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McInerny S, Mascarenhas L, Yanes T, Petelin L, Chenevix-Trench G, Southey MC, Young MA, James PA. Using polygenic risk modification to improve breast cancer prevention: study protocol for the PRiMo multicentre randomised controlled trial. BMJ Open 2024; 14:e087874. [PMID: 39107016 DOI: 10.1136/bmjopen-2024-087874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/09/2024] Open
Abstract
INTRODUCTION Established personal and familial risk factors contribute collectively to a woman's risk of breast or ovarian cancer. Existing clinical services offer genetic testing for pathogenic variants in high-risk genes to investigate these risks but recent information on the role of common genomic variants, in the form of a Polygenic Risk Score (PRS), has provided the potential to further personalise breast and ovarian cancer risk assessment. Data from cohort studies support the potential of an integrated risk assessment to improve targeted risk management but experience of this approach in clinical practice is limited. METHODS AND ANALYSIS The polygenic risk modification trial is an Australian multicentre prospective randomised controlled trial of integrated risk assessment including personal and family risk factors with inclusion of breast and ovarian PRS vs standard care. The study will enrol women, unaffected by cancer, undergoing predictive testing at a familial cancer clinic for a pathogenic variant in a known breast cancer (BC) or ovarian cancer (OC) predisposition gene (BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D). Array-based genotyping will be used to generate breast cancer (313 SNP) and ovarian cancer (36 SNP) PRS. A suite of materials has been developed for the trial including an online portal for patient consent and questionnaires, and a clinician education programme to train healthcare providers in the use of integrated risk assessment. Long-term follow-up will evaluate differences in the assessed risk and management advice, patient risk management intentions and adherence, patient-reported experience and outcomes, and the health service implications of personalised risk assessment. ETHICS AND DISSEMINATION This study has been approved by the Human Research Ethics Committee of Peter MacCallum Cancer Centre and at all participating centres. Study findings will be disseminated via peer-reviewed publications and conference presentations, and directly to participants. TRIAL REGISTRATION NUMBER ACTRN12621000009819.
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Affiliation(s)
- Simone McInerny
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Parkville Familial Cancer Centre, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Lyon Mascarenhas
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Parkville Familial Cancer Centre, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Tatiane Yanes
- Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Lara Petelin
- The Daffodil Centre, joint venture with Cancer Council NSW, The University of Sydney, Sydney, New South Wales, Australia
- The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Georgia Chenevix-Trench
- Cancer Genetics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Melissa C Southey
- Precision Medicine, Monash University School of Clinical Sciences at Monash Health, Clayton, Victoria, Australia
- Cancer Council Victoria Cancer Epidemiology Division, Melbourne, Victoria, Australia
| | - Mary-Anne Young
- Clinical Translation and Engagement Platform, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia
| | - Paul A James
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Parkville Familial Cancer Centre, The Royal Melbourne Hospital, Parkville, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
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Valentini V, Bucalo A, Conti G, Celli L, Porzio V, Capalbo C, Silvestri V, Ottini L. Gender-Specific Genetic Predisposition to Breast Cancer: BRCA Genes and Beyond. Cancers (Basel) 2024; 16:579. [PMID: 38339330 PMCID: PMC10854694 DOI: 10.3390/cancers16030579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Among neoplastic diseases, breast cancer (BC) is one of the most influenced by gender. Despite common misconceptions associating BC as a women-only disease, BC can also occur in men. Additionally, transgender individuals may also experience BC. Genetic risk factors play a relevant role in BC predisposition, with important implications in precision prevention and treatment. The genetic architecture of BC susceptibility is similar in women and men, with high-, moderate-, and low-penetrance risk variants; however, some sex-specific features have emerged. Inherited high-penetrance pathogenic variants (PVs) in BRCA1 and BRCA2 genes are the strongest BC genetic risk factor. BRCA1 and BRCA2 PVs are more commonly associated with increased risk of female and male BC, respectively. Notably, BRCA-associated BCs are characterized by sex-specific pathologic features. Recently, next-generation sequencing technologies have helped to provide more insights on the role of moderate-penetrance BC risk variants, particularly in PALB2, CHEK2, and ATM genes, while international collaborative genome-wide association studies have contributed evidence on common low-penetrance BC risk variants, on their combined effect in polygenic models, and on their role as risk modulators in BRCA1/2 PV carriers. Overall, all these studies suggested that the genetic basis of male BC, although similar, may differ from female BC. Evaluating the genetic component of male BC as a distinct entity from female BC is the first step to improve both personalized risk assessment and therapeutic choices of patients of both sexes in order to reach gender equality in BC care. In this review, we summarize the latest research in the field of BC genetic predisposition with a particular focus on similarities and differences in male and female BC, and we also discuss the implications, challenges, and open issues that surround the establishment of a gender-oriented clinical management for BC.
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Affiliation(s)
- Virginia Valentini
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Agostino Bucalo
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Giulia Conti
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Ludovica Celli
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Virginia Porzio
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Carlo Capalbo
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
- Medical Oncology Unit, Sant’Andrea University Hospital, 00189 Rome, Italy
| | - Valentina Silvestri
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Laura Ottini
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
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Abstract
Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.
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Affiliation(s)
- Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul D P Pharoah
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Wisesty UN, Mengko TR, Purwarianti A, Pancoro A. Temporal convolutional network for a Fast DNA mutation detection in breast cancer data. PLoS One 2023; 18:e0285981. [PMID: 37228159 DOI: 10.1371/journal.pone.0285981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 05/07/2023] [Indexed: 05/27/2023] Open
Abstract
Early detection of breast cancer can be achieved through mutation detection in DNA sequences, which can be acquired through patient blood samples. Mutation detection can be performed using alignment and machine learning techniques. However, alignment techniques require reference sequences, and machine learning techniques still cannot predict index mutation and require supporting tools. Therefore, in this research, a Temporal Convolutional Network (TCN) model was proposed to detect the type and index mutation faster and without reference sequences and supporting tools. The architecture of the proposed TCN model is specifically designed for sequential labeling tasks on DNA sequence data. This allows for the detection of the mutation type of each nucleotide in the sequence, and if the nucleotide has a mutation, the index mutation can be obtained. The proposed model also uses 2-mers and 3-mers mapping techniques to improve detection performance. Based on the tests that have been carried out, the proposed TCN model can achieve the highest F1-score of 0.9443 for COSMIC dataset and 0.9629 for RSCM dataset, Additionally, the proposed TCN model can detect index mutation six times faster than BiLSTM model. Furthermore, the proposed model can detect type and index mutations based on the patient's DNA sequence, without the need for reference sequences or other additional tools.
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Affiliation(s)
- Untari Novia Wisesty
- Bandung Institute of Technology, Doctoral Program of Electrical Engineering and Informatics, School of Electrical and Information Engineering, Bandung, Indonesia
- School of Computing, Telkom University, Bandung, Indonesia
| | - Tati Rajab Mengko
- Bandung Institute of Technology, School of Electrical and Information Engineering, Bandung, Indonesia
| | - Ayu Purwarianti
- Bandung Institute of Technology, School of Electrical and Information Engineering, Bandung, Indonesia
- U-CoE AI-VLB, Bandung, Indonesia
| | - Adi Pancoro
- Bandung Institute of Technology, School of Life Sciences and Technology, Bandung, Indonesia
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Mavaddat N, Ficorella L, Carver T, Lee A, Cunningham AP, Lush M, Dennis J, Tischkowitz M, Downes K, Hu D, Hahnen E, Schmutzler RK, Stockley TL, Downs GS, Zhang T, Chiarelli AM, Bojesen SE, Liu C, Chung WK, Pardo M, Feliubadaló L, Balmaña J, Simard J, Antoniou AC, Easton DF. Incorporating Alternative Polygenic Risk Scores into the BOADICEA Breast Cancer Risk Prediction Model. Cancer Epidemiol Biomarkers Prev 2023; 32:422-427. [PMID: 36649146 PMCID: PMC9986688 DOI: 10.1158/1055-9965.epi-22-0756] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/09/2022] [Accepted: 01/10/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The multifactorial risk prediction model BOADICEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component - the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA. METHODS The mean, SD, and proportion of the overall polygenic component explained by the PRS (α2) need to be estimated. $\alpha $ was estimated using logistic regression, where the age-specific log-OR is constrained to be a function of the age-dependent polygenic relative risk in BOADICEA; and using a retrospective likelihood (RL) approach that models, in addition, the unmeasured polygenic component. RESULTS Parameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and implementation studies. The logistic regression approach underestimates $\alpha $, as compared with the RL estimates. The RL $\alpha $ estimates were very close to those obtained by assuming proportionality to the OR per 1 SD, with the constant of proportionality estimated using the 313 SNP PRS. Small variations in the SNPs included in the PRS can lead to large differences in the mean. CONCLUSIONS BOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model. IMPACT : The methods described facilitate comprehensive breast cancer risk assessment.
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Affiliation(s)
- Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Lorenzo Ficorella
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Tim Carver
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Alex P. Cunningham
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Marc Tischkowitz
- Department of Medical Genetics and National Institute for Health Research, Cambridge Biomedical Research Centre, The University of Cambridge, Cambridge, United Kingdom
| | - Kate Downes
- Cambridge Genomics Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Donglei Hu
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, California
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Rita K. Schmutzler
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Tracy L. Stockley
- Advanced Molecular Diagnostics Laboratory, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, The University of Toronto, Ontario, Canada
- Division of Clinical Laboratory Genetics, Laboratory Medicine Program, University Health Network, Toronto, Canada
| | - Gregory S. Downs
- Advanced Molecular Diagnostics Laboratory, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Division of Clinical Laboratory Genetics, Laboratory Medicine Program, University Health Network, Toronto, Canada
| | - Tong Zhang
- Advanced Molecular Diagnostics Laboratory, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Anna M. Chiarelli
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Ontario Health, Cancer Care Ontario, Toronto, Ontario, Canada
| | - Stig E. Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
| | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, New York
| | - Monica Pardo
- Hereditary Cancer Genetics Group, Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
| | - Lidia Feliubadaló
- Hereditary Cancer Program, Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, Spain
- Program in Molecular Mechanisms and Experimental Therapy in Oncology (Oncobell), IDIBELL, L'Hospitalet de Llobregat, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Judith Balmaña
- Hereditary Cancer Genetics Group, Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
- Medical Oncology Department, University Hospital of Vall d'Hebron, Barcelona, Spain
| | - Jacques Simard
- Department of Molecular Medicine, Université Laval and CHU de Québec-Université Laval Research Center, Québec, Canada
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
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6
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Affiliation(s)
- Cathryn M Lewis
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Lewis, Vassos); Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London (Lewis)
| | - Evangelos Vassos
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Lewis, Vassos); Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London (Lewis)
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