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Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab.
Li, Hongyan; Feng, Bingjian; Miron, Alexander; Chen, Xiaoqing; Beesley, Jonathan; Bimeh, Emmanuella; Barrowdale, Daniel; John, Esther M; Daly, Mary B; Andrulis, Irene L; Buys, Saundra S; Kraft, Peter; Thorne, Heather; Chenevix-Trench, Georgia; Southey, Melissa C; Antoniou, Antonis C; James, Paul A; Terry, Mary Beth; Phillips, Kelly-Anne; Hopper, John L; Mitchell, Gillian; Goldgar, David E.
  • Li H; Cancer Control and Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.
  • Feng B; Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • Miron A; Dana Farber Cancer Institute, Boston, Massachusetts, USA.
  • Chen X; Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA.
  • Beesley J; Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Bimeh E; Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Barrowdale D; Division of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, USA.
  • John EM; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Daly MB; Cancer Prevention Institute of California, Fremont, California, USA.
  • Andrulis IL; Department of Health Research and Policy (Epidemiology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA.
  • Buys SS; Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA.
  • Kraft P; Lunenfeld-Tanenbaum Research Institute, Department of Molecular Genetics, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada.
  • Thorne H; Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
  • Southey MC; Research Division, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.
  • Antoniou AC; Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • James PA; Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Australia.
  • Terry MB; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Phillips KA; Familial Cancer Centre, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.
  • Hopper JL; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
  • Mitchell G; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.
  • Goldgar DE; Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA.
Genet Med ; 19(1): 30-35, 2017 01.
Article en En | MEDLINE | ID: mdl-27171545
ABSTRACT

PURPOSE:

This study examined the utility of sets of single-nucleotide polymorphisms (SNPs) in familial but non-BRCA-associated breast cancer (BC).

METHODS:

We derived a polygenic risk score (PRS) based on 24 known BC risk SNPs for 4,365 women from the Breast Cancer Family Registry and Kathleen Cuningham Consortium Foundation for Research into Familial Breast Cancer familial BC cohorts. We compared scores for women based on cancer status at baseline; 2,599 women unaffected at enrollment were followed-up for an average of 7.4 years. Cox proportional hazards regression was used to analyze the association of PRS with BC risk. The BOADICEA risk prediction algorithm was used to measure risk based on family history alone.

RESULTS:

The mean PRS at baseline was 2.25 (SD, 0.35) for affected women and was 2.17 (SD, 0.35) for unaffected women from combined cohorts (P < 10-6). During follow-up, 205 BC cases occurred. The hazard ratios for continuous PRS (per SD) and upper versus lower quintiles were 1.38 (95% confidence interval 1.22-1.56) and 3.18 (95% confidence interval 1.84-5.23) respectively. Based on their PRS-based predicted risk, management for up to 23% of women could be altered.

CONCLUSION:

Including BC-associated SNPs in risk assessment can provide more accurate risk prediction than family history alone and can influence recommendations for cancer screening and prevention modalities for high-risk women.Genet Med 19 1, 30-35.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Predisposición Genética a la Enfermedad / Herencia Multifactorial / Detección Precoz del Cáncer Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Año: 2017 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Predisposición Genética a la Enfermedad / Herencia Multifactorial / Detección Precoz del Cáncer Tipo de estudio: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Año: 2017 Tipo del documento: Article