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McDonald JA, Liao Y, Knight JA, John EM, Kurian AW, Daly M, Buys SS, Huang Y, Frost CJ, Andrulis IL, Colonna SV, Friedlander ML, Hopper JL, Chung WK, Genkinger JM, MacInnis RJ, Terry MB. Pregnancy-Related Factors and Breast Cancer Risk for Women Across a Range of Familial Risk. JAMA Netw Open 2024; 7:e2427441. [PMID: 39186276 DOI: 10.1001/jamanetworkopen.2024.27441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/27/2024] Open
Abstract
Importance Few studies have investigated whether the associations between pregnancy-related factors and breast cancer (BC) risk differ by underlying BC susceptibility. Evidence regarding variation in BC risk is critical to understanding BC causes and for developing effective risk-based screening guidelines. Objective To examine the association between pregnancy-related factors and BC risk, including modification by a of BC where scores are based on age and BC family history. Design, Setting, and Participants This cohort study included participants from the prospective Family Study Cohort (ProF-SC), which includes the 6 sites of the Breast Cancer Family Registry (US, Canada, and Australia) and the Kathleen Cuningham Foundation Consortium (Australia). Analyses were performed in a cohort of women enrolled from 1992 to 2011 without any personal history of BC who were followed up through 2017 with a median (range) follow-up of 10 (1-23) years. Data were analyzed from March 1992 to March 2017. Exposures Parity, number of full-term pregnancies (FTP), age at first FTP, years since last FTP, and breastfeeding. Main Outcomes and Measures BC diagnoses were obtained through self-report or report by a first-degree relative and confirmed through pathology and data linkages. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% CIs for each exposure, examining modification by PARS of BC. Differences were assessed by estrogen receptor (ER) subtype. Results The study included 17 274 women (mean [SD] age, 46.7 [15.1] years; 791 African American or Black participants [4.6%], 1399 Hispanic or Latinx participants [8.2%], and 13 790 White participants [80.7%]) with 943 prospectively ascertained BC cases. Compared with nulliparous women, BC risk was higher after a recent pregnancy for those women with higher PARS (last FTP 0-5 years HR for interaction, 1.53; 95% CI, 1.13-2.07; P for interaction < .001). Associations between other exposures were limited to ER-negative disease. ER-negative BC was positively associated with increasing PARS and increasing years since last FTP (P for interaction < .001) with higher risk for recent pregnancy vs nulliparous women (last FTP 0-5 years HR for interaction, 1.54; 95% CI, 1.03-2.31). ER-negative BC was positively associated with increasing PARS and being aged 20 years or older vs less than 20 years at first FTP (P for interaction = .002) and inversely associated with multiparity vs nulliparity (P for interaction = .01). Conclusions and Relevance In this cohort study of women with no prior BC diagnoses, associations between pregnancy-related factors and BC risk were modified by PARS, with greater associations observed for ER-negative BC.
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Affiliation(s)
| | - Yuyan Liao
- Columbia University Irving Medical Center, New York, New York
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Esther M John
- Stanford University School of Medicine, Stanford, California
| | | | - Mary Daly
- Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Saundra S Buys
- University of Utah Health Sciences Center, Salt Lake City
| | - Yun Huang
- Ministry of Education, Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine and School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Caren J Frost
- College of Social Work, The University of Utah, Salt Lake City
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Sarah V Colonna
- University of Utah Health Huntsman Cancer Institute, Salt Lake City
| | | | - John L Hopper
- Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Wendy K Chung
- Columbia University Irving Medical Center, New York, New York
| | | | - Robert J MacInnis
- Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Council Victoria, East Melbourne, Victoria, Australia
| | - Mary Beth Terry
- Columbia University Irving Medical Center, New York, New York
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Ficorella L, Yang X, Easton DF, Antoniou AC. BOADICEA model: updates to the BRCA2 breast cancer risks for ages 60 years and older. BJC REPORTS 2024; 2:53. [PMID: 39072245 PMCID: PMC11269170 DOI: 10.1038/s44276-024-00079-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/26/2024] [Accepted: 07/03/2024] [Indexed: 07/30/2024]
Abstract
Breast cancer risks in older BRCA2 pathogenic variant carriers are understudied. Recent studies show a marked decline in the relative risk at older ages. We used data from two large studies to update the breast cancer risks in the BOADICEA model for BRCA2 carriers 60 years and older.
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Affiliation(s)
- Lorenzo Ficorella
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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Nguyen NH, Dodd-Eaton EB, Corredor JL, Woodman-Ross J, Green S, Gutierrez AM, Arun BK, Wang W. Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data. J Clin Oncol 2024; 42:2186-2195. [PMID: 38569124 PMCID: PMC11191065 DOI: 10.1200/jco.23.01926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/02/2023] [Accepted: 02/07/2024] [Indexed: 04/05/2024] Open
Abstract
PURPOSE There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize that this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in real clinical sessions, as compared with the commonly used research cohorts that are meticulously collected. MATERIALS AND METHODS Genetic counselors (GCs) collect family history when patients (ie, probands) come to MD Anderson Cancer Center for risk assessment of Li-Fraumeni syndrome, a genetic disorder characterized by deleterious germline mutations in the TP53 gene. Our clinical counseling-based (CCB) cohort consists of 3,297 individuals across 124 families (522 cases of single primary cancer and 125 cases of multiple primary cancers). We applied our software suite LFSPRO to make risk predictions and assessed performance in discrimination using AUC and in calibration using observed/expected (O/E) ratio. RESULTS For prediction of deleterious TP53 mutations, we achieved an AUC of 0.78 (95% CI, 0.71 to 0.85) and an O/E ratio of 1.66 (95% CI, 1.53 to 1.80). Using the LFSPRO.MPC model to predict the onset of the second cancer, we obtained an AUC of 0.70 (95% CI, 0.58 to 0.82). Using the LFSPRO.CS model to predict the onset of different cancer types as the first primary, we achieved AUCs between 0.70 and 0.83 for sarcoma, breast cancer, or other cancers combined. CONCLUSION We describe a study that fills in the critical gap in knowledge for the utility of risk prediction models. Using a CCB cohort, our previously validated models have demonstrated good performance and outperformed the standard clinical criteria. Our study suggests that better risk counseling may be achieved by GCs using these already-developed mathematical models.
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Affiliation(s)
- Nam H. Nguyen
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
- Rice University, Department of Statistics, Houston, TX
| | - Elissa B. Dodd-Eaton
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
| | - Jessica L. Corredor
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Jacynda Woodman-Ross
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Sierra Green
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Angelica M. Gutierrez
- The University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX
| | - Banu K. Arun
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
- The University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX
| | - Wenyi Wang
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
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4
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Hopper JL, Li S, MacInnis RJ, Dowty JG, Nguyen TL, Bui M, Dite GS, Esser VFC, Ye Z, Makalic E, Schmidt DF, Goudey B, Alpen K, Kapuscinski M, Win AK, Dugué PA, Milne RL, Jayasekara H, Brooks JD, Malta S, Calais-Ferreira L, Campbell AC, Young JT, Nguyen-Dumont T, Sung J, Giles GG, Buchanan D, Winship I, Terry MB, Southey MC, Jenkins MA. Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies. Genet Epidemiol 2024. [PMID: 38504141 DOI: 10.1002/gepi.22555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/29/2024] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Karen Alpen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Miroslaw Kapuscinski
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
- Genetic Medicine, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Harindra Jayasekara
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Jennifer D Brooks
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Sue Malta
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Lucas Calais-Ferreira
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Alexander C Campbell
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Jesse T Young
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Centre for Adolescent Health, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
- Justice Health Group, Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, South Korea
- Genome Medicine Institute, Seoul National University, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel Buchanan
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ingrid Winship
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
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Li S, Dite GS, MacInnis RJ, Bui M, Nguyen TL, Esser VFC, Ye Z, Dowty JG, Makalic E, Sung J, Giles GG, Southey MC, Hopper JL. Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses. Genet Epidemiol 2024. [PMID: 38472646 DOI: 10.1002/gepi.22556] [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: 07/31/2023] [Accepted: 02/23/2024] [Indexed: 03/14/2024]
Abstract
A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
- Genomic Medicine Institute, Seoul National University, Euigwahakgwan #402, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, 1st GwanakRo, Seoul, South Korea
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
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6
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Ho WK, Hassan NT, Yoon SY, Yang X, Lim JM, Binte Ishak ND, Ho PJ, Wijaya EA, Ng PPS, Luccarini C, Allen J, Tai MC, Chiang J, Zhang Z, See MH, Thong MK, Woo YL, Dunning AM, Hartman M, Yip CH, Mohd Taib NA, Easton DF, Li J, Ngeow J, Antoniou AC, Teo SH. Age-specific breast and ovarian cancer risks associated with germline BRCA1 or BRCA2 pathogenic variants - an Asian study of 572 families. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 44:101017. [PMID: 38333895 PMCID: PMC10851205 DOI: 10.1016/j.lanwpc.2024.101017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/28/2023] [Accepted: 01/11/2024] [Indexed: 02/10/2024]
Abstract
Background Clinical management of Asian BRCA1 and BRCA2 pathogenic variants (PV) carriers remains challenging due to imprecise age-specific breast (BC) and ovarian cancer (OC) risks estimates. We aimed to refine these estimates using six multi-ethnic studies in Asia. Methods Data were collected on 271 BRCA1 and 301 BRCA2 families from Malaysia and Singapore, ascertained through population/hospital-based case-series (88%) and genetic clinics (12%). Age-specific cancer risks were estimated using a modified segregation analysis method, adjusted for ascertainment. Findings BC and OC relative risks (RRs) varied across age groups for both BRCA1 and BRCA2. The age-specific RR estimates were similar across ethnicities and country of residence. For BRCA1 carriers of Malay, Indian and Chinese ancestry born between 1950 and 1959 in Malaysia, the cumulative risk (95% CI) of BC by age 80 was 40% (36%-44%), 49% (44%-53%) and 55% (51%-60%), respectively. The corresponding estimates for BRCA2 were 29% (26-32%), 36% (33%-40%) and 42% (38%-45%). The corresponding cumulative BC risks for Singapore residents from the same birth cohort, where the underlying population cancer incidences are higher compared to Malaysia, were higher, varying by ancestry group between 57 and 61% for BRCA1, and between 43 and 47% for BRCA2 carriers. The cumulative risk of OC by age 80 was 31% (27-36%) for BRCA1 and 12% (10%-15%) for BRCA2 carriers in Malaysia born between 1950 and 1959; and 42% (34-50%) for BRCA1 and 20% (14-27%) for BRCA2 carriers of the same birth cohort in Singapore. There was evidence of increased BC and OC risks for women from >1960 birth cohorts (p-value = 3.6 × 10-5 for BRCA1 and 0.018 for BRCA2). Interpretation The absolute age-specific cancer risks of Asian carriers vary depending on the underlying population-specific cancer incidences, and hence should be customised to allow for more accurate cancer risk management. Funding Wellcome Trust [grant no: v203477/Z/16/Z]; CRUK (PPRPGM-Nov20∖100002).
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Affiliation(s)
- Weang-Kee Ho
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, 43500, Selangor, Malaysia
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Nur Tiara Hassan
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Sook-Yee Yoon
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
| | - Joanna M.C. Lim
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | | | - Peh Joo Ho
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore
| | - Eldarina A. Wijaya
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Patsy Pei-Sze Ng
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, 2 Worts' Causeway, CB1 8RN, Cambridge, UK
| | - Jamie Allen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, UK
| | - Mei-Chee Tai
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Jianbang Chiang
- Cancer Genetics Service, National Cancer Centre Singapore, Singapore
| | - Zewen Zhang
- Cancer Genetics Service, National Cancer Centre Singapore, Singapore
| | - Mee-Hoong See
- Department of Surgery, Faculty of Medicine, University of Malaya, Jalan Universiti, Kuala Lumpur, 50630, Malaysia
| | - Meow-Keong Thong
- Genetic Medicine Unit, University of Malaya Medical Center, Kuala Lumpur, Malaysia
| | - Yin-Ling Woo
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, 2 Worts' Causeway, CB1 8RN, Cambridge, UK
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Surgery, National University Hospital and National University Health System, Singapore, Singapore
| | - Cheng-Har Yip
- Subang Jaya Medical Centre, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Nur Aishah Mohd Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, Jalan Universiti, Kuala Lumpur, 50630, Malaysia
- University Malaya Cancer Research Institute, 50603, Kuala Lumpur, Malaysia
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, 2 Worts' Causeway, CB1 8RN, Cambridge, UK
| | - Jingmei Li
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore
| | - Joanne Ngeow
- Cancer Genetics Service, National Cancer Centre Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
| | - Soo-Hwang Teo
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
- Department of Surgery, Faculty of Medicine, University of Malaya, Jalan Universiti, Kuala Lumpur, 50630, Malaysia
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Rodriguez J, Grassmann F, Xiao Q, Eriksson M, Mao X, Bajalica-Lagercrantz S, Hall P, Czene K. Investigation of Genetic Alterations Associated With Interval Breast Cancer. JAMA Oncol 2024; 10:372-379. [PMID: 38270937 PMCID: PMC10811589 DOI: 10.1001/jamaoncol.2023.6287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/16/2023] [Indexed: 01/26/2024]
Abstract
Importance Breast cancers (BCs) diagnosed between 2 screening examinations are called interval cancers (ICs), and they have worse clinicopathological characteristics and poorer prognosis than screen-detected cancers (SDCs). However, the association of rare germline genetic variants with IC have not been studied. Objective To evaluate whether rare germline deleterious protein-truncating variants (PTVs) can be applied to discriminate between IC and SDC while considering mammographic density. Design, Setting, and Participants This population-based genetic association study was based on women aged 40 to 76 years who were attending mammographic screening in Sweden. All women with a diagnosis of BC between January 2001 and January 2016 were included, together with age-matched controls. Patients with BC were followed up for survival until 2021. Statistical analysis was performed from September 2021 to December 2022. Exposure Germline PTVs in 34 BC susceptibility genes as analyzed by targeted sequencing. Main Outcomes and Measures Odds ratios (ORs) were used to compare IC with SDC using logistic regression. Hazard ratios were used to investigate BC-specific survival using Cox regression. Results All 4121 patients with BC (IC, n = 1229; SDC, n = 2892) were female, with a mean (SD) age of 55.5 (7.1) years. There were 5631 age-matched controls. The PTVs of the ATM, BRCA1, BRCA2, CHEK2, and PALB2 genes were more common in patients with IC compared with SDC (OR, 1.48; 95% CI, 1.06-2.05). This association was primarily influenced by BRCA1/2 and PALB2 variants. A family history of BC together with PTVs of any of these genes synergistically increased the probability of receiving a diagnosis of IC rather than SDC (OR, 3.95; 95% CI, 1.97-7.92). Furthermore, 10-year BC-specific survival revealed that if a patient received a diagnosis of an IC, carriers of PTVs in any of these 5 genes had significantly worse survival compared with patients not carrying any of them (hazard ratio, 2.04; 95% CI, 1.06-3.92). All of these associations were further pronounced in a subset of patients with IC who had a low mammographic density at prior screening examination. Conclusions and Relevance The results of this study may be helpful in future optimizations of screening programs that aim to lower mortality as well as the clinical treatment of patients with BC.
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Affiliation(s)
- Juan Rodriguez
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Health and Medical University, Potsdam, Germany
| | - Qingyang Xiao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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8
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Nguyen NH, Dodd-Eaton EB, Peng G, Corredor JL, Jiao W, Woodman-Ross J, Arun BK, Wang W. LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious Germline TP53 Mutations. JCO Clin Cancer Inform 2024; 8:e2300167. [PMID: 38346271 PMCID: PMC10871774 DOI: 10.1200/cci.23.00167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/13/2023] [Accepted: 12/19/2023] [Indexed: 02/15/2024] Open
Abstract
PURPOSE LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the TP53 gene. To facilitate the use of these models in clinics, we developed LFSPROShiny, an interactive R/Shiny interface of LFSPRO that allows genetic counselors (GCs) to perform risk predictions without any programming components and further visualize the risk profiles of their patients to aid the decision-making process. METHODS LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing risk model that predicts cancer-specific risks for the first primary and a recurrent-event model that predicts the risk of a second primary tumor. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. On receiving the family history as input, LFSPROShiny renders the family into a pedigree and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. RESULTS We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. CONCLUSION Since December 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at the MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.
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Affiliation(s)
- Nam H Nguyen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Statistics, Rice University, Houston, TX
| | - Elissa B Dodd-Eaton
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Gang Peng
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Jessica L Corredor
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Wenwei Jiao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Statistics, North Caroline State University, Raleigh, NC
| | - Jacynda Woodman-Ross
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Banu K Arun
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
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Abstract
Multiple tools exist to assess a patient's breast cancer risk. The choice of risk model depends on the patient's risk factors and how the calculation will impact care. High-risk patients-those with a lifetime breast cancer risk of ≥20%-are, for instance, eligible for supplemental screening with breast magnetic resonance imaging. Those with an elevated short-term breast cancer risk (frequently defined as a 5-year risk ≥1.66%) should be offered endocrine prophylaxis. High-risk patients should also receive guidance on modification of lifestyle factors that affect breast cancer risk.
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Affiliation(s)
- Amy E Cyr
- Department of Medicine, Washington University, Box 8056, 660 South Euclid Avenue, Saint Louis, MO 63110, USA.
| | - Kaitlyn Kennard
- Department of Surgery, Washington University, Box 8051, 660 South Euclid Avenue, Saint louis, MO 63110, USA
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10
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Nguyen NH, Dodd-Eaton EB, Corredor JL, Woodman-Ross J, Green S, Hernandez ND, Gutierrez Barrera AM, Arun BK, Wang W. Validating risk prediction models for multiple primaries and competing cancer outcomes in families with Li-Fraumeni syndrome using clinically ascertained data at a single institute. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.31.23294849. [PMID: 37693464 PMCID: PMC10491358 DOI: 10.1101/2023.08.31.23294849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Purpose There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in real clinical sessions, as compared to the commonly used research cohorts that are meticulously collected. Patients and methods Genetic counselors (GCs) collect family history when patients (i.e., probands) come to MD Anderson Cancer Center for risk assessment of Li-Fraumeni syndrome, a genetic disorder characterized by deleterious germline mutations in the TP53 gene. Our clinical counseling-based (CCB) cohort consists of 3,297 individuals across 124 families (522 cases of single primary cancer and 125 cases of multiple primary cancers). We applied our software suite LFSPRO to make risk predictions and assessed performance in discrimination using area under the curve (AUC), and in calibration using observed/expected (O/E) ratio. Results For prediction of deleterious TP53 mutations, we achieved an AUC of 0.81 (95% CI, 0.70 - 0.91) and an O/E ratio of 0.96 (95% CI, 0.70 - 1.21). Using the LFSPRO.MPC model to predict the onset of the second cancer, we obtained an AUC of 0.70 (95% CI, 0.58 - 0.82). Using the LFSPRO.CS model to predict the onset of different cancer types as the first primary, we achieved AUCs between 0.70 and 0.83 for sarcoma, breast cancer, or other cancers combined. Conclusion We describe a study that fills in the critical gap in knowledge for the utility of risk prediction models. Using a CCB cohort, our previously validated models have demonstrated good performance and outperformed the standard clinical criteria. Our study suggests better risk counseling may be achieved by GCs using these already-developed mathematical models.
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Affiliation(s)
- Nam H. Nguyen
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
- Rice University, Department of Statistics, Houston, TX
| | - Elissa B. Dodd-Eaton
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
| | - Jessica L. Corredor
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Jacynda Woodman-Ross
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Sierra Green
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | - Nathaniel D. Hernandez
- The University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX
| | | | - Banu K. Arun
- The University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX
| | - Wenyi Wang
- The University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX
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11
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Adedokun B, Ademola A, Makumbi T, Odedina S, Agwai I, Ndom P, Gakwaya A, Ogundiran T, Ojengbede O, Huo D, Olopade OI. Unawareness of breast cancer family history among African women. Pan Afr Med J 2023; 45:188. [PMID: 38020349 PMCID: PMC10656588 DOI: 10.11604/pamj.2023.45.188.21616] [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: 01/23/2020] [Accepted: 02/04/2020] [Indexed: 12/01/2023] Open
Abstract
Introduction comprehensive cancer risk assessment services are lacking in most sub-Saharan African countries and the use of accurate family history (FH) information could serve as a cheap strategy for risk evaluation. The aim of this study is to determine the proportion of women unaware of family history of cancer among female relatives and associated socio-demographic characteristics. Methods using case-control data on breast cancer among 4294 women in Nigeria, Uganda and Cameroon, we investigated the proportion of women unaware of family history of cancer among their female relatives. The association between participants' response to their awareness of female relatives' cancer history and socio-demographic characteristics was analysed according to case-control status, family side and distance of relation. Results: the proportion of women unaware if any relative had cancer was 33%, and was significantly higher among controls (43.2%) compared to 23.9% among cases (p<0.001) (Adjusted Odds Ratio (OR) = 2.51, 95% CI = 2.14 - 2.95). Age, education and marital status remained significantly associated with being unaware of FH among controls on multiple regression. Conclusion about a third of women interviewed did not know about cancer history in at least one of their female relatives. Efforts aimed at improving cancer awareness in sub-Saharan Africa (SSA) are needed. Our findings could be useful for future studies of cancer risk assessment in SSA.
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Affiliation(s)
- Babatunde Adedokun
- Center for Clinical Cancer Genetics and Global Health, University of Chicago, Chicago, United States of America
| | | | | | - Stella Odedina
- Center for Population and Reproductive Health, University of Ibadan, Ibadan, Nigeria
| | - Imaria Agwai
- Center for Population and Reproductive Health, University of Ibadan, Ibadan, Nigeria
| | - Paul Ndom
- Hôpital Général Yaoundé, Yaoundé, Cameroon
| | - Antony Gakwaya
- School of Medicine, St. Augustine International University, Kampala, Uganda
| | | | - Oladosu Ojengbede
- Center for Population and Reproductive Health, University of Ibadan, Ibadan, Nigeria
| | - Dezheng Huo
- Center for Clinical Cancer Genetics and Global Health, University of Chicago, Chicago, United States of America
| | - Olufunmilayo I. Olopade
- Center for Clinical Cancer Genetics and Global Health, University of Chicago, Chicago, United States of America
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12
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Nguyen NH, Dodd-Eaton EB, Peng G, Corredor JL, Jiao W, Woodman-Ross J, Arun BK, Wang W. LFSPROShiny: an interactive R/Shiny app for prediction and visualization of cancer risks in families with deleterious germline TP53 mutations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.11.23293956. [PMID: 37645796 PMCID: PMC10462184 DOI: 10.1101/2023.08.11.23293956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Purpose LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the TP53 gene. To facilitate the use of these models in clinics, we developed LFSPROShiny, an interactive R/Shiny interface of LFSPRO that allows genetic counselors (GCs) to perform risk predictions without any programming components, and further visualize the risk profiles of their patients to aid the decision-making process. Methods LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing-risk model that predicts cancer-specific risks for the first primary, and a recurrent-event model that predicts the risk of a second primary tumor. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. Upon receiving the family history as input, LFSPROShiny renders the family into a pedigree, and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. Results We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics, from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. Conclusion Since Dec 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.
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Affiliation(s)
- Nam H Nguyen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Statistics, Rice University, Houston, TX
| | - Elissa B Dodd-Eaton
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Gang Peng
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Jessica L. Corredor
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Wenwei Jiao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Statistics, North Caroline State University, Raleigh, NC
| | - Jacynda Woodman-Ross
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Banu K. Arun
- Department of Clinical Cancer Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
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13
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Foroughi O, Madraswala S, Hayes J, Glover K, Lee L, Chaki M, Redpath S, Yu AW, Chiu D, Amanti KG, Gustavsen G. Barriers to g BRCA Testing in High-Risk HER2-Negative Early Breast Cancer. J Pers Med 2023; 13:1228. [PMID: 37623478 PMCID: PMC10455724 DOI: 10.3390/jpm13081228] [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: 06/30/2023] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023] Open
Abstract
Despite the OlympiA trial demonstrating that early-stage, high-risk, HER2- germline BRCA1 and BRCA2 mutation (gBRCAm) positive breast cancer patients can benefit from PARPi in the adjuvant setting, the gBRCA testing rate in early-stage HR+/HER2- patients remains suboptimal compared to that in early-stage TNBC patients. To better understand the perceived barriers associated with gBRCA testing in HR+/HER2- disease, a quantitative survey was conducted across stakeholders (n = 430) including medical oncologists, surgeons, nurses, physician assistants, payers, and patients. This study revealed that while payers claim to cover gBRCA testing, poor clinician documentation and overutilization are key challenges. Therefore, payers place utilization management controls on gBRCA testing due to their impression that clinicians overtest. These controls have led to healthcare professionals experiencing payer pushback in the form of reimbursement limitations and denials. The perceived challenges to gBRCA testing stem from the lack of consensus dictating which patients are high risk and should be tested. While payers define high risk based on the CPS + EG score from the OlympiA trial, HCPs adopt a broader definition including genomic risk scores, lymph node involvement, and tumor grade and size. A dialogue to harmonize risk classification and testing eligibility across stakeholders is critical to address this disconnect and increase gBRCA testing in appropriate patients.
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Affiliation(s)
| | | | | | | | - Liam Lee
- AstraZeneca, Gaithersburg, MD 20878, USA
- Merck & Co., Inc., Rahway, NJ 07065, USA
| | | | | | | | - David Chiu
- Merck & Co., Inc., Rahway, NJ 07065, USA
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Hu C, Nagaraj AB, Shimelis H, Montalban G, Lee KY, Huang H, Lumby CA, Na J, Susswein LR, Roberts ME, Marshall ML, Hiraki S, LaDuca H, Chao E, Yussuf A, Pesaran T, Neuhausen SL, Haiman CA, Kraft P, Lindstrom S, Palmer JR, Teras LR, Vachon CM, Yao S, Ong I, Nathanson KL, Weitzel JN, Boddicker N, Gnanaolivu R, Polley EC, Mer G, Cui G, Karam R, Richardson ME, Domchek SM, Yadav S, Hruska KS, Dolinsky J, Weroha SJ, Hart SN, Simard J, Masson JY, Pang YP, Couch FJ. Functional and Clinical Characterization of Variants of Uncertain Significance Identifies a Hotspot for Inactivating Missense Variants in RAD51C. Cancer Res 2023; 83:2557-2571. [PMID: 37253112 PMCID: PMC10390864 DOI: 10.1158/0008-5472.can-22-2319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/07/2022] [Accepted: 05/25/2023] [Indexed: 06/01/2023]
Abstract
Pathogenic protein-truncating variants of RAD51C, which plays an integral role in promoting DNA damage repair, increase the risk of breast and ovarian cancer. A large number of RAD51C missense variants of uncertain significance (VUS) have been identified, but the effects of the majority of these variants on RAD51C function and cancer predisposition have not been established. Here, analysis of 173 missense variants by a homology-directed repair (HDR) assay in reconstituted RAD51C-/- cells identified 30 nonfunctional (deleterious) variants, including 18 in a hotspot within the ATP-binding region. The deleterious variants conferred sensitivity to cisplatin and olaparib and disrupted formation of RAD51C/XRCC3 and RAD51B/RAD51C/RAD51D/XRCC2 complexes. Computational analysis indicated the deleterious variant effects were consistent with structural effects on ATP-binding to RAD51C. A subset of the variants displayed similar effects on RAD51C activity in reconstituted human RAD51C-depleted cancer cells. Case-control association studies of deleterious variants in women with breast and ovarian cancer and noncancer controls showed associations with moderate breast cancer risk [OR, 3.92; 95% confidence interval (95% CI), 2.18-7.59] and high ovarian cancer risk (OR, 14.8; 95% CI, 7.71-30.36), similar to protein-truncating variants. This functional data supports the clinical classification of inactivating RAD51C missense variants as pathogenic or likely pathogenic, which may improve the clinical management of variant carriers. SIGNIFICANCE Functional analysis of the impact of a large number of missense variants on RAD51C function provides insight into RAD51C activity and information for classification of the cancer relevance of RAD51C variants.
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Affiliation(s)
| | | | | | - Gemma Montalban
- CHU de Quebec-Université Laval Research Center, Université Laval, Quebec City, Quebec, Canada
| | | | | | | | - Jie Na
- Mayo Clinic, Rochester, Minnesota
| | | | | | | | | | | | | | | | | | | | | | - Peter Kraft
- T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Sara Lindstrom
- Department of Epidemiology, University of Washington, Seattle, Washington
| | - Julie R. Palmer
- Slone Epidemiology Center at Boston University, Boston, Massachusetts
| | - Lauren R. Teras
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia
| | | | - Song Yao
- Roswell Park Comprehensive Cancer Center, Buffalo, New York
| | - Irene Ong
- University of Wisconsin-Madison, Madison, Wisconsin
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jacques Simard
- CHU de Quebec-Université Laval Research Center, Université Laval, Quebec City, Quebec, Canada
| | - Jean Yves Masson
- CHU de Quebec-Université Laval Research Center, Université Laval, Quebec City, Quebec, Canada
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15
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Wang H, MacInnis RJ, Li S. Family history and breast cancer risk for Asian women: a systematic review and meta-analysis. BMC Med 2023; 21:239. [PMID: 37400822 DOI: 10.1186/s12916-023-02950-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 06/19/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Studies of women of European ancestry have shown that the average familial relative risk for first-degree relatives of women with breast cancer is approximately twofold, but little is known for Asian women. We aimed to provide evidence for the association between family history and breast cancer risk for Asian women by systematically reviewing published literature. METHODS Studies reporting the familial relative risk of breast cancer for Asian women were searched in three online databases and complemented by a manual search. Odds ratios (ORs) for the association between family history and breast cancer risk were pooled across all included studies and by subgroups in terms of the type of family history, age, menopausal status and geographical region. RESULTS The pooled OR for women who have a first-degree relative with breast cancer was 2.46 (95% confidence interval [CI]: 2.03, 2.97). There was no evidence that the familial risk differed by the type of affected relative (mother versus sisters), the woman's age (< 50 years versus ≥ 50 years), menopausal status (pre versus post) and geographical region (East and Southeast Asia versus other regions) (all P > 0.3). The pooled ORs for women of Asian ancestry with a family history in any relative were similar for those living in non-Asian countries (2.26, 95% CI: 1.42, 3.59) compared with those living in Asian countries (2.18, 95% CI: 1.85, 2.58). CONCLUSIONS Family history of breast cancer is associated with an approximately twofold relative risk of breast cancer for Asian women, which is of similar magnitude to that observed for women of European ancestry. This implies that similar familial factors are implicated in breast cancer risk between women of European and Asian ancestries. Genetic factors are likely to play a substantial role in explaining the breast cancer familial risk for Asian women, as similar risks were observed across different living environments and cultures.
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Affiliation(s)
- Heran Wang
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- China Astronaut Research and Training Centre, Beijing, 100094, China
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia.
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia.
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, 3051, Australia.
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Casauria S, Lewis S, Lynch F, Saffery R. Australian parental perceptions of genomic newborn screening for non-communicable diseases. Front Genet 2023; 14:1209762. [PMID: 37434950 PMCID: PMC10330815 DOI: 10.3389/fgene.2023.1209762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/15/2023] [Indexed: 07/13/2023] Open
Abstract
Background: Newborn bloodspot screening (NBS) programs have improved neonatal healthcare since the 1960s. Genomic sequencing now offers potential to generate polygenic risk score (PRS) that could be incorporated into NBS programs, shifting the focus from treatment to prevention of future noncommunicable disease (NCD). However, Australian parents' knowledge and attitudes regarding PRS for NBS is currently unknown. Methods: Parents with at least one Australian-born child under 18 years were invited via social media platforms to complete an online questionnaire aimed at examining parents' knowledge of NCDs, PRS, and precision medicine, their opinions on receiving PRS for their child, and considerations of early-intervention strategies to prevent the onset of disease. Results: Of 126 participants, 90.5% had heard the term "non-communicable disease or chronic condition," but only 31.8% and 34.4% were aware of the terms "polygenic risk score" and "precision medicine" respectively. A large proportion of participants said they would consider screening their newborn to receive a PRS for allergies (77.9%), asthma (81.0%), cancer (64.8%), cardiovascular disease (65.7%), mental illness (56.7%), obesity (49.5%), and type 2 diabetes (66.7%). Additionally, participants would primarily consider diet and exercise as interventions for specific NCDs. Discussion: The results from this study will inform future policy for genomic NBS, including expected rate of uptake and interventions that parents would consider employing to prevent the onset of disease.
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Affiliation(s)
- Sarah Casauria
- Murdoch Children’s Research Institute, Melbourne, VIC, Australia
- Australian Genomics, Melbourne, VIC, Australia
| | - Sharon Lewis
- Murdoch Children’s Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
| | - Fiona Lynch
- Murdoch Children’s Research Institute, Melbourne, VIC, Australia
- Melbourne Law School, University of Melbourne, Parkville, VIC, Australia
| | - Richard Saffery
- Murdoch Children’s Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
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Møller NB, Boonen DS, Feldner ES, Hao Q, Larsen M, Lænkholm AV, Borg Å, Kvist A, Törngren T, Jensen UB, Boonen SE, Thomassen M, Terkelsen T. Validation of the BOADICEA model for predicting the likelihood of carrying pathogenic variants in eight breast and ovarian cancer susceptibility genes. Sci Rep 2023; 13:8536. [PMID: 37237042 PMCID: PMC10220031 DOI: 10.1038/s41598-023-35755-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 05/23/2023] [Indexed: 05/28/2023] Open
Abstract
BOADICEA is a comprehensive risk prediction model for breast and/or ovarian cancer (BC/OC) and for carrying pathogenic variants (PVs) in cancer susceptibility genes. In addition to BRCA1 and BRCA2, BOADICEA version 6 includes PALB2, CHEK2, ATM, BARD1, RAD51C and RAD51D. To validate its predictions for these genes, we conducted a retrospective study including 2033 individuals counselled at clinical genetics departments in Denmark. All counselees underwent comprehensive genetic testing by next generation sequencing on suspicion of hereditary susceptibility to BC/OC. Likelihoods of PVs were predicted from information about diagnosis, family history and tumour pathology. Calibration was examined using the observed-to-expected ratio (O/E) and discrimination using the area under the receiver operating characteristics curve (AUC). The O/E was 1.11 (95% CI 0.97-1.26) for all genes combined. At sub-categories of predicted likelihood, the model performed well with limited misestimation at the extremes of predicted likelihood. Discrimination was acceptable with an AUC of 0.70 (95% CI 0.66-0.74), although discrimination was better for BRCA1 and BRCA2 than for the other genes in the model. This suggests that BOADICEA remains a valid decision-making aid for determining which individuals to offer comprehensive genetic testing for hereditary susceptibility to BC/OC despite suboptimal calibration for individual genes in this population.
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Affiliation(s)
- Nanna Bæk Møller
- Department of Clinical Genetics, Aarhus University Hospital, Brendstrupgårdsvej 21, 8200, Aarhus N, Denmark
| | - Desirée Sofie Boonen
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Elisabeth Simone Feldner
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Qin Hao
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Martin Larsen
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Anne-Vibeke Lænkholm
- Department of Surgical Pathology, Zealand University Hospital, Roskilde, Denmark
| | - Åke Borg
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Anders Kvist
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Therese Törngren
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Uffe Birk Jensen
- Department of Clinical Genetics, Aarhus University Hospital, Brendstrupgårdsvej 21, 8200, Aarhus N, Denmark
| | - Susanne Eriksen Boonen
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark.
| | - Thorkild Terkelsen
- Department of Clinical Genetics, Aarhus University Hospital, Brendstrupgårdsvej 21, 8200, Aarhus N, Denmark.
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18
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Allman R, Mu Y, Dite GS, Spaeth E, Hopper JL, Rosner BA. Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk. Breast Cancer Res Treat 2023; 198:335-347. [PMID: 36749458 PMCID: PMC10020257 DOI: 10.1007/s10549-022-06834-7] [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: 06/16/2022] [Accepted: 12/02/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS). METHODS Using nested case-control data from the Nurses' Health Study, we compared the models' association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk. RESULTS The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035). CONCLUSION BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.
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Affiliation(s)
- Richard Allman
- Genetic Technologies Limited, 60-66 Hanover St, Fitzroy, VIC, 3065, Australia.
| | - Yi Mu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Gillian S Dite
- Genetic Technologies Limited, 60-66 Hanover St, Fitzroy, VIC, 3065, Australia
| | | | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Bernard A Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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19
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Lang N, Ayme A, Ming C, Combes JD, Chappuis VN, Friedlaender A, Vuilleumier A, Sandoval JL, Viassolo V, Chappuis PO, Labidi-Galy SI. Chemotherapy-related agranulocytosis as a predictive factor for germline BRCA1 pathogenic variants in breast cancer patients: a retrospective cohort study. Swiss Med Wkly 2023; 153:40055. [PMID: 37011610 DOI: 10.57187/smw.2023.40055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Carriers of germline pathogenic variants of the BRCA1 gene (gBRCA1) tend to have a higher incidence of haematological toxicity upon exposure to chemotherapy. We hypothesised that the occurrence of agranulocytosis during the first cycle of (neo-)adjuvant chemotherapy (C1) in breast cancer (BC) patients could predict gBRCA1 pathogenic variants. PATIENTS AND METHODS The study population included non-metastatic BC patients selected for genetic counselling at Hôpitaux Universitaires de Genève (Jan. 1998 to Dec. 2017) with available mid-cycle blood counts performed during C1. The BOADICEA and Manchester scoring system risk-prediction models were applied. The primary outcome was the predicted likelihood of harbouring gBRCA1 pathogenic variants among patients presenting agranulocytosis during C1. RESULTS Three hundred seven BC patients were included: 32 (10.4%) gBRCA1, 27 (8.8%) gBRCA2, and 248 (81.1%) non-heterozygotes. Mean age at diagnosis was 40 years. Compared with non-heterozygotes, gBRCA1 heterozygotes more frequently had grade 3 BC (78.1%; p = 0.014), triple-negative subtype (68.8%; p <0.001), bilateral BC (25%; p = 0.004), and agranulocytosis following the first cycle of (neo-)adjuvant chemotherapy (45.8%; p = 0.002). Agranulocytosis and febrile neutropenia that developed following the first cycle of chemotherapy were independently predictive for gBRCA1 pathogenic variants (odds ratio: 6.1; p = 0.002). The sensitivity, specificity, positive predictive value, and negative predictive value for agranulocytosis predicting gBRCA1 were 45.8% (25.6-67.2%), 82.8% (77.5-87.3%), 22.9% (6.1-37.3%), and 93.4% (88.9-96.4%), respectively. Agranulocytosis substantially improved the positive predictive value of the risk-prediction models used for gBRCA1 evaluation. CONCLUSION Agranulocytosis following the first cycle of (neo-)adjuvant chemotherapy is an independent predictive factor for gBRCA1 detection in non-metastatic BC patients.
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Affiliation(s)
- Noémie Lang
- Department of Oncology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Aurélie Ayme
- Department of Diagnostics, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Chang Ming
- Department of Clinical Research, Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Jean-Damien Combes
- Infections and Cancer Epidemiology Group, International Agency for Research on Cancer, Lyon, France
| | - Victor N Chappuis
- Department of Oncology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Alex Friedlaender
- Department of Oncology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Aurélie Vuilleumier
- Department of Oncology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - José L Sandoval
- Department of Oncology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Valeria Viassolo
- Department of Oncology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Pierre O Chappuis
- Department of Oncology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
- Department of Diagnostics, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - S Intidhar Labidi-Galy
- Department of Oncology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
- Department of Diagnostics, Hôpitaux Universitaires de Genève, Geneva, Switzerland
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20
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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: 11] [Impact Index Per Article: 11.0] [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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21
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Spalluto LB, Bonnet K, Sonubi C, Ernst LL, Wahab R, Reid SA, Agrawal P, Gregory K, Davis KM, Lewis JA, Berardi E, Hartsfield C, Selove R, Sanderson M, Schlundt D, Audet CM. Barriers to Implementation of Breast Cancer Risk Assessment: The Health Care Team Perspective. J Am Coll Radiol 2023; 20:342-351. [PMID: 36922108 PMCID: PMC10042588 DOI: 10.1016/j.jacr.2022.12.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/19/2022] [Accepted: 12/24/2022] [Indexed: 03/16/2023]
Abstract
PURPOSE To assess health care professionals' perceptions of barriers to the utilization of breast cancer risk assessment tools in the public health setting through a series of one-on-one interviews with health care team members. METHODS We conducted a cross-sectional qualitative study consisting of one-on-one semistructured telephone interviews with health care team members in the public health setting in the state of Tennessee between May 2020 and October 2020. An iterative inductive-deductive approach was used for qualitative analysis of interview data, resulting in the development of a conceptual framework to depict influences of provider behavior in the utilization of breast cancer risk assessment. RESULTS A total of 24 interviews were completed, and a framework of influences of provider behavior in the utilization of breast cancer risk assessment was developed. Participants identified barriers to the utilization of breast cancer risk assessment (knowledge and understanding of risk assessment tools, workflow challenges, and availability of personnel); patient-level barriers as perceived by health care team members (psychological, economic, educational, and environmental); and strategies to increase the utilization of breast cancer risk assessment at the provider level (leadership buy-in, training, supportive policies, and incentives) and patient level (improved communication and better understanding of patients' perceived cancer risk and severity of cancer). CONCLUSIONS Understanding barriers to implementation of breast cancer risk assessment and strategies to overcome these barriers as perceived by health care team members offers an opportunity to improve implementation of risk assessment and to identify a racially, geographically, and socioeconomically diverse population of young women at high risk for breast cancer.
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Affiliation(s)
- Lucy B Spalluto
- Vice Chair of Health Equity, Associate Director of Diversity and Inclusion, Department of Radiology, Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt-Ingram Cancer Center, Nashville, Tennessee; and Veterans Health Administration-Tennessee Valley Health Care System Geriatric Research, Education and Clinical Center (GRECC), Nashville, Tennessee; RSNA Cochair, Health Equity Committee.
| | - Kemberlee Bonnet
- Department of Psychology, Vanderbilt University, Nashville, Tennessee
| | - Chiamaka Sonubi
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Laura L Ernst
- Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Rifat Wahab
- Department of Radiology, University of Cincinnati, Cincinnati, Ohio. https://twitter.com/RifatWahab
| | - Sonya A Reid
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, and Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Pooja Agrawal
- University of Texas Medical Branch, John Sealy School of Medicine, Galveston, Texas
| | - Kris Gregory
- R. Ken Coit College of Pharmacy, University of Arizona, Tucson, Arizona
| | - Katie M Davis
- Section Chief, Breast Imaging, Department of Radiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jennifer A Lewis
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee; Co-director clinical lung screening program, Veterans Health Administration-Tennessee Valley Health Care System Geriatric Research, Education and Clinical Center (GRECC), Nashville, Tennessee; and Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, Tennessee; Rescue Lung Rescue Life Society Board Member
| | - Elizabeth Berardi
- Program Director, Tennessee Breast and Cervical Screening Program, Tennessee Department of Health, Nashville, Tennessee
| | - Crissy Hartsfield
- Clinical Programs Administrator, Division of Family Health and Wellness, Tennessee Department of Health, Nashville, Tennessee
| | - Rebecca Selove
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, and Director, Center for Prevention Research, Tennessee State University, Nashville, Tennessee
| | - Maureen Sanderson
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, and Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee
| | - David Schlundt
- Department of Psychology, Vanderbilt University, Nashville, Tennessee
| | - Carolyn M Audet
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee; Associate Director of the Vanderbilt Center for Clinical Quality and Implementation Research and Associate Director of Research in Vanderbilt Institute for Global Health
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22
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Spalluto LB, Bonnet K, Sonubi C, Reid SA, Lewis JA, Ernst LL, Davis KM, Wahab R, Agrawal P, D'Agostino C, Gregory K, Berardi E, Hartsfield C, Sanderson M, Selove R, Schlundt D, Audet CM. Black Women's Perspectives on Breast Cancer Risk Assessment. J Am Coll Radiol 2023; 20:314-323. [PMID: 36922105 PMCID: PMC10027374 DOI: 10.1016/j.jacr.2023.01.003] [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: 10/09/2022] [Revised: 12/22/2022] [Accepted: 01/27/2023] [Indexed: 03/14/2023]
Abstract
PURPOSE The aim of this study was to gather the perspectives of Black women on breast cancer risk assessment through a series of one-on-one interviews. METHODS The authors conducted a cross-sectional qualitative study consisting of one-on-one semistructured telephone interviews with Black women in Tennessee between September 2020 and November 2020. Guided by the Health Belief Model, qualitative analysis of interview data was performed in an iterative inductive and deductive approach and resulted in the development of a conceptual framework to depict influences on a woman's decision to engage with breast cancer risk assessment. RESULTS A total of 37 interviews were completed, and a framework of influences on a woman's decision to engage in breast cancer risk assessment was developed. Study participants identified several emerging themes regarding women's perspectives on breast cancer risk assessment and potential influences on women's decisions to engage with risk assessment. Much of women's decision context was based on risk appraisal (perceived severity of cancer and susceptibility of cancer), emotions (fear and trust), and perceived risks and benefits of having risk assessment. The decision was further influenced by modifiers such as communication, the risk assessment protocol, access to health care, knowledge, and health status. Perceived challenges to follow-up if identified as high risk also influenced women's decisions to pursue risk assessment. CONCLUSIONS Black women in this study identified several barriers to engagement with breast cancer risk assessment. Efforts to overcome these barriers and increase the use of breast cancer risk assessment can potentially serve as a catalyst to address existing breast cancer disparities. Continued work is needed to develop patient-centric strategies to overcome identified barriers.
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Affiliation(s)
- Lucy B Spalluto
- Vice Chair of Health Equity, Associate Director of Diversity and Inclusion, Department of Radiology, Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt-Ingram Cancer Center, Nashville, Tennessee; and Veterans Health Administration-Tennessee Valley Health Care System Geriatric Research, Education and Clinical Center (GRECC), Nashville, Tennessee; RSNA Cochair, Health Equity Committee.
| | - Kemberlee Bonnet
- Department of Psychology, Vanderbilt University, Nashville, Tennessee
| | - Chiamaka Sonubi
- Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Sonya A Reid
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, and Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jennifer A Lewis
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee; Co-director clinical lung screening program, Veterans Health Administration-Tennessee Valley Health Care System Geriatric Research, Education and Clinical Center (GRECC), Nashville, Tennessee; and Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, Tennessee; Rescue Lung Rescue Life Society Board Member
| | - Laura L Ernst
- Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Katie M Davis
- Section Chief, Breast Imaging, Department of Radiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rifat Wahab
- Department of Radiology, University of Cincinnati, Cincinnati, Ohio. https://twitter.com/%20RifatWahab
| | - Pooja Agrawal
- University of Texas Medical Branch, John Sealy School of Medicine, Galveston, Texas
| | - Chloe D'Agostino
- Department of Psychology, Vanderbilt University, Nashville, Tennessee
| | - Kris Gregory
- R. Ken Coit College of Pharmacy, University of Arizona, Tucson, Arizona
| | - Elizabeth Berardi
- Program Director, Tennessee Breast and Cervical Screening Program, Tennessee Department of Health, Nashville, Tennessee
| | - Crissy Hartsfield
- Clinical Programs Administrator, Division of Family Health and Wellness, Tennessee Department of Health, Nashville, Tennessee
| | - Maureen Sanderson
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, and Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee
| | - Rebecca Selove
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, and Director, Center for Prevention Research, Tennessee State University, Nashville, Tennessee
| | - David Schlundt
- Department of Psychology, Vanderbilt University, Nashville, Tennessee
| | - Carolyn M Audet
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee; Associate Director of the Vanderbilt Center for Clinical Quality and Implementation Research and Associate Director of Research in Vanderbilt Institute for Global Health
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23
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Nyberg T, Brook MN, Ficorella L, Lee A, Dennis J, Yang X, Wilcox N, Dadaev T, Govindasami K, Lush M, Leslie G, Lophatananon A, Muir K, Bancroft E, Easton DF, Tischkowitz M, Kote-Jarai Z, Eeles R, Antoniou AC. CanRisk-Prostate: A Comprehensive, Externally Validated Risk Model for the Prediction of Future Prostate Cancer. J Clin Oncol 2023; 41:1092-1104. [PMID: 36493335 PMCID: PMC9928632 DOI: 10.1200/jco.22.01453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/26/2022] [Accepted: 10/07/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Prostate cancer (PCa) is highly heritable. No validated PCa risk model currently exists. We therefore sought to develop a genetic risk model that can provide personalized predicted PCa risks on the basis of known moderate- to high-risk pathogenic variants, low-risk common genetic variants, and explicit cancer family history, and to externally validate the model in an independent prospective cohort. MATERIALS AND METHODS We developed a risk model using a kin-cohort comprising individuals from 16,633 PCa families ascertained in the United Kingdom from 1993 to 2017 from the UK Genetic Prostate Cancer Study, and complex segregation analysis adjusting for ascertainment. The model was externally validated in 170,850 unaffected men (7,624 incident PCas) recruited from 2006 to 2010 to the independent UK Biobank prospective cohort study. RESULTS The most parsimonious model included the effects of pathogenic variants in BRCA2, HOXB13, and BRCA1, and a polygenic score on the basis of 268 common low-risk variants. Residual familial risk was modeled by a hypothetical recessively inherited variant and a polygenic component whose standard deviation decreased log-linearly with age. The model predicted familial risks that were consistent with those reported in previous observational studies. In the validation cohort, the model discriminated well between unaffected men and men with incident PCas within 5 years (C-index, 0.790; 95% CI, 0.783 to 0.797) and 10 years (C-index, 0.772; 95% CI, 0.768 to 0.777). The 50% of men with highest predicted risks captured 86.3% of PCa cases within 10 years. CONCLUSION To our knowledge, this is the first validated risk model offering personalized PCa risks. The model will assist in counseling men concerned about their risk and can facilitate future risk-stratified population screening approaches.
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Affiliation(s)
- Tommy Nyberg
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Mark N. Brook
- Oncogenetics Team, Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
| | - Lorenzo Ficorella
- 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
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Naomi Wilcox
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Tokhir Dadaev
- Oncogenetics Team, Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
| | - Koveela Govindasami
- Oncogenetics Team, Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Goska Leslie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Elizabeth Bancroft
- Oncogenetics Team, Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
- Cancer Genetics Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Marc Tischkowitz
- Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Zsofia Kote-Jarai
- Oncogenetics Team, Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
| | - Rosalind Eeles
- Oncogenetics Team, Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
- Cancer Genetics Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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24
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Guan Z, Huang T, McCarthy AM, Hughes K, Semine A, Uno H, Trippa L, Parmigiani G, Braun D. Combining Breast Cancer Risk Prediction Models. Cancers (Basel) 2023; 15:1090. [PMID: 36831433 PMCID: PMC9953824 DOI: 10.3390/cancers15041090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.
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Affiliation(s)
- Zoe Guan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA
| | | | - Anne Marie McCarthy
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kevin Hughes
- Department of Surgery, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Alan Semine
- Advanced Image Enhancement, Fall River, MA 02720, USA
| | - Hajime Uno
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Lorenzo Trippa
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Giovanni Parmigiani
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Danielle Braun
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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25
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Barnard ME, Meeks H, Jarboe EA, Albro J, Camp NJ, Doherty JA. Familial risk of epithelial ovarian cancer after accounting for gynaecological surgery: a population-based study. J Med Genet 2023; 60:119-127. [PMID: 35534206 PMCID: PMC9643667 DOI: 10.1136/jmedgenet-2021-108402] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/14/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Uptake of risk-reducing surgery has increased among women at high risk of epithelial ovarian cancer. We sought to characterise familial risk of epithelial ovarian cancer histotypes in a population-based study after accounting for gynaecological surgeries, including bilateral oophorectomy. METHODS We compared risk of epithelial ovarian cancer in relatives of 3536 epithelial ovarian cancer cases diagnosed in 1966-2016 and relatives of 35 326 matched controls. We used Cox competing risk models, incorporating bilateral oophorectomy as a competing risk, to estimate the relative risk of ovarian cancer in first-degree (FDR), second-degree (SDR) and third-degree (TDR) relatives from 1966 to 2016. We also estimated relative risks in time periods before (1966-1994, 1995-2004) and after (2005-2016) formal recommendations were made for prophylactic oophorectomy among women with pathogenic variants in BRCA1/2. RESULTS The relative risks of epithelial ovarian cancer in FDRs, SDRs and TDRs of cases versus controls were 1.68 (95% CI 1.39 to 2.04), 1.51 (95% CI 1.30 to 1.75) and 1.34 (95% CI 1.20 to 1.48), respectively. Relative risks were greatest for high-grade serous, mucinous and 'other epithelial' histotypes. Relative risks were attenuated for case FDRs, but not for SDRs or TDRs, from 2005 onwards, consistent with the timing of recommendations for prophylactic surgery. CONCLUSION Familial risk of epithelial ovarian cancer extends to TDRs, especially for high-grade serous and mucinous histotypes. Distant relatives share genes but minimal environment, highlighting the importance of germline inherited genetics in ovarian cancer aetiology. Increased ovarian cancer risk in distant relatives has implications for counselling and recommendations for prophylactic surgeries that, from our data, appear only to reach FDRs.
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Affiliation(s)
- Mollie E Barnard
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA .,Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Huong Meeks
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Elke A Jarboe
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA,Departments of Pathology and Obstetrics and Gynecology, University of Utah, Salt Lake City, Utah, USA
| | - James Albro
- Intermountain Biorepository, Intermountain Healthcare, Salt Lake City, Utah, USA,Department of Pathology, Intermountain Medical Center, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Nicola J Camp
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Jennifer A Doherty
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA,Departments of Population Health Sciences and Obstetrics and Gynecology, University of Utah, Salt Lake City, Utah, USA
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26
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Lee A, Mavaddat N, Cunningham A, Carver T, Ficorella L, Archer S, Walter FM, Tischkowitz M, Roberts J, Usher-Smith J, Simard J, Schmidt MK, Devilee P, Zadnik V, Jürgens H, Mouret-Fourme E, De Pauw A, Rookus M, Mooij TM, Pharoah PP, Easton DF, Antoniou AC. Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence. J Med Genet 2022; 59:1206-1218. [PMID: 36162851 PMCID: PMC9691826 DOI: 10.1136/jmedgenet-2022-108471] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 04/23/2022] [Indexed: 01/12/2023]
Abstract
BACKGROUND BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) for breast cancer and the epithelial tubo-ovarian cancer (EOC) models included in the CanRisk tool (www.canrisk.org) provide future cancer risks based on pathogenic variants in cancer-susceptibility genes, polygenic risk scores, breast density, questionnaire-based risk factors and family history. Here, we extend the models to include the effects of pathogenic variants in recently established breast cancer and EOC susceptibility genes, up-to-date age-specific pathology distributions and continuous risk factors. METHODS BOADICEA was extended to further incorporate the associations of pathogenic variants in BARD1, RAD51C and RAD51D with breast cancer risk. The EOC model was extended to include the association of PALB2 pathogenic variants with EOC risk. Age-specific distributions of oestrogen-receptor-negative and triple-negative breast cancer status for pathogenic variant carriers in these genes and CHEK2 and ATM were also incorporated. A novel method to include continuous risk factors was developed, exemplified by including adult height as continuous. RESULTS BARD1, RAD51C and RAD51D explain 0.31% of the breast cancer polygenic variance. When incorporated into the multifactorial model, 34%-44% of these carriers would be reclassified to the near-population and 15%-22% to the high-risk categories based on the UK National Institute for Health and Care Excellence guidelines. Under the EOC multifactorial model, 62%, 35% and 3% of PALB2 carriers have lifetime EOC risks of <5%, 5%-10% and >10%, respectively. Including height as continuous, increased the breast cancer relative risk variance from 0.002 to 0.010. CONCLUSIONS These extensions will allow for better personalised risks for BARD1, RAD51C, RAD51D and PALB2 pathogenic variant carriers and more informed choices on screening, prevention, risk factor modification or other risk-reducing options.
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Affiliation(s)
- Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Alex Cunningham
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Tim Carver
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Lorenzo Ficorella
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Stephanie Archer
- Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Fiona M Walter
- Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Marc Tischkowitz
- Department of Medical Genetics and National Institute for Health Research, Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Jonathan Roberts
- Department of Medical Genetics and National Institute for Health Research, Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Juliet Usher-Smith
- Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jacques Simard
- Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Université Laval, Quebec, Quebec, Canada
| | - Marjanka K Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Peter Devilee
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Vesna Zadnik
- Epidemiology and Cancer Registry, Institute of Oncology, Ljubljana, Slovenia
| | - Hannes Jürgens
- Clinic of Hematology and Oncology, Tartu University Hospital, Tartu, Estonia
| | | | | | - Matti Rookus
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Thea M Mooij
- Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Paul Pd Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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Yang X, Eriksson M, Czene K, Lee A, Leslie G, Lush M, Wang J, Dennis J, Dorling L, Carvalho S, Mavaddat N, Simard J, Schmidt MK, Easton DF, Hall P, Antoniou AC. Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study. J Med Genet 2022; 59:1196-1205. [PMID: 36162852 PMCID: PMC9691822 DOI: 10.1136/jmg-2022-108806] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/24/2022] [Indexed: 01/13/2023]
Abstract
BACKGROUND The multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large independent prospective cohort. METHODS We validated BOADICEA (V.6) in the Swedish KARolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort including 66 415 women of European ancestry (median age 54 years, IQR 45-63; 816 incident breast cancers) without previous cancer diagnosis. We calculated 5-year risks on the basis of questionnaire-based risk factors, pedigree-structured first-degree family history, mammographic density (BI-RADS), a validated breast cancer polygenic risk score (PRS) based on 313-SNPs, and pathogenic variant status in 8 breast cancer susceptibility genes: BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D and BARD1. Calibration was assessed by comparing observed and expected risks in deciles of predicted risk and the calibration slope. The discriminatory ability was assessed using the area under the curve (AUC). RESULTS Among the individual model components, the PRS contributed most to breast cancer risk stratification. BOADICEA was well calibrated in predicting the risks for low-risk and high-risk women when all, or subsets of risk factors are included in the risk prediction. Discrimination was maximised when all risk factors are considered (AUC=0.70, 95% CI: 0.66 to 0.73; expected-to-observed ratio=0.88, 95% CI: 0.75 to 1.04; calibration slope=0.97, 95% CI: 0.95 to 0.99). The full multifactorial model classified 3.6% women as high risk (5-year risk ≥3%) and 11.1% as very low risk (5-year risk <0.33%). CONCLUSION The multifactorial BOADICEA model provides valid breast cancer risk predictions and a basis for personalised decision-making on disease prevention and screening.
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Affiliation(s)
- Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Goska Leslie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Jean Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Leila Dorling
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Sara Carvalho
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Jacques Simard
- Department of Molecular Medicine, Université Laval and CHU de Québec-Université Laval Research Center, Quebec City, Quebec, Canada
| | - Marjanka K Schmidt
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Devision of Molecular Pathology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
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28
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Giardiello D, Hooning MJ, Hauptmann M, Keeman R, Heemskerk-Gerritsen BAM, Becher H, Blomqvist C, Bojesen SE, Bolla MK, Camp NJ, Czene K, Devilee P, Eccles DM, Fasching PA, Figueroa JD, Flyger H, García-Closas M, Haiman CA, Hamann U, Hopper JL, Jakubowska A, Leeuwen FE, Lindblom A, Lubiński J, Margolin S, Martinez ME, Nevanlinna H, Nevelsteen I, Pelders S, Pharoah PDP, Siesling S, Southey MC, van der Hout AH, van Hest LP, Chang-Claude J, Hall P, Easton DF, Steyerberg EW, Schmidt MK. PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients. BREAST CANCER RESEARCH : BCR 2022; 24:69. [PMID: 36271417 PMCID: PMC9585761 DOI: 10.1186/s13058-022-01567-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/07/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. METHODS We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. RESULTS The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.
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Affiliation(s)
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Institute of Biomedicine, EURAC Research Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Michael Hauptmann
- Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | | | - Heiko Becher
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.,Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - 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
| | - Manjeet K Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Nicola J Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Peter A Fasching
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.,Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany
| | - Jonine D Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.,Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK.,Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - John L Hopper
- Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.,Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Floor E Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden.,Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
| | - Maria Elena Martinez
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.,Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ines Nevelsteen
- Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Louven, Belgium
| | - Saskia Pelders
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.,Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands.,Department of HealthTechnology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Annemieke H van der Hout
- Department of Genetics, University Medical Center Groningen, University Groningen, Groningen, The Netherlands
| | - Liselotte P van Hest
- Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.,Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.,Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. .,Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
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29
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Li S, MacInnis RJ, Lee A, Nguyen-Dumont T, Dorling L, Carvalho S, Dite GS, Shah M, Luccarini C, Wang Q, Milne RL, Jenkins MA, Giles GG, Dunning AM, Pharoah PDP, Southey MC, Easton DF, Hopper JL, Antoniou AC. Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction. Am J Hum Genet 2022; 109:1777-1788. [PMID: 36206742 PMCID: PMC9606477 DOI: 10.1016/j.ajhg.2022.09.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/12/2022] [Indexed: 01/25/2023] Open
Abstract
Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3051, Australia.
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
| | - Leila Dorling
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Sara Carvalho
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Genetic Technologies Ltd., Fitzroy, VIC 3065, Australia
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC 3053, Australia
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
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30
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Hakkaart C, Pearson JF, Marquart L, Dennis J, Wiggins GAR, Barnes DR, Robinson BA, Mace PD, Aittomäki K, Andrulis IL, Arun BK, Azzollini J, Balmaña J, Barkardottir RB, Belhadj S, Berger L, Blok MJ, Boonen SE, Borde J, Bradbury AR, Brunet J, Buys SS, Caligo MA, Campbell I, Chung WK, Claes KBM, Collonge-Rame MA, Cook J, Cosgrove C, Couch FJ, Daly MB, Dandiker S, Davidson R, de la Hoya M, de Putter R, Delnatte C, Dhawan M, Diez O, Ding YC, Domchek SM, Donaldson A, Eason J, Easton DF, Ehrencrona H, Engel C, Evans DG, Faust U, Feliubadaló L, Fostira F, Friedman E, Frone M, Frost D, Garber J, Gayther SA, Gehrig A, Gesta P, Godwin AK, Goldgar DE, Greene MH, Hahnen E, Hake CR, Hamann U, Hansen TVO, Hauke J, Hentschel J, Herold N, Honisch E, Hulick PJ, Imyanitov EN, Isaacs C, Izatt L, Izquierdo A, Jakubowska A, James PA, Janavicius R, John EM, Joseph V, Karlan BY, Kemp Z, Kirk J, Konstantopoulou I, Koudijs M, Kwong A, Laitman Y, Lalloo F, Lasset C, Lautrup C, Lazaro C, Legrand C, Leslie G, Lesueur F, Mai PL, Manoukian S, Mari V, Martens JWM, McGuffog L, Mebirouk N, Meindl A, Miller A, Montagna M, Moserle L, Mouret-Fourme E, Musgrave H, Nambot S, Nathanson KL, Neuhausen SL, Nevanlinna H, Yie JNY, Nguyen-Dumont T, Nikitina-Zake L, Offit K, Olah E, Olopade OI, Osorio A, Ott CE, Park SK, Parsons MT, Pedersen IS, Peixoto A, Perez-Segura P, Peterlongo P, Pocza T, Radice P, Ramser J, Rantala J, Rodriguez GC, Rønlund K, Rosenberg EH, Rossing M, Schmutzler RK, Shah PD, Sharif S, Sharma P, Side LE, Simard J, Singer CF, Snape K, Steinemann D, Stoppa-Lyonnet D, Sutter C, Tan YY, Teixeira MR, Teo SH, Thomassen M, Thull DL, Tischkowitz M, Toland AE, Trainer AH, Tripathi V, Tung N, van Engelen K, van Rensburg EJ, Vega A, Viel A, Walker L, Weitzel JN, Wevers MR, Chenevix-Trench G, Spurdle AB, Antoniou AC, Walker LC. Copy number variants as modifiers of breast cancer risk for BRCA1/BRCA2 pathogenic variant carriers. Commun Biol 2022; 5:1061. [PMID: 36203093 PMCID: PMC9537519 DOI: 10.1038/s42003-022-03978-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 09/12/2022] [Indexed: 11/23/2022] Open
Abstract
The contribution of germline copy number variants (CNVs) to risk of developing cancer in individuals with pathogenic BRCA1 or BRCA2 variants remains relatively unknown. We conducted the largest genome-wide analysis of CNVs in 15,342 BRCA1 and 10,740 BRCA2 pathogenic variant carriers. We used these results to prioritise a candidate breast cancer risk-modifier gene for laboratory analysis and biological validation. Notably, the HR for deletions in BRCA1 suggested an elevated breast cancer risk estimate (hazard ratio (HR) = 1.21), 95% confidence interval (95% CI = 1.09-1.35) compared with non-CNV pathogenic variants. In contrast, deletions overlapping SULT1A1 suggested a decreased breast cancer risk (HR = 0.73, 95% CI 0.59-0.91) in BRCA1 pathogenic variant carriers. Functional analyses of SULT1A1 showed that reduced mRNA expression in pathogenic BRCA1 variant cells was associated with reduced cellular proliferation and reduced DNA damage after treatment with DNA damaging agents. These data provide evidence that deleterious variants in BRCA1 plus SULT1A1 deletions contribute to variable breast cancer risk in BRCA1 carriers.
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Affiliation(s)
- Christopher Hakkaart
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - John F Pearson
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Louise Marquart
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Public Health, University of Queensland, Brisbane, Australia
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - George A R Wiggins
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Daniel R Barnes
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Bridget A Robinson
- Department of Medicine, University of Otago, Christchurch, New Zealand
- Canterbury Regional Cancer and Haematology Service, Canterbury District Health Board, Christchurch Hospital, Christchurch, New Zealand
| | - Peter D Mace
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Kristiina Aittomäki
- Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Banu K Arun
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jacopo Azzollini
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Judith Balmaña
- Hereditary cancer Genetics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Hospital Campus, Barcelona, Spain
- Department of Medical Oncology, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Rosa B Barkardottir
- Department of Pathology, Landspitali University Hospital, Reykjavik, Iceland
- BMC (Biomedical Centre), Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Sami Belhadj
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lieke Berger
- Department of Clinical Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Marinus J Blok
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Susanne E Boonen
- Department of Clinical Genetics, Odense University Hospital, Odence C, Denmark
| | - Julika Borde
- 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
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Angela R Bradbury
- Department of Medicine, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Joan Brunet
- Hereditary Cancer Program, Catalan Institute of Oncology (ICO), ONCOBELL-IDIBELL-IGTP, CIBERONC, Barcelona, Spain
| | - Saundra S Buys
- Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Maria A Caligo
- SOD Genetica Molecolare, University Hospital, Pisa, Italy
| | - Ian Campbell
- Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | | | | | - Jackie Cook
- Sheffield Clinical Genetics Service, Sheffield Children's Hospital, Sheffield, UK
| | - Casey Cosgrove
- Gynecologic Oncology, Translational Therapeutics, Department of Obstetrics and Gynecology, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Sita Dandiker
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rosemarie Davidson
- Department of Clinical Genetics, Queen Elizabeth University Hospital, Glasgow, UK
| | - Miguel de la Hoya
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
| | - Robin de Putter
- Centre for Medical Genetics, Ghent University Hospital, Gent, Belgium
| | - Capucine Delnatte
- Oncogénétique, Institut de Cancérologie de l'Ouest siteRené Gauducheau, Saint Herblain, France
| | - Mallika Dhawan
- Cancer Genetics and Prevention Program, University of California San Francisco, San Francisco, CA, USA
| | - Orland Diez
- Hereditary cancer Genetics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Hospital Campus, Barcelona, Spain
- Area of Clinical and Molecular Genetics, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Yuan Chun Ding
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Susan M Domchek
- Basser Center for BRCA, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Alan Donaldson
- Clinical Genetics Department, St Michael's Hospital, Bristol, UK
| | - Jacqueline Eason
- Nottingham Clinical Genetics Service, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Hans Ehrencrona
- Department of Clinical Genetics and Pathology, Laboratory Medicine, Skåne University Hospital, Lund, Sweden
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - 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, UK
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Ulrike Faust
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Lidia Feliubadaló
- Hereditary Cancer Program, Catalan Institute of Oncology (ICO), ONCOBELL-IDIBELL-IGTP, CIBERONC, Barcelona, Spain
| | - Florentia Fostira
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research 'Demokritos', Athens, Greece
| | - Eitan Friedman
- The Susanne Levy Gertner Oncogenetics Unit, Chaim Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Israel
| | - Megan Frone
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Debra Frost
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Judy Garber
- Cancer Risk and Prevention Clinic, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Simon A Gayther
- Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrea Gehrig
- Department of Human Genetics, University Würzburg, Würzburg, Germany
| | - Paul Gesta
- Service Régional Oncogénétique Poitou-Charentes, CH Niort, Niort, France
| | - Andrew K Godwin
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | - David E Goldgar
- Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Mark H Greene
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Eric Hahnen
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thomas V O Hansen
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jan Hauke
- 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
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julia Hentschel
- Institute of Human Genetics, University Hospital Leipzig, Leipzig, Germany
| | - Natalie Herold
- 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
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ellen Honisch
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Peter J Hulick
- Center for Medical Genetics, NorthShore University HealthSystem, Evanston, IL, USA
- The University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | | | - Claudine Isaacs
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Louise Izatt
- Clinical Genetics, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Angel Izquierdo
- Hereditary Cancer Program, Catalan Institute of Oncology (ICO), ONCOBELL-IDIBELL-IGTP, CIBERONC, Barcelona, Spain
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Paul A James
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
| | - Ramunas Janavicius
- Faculty of Medicine, Institute of Biomedical Sciences, Dept. Of Human and Medical Genetics, Vilnius University, Vilnius, Lithuania
- State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Esther M John
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Vijai Joseph
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Beth Y Karlan
- David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of California at Los Angeles, Los Angeles, CA, USA
| | - Zoe Kemp
- Breast and Cancer Genetics Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - Judy Kirk
- Familial Cancer Service, Weatmead Hospital, Wentworthville, New South Wales, Australia
| | - Irene Konstantopoulou
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research 'Demokritos', Athens, Greece
| | - Marco Koudijs
- Department of Medical Genetics, University Medical Center, Utrecht, The Netherlands
| | - Ava Kwong
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong, China
- Department of Surgery, The University of Hong Kong, Hong Kong, China
- Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Hong Kong, China
| | - Yael Laitman
- The Susanne Levy Gertner Oncogenetics Unit, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Fiona Lalloo
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Christine Lasset
- Unité de Prévention et d'Epidémiologie Génétique, Centre Léon Bérard, Lyon, France
| | - Charlotte Lautrup
- Department of Clinical Genetics, Aarhus University Hospital, Aarhus N, Denmark
| | - Conxi Lazaro
- Hereditary Cancer Program, Catalan Institute of Oncology (ICO), ONCOBELL-IDIBELL-IGTP, CIBERONC, Barcelona, Spain
| | | | - Goska Leslie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Fabienne Lesueur
- Genetic Epidemiology of Cancer team, Inserm U900, Paris, France
- Institut Curie, Paris, France
- Mines ParisTech, Fontainebleau, France
| | - Phuong L Mai
- Magee-Womens Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Siranoush Manoukian
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Véronique Mari
- Département d'Hématologie-Oncologie Médicale, Centre Antoine Lacassagne, Nice, France
| | - John W M Martens
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Lesley McGuffog
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Noura Mebirouk
- Genetic Epidemiology of Cancer team, Inserm U900, Paris, France
- Institut Curie, Paris, France
- Mines ParisTech, Fontainebleau, France
| | - Alfons Meindl
- Department of Gynecology and Obstetrics, University of Munich, Campus Großhadern, Munich, Germany
| | - Austin Miller
- NRG Oncology, Statistics and Data Management Center, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Marco Montagna
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Lidia Moserle
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | | | - Hannah Musgrave
- Department of Clinical Genetics, Yorkshire Regional Genetics Service, Chapel Allerton Hospital, Leeds, UK
| | - Sophie Nambot
- Unité d'oncogénétique, Centre de Lutte Contre le Cancer, Centre Georges-François Leclerc, Dijon, France
| | - Katherine L Nathanson
- Basser Center for BRCA, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Joanne Ngeow Yuen Yie
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Cancer Genetics Service, National Cancer Centre, Singapore, Singapore
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Edith Olah
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
| | | | - Ana Osorio
- Familial Cancer Clinical Unit, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO) and Spanish Network on Rare Diseases (CIBERER), Madrid, Spain
| | - Claus-Eric Ott
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Michael T Parsons
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Inge Sokilde Pedersen
- Molecular Diagnostics, Aalborg University Hospital, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Ana Peixoto
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | - Pedro Perez-Segura
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), Madrid, Spain
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
| | - Timea Pocza
- Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Juliane Ramser
- Division of Gynaecology and Obstetrics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
| | | | - Gustavo C Rodriguez
- Division of Gynecologic Oncology, NorthShore University HealthSystem, University of Chicago, Evanston, IL, USA
| | - Karina Rønlund
- Department of Clinical Genetics, University Hospital of Southern Denmark, Vejle Hospital, Vejle, Denmark
| | - Efraim H Rosenberg
- Department of Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Maria Rossing
- Center for Genomic Medicine, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Rita K Schmutzler
- 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
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Payal D Shah
- Department of Medicine, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Saba Sharif
- West Midlands Regional Genetics Service, Birmingham Women's Hospital Healthcare NHS Trust, Birmingham, UK
| | - Priyanka Sharma
- Department of Internal Medicine, Division of Medical Oncology, University of Kansas Medical Center, Westwood, KS, USA
| | | | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Québec City, QC, Canada
| | - Christian F Singer
- Dept of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Katie Snape
- Medical Genetics Unit, St George's, University of London, London, UK
| | - Doris Steinemann
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Dominique Stoppa-Lyonnet
- Service de Génétique, Institut Curie, Paris, France
- Department of Tumour Biology, INSERM U830, Paris, France
- Université Paris Cité, Paris, France
| | - Christian Sutter
- Institute of Human Genetics, University Hospital Heidelberg, Heidelberg, Germany
| | - Yen Yen Tan
- Dept of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Soo Hwang Teo
- Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, Odence C, Denmark
| | - Darcy L Thull
- Department of Medicine, Magee-Womens Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Marc Tischkowitz
- Program in Cancer Genetics, Departments of Human Genetics and Oncology, McGill University, Montréal, QC, Canada
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Amanda E Toland
- Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA
| | - Alison H Trainer
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
- Department of medicine, University Of Melbourne, Melbourne, Victoria, Australia
| | - Vishakha Tripathi
- South East Thames Regional Genetics Service, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Nadine Tung
- Department of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Klaartje van Engelen
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Ana Vega
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
| | - Alessandra Viel
- Division of Functional onco-genomics and genetics, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy
| | - Lisa Walker
- Oxford Regional Genetics Service, Churchill Hospital, Oxford, UK
| | - Jeffrey N Weitzel
- Latin American School of Oncology, Tuxtla Gutiérrez, Chiapas, Mexico
| | | | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Amanda B Spurdle
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Logan C Walker
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand.
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Rooney MM, Miller KN, Plichta JK. Genetics of Breast Cancer. Surg Clin North Am 2022; 103:35-47. [DOI: 10.1016/j.suc.2022.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Liang JW, Idos GE, Hong C, Gruber SB, Parmigiani G, Braun D. Statistical methods for Mendelian models with multiple genes and cancers. Genet Epidemiol 2022; 46:395-414. [PMID: 35583099 PMCID: PMC9452449 DOI: 10.1002/gepi.22460] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/06/2022] [Accepted: 05/05/2022] [Indexed: 01/29/2023]
Abstract
Risk evaluation to identify individuals who are at greater risk of cancer as a result of heritable pathogenic variants is a valuable component of individualized clinical management. Using principles of Mendelian genetics, Bayesian probability theory, and variant-specific knowledge, Mendelian models derive the probability of carrying a pathogenic variant and developing cancer in the future, based on family history. Existing Mendelian models are widely employed, but are generally limited to specific genes and syndromes. However, the upsurge of multigene panel germline testing has spurred the discovery of many new gene-cancer associations that are not presently accounted for in these models. We have developed PanelPRO, a flexible, efficient Mendelian risk prediction framework that can incorporate an arbitrary number of genes and cancers, overcoming the computational challenges that arise because of the increased model complexity. We implement an 11-gene, 11-cancer model, the largest Mendelian model created thus far, based on this framework. Using simulations and a clinical cohort with germline panel testing data, we evaluate model performance, validate the reverse-compatibility of our approach with existing Mendelian models, and illustrate its usage. Our implementation is freely available for research use in the PanelPRO R package.
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Affiliation(s)
- Jane W. Liang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gregory E. Idos
- Center for Precision Medicine, City of Hope, Duarte, CA, USA
| | - Christine Hong
- Center for Precision Medicine, City of Hope, Duarte, CA, USA
| | | | - Giovanni Parmigiani
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
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C.E DK, C. VTT, J.C. EM, G.W.M. LE, Irene H, Mariette G, J.T. VGR, Willem V, D. LK, J.M. BF, M.E. BA. The Impact of BRCA1- and BRCA2 Mutations on Ovarian Reserve Status. Reprod Sci 2022; 30:270-282. [PMID: 35705781 PMCID: PMC9810575 DOI: 10.1007/s43032-022-00997-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/02/2022] [Indexed: 01/07/2023]
Abstract
This study aimed to investigate whether female BRCA1- and BRCA2 mutation carriers have a reduced ovarian reserve status, based on serum anti-Mullerian hormone (AMH) levels, antral follicle count (AFC) and ovarian response to ovarian hyperstimulation. A prospective, multinational cohort study was performed between October 2014 and December 2019. Normo-ovulatory women, aged 18-41 years old, applying for their first PGT-cycle for reason of a BRCA mutation (cases) or other genetic diseases unrelated to ovarian reserve (controls), were asked to participate. All participants underwent a ICSI-PGT cycle with a long-agonist protocol for controlled ovarian hyperstimulation. Linear and logistic regression models were used to compare AMH, AFC and ovarian response in cases and controls. Sensitivity analyses were conducted on BRCA1- and BRCA2 mutation carrier subgroups. Thirty-six BRCA mutation carriers (18 BRCA1- and 18 BRCA2 mutation carriers) and 126 controls, with mean female age 30.4 years, were included in the primary analysis. Unadjusted median AMH serum levels (IQR) were 2.40 (1.80-3.00) ng/ml in BRCA mutation carriers and 2.15 (1.30-3.40) ng/ml in controls (p = 0.45), median AFC (IQR) was 15.0 (10.8-20.3) and 14.5 (9.0-20.0), p = 0.54, respectively. Low response rate was 22.6% among BRCA mutation carriers and 9.3% among controls, p = 0.06. Median number of retrieved oocytes was 9 (6-14) in carriers and 10 (7-13) in controls, p = 0.36. No substantial differences were observed between BRCA1- and BRCA2 mutation carriers. Based on several biomarkers, no meaningful differences in ovarian reserve status were observed in female BRCA mutation carriers compared to controls in the context of ICSI-PGT treatment.
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Affiliation(s)
- Drechsel Katja C.E
- Department of Reproductive Medicine, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - van Tilborg Theodora C.
- Department of Reproductive Medicine, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - Eijkemans Marinus J.C.
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - Lentjes Eef G.W.M.
- Central Diagnostic Laboratory (CDL), University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - Homminga Irene
- Department of Obstetrics and Gynaecology, Section Reproductive Medicine, University of Groningen, University Medical Centre Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
| | - Goddijn Mariette
- Department of Obstetrics and Gynaecology, Centre for Reproductive Medicine Amsterdam UMC, University of Amsterdam, Meibergdreef 9, AZ 1105 Amsterdam, The Netherlands
| | - van Golde Ron J.T.
- Department of Obstetrics and Gynaecology, Maastricht University Medical Centre, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands ,GROW - School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Verpoest Willem
- Centre for Reproductive Medicine, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
| | - Lichtenbelt Klaske D.
- Department of Genetics, University Medical Centre Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
| | - Broekmans Frank J.M.
- Department of Reproductive Medicine, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, 3508 GA Utrecht, The Netherlands
| | - Bos Anna M.E.
- Department of Reproductive Medicine, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, 3508 GA Utrecht, The Netherlands
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Kartsonaki C, Pang Y, Millwood I, Yang L, Guo Y, Walters R, Lv J, Hill M, Yu C, Chen Y, Chen X, O’Neill E, Chen J, Travis RC, Clarke R, Li L, Chen Z, Holmes MV. Circulating proteins and risk of pancreatic cancer: a case-subcohort study among Chinese adults. Int J Epidemiol 2022; 51:817-829. [PMID: 35064782 PMCID: PMC9189974 DOI: 10.1093/ije/dyab274] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 12/31/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Pancreatic cancer has a very poor prognosis. Biomarkers that may help predict or diagnose pancreatic cancer may lead to earlier diagnosis and improved survival. METHODS The prospective China Kadoorie Biobank (CKB) recruited 512 891 adults aged 30-79 years during 2004-08, recording 702 incident cases of pancreatic cancer during 9 years of follow-up. We conducted a case-subcohort study measuring 92 proteins in 610 cases and a subcohort of 623 individuals, using the OLINK immuno-oncology panel in stored baseline plasma samples. Cox regression with the Prentice pseudo-partial likelihood was used to estimate adjusted hazard ratios (HRs) for risk of pancreatic cancer by protein levels. RESULTS Among 1233 individuals (including 610 cases), several chemokines, interleukins, growth factors and membrane proteins were associated with risk of pancreatic cancer, with adjusted HRs per 1 standard deviation (SD) of 0.86 to 1.86, including monocyte chemotactic protein 3 (MCP3/CCL7) {1.29 [95% CI (confidence interval) (1.10, 1.51)]}, angiopoietin-2 (ANGPT2) [1.27 (1.10, 1.48)], interleukin-18 (IL18) [1.24 (1.07, 1.43)] and interleukin-6 (IL6) [1.21 (1.06, 1.38)]. Associations between some proteins [e.g. matrix metalloproteinase-7 (MMP7), hepatocyte growth factor (HGF) and tumour necrosis factor receptor superfamily member 9 [TNFRSF9)] and risk of pancreatic cancer were time-varying, with higher levels associated with higher short-term risk. Within the first year, the discriminatory ability of a model with known risk factors (age, age squared, sex, region, smoking, alcohol, education, diabetes and family history of cancer) was increased when several proteins were incorporated (weighted C-statistic changed from 0.85 to 0.99; P for difference = 4.5 × 10-5), although only a small increase in discrimination (0.77 to 0.79, P = 0.04) was achieved for long-term risk. CONCLUSIONS Several plasma proteins were associated with subsequent diagnosis of pancreatic cancer. The potential clinical utility of these biomarkers warrants further investigation.
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Affiliation(s)
- Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Iona Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yu Guo
- CKB Project Department, Chinese Academy of Medical Sciences, Beijing, China
| | - Robin Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiaofang Chen
- NCDs Prevention and Control Department, Pengzhou CDC, Pengzhou City, Sichuan Province, China
| | - Eric O’Neill
- Department of Oncology, University of Oxford, Oxford, UK
| | - Junshi Chen
- NHD Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, China
| | - Ruth C Travis
- Cancer Epidemiology Unit (CEU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Michael V Holmes
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, John Radcliffe University Hospital, Oxford, UK
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Forrai G, Kovács E, Ambrózay É, Barta M, Borbély K, Lengyel Z, Ormándi K, Péntek Z, Tünde T, Sebő É. Use of Diagnostic Imaging Modalities in Modern Screening, Diagnostics and Management of Breast Tumours 1st Central-Eastern European Professional Consensus Statement on Breast Cancer. Pathol Oncol Res 2022; 28:1610382. [PMID: 35755417 PMCID: PMC9214693 DOI: 10.3389/pore.2022.1610382] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/29/2022] [Indexed: 12/11/2022]
Abstract
Breast radiologists and nuclear medicine specialists updated their previous recommendation/guidance at the 4th Hungarian Breast Cancer Consensus Conference in Kecskemét. A recommendation is hereby made that breast tumours should be screened, diagnosed and treated according to these guidelines. These professional guidelines include the latest technical developments and research findings, including the role of imaging methods in therapy and follow-up. It includes details on domestic development proposals and also addresses related areas (forensic medicine, media, regulations, reimbursement). The entire material has been agreed with the related medical disciplines.
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Affiliation(s)
- Gábor Forrai
- GÉ-RAD Kft., Budapest, Hungary
- Duna Medical Center, Budapest, Hungary
| | - Eszter Kovács
- GÉ-RAD Kft., Budapest, Hungary
- Duna Medical Center, Budapest, Hungary
| | | | | | - Katalin Borbély
- National Institute of Oncology, Budapest, Hungary
- Ministry of Human Capacities, Budapest, Hungary
| | | | | | | | - Tasnádi Tünde
- Dr Réthy Pál Member Hospital of Békés County Central Hospital, Békéscsaba, Hungary
| | - Éva Sebő
- Kenézy Gyula University Hospital, University of Debrecen, Debrecen, Hungary
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Moorthie S, Babb de Villiers C, Burton H, Kroese M, Antoniou AC, Bhattacharjee P, Garcia-Closas M, Hall P, Schmidt MK. Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention. Prev Med 2022; 159:107075. [PMID: 35526672 DOI: 10.1016/j.ypmed.2022.107075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/05/2022] [Accepted: 05/02/2022] [Indexed: 12/24/2022]
Abstract
Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.
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Affiliation(s)
- Sowmiya Moorthie
- PHG Foundation, University of Cambridge, Cambridge, UK; Cambridge Public Health, University of Cambridge School of Clinical Medicine, Forvie Site, Cambridge Biomedical Campus, Cambridge CB2 0SR, United Kingdom.
| | | | - Hilary Burton
- PHG Foundation, University of Cambridge, Cambridge, UK
| | - Mark Kroese
- PHG Foundation, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Proteeti Bhattacharjee
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
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Abstract
Translational medicine, the exchange between laboratory (bench) and the clinic (bedside), is decidedly taking on a vital role. Many companies are now focusing on a translational medicinal approach as a therapeutic strategy in decision making upon realizing the expenses of drug attrition in late-stage advancement. In addition, the utility of biomarkers in clinical decision and therapy guidance seeks to improve the patient outcomes and decrease wasteful and harmful treatment. Efficient biomarkers are crucial for the advancement of diagnoses, better molecular targeted therapy, along with therapeutic advantages in a broad spectrum of various diseases. Despite recent advances in the discovery of biomarkers, the advancement route to a clinically validated biomarker remains intensely challenging, and many of the candidate biomarkers do not progress to clinical applications, thereby widening the innovation gap between research and application. The present article will focus on the clinical view of biomarkers in a reverse design, addressing how a biomarker program should appear if it is expected to create an impact on personalized medicine and patient care.
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38
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Li S, Silvestri V, Leslie G, Rebbeck TR, Neuhausen SL, Hopper JL, Nielsen HR, Lee A, Yang X, McGuffog L, Parsons MT, Andrulis IL, Arnold N, Belotti M, Borg Å, Buecher B, Buys SS, Caputo SM, Chung WK, Colas C, Colonna SV, Cook J, Daly MB, de la Hoya M, de Pauw A, Delhomelle H, Eason J, Engel C, Evans DG, Faust U, Fehm TN, Fostira F, Fountzilas G, Frone M, Garcia-Barberan V, Garre P, Gauthier-Villars M, Gehrig A, Glendon G, Goldgar DE, Golmard L, Greene MH, Hahnen E, Hamann U, Hanson H, Hassan T, Hentschel J, Horvath J, Izatt L, Janavicius R, Jiao Y, John EM, Karlan BY, Kim SW, Konstantopoulou I, Kwong A, Laugé A, Lee JW, Lesueur F, Mebirouk N, Meindl A, Mouret-Fourme E, Musgrave H, Ngeow Yuen Yie J, Niederacher D, Park SK, Pedersen IS, Ramser J, Ramus SJ, Rantala J, Rashid MU, Reichl F, Ritter J, Rump A, Santamariña M, Saule C, Schmidt G, Schmutzler RK, Senter L, Shariff S, Singer CF, Southey MC, Stoppa-Lyonnet D, Sutter C, Tan Y, Teo SH, Terry MB, Thomassen M, Tischkowitz M, Toland AE, Torres D, Vega A, Wagner SA, Wang-Gohrke S, Wappenschmidt B, Weber BHF, Yannoukakos D, Spurdle AB, Easton DF, Chenevix-Trench G, Ottini L, Antoniou AC. Cancer Risks Associated With BRCA1 and BRCA2 Pathogenic Variants. J Clin Oncol 2022; 40:1529-1541. [PMID: 35077220 PMCID: PMC9084432 DOI: 10.1200/jco.21.02112] [Citation(s) in RCA: 92] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/19/2021] [Accepted: 12/20/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To provide precise age-specific risk estimates of cancers other than female breast and ovarian cancers associated with pathogenic variants (PVs) in BRCA1 and BRCA2 for effective cancer risk management. METHODS We used data from 3,184 BRCA1 and 2,157 BRCA2 families in the Consortium of Investigators of Modifiers of BRCA1/2 to estimate age-specific relative (RR) and absolute risks for 22 first primary cancer types adjusting for family ascertainment. RESULTS BRCA1 PVs were associated with risks of male breast (RR = 4.30; 95% CI, 1.09 to 16.96), pancreatic (RR = 2.36; 95% CI, 1.51 to 3.68), and stomach (RR = 2.17; 95% CI, 1.25 to 3.77) cancers. Associations with colorectal and gallbladder cancers were also suggested. BRCA2 PVs were associated with risks of male breast (RR = 44.0; 95% CI, 21.3 to 90.9), stomach (RR = 3.69; 95% CI, 2.40 to 5.67), pancreatic (RR = 3.34; 95% CI, 2.21 to 5.06), and prostate (RR = 2.22; 95% CI, 1.63 to 3.03) cancers. The stomach cancer RR was higher for females than males (6.89 v 2.76; P = .04). The absolute risks to age 80 years ranged from 0.4% for male breast cancer to approximately 2.5% for pancreatic cancer for BRCA1 carriers and from approximately 2.5% for pancreatic cancer to 27% for prostate cancer for BRCA2 carriers. CONCLUSION In addition to female breast and ovarian cancers, BRCA1 and BRCA2 PVs are associated with increased risks of male breast, pancreatic, stomach, and prostate (only BRCA2 PVs) cancers, but not with the risks of other previously suggested cancers. The estimated age-specific risks will refine cancer risk management in men and women with BRCA1/2 PVs.
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Affiliation(s)
- Shuai Li
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | | | - Goska Leslie
- Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Timothy R. Rebbeck
- Harvard T.H. Chan School of Public Health, Boston, MA
- Dana-Farber Cancer Institute, Boston, MA
| | - Susan L. Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA
| | - John L. Hopper
- Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Andrew Lee
- Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Xin Yang
- Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Lesley McGuffog
- Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Michael T. Parsons
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Irene L. Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Norbert Arnold
- Department of Gynaecology and Obstetrics, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel, Germany
- Institute of Clinical Molecular Biology, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel, Germany
| | - Muriel Belotti
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Åke Borg
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Bruno Buecher
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Saundra S. Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT
| | - Sandrine M. Caputo
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY
| | - Chrystelle Colas
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Sarah V. Colonna
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT
| | - Jackie Cook
- Sheffield Clinical Genetics Service, Sheffield Children's Hospital, Sheffield, United Kingdom
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA
| | - Miguel de la Hoya
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clinico San Carlos), Madrid, Spain
| | - Antoine de Pauw
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Hélène Delhomelle
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Jacqueline Eason
- Nottingham Clinical Genetics Service, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - 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 Center, Manchester, United Kingdom
- North West Genomics Laboratory Hub, Manchester Center for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Center, Manchester, United Kingdom
| | - Ulrike Faust
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Tanja N. Fehm
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Florentia Fostira
- Molecular Diagnostics Laboratory, INRASTES, National Center for Scientific Research “Demokritos”, Athens, Greece
| | - George Fountzilas
- Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece
- Department of Medical Oncology, German Oncology Center, Limassol, Cyprus
| | - Megan Frone
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Vanesa Garcia-Barberan
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clinico San Carlos), Madrid, Spain
| | - Pilar Garre
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clinico San Carlos), Madrid, Spain
| | - Marion Gauthier-Villars
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Andrea Gehrig
- Department of Human Genetics, University Würzburg, Würzburg, Germany
| | - Gord Glendon
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
| | - David E. Goldgar
- Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT
| | - Lisa Golmard
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Mark H. Greene
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - 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
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Helen Hanson
- Southwest Thames Regional Genetics Service, St George's Hospital, London, United Kingdom
| | - Tiara Hassan
- Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
| | - Julia Hentschel
- Institute of Human Genetics, University Hospital Leipzig, Leipzig, Germany
| | - Judit Horvath
- Institute of Human Genetics, University of Münster, Münster, Germany
| | - Louise Izatt
- Clinical Genetics Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Ramunas Janavicius
- Faculty of Medicine, Department of Human and Medical Genetics, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
- State Research Institute Center for Innovative Medicine, Vilnius, Lithuania
| | - Yue Jiao
- Genetic Epidemiology of Cancer Team, Inserm U900, Paris, France
- Institut Curie, Paris, France
- Mines ParisTech, Fontainebleau, France
| | - Esther M. John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Beth Y. Karlan
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA
| | - Sung-Won Kim
- Department of Surgery, Daerim Saint Mary's Hospital, Seoul, South Korea
| | - Irene Konstantopoulou
- Molecular Diagnostics Laboratory, INRASTES, National Center for Scientific Research “Demokritos”, Athens, Greece
| | - Ava Kwong
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong
- Department of Surgery, The University of Hong Kong, Hong Kong
- Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Hong Kong
| | - Anthony Laugé
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Jong Won Lee
- Department of Surgery, Ulsan University College of Medicine and Asan Medical Center, Seoul, South Korea
| | - Fabienne Lesueur
- Genetic Epidemiology of Cancer Team, Inserm U900, Paris, France
- Institut Curie, Paris, France
- Mines ParisTech, Fontainebleau, France
| | - Noura Mebirouk
- Genetic Epidemiology of Cancer Team, Inserm U900, Paris, France
- Institut Curie, Paris, France
- Mines ParisTech, Fontainebleau, France
| | - Alfons Meindl
- Department of Gynecology and Obstetrics, University of Munich, Campus Großhadern, Munich, Germany
- Division of Gynaecology and Obstetrics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
| | - Emmanuelle Mouret-Fourme
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Hannah Musgrave
- Yorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Joanne Ngeow Yuen Yie
- Cancer Genetics Service, National Cancer Center, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Dieter Niederacher
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Sue K. Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University, Seoul, South Korea
| | - Inge Sokilde Pedersen
- Molecular Diagnostics, Aalborg University Hospital, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Juliane Ramser
- Division of Gynaecology and Obstetrics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
| | - Susan J. Ramus
- Faculty of Medicine, School of Women's and Children's Health, University of NSW Sydney, Sydney, New South Wales, Australia
- Adult Cancer Program, Lowy Cancer Research Center, University of NSW Sydney, Sydney, New South Wales, Australia
| | | | - Muhammad U. Rashid
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Center (SKMCH & RC), Lahore, Pakistan
| | - Florian Reichl
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Julia Ritter
- Institute of Medical and Human Genetics, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Rump
- Faculty of Medicine Carl Gustav Carus, Institute for Clinical Genetics, TU Dresden, Dresden, Germany
| | - Marta Santamariña
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Fundación Pública Galega Medicina Xenómica, Santiago De Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, Spain
| | - Claire Saule
- Service de Génétique, Institut Curie, Paris, France
- Paris Sciences Lettres Research University, Paris, France
| | - Gunnar Schmidt
- Institute of Human Genetics, Hannover Medical School, Hannover, 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
- Faculty of Medicine, Center for Molecular Medicine Cologne (CMMC), University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Leigha Senter
- Clinical Cancer Genetics Program, Division of Human Genetics, Department of Internal Medicine, The Comprehensive Cancer Center, The Ohio State University, Columbus, OH
| | - Saba Shariff
- West Midlands Regional Genetics Service, Birmingham Women's Hospital Healthcare NHS Trust, Birmingham, United Kingdom
| | - Christian F. Singer
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Dominique Stoppa-Lyonnet
- Service de Génétique, Institut Curie, Paris, France
- Department of Tumour Biology, INSERM U830, Paris, France
- Université Paris Descartes, Paris, France
| | - Christian Sutter
- Institute of Human Genetics, University Hospital Heidelberg, Heidelberg, Germany
| | - Yen Tan
- Department of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Soo Hwang Teo
- Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, Odence, Denmark
| | - Marc Tischkowitz
- Program in Cancer Genetics, Departments of Human Genetics and Oncology, McGill University, Montréal, QC, Canada
- Department of Medical Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Amanda E. Toland
- Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH
| | - Diana Torres
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Human Genetics, Pontificia Universidad Javeriana, Bogota, Colombia
| | - Ana Vega
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid, Spain
- Fundación Pública Galega Medicina Xenómica, Santiago De Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, Spain
| | - Sebastian A. Wagner
- Department of Medicine, Hematology/Oncology, Goethe-University Frankfurt, Frankfurt, Germany
| | - Shan Wang-Gohrke
- Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
| | - Barbara Wappenschmidt
- 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
| | - Bernhard H. F. Weber
- Institute of Human Genetics, University Regensburg, Regensburg, Germany
- Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, INRASTES, National Center for Scientific Research “Demokritos”, Athens, Greece
| | - Amanda B. Spurdle
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Douglas F. Easton
- Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Laura Ottini
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Antonis C. Antoniou
- Center for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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Pensabene M, Von Arx C, De Laurentiis M. Male Breast Cancer: From Molecular Genetics to Clinical Management. Cancers (Basel) 2022; 14:2006. [PMID: 35454911 PMCID: PMC9030724 DOI: 10.3390/cancers14082006] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 12/18/2022] Open
Abstract
MBC is a rare disease accounting for almost 1% of all cancers in men and less than 1% of breast cancer. Emerging data on the genetic drivers of predisposition for MBC are available and different risk factors have been associated with its pathogenesis. Genetic alterations, such as pathogenetic variants in BRCA1/2 and other moderate-/low-penetrance genes, along with non-genetic risk factors, have been recognized as pathogenic factors for MBC. Preventive and therapeutic implications could be related to the detection of alterations in predisposing genes, especially BRCA1/2, and to the identification of oncogenic drivers different from FBC. However, approved treatments for MBC remain the same as FBC. Cancer genetic counseling has to be considered in the diagnostic work-up of MBC with or without positive oncological family history. Here, we review the literature, reporting recent data about this malignancy with a specific focus on epidemiology, and genetic and non-genetic risk factors. We introduce the perspective of cancer genetic counseling for MBC patients and their healthy at-risk family members, with a focus on different hereditary cancer syndromes.
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Affiliation(s)
- Matilde Pensabene
- National Cancer Institute, IRCCS Fondazione G. Pascale, 80131 Naples, Italy; (C.V.A.); (M.D.L.)
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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: 42] [Impact Index Per Article: 21.0] [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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Ye Z, Li S, Dite GS, Nguyen TL, MacInnis RJ, Andrulis IL, Buys SS, Daly MB, John EM, Kurian AW, Genkinger JM, Chung WK, Phillips KA, Thorne H, Thorne H, Winship IM, Milne RL, Dugué PA, Southey MC, Giles GG, Terry MB, Hopper JL. Weight is More Informative than Body Mass Index for Predicting Postmenopausal Breast Cancer Risk: Prospective Family Study Cohort (ProF-SC). Cancer Prev Res (Phila) 2022; 15:185-191. [PMID: 34965921 PMCID: PMC8977841 DOI: 10.1158/1940-6207.capr-21-0164] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/17/2021] [Accepted: 12/20/2021] [Indexed: 01/07/2023]
Abstract
We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (ΔAIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (ΔAIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (ΔAIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk. PREVENTION RELEVANCE Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Genetic Technologies Limited, Fitzroy, Victoria, Australia
| | - Tuong L. Nguyen
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
| | - Robert J. MacInnis
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Irene L. Andrulis
- Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Saundra S. Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, Utah
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Esther M. John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
| | - Allison W. Kurian
- Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, California
| | - Jeanine M. Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York City, New York
| | - Wendy K. Chung
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York City, New York
- Departments of Pediatrics and Medicine, Columbia University, New York City, New York
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Heather Thorne
- Research Department, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Heather Thorne
- Research Department, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | | | - Ingrid M. Winship
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Roger L. Milne
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The Melbourne Medical School, The University of Melbourne, Melbourne, Australia
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York City, New York
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, Victoria, Australia
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Fanale D, Pivetti A, Cancelliere D, Spera A, Bono M, Fiorino A, Pedone E, Barraco N, Brando C, Perez A, Guarneri MF, Russo TDB, Vieni S, Guarneri G, Russo A, Bazan V. BRCA1/2 variants of unknown significance in hereditary breast and ovarian cancer (HBOC) syndrome: looking for the hidden meaning. Crit Rev Oncol Hematol 2022; 172:103626. [PMID: 35150867 DOI: 10.1016/j.critrevonc.2022.103626] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/28/2022] [Accepted: 02/07/2022] [Indexed: 01/04/2023] Open
Abstract
Hereditary breast and ovarian cancer syndrome is caused by germline mutations in BRCA1/2 genes. These genes are very large and their mutations are heterogeneous and scattered throughout the coding sequence. In addition to the above-mentioned mutations, variants of uncertain/unknown significance (VUSs) have been identified in BRCA genes, which make more difficult the clinical management of the patient and risk assessment. In the last decades, several laboratories have developed different databases that contain more than 2000 variants for the two genes and integrated strategies which include multifactorial prediction models based on direct and indirect genetic evidence, to classify the VUS and attribute them a clinical significance associated with a deleterious, high-low or neutral risk. This review provides a comprehensive overview of literature studies concerning the VUSs, in order to assess their impact on the population and provide new insight for the appropriate patient management in clinical practice.
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Affiliation(s)
- Daniele Fanale
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Alessia Pivetti
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Daniela Cancelliere
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Antonio Spera
- Department of Radiotherapy, San Giovanni di Dio Hospital, ASP of Agrigento, Agrigento, Italy
| | - Marco Bono
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Alessia Fiorino
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Erika Pedone
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Nadia Barraco
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Chiara Brando
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Alessandro Perez
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | | | - Tancredi Didier Bazan Russo
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Salvatore Vieni
- Division of General and Oncological Surgery, Department of Surgical, Oncological and Oral Sciences, University of Palermo, Italy
| | - Girolamo Guarneri
- Gynecology Section, Mother - Child Department, University of Palermo, 90127 Palermo, Italy
| | - Antonio Russo
- Section of Medical Oncology, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy.
| | - Viviana Bazan
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90127 Palermo, Italy
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43
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Niehoff NM, Terry MB, Bookwalter DB, Kaufman JD, O'Brien KM, Sandler DP, White AJ. Air Pollution and Breast Cancer: An Examination of Modification By Underlying Familial Breast Cancer Risk. Cancer Epidemiol Biomarkers Prev 2022; 31:422-429. [PMID: 34906967 PMCID: PMC8825697 DOI: 10.1158/1055-9965.epi-21-1140] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/18/2021] [Accepted: 12/03/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND An increased familial risk of breast cancer may be due to both shared genetics and environment. Women with a breast cancer family history may have a higher prevalence of breast cancer-related gene variants and thus increased susceptibility to environmental exposures. We evaluated whether air pollutant and breast cancer associations varied by familial risk. METHODS Sister Study participants living in the contiguous United States at enrollment (2003-2009; N = 48,453), all of whom had at least one first-degree relative with breast cancer, were followed for breast cancer. Annual NO2 and PM2.5 concentrations were estimated at the enrollment addresses. We predicted 1-year familial breast cancer risk using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). Using Cox regression, we estimated HRs and 95% confidence intervals (CI) for associations between each pollutant dichotomized at the median and breast cancer with interaction terms to examine modification by BOADICEA score. RESULTS NO2 was associated with a higher breast cancer risk among those with BOADICEA score >90th percentile (HR, 1.28; 95% CI, 1.05-1.56) but not among those with BOADICEA score ≤90th percentile (HR, 0.98; 95% CI, 0.90-1.06; P interaction = 0.01). In contrast to NO2, associations between PM2.5 and breast cancer did not vary between individuals with BOADICEA score >90th percentile and ≤90th percentile (P interaction = 0.26). CONCLUSIONS Our results provide additional evidence that air pollution may be implicated in breast cancer, particularly among women with a higher familial risk. IMPACT Women at higher underlying breast cancer risk may benefit more from interventions to reduce exposure to NO2.
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Affiliation(s)
- Nicole M Niehoff
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina.
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York and the Herbert Irving Comprehensive Cancer Center, New York, New York
| | | | - Joel D Kaufman
- Department of Environmental and Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, Seattle, Washington
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
| | - Alexandra J White
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
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Le TNN, Tran VK, Nguyen TT, Vo NS, Hoang TH, Vo HL, Nguyen THT, Nguyen PD, Nguyen VT, Ta TV, Tran HT. BRCA1/2 Mutations in Vietnamese Patients with Hereditary Breast and Ovarian Cancer Syndrome. Genes (Basel) 2022; 13:genes13020268. [PMID: 35205313 PMCID: PMC8872259 DOI: 10.3390/genes13020268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 11/18/2022] Open
Abstract
(1) Background: Individuals with BRCA1/2 gene mutations are at increased risk of breast and ovarian cancer. The prevalence of BRCA1/2 mutations varies by race and ethnicity, and the prevalence and the risks associated with most BRCA1/2 mutations has not been unknown in the Vietnamese population. We herein screen the entire BRCA1 and BRCA2 genes for breast and ovarian cancer patients with a family history of breast cancer and ovarian cancer, thereby, suggesting a risk score associated with carrier status and history for aiding personalized treatment; (2) Methods: Between December 2017 and December 2019, Vietnamese patients who had a pathological diagnosis of breast and epithelial ovarian cancer were followed up, prospectively, after treatment from two large institutions in Vietnam. Blood samples from 33 Vietnamese patients with hereditary breast and ovarian cancers (HBOC) syndrome were collected and analyzed using Next Generation Sequencing; (3) Results: Eleven types of mutations in both BRCA1 (in nine patients) and BRCA2 (in three patients) were detected, two of which (BRCA1:p.Tyr1666Ter and BRCA2:p.Ser1341Ter) have not been previously documented in the literature. Seven out of 19 patient’s relatives had BRCA1/2 gene mutations. All selected patients were counselled about the likelihood of cancer rising and prophylactic screening and procedures. The study established a risk score associated with the cohorts based on carrier status and family history; (4) Conclusions: Our findings suggested the implications for the planning of a screening programme for BRCA1 and BRCA2 genes testing in breast and ovarian cancer patients and genetic screening in their relatives. BRCA1/2 mutation carriers without cancer should have early and regular cancer screening, and prophylactic measures. This study could be beneficial for a diverse group in a large population-specific cohort, related to HBOC Syndrome.
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Affiliation(s)
- Trong-Nhan N. Le
- Hanoi Medical University, Hanoi 100000, Vietnam; (T.-N.N.L.); (V.-K.T.); (T.-T.N.); (H.-L.V.); (T.-H.T.N.); (V.-T.N.); (T.-V.T.)
| | - Van-Khanh Tran
- Hanoi Medical University, Hanoi 100000, Vietnam; (T.-N.N.L.); (V.-K.T.); (T.-T.N.); (H.-L.V.); (T.-H.T.N.); (V.-T.N.); (T.-V.T.)
| | - Thu-Thuy Nguyen
- Hanoi Medical University, Hanoi 100000, Vietnam; (T.-N.N.L.); (V.-K.T.); (T.-T.N.); (H.-L.V.); (T.-H.T.N.); (V.-T.N.); (T.-V.T.)
| | - Nam S. Vo
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi 100000, Vietnam; (N.S.V.); (T.H.H.)
| | - Tham H. Hoang
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi 100000, Vietnam; (N.S.V.); (T.H.H.)
| | - Hoang-Long Vo
- Hanoi Medical University, Hanoi 100000, Vietnam; (T.-N.N.L.); (V.-K.T.); (T.-T.N.); (H.-L.V.); (T.-H.T.N.); (V.-T.N.); (T.-V.T.)
| | - Thanh-Hai T. Nguyen
- Hanoi Medical University, Hanoi 100000, Vietnam; (T.-N.N.L.); (V.-K.T.); (T.-T.N.); (H.-L.V.); (T.-H.T.N.); (V.-T.N.); (T.-V.T.)
| | - Phuoc-Dung Nguyen
- National Institute of Hematology and Blood Transfusion, Hanoi 100000, Vietnam;
| | - Viet-Tien Nguyen
- Hanoi Medical University, Hanoi 100000, Vietnam; (T.-N.N.L.); (V.-K.T.); (T.-T.N.); (H.-L.V.); (T.-H.T.N.); (V.-T.N.); (T.-V.T.)
| | - Thanh-Van Ta
- Hanoi Medical University, Hanoi 100000, Vietnam; (T.-N.N.L.); (V.-K.T.); (T.-T.N.); (H.-L.V.); (T.-H.T.N.); (V.-T.N.); (T.-V.T.)
- Hanoi Medical University Hospital, Hanoi Medical University, Hanoi 100000, Vietnam
| | - Huy-Thinh Tran
- Hanoi Medical University, Hanoi 100000, Vietnam; (T.-N.N.L.); (V.-K.T.); (T.-T.N.); (H.-L.V.); (T.-H.T.N.); (V.-T.N.); (T.-V.T.)
- Hanoi Medical University Hospital, Hanoi Medical University, Hanoi 100000, Vietnam
- Correspondence: ; Tel.: +84-243-852-3798/244; Fax: +84-24-3852-5115
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Saghatchian M, Abehsera M, Yamgnane A, Geyl C, Gauthier E, Hélin V, Bazire M, Villoing-Gaudé L, Reyes C, Gentien D, Golmard L, Stoppa-Lyonnet D. Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: results from a dedicated risk clinic. Breast Cancer Res Treat 2022; 192:375-383. [PMID: 34994879 PMCID: PMC8739506 DOI: 10.1007/s10549-021-06445-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/08/2021] [Indexed: 11/02/2022]
Abstract
PURPOSE A personalized approach to prevention and early detection based on known risk factors should contribute to early diagnosis and treatment of breast cancer. We initiated a risk assessment clinic for all women wishing to undergo an individual breast cancer risk assessment. METHODS Women underwent a complete breast cancer assessment including a questionnaire, mammogram with evaluation of breast density, collection of saliva sample, consultation with a radiologist, and a breast cancer specialist. Women aged 40 or older, with 0 or 1 first-degree relative with breast cancer diagnosed after the age of 40 were eligible for risk assessment using MammoRisk, a machine learning-based tool that provides an individual 5-year estimated risk of developing breast cancer based on the patient's clinical data and breast density, with or without polygenic risk scores (PRSs). DNA was extracted from saliva samples for genotyping of 76 single-nucleotide polymorphisms. The individual risk was communicated to the patient, with individualized screening and prevention recommendations. RESULTS A total of 290 women underwent breast cancer assessment, among which 196 women (68%) were eligible for risk assessment using MammoRisk (median age 52, range 40-72). When PRS was added to MammoRisk, 40% (n = 78) of patients were assigned a different risk category, with 28% (n = 55) of patients changing from intermediate to moderate or high risk. CONCLUSION Individual risk assessment is feasible in the general population. Screening recommendations could be given based on individual risk. The use of PRS changed the risk score and screening recommendations in 40% of women.
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Affiliation(s)
- Mahasti Saghatchian
- American Hospital of Paris, Neuilly-sur-Seine, France. .,Paris-Descartes University, Paris, France.
| | - Marc Abehsera
- American Hospital of Paris, Neuilly-sur-Seine, France
| | | | - Caroline Geyl
- American Hospital of Paris, Neuilly-sur-Seine, France
| | | | | | | | | | | | | | - Lisa Golmard
- INSERM U830 D.R.U.M. Team, Institut Curie Hospital, Paris-University, Paris, France
| | - Dominique Stoppa-Lyonnet
- Institut Curie, Paris, France.,INSERM U830 D.R.U.M. Team, Institut Curie Hospital, Paris-University, Paris, France
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46
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Li H, Engel C, de la Hoya M, Peterlongo P, Yannoukakos D, Livraghi L, Radice P, Thomassen M, Hansen TVO, Gerdes AM, Nielsen HR, Caputo SM, Zambelli A, Borg A, Solano A, Thomas A, Parsons MT, Antoniou AC, Leslie G, Yang X, Chenevix-Trench G, Caldes T, Kwong A, Pedersen IS, Lautrup CK, John EM, Terry MB, Hopper JL, Southey MC, Andrulis IL, Tischkowitz M, Janavicius R, Boonen SE, Kroeldrup L, Varesco L, Hamann U, Vega A, Palmero EI, Garber J, Montagna M, Van Asperen CJ, Foretova L, Greene MH, Selkirk T, Moller P, Toland AE, Domchek SM, James PA, Thorne H, Eccles DM, Nielsen SM, Manoukian S, Pasini B, Caligo MA, Lazaro C, Kirk J, Wappenschmidt B, Spurdle AB, Couch FJ, Schmutzler R, Goldgar DE. Risks of breast and ovarian cancer for women harboring pathogenic missense variants in BRCA1 and BRCA2 compared with those harboring protein truncating variants. Genet Med 2022; 24:119-129. [PMID: 34906479 PMCID: PMC10170303 DOI: 10.1016/j.gim.2021.08.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/22/2021] [Accepted: 08/25/2021] [Indexed: 12/20/2022] Open
Abstract
PURPOSE Germline genetic testing for BRCA1 and BRCA2 variants has been a part of clinical practice for >2 decades. However, no studies have compared the cancer risks associated with missense pathogenic variants (PVs) with those associated with protein truncating (PTC) variants. METHODS We collected 582 informative pedigrees segregating 1 of 28 missense PVs in BRCA1 and 153 pedigrees segregating 1 of 12 missense PVs in BRCA2. We analyzed 324 pedigrees with PTC variants in BRCA1 and 214 pedigrees with PTC variants in BRCA2. Cancer risks were estimated using modified segregation analysis. RESULTS Estimated breast cancer risks were markedly lower for women aged >50 years carrying BRCA1 missense PVs than for the women carrying BRCA1 PTC variants (hazard ratio [HR] = 3.9 [2.4-6.2] for PVs vs 12.8 [5.7-28.7] for PTC variants; P = .01), particularly for missense PVs in the BRCA1 C-terminal domain (HR = 2.8 [1.4-5.6]; P = .005). In case of BRCA2, for women aged >50 years, the HR was 3.9 (2.0-7.2) for those heterozygous for missense PVs compared with 7.0 (3.3-14.7) for those harboring PTC variants. BRCA1 p.[Cys64Arg] and BRCA2 p.[Trp2626Cys] were associated with particularly low risks of breast cancer compared with other PVs. CONCLUSION These results have important implications for the counseling of at-risk women who harbor missense PVs in the BRCA1/2 genes.
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Affiliation(s)
- Hongyan Li
- Cancer Control and Population Science, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Miguel de la Hoya
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos, Madrid, Spain
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM - the FIRC Institute of Molecular Oncology, Milan, Italy
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, National Centre for Scientific Research "Demokritos", INRASTES Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, Athens, Greece
| | - Luca Livraghi
- Medical Oncology Unit, AZIENDA SOCIO SANITARIA TERRITORIALE PAPA GIOVANNI XXIII, Bergamo, Italy; University of Siena, Siena, Italy
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Thomas V O Hansen
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Anne-Marie Gerdes
- Department of Clinical Genetics, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Henriette R Nielsen
- Department of Clinical Genetics Sygehus Lillebaelt, Vejle Hospital, Vejle, Denmark
| | - Sandrine M Caputo
- Service de Génétique, Institut Curie, Paris, France; Paris Sciences and Lettres Research University, Paris, France
| | - Alberto Zambelli
- Medical Oncology Unit, AZIENDA SOCIO SANITARIA TERRITORIALE PAPA GIOVANNI XXIII, Bergamo, Italy
| | - Ake Borg
- Divisions of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Angela Solano
- INBIOMED, Faculty of Medicine, University of Buenos Aires, CONICET and Genotyping Laboratory, Department of Clinical Chemistry, CEMIC, Buenos Aires, Argentina
| | - Abigail Thomas
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Michael T Parsons
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Goska Leslie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Trinidad Caldes
- Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos, Madrid, Spain
| | - Ava Kwong
- Cancer Genetics Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong; Department of Surgery, LKS Faculty of Medicine,University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Inge Søkilde Pedersen
- Molecular Diagnostics, Aalborg University Hospital, Aalborg, Denmark; Clinical Cancer Research Center and Department of Clinical Genetics, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, The Faculty of Medicine, Aalborg University of Aalborg, Aalborg, Denmark
| | - Charlotte K Lautrup
- Clinical Cancer Research Center and Department of Clinical Genetics, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, The Faculty of Medicine, Aalborg University of Aalborg, Aalborg, Denmark
| | - Esther M John
- Department of Epidemiology & Population Health and Stanford Cancer Institute, School of Medicine, Stanford University, Stanford, CA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia; Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Marc Tischkowitz
- Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, University of Cambridge, Cambridge, United Kingdom
| | - Ramunas Janavicius
- Department of Human and Medical Genetics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania; State Research Institute Centre for Innovative Medicine, Vilnius, Lithuania
| | - Susanne E Boonen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Lone Kroeldrup
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Liliana Varesco
- Unit of Hereditary Cancer, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ana Vega
- Fundación Pública galega Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica-USC, CIBERER, IDIS, Santiago de Compostela, Spain
| | - Edenir I Palmero
- Molecular Oncology Research Center, Barretos Cancer Hospital, São Paulo, Brazil; National Cancer Institute, Rio de Janeiro, Brazil
| | - Judy Garber
- Center for Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA
| | - Marco Montagna
- Immunology and Molecular Oncology Unit, IOV - Istituto Oncologico Veneto - IRCCS, Padova, Italy
| | - Christi J Van Asperen
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Mark H Greene
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Tina Selkirk
- NorthShore University HealthSystem, University of Chicago, Evanston, IL
| | - Pal Moller
- Department of Tumor Biology, Institute of Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway; Center for Hereditary Tumors, HELIOS-Klinikum Wuppertal, University of Witten-Herdecke, Wuppertal, Germany
| | - Amanda E Toland
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus, OH
| | - Susan M Domchek
- Basser Center for BRCA, Abramson Cancer Center, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Paul A James
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; The Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Heather Thorne
- The Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Sarah M Nielsen
- Center for Clinical Cancer Genetics, The University of Chicago, Chicago, IL
| | - Siranoush Manoukian
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Barbara Pasini
- Medical Genetics Unit, Department of Medical Sciences, University of Torino, Torino, Italy
| | - Maria A Caligo
- SOD Genetica Molecolare, University Hospital, Pisa, Italy
| | - Conxi Lazaro
- ONCOBELL-IDIBELL-IDIBGI-IGTP, CIBERONC, Hereditary Cancer Program, Catalan Institute of Oncology, Barcelona, Spain
| | - Judy Kirk
- Familial Cancer Service, Crown Princess Mary Cancer Centre, Westmead Hospital, Sydney Medical School, University of Sydney, Centre for Cancer Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Barbara Wappenschmidt
- Center for Hereditary Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Center for Integrated Oncology (CIO), Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Amanda B Spurdle
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Rita Schmutzler
- Center for Hereditary Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Center for Integrated Oncology (CIO), Faculty of Medicine, University of Cologne, Cologne, Germany
| | - David E Goldgar
- Cancer Control and Population Science, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT; Department of Dermatology, University of Utah School of Medicine, Salt Lake City, UT.
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Kehm RD, MacInnis RJ, John EM, Liao Y, Kurian AW, Genkinger JM, Knight JA, Colonna SV, Chung WK, Milne R, Zeinomar N, Dite GS, Southey MC, Giles GG, McLachlan SA, Whitaker KD, Friedlander ML, Weideman PC, Glendon G, Nesci S, Phillips KA, Andrulis IL, Buys SS, Daly MB, Hopper JL, Terry MB. Recreational Physical Activity and Outcomes After Breast Cancer in Women at High Familial Risk. JNCI Cancer Spectr 2021; 5:pkab090. [PMID: 34950851 PMCID: PMC8692829 DOI: 10.1093/jncics/pkab090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/08/2021] [Accepted: 10/14/2021] [Indexed: 12/13/2022] Open
Abstract
Background Recreational physical activity (RPA) is associated with improved survival after breast cancer (BC) in average-risk women, but evidence is limited for women who are at increased familial risk because of a BC family history or BRCA1 and BRCA2 pathogenic variants (BRCA1/2 PVs). Methods We estimated associations of RPA (self-reported average hours per week within 3 years of BC diagnosis) with all-cause mortality and second BC events (recurrence or new primary) after first invasive BC in women in the Prospective Family Study Cohort (n = 4610, diagnosed 1993-2011, aged 22-79 years at diagnosis). We fitted Cox proportional hazards regression models adjusted for age at diagnosis, demographics, and lifestyle factors. We tested for multiplicative interactions (Wald test statistic for cross-product terms) and additive interactions (relative excess risk due to interaction) by age at diagnosis, body mass index, estrogen receptor status, stage at diagnosis, BRCA1/2 PVs, and familial risk score estimated from multigenerational pedigree data. Statistical tests were 2-sided. Results We observed 1212 deaths and 473 second BC events over a median follow-up from study enrollment of 11.0 and 10.5 years, respectively. After adjusting for covariates, RPA (any vs none) was associated with lower all-cause mortality of 16.1% (95% confidence interval [CI] = 2.4% to 27.9%) overall, 11.8% (95% CI = -3.6% to 24.9%) in women without BRCA1/2 PVs, and 47.5% (95% CI = 17.4% to 66.6%) in women with BRCA1/2 PVs (RPA*BRCA1/2 multiplicative interaction P = .005; relative excess risk due to interaction = 0.87, 95% CI = 0.01 to 1.74). RPA was not associated with risk of second BC events. Conclusion Findings support that RPA is associated with lower all-cause mortality in women with BC, particularly in women with BRCA1/2 PVs.
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Affiliation(s)
- Rebecca D Kehm
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Esther M John
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Allison W Kurian
- Division of Medical Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeanine M Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Sarah V Colonna
- Division of Medical Oncology, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY, USA
| | - Roger Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Nur Zeinomar
- Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Sue-Anne McLachlan
- Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Medical Oncology, St Vincent’s Hospital, Fitzroy, Melbourne, Victoria, Australia
| | - Kristen D Whitaker
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Michael L Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Oncology, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Prue C Weideman
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Gord Glendon
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Stephanie Nesci
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Departments of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA
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Reisel D, Baran C, Manchanda R. Preventive population genomics: The model of BRCA related cancers. ADVANCES IN GENETICS 2021; 108:1-33. [PMID: 34844711 DOI: 10.1016/bs.adgen.2021.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Preventive population genomics offers the prospect of population stratification for targeting screening and prevention and tailoring care to those at greatest risk. Within cancer, this approach is now within reach, given our expanding knowledge of its heritable components, improved ability to predict risk, and increasing availability of effective preventive strategies. Advances in technology and bioinformatics has made population-testing technically feasible. The BRCA model provides 30 years of insight and experience of how to conceive of and construct care and serves as an initial model for preventive population genomics. Population-based BRCA-testing in the Jewish population is feasible, acceptable, reduces anxiety, does not detrimentally affect psychological well-being or quality of life, is cost-effective and is now beginning to be implemented. Population-based BRCA-testing and multigene panel testing in the wider general population is cost-effective for numerous health systems and can save thousands more lives than the current clinical strategy. There is huge potential for using both genetic and non-genetic information in complex risk prediction algorithms to stratify populations for risk adapted screening and prevention. While numerous strides have been made in the last decade several issues need resolving for population genomics to fulfil its promise and potential for maximizing precision prevention. Healthcare systems need to overcome significant challenges associated with developing delivery pathways, infrastructure expansion including laboratory services, clinical workforce training, scaling of management pathways for screening and prevention. Large-scale real world population studies are needed to evaluate context specific population-testing implementation models for cancer risk prediction, screening and prevention.
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Affiliation(s)
- Dan Reisel
- EGA Institute for Women's Health, University College London, London, United Kingdom
| | - Chawan Baran
- Wolfson Institute of Preventive Medicine, CRUK Barts Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
| | - Ranjit Manchanda
- Wolfson Institute of Preventive Medicine, CRUK Barts Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom; Department of Gynaecological Oncology, St Bartholomew's Hospital, London, United Kingdom; Department of Health Services Research, London School of Hygiene & Tropical Medicine, London, United Kingdom.
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Wang Y, Zhu M, Ma H, Shen H. Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention. MEDICAL REVIEW (BERLIN, GERMANY) 2021; 1:129-149. [PMID: 37724297 PMCID: PMC10471106 DOI: 10.1515/mr-2021-0025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/13/2021] [Indexed: 09/20/2023]
Abstract
Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.
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Affiliation(s)
- Yuzhuo Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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50
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A risk prediction tool for individuals with a family history of breast, ovarian, or pancreatic cancer: BRCAPANCPRO. Br J Cancer 2021; 125:1712-1717. [PMID: 34703010 DOI: 10.1038/s41416-021-01580-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 09/07/2021] [Accepted: 10/04/2021] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Identifying families with an underlying inherited cancer predisposition is a major goal of cancer prevention efforts. Mendelian risk models have been developed to better predict the risk associated with a pathogenic variant of developing breast/ovarian cancer (with BRCAPRO) and the risk of developing pancreatic cancer (PANCPRO). Given that pathogenic variants involving BRCA2 and BRCA1 predispose to all three of these cancers, we developed a joint risk model to capture shared susceptibility. METHODS We expanded the existing framework for PANCPRO and BRCAPRO to jointly model risk of pancreatic, breast, and ovarian cancer and validated this new model, BRCAPANCPRO on three data sets each reflecting the common target populations. RESULTS BRCAPANCPRO outperformed the prior BRCAPRO and PANCPRO models and yielded good discrimination for differentiating BRCA1 and BRCA2 carriers from non-carriers (AUCs 0.79, 95% CI: 0.73-0.84 and 0.70, 95% CI: 0.60-0.80) in families seen in high-risk clinics and pancreatic cancer family registries, respectively. In addition, BRCAPANCPRO was reasonably well calibrated for predicting future risk of pancreatic cancer (observed-to-expected (O/E) ratio = 0.81 [0.69, 0.94]). DISCUSSION The BRCAPANCPRO model provides improved risk assessment over our previous risk models, particularly for pedigrees with a co-occurrence of pancreatic cancer and breast and/or ovarian cancer.
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