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CC BY-NC-ND 4.0 · Geburtshilfe Frauenheilkd 2018; 78(05): 481-492
DOI: 10.1055/a-0603-4350
GebFra Science
Review
Georg Thieme Verlag KG Stuttgart · New York

Risk, Prediction and Prevention of Hereditary Breast Cancer – Large-Scale Genomic Studies in Times of Big and Smart Data

Risiko, Vorhersage und Prävention von erblichem Brustkrebs – groß angelegte genomische Studien in Zeiten von Big und Smart Data
Marius Wunderle
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Gregor Olmes
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Naiba Nabieva
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Lothar Häberle
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
2   Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Sebastian M. Jud
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Alexander Hein
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Claudia Rauh
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Carolin C. Hack
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Ramona Erber
3   Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Arif B. Ekici
4   Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Juliane Hoyer
4   Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Georgia Vasileiou
4   Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Cornelia Kraus
4   Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
André Reis
4   Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Arndt Hartmann
3   Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Rüdiger Schulz-Wendtland
5   Institute of Diagnostic Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Michael P. Lux
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Matthias W. Beckmann
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
,
Peter A. Fasching
1   Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
› Author Affiliations
Further Information

Publication History

received 08 April 2018
revised 09 April 2018

accepted 09 April 2018

Publication Date:
04 June 2018 (online)

Also available at
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Abstract

Over the last two decades genetic testing for mutations in BRCA1 and BRCA2 has become standard of care for women and men who are at familial risk for breast or ovarian cancer. Currently, genetic testing more often also includes so-called panel genes, which are assumed to be moderate-risk genes for breast cancer. Recently, new large-scale studies provided more information about the risk estimation of those genes. The utilization of information on panel genes with regard to their association with the individual breast cancer risk might become part of future clinical practice. Furthermore, large efforts have been made to understand the influence of common genetic variants with a low impact on breast cancer risk. For this purpose, almost 450 000 individuals have been genotyped for almost 500 000 genetic variants in the OncoArray project. Based on first results it can be assumed that – together with previously identified common variants – more than 170 breast cancer risk single nucleotide polymorphisms can explain up to 18% of familial breast cancer risk. The knowledge about genetic and non-genetic risk factors and its implementation in clinical practice could especially be of use for individualized prevention. This includes an individualized risk prediction as well as the individualized selection of screening methods regarding imaging and possible lifestyle interventions. The aim of this review is to summarize the most recent developments in this area and to provide an overview on breast cancer risk genes, risk prediction models and their utilization for the individual patient.

Zusammenfassung

In den letzten 2 Jahrzehnten wurden genetische Testungen zur Erkennung von BRCA1- und BRCA2-Mutationen Teil der Standardversorgung für Personen mit einem erhöhten familiären Risiko, an Brust- oder Eierstockkrebs zu erkranken. Zurzeit wird bei genetischen Testungen immer öfters auch nach Mutationen in sogenannten Panel-Genen gesucht, von denen angenommen wird, dass sie mit einem mittleren Erkrankungsrisiko für Brustkrebs einhergehen. Vor Kurzem wurden die Ergebnisse neuer großangelegter Studien publiziert, die mehr Informationen über die Risikoabschätzung für diese Gene bieten. Die Nutzung dieses neuen Wissens über Panel-Gene und des damit verbundenen individuellen Erkrankungsrisikos könnte in Zukunft klinischer Alltag sein. Dazu kommt, dass auch große Anstrengungen unternommen wurden, um den Einfluss häufig vorkommender genetischer Varianten, die nur geringe Auswirkungen auf das Brustkrebsrisiko haben, zu verstehen. Zu diesem Zwecke wurde im Zuge des OncoArray-Projekts eine Genotypisierung von annähernd 500 000 genetischen Varianten bei fast 450 000 Personen vorgenommen. Basierend auf den ersten Zwischenergebnissen wird nun angenommen, dass es zusammen mit den bereits zuvor identifizierten häufig vorkommenden Varianten mehr als 170 Einzelnukleotid-Polymorphismen gibt, die ein Brustkrebsrisiko bergen und die bis zu 18% des familiären Risikos, an Brustkrebs zu erkranken, erklären können. Die Umsetzung des Wissens von genetischen und nicht genetischen Risikofaktoren in die klinische Praxis könnte besonders für individuelle Präventionsmaßnahmen von Nutzen sein. Hierzu zählen sowohl die individuelle Risikovorhersage, die individualisierte Auswahl von bildgebenden Verfahren für Vorsorgeuntersuchungen sowie potenzielle Lebensstil-Interventionen. Ziel dieses Artikels ist es, die neuesten Entwicklungen auf diesem Gebiet zusammenzufassen sowie einen Überblick über Brustkrebsrisikogene, Risikovorhersagemodelle und deren Nutzen für individuelle Patientinnen zu geben.

Key words

breast cancer - risk - BRCA1 - BRCA2 - panel genes - SNP

Schlüsselwörter

Brustkrebs - Risiko - BRCA1 - BRCA2 - Panel-Gene - SNP - Einzelnukleotid-Polymorphismen
 

  • References

  • 1 Leitlinienprogramm Onkologie (Deutsche Krebsgesellschaft/Deutsche Krebshilfe/AWMF). S3-Leitlinie Früherkennung, Diagnose, Therapie und Nachsorge des Mammakarzinoms, Version 4.0, 2017 AWMF Registernummer: 032-045OL (2017). Online: https://www.leitlinienprogramm-onkologie.de/fileadmin/user_upload/LL_Mammakarzinom_Langversion_4.0.pdf last access: 01.04.2018
    PubMedGoogle Scholar
  • 2 AGO Breast Committee. Diagnosis and Treatment of Patients with Primary and Metastatic Breast Cancer (2018). Online: https://www.ago-online.de/fileadmin/downloads/leitlinien/mamma/2018-03/EN/AGO_2018_PDF_Englisch_with_References.zip last access: 01.04.2018
    PubMedGoogle Scholar
  • 3 Kurian AW, Idos G, Culver J. et al. Safety of multiplex gene testing for inherited cancer risk: Interim analysis of a clinical trial. J Clin Oncol 2016; 34 (Suppl.) Abstr.. 1503
    CrossrefPubMedGoogle Scholar
  • 4 Couch FJ, Shimelis H, Hu C. et al. Associations Between Cancer Predisposition Testing Panel Genes and Breast Cancer. JAMA Oncol 2017; 3: 1190-1196
    CrossrefPubMedGoogle Scholar
  • 5 Copson ER, Maishman TC, Tapper WJ. et al. Germline BRCA mutation and outcome in young-onset breast cancer (POSH): a prospective cohort study. Lancet Oncol 2018; 19: 169-180
    CrossrefPubMedGoogle Scholar
  • 6 Fasching PA. Breast cancer in young women: do BRCA1 or BRCA2 mutations matter?. Lancet Oncol 2018; 19: 150-151
    CrossrefPubMedGoogle Scholar
  • 7 Couch FJ, Hart SN, Sharma P. et al. Inherited mutations in 17 breast cancer susceptibility genes among a large triple-negative breast cancer cohort unselected for family history of breast cancer. J Clin Oncol 2015; 33: 304-311
    CrossrefPubMedGoogle Scholar
  • 8 Plon SE, Eccles DM, Easton D. et al. Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum Mutat 2008; 29: 1282-1291
    CrossrefPubMedGoogle Scholar
  • 9 Exome Aggregation Consortium. ExAC Browser (Beta) (2018). Online: http://exac.broadinstitute.org/ last access: 01.04.2018
    PubMedGoogle Scholar
  • 10 The FLOSSIES project. A database of germline genomic variation in healthy older women (2018). Online: https://whi.color.com/ last access: 01.04.2018
    PubMedGoogle Scholar
  • 11 Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490: 61-70
    CrossrefPubMedGoogle Scholar
  • 12 National Institutes of Health; National Center for Biotechnology Information, US National Library of Medicine. The database of Genotypes and Phenotypes (dbGaP) (2018). Online: https://www.ncbi.nlm.nih.gov/gap last access: 01.04.2018
    PubMedGoogle Scholar
  • 13 Hauke J, Horvath J, Gross E. et al. Gene panel testing of 5589 BRCA1/2-negative index patients with breast cancer in a routine diagnostic setting: results of the German Consortium for Hereditary Breast and Ovarian Cancer. Cancer Med 2018; DOI: 10.1002/cam4.1376.
    CrossrefPubMedGoogle Scholar
  • 14 Couch F, Shimelis H, LaDuca H. et al. Triple negative breast cancer predisposition genes [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5–9; San Antonio, TX Philadelphia (PA): AACR. Cancer Res 2018; 78: Abstr.. PD1-01
    PubMedGoogle Scholar
  • 15 National Institutes of Health; National Center for Biotechnology Information, US National Library of Medicine. ClinVar (2018). Online: https://www.ncbi.nlm.nih.gov/clinvar/ last access: 01.04.2018
    PubMedGoogle Scholar
  • 16 The ENIGMA consortium. Evidence-based Network for the Interpretation of Germline Mutant Alleles (2018). Online: https://enigmaconsortium.org/ last access: 01.04.2018
    PubMedGoogle Scholar
  • 17 National Institutes of Health; National Center for Biotechnology Information, US National Library of Medicine. GTR: Genetic Testing Registry (2018). Online: https://www.ncbi.nlm.nih.gov/gtr/ last access: 01.04.2018
    PubMedGoogle Scholar
  • 18 Couch FJ, Shimelis H, Hart SN. et al. Cancer risks and response to targeted therapy associated with BRCA2 variants of uncertain significance [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5–9; San Antonio, TX Philadelphia (PA): AACR. Cancer Res 2018; 78: Abstr.. GS4-06
    CrossrefPubMedGoogle Scholar
  • 19 United States Food and Drug Administration (FDA). FDA approves first treatment for breast cancer with a certain inherited genetic mutation. 2018. Online: https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm592347.htm last access: 01.04.2018
    PubMedGoogle Scholar
  • 20 Fasching PA, Hu C, Hart SN. et al. Cancer predisposition genes in metastatic breast cancer – Association with metastatic pattern, prognosis, patient and tumor characteristics [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5–9; San Antonio, TX Philadelphia (PA): AACR. Cancer Res 2018; 78: Abstr.. PD1-02
    PubMedGoogle Scholar
  • 21 Cox A, Dunning AM, Garcia-Closas M. et al. A common coding variant in CASP8 is associated with breast cancer risk. Nat Genet 2007; 39: 352-358
    CrossrefPubMedGoogle Scholar
  • 22 Easton DF, Pooley KA, Dunning AM. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007; 447: 1087-1093
    CrossrefPubMedGoogle Scholar
  • 23 Hunter DJ, Kraft P, Jacobs KB. et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 2007; 39: 870-874
    CrossrefPubMedGoogle Scholar
  • 24 Stacey SN, Manolescu A, Sulem P. et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 2007; 39: 865-869
    CrossrefPubMedGoogle Scholar
  • 25 Stacey SN, Manolescu A, Sulem P. et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 2008; 40: 703-706
    CrossrefPubMedGoogle Scholar
  • 26 Ahmed S, Thomas G, Ghoussaini M. et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet 2009; 41: 585-590
    CrossrefPubMedGoogle Scholar
  • 27 Thomas G, Jacobs KB, Kraft P. et al. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet 2009; 41: 579-584
    CrossrefPubMedGoogle Scholar
  • 28 Zheng W, Long J, Gao YT. et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet 2009; 41: 324-328
    CrossrefPubMedGoogle Scholar
  • 29 Antoniou AC, Wang X, Fredericksen ZS. et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet 2010; 42: 885-892
    CrossrefPubMedGoogle Scholar
  • 30 Turnbull C, Ahmed S, Morrison J. et al. Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet 2010; 42: 504-507
    CrossrefPubMedGoogle Scholar
  • 31 Fletcher O, Johnson N, Orr N. et al. Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study. J Natl Cancer Inst 2011; 103: 425-435
    CrossrefPubMedGoogle Scholar
  • 32 Haiman CA, Chen GK, Vachon CM. et al. A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat Genet 2011; 43: 1210-1214
    CrossrefPubMedGoogle Scholar
  • 33 Ghoussaini M, Fletcher O, Michailidou K. et al. Genome-wide association analysis identifies three new breast cancer susceptibility loci. Nat Genet 2012; 44: 312-318
    CrossrefPubMedGoogle Scholar
  • 34 Siddiq A, Couch FJ, Chen GK. et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Hum Mol Genet 2012; 21: 5373-5384
    CrossrefPubMedGoogle Scholar
  • 35 Michailidou K, Hall P, Gonzalez-Neira A. et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet 2013; 45: 353-361 361e1–361e2
    CrossrefPubMedGoogle Scholar
  • 36 Bojesen SE, Pooley KA, Johnatty SE. et al. Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer. Nat Genet 2013; 45: 371-384 384e1–384e2
    CrossrefPubMedGoogle Scholar
  • 37 French JD, Ghoussaini M, Edwards SL. et al. Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers. Am J Hum Genet 2013; 92: 489-503
    CrossrefPubMedGoogle Scholar
  • 38 Garcia-Closas M, Couch FJ, Lindstrom S. et al. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet 2013; 45: 392-398 398e1–398e2
    CrossrefPubMedGoogle Scholar
  • 39 Michailidou K, Beesley J, Lindstrom S. et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet 2015; 47: 373-380
    CrossrefPubMedGoogle Scholar
  • 40 Amos CI, Dennis J, Wang Z. et al. The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers. Cancer Epidemiol Biomarkers Prev 2017; 26: 126-135
    CrossrefPubMedGoogle Scholar
  • 41 Michailidou K, Lindstrom S, Dennis J. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 2017; 551: 92-94
    CrossrefPubMedGoogle Scholar
  • 42 Milne RL, Kuchenbaecker KB, Michailidou K. et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet 2017; 49: 1767-1778
    CrossrefPubMedGoogle Scholar
  • 43 Fasching PA, Loehberg CR, Strissel PL. et al. Single nucleotide polymorphisms of the aromatase gene (CYP19A1), HER2/neu status, and prognosis in breast cancer patients. Breast Cancer Res Treat 2008; 112: 89-98
    CrossrefPubMedGoogle Scholar
  • 44 Hein A, Bayer CM, Schrauder MG. et al. Polymorphisms in the RANK/RANKL genes and their effect on bone specific prognosis in breast cancer patients. Biomed Res Int 2014; 2014: 842452
    PubMedGoogle Scholar
  • 45 Hein A, Lambrechts D, von Minckwitz G. et al. Genetic variants in VEGF pathway genes in neoadjuvant breast cancer patients receiving bevacizumab: Results from the randomized phase III GeparQuinto study. Int J Cancer 2015; 137: 2981-2988
    CrossrefPubMedGoogle Scholar
  • 46 Province MA, Goetz MP, Brauch H. et al. CYP2D6 genotype and adjuvant tamoxifen: meta-analysis of heterogeneous study populations. Clin Pharmacol Ther 2014; 95: 216-227
    CrossrefPubMedGoogle Scholar
  • 47 Schroth W, Goetz MP, Hamann U. et al. Association between CYP2D6 polymorphisms and outcomes among women with early stage breast cancer treated with tamoxifen. JAMA 2009; 302: 1429-1436
    CrossrefPubMedGoogle Scholar
  • 48 Schroth W, Hamann U, Fasching PA. et al. CYP2D6 polymorphisms as predictors of outcome in breast cancer patients treated with tamoxifen: expanded polymorphism coverage improves risk stratification. Clin Cancer Res 2010; 16: 4468-4477
    CrossrefPubMedGoogle Scholar
  • 49 Fasching PA, Haberle L, Rack B. et al. Clinical validation of genetic variants associated with in vitro chemotherapy-related lymphoblastoid cell toxicity. Oncotarget 2017; 8: 78133-78143
    PubMedGoogle Scholar
  • 50 Fasching PA, Pharoah PD, Cox A. et al. The role of genetic breast cancer susceptibility variants as prognostic factors. Hum Mol Genet 2012; 21: 3926-3939
    CrossrefPubMedGoogle Scholar
  • 51 Azzato EM, Tyrer J, Fasching PA. et al. Association between a germline OCA2 polymorphism at chromosome 15q13.1 and estrogen receptor-negative breast cancer survival. J Natl Cancer Inst 2010; 102: 650-662
    CrossrefPubMedGoogle Scholar
  • 52 Fagerholm R, Schmidt MK, Khan S. et al. The SNP rs6500843 in 16p13.3 is associated with survival specifically among chemotherapy-treated breast cancer patients. Oncotarget 2015; 6: 7390-7407
    PubMedGoogle Scholar
  • 53 Guo Q, Schmidt MK, Kraft P. et al. Identification of novel genetic markers of breast cancer survival. J Natl Cancer Inst 2015; 107: pii:djv081
    PubMedGoogle Scholar
  • 54 Jamshidi M, Fagerholm R, Khan S. et al. SNP-SNP interaction analysis of NF-kappaB signaling pathway on breast cancer survival. Oncotarget 2015; 6: 37979-37994
    CrossrefPubMedGoogle Scholar
  • 55 Pirie A, Guo Q, Kraft P. et al. Common germline polymorphisms associated with breast cancer-specific survival. Breast Cancer Res 2015; 17: 58
    CrossrefPubMedGoogle Scholar
  • 56 Muranen TA, Blomqvist C, Dork T. et al. Patient survival and tumor characteristics associated with CHEK2:p.I157T - findings from the Breast Cancer Association Consortium. Breast Cancer Res 2016; 18: 98
    CrossrefPubMedGoogle Scholar
  • 57 Curtit E, Pivot X, Henriques J. et al. Assessment of the prognostic role of a 94-single nucleotide polymorphisms risk score in early breast cancer in the SIGNAL/PHARE prospective cohort: no correlation with clinico-pathological characteristics and outcomes. Breast Cancer Res 2017; 19: 98
    CrossrefPubMedGoogle Scholar
  • 58 Fagerholm R, Khan S, Schmidt MK. et al. TP53-based interaction analysis identifies cis-eQTL variants for TP53BP2, FBXO28, and FAM53A that associate with survival and treatment outcome in breast cancer. Oncotarget 2017; 8: 18381-18398
    CrossrefPubMedGoogle Scholar
  • 59 Barrdahl M, Rudolph A, Hopper JL. et al. Gene-environment interactions involving functional variants: Results from the Breast Cancer Association Consortium. Int J Cancer 2017; 141: 1830-1840
    CrossrefPubMedGoogle Scholar
  • 60 Rudolph A, Milne RL, Truong T. et al. Investigation of gene-environment interactions between 47 newly identified breast cancer susceptibility loci and environmental risk factors. Int J Cancer 2015; 136: E685-E696
    CrossrefPubMedGoogle Scholar
  • 61 Rudolph A, Fasching PA, Behrens S. et al. A comprehensive evaluation of interaction between genetic variants and use of menopausal hormone therapy on mammographic density. Breast Cancer Res 2015; 17: 110
    CrossrefPubMedGoogle Scholar
  • 62 Schoeps A, Rudolph A, Seibold P. et al. Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions. Genet Epidemiol 2014; 38: 84-93
    CrossrefPubMedGoogle Scholar
  • 63 Nickels S, Truong T, Hein R. et al. Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors. PLoS Genet 2013; 9: e1003284
    CrossrefPubMedGoogle Scholar
  • 64 Fischer C, Kuchenbacker K, Engel C. et al. 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; 50: 360-367
    CrossrefPubMedGoogle Scholar
  • 65 Cuzick J. Version 8 of Tyrer-Cuzick model (2017). Online: http://www.ems-trials.org/riskevaluator/ last access: 01.04.2018
    PubMedGoogle Scholar
  • 66 Lee AJ, Cunningham AP, Kuchenbaecker KB. et al. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br J Cancer 2014; 110: 535-545
    CrossrefPubMedGoogle Scholar
  • 67 Mazzola E, Blackford A, Parmigiani G. et al. Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO. Cancer Inform 2015; 14: 147-157
    PubMedGoogle Scholar
  • 68 Pankratz VS, Degnim AC, Frank RD. et al. Model for individualized prediction of breast cancer risk after a benign breast biopsy. J Clin Oncol 2015; 33: 923-929
    CrossrefPubMedGoogle Scholar
  • 69 Brentnall AR, Harkness EF, Astley SM. et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res 2015; 17: 147
    CrossrefPubMedGoogle Scholar
  • 70 Schonberg MA, Li VW, Eliassen AH. et al. Performance of the Breast Cancer Risk Assessment Tool Among Women Age 75 Years and Older. J Natl Cancer Inst 2015; DOI: 10.1093/jnci/djv348.
    CrossrefPubMedGoogle Scholar
  • 71 Antoniou AC, Pharoah PP, Smith P. et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer 2004; 91: 1580-1590
    CrossrefPubMedGoogle Scholar
  • 72 Lee AJ, Cunningham AP, Tischkowitz M. et al. Incorporating truncating variants in PALB2, CHEK2, and ATM into the BOADICEA breast cancer risk model. Genet Med 2016; 18: 1190-1198
    CrossrefPubMedGoogle Scholar
  • 73 Parmigiani G, Berry D, Aguilar O. Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. Am J Hum Genet 1998; 62: 145-158
    CrossrefPubMedGoogle Scholar
  • 74 Collins IM, Bickerstaffe A, Ranaweera T. et al. iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management. Breast Cancer Res Treat 2016; 156: 171-182
    CrossrefPubMedGoogle Scholar
  • 75 Mavaddat N, Pharoah PD, Michailidou K. et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst 2015; 107: pii:djv036
    PubMedGoogle Scholar
  • 76 Garcia-Closas M, Gunsoy NB, Chatterjee N. Combined associations of genetic and environmental risk factors: implications for prevention of breast cancer. J Natl Cancer Inst 2014; 106: pii:dju305
    CrossrefPubMedGoogle Scholar
  • 77 Rudolph A, Song M, Brook MN. et al. Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium. Int J Epidemiol 2018; DOI: 10.1093/ije/dyx242.
    CrossrefPubMedGoogle Scholar
  • 78 Vachon CM, Pankratz VS, Scott CG. et al. The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst 2015; 107: pii:dju397
    CrossrefPubMedGoogle Scholar
  • 79 Cuzick J, Brentnall AR, Segal C. et al. Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results From Two Randomized Tamoxifen Prevention Trials. J Clin Oncol 2017; 35: 743-750
    CrossrefPubMedGoogle Scholar
  • 80 Lee CP, Irwanto A, Salim A. et al. Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population. Breast Cancer Res 2014; 16: R64
    CrossrefPubMedGoogle Scholar
  • 81 Mealiffe ME, Stokowski RP, Rhees BK. et al. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst 2010; 102: 1618-1627
    CrossrefPubMedGoogle Scholar
  • 82 Wacholder S, Hartge P, Prentice R. et al. Performance of common genetic variants in breast-cancer risk models. N Engl J Med 2010; 362: 986-993
    CrossrefPubMedGoogle Scholar
  • 83 Heywang-Koebrunner S, Bock K, Heindel W. et al. Mammography Screening – as of 2013. Geburtsh Frauenheilk 2013; 73: 1007-1016
    Article in Thieme ConnectPubMedGoogle Scholar
  • 84 Heidinger O, Batzler WU, Krieg V. et al. The incidence of interval cancers in the German mammography screening program: results from the population-based cancer registry in North Rhine-Westphalia. Dtsch Arztebl Int 2012; 109: 781-787
    PubMedGoogle Scholar
  • 85 Urbschat I, Heidinger O. [Determination of interval cancer rates in the German mammography screening program using population-based cancer registry data]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2014; 57: 68-76
    CrossrefPubMedGoogle Scholar
  • 86 Schulz-Wendtland R, Wittenberg T, Michel T. et al. [Future of mammography-based imaging]. Radiologe 2014; 54: 217-223
    CrossrefPubMedGoogle Scholar
  • 87 Stone J, Thompson DJ, Dos Santos Silva I. et al. Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures. Cancer Res 2015; 75: 2457-2467
    CrossrefPubMedGoogle Scholar
  • 88 Lindstrom S, Thompson DJ, Paterson AD. et al. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun 2014; 5: 5303
    CrossrefPubMedGoogle Scholar
  • 89 Vachon CM, Scott CG, Fasching PA. et al. Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomarkers Prev 2012; 21: 1156-1166
    CrossrefPubMedGoogle Scholar
  • 90 Haberle L, Fasching PA, Brehm B. et al. Mammographic density is the main correlate of tumors detected on ultrasound but not on mammography. Int J Cancer 2016; 139: 1967-1974
    CrossrefPubMedGoogle Scholar
  • 91 Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology 2002; 225: 165-175
    CrossrefPubMedGoogle Scholar
  • 92 Emons J, Wunderle M, Hartmann A. et al. Initial clinical results with a fusion prototype for mammography and three-dimensional ultrasound with a standard mammography system and a standard ultrasound probe. Acta Radiol 2018; DOI: 10.1177/0284185118762249.
    CrossrefPubMedGoogle Scholar
  • 93 Schulz-Wendtland R, Jud SM, Fasching PA. et al. A Standard Mammography Unit – Standard 3D Ultrasound Probe Fusion Prototype: First Results. Geburtsh Frauenheilk 2017; 77: 679-685
    Article in Thieme ConnectPubMedGoogle Scholar
  • 94 Brandt KR, Scott CG, Ma L. et al. Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening. Radiology 2016; 279: 710-719
    CrossrefPubMedGoogle Scholar
  • 95 Haberle L, Hack CC, Heusinger K. et al. Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound. Eur J Med Res 2017; 22: 30
    CrossrefPubMedGoogle Scholar
  • 96 Haberle L, Hein A, Rubner M. et al. Predicting Triple-Negative Breast Cancer Subtype Using Multiple Single Nucleotide Polymorphisms for Breast Cancer Risk and Several Variable Selection Methods. Geburtsh Frauenheilk 2017; 77: 667-678
    Article in Thieme ConnectPubMedGoogle Scholar
  • 97 Tresp V, Overhage JM, Bundschus M. et al. Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare. P Ieee 2016; 104: 2180-2206
    CrossrefPubMedGoogle Scholar
  • 98 ASSURE Consortium. Final Report Summary – ASSURE (Adapting Breast Cancer Screening Strategy Using Personalised RiskEstimation) (2016). Online: https://cordis.europa.eu/result/rcn/187468_en.html last access: 01.04.2018
    PubMedGoogle Scholar
  • 99 Evans DG, Astley S, Stavrinos P. et al. Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study. Southampton, UK: NIHR Journals Library; 2016
    Google Scholar
  • 100 Shieh Y, Eklund M, Madlensky L. et al. Breast Cancer Screening in the Precision Medicine Era: Risk-Based Screening in a Population-Based Trial. J Natl Cancer Inst 2017; 109: pii:djw290
    CrossrefPubMedGoogle Scholar
  • 101 Unicancer. Randomized, Comparison Of Risk-Stratified versus Standard Breast Cancer Screening In European Women Aged 40-74 (2017). Online: https://cordis.europa.eu/project/rcn/212694_en.html last access: 01.04.2018
    PubMedGoogle Scholar
  • 102 Simard J, Chiarelli AM. Personalized risk assessment for prevention and early detection of breast cancer: Integration and Implementation (2018). Online: https://www.genomecanada.ca/sites/default/files/2017lsarp_backgrounder_en.pdf last access: 01.04.2018
    PubMedGoogle Scholar
  • 103 Kast K, Rhiem K, Wappenschmidt B. et al. Prevalence of BRCA1/2 germline mutations in 21 401 families with breast and ovarian cancer. J Med Genet 2016; 53: 465-471
    CrossrefPubMedGoogle Scholar
  • 104 Kuchenbaecker KB, Hopper JL, Barnes DR. et al. Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers. JAMA 2017; 317: 2402-2416
    CrossrefPubMedGoogle Scholar
  • 105 Schneider K, Zelley K, Nichols KE. et al. Li-Fraumeni Syndrome. In: Adam MP, Ardinger HH, Pagon RA. et al. eds. GeneReviews((R)). Seattle, WA: University of Washington, Seattle; 1993
    Google Scholar
  • 106 Tan MH, Mester JL, Ngeow J. et al. Lifetime cancer risks in individuals with germline PTEN mutations. Clin Cancer Res 2012; 18: 400-407
    CrossrefPubMedGoogle Scholar
  • 107 van Lier MG, Wagner A, Mathus-Vliegen EM. et al. High cancer risk in Peutz-Jeghers syndrome: a systematic review and surveillance recommendations. Am J Gastroenterol 2010; 105: 1258-1264 author reply 1265
    CrossrefPubMedGoogle Scholar
  • 108 Hansford S, Kaurah P, Li-Chang H. et al. Hereditary Diffuse Gastric Cancer Syndrome: CDH1 Mutations and Beyond. JAMA Oncol 2015; 1: 23-32
    CrossrefPubMedGoogle Scholar
  • 109 Milne RL, Benitez J, Nevanlinna H. et al. Risk of estrogen receptor-positive and -negative breast cancer and single-nucleotide polymorphism 2q35-rs13387042. J Natl Cancer Inst 2009; 101: 1012-1018
    CrossrefPubMedGoogle Scholar
  • 110 Stevens KN, Fredericksen Z, Vachon CM. et al. 19p13.1 is a triple-negative-specific breast cancer susceptibility locus. Cancer Res 2012; 72: 1795-1803
    CrossrefPubMedGoogle Scholar
  • 111 Gail MH, Benichou J. Validation studies on a model for breast cancer risk. J Natl Cancer Inst 1994; 86: 573-575
    CrossrefPubMedGoogle Scholar
  • 112 National Institutes of Health. Breast Cancer Risk Assessment Tool. 2011, last update 05/2011. Online: http://www.cancer.gov/bcrisktool last access: 01.04.2018
    PubMedGoogle Scholar
  • 113 Claus EB, Risch N, Thompson WD. The calculation of breast cancer risk for women with a first degree family history of ovarian cancer. Breast Cancer Res Treat 1993; 28: 115-120
    CrossrefPubMedGoogle Scholar
  • 114 Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004; 23: 1111-1130
    CrossrefPubMedGoogle Scholar
  • 115 Cuzick J, Sestak I, Cawthorn S. et al. Tamoxifen for prevention of breast cancer: extended long-term follow-up of the IBIS-I breast cancer prevention trial. Lancet Oncol 2015; 16: 67-75
    CrossrefPubMedGoogle Scholar
  • 116 Tice JA, Cummings SR, Smith-Bindman R. et al. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med 2008; 148: 337-347
    CrossrefPubMedGoogle Scholar
  • 117 Darabi H, Czene K, Zhao W. et al. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast Cancer Res 2012; 14: R25
    CrossrefPubMedGoogle Scholar
  • 118 Eriksson M, Czene K, Pawitan Y. et al. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res 2017; 19: 29
    CrossrefPubMedGoogle Scholar