Publications by authors named "Nasim Mavaddat"

29 Publications

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Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors.

J Med Genet 2021 Nov 29. Epub 2021 Nov 29.

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Background: Epithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify women at higher risk who could benefit from targeted screening and prevention.

Methods: We developed a multifactorial EOC risk model for women of European ancestry incorporating the effects of pathogenic variants (PVs) in , , , and , a Polygenic Risk Score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively.

Results: Based on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the first to 99th percentiles of the EOC risk distribution. The corresponding range for women with an affected first-degree relative is 1.9%-10.3%. Based on the combined risk distribution, 33% of PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well calibrated in quintiles of predicted risk.

Conclusion: This multifactorial risk model can facilitate stratification, in particular among women with FH of cancer and/or moderate-risk and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).
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http://dx.doi.org/10.1136/jmedgenet-2021-107904DOI Listing
November 2021

Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women.

N Engl J Med 2021 02 20;384(5):428-439. Epub 2021 Jan 20.

The authors' affiliations are as follows: the Centre for Cancer Genetic Epidemiology, Departments of Public Health and Primary Care (L.D., S. Carvalho, J.A., K.A.P., Q.W., M.K.B., J.D., B.D., N. Mavaddat, K. Michailidou, A.C.A., P.D.P.P., D.F.E.) and Oncology (C.L., P.A.H., C. Baynes, D.M.C., L.F., V.R., M. Shah, P.D.P.P., A.M.D., D.F.E.), University of Cambridge, Cambridge, the Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine (A. Campbell, D.J.P.), and the Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology (D.J.P.), University of Edinburgh, the Cancer Research UK Edinburgh Centre (D.A.C., J.F.), and the Usher Institute of Population Health Sciences and Informatics, University of Edinburgh Medical School (A. Campbell, J.F.), Edinburgh, the Divisions of Informatics, Imaging, and Data Sciences (E.F.H.), Cancer Sciences (A. Howell), Population Health, Health Services Research, and Primary Care (A. Lophatananon, K. Muir), and Evolution and Genomic Sciences, School of Biological Sciences (W.G.N., E.M.V., D.G.E.), University of Manchester, the NIHR Manchester Biomedical Research Unit (E.F.H.) and the Nightingale Breast Screening Centre, Wythenshawe Hospital (E.F.H., H.I.), Academic Health Science Centre and North West Genomics Laboratory Hub, and the Manchester Centre for Genomic Medicine, St. Mary's Hospital, Manchester University NHS Foundation Trust (W.G.N., E.M.V., D.G.E.), Manchester, the School of Cancer and Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy's Campus, King's College London, London (E.J.S.), the Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham (I.T.), and the Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford (I.T.) - all in the United Kingdom; the Human Genotyping-CEGEN Unit, Human Cancer Genetic Program (A.G.-N., M.R.A., N.Á., B.H., R.N.-T.), and the Human Genetics Group (V.F., A.O., J.B.), Spanish National Cancer Research Center, Centro de Investigación en Red de Enfermedades Raras (A.O., J.B.), Servicio de Oncología Médica, Hospital Universitario La Paz (M.P.Z.), and Molecular Oncology Laboratory, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (M. de la Hoya), Madrid, the Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela, Complejo Hospitalario Universitario de Santiago (A. Carracedo, M.G.-D.), and Centro de Investigación en Red de Enfermedades Raras y Centro Nacional de Genotipado, Universidad de Santiago de Compostela (A. Carracedo), Santiago de Compostela, the Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galeo de Saúde, Vigo (J.E.C.), and Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Oviedo (J.I.A.P.) - all in Spain; the Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund (C. Wahlström, J.V., M.L., T. Törngren, Å.B., A.K.), the Department of Oncology, Örebro University Hospital, Örebro (C. Blomqvist), and the Departments of Medical Epidemiology and Biostatistics (K.C., M.E., M.G., P. Hall, W.H., K.H.), Oncology, Södersjukhuset (P. Hall, S. Margolin), Molecular Medicine and Surgery (A. Lindblom), and Clinical Science and Education, Södersjukhuset (S. Margolin, C. Wendt), Karolinska Institutet, and the Department of Clinical Genetics, Karolinska University Hospital (A. Lindblom), Stockholm - all in Sweden; the Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD (M.T.P., C.F., G.C.-T., A.B.S.), the Cancer Epidemiology Division, Cancer Council Victoria (G.G.G., R.J.M., R.L.M.), the Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health (G.G.G., R.J.M., R.L.M.), and the Department of Clinical Pathology (M.C.S.), University of Melbourne, Anatomical Pathology, Alfred Hospital (C.M.), and the Cancer Epidemiology Division, Cancer Council Victoria (M.C.S.), Melbourne, VIC, and Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC (G.G.G., M.C.S., R.L.M.) - all in Australia; the Division of Molecular Pathology (R.K., S. Cornelissen, M.K.S.), Family Cancer Clinic (F.B.L.H., L.E.K.), Department of Epidemiology (M.A.R.), and Division of Psychosocial Research and Epidemiology (M.K.S.), the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, Division Laboratories, Pharmacy and Biomedical Genetics, Department of Genetics, University Medical Center, Utrecht (M.G.E.M.A.), the Department of Clinical Genetics, Erasmus University Medical Center (J.M.C., A.M.W.O.), and the Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute (B.A.M.H.-G., A. Hollestelle, M.J.H.), Rotterdam, the Department of Clinical Genetics, Maastricht University Medical Center, Maastricht (E.B.G.G.), the Departments of Human Genetics (I.M.M.L., M.P.G.V., P.D.), Clinical Genetics (C.J.A.), and Pathology (P.D.), Leiden University Medical Center, Leiden, the Department of Human Genetics, Radboud University Medical Center, Nijmegen (A.R.M.), and the Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen (J.C.O.) - all in the Netherlands; the Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute (B.D.), and the Division of Cancer Epidemiology and Genetics, National Cancer Institute (T.A., S.J.C., X.R.Y., M.G.-C.), National Institutes of Health, Bethesda, MD; the Department of Pathology, Brigham and Women's Hospital, Harvard Medical School (B.D.), and the Department of Nutrition, Harvard T.H. Chan School of Public Health (R.M.V.D.), Boston; the Departments of Clinical Genetics (K.A.), Oncology (C. Blomqvist), and Obstetrics and Gynecology (H.N., M. Suvanto), Helsinki University Hospital, University of Helsinki, Helsinki, and the Unit of Clinical Oncology, Kuopio University Hospital (P. Auvinen), the Institute of Clinical Medicine, Oncology (P. Auvinen), the Translational Cancer Research Area (J.M.H., V.-M.K., A. Mannermaa), and the Institute of Clinical Medicine, Pathology, and Forensic Medicine (J.M.H., V.-M.K., A. Mannermaa), University of Eastern Finland, and the Biobank of Eastern Finland, Kuopio University Hospital (V.-M.K., A. Mannermaa), Kuopio - both in Finland; the N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus (N.N.A., N.V.B.); the Department of Gynecology and Obstetrics and Institute of Clinical Molecular Biology, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel (N.A.), the Institute of Medical Biometry and Epidemiology (H. Becher) and Cancer Epidemiology Group (T.M., J.C.-C.), University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, the Department of Gynecology and Obstetrics (M.W.B., P.A.F., L.H.) and Institute of Human Genetics (A.B.E.), University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-European Metropolitan Region of Nuremberg, Erlangen, the Division of Cancer Epidemiology (S.B., A. Jung, P.M.K., J.C.-C.), Molecular Epidemiology Group, C080 (B. Burwinkel, H.S.), Division of Pediatric Neurooncology (A.F.), and Molecular Genetics of Breast Cancer (U.H., M.M., M.U.R., D.T.), German Cancer Research Center, Molecular Biology of Breast Cancer, University Women's Clinic Heidelberg, University of Heidelberg (B. Burwinkel, A.S., H.S.), Hopp Children's Cancer Center (A.F.), Faculty of Medicine, University of Heidelberg (P.M.K.), and National Center for Tumor Diseases, University Hospital and German Cancer Research Center (A.S., C.S.), Heidelberg, the Department of Radiation Oncology (N.V.B., M. Bremer, H.C.) and the Gynecology Research Unit (N.V.B., T.D., P. Hillemanns, T.-W.P.-S., P.S.), Hannover Medical School, Hannover, the Institute of Human Genetics, University of Münster, Münster (N.B.-M.), Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart (H. Brauch, W.-Y.L.), iFIT-Cluster of Excellence, University of Tübingen, and the German Cancer Consortium, German Cancer Research Center, Partner Site Tübingen (H. Brauch), and the University of Tübingen (W.-Y.L.), Tübingen, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum, Bochum (T.B.), Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig (C.E.), Center for Hereditary Breast and Ovarian Cancer (E.H., R.K.S.) and Center for Integrated Oncology (E.H., R.K.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, the Department of Internal Medicine, Evangelische Kliniken Bonn, Johanniter Krankenhaus, Bonn (Y.-D.K.), the Department of Gynecology and Obstetrics, University of Munich, Campus Großhadern, Munich (A. Meindl), and the Institute of Pathology, Städtisches Klinikum Karlsruhe, Karlsruhe (T.R.) - all in Germany; the Gynecological Cancer Registry, Centre Georges-François Leclerc, Dijon (P. Arveux), and the Center for Research in Epidemiology and Population Health, Team Exposome and Heredity, INSERM, University Paris-Saclay, Villejuif (E.C.-D., P.G., T. Truong) - both in France; the Institute of Biochemistry and Genetics, Ufa Federal Research Center of the Russian Academy of Sciences (M. Bermisheva, E.K.), the Department of Genetics and Fundamental Medicine, Bashkir State University (E.K., D.P., Y.V.), and the Ufa Research Institute of Occupational Health and Human Ecology (Y.V.), Ufa, Russia; the Department of Genetics and Pathology (K.B., A. Jakubowska, J. Lubiński, K.P.) and the Independent Laboratory of Molecular Biology and Genetic Diagnostics (A. Jakubowska), Pomeranian Medical University, Szczecin, Poland; the Copenhagen General Population Study, the Department of Clinical Biochemistry (S.E.B., B.G.N.), and the Department of Breast Surgery (H.F.), Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, and the Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen (S.E.B., B.G.N.) - both in Denmark; the Division of Cancer Prevention and Genetics, European Institute of Oncology Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) (B. Bonanni), the Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano (S. Manoukian), the Genome Diagnostics Program, FIRC Institute of Molecular Oncology (P.P.), and the Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (P.R.), Milan; the Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet (A.-L.B.-D., G.I.G.A., V.N.K.), and the Institute of Clinical Medicine, Faculty of Medicine, University of Oslo (A.-L.B.-D., V.N.K.), Oslo; Medical Faculty, Universidad de La Sabana (I.B.), and the Clinical Epidemiology and Biostatistics Department (F.G.) and Institute of Human Genetics (D.T.), Pontificia Universidad Javeriana, Bogota, Colombia; the Department of Internal Medicine and Huntsman Cancer Institute, University of Utah (N.J.C., M.J.M., J.A.W.), and the Intermountain Healthcare Biorepository and Department of Pathology, Intermountain Healthcare (M.H.C.), Salt Lake City; the David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California, Los Angeles (P.A.F.), and Moores Cancer Center (M.G.-D., M.E.M.) and the Department of Family Medicine and Public Health (M.E.M.), University of California San Diego, La Jolla; the Departments of Medical Oncology (V.G., D.M.) and Pathology (M.T.), University Hospital of Heraklion, Heraklion, and the Department of Oncology, University Hospital of Larissa, Larissa (E.S.) - both in Greece; the Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital (G.G., I.L.A.), the Departments of Laboratory Medicine and Pathobiology (A.M.M.) and Molecular Genetics (I.L.A.), University of Toronto, and the Laboratory Medicine Program, University Health Network (A.M.M.), Toronto, and the Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, QC (J.S.) - both in Canada; the Department of Electron Microscopy and Molecular Pathology (A. Hadjisavvas, K.K., M.A.L.), the Cyprus School of Molecular Medicine (A. Hadjisavvas, K.K., M.A.L., K. Michailidou), and the Biostatistics Unit (K. Michailidou), Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus; the Saw Swee Hock School of Public Health (M. Hartman, R.M.V.D.) and the Department of Medicine, Yong Loo Lin School of Medicine (R.M.V.D.), National University of Singapore, the Department of Surgery, National University Health System (M. Hartman, J. Li), and the Human Genetics Division, Genome Institute of Singapore (J. Li), Singapore; the Department of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia (W.K.H.), and the Breast Cancer Research Programme, Cancer Research Malaysia (W.K.H., P.S.N., S.-Y.Y., S.H.T.), Selangor, and the Breast Cancer Research Unit, Cancer Research Institute (N.A.M.T.), and the Department of Surgery, Faculty of Medicine (N.A.M.T., P.S.N., S.H.T.), University Malaya, Kuala Lumpur - both in Malaysia; Surgery, School of Medicine, National University of Ireland, Galway (M.J.K., N. Miller); the Department of Surgery, Daerim Saint Mary's Hospital (S.-W.K.), the Department of Surgery, Ulsan University College of Medicine and Asan Medical Center (J.W.L.), the Department of Surgery, Soonchunhyang University College of Medicine and Soonchunhyang University Hospital (M.H.L.), Integrated Major in Innovative Medical Science, Seoul National University College of Medicine (S.K.P.), and the Cancer Research Institute, Seoul National University (S.K.P.), Seoul, South Korea; the Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, Pakistan (M.U.R.); and the National Cancer Institute, Ministry of Public Health, Nonthaburi, Thailand (S.T.).

Background: Genetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking.

Methods: We used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity.

Results: Protein-truncating variants in 5 genes (, , , , and ) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (, , , and ) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in and , odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in , , , , , and , odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in , , and were associated with a risk of breast cancer overall with a P value of less than 0.001. For , , and , missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants.

Conclusions: The results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.).
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http://dx.doi.org/10.1056/NEJMoa1913948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611105PMC
February 2021

Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk.

Am J Hum Genet 2020 11 5;107(5):837-848. Epub 2020 Oct 5.

Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong; Hong Kong Sanatorium and Hospital, Department of Pathology, Happy Valley, Hong Kong.

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS was quantified using Cox regression analyses. We assessed PRS interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10 percentile and 20.5% at the 90 percentile of PRS. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.
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http://dx.doi.org/10.1016/j.ajhg.2020.09.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675034PMC
November 2020

Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening.

Radiology 2020 11 8;297(2):327-333. Epub 2020 Sep 8.

From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.).

Background Mammography screening reduces breast cancer mortality, but a proportion of breast cancers are missed and are detected at later stages or develop during between-screening intervals. Purpose To develop a risk model based on negative mammograms that identifies women likely to be diagnosed with breast cancer before or at the next screening examination. Materials and Methods This study was based on the prospective screening cohort Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA), 2011-2017. An image-based risk model was developed by using the Stratus method and computer-aided detection mammographic features (density, masses, microcalcifications), differences in the left and right breasts, and age. The lifestyle extended model included menopausal status, family history of breast cancer, body mass index, hormone replacement therapy, and use of tobacco and alcohol. The genetic extended model included a polygenic risk score with 313 single nucleotide polymorphisms. Age-adjusted relative risks and tumor subtype specific risks were estimated by using logistic regression, and absolute risks were calculated. Results Of 70 877 participants in the KARMA cohort, 974 incident cancers were sampled from 9376 healthy women (mean age, 54 years ± 10 [standard deviation]). The area under the receiver operating characteristic curve (AUC) for the image-based model was 0.73 (95% confidence interval [CI]: 0.71, 0.74). The AUCs for the lifestyle and genetic extended models were 0.74 (95% CI: 0.72, 0.75) and 0.77 (95% CI: 0.75, 0.79), respectively. There was a relative eightfold difference in risk between women at high risk and those at general risk. High-risk women were more likely to be diagnosed with stage II cancers and with tumors 20 mm or larger and were less likely to have stage I and estrogen receptor-positive tumors. The image-based model was validated in three external cohorts. Conclusion By combining three mammographic features, differences in the left and right breasts, and optionally lifestyle factors and family history and a polygenic risk score, the model identified women at high likelihood of being diagnosed with breast cancer within 2 years of a negative screening examination and in possible need of supplemental screening. © RSNA, 2020
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http://dx.doi.org/10.1148/radiol.2020201620DOI Listing
November 2020

European polygenic risk score for prediction of breast cancer shows similar performance in Asian women.

Nat Commun 2020 07 31;11(1):3833. Epub 2020 Jul 31.

Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore, Singapore.

Polygenic risk scores (PRS) have been shown to predict breast cancer risk in European women, but their utility in Asian women is unclear. Here we evaluate the best performing PRSs for European-ancestry women using data from 17,262 breast cancer cases and 17,695 controls of Asian ancestry from 13 case-control studies, and 10,255 Chinese women from a prospective cohort (413 incident breast cancers). Compared to women in the middle quintile of the risk distribution, women in the highest 1% of PRS distribution have a ~2.7-fold risk and women in the lowest 1% of PRS distribution has ~0.4-fold risk of developing breast cancer. There is no evidence of heterogeneity in PRS performance in Chinese, Malay and Indian women. A PRS developed for European-ancestry women is also predictive of breast cancer risk in Asian women and can help in developing risk-stratified screening programmes in Asia.
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http://dx.doi.org/10.1038/s41467-020-17680-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395776PMC
July 2020

Polygenic risk scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants.

Genet Med 2020 10 15;22(10):1653-1666. Epub 2020 Jul 15.

Royal Devon & Exeter Hospital, Department of Clinical Genetics, Exeter, UK.

Purpose: We assessed the associations between population-based polygenic risk scores (PRS) for breast (BC) or epithelial ovarian cancer (EOC) with cancer risks for BRCA1 and BRCA2 pathogenic variant carriers.

Methods: Retrospective cohort data on 18,935 BRCA1 and 12,339 BRCA2 female pathogenic variant carriers of European ancestry were available. Three versions of a 313 single-nucleotide polymorphism (SNP) BC PRS were evaluated based on whether they predict overall, estrogen receptor (ER)-negative, or ER-positive BC, and two PRS for overall or high-grade serous EOC. Associations were validated in a prospective cohort.

Results: The ER-negative PRS showed the strongest association with BC risk for BRCA1 carriers (hazard ratio [HR] per standard deviation = 1.29 [95% CI 1.25-1.33], P = 3×10). For BRCA2, the strongest association was with overall BC PRS (HR = 1.31 [95% CI 1.27-1.36], P = 7×10). HR estimates decreased significantly with age and there was evidence for differences in associations by predicted variant effects on protein expression. The HR estimates were smaller than general population estimates. The high-grade serous PRS yielded the strongest associations with EOC risk for BRCA1 (HR = 1.32 [95% CI 1.25-1.40], P = 3×10) and BRCA2 (HR = 1.44 [95% CI 1.30-1.60], P = 4×10) carriers. The associations in the prospective cohort were similar.

Conclusion: Population-based PRS are strongly associated with BC and EOC risks for BRCA1/2 carriers and predict substantial absolute risk differences for women at PRS distribution extremes.
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http://dx.doi.org/10.1038/s41436-020-0862-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521995PMC
October 2020

Combined Associations of a Polygenic Risk Score and Classical Risk Factors With Breast Cancer Risk.

J Natl Cancer Inst 2021 03;113(3):329-337

Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.
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http://dx.doi.org/10.1093/jnci/djaa056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936056PMC
March 2021

Risk-reducing salpingo-oophorectomy, natural menopause, and breast cancer risk: an international prospective cohort of BRCA1 and BRCA2 mutation carriers.

Breast Cancer Res 2020 01 16;22(1). Epub 2020 Jan 16.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia.

Background: The effect of risk-reducing salpingo-oophorectomy (RRSO) on breast cancer risk for BRCA1 and BRCA2 mutation carriers is uncertain. Retrospective analyses have suggested a protective effect but may be substantially biased. Prospective studies have had limited power, particularly for BRCA2 mutation carriers. Further, previous studies have not considered the effect of RRSO in the context of natural menopause.

Methods: A multi-centre prospective cohort of 2272 BRCA1 and 1605 BRCA2 mutation carriers was followed for a mean of 5.4 and 4.9 years, respectively; 426 women developed incident breast cancer. RRSO was modelled as a time-dependent covariate in Cox regression, and its effect assessed in premenopausal and postmenopausal women.

Results: There was no association between RRSO and breast cancer for BRCA1 (HR = 1.23; 95% CI 0.94-1.61) or BRCA2 (HR = 0.88; 95% CI 0.62-1.24) mutation carriers. For BRCA2 mutation carriers, HRs were 0.68 (95% CI 0.40-1.15) and 1.07 (95% CI 0.69-1.64) for RRSO carried out before or after age 45 years, respectively. The HR for BRCA2 mutation carriers decreased with increasing time since RRSO (HR = 0.51; 95% CI 0.26-0.99 for 5 years or longer after RRSO). Estimates for premenopausal women were similar.

Conclusion: We found no evidence that RRSO reduces breast cancer risk for BRCA1 mutation carriers. A potentially beneficial effect for BRCA2 mutation carriers was observed, particularly after 5 years following RRSO. These results may inform counselling and management of carriers with respect to RRSO.
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http://dx.doi.org/10.1186/s13058-020-1247-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966793PMC
January 2020

Correction: BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors.

Genet Med 2019 Jun;21(6):1462

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, The Strangeways Research Laboratory, University of Cambridge, Cambridge, UK.

This has now been corrected in both the PDF and HTML versions of the Article. The authors regret this error.
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http://dx.doi.org/10.1038/s41436-019-0459-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608223PMC
June 2019

BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors.

Genet Med 2019 08 15;21(8):1708-1718. Epub 2019 Jan 15.

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, The Strangeways Research Laboratory, University of Cambridge, Cambridge, UK.

Purpose: Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs).

Methods: BOADICEA incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information.

Results: Among all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of women predicted to have a lifetime risk of ≥17-<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of ≥30% (high risk).

Conclusion: This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening.
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http://dx.doi.org/10.1038/s41436-018-0406-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6687499PMC
August 2019

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Am J Hum Genet 2019 01 13;104(1):21-34. Epub 2018 Dec 13.

Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland; Department of Oncology, Örebro University Hospital, Örebro 70185, Sweden.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
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http://dx.doi.org/10.1016/j.ajhg.2018.11.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323553PMC
January 2019

Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium.

Int J Epidemiol 2018 04;47(2):526-536

Department of Laboratory Medicine and Pathology.

Background: Polygenic risk scores (PRS) for breast cancer can be used to stratify the population into groups at substantially different levels of risk. Combining PRS and environmental risk factors will improve risk prediction; however, integrating PRS into risk prediction models requires evaluation of their joint association with known environmental risk factors.

Methods: Analyses were based on data from 20 studies; datasets analysed ranged from 3453 to 23 104 invasive breast cancer cases and similar numbers of controls, depending on the analysed environmental risk factor. We evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS with reproductive history, alcohol consumption, menopausal hormone therapy (MHT), height and body mass index (BMI). We tested the null hypothesis of multiplicative joint associations for PRS and each of the environmental factors, and performed global and tail-based goodness-of-fit tests in logistic regression models. The outcomes were breast cancer overall and by estrogen receptor (ER) status.

Results: The strongest evidence for a non-multiplicative joint associations with the 77-SNP PRS was for alcohol consumption (P-interaction = 0.009), adult height (P-interaction = 0.025) and current use of combined MHT (P-interaction = 0.038) in ER-positive disease. Risk associations for these factors by percentiles of PRS did not follow a clear dose-response. In addition, global and tail-based goodness of fit tests showed little evidence for departures from a multiplicative risk model, with alcohol consumption showing the strongest evidence for ER-positive disease (P = 0.013 for global and 0.18 for tail-based tests).

Conclusions: The combined effects of the 77-SNP PRS and environmental risk factors for breast cancer are generally well described by a multiplicative model. Larger studies are required to confirm possible departures from the multiplicative model for individual risk factors, and assess models specific for ER-negative disease.
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http://dx.doi.org/10.1093/ije/dyx242DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5913605PMC
April 2018

Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers.

J Natl Cancer Inst 2017 07;109(7)

Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates.

Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS.

Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P =  8.2×10 -53 ). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P =  7.2×10 -20 ). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS.

Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management.
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http://dx.doi.org/10.1093/jnci/djw302DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408990PMC
July 2017

Polygenic risk score is associated with increased disease risk in 52 Finnish breast cancer families.

Breast Cancer Res Treat 2016 08 20;158(3):463-9. Epub 2016 Jul 20.

Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 8, P.O.Box 700, 00029 HUS, Helsinki, Finland.

The risk of developing breast cancer is increased in women with family history of breast cancer and particularly in families with multiple cases of breast or ovarian cancer. Nevertheless, many women with a positive family history never develop the disease. Polygenic risk scores (PRSs) based on the risk effects of multiple common genetic variants have been proposed for individual risk assessment on a population level. We investigate the applicability of the PRS for risk prediction within breast cancer families. We studied the association between breast cancer risk and a PRS based on 75 common genetic variants in 52 Finnish breast cancer families including 427 genotyped women and pedigree information on ~4000 additional individuals by comparing the affected to healthy family members, as well as in a case-control dataset comprising 1272 healthy population controls and 1681 breast cancer cases with information on family history. Family structure was summarized using the BOADICEA risk prediction model. The PRS was associated with increased disease risk in women with family history of breast cancer as well as in women within the breast cancer families. The odds ratio (OR) for breast cancer within the family dataset was 1.55 [95 % CI 1.26-1.91] per unit increase in the PRS, similar to OR in unselected breast cancer cases of the case-control dataset (1.49 [1.38-1.62]). High PRS-values were informative for risk prediction in breast cancer families, whereas for the low PRS-categories the results were inconclusive. The PRS is informative in women with family history of breast cancer and should be incorporated within pedigree-based clinical risk assessment.
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http://dx.doi.org/10.1007/s10549-016-3897-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963452PMC
August 2016

Post-sanctions era in Iran: opportunity for science and publication.

Lancet 2016 Jul;388(10039):28-9

Health Sciences, Warwick Medical School, Coventry, UK.

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http://dx.doi.org/10.1016/S0140-6736(16)30897-2DOI Listing
July 2016

Prediction of breast cancer risk based on profiling with common genetic variants.

J Natl Cancer Inst 2015 May 8;107(5). Epub 2015 Apr 8.

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK (NM, PDPP, KM, MKB, QW, JD, RL, JBr, DFE); Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK (PDPP, JT, AMD, MS, CL, CB, SA, MM, CSH, DFE); Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK (MNB, ASw, MJS); Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Denmark (SEB, BGN, SFN); Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Herlev, Denmark (SEB, BGN, SFN); Faculty of Health and Medical Sciences, Copenhagen University Hospital, Copenhagen, Herlev, Denmark (SEB, BGN); Department of Breast Surgery, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Herlev, Denmark (HF); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (KC, HD, ME, KH, PHa); Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK (JP, IdSS, FD); Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK (NJ, AA, NO, MGC); Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands (MKS, AB, SV, EJR); Division of Breast Cancer Research, Institute of Cancer Research, London, UK (ASw); Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD (JF, SJC, LB, ASi, MD); Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland (JLis); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN (FJC); Department of Health Sciences Research, Mayo Clinic, Rochester, MN (JEO, CV, VSP, SS); Vesalius Research Center, VIB, Leuven, Belgium (DL); Laboratory for Translational Genetics, Department of Oncology, University of

Background: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking.

Methods: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates.

Results: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer.

Conclusions: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.
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http://dx.doi.org/10.1093/jnci/djv036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754625PMC
May 2015

Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE.

J Natl Cancer Inst 2013 Jun 29;105(11):812-22. Epub 2013 Apr 29.

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Manchester, UK.

Background: Reliable estimates of cancer risk are critical for guiding management of BRCA1 and BRCA2 mutation carriers. The aims of this study were to derive penetrance estimates for breast cancer, ovarian cancer, and contralateral breast cancer in a prospective series of mutation carriers and to assess how these risks are modified by common breast cancer susceptibility alleles.

Methods: Prospective cancer risks were estimated using a cohort of 978 BRCA1 and 909 BRCA2 carriers from the United Kingdom. Nine hundred eighty-eight women had no breast or ovarian cancer diagnosis at baseline, 1509 women were unaffected by ovarian cancer, and 651 had been diagnosed with unilateral breast cancer. Cumulative risks were obtained using Kaplan-Meier estimates. Associations between cancer risk and covariables of interest were evaluated using Cox regression. All statistical tests were two-sided.

Results: The average cumulative risks by age 70 years for BRCA1 carriers were estimated to be 60% (95% confidence interval [CI] = 44% to 75%) for breast cancer, 59% (95% CI = 43% to 76%) for ovarian cancer, and 83% (95% CI = 69% to 94%) for contralateral breast cancer. For BRCA2 carriers, the corresponding risks were 55% (95% CI = 41% to 70%) for breast cancer, 16.5% (95% CI = 7.5% to 34%) for ovarian cancer, and 62% (95% CI = 44% to 79.5%) for contralateral breast cancer. BRCA2 carriers in the highest tertile of risk, defined by the joint genotype distribution of seven single nucleotide polymorphisms associated with breast cancer risk, were at statistically significantly higher risk of developing breast cancer than those in the lowest tertile (hazard ratio = 4.1, 95% CI = 1.2 to 14.5; P = .02).

Conclusions: Prospective risk estimates confirm that BRCA1 and BRCA2 carriers are at high risk of developing breast, ovarian, and contralateral breast cancer. Our results confirm findings from retrospective studies that common breast cancer susceptibility alleles in combination are predictive of breast cancer risk for BRCA2 carriers.
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http://dx.doi.org/10.1093/jnci/djt095DOI Listing
June 2013

Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium.

J Med Genet 2013 Jun 6;50(6):360-7. Epub 2013 Apr 6.

Institute of Human Genetics, University of Heidelberg, Im Neuenheimer Feld 366, Heidelberg 69120, Germany.

Background: Risk prediction models are widely used in clinical genetic counselling. Despite their frequent use, the genetic risk models BOADICEA, BRCAPRO, IBIS and extended Claus model (eCLAUS), used to estimate BRCA1/2 mutation carrier probabilities, have never been comparatively evaluated in a large sample from central Europe. Additionally, a novel version of BOADICEA that incorporates tumour pathology information has not yet been validated.

Patients And Methods: Using data from 7352 German families we estimated BRCA1/2 carrier probabilities under each model and compared their discrimination and calibration. The incremental value of using pathology information in BOADICEA was assessed in a subsample of 4928 pedigrees with available data on breast tumour molecular markers oestrogen receptor, progesterone receptor and human epidermal growth factor 2.

Results: BRCAPRO (area under receiver operating characteristic curve (AUC)=0.80 (95% CI 0.78 to 0.81)) and BOADICEA (AUC=0.79 (0.78-0.80)), had significantly higher diagnostic accuracy than IBIS and eCLAUS (p<0.001). The AUC increased when pathology information was used in BOADICEA: AUC=0.81 (95% CI 0.80 to 0.83, p<0.001). At carrier thresholds of 10% and 15%, the net reclassification index was +3.9% and +5.4%, respectively, when pathology was included in the model. Overall, calibration was best for BOADICEA and worst for eCLAUS. With eCLAUS, twice as many mutation carriers were predicted than observed.

Conclusions: Our results support the use of BRCAPRO and BOADICEA for decision making regarding genetic testing for BRCA1/2 mutations. However, model calibration has to be improved for this population. eCLAUS should not be used for estimating mutation carrier probabilities in clinical settings. Whenever possible, breast tumour molecular marker information should be taken into account.
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http://dx.doi.org/10.1136/jmedgenet-2012-101415DOI Listing
June 2013

Pathology of breast and ovarian cancers among BRCA1 and BRCA2 mutation carriers: results from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA).

Cancer Epidemiol Biomarkers Prev 2012 Jan 5;21(1):134-47. Epub 2011 Dec 5.

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.

Background: Previously, small studies have found that BRCA1 and BRCA2 breast tumors differ in their pathology. Analysis of larger datasets of mutation carriers should allow further tumor characterization.

Methods: We used data from 4,325 BRCA1 and 2,568 BRCA2 mutation carriers to analyze the pathology of invasive breast, ovarian, and contralateral breast cancers.

Results: There was strong evidence that the proportion of estrogen receptor (ER)-negative breast tumors decreased with age at diagnosis among BRCA1 (P-trend = 1.2 × 10(-5)), but increased with age at diagnosis among BRCA2, carriers (P-trend = 6.8 × 10(-6)). The proportion of triple-negative tumors decreased with age at diagnosis in BRCA1 carriers but increased with age at diagnosis of BRCA2 carriers. In both BRCA1 and BRCA2 carriers, ER-negative tumors were of higher histologic grade than ER-positive tumors (grade 3 vs. grade 1; P = 1.2 × 10(-13) for BRCA1 and P = 0.001 for BRCA2). ER and progesterone receptor (PR) expression were independently associated with mutation carrier status [ER-positive odds ratio (OR) for BRCA2 = 9.4, 95% CI: 7.0-12.6 and PR-positive OR = 1.7, 95% CI: 1.3-2.3, under joint analysis]. Lobular tumors were more likely to be BRCA2-related (OR for BRCA2 = 3.3, 95% CI: 2.4-4.4; P = 4.4 × 10(-14)), and medullary tumors BRCA1-related (OR for BRCA2 = 0.25, 95% CI: 0.18-0.35; P = 2.3 × 10(-15)). ER-status of the first breast cancer was predictive of ER-status of asynchronous contralateral breast cancer (P = 0.0004 for BRCA1; P = 0.002 for BRCA2). There were no significant differences in ovarian cancer morphology between BRCA1 and BRCA2 carriers (serous: 67%; mucinous: 1%; endometrioid: 12%; clear-cell: 2%). CONCLUSIONS/IMPACT: Pathologic characteristics of BRCA1 and BRCA2 tumors may be useful for improving risk-prediction algorithms and informing clinical strategies for screening and prophylaxis.
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http://dx.doi.org/10.1158/1055-9965.EPI-11-0775DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3272407PMC
January 2012

Genetic susceptibility to breast cancer.

Mol Oncol 2010 Jun 21;4(3):174-91. Epub 2010 May 21.

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, United Kingdom.

Genetic and lifestyle/environmental factors are implicated in the aetiology of breast cancer. This review summarizes the current state of knowledge on rare high penetrance mutations, as well as moderate and low-penetrance genetic variants implicated in breast cancer aetiology. We summarize recent discoveries from large collaborative efforts to combine data from candidate gene studies, and to conduct genome-wide association studies (GWAS), primarily in breast cancers in the general population. These findings are compared with results from collaborative efforts aiming to identify genetic modifiers in BRCA1 and BRCA2 carriers. Breast cancer is a heterogeneous disease, and tumours from BRCA1 and BRCA2 carriers display distinct pathological characteristics when compared with tumours unselected for family history. The relationship between genetic variants and pathological subtypes of breast cancer, and the implication of discoveries of novel genetic variants to risk prediction in BRCA1/2 mutation carriers and in populations unselected for mutation carrier status, are discussed.
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http://dx.doi.org/10.1016/j.molonc.2010.04.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5527934PMC
June 2010

Incorporating tumour pathology information into breast cancer risk prediction algorithms.

Breast Cancer Res 2010 18;12(3):R28. Epub 2010 May 18.

Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, UK.

Introduction: Mutations in BRCA1 and BRCA2 confer high risks of breast cancer and ovarian cancer. The risk prediction algorithm BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) may be used to compute the probabilities of carrying mutations in BRCA1 and BRCA2 and help to target mutation screening. Tumours from BRCA1 and BRCA2 mutation carriers display distinctive pathological features that could be used to better discriminate between BRCA1 mutation carriers, BRCA2 mutation carriers and noncarriers. In particular, oestrogen receptor (ER)-negative status, triple-negative (TN) status, and expression of basal markers are predictive of BRCA1 mutation carrier status.

Methods: We extended BOADICEA by treating breast cancer subtypes as distinct disease end points. Age-specific expression of phenotypic markers in a series of tumours from 182 BRCA1 mutation carriers, 62 BRCA2 mutation carriers and 109 controls from the Breast Cancer Linkage Consortium, and over 300,000 tumours from the general population obtained from the Surveillance Epidemiology, and End Results database, were used to calculate age-specific and genotype-specific incidences of each disease end point. The probability that an individual carries a BRCA1 or BRCA2 mutation given their family history and tumour marker status of family members was computed in sample pedigrees.

Results: The cumulative risk of ER-negative breast cancer by age 70 for BRCA1 mutation carriers was estimated to be 55% and the risk of ER-positive disease was 18%. The corresponding risks for BRCA2 mutation carriers were 21% and 44% for ER-negative and ER-positive disease, respectively. The predicted BRCA1 carrier probabilities among ER-positive breast cancer cases were less than 1% at all ages. For women diagnosed with breast cancer below age 50 years, these probabilities rose to more than 5% in ER-negative breast cancer, 7% in TN disease and 24% in TN breast cancer expressing both CK5/6 and CK14 cytokeratins. Large differences in mutation probabilities were observed by combining ER status and other informative markers with family history.

Conclusions: This approach combines both full pedigree and tumour subtype data to predict BRCA1/2 carrier probabilities. Prediction of BRCA1/2 carrier status, and hence selection of women for mutation screening, may be substantially improved by combining tumour pathology with family history of cancer.
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http://dx.doi.org/10.1186/bcr2576DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917017PMC
November 2010

Familial relative risks for breast cancer by pathological subtype: a population-based cohort study.

Breast Cancer Res 2010 10;12(1):R10. Epub 2010 Feb 10.

Cancer Research UK, Genetic Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK.

Introduction: The risk of breast cancer to first degree relatives of breast cancer patients is approximately twice that of the general population. Breast cancer, however, is a heterogeneous disease and it is plausible that the familial relative risk (FRR) for breast cancer may differ by the pathological subtype of the tumour. The contribution of genetic variants associated with breast cancer susceptibility to the subtype-specific FRR is still unclear.

Methods: We computed breast cancer FRR for subtypes of breast cancer by comparing breast cancer incidence in relatives of breast cancer cases from a population-based series with known estrogen receptor (ER), progesterone receptor (PR) or human epidermal growth factor receptor 2 (HER2) status with that expected from the general population. We estimated the contribution to the FRR of genetic variants associated with breast cancer susceptibility using subtype-specific genotypic relative risks and allele frequencies for each variant.

Results: At least one marker was measured for 4,590 breast cancer cases, who reported 9,014 affected and unaffected first-degree female relatives. There was no difference between the breast cancer FRR for relatives of patients with ER-negative (FRR = 1.78, 95% confidence intervals (CI): 1.44 to 2.11) and ER-positive disease (1.82, 95% CI: 1.67 to 1.98), P = 0.99. There was some suggestion that the breast cancer FRR for relatives of patients with ER-negative disease was higher than that for ER-positive disease for ages of the relative less than 50 years old (FRR = 2.96, 95% CI: 2.04 to 3.87; and 2.05, 95% CI: 1.70 to 2.40 respectively; P = 0.07), and that the breast cancer FRR for relatives of patients with ER-positive disease was higher than for ER-negative disease when the age of the relative was greater than 50 years (FRR = 1.76, 95% CI: 1.59 to 1.93; and 1.41, 95% CI: 1.08 to 1.74 respectively, P = 0.06). We estimated that mutations in BRCA1 and BRCA2 explain 32% of breast cancer FRR for relatives of patients with ER-negative and 9.4% of the breast cancer FRR for relatives of patients with ER-positive disease. Twelve recently identified common breast cancer susceptibility variants were estimated to explain 1.9% and 9.6% of the FRR to relatives of patients with ER-negative and ER-positive disease respectively.

Conclusions: FRR for breast cancer was significantly increased for both ER-negative and ER-positive disease. Including receptor status in conjunction with genetic status may aid risk prediction in women with a family history.
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http://dx.doi.org/10.1186/bcr2476DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2880431PMC
August 2010

Common genetic variation in candidate genes and susceptibility to subtypes of breast cancer.

Cancer Epidemiol Biomarkers Prev 2009 Jan;18(1):255-9

Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.

Association studies have been widely used to search for common low-penetrance susceptibility alleles to breast cancer in general. However, breast cancer is a heterogeneous disease and it has been suggested that it may be possible to identify additional susceptibility alleles by restricting analyses to particular subtypes. We used data on 710 single nucleotide polymorphisms (SNP) in 120 candidate genes from a large candidate gene association study of up to 4,470 cases and 4,560 controls to compare the results of analyses of "overall" breast cancer with subgroup analyses based on the major clinicopathologic characteristics of breast cancer (stage, grade, morphology, and hormone receptor status). No SNP was highly significant in overall effects analysis. Subgroup analysis resulted in substantial reordering of ranks of SNPs, as assessed by the magnitude of the test statistics, and some associations that were not significant for an overall effect were detected in subgroups at a nominal 5% level adjusted for multiple testing. The most significant association of CCND1 SNP rs3212879 with estrogen receptor-negative tumor types (P = 0.001) did not reach genome-wide significance levels. These results show that it may be possible to detect associations using subgroup analysis that are missed in overall effects analysis. If the associations we found can be replicated in independent studies, they may provide important insights into disease mechanisms in breast cancer.
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http://dx.doi.org/10.1158/1055-9965.EPI-08-0704DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655077PMC
January 2009

CD161 (human NKR-P1A) signaling in NK cells involves the activation of acid sphingomyelinase.

J Immunol 2006 Feb;176(4):2397-406

Immunology Division, Department of Pathology, University of Cambridge, UK.

NK and NKT cells play a major role in both innate immunity and in influencing the development of adaptive immune responses. CD161 (human NKR-P1A), a protein encoded in the NK gene complex, is a major phenotypic marker of both these cell types and is thought to be involved in the regulation of NK and NKT cell function. However, the mechanisms of action and signaling pathways of CD161 are poorly understood. To identify molecules able to interact with the cytoplasmic tail of human CD161 (NKR-P1A), we have conducted a yeast two-hybrid screen and identified acid sphingomyelinase as a novel intracellular signaling pathway linked to CD161. mAb-mediated cross-linking of CD161, in both transfectants and primary human NK cells, triggers the activation of acid, but not neutral sphingomyelinase. The sphingomyelinases represent the catabolic pathway for N-acyl-sphingosine (ceramide) generation, an emerging second messenger with key roles in the induction of apoptosis, proliferation, and differentiation. These data therefore define a novel signal transduction pathway for the CD161 (NKR-P1A) receptor and provide fresh insights into NK and NKT cell biology.
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http://dx.doi.org/10.4049/jimmunol.176.4.2397DOI Listing
February 2006

Reaction between ellagic acid and an ultimate carcinogen.

J Chem Inf Model 2005 Nov-Dec;45(6):1564-70

Laboratoire de Physique Moléculaire, UMR CNRS 6624, Faculté des Sciences et Techniques, La Bouloie, Université de Franche-Comté, 25030 Besançon Cedex, France.

The reaction coordinate between a typical ultimate carcinogen benzo[a]pyrene-7,8-diol-9,10-epoxide (BPDE) and ellagic acid, a proven chemopreventive agent active against cancers caused by polycyclic aromatic hydrocarbons (PAHs), was examined by density functional theory (DFT) and semiempirical MO calculations, and activation energy was calculated. The effect of a polar environment was included using Tomasi and the Langevin dipoles methods. The calculated BPDE/ellagic acid reaction free energy of activation is found to be in decent agreement with experimental data [Sayer, J. M. et al. J. Am. Chem. Soc. 1982, 104, 5562-5564]. This work sheds light on the mechanism of action of ellagic acid. Quantum chemical calculations of this kind are valuable for the design of ellagic acid derivatives with even lower activation energy and increased reactivity toward ultimate carcinogens as well as controlled reactivity toward DNA.
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http://dx.doi.org/10.1021/ci050163cDOI Listing
June 2006

Switching from a restricted to an effective CD4 T cell response by activating CD8+ murine dendritic cells with a Toll-like receptor 9 ligand.

Eur J Immunol 2005 Nov;35(11):3209-20

The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.

Freshly isolated quiescent splenic dendritic cell (DC) subtypes differ in their capacity to activate naive CD4 T cells in culture. The CD8+ DC showed a reduced capacity to stimulate T cell proliferation compared to either of the CD8- DC subsets, regardless of antigen and DC dose. In contrast to CD8- DC, the quiescent CD8+ DC did not induce IFN-gamma production from CD4 T cells. The difference between the DC subtypes appeared to be at the level of initial surface molecule interactions, but could not be attributed to differences in expression of MHC class II or B7 family molecules, or to the expression of Fas ligand on DC. However, when activated by inclusion of the Toll-like receptor 9 ligand CpG in culture, CD8+ DC became potent stimulators of both CD4 T cell proliferation and IFN-gamma production. In contrast, similar activation of CD8- DC produced a more modest increase in capacity to stimulate CD4 T cell proliferation and no increase in capacity to stimulate IFN-gamma production. The difference between a quiescent and an activated state is therefore more extreme for CD8+ than for CD8- DC. The especially tight regulation of the activity of CD8+ DC may be essential for the maintenance of self tolerance.
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http://dx.doi.org/10.1002/eji.200526231DOI Listing
November 2005

The ligand-binding face of the semaphorins revealed by the high-resolution crystal structure of SEMA4D.

Nat Struct Biol 2003 Oct 7;10(10):843-8. Epub 2003 Sep 7.

Division of Structural Biology, Henry Wellcome Building for Genomic Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK.

Semaphorins, proteins characterized by an extracellular sema domain, regulate axon guidance, immune function and angiogenesis. The crystal structure of SEMA4D (residues 1-657) shows the sema topology to be a seven-bladed beta-propeller, revealing an unexpected homology with integrins. The sema beta-propeller contains a distinctive 77-residue insertion between beta-strands C and D of blade 5. Blade 7 is followed by a domain common to plexins, semaphorins and integrins (PSI domain), which forms a compact cysteine knot abutting the side of the propeller, and an Ig-like domain. The top face of the beta-propeller presents prominent loops characteristic of semaphorins. In addition to limited contact between the Ig-like domains, the homodimer is stabilized through extensive interactions between the top faces in a sector of the beta-propeller used for heterodimerization in integrins. This face of the propeller also mediates ligand binding in integrins, and functional data for semaphorin-receptor interactions map to the equivalent surface.
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http://dx.doi.org/10.1038/nsb977DOI Listing
October 2003
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