made an attempt to critically analyze the role of SNPs and associated genes in the development of prostate cancer and their implications in diagnostics and therapeutics. A better understanding of the role of SNPs in prostate cancer susceptibility may improve risk prediction, enhance fine-mapping, and furnish new insights into the underlying pathophysiology of prostate cancer.
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Affiliation(s)
- Khaled S Allemailem
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim UniversityBuraydah, Saudi Arabia
| | - Ahmad Almatroudi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim UniversityBuraydah, Saudi Arabia
| | - Faris Alrumaihi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim UniversityBuraydah, Saudi Arabia
| | - Nahlah Makki Almansour
- Department of Biology, College of Science, University of Hafr Al BatinHafr Al Batin, Saudi Arabia
| | - Fahad M Aldakheel
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud UniversityRiyadh, Saudi Arabia
- Prince Sattam Chair for Epidemiology and Public Health Research, College of Medicine, King Saud UniversityRiyadh, Saudi Arabia
| | - Rafiq Ahmad Rather
- Advanced Centre for Human Genetics, Sher-i-Kashmir Institute of Medical ScienceSrinagar, Jammu and Kashmir, India
| | - Dil Afroze
- Advanced Centre for Human Genetics, Sher-i-Kashmir Institute of Medical ScienceSrinagar, Jammu and Kashmir, India
| | - Bilal Rah
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim UniversityBuraydah, Saudi Arabia
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23
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Mbuya-Bienge C, Pashayan N, Brooks JD, Dorval M, Chiquette J, Eloy L, Turgeon A, Lambert-Côté L, Paquette JS, Lévesque E, Hagan J, Walker MJ, Lapointe J, Dalpé G, Granados Moreno P, Blackmore K, Wolfson M, Joly Y, Broeders M, Knoppers BM, Chiarelli AM, Simard J, Nabi H. Women's Views on Multifactorial Breast Cancer Risk Assessment and Risk-Stratified Screening: A Population-Based Survey from Four Provinces in Canada. J Pers Med 2021; 11:jpm11020095. [PMID: 33540785 PMCID: PMC7912955 DOI: 10.3390/jpm11020095] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 12/03/2022] Open
Abstract
Risk-stratified screening for breast cancer (BC) is increasingly considered as a promising approach. However, its implementation is challenging and needs to be acceptable to women. We examined Canadian women’s attitudes towards, comfort level about, and willingness to take part in BC risk-stratified screening. We conducted an online survey in women aged 30 to 69 years in four Canadian provinces. In total, 4293 women completed the questionnaire (response rate of 63%). The majority of women (63.5% to 72.8%) expressed favorable attitudes towards BC risk-stratified screening. Most women reported that they would be comfortable providing personal and genetic information for BC risk assessment (61.5% to 67.4%) and showed a willingness to have their BC risk assessed if offered (74.8%). Most women (85.9%) would also accept an increase in screening frequency if they were at higher risk, but fewer (49.3%) would accept a reduction in screening frequency if they were at lower risk. There were few differences by province; however, outcomes varied by age, education level, marital status, income, perceived risk, history of BC, prior mammography, and history of genetic test for BC (all p ≤ 0.01). Risk-based BC screening using multifactorial risk assessment appears to be acceptable to most women. This suggests that the implementation of this approach is likely to be well-supported by Canadian women.
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Affiliation(s)
- Cynthia Mbuya-Bienge
- CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada; (C.M.-B.); (M.D.); (J.C.); (A.T.); (L.L.-C.); (J.L.); (J.S.)
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Nora Pashayan
- Department of Applied Health Research, Institute of Epidemiology and Healthcare, University College, London WC1E 6BT, UK;
| | - Jennifer D. Brooks
- Dalla Lana School of Public Health Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (J.D.B.); (M.J.W.); (A.M.C.)
| | - Michel Dorval
- CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada; (C.M.-B.); (M.D.); (J.C.); (A.T.); (L.L.-C.); (J.L.); (J.S.)
- Faculty of Pharmacy, Université Laval, Quebec City, QC G1V 4G2, Canada
- CISSS de Chaudière-Appalaches Research Center, Lévis, QC G6V 3Z1, Canada
| | - Jocelyne Chiquette
- CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada; (C.M.-B.); (M.D.); (J.C.); (A.T.); (L.L.-C.); (J.L.); (J.S.)
- CHU de Québec—Université Laval, Quebec City, QC G1S 4L8, Canada
- Département de Médecine Familiale et de Médecine d’Urgence, Université Laval, Quebec City, QC G1V 4G2, Canada;
| | - Laurence Eloy
- Québec Cancer Program, Ministère de la Santé et des Services Sociaux, Quebec City, QC G1S 2M1, Canada;
| | - Annie Turgeon
- CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada; (C.M.-B.); (M.D.); (J.C.); (A.T.); (L.L.-C.); (J.L.); (J.S.)
| | - Laurence Lambert-Côté
- CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada; (C.M.-B.); (M.D.); (J.C.); (A.T.); (L.L.-C.); (J.L.); (J.S.)
| | - Jean-Sébastien Paquette
- Département de Médecine Familiale et de Médecine d’Urgence, Université Laval, Quebec City, QC G1V 4G2, Canada;
| | - Emmanuelle Lévesque
- Centre of Genomics and Policy, McGill University, Montreal, QC H3A 0G1, Canada; (E.L.); (J.H.); (G.D.); (P.G.M.); (Y.J.); (B.M.K.)
| | - Julie Hagan
- Centre of Genomics and Policy, McGill University, Montreal, QC H3A 0G1, Canada; (E.L.); (J.H.); (G.D.); (P.G.M.); (Y.J.); (B.M.K.)
| | - Meghan J. Walker
- Dalla Lana School of Public Health Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (J.D.B.); (M.J.W.); (A.M.C.)
- Ontario Health, Cancer Care Ontario, Toronto, ON M5G 2L3, Canada;
| | - Julie Lapointe
- CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada; (C.M.-B.); (M.D.); (J.C.); (A.T.); (L.L.-C.); (J.L.); (J.S.)
| | - Gratien Dalpé
- Centre of Genomics and Policy, McGill University, Montreal, QC H3A 0G1, Canada; (E.L.); (J.H.); (G.D.); (P.G.M.); (Y.J.); (B.M.K.)
| | - Palmira Granados Moreno
- Centre of Genomics and Policy, McGill University, Montreal, QC H3A 0G1, Canada; (E.L.); (J.H.); (G.D.); (P.G.M.); (Y.J.); (B.M.K.)
| | | | - Michael Wolfson
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON K1G 5Z3, Canada;
| | - Yann Joly
- Centre of Genomics and Policy, McGill University, Montreal, QC H3A 0G1, Canada; (E.L.); (J.H.); (G.D.); (P.G.M.); (Y.J.); (B.M.K.)
| | - Mireille Broeders
- Radboud Institute for Health Sciences, Radboud University Medical Center, 525 EZ Nijmegen, The Netherlands;
- Dutch Expert Centre for Screening, 6538 SW Nijmegen, The Netherlands
| | - Bartha M. Knoppers
- Centre of Genomics and Policy, McGill University, Montreal, QC H3A 0G1, Canada; (E.L.); (J.H.); (G.D.); (P.G.M.); (Y.J.); (B.M.K.)
| | - Anna M. Chiarelli
- Dalla Lana School of Public Health Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (J.D.B.); (M.J.W.); (A.M.C.)
- Ontario Health, Cancer Care Ontario, Toronto, ON M5G 2L3, Canada;
| | - Jacques Simard
- CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada; (C.M.-B.); (M.D.); (J.C.); (A.T.); (L.L.-C.); (J.L.); (J.S.)
- Department of Molecular Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 4G2, Canada
| | - Hermann Nabi
- CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada; (C.M.-B.); (M.D.); (J.C.); (A.T.); (L.L.-C.); (J.L.); (J.S.)
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada
- Université Laval Cancer Research Center, Quebec City, QC G1R 3S3, Canada
- Correspondence: ; Tel.: +1-418-682-7511 (ext. 82800)
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The STHLM3-model, Risk-based Prostate Cancer Testing Identifies Men at High Risk Without Inducing Negative Psychosocial Effects. EUR UROL SUPPL 2021; 24:43-51. [PMID: 34337495 PMCID: PMC8317863 DOI: 10.1016/j.euros.2020.12.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2020] [Indexed: 11/24/2022] Open
Abstract
Background The new STHLM3 test, combining protein markers, genetic markers, and clinical data to assess a man's prostate cancer (PCa) risk, has been investigated in Sweden within the frame of the STHLM3 trial. Objective To assess whether the STHLM3 test influences men's worry level, PCa knowledge, attitude, and health-related quality of life (HRQoL). Design setting and participants Invitations with login to the web survey were mailed to 10 000 men, 50-69 yr of age, who were eligible for the STHLM3 trial. The survey was sent 3 mo before invitation to the STHLM3 trial (baseline) and 5 mo after STHLM3 (follow-up). At baseline, the men were unaware of the upcoming invitation to STHLM3. The survey covered the following: PCa-specific worry and perceived vulnerability, knowledge about PCa, attitude toward PCa testing and health behavior, and HRQoL. Outcome measurements and statistical analysis Survey scores were compared between baseline and follow-up by using the nonparametric Wilcoxon-signed rank tests for paired samples. Analysis of covariance was performed for PCa risk group comparisons. Results and limitations A total of 994 men (10%) responded to our survey at baseline and follow-up, and were assessed as follows: low risk: 421 men; intermediate risk: 421 men; and high risk:152, of whom 59 were diagnosed with PCa after further investigation. In men assessed as having low and intermediate risk, level of worrying decreased at follow-up (p < 0.001), whereas no changes were observed in men at high risk. Moreover, no HRQoL changes were observed over time. The low response rate is the main limitation. Conclusions We found that the STHLM3 model, a risk-based PCa test, showed no negative impact on the well-being of men. Patient summary Since our results suggest that the risk-based screening as used in STHLM3 did not induce negative psychological effects on the participants, we can recommend this risk-based approach for population-based prostate cancer screening.
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25
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Huynh-Le MP, Fan CC, Karunamuni R, Walsh EI, Turner EL, Lane JA, Martin RM, Neal DE, Donovan JL, Hamdy FC, Parsons JK, Eeles RA, Easton DF, Kote-Jarai ZS, Al Olama AA, Garcia SB, Muir K, Gronberg H, Wiklund F, Aly M, Schleutker J, Sipeky C, Tammela TLJ, Nordestgaard BG, Key TJ, Travis RC, Pharoah PDP, Pashayan N, Khaw KT, Thibodeau SN, McDonnell SK, Schaid DJ, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Wokolorczyk D, Kluzniak W, Cannon-Albright LA, Brenner H, Schöttker B, Holleczek B, Park JY, Sellers TA, Lin HY, Slavov CK, Kaneva RP, Mitev VI, Batra J, Clements JA, Spurdle AB, Teixeira MR, Paulo P, Maia S, Pandha H, Michael A, Mills IG, Andreassen OA, Dale AM, Seibert TM. A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data. Cancer Epidemiol Biomarkers Prev 2020; 29:1731-1738. [PMID: 32581112 PMCID: PMC7483627 DOI: 10.1158/1055-9965.epi-19-1527] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/25/2020] [Accepted: 06/15/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. METHODS United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. RESULTS The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. CONCLUSIONS PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. IMPACT Personalized genetic risk assessments could inform prostate cancer screening decisions.
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Affiliation(s)
- Minh-Phuong Huynh-Le
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Eleanor I. Walsh
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - Emma L. Turner
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
| | - J. Athene Lane
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Richard M. Martin
- Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - David E. Neal
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Oncology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge UK
| | - Jenny L. Donovan
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Freddie C. Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - J. Kellogg Parsons
- Department of Urology, University of California, San Diego, La Jolla, CA, USA
| | - Rosalind A. Eeles
- The Institute of Cancer Research, London, UK
- Royal Marsden NHS Foundation Trust, London, UK
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | | | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, UK
| | - Sara Benlloch Garcia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
- Department of Urology, Karolinska University Hospital, Stockholm, Sweden
| | - Johanna Schleutker
- Institute of Biomedicine, University of Turku, Turku Finland
- Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, Turku, Finland
| | - Csilla Sipeky
- Institute of Biomedicine, University of Turku, Turku Finland
| | - Teuvo LJ Tammela
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, FI-33014 Tampere University, Finland
- Department of Urology, University of Tampere, Finland
| | - Børge G. Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Copenhagen, Denmark
| | | | | | - Paul D. P. Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Cambridge, UK
| | - Nora Pashayan
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Cambridge, UK
- University College London, Department of Applied Health Research, London, UK
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
| | - Stephen N. Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Shannon K. McDonnell
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Daniel J. Schaid
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA
| | | | - Walther Vogel
- Institute for Human Genetics, University Hospital Ulm, Ulm, Germany
| | | | - Kathleen Herkommer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Urology, Munich, Germany
| | - Adam S. Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, Boston, MA, USA
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Dominika Wokolorczyk
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Wojciech Kluzniak
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Lisa A. Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Bernd Holleczek
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Saarland Cancer Registry, D-66119 Saarbrücken, Germany
| | - Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Thomas A. Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Hui-Yi Lin
- School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Chavdar Kroumov Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University of Sofia, Sofia, Bulgaria
| | - Radka P. Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Vanio I. Mitev
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Jyotsna Batra
- Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre-Qld, Translational Research Institute, Brisbane, Queensland, Australia
| | - Judith A. Clements
- Australian Prostate Cancer Research Centre-Qld, Translational Research Institute, Brisbane, Queensland, Australia
- Translational Research Institute, Brisbane, Queensland, Australia
| | - Amanda B. Spurdle
- Molecular Cancer Epidemiology Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane, Australia
| | | | - Manuel R. Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
| | | | | | - Ian G. Mills
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Tyler M. Seibert
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
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Hamdy FC, Donovan JL, Lane JA, Mason M, Metcalfe C, Holding P, Wade J, Noble S, Garfield K, Young G, Davis M, Peters TJ, Turner EL, Martin RM, Oxley J, Robinson M, Staffurth J, Walsh E, Blazeby J, Bryant R, Bollina P, Catto J, Doble A, Doherty A, Gillatt D, Gnanapragasam V, Hughes O, Kockelbergh R, Kynaston H, Paul A, Paez E, Powell P, Prescott S, Rosario D, Rowe E, Neal D. Active monitoring, radical prostatectomy and radical radiotherapy in PSA-detected clinically localised prostate cancer: the ProtecT three-arm RCT. Health Technol Assess 2020; 24:1-176. [PMID: 32773013 PMCID: PMC7443739 DOI: 10.3310/hta24370] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Prostate cancer is the most common cancer among men in the UK. Prostate-specific antigen testing followed by biopsy leads to overdetection, overtreatment as well as undertreatment of the disease. Evidence of treatment effectiveness has lacked because of the paucity of randomised controlled trials comparing conventional treatments. OBJECTIVES To evaluate the effectiveness of conventional treatments for localised prostate cancer (active monitoring, radical prostatectomy and radical radiotherapy) in men aged 50-69 years. DESIGN A prospective, multicentre prostate-specific antigen testing programme followed by a randomised trial of treatment, with a comprehensive cohort follow-up. SETTING Prostate-specific antigen testing in primary care and treatment in nine urology departments in the UK. PARTICIPANTS Between 2001 and 2009, 228,966 men aged 50-69 years received an invitation to attend an appointment for information about the Prostate testing for cancer and Treatment (ProtecT) study and a prostate-specific antigen test; 82,429 men were tested, 2664 were diagnosed with localised prostate cancer, 1643 agreed to randomisation to active monitoring (n = 545), radical prostatectomy (n = 553) or radical radiotherapy (n = 545) and 997 chose a treatment. INTERVENTIONS The interventions were active monitoring, radical prostatectomy and radical radiotherapy. TRIAL PRIMARY OUTCOME MEASURE Definite or probable disease-specific mortality at the 10-year median follow-up in randomised participants. SECONDARY OUTCOME MEASURES Overall mortality, metastases, disease progression, treatment complications, resource utilisation and patient-reported outcomes. RESULTS There were no statistically significant differences between the groups for 17 prostate cancer-specific (p = 0.48) and 169 all-cause (p = 0.87) deaths. Eight men died of prostate cancer in the active monitoring group (1.5 per 1000 person-years, 95% confidence interval 0.7 to 3.0); five died of prostate cancer in the radical prostatectomy group (0.9 per 1000 person-years, 95% confidence interval 0.4 to 2.2 per 1000 person years) and four died of prostate cancer in the radical radiotherapy group (0.7 per 1000 person-years, 95% confidence interval 0.3 to 2.0 per 1000 person years). More men developed metastases in the active monitoring group than in the radical prostatectomy and radical radiotherapy groups: active monitoring, n = 33 (6.3 per 1000 person-years, 95% confidence interval 4.5 to 8.8); radical prostatectomy, n = 13 (2.4 per 1000 person-years, 95% confidence interval 1.4 to 4.2 per 1000 person years); and radical radiotherapy, n = 16 (3.0 per 1000 person-years, 95% confidence interval 1.9 to 4.9 per 1000 person-years; p = 0.004). There were higher rates of disease progression in the active monitoring group than in the radical prostatectomy and radical radiotherapy groups: active monitoring (n = 112; 22.9 per 1000 person-years, 95% confidence interval 19.0 to 27.5 per 1000 person years); radical prostatectomy (n = 46; 8.9 per 1000 person-years, 95% confidence interval 6.7 to 11.9 per 1000 person-years); and radical radiotherapy (n = 46; 9.0 per 1000 person-years, 95% confidence interval 6.7 to 12.0 per 1000 person years; p < 0.001). Radical prostatectomy had the greatest impact on sexual function/urinary continence and remained worse than radical radiotherapy and active monitoring. Radical radiotherapy's impact on sexual function was greatest at 6 months, but recovered somewhat in the majority of participants. Sexual and urinary function gradually declined in the active monitoring group. Bowel function was worse with radical radiotherapy at 6 months, but it recovered with the exception of bloody stools. Urinary voiding and nocturia worsened in the radical radiotherapy group at 6 months but recovered. Condition-specific quality-of-life effects mirrored functional changes. No differences in anxiety/depression or generic or cancer-related quality of life were found. At the National Institute for Health and Care Excellence threshold of £20,000 per quality-adjusted life-year, the probabilities that each arm was the most cost-effective option were 58% (radical radiotherapy), 32% (active monitoring) and 10% (radical prostatectomy). LIMITATIONS A single prostate-specific antigen test and transrectal ultrasound biopsies were used. There were very few non-white men in the trial. The majority of men had low- and intermediate-risk disease. Longer follow-up is needed. CONCLUSIONS At a median follow-up point of 10 years, prostate cancer-specific mortality was low, irrespective of the assigned treatment. Radical prostatectomy and radical radiotherapy reduced disease progression and metastases, but with side effects. Further work is needed to follow up participants at a median of 15 years. TRIAL REGISTRATION Current Controlled Trials ISRCTN20141297. FUNDING This project was funded by the National Institute for Health Research Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 37. See the National Institute for Health Research Journals Library website for further project information.
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Affiliation(s)
- Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - J Athene Lane
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Malcolm Mason
- School of Medicine, University of Cardiff, Cardiff, UK
| | - Chris Metcalfe
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Peter Holding
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Julia Wade
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Sian Noble
- Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Grace Young
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael Davis
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Tim J Peters
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Emma L Turner
- Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Jon Oxley
- Department of Cellular Pathology, North Bristol NHS Trust, Bristol, UK
| | - Mary Robinson
- Department of Cellular Pathology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - John Staffurth
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Eleanor Walsh
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Jane Blazeby
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Richard Bryant
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Prasad Bollina
- Department of Urology and Surgery, Western General Hospital, University of Edinburgh, Edinburgh, UK
| | - James Catto
- Academic Urology Unit, University of Sheffield, Sheffield, UK
| | - Andrew Doble
- Department of Urology, Addenbrooke's Hospital, Cambridge, UK
| | - Alan Doherty
- Department of Urology, Queen Elizabeth Hospital, Birmingham, UK
| | - David Gillatt
- Department of Urology, Southmead Hospital and Bristol Urological Institute, Bristol, UK
| | | | - Owen Hughes
- Department of Urology, Cardiff and Vale University Health Board, Cardiff, UK
| | - Roger Kockelbergh
- Department of Urology, University Hospitals of Leicester, Leicester, UK
| | - Howard Kynaston
- Department of Urology, Cardiff and Vale University Health Board, Cardiff, UK
| | - Alan Paul
- Department of Urology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Edgar Paez
- Department of Urology, Freeman Hospital, Newcastle upon Tyne, UK
| | - Philip Powell
- Department of Urology, Freeman Hospital, Newcastle upon Tyne, UK
| | - Stephen Prescott
- Department of Urology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Derek Rosario
- Academic Urology Unit, University of Sheffield, Sheffield, UK
| | - Edward Rowe
- Department of Urology, Southmead Hospital and Bristol Urological Institute, Bristol, UK
| | - David Neal
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Academic Urology Group, University of Cambridge, Cambridge, UK
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Yanes T, McInerney-Leo AM, Law MH, Cummings S. The emerging field of polygenic risk scores and perspective for use in clinical care. Hum Mol Genet 2020; 29:R165-R176. [DOI: 10.1093/hmg/ddaa136] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/30/2020] [Accepted: 07/01/2020] [Indexed: 02/06/2023] Open
Abstract
Abstract
Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.
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Affiliation(s)
- Tatiane Yanes
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Aideen M McInerney-Leo
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Matthew H Law
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Herston QLD 4006, Australia
- Faculty of Health, School of Biomedical Sciences, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove QLD 4059, Australia
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Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations. Br J Cancer 2020; 123:860-867. [PMID: 32565540 PMCID: PMC7463251 DOI: 10.1038/s41416-020-0937-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 12/17/2022] Open
Abstract
Background The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices. Methods We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20–80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol. Results The predictive accuracy of ML-based algorithms (0.843 ≤ AU-ROC ≤ 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as ‘near population’ risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50. Conclusion ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening.
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Gail MH, Pee D. Robustness of risk-based allocation of resources for disease prevention. Stat Methods Med Res 2020; 29:3511-3524. [PMID: 32552454 DOI: 10.1177/0962280220930055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Risk models for disease incidence can be useful for allocating resources for disease prevention if risk assessment is not too expensive. Assume there is a preventive intervention that should be given to everyone, but preventive resources are limited. We optimize risk-based prevention strategies and investigate robustness to modeling assumptions. The optimal strategy defines the proportion of the population to be given risk assessment and who should be offered intervention. The optimal strategy depends on the ratio of available resources to resources needed to intervene on everyone, and on the ratio of the costs of risk assessment to intervention. Risk assessment is not recommended if it is too expensive. Preventive efficiency decreases with decreasing compliance to risk assessment or intervention. Risk measurement error has little effect nor does misspecification of the risk distribution. Ignoring population substructure has small effects on optimal prevention strategy but can lead to modest over- or under-spending. We give conditions under which ignoring population substructure has no effect on optimal strategy. Thus, a simple one-population model offers robust guidance on prevention strategy but requires data on available resources, costs of risk assessment and intervention, population risk distribution, and probabilities of acceptance of risk assessment and intervention.
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Affiliation(s)
- Mitchell H Gail
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - David Pee
- Rockville Office, Information Management Services, Inc., Rockville, MD, USA
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Brentnall AR, van Veen EM, Harkness EF, Rafiq S, Byers H, Astley SM, Sampson S, Howell A, Newman WG, Cuzick J, Evans DGR. A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density. Int J Cancer 2020; 146:2122-2129. [PMID: 31251818 PMCID: PMC7065068 DOI: 10.1002/ijc.32541] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 05/28/2019] [Indexed: 01/03/2023]
Abstract
Panels of single nucleotide polymorphisms (SNPs) stratify risk for breast cancer in women from the general population, but studies are needed assess their use in a fully comprehensive model including classical risk factors, mammographic density and more than 100 SNPs associated with breast cancer. A case-control study was designed (1,668 controls, 405 cases) in women aged 47-73 years attending routine screening in Manchester UK, and enrolled in a wider study to assess methods for risk assessment. Risk from classical questionnaire risk factors was assessed using the Tyrer-Cuzick model; mean percentage visual mammographic density was scored by two independent readers. DNA extracted from saliva was genotyped at selected SNPs using the OncoArray. A predefined polygenic risk score based on 143 SNPs was calculated (SNP143). The odds ratio (OR, and 95% confidence interval, CI) per interquartile range (IQ-OR) of SNP143 was estimated unadjusted and adjusted for Tyrer-Cuzick and breast density. Secondary analysis assessed risk by oestrogen receptor (ER) status. The primary polygenic risk score was well calibrated (O/E OR 1.10, 95% CI 0.86-1.34) and accuracy was retained after adjustment for Tyrer-Cuzick risk and mammographic density (IQ-OR unadjusted 2.12, 95% CI% 1.75-2.42; adjusted 2.06, 95% CI 1.75-2.42). SNP143 was a risk factor for ER+ and ER- breast cancer (adjusted IQ-OR, ER+ 2.11, 95% CI 1.78-2.51; ER- 1.81, 95% CI 1.16-2.84). In conclusion, polygenic risk scores based on a large number of SNPs improve risk stratification in combination with classical risk factors and mammographic density, and SNP143 was similarly predictive for ER-positive and ER-negative disease.
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Affiliation(s)
- Adam R. Brentnall
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The LondonQueen Mary University of LondonLondonUnited Kingdom
| | - Elke M. van Veen
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and HealthUniversity of Manchester, Manchester Academic Health Science CentreManchesterUnited Kingdom
| | - Elaine F. Harkness
- Prevention Breast Cancer Centre and Nightingale Breast Screening CentreUniversity Hospital of South ManchesterManchesterUnited Kingdom
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and HealthUniversity of ManchesterManchesterUnited Kingdom
- Manchester Academic Health Science CentreUniversity of ManchesterManchesterUnited Kingdom
| | - Sajjad Rafiq
- School of Public Health, Epidemiology & BiostatisticsImperial College LondonLondonUnited Kingdom
| | - Helen Byers
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and HealthUniversity of Manchester, Manchester Academic Health Science CentreManchesterUnited Kingdom
| | - Susan M. Astley
- Prevention Breast Cancer Centre and Nightingale Breast Screening CentreUniversity Hospital of South ManchesterManchesterUnited Kingdom
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and HealthUniversity of ManchesterManchesterUnited Kingdom
- Manchester Academic Health Science CentreUniversity of ManchesterManchesterUnited Kingdom
- Manchester Breast Centre, Manchester Cancer Research CentreUniversity of ManchesterManchesterUnited Kingdom
| | - Sarah Sampson
- Prevention Breast Cancer Centre and Nightingale Breast Screening CentreUniversity Hospital of South ManchesterManchesterUnited Kingdom
| | - Anthony Howell
- Prevention Breast Cancer Centre and Nightingale Breast Screening CentreUniversity Hospital of South ManchesterManchesterUnited Kingdom
- The Christie NHS Foundation TrustManchesterUnited Kingdom
- Manchester Breast Centre, Manchester Cancer Research CentreUniversity of ManchesterManchesterUnited Kingdom
| | - William G. Newman
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and HealthUniversity of Manchester, Manchester Academic Health Science CentreManchesterUnited Kingdom
- Manchester Centre for Genomic MedicineManchester University NHS Foundation TrustManchesterUnited Kingdom
- Manchester Breast Centre, Manchester Cancer Research CentreUniversity of ManchesterManchesterUnited Kingdom
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Charterhouse Square, Barts and The LondonQueen Mary University of LondonLondonUnited Kingdom
| | - Dafydd Gareth R. Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and HealthUniversity of Manchester, Manchester Academic Health Science CentreManchesterUnited Kingdom
- Prevention Breast Cancer Centre and Nightingale Breast Screening CentreUniversity Hospital of South ManchesterManchesterUnited Kingdom
- The Christie NHS Foundation TrustManchesterUnited Kingdom
- Manchester Centre for Genomic MedicineManchester University NHS Foundation TrustManchesterUnited Kingdom
- Manchester Breast Centre, Manchester Cancer Research CentreUniversity of ManchesterManchesterUnited Kingdom
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Huynh-Le MP, Myklebust TÅ, Feng CH, Karunamuni R, Johannesen TB, Dale AM, Andreassen OA, Seibert TM. Age dependence of modern clinical risk groups for localized prostate cancer-A population-based study. Cancer 2020; 126:1691-1699. [PMID: 31899813 PMCID: PMC7103486 DOI: 10.1002/cncr.32702] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 12/03/2019] [Accepted: 12/13/2019] [Indexed: 01/02/2023]
Abstract
BACKGROUND Optimal prostate cancer (PCa) screening strategies will focus on men likely to have potentially lethal disease. Age-specific incidence rates (ASIRs) by modern clinical risk groups could inform risk stratification efforts for screening. METHODS This cross-sectional population study identified all men diagnosed with PCa in Norway from 2014 to 2017 (n = 20,356). Age, Gleason score (primary plus secondary), and clinical stage were extracted. Patients were assigned to clinical risk groups: low, favorable intermediate, unfavorable intermediate, high, regional, and metastatic. Chi-square tests analyzed the independence of Gleason scores and modern PCa risk groups with age. ASIRs for each risk group were calculated as the product of Norwegian ASIRs for all PCa and the proportions observed for each risk category. RESULTS Older age was significantly associated with a higher Gleason score and more advanced disease. The percentages of men with Gleason 8 to 10 disease among men aged 55 to 59, 65 to 69, 75 to 79, and 85 to 89 years were 16.5%, 23.4%, 37.2%, and 59.9%, respectively (P < .001); the percentages of men in the same age groups with at least high-risk disease were 29.3%, 39.1%, 60.4%, and 90.6%, respectively (P < .001). The maximum ASIRs (per 100,000 men) for low-risk, favorable intermediate-risk, unfavorable intermediate-risk, high-risk, regional, and metastatic disease were 157.1 for those aged 65 to 69 years, 183.8 for those aged 65 to 69 years, 194.8 for those aged 70 to 74 years, 408.3 for those aged 75 to 79 years, 159.7 for those aged ≥85 years, and 314.0 for those aged ≥85 years, respectively. At the ages of 75 to 79 years, the ASIR of high-risk disease was approximately 6 times greater than the ASIR at 55 to 59 years. CONCLUSIONS The risk of clinically significant localized PCa increases with age. Healthy older men may benefit from screening.
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Affiliation(s)
- Minh-Phuong Huynh-Le
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Tor Åge Myklebust
- Department of Registration, Cancer Registry of Norway, Oslo, Norway
- Department of Research and Innovation, Møre and Romsdal Hospital Trust, Alesund, Norway
| | - Christine H. Feng
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | | | - Anders M. Dale
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Ole A. Andreassen
- NORMENT & K.G. Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Tyler M. Seibert
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
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Al Ajmi K, Lophatananon A, Mekli K, Ollier W, Muir KR. Association of Nongenetic Factors With Breast Cancer Risk in Genetically Predisposed Groups of Women in the UK Biobank Cohort. JAMA Netw Open 2020; 3:e203760. [PMID: 32329772 PMCID: PMC7182796 DOI: 10.1001/jamanetworkopen.2020.3760] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
IMPORTANCE The association between noninherited factors, including lifestyle factors, and the risk of breast cancer (BC) in women and the association between BC and genetic makeup are only partly characterized. A study using data on current genetic stratification may help in the characterization. OBJECTIVE To examine the association between healthier lifestyle habits and BC risk in genetically predisposed groups. DESIGN, SETTING, AND PARTICIPANTS Data from UK Biobank, a prospective cohort comprising 2728 patients with BC and 88 489 women without BC, were analyzed. The data set used for the analysis was closed on March 31, 2019. The analysis was restricted to postmenopausal white women. Classification of healthy lifestyle was based on Cancer Research UK guidance (healthy weight, regular exercise, no use of hormone replacement therapy for more than 5 years, no oral contraceptive use, and alcohol intake <3 times/wk). Three groups were established: favorable (≥4 healthy factors), intermediate (2-3 healthy factors), and unfavorable (≤1 healthy factor). The genetic contribution was estimated using the polygenic risk scores of 305 preselected single-nucleotide variations. Polygenic risk scores were categorized into 3 tertiles (low, intermediate, and high). MAIN OUTCOMES AND MEASURES Cox proportional hazards regression was used to assess the hazard ratios (HRs) of the lifestyles and polygenic risk scores associated with a malignant neoplasm of the breast. RESULTS Mean (SD) age of the 2728 women with BC was 60.1 (5.5) years, and mean age of the 88 489 women serving as controls was 59.4 (4.9) years. The median follow-up time for the cohort was 10 years (maximum 13 years) (interquartile range, 9.44-10.82 years). Women with BC had a higher body mass index (relative risk [RR], 1.14; 95% CI, 1.05-1.23), performed less exercise (RR, 1.12; 95% CI, 1.01-1.25), used hormonal replacement therapy for longer than 5 years (RR, 1.23; 95% CI, 1.13-1.34), used more oral contraceptives (RR, 1.02; 95% CI, 0.93-1.12), and had greater alcohol intake (RR, 1.11; 95% CI, 1.03-1.19) compared with the controls. Overall, 20 657 women (23.3%) followed a favorable lifestyle, 60 195 women (68.0%) followed an intermediate lifestyle, and 7637 women (8.6%) followed an unfavorable lifestyle. The RR of the highest genetic risk group was 2.55 (95% CI, 2.28-2.84), and the RR of the most unfavorable lifestyle category was 1.44 (95% CI, 1.25-1.65). The association of lifestyle and BC within genetic subgroups showed lower HRs among women following a favorable lifestyle compared with intermediate and unfavorable lifestyles among all of the genetic groups: women with an unfavorable lifestyle had a higher risk of BC in the low genetic group (HR, 1.63; 95% CI, 1.13-2.34), intermediate genetic group (HR, 1.94; 95% CI, 1.46-2.58), and high genetic group (HR, 1.39; 95% CI, 1.11-1.74) compared with the reference group of favorable lifestyle. Intermediate lifestyle was also associated with a higher risk of BC among the low genetic group (HR, 1.40; 95% CI, 1.09-1.80) and the intermediate genetic group (HR, 1.37; 95% CI, 1.12-1.68). CONCLUSIONS AND RELEVANCE In this cohort study of data on women in the UK Biobank, a healthier lifestyle with more exercise, healthy weight, low alcohol intake, no oral contraceptive use, and no or limited hormonal replacement therapy use appeared to be associated with a reduced level of risk for BC, even if the women were at higher genetic risk for BC.
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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, University of Manchester, Manchester, United Kingdom
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, University of Manchester, Manchester, United Kingdom
| | - Krisztina Mekli
- Cathie Marsh Institute for Social Research, School of Social Sciences, Faculty of Humanities, University of Manchester, Manchester, United Kingdom
| | - William Ollier
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, University of Manchester, Manchester, United Kingdom
- School of Healthcare Science, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, United Kingdom
| | - Kenneth R Muir
- Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine and Health, Centre for Epidemiology, University of Manchester, Manchester, United Kingdom
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Wang C, Brentnall AR, Mainprize J, Yaffe M, Cuzick J, Harvey JA. External validation of a mammographic texture marker for breast cancer risk in a case-control study. J Med Imaging (Bellingham) 2020; 7:014003. [PMID: 32064299 PMCID: PMC7013151 DOI: 10.1117/1.jmi.7.1.014003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 01/17/2020] [Indexed: 12/03/2022] Open
Abstract
Purpose: The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting. Approach: A case–control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography). Results: MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors (IQ-OR=1.16, 95%CI 0.92 to 1.46), nor after further adjustment for BI-RADS density (IQ-OR=0.92, 95%CI 0.76 to 1.10). There was weak evidence that MMTEXT was more predictive for cancers that were not detected by mammography (unadjusted for density: IQ-OR=1.46, 95%CI 0.99 to 2.15 versus 1.03, 95%CI 0.79 to 1.35, Phet 0.10; adjusted for density: IQ-OR=1.11, 95%CI 0.70 to 1.77 versus 0.76, 95%CI 0.55 to 1.05, Phet 0.21). Conclusions: MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation.
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Affiliation(s)
- Chao Wang
- Kingston University and St. George's, University of London, Faculty of Health, Social Care and Education, London, United Kingdom
| | - Adam R Brentnall
- Queen Mary University of London, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Centre for Cancer Prevention, London, United Kingdom
| | - James Mainprize
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Department of Medical Biophysics, Toronto, Ontario, Canada
| | - Martin Yaffe
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Department of Medical Biophysics, Toronto, Ontario, Canada
| | - Jack Cuzick
- Queen Mary University of London, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Centre for Cancer Prevention, London, United Kingdom
| | - Jennifer A Harvey
- University of Virginia, Health Sciences Center, Department of Radiology and Medical Imaging, Charlottesville, Virginia, United States
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Román M, Sala M, Domingo L, Posso M, Louro J, Castells X. Personalized breast cancer screening strategies: A systematic review and quality assessment. PLoS One 2019; 14:e0226352. [PMID: 31841563 PMCID: PMC6913984 DOI: 10.1371/journal.pone.0226352] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/25/2019] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The effectiveness of breast cancer screening is still under debate. Our objective was to systematically review studies assessing personalized breast cancer screening strategies based on women's individual risk and to conduct a risk of bias assessment. METHODS We followed the standard methods of The Cochrane Collaboration and PRISMA declaration and searched the MEDLINE, EMBASE and Clinical Trials databases for studies published in English. The quality of the studies was assessed using the ISPOR-AMCP-NPC Questionnaire and The Cochrane Risk of Bias Tool. Two independent reviewers screened full texts and evaluated the risk of bias. RESULTS Out of the 1533 initially retrieved citations, we included 13 studies. Three studies were randomized controlled trials, while nine were mathematical modeling studies, and one was an observational pilot study. The trials are in the recruitment phase and have not yet reported their results. All three trials used breast density and age to define risk groups, and two of them included family history, previous biopsies, and genetic information. Among the mathematical modeling studies, the main risk factors used to define risk groups were breast density, age, family history, and previous biopsies. Six studies used genetic information to define risk groups. The most common outcome measures were the gain in quality-adjusted life years (QALY), absolute costs, and incremental cost-effectiveness ratio (ICER), while the main outcome in the observational study was the detection rate. In all models, personalized screening strategies were shown to be effective. The randomized trials were of good quality. The modeling studies showed moderate risk of bias but there was wide variability across studies. The observational study showed a low risk of bias but its utility was moderate due to its pilot design and its relatively small scale. CONCLUSIONS There is some evidence of the effectiveness of screening personalization in terms of QUALYs and ICER from the modeling studies and the observational study. However, evidence is lacking on feasibility and acceptance by the target population. REVIEW REGISTRATION PROSPERO: CRD42018110483.
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Affiliation(s)
- Marta Román
- Department of Epidemiology and Evaluation, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - Maria Sala
- Department of Epidemiology and Evaluation, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - Laia Domingo
- Department of Epidemiology and Evaluation, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - Margarita Posso
- Department of Epidemiology and Evaluation, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - Javier Louro
- Department of Epidemiology and Evaluation, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - Xavier Castells
- Department of Epidemiology and Evaluation, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Network on Health Services in Chronic Diseases (REDISSEC), Spain
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Cenin DR, Naber SK, de Weerdt AC, Jenkins MA, Preen DB, Ee HC, O'Leary PC, Lansdorp-Vogelaar I. Cost-Effectiveness of Personalized Screening for Colorectal Cancer Based on Polygenic Risk and Family History. Cancer Epidemiol Biomarkers Prev 2019; 29:10-21. [PMID: 31748260 DOI: 10.1158/1055-9965.epi-18-1123] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 02/20/2019] [Accepted: 10/23/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND There is growing evidence for personalizing colorectal cancer screening based on risk factors. We compared the cost-effectiveness of personalized colorectal cancer screening based on polygenic risk and family history to uniform screening. METHODS Using the MISCAN-Colon model, we simulated a cohort of 100 million 40-year-olds, offering them uniform or personalized screening. Individuals were categorized based on polygenic risk and family history of colorectal cancer. We varied screening strategies by start age, interval and test and estimated costs, and quality-adjusted life years (QALY). In our analysis, we (i) assessed the cost-effectiveness of uniform screening; (ii) developed personalized screening scenarios based on optimal screening strategies by risk group; and (iii) compared the cost-effectiveness of both. RESULTS At a willingness-to-pay threshold of $50,000/QALY, the optimal uniform screening scenario was annual fecal immunochemical testing (FIT) from ages 50 to 74 years, whereas for personalized screening the optimal screening scenario consisted of annual and biennial FIT screening except for those at highest risk who were offered 5-yearly colonoscopy from age 50 years. Although these scenarios gained the same number of QALYs (17,887), personalized screening was not cost-effective, costing an additional $428,953 due to costs associated with determining risk (assumed to be $240 per person). Personalized screening was cost-effective when these costs were less than ∼$48. CONCLUSIONS Uniform colorectal cancer screening currently appears more cost-effective than personalized screening based on polygenic risk and family history. However, cost-effectiveness is highly dependent on the cost of determining risk. IMPACT Personalized screening could become increasingly viable as costs for determining risk decrease.
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Affiliation(s)
- Dayna R Cenin
- Erasmus MC, University Medical Center Rotterdam, Department of Public Health, Rotterdam, the Netherlands. .,Centre for Health Services Research, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia.,Health Systems and Health Economics, School of Public Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Steffie K Naber
- Erasmus MC, University Medical Center Rotterdam, Department of Public Health, Rotterdam, the Netherlands
| | - Anne C de Weerdt
- Erasmus MC, University Medical Center Rotterdam, Department of Public Health, Rotterdam, the Netherlands
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Preen
- Centre for Health Services Research, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
| | - Hooi C Ee
- Department of Gastroenterology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Peter C O'Leary
- Health Systems and Health Economics, School of Public Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.,Division of Obstetrics and Gynaecology, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia.,Clinical Biochemistry, PathWest Laboratory Medicine, QE2 Medical Centre, Nedlands, Western Australia, Australia
| | - Iris Lansdorp-Vogelaar
- Erasmus MC, University Medical Center Rotterdam, Department of Public Health, Rotterdam, the Netherlands
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Naber SK, Kundu S, Kuntz KM, Dotson WD, Williams MS, Zauber AG, Calonge N, Zallen DT, Ganiats TG, Webber EM, Goddard KAB, Henrikson NB, van Ballegooijen M, Janssens ACJW, Lansdorp-Vogelaar I. Cost-Effectiveness of Risk-Stratified Colorectal Cancer Screening Based on Polygenic Risk: Current Status and Future Potential. JNCI Cancer Spectr 2019; 4:pkz086. [PMID: 32025627 PMCID: PMC6988584 DOI: 10.1093/jncics/pkz086] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 07/03/2019] [Accepted: 10/11/2019] [Indexed: 12/22/2022] Open
Abstract
Background Although uniform colonoscopy screening reduces colorectal cancer (CRC) mortality, risk-based screening may be more efficient. We investigated whether CRC screening based on polygenic risk is a cost-effective alternative to current uniform screening, and if not, under what conditions it would be. Methods The MISCAN-Colon model was used to simulate a hypothetical cohort of US 40-year-olds. Uniform screening was modeled as colonoscopy screening at ages 50, 60, and 70 years. For risk-stratified screening, individuals underwent polygenic testing with current and potential future discriminatory performance (area under the receiver-operating curve [AUC] of 0.60 and 0.65–0.80, respectively). Polygenic testing results were used to create risk groups, for which colonoscopy screening was optimized by varying the start age (40–60 years), end age (70–85 years), and interval (1–20 years). Results With current discriminatory performance, optimal screening ranged from once-only colonoscopy at age 60 years for the lowest-risk group to six colonoscopies at ages 40–80 years for the highest-risk group. While maintaining the same health benefits, risk-stratified screening increased costs by $59 per person. Risk-stratified screening could become cost-effective if the AUC value would increase beyond 0.65, the price per polygenic test would drop to less than $141, or risk-stratified screening would lead to a 5% increase in screening participation. Conclusions Currently, CRC screening based on polygenic risk is unlikely to be cost-effective compared with uniform screening. This is expected to change with a greater than 0.05 increase in AUC value, a greater than 30% reduction in polygenic testing costs, or a greater than 5% increase in adherence with screening.
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Affiliation(s)
- Steffie K Naber
- See the Notes section for the full list of authors' affiliations
| | - Suman Kundu
- See the Notes section for the full list of authors' affiliations
| | - Karen M Kuntz
- See the Notes section for the full list of authors' affiliations
| | - W David Dotson
- See the Notes section for the full list of authors' affiliations
| | - Marc S Williams
- See the Notes section for the full list of authors' affiliations
| | - Ann G Zauber
- See the Notes section for the full list of authors' affiliations
| | - Ned Calonge
- See the Notes section for the full list of authors' affiliations
| | - Doris T Zallen
- See the Notes section for the full list of authors' affiliations
| | | | | | | | - Nora B Henrikson
- See the Notes section for the full list of authors' affiliations
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37
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Gail MH, Pfeiffer RM. Breast Cancer Risk Model Requirements for Counseling, Prevention, and Screening. J Natl Cancer Inst 2019; 110:994-1002. [PMID: 29490057 DOI: 10.1093/jnci/djy013] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/16/2018] [Indexed: 01/05/2023] Open
Abstract
Background Incorporation of polygenic risk scores and mammographic density into models to predict breast cancer incidence can increase discriminatory accuracy (area under the receiver operating characteristic curve [AUC]) from 0.6 for models based only on epidemiologic factors to 0.7. It is timely to assess what impact these improvements will have on individual counseling and on public health prevention and screening strategies, and to determine what further improvements are needed. Methods We studied various clinical and public health applications using a log-normal distribution of risk. Results Provided they are well calibrated, even risk models with AUCs of 0.6 to 0.7 provide useful perspective for individual counseling and for weighing the harms and benefits of preventive interventions in the clinic. At the population level, they are helpful for designing preventive intervention trials, for assessing reductions in absolute risk from reducing exposure to modifiable risk factors, and for resource allocation (although a higher AUC would be desirable for risk-based allocation). Other public health applications require higher AUCs that can only be achieved with risk predictors 1.6 to 8.8 times as strong as all those yet discovered combined. Such applications are preventing an appreciable proportion of population disease when employing a high-risk prevention strategy and deciding who should be screened for subclinical disease. Conclusions Current and foreseeable risk models are useful for counseling and some prevention activities, but given the daunting challenge of achieving, for example, an AUC of 0.8, considerable effort should be put into finding effective preventive interventions and screening strategies with fewer adverse effects.
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Affiliation(s)
- Mitchell H Gail
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Ruth M Pfeiffer
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
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38
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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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Ming C, Viassolo V, Probst-Hensch N, Chappuis PO, Dinov ID, Katapodi MC. Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models. Breast Cancer Res 2019; 21:75. [PMID: 31221197 PMCID: PMC6585114 DOI: 10.1186/s13058-019-1158-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 05/28/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53-0.64). Machine learning (ML) offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of those tools. The purpose of this study was to compare the discriminatory accuracy of ML-based estimates against a pair of established methods-the Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models. METHODS We quantified and compared the performance of eight different ML methods to the performance of BCRAT and BOADICEA using eight simulated datasets and two retrospective samples: a random population-based sample of U.S. breast cancer patients and their cancer-free female relatives (N = 1143), and a clinical sample of Swiss breast cancer patients and cancer-free women seeking genetic evaluation and/or testing (N = 2481). RESULTS Predictive accuracy (AU-ROC curve) reached 88.28% using ML-Adaptive Boosting and 88.89% using ML-random forest versus 62.40% with BCRAT for the U.S. population-based sample. Predictive accuracy reached 90.17% using ML-adaptive boosting and 89.32% using ML-Markov chain Monte Carlo generalized linear mixed model versus 59.31% with BOADICEA for the Swiss clinic-based sample. CONCLUSIONS There was a striking improvement in the accuracy of classification of women with and without breast cancer achieved with ML algorithms compared to the state-of-the-art model-based approaches. High-accuracy prediction techniques are important in personalized medicine because they facilitate stratification of prevention strategies and individualized clinical management.
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Affiliation(s)
- Chang Ming
- Nursing Science, Faculty of Medicine, University of Basel, Bernoullistrasse 28, Room 118, 4056, Basel, Switzerland.
| | - Valeria Viassolo
- Oncogenetics and Cancer Prevention, Geneva University Hospitals, Geneva, Switzerland
| | - Nicole Probst-Hensch
- Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
| | - Pierre O Chappuis
- Oncogenetics and Cancer Prevention, Geneva University Hospitals, Geneva, Switzerland.,Genetic Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Ivo D Dinov
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA.,Statistics Online Computational resource, University of Michigan, Ann Arbor, MI, USA.,University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Maria C Katapodi
- Nursing Science, Faculty of Medicine, University of Basel, Bernoullistrasse 28, Room 118, 4056, Basel, Switzerland.,University of Michigan School of Nursing, Ann Arbor, MI, USA
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40
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Why the Gold Standard Approach by Mammography Demands Extension by Multiomics? Application of Liquid Biopsy miRNA Profiles to Breast Cancer Disease Management. Int J Mol Sci 2019; 20:ijms20122878. [PMID: 31200461 PMCID: PMC6627787 DOI: 10.3390/ijms20122878] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 06/07/2019] [Accepted: 06/11/2019] [Indexed: 02/06/2023] Open
Abstract
In the global context, the epidemic of breast cancer (BC) is evident for the early 21st century. Evidence shows that national mammography screening programs have sufficiently reduced BC related mortality. Therefore, the great utility of the mammography-based screening is not an issue. However, both false positive and false negative BC diagnosis, excessive biopsies, and irradiation linked to mammography application, as well as sub-optimal mammography-based screening, such as in the case of high-dense breast tissue in young females, altogether increase awareness among the experts regarding the limitations of mammography-based screening. Severe concerns regarding the mammography as the “golden standard” approach demanding complementary tools to cover the evident deficits led the authors to present innovative strategies, which would sufficiently improve the quality of the BC management and services to the patient. Contextually, this article provides insights into mammography deficits and current clinical data demonstrating the great potential of non-invasive diagnostic tools utilizing circulating miRNA profiles as an adjunct to conventional mammography for the population screening and personalization of BC management.
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Briggs S, Slade I. Evaluating the Integration of Genomics into Cancer Screening Programmes: Challenges and Opportunities. CURRENT GENETIC MEDICINE REPORTS 2019; 7:63-74. [PMID: 32117599 PMCID: PMC7019642 DOI: 10.1007/s40142-019-00162-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
PURPOSE OF REVIEW As the costs of genomic testing have fallen, and our understanding of genetic susceptibility to cancers has grown, there has been increasing interest in incorporating testing for cancer susceptibility genes, and polygenic risk estimates, into population cancer screening. A growing body of evidence suggests that this would be both clinically and cost-effective. In this article, we aim to explore the frameworks used to evaluate screening programmes, evaluate whether population screening for cancer susceptibility can be assessed using these standards, and consider additional issues and outcomes of importance in this context. RECENT FINDINGS There are tensions between traditional approaches of genetic testing (utilising tests with high sensitivity and specificity) and the principles of population screening (in which the screening test typically has low specificity), as well as the frameworks used to evaluate the two. Despite the existence of many screening guidelines, including consensus papers, these often do not align fully with broader considerations of genetic test evaluation. Population screening for genetic risk in cancer shifts the focus from diagnostics to prognostication and has wider implications for personal and familial health than existing screening programmes. In addition, understanding of the prevalence and penetrance of cancer susceptibility genes, required by many screening guidelines, may only be obtainable through population-level testing; prospective multi-disciplinary research alongside implementation will be essential. SUMMARY Appropriate evaluation of genetic screening for cancer risk will require modification of existing screening frameworks to incorporate additional complexity of outcomes and population values. As evidence supporting population screening for cancer susceptibility mounts, development of an appropriate evaluative framework, and expansion of public dialogue will be key to informing policy.
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Affiliation(s)
- Sarah Briggs
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN UK
| | - Ingrid Slade
- Wellcome Centre for Ethics and Humanities and Ethox Centre, Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Oxford, OX3 7LF UK
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42
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Eklund M, Broglio K, Yau C, Connor JT, Stover Fiscalini A, Esserman LJ. The WISDOM Personalized Breast Cancer Screening Trial: Simulation Study to Assess Potential Bias and Analytic Approaches. JNCI Cancer Spectr 2019; 2:pky067. [PMID: 31360882 PMCID: PMC6649825 DOI: 10.1093/jncics/pky067] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 09/05/2018] [Accepted: 10/09/2018] [Indexed: 12/20/2022] Open
Abstract
Background WISDOM (Women Informed to Screen Depending on Measures of Risk) is a randomized trial to assess whether personalized breast cancer screening—where women are screened biannually, annually, biennially, or not at all depending on risk and age—can prevent as many advanced (stage IIB or higher) cancers as annual screening in women ages 40–74 years across 5 years of trial time. The short study time in combination with design choices of not requiring study entry and exit mammograms for all participants may introduce different sources of bias in favor of either the personalized or the annual arm. Methods We designed a simulation model and performed 5000 virtual WISDOM trials to assess potential biases. Each virtual trial simulated 65 000 randomly assigned participants who were each assigned a risk stratum and a time to stage of at least IIB cancer sampled from an exponential distribution with the hazard rate based on the risk stratum. Results from the virtual trials were used to evaluate two candidate analysis strategies with respect to susceptibility for introducing bias: 1) difference between arms in total number of events over total trial time, and 2) difference in number of events within complete screening cycles. Results Based on the simulations, about 86 stage IIB or higher cancers will be detected within the trial and the total exposure time will be about 74 000 years in each arm. Potential ascertainment bias is introduced at study entry and exit. Analysis strategy 1 works better for the nonscreened stratum, whereas method 2 is considerably more unbiased for the strata of women screened biennially or every 6 months. Conclusion Combining the two candidate analysis approaches gives a reasonably unbiased analysis based on the simulations and is the method we will use for the primary analysis in WISDOM. Publishing the WISDOM analysis approach provides transparency and can aid the design and analysis of other individualized screening trials.
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Affiliation(s)
- Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Intitutet, Stockholm, Sweden
| | | | - Christina Yau
- Department of Surgery, University of California San Francisco, San Francisco, CA.,Buck Institute for Research on Aging, Novato, CA
| | - Jason T Connor
- University of Central Florida College of Medicine, Orlando, FL.,Confluence Stat, Orlando, FL
| | | | - Laura J Esserman
- Department of Surgery, University of California San Francisco, San Francisco, CA
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43
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Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A, Tyrer JP, Chen TH, Wang Q, Bolla MK, Yang X, Adank MA, Ahearn T, Aittomäki K, Allen J, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Auer PL, Auvinen P, Barrdahl M, Beane Freeman LE, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bernstein L, Blomqvist C, Bogdanova NV, Bojesen SE, Bonanni B, Børresen-Dale AL, Brauch H, Bremer M, Brenner H, Brentnall A, Brock IW, Brooks-Wilson A, Brucker SY, Brüning T, Burwinkel B, Campa D, Carter BD, Castelao JE, Chanock SJ, Chlebowski R, Christiansen H, Clarke CL, Collée JM, Cordina-Duverger E, Cornelissen S, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dos-Santos-Silva I, Dumont M, Durcan L, Dwek M, Eccles DM, Ekici AB, Eliassen AH, Ellberg C, Engel C, Eriksson M, Evans DG, Fasching PA, Figueroa J, Fletcher O, Flyger H, Försti A, Fritschi L, Gabrielson M, Gago-Dominguez M, Gapstur SM, García-Sáenz JA, Gaudet MM, Georgoulias V, Giles GG, Gilyazova IR, Glendon G, Goldberg MS, Goldgar DE, González-Neira A, Grenaker Alnæs GI, Grip M, Gronwald J, Grundy A, Guénel P, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hamann U, Hankinson SE, Harkness EF, Hart SN, He W, Hein A, Heyworth J, Hillemanns P, Hollestelle A, Hooning MJ, Hoover RN, Hopper JL, Howell A, Huang G, Humphreys K, Hunter DJ, Jakimovska M, Jakubowska A, Janni W, John EM, Johnson N, Jones ME, Jukkola-Vuorinen A, Jung A, Kaaks R, Kaczmarek K, Kataja V, Keeman R, Kerin MJ, Khusnutdinova E, Kiiski JI, Knight JA, Ko YD, Kosma VM, Koutros S, Kristensen VN, Krüger U, Kühl T, Lambrechts D, Le Marchand L, Lee E, Lejbkowicz F, Lilyquist J, Lindblom A, Lindström S, Lissowska J, Lo WY, Loibl S, Long J, Lubiński J, Lux MP, MacInnis RJ, Maishman T, Makalic E, Maleva Kostovska I, Mannermaa A, Manoukian S, Margolin S, Martens JWM, Martinez ME, Mavroudis D, McLean C, Meindl A, Menon U, Middha P, Miller N, Moreno F, Mulligan AM, Mulot C, Muñoz-Garzon VM, Neuhausen SL, Nevanlinna H, Neven P, Newman WG, Nielsen SF, Nordestgaard BG, Norman A, Offit K, Olson JE, Olsson H, Orr N, Pankratz VS, Park-Simon TW, Perez JIA, Pérez-Barrios C, Peterlongo P, Peto J, Pinchev M, Plaseska-Karanfilska D, Polley EC, Prentice R, Presneau N, Prokofyeva D, Purrington K, Pylkäs K, Rack B, Radice P, Rau-Murthy R, Rennert G, Rennert HS, Rhenius V, Robson M, Romero A, Ruddy KJ, Ruebner M, Saloustros E, Sandler DP, Sawyer EJ, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schumacher F, Schürmann P, Schwentner L, Scott C, Scott RJ, Seynaeve C, Shah M, Sherman ME, Shrubsole MJ, Shu XO, Slager S, Smeets A, Sohn C, Soucy P, Southey MC, Spinelli JJ, Stegmaier C, Stone J, Swerdlow AJ, Tamimi RM, Tapper WJ, Taylor JA, Terry MB, Thöne K, Tollenaar RAEM, Tomlinson I, Truong T, Tzardi M, Ulmer HU, Untch M, Vachon CM, van Veen EM, Vijai J, Weinberg CR, Wendt C, Whittemore AS, Wildiers H, Willett W, Winqvist R, Wolk A, Yang XR, Yannoukakos D, Zhang Y, Zheng W, Ziogas A, Dunning AM, Thompson DJ, Chenevix-Trench G, Chang-Claude J, Schmidt MK, Hall P, Milne RL, Pharoah PDP, Antoniou AC, Chatterjee N, Kraft P, García-Closas M, Simard J, Easton DF. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet 2019; 104:21-34. [PMID: 30554720 PMCID: PMC6323553 DOI: 10.1016/j.ajhg.2018.11.002] [Citation(s) in RCA: 594] [Impact Index Per Article: 118.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 11/03/2018] [Indexed: 12/29/2022] Open
Abstract
Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
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Affiliation(s)
- Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, 1683 Nicosia, Cyprus
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Laura Fachal
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Jonathan P Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Ting-Huei Chen
- Department of Mathematics and Statistics, Laval University, Québec City, QC G1V 0A6, Canada
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Muriel A Adank
- Family Cancer Clinic, the Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, 1066 CX, the Netherlands
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Kristiina Aittomäki
- Department of Clinical Genetics, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland
| | - Jamie Allen
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Hoda Anton-Culver
- Department of Epidemiology, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA 92617, USA
| | - Natalia N Antonenkova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk 223040, Belarus
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Kristan J Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Paul L Auer
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53205, USA
| | - Päivi Auvinen
- Cancer Center, Kuopio University Hospital, Kuopio 70210, Finland; Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio 70210, Finland; Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland
| | - Myrto Barrdahl
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Laura E Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Javier Benitez
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain; Biomedical Network on Rare Diseases (CIBERER), Madrid 28029, Spain
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa 450054, Russia
| | - Leslie Bernstein
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland; Department of Oncology, Örebro University Hospital, Örebro 70185, Sweden
| | - Natalia V Bogdanova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk 223040, Belarus; Department of Radiation Oncology, Hannover Medical School, Hannover 30625, Germany; Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics, IEO, European Institute of Oncology IRCCS, Milan 20141, Italy
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo 0450, Norway
| | - Hiltrud Brauch
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart 70376, Germany; University of Tübingen, Tübingen 72074, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Michael Bremer
- Department of Radiation Oncology, Hannover Medical School, Hannover 30625, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany
| | - Adam Brentnall
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London EC1M 6BQ, UK
| | - Ian W Brock
- Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK
| | - Angela Brooks-Wilson
- Genome Sciences Centre, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada; Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Sara Y Brucker
- Department of Gynecology and Obstetrics, University of Tübingen, Tübingen 72076, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum 44789, Germany
| | - Barbara Burwinkel
- Department of Obstetrics and Gynecology, University of Heidelberg, Heidelberg 69120, Germany; Molecular Epidemiology Group, C080, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Daniele Campa
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Department of Biology, University of Pisa, Pisa 56126, Italy
| | - Brian D Carter
- Epidemiology Research Program, American Cancer Society, Atlanta, GA 30303, USA
| | - Jose E Castelao
- Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo 36312, Spain
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Rowan Chlebowski
- Division of Medical Oncology and Hematology, University of California at Los Angeles, Los Angeles, CA 90024, USA
| | - Hans Christiansen
- Department of Radiation Oncology, Hannover Medical School, Hannover 30625, Germany
| | - Christine L Clarke
- Westmead Institute for Medical Research, University of Sydney, Sydney, NSW 2145, Australia
| | - J Margriet Collée
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam 3015 CN, the Netherlands
| | - Emilie Cordina-Duverger
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif 94805, France
| | - Sten Cornelissen
- Division of Molecular Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Angela Cox
- Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK
| | - Simon S Cross
- Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield S10 2TN, UK
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Martine Dumont
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Université Laval, Québec City, QC G1V 4G2, Canada
| | - Lorraine Durcan
- Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK; Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK
| | - Miriam Dwek
- School of Life Sciences, University of Westminster, London W1B 2HW, UK
| | - Diana M Eccles
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK
| | - Arif B Ekici
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
| | - Carolina Ellberg
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund 222 42, Sweden
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig 04107, Germany; LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig 04103, Germany
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - D Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9WL, UK; North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany; David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA; Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh EH16 4TJ, UK; Cancer Research UK Edinburgh Centre, Edinburgh EH4 2XR, UK
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW7 3RP, UK
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark
| | - Asta Försti
- Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Center for Primary Health Care Research, Clinical Research Center, Lund University, Malmö 205 02, Sweden
| | - Lin Fritschi
- School of Public Health, Curtin University, Perth, WA 6102, Australia
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela 15706, Spain; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, GA 30303, USA
| | - José A García-Sáenz
- Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid 28040, Spain
| | - Mia M Gaudet
- Epidemiology Research Program, American Cancer Society, Atlanta, GA 30303, USA
| | - Vassilios Georgoulias
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion 711 10, Greece
| | - Graham G Giles
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Irina R Gilyazova
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa 450054, Russia; Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa 450076, Russia
| | - Gord Glendon
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Mark S Goldberg
- Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada; Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, QC H4A 3J1, Canada
| | - David E Goldgar
- Department of Dermatology and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Anna González-Neira
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Grethe I Grenaker Alnæs
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway
| | - Mervi Grip
- Department of Surgery, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
| | - Jacek Gronwald
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland
| | - Anne Grundy
- Centre de Recherche du Centre Hospitalier de Université de Montréal (CHUM), Université de Montréal, Montréal, QC H2X 0A9, Canada
| | - Pascal Guénel
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif 94805, France
| | - Lothar Haeberle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - Eric Hahnen
- Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne 50937, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Niclas Håkansson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Susan E Hankinson
- Department of Biostatistics & Epidemiology, University of Massachusetts, Amherst, Amherst, MA 1003, USA
| | - Elaine F Harkness
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PT, UK; Nightingale Breast Screening Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester M23 9LT, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Steven N Hart
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - Alexander Hein
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - Jane Heyworth
- School of Population and Global Health, University of Western Australia, Perth, WA 6009, Australia
| | - Peter Hillemanns
- Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Antoinette Hollestelle
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam 3015 CN, the Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam 3015 CN, the Netherlands
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester M13 9PL, UK
| | - Guanmengqian Huang
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden
| | - David J Hunter
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Milena Jakimovska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov," Macedonian Academy of Sciences and Arts, Skopje 1000, Republic of Macedonia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland; Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin 71-252, Poland
| | - Wolfgang Janni
- Department of Gynecology and Obstetrics, University Hospital Ulm, Ulm 89075, Germany
| | - Esther M John
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Nichola Johnson
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW7 3RP, UK
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SM2 5NG, UK
| | - Arja Jukkola-Vuorinen
- Department of Oncology, Tampere University Hospital, Tampere, Finland Box 2000, 33521 Tampere, Finland
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Katarzyna Kaczmarek
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland
| | - Vesa Kataja
- Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland; Central Finland Health Care District, Jyväskylä Central Hospital, Jyväskylä 40620, Finland
| | - Renske Keeman
- Division of Molecular Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands
| | - Michael J Kerin
- Surgery, School of Medicine, National University of Ireland, Galway H91TK33, Ireland
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa 450054, Russia; Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa 450076, Russia
| | - Johanna I Kiiski
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland
| | - Julia A Knight
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5T 3L9, Canada; Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Yon-Dschun Ko
- Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn 53177, Germany
| | - Veli-Matti Kosma
- Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland; Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio 70210, Finland; Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio 70210, Finland
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Vessela N Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo 0450, Norway
| | - Ute Krüger
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund 222 42, Sweden
| | - Tabea Kühl
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, Leuven 3000, Belgium; Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven 3000, Belgium
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Eunjung Lee
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Flavio Lejbkowicz
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel
| | - Jenna Lilyquist
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, and Department of Clinical Genetics, Karolinska University Hospital, Stockholm 171 76, Sweden
| | - Sara Lindström
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98195, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M Sklodowska-Curie Cancer Center - Oncology Institute, Warsaw 02-034, Poland
| | - Wing-Yee Lo
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart 70376, Germany; University of Tübingen, Tübingen 72074, Germany
| | - Sibylle Loibl
- German Breast Group, GmbH, Neu Isenburg 63263, Germany
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland
| | - Michael P Lux
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | - Robert J MacInnis
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Tom Maishman
- Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK; Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton SO17 6YD, UK
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Ivana Maleva Kostovska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov," Macedonian Academy of Sciences and Arts, Skopje 1000, Republic of Macedonia
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland; Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio 70210, Finland; Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio 70210, Finland
| | - Siranoush Manoukian
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan 20133, Italy
| | - Sara Margolin
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm 118 83, Sweden; Department of Oncology, Södersjukhuset, Stockholm 118 83, Sweden
| | - John W M Martens
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam 3015 CN, the Netherlands
| | - Maria Elena Martinez
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA; Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA 92093, USA
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion 711 10, Greece
| | - Catriona McLean
- Anatomical Pathology, The Alfred Hospital, Melbourne, VIC 3004, Australia
| | - Alfons Meindl
- Department of Gynecology and Obstetrics, Ludwig Maximilian University of Munich, Munich 80336, Germany
| | - Usha Menon
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, University College London, London WC1V 6LJ, UK
| | - Pooja Middha
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Faculty of Medicine, University of Heidelberg, Heidelberg 69120, Germany
| | - Nicola Miller
- Surgery, School of Medicine, National University of Ireland, Galway H91TK33, Ireland
| | - Fernando Moreno
- Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid 28040, Spain
| | - Anna Marie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada; Laboratory Medicine Program, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Claire Mulot
- Université Paris Sorbonne Cité, INSERM UMR-S1147, Paris 75270, France
| | | | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland
| | - Patrick Neven
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven 3000, Belgium
| | - William G Newman
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9WL, UK; North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Sune F Nielsen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark
| | - Børge G Nordestgaard
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Aaron Norman
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Håkan Olsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund 222 42, Sweden
| | - Nick Orr
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast BT7 1NN, UK
| | - V Shane Pankratz
- University of New Mexico Health Sciences Center, University of New Mexico, Albuquerque, NM 87131, USA
| | | | - Jose I A Perez
- Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Oviedo 33012, Spain
| | - Clara Pérez-Barrios
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid 28222, Spain
| | - Paolo Peterlongo
- Genome Diagnostic Program, IFOM the FIRC (Italian Foundation for Cancer Research) Institute of Molecular Oncology, Milan 20139, Italy
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Mila Pinchev
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov," Macedonian Academy of Sciences and Arts, Skopje 1000, Republic of Macedonia
| | - Eric C Polley
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Ross Prentice
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Nadege Presneau
- School of Life Sciences, University of Westminster, London W1B 2HW, UK
| | - Darya Prokofyeva
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa 450076, Russia
| | - Kristen Purrington
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu 90220, Finland; Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu 90220, Finland
| | - Brigitte Rack
- Department of Gynecology and Obstetrics, University Hospital Ulm, Ulm 89075, Germany
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan 20133, Italy
| | - Rohini Rau-Murthy
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Gad Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel
| | - Hedy S Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel
| | - Valerie Rhenius
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Mark Robson
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid 28222, Spain
| | - Kathryn J Ruddy
- Department of Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthias Ruebner
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany
| | | | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA
| | - Elinor J Sawyer
- Research Oncology, Guy's Hospital, King's College London, London SE1 9RT, UK
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Rita K Schmutzler
- Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne 50937, Germany; Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany
| | - Andreas Schneeweiss
- National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg 69120, Germany
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SM2 5NG, UK
| | - Fredrick Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Peter Schürmann
- Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Lukas Schwentner
- Department of Gynecology and Obstetrics, University Hospital Ulm, Ulm 89075, Germany
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Rodney J Scott
- Division of Molecular Medicine, Pathology North, John Hunter Hospital, Newcastle, NSW 2305, Australia; Discipline of Medical Genetics, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, Callaghan, NSW 2308, Australia; Hunter Medical Research Institute, John Hunter Hospital, Newcastle, NSW 2305, Australia
| | - Caroline Seynaeve
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam 3015 CN, the Netherlands
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Mark E Sherman
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, FL 32224, USA
| | - Martha J Shrubsole
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Susan Slager
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Ann Smeets
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven 3000, Belgium
| | - Christof Sohn
- National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg 69120, Germany
| | - Penny Soucy
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Université Laval, Québec City, QC G1V 4G2, Canada
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - John J Spinelli
- Population Oncology, BC Cancer, Vancouver, BC V5Z 1G1, Canada; School of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | | | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; The Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Perth, WA 6000, Australia
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London SM2 5NG, UK; Division of Breast Cancer Research, The Institute of Cancer Research, London SW7 3RP, UK
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
| | - William J Tapper
- Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA; Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Kathrin Thöne
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Ian Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 7BN, UK
| | - Thérèse Truong
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif 94805, France
| | - Maria Tzardi
- Department of Pathology, University Hospital of Heraklion, Heraklion 711 10, Greece
| | - Hans-Ulrich Ulmer
- Frauenklinik der Stadtklinik Baden-Baden, Baden-Baden 76532, Germany
| | - Michael Untch
- Department of Gynecology and Obstetrics, Helios Clinics Berlin-Buch, Berlin 13125, Germany
| | - Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Elke M van Veen
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9WL, UK; North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Joseph Vijai
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA
| | - Camilla Wendt
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm 118 83, Sweden; Department of Oncology, Södersjukhuset, Stockholm 118 83, Sweden
| | - Alice S Whittemore
- Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hans Wildiers
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven 3000, Belgium
| | - Walter Willett
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu 90220, Finland; Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu 90220, Finland
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden; Department of Surgical Sciences, Uppsala University, Uppsala 751 05, Sweden
| | - Xiaohong R Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research "Demokritos," Athens 15310, Greece
| | - Yan Zhang
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Argyrios Ziogas
- Department of Epidemiology, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA 92617, USA
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Marjanka K Schmidt
- Division of Molecular Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; Division of Psychosocial Research and Epidemiology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam 1066 CX, the Netherlands
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden; Department of Oncology, Södersjukhuset, Stockholm 118 83, Sweden
| | - Roger L Milne
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Nilanjan Chatterjee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA; Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD 21205, USA; Department of Oncology, School of Medicine, John Hopkins University, Baltimore, MD 21205, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA 02115, USA; Program in Genetic Epidemiology and Statistical Genetics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Université Laval, Québec City, QC G1V 4G2, Canada
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
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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: 183] [Impact Index Per Article: 30.5] [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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Cancer genetics, precision prevention and a call to action. Nat Genet 2018; 50:1212-1218. [PMID: 30158684 DOI: 10.1038/s41588-018-0202-0] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 06/05/2018] [Indexed: 01/10/2023]
Abstract
More than 15 years have passed since the identification, through linkage, of 'first-wave' susceptibility genes for common cancers (BRCA1, BRCA2, MLH1 and MSH2). These genes have strong frequency-penetrance profiles, such that the associated clinical utility probably remains relevant regardless of the context of ascertainment. 'Second-wave' genes, not tractable by linkage, were subsequently identified by mutation screening of candidate genes (PALB2, ATM, CHEK2, BRIP1, RAD51C and RAD51D). Their innately weaker frequency-penetrance profiles have rendered delineation of cancer associations, risks and variant pathogenicity challenging, thereby compromising their clinical application. Early germline exome-sequencing endeavors for common cancers did not yield the long-anticipated slew of 'next-wave' genes but instead implied a highly polygenic genomic architecture requiring much larger experiments to make any substantive inroads into gene discovery. As such, the 'genetic economics' of frequency penetrance clearly indicates that focused identification of carriers of first-wave-gene mutations is most impactful for cancer control. With screening, prevention and early detection at the forefront of the cancer management agenda, we propose that the time is nigh for the initiation of national population-testing programs to identify carriers of first-wave gene mutation carriers. To fully deliver a precision prevention program, long-term, large-scale mutation studies that capture longitudinal clinical data and serial biosamples are required.
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Näslund-Koch C, Nordestgaard BG, Bojesen SE. Common breast cancer risk alleles and risk assessment: a study on 35 441 individuals from the Danish general population. Ann Oncol 2018; 28:175-181. [PMID: 28177461 DOI: 10.1093/annonc/mdw536] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background We hypothesized that common breast cancer risk alleles are associated with incidences of breast cancer and other cancers in the general population, and identify low risk women among those invited for screening mammography. Participants and Methods About 35 441 individuals from the Danish general population were followed in Danish health registries for up to 21 years after blood sampling. After genotyping 72 breast cancer risk loci, each with 0–2 alleles, the sum for each individual was calculated. We used the simple allele sum instead of the conventional polygenic risk score, as it is likely more sensitive in detecting associations with risks of other endpoints than breast cancer. Results Breast cancer incidence in the 19 010 women was increased across allele sum quintiles (log-rank trend test; P = 1×10 − 12), but not incidence of other cancers (P = 0.41). Age- and study-adjusted hazard ratio for the fifth versus the first allele sum quintile was 1.82 (95% confidence interval; 1.53–2.18). Corresponding hazard ratios per allele were 1.04 (1.03–1.05) and 1.05 (1.02–1.08) for breast cancer incidence and mortality, similar across risk factors. In 50-year-old women, the starting age for screening mammography in Denmark, the average 5-year breast cancer risk was 1.5%, overall and 1.1%, 1.4%, 1.6%, 1.7%, 2.1%, for the first through fifth quintile, respectively. Based on age, nulliparity, familial history, and allele sum, 25% of women aged 50–69 years, and 94% of women aged 40–49 years, had absolute 5-year breast cancer risks ≤ 1.5%. Using polygenic risk score led to similar results. Conclusion Common breast cancer risk alleles are associated with incidence and mortality of breast cancer in the general population, but not with other cancers. After including breast cancer allele sum in risk assessment, 25% of women currently being offered screening mammography had an absolute 5-year risk below the cutoff of average risk for a 50-year-old woman.
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Affiliation(s)
- C Näslund-Koch
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev,The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen
| | - B G Nordestgaard
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev,The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen,The Copenhagen City Heart Study, Frederiksberg Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - S E Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev,The Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen,The Copenhagen City Heart Study, Frederiksberg Hospital, Copenhagen University Hospital, Copenhagen, Denmark
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Seibert TM, Fan CC, Wang Y, Zuber V, Karunamuni R, Parsons JK, Eeles RA, Easton DF, Kote-Jarai ZS, Al Olama AA, Garcia SB, Muir K, Grönberg H, Wiklund F, Aly M, Schleutker J, Sipeky C, Tammela TL, Nordestgaard BG, Nielsen SF, Weischer M, Bisbjerg R, Røder MA, Iversen P, Key TJ, Travis RC, Neal DE, Donovan JL, Hamdy FC, Pharoah P, Pashayan N, Khaw KT, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Wokolorczyk D, Kluzniak W, Cannon-Albright L, Brenner H, Cuk K, Saum KU, Park JY, Sellers TA, Slavov C, Kaneva R, Mitev V, Batra J, Clements JA, Spurdle A, Teixeira MR, Paulo P, Maia S, Pandha H, Michael A, Kierzek A, Karow DS, Mills IG, Andreassen OA, Dale AM. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts. BMJ 2018; 360:j5757. [PMID: 29321194 PMCID: PMC5759091 DOI: 10.1136/bmj.j5757] [Citation(s) in RCA: 127] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/04/2017] [Indexed: 02/02/2023]
Abstract
OBJECTIVES To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age. DESIGN Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa. SETTING Multiple institutions that were members of international PRACTICAL consortium. PARTICIPANTS All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men. MAIN OUTCOME MEASURES Prediction with hazard score of age of onset of aggressive cancer in validation set. RESULTS In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10-16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score. CONCLUSIONS Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.
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Affiliation(s)
- Tyler M Seibert
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Yunpeng Wang
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Verena Zuber
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - Roshan Karunamuni
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, USA
| | - J Kellogg Parsons
- Department of Surgery, University of California, San Diego, La Jolla, CA, USA
| | - Rosalind A Eeles
- Institute of Cancer Research, London, SM2 5NG, UK
- Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | | | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, R3, Box 83, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Sara Benlloch Garcia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Kenneth Muir
- Institute of Population Health, University of Manchester, Manchester, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Solna, 171 76 Stockholm, Sweden
- Department of Urology, Karolinska University Hospital, Solna, 171 76 Stockholm, Sweden
| | - Johanna Schleutker
- Department of Medical Biochemistry and Genetics, Institute of Biomedicine, Kiinamyllynkatu 10, FI-20014 University of Turku, Finland
- Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku, Finland
- BioMediTech, 30014 University of Tampere, Tampere, Finland
| | - Csilla Sipeky
- Department of Medical Biochemistry and Genetics, Institute of Biomedicine, Kiinamyllynkatu 10, FI-20014 University of Turku, Finland
- Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku, Finland
| | - Teuvo Lj Tammela
- Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Finland
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Sune F Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Maren Weischer
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Rasmus Bisbjerg
- Department of Urology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - M Andreas Røder
- Copenhagen Prostate Cancer Centre, Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Peter Iversen
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Copenhagen Prostate Cancer Centre, Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Tim J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health University of Oxford, Oxford OX3 7LF, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health University of Oxford, Oxford OX3 7LF, UK
| | - David E Neal
- Nuffield Department of Surgical Sciences, Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
- University of Cambridge, Department of Oncology, Box 279, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Jenny L Donovan
- School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Nora Pashayan
- University College London, Department of Applied Health Research, London WC1E 7HB, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge UK
| | - Christiane Maier
- Institute of Human Genetics, University Hospital of Ulm, Ulm, Germany
| | - Walther Vogel
- Institute of Human Genetics, University Hospital of Ulm, Ulm, Germany
| | - Manuel Luedeke
- Institute of Human Genetics, University Hospital of Ulm, Ulm, Germany
| | - Kathleen Herkommer
- Department of Urology, Klinikum rechts der Isar der Technischen Universitaet Muenchen, Munich, Germany
| | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, 75 Francis Street, Boston, MA 02115, USA
| | - Cezary Cybulski
- International Hereditary Cancer Centre, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Dominika Wokolorczyk
- International Hereditary Cancer Centre, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Wojciech Kluzniak
- International Hereditary Cancer Centre, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Lisa Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Katarina Cuk
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kai-Uwe Saum
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Thomas A Sellers
- Office of the Center Director, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA
| | - Chavdar Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University, Sofia, Bulgaria
| | - Radka Kaneva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, 2 Zdrave Str, 1431 Sofia, Bulgaria
| | - Vanio Mitev
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, 2 Zdrave Str, 1431 Sofia, Bulgaria
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
| | - Judith A Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
| | - Amanda Spurdle
- Molecular Cancer Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
- Australian Prostate Cancer BioResource, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | | | | | | | - David S Karow
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Ian G Mills
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
- Centre for Cancer Research and Cell Biology, Queens University Belfast, Belfast, UK
- Nuffield Department of Surgical Sciences, Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
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48
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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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Sud A, Kinnersley B, Houlston RS. Genome-wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer 2017; 17:692-704. [PMID: 29026206 DOI: 10.1038/nrc.2017.82] [Citation(s) in RCA: 229] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Genome-wide association studies (GWAS) provide an agnostic approach for investigating the genetic basis of complex diseases. In oncology, GWAS of nearly all common malignancies have been performed, and over 450 genetic variants associated with increased risks have been identified. As well as revealing novel pathways important in carcinogenesis, these studies have shown that common genetic variation contributes substantially to the heritable risk of many common cancers. The clinical application of GWAS is starting to provide opportunities for drug discovery and repositioning as well as for cancer prevention. However, deciphering the functional and biological basis of associations is challenging and is in part a barrier to fully unlocking the potential of GWAS.
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Affiliation(s)
- Amit Sud
- Division of Genetics and Epidemiology, The Institute of Cancer Research
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research
- Division of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London SM2 5NG, UK
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50
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Heijnsdijk EA, Bangma CH, Borràs JM, de Carvalho TM, Castells X, Eklund M, Espinàs JA, Graefen M, Grönberg H, Lansdorp-Vogelaar I, Leeuwen PJV, Nelen V, Recker F, Roobol MJ, Vandenbulcke P, de Koning HJ. Summary statement on screening for prostate cancer in Europe. Int J Cancer 2017; 142:741-746. [DOI: 10.1002/ijc.31102] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 08/15/2017] [Accepted: 08/21/2017] [Indexed: 11/06/2022]
Affiliation(s)
- Eveline A.M. Heijnsdijk
- Department of Public Health; Erasmus Medical Center, Wytemaweg 80; CN Rotterdam 3015 The Netherlands
| | - Chris H. Bangma
- Department of Urology; Erasmus Medical Center, Wytemaweg 80; CN Rotterdam 3015 The Netherlands
| | - Josep M. Borràs
- Department of Health; Catalan Institute of Oncology, Avinguda de la Granvia de l'Hospitalet 199-203; Barcelona 08908 Spain
| | - Tiago M. de Carvalho
- Department of Public Health; Erasmus Medical Center, Wytemaweg 80; CN Rotterdam 3015 The Netherlands
| | - Xavier Castells
- Department of Epidemiology and Evaluation; IMIM (Hospital del Mar Medical Research Institute), Passeig Marítim, 25-29; Barcelona 08003 Spain
| | - Martin Eklund
- Department of medical epidemiology and biostatistics; Karolinska Institutet, Karolinska vägen; Solna Stockholm 171 76 Sweden
| | - Josep A. Espinàs
- Department of Health; Catalan Institute of Oncology, Avinguda de la Granvia de l'Hospitalet 199-203; Barcelona 08908 Spain
| | - Markus Graefen
- Martini-Clinic, Prostate Cancer Center, University Medical Center Hamburg-Eppendorf, Martinistrasse 52 Building O43; Hamburg 20246 Germany
| | - Henrik Grönberg
- Department of medical epidemiology and biostatistics; Karolinska Institutet, Karolinska vägen; Solna Stockholm 171 76 Sweden
| | - Iris Lansdorp-Vogelaar
- Department of Public Health; Erasmus Medical Center, Wytemaweg 80; CN Rotterdam 3015 The Netherlands
| | - Pim J. van Leeuwen
- Department of Urology; Erasmus Medical Center, Wytemaweg 80; CN Rotterdam 3015 The Netherlands
| | - Vera Nelen
- Provinciaal Instituut voor Hygiëne, Kronenburgstraat 45; Antwerp 2000 Belgium
| | - Franz Recker
- Department of Urology; Kantonsspital Aarau, Tellstrasse 25; Aarau 5001 Switzerland
| | - Monique J. Roobol
- Department of Urology; Erasmus Medical Center, Wytemaweg 80; CN Rotterdam 3015 The Netherlands
| | - Pieter Vandenbulcke
- Agentschap Zorg en gezondheid, Lange Kievitstraat 111-113; Antwerp 2018 Belgium
| | - Harry J. de Koning
- Department of Public Health; Erasmus Medical Center, Wytemaweg 80; CN Rotterdam 3015 The Netherlands
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