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

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.

 

MissingLinkText

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.

 

MissingLinkText

Key words

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

Schlüsselwörter

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

Genetic Variants of High and Moderate Penetrance

With technical advances, continuously falling genotyping costs and easier access to databases for the interpretation of genotyping results, genetic testing is on the verge of a broader implementation in clinical practice. Testing for BRCA1 and BRCA2 is already part of clinical routine testing according to current guidelines [1], [2]. Further genes belong to a so-called panel testing [2] and seem – under trial conditions – not to be harmful with regard to clinical decisions based on the availability of those results [3]. While many of these genes have a function in the context of homologous repair (BRCA1/2, BARD1, BRIP1, PALB2, RAD51C/D, NBN, MRE11, ATM), others have been described to come out of a different or to have an additional functional context (TP53, PTEN, STK11, CDH1, CHEK2, ATM, MLH1, MSH2, MSH6, PMS2).

A broader application of genetic testing might be problematic with regard to several considerations. One aspect is the knowledge about risk effects and clinical implications: Most of the mutations in panel genes are rare. CHEK2 is the most frequently mutated gene after BRCA1/2 and has mutation frequencies in breast cancer patients of about 1.5% and in healthy individuals of about 0.65% [4]. All other mutations are observed less frequently. Therefore, in these mutations an interpretation with regard to breast cancer risk and clinical implications (e.g. therapy efficacy or prognosis) is more difficult than in BRCA1/2. The discussion concerning the prognostic relevance of BRCA1/2, for instance, is still ongoing [5], [6], which makes it clear that respective knowledge is specifically missing even more in rarer panel genes. Large studies in triple negative breast cancer (TNBC) also do not yield a high enough sample size to address the clinical meaning of panel genes other than BRCA1/2 in this patient population [7]. Another aspect is that an increase of genetic testing also leads to an increase of genetic test results that have to be interpreted as variants of uncertain significance [8]. These examples illustrate that still a lot of knowledge has to be acquired before these genes can be added to routine treatment or screening recommendations.

However, the interpretation of genetic variants becomes easier with genetic information from large databases being available for approved research projects. Examples for these datasets and databases are the Exome Aggregation Consortium (ExAC) [9], the FLOSSIES dataset [10], The Cancer Genome Atlas (TCGA) [11] or the database of Genotypes and Phenotypes (dbGaP) [12]. There are several examples on how these data are used for risk calculations of rarer panel genes [4], [13], [14]. A large study with more than 65 000 breast cancer patients and healthy women provided odds ratios with reasonable confidence intervals for the majority of the currently used panel genes ([Table 1]). Furthermore, information about the interpretation of test results in clinical practice is also easier to access as findings are provided in structured databases such as the database on clinical variations (ClinVar) [15] or the Genetic Testing Registry (GTR) [17], or are directly exchanged between clinicians and researchers in large international consortia like ENIGMA (Evidence-based Network for the Interpretation of Germline Mutant Alleles) [16]. With regard to unclassified variants, improved in vitro experiments might help in shortening the time frames in which their functional meaning can be assessed [18].

Table 1 Panel genes for breast cancer.

Gene	Mutation frequency	Risk for breast cancer
*  Mutation frequency in German high risk families with breast and/or ovarian cancer according to the family criteria of the German Consortium for Hereditary Breast and Ovarian Cancer. **  Mutation frequency in the general population. ***  Mutation frequency in Northern American families with breast, ovarian, colorectal or pancreatic cancer. Abbreviations: OR: odds ratio; CI: confidence interval.
BRCA1	15.9 [103]*	72% (95% CI, 65 – 79%) risk at age 80 [104]
BRCA2	8.3 [103]*	69% (95% CI, 61 – 77%) risk at age 80 [104]
TP53	1 : 5000 – 1 : 20 000**	50 – 90% lifetime risk [105]
PTEN	1 : 200 000**	50 – 85% lifetime risk [106]
STK11	1 : 8000 – 1 : 200 000**	32 – 54% lifetime risk [107]
CDH1	Unknown**	52% risk of lobular breast cancer at age 75 [108]
PALB2	0.80 [4]***	OR 7.46 (95% CI, 5.12 – 11.19; p = 4.31 × 10−38) lifetime risk [4]
RAD51D	0.07 [4]***	OR 3.07 (95% CI, 1.21 – 7.88; p = 0.01) lifetime risk [4]
ATM	0.94 [4]***	OR 2.78 (95% CI, 2.22 – 3.62; p = 2.42 × 10−19) lifetime risk [4]
CHEK2	1.46 [4]***	OR 2.26 (95% CI, 1.89 – 2.72; p = 1.75 × 10−20) lifetime risk [4]
BARD1	0.18 [4]***	OR 2.16 (95% CI, 1.31 – 3.63; p = 2.26 × 10−3) lifetime risk [4]
MSH6	0.21 [4]***	OR 1.93 (95% CI, 1.16 – 3.27; p = 0.01) lifetime risk [4]
BRIP1	0.25 [4]***	OR 1.63 (95% CI, 1.11 – 2.41; p = 0.01) lifetime risk [4]
MSH2	0.06 [4]***	OR 2.46 (95% CI, 0.81 – 6.93; p = 0.11) lifetime risk [4]
MLH1	0.03 [4]***	OR 1.15 (95% CI, 0.30 – 4.19; p > 0.99) lifetime risk [4]
NBN	0.17 [4]***	OR 1.13 (95% CI, 0.73 – 1.75; p = 0.59) lifetime risk [4]
MRE11A	0.07 [4]***	OR 0.86 (95% CI, 0.46 – 1.57; p = 0.65) lifetime risk [4]
PMS2	0.11 [4]***	OR 0.82 (95% CI, 0.44 – 1.47; p = 0.56) lifetime risk [4]
RAD51C	0.09 [4]***	OR 0.78 (95% CI, 0.47 – 1.37; p = 0.43) lifetime risk [4]

As poly ADP-ribose polymerase (PARP) inhibitors have been approved for the treatment of BRCA1/2 mutated advanced breast cancer patients [19], genetic testing could be performed in this patient population. In a recent study mutation frequencies of an unselected cohort of advanced breast cancer patients have been described for BRCA1/2 and other panel genes [20], which could help in deciding what kind of specified patient collective should be screened for genetic testing. Information about therapy efficacy of chemotherapy, PARP inhibitors or immunotherapies is still completely missing regarding the other panel genes.

 

MissingLinkText

Genetic Variants of Low Penetrance

Up to 2013 a total of 26 single nucleotide polymorphisms (SNPs; common variants) had been discovered by several independent genome wide association studies (GWAS) and one SNP in CASP8 by a candidate gene approach [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34]. These common variants explain up to 9% of the excess of familial breast cancer. Together with high penetrance mutations in genes like BRCA1, BRCA2, PALB2 and further alleles in moderate-risk genes like ATM, CHEK2 and others, another ~ 20% could be explained, so that taken together at that time up to 29% of familial breast cancer could be explained [33].

After the validation of these 27 common variants an unparalleled effort was made to join more than 55 000 breast cancer patients and 53 000 healthy women with germline DNA and clinical data available to identify and validate further common variants. For that purpose, the Collaborative Oncological Gene-environment Study (COGS; https://www.nature.com/icogs/) was formed designing an Illumina custom iSelect SNP genotyping array (iCOGS array) comprising more than 210 000 SNPs selected from previous GWAS and candidate gene nominations [35]. This project increased the number of validated common risk variants first to 77 [35], [36], [37], [38] and by a further meta-analysis together with other GWAS to a total of 102 SNPs [39]. With these loci ~ 16% of familial breast cancer risk could be explained with common risk variants.

With about 36% (20% due to higher penetrance alleles and 16% due to common risk variants) of familial breast cancer risk explainable, further genetic risk factors have to be assumed to complete the knowledge about familial breast cancer risk. One of the most recent efforts is the OncoArray network [40] (https://epi.grants.cancer.gov/oncoarray/). In this further attempt a chip with more than 530 000 SNPs was constructed. These SNPs comprised about 230 000 SNPs serving as a GWAS backbone. Further about 330 000 SNPs were selected by several consortia (TRICL, BCAC/DRIVE/CIMBA, FOCI/OCAC, ELLIPSE/PRACTICAL and CORECT) for several reasons (e.g. fine-mapping, SNPs from existing GWAS, rare variants, candidate SNPs, SNPs from relevant tumor genes, functional SNPs, SNPs associated with survival) [40]. Of those SNPs more than 494 000 passed quality control and more than 447 000 samples were successfully genotyped from patients with breast, colon, lung, ovary and prostate cancer as well as from healthy women in the control group. With these data a genome wide association study could be performed with more than 137 000 breast cancer patients and more than 119 000 healthy women. This revealed an additional 75 common variants that could be validated as breast cancer risk loci [41], [42]. We have summarized all validated risk SNPs in [Table 2], along with the respective gene names or regions, the minor allele frequencies and the odds ratios. It is assumed that about 18% of the familial relative risk can be explained with these additional common variants [41].

Table 2 Validated SNPs in sporadic breast cancer.

Region; Closest Gene	SNP-Number (MAF)	OR (95% CI), Citation
Abbreviations: SNP: single nucleotide polymorphism; MAF: minor allele frequency; OR: odds ratio; CI: confidence interval.
1p36.22; PEX14	rs616488 (0.33)	0.94 (0.92 – 0.96) [35]
1p36.13; KLHDC7A	rs2992756 (0.49)	1.06 (1.04 – 1.08) [41]
1p34.2; HIVEP3	rs79724016 (0.03)	0.93 (0.88 – 0.97) [41]
1p34.2	rs4233486 (0.36)	0.97 (0.95 – 0.98) [41]
1p34.1; PIK3R3	rs1707302 (0.34)	0.96 (0.95 – 0.98) [41]
1p32.3	rs140850326 (0.49)	0.97 (0.95 – 0.99) [41]
1p22.3	rs17426269 (0.15)	1.05 (1.02 – 1.07) [41]
1p13.2; AP4B1, DCLRE1B	rs11552449 (0.17)	1.07 (1.04 – 1.10) [35]
1p12	rs7529522 (0.23)	1.06 (1.04 – 1.08) [41]
1p11.2; EMBP1	rs11249433 (0.40)	1.09 (1.07 – 1.11) [27], [35]
1q21.1; NBPF10, RNF115	rs12405132 (0.36)	0.95 (0.93 – 0.97) [39]
1q21.2; OTUD7B	rs12048493 (0.34)	1.07 (1.05 – 1.10) [39]
1q22; TRIM46	rs4971059 (0.35)	1.05 (1.03 – 1.07) [41]
1q32.1; MDM4	rs4245739 (0.26)	1.02 (1.00 – 1.04) [38]
1q32.1; LGR6	rs6678914 (0.41)	1.00 (0.98 – 1.02) [38]
1q32.1; PHLDA3	rs35383942 (0.06)	1.12 (1.08 – 1.17) [41]
1q41; ESRRG	rs11117758 (0.21)	0.95 (0.93 – 0.97) [41]
1q43; EXO1	rs72755295 (0.03)	1.15 (1.09 – 1.22) [39]
2p25.1; GRHL1	rs113577745 (0.10)	1.08 (1.05 – 1.11) [41]
2p24.1	rs12710696 (0.36)	1.04 (1.01 – 1.06) [38]
2p23.3; ADCY3	rs6725517 (0.41)	0.96 (0.94 – 0.98) [41]
2p23.3; NCOA1	rs200648189 (0.19)	0.94 (0.91 – 0.97) [42]
2q13; BCL2L11	rs71801447 (0.06)	1.09 (1.05 – 1.13) [41]
2q14.2	rs4849887 (0.10)	0.91 (0.88 – 0.94) [35]
2q31.1; CDCA7	rs1550623 (0.16)	0.94 (0.92 – 0.97) [35]
2q31.1; METAP1D, DLX1, DLX2	rs2016394 (0.48)	0.95 (0.93 – 0.97) [35]
2q33.1; CASP8	rs1045485 (0.13)	0.97 (0.94 – 1.00) [21], [35]
2q35; LOC101928278, LOC105373874	rs13387042 (0.47)	0.88 (0.86 – 0.90) [24], [35], [109]
2q35; DIRC3	rs16857609 (0.26)	1.08 (1.06 – 1.10) [35]
2q36.3	rs12479355 (0.21)	0.96 (0.94 – 0.98) [41]
3p26.2; ITPR1, EGOT	rs6762644 (0.40)	1.07 (1.04 – 1.09) [35]
3p24.1; SLC4A7	rs4973768 (0.47)	1.10 (1.08 – 1.12) [26], [35]
3p24.1; TGFBR2	rs12493607 (0.35)	1.06 (1.03 – 1.08) [35]
3p21.3	rs6796502 (0.09)	0.92 (0.89 – 0.95) [39]
3p13; FOXP1	rs6805189 (0.48)	0.97 (0.95 – 0.99) [41]
3p12.1; VGLL3	rs13066793 (0.09)	0.94 (0.91 – 0.97) [41]
3p12.1; CMSS1, FILIP1L	rs9833888 (0.22)	1.06 (1.04 – 1.08) [41]
3q23; ZBTB38	rs34207738 (0.41)	1.06 (1.04 – 1.08) [41]
3q26.31	rs58058861 (0.21)	1.06 (1.04 – 1.09) [41]
4p14	rs6815814 (0.26)	1.06 (1.04 – 1.08) [41]
4q21.23; HELQ	rs84370124 (0.47)	1.04 (1.02 – 1.05) [41]
4q22.1; LOC105 369 192	rs10022462 (0.44)	1.04 (1.02 – 1.06) [41]
4q24; TET2	rs9790517 (0.23)	1.05 (1.03 – 1.08) [35]
4q28.1	rs77528541 (0.13)	0.95 (0.92 – 0.97) [41]
4q34.1; ADAM29	rs6828523 (0.13)	0.90 (0.87 – 0.92) [35]
5p15.33; TERT	rs10069690 (0.26)	1.06 (1.04 – 1.09) [32], [35]
5p15.33; TERT	rs2736108 (0.29)	0.94 (0.92 – 0.95) [36]
5p15.33; AHRR	rs116095464 (0.05)	1.06 (1.02 – 1.10) [41]
5p15.1; LOC401176	rs13162653 (0.45)	0.95 (0.93 – 0.97) [39]
5p13.3; SUB1	rs2012709 (0.46)	1.05 (1.03 – 1.08) [39]
5p12	rs10941679 (0.25)	1.13 (1.10 – 1.15) [25], [35]
5q11.1	rs35951924 (0.32)	0.95(0.93 – 0.97) [41]
5q11.1	rs72749841 (0.16)	0.93(0.91 – 0.96) [41]
5q11.2; MAP3K1	rs889312 (0.28)	1.12 (1.10 – 1.15) [22], [35]
5q11.2; RAB3C	rs10472076 (0.38)	1.05 (1.03 – 1.07) [35]
5q12.1; PDE4D	rs1353747 (0.10)	0.92 (0.89 – 0.95) [35]
5q14; ATG10	rs7707921 (0.23)	0.93 (0.91 – 0.95) [39]
5q22.1; NREP	rs6882649 (0.34)	0.97(0.95 – 0.99) [41]
5q31.1; HSPA4	rs6596100 (0.25)	0.94(0.92 – 0.96) [41]
5q33.3; EBF1	rs1432679 (0.43)	1.07 (1.05 – 1.09) [35]
5q35.1	rs4562056 (0.33)	1.05(1.03 – 1.07) [41]
6p25.3; FOXQ1	rs11242675 (0.39)	0.94 (0.92 – 0.96) [35]
6p23; ANBP9	rs204247 (0.43)	1.05 (1.03 – 1.07) [35]
6p22.3; ATXN1	rs3819405 (0.33)	0.96 (0.94 – 0.97) [41]
6p22.3; CDKAL1	rs2223621 (0.38)	1.04 (1.02 – 1.06) [41]
6p22.2	rs71557345 (0.07)	0.92 (0.88 – 0.96) [41]
6p22.1	rs9257408 (0.38)	1.05 (1.03 – 1.08) [39]
6q14; LOC105377871	rs17530068	1.12 (1.08 – 1.16) [34]
6q14.1	rs12207986 (0.47)	0.97 (0.95 – 0.98) [41]
6q14.1	rs17529111 (0.22)	1.05 (1.03 – 1.08) [35]
6q23.1; L3MBTL3	rs6569648 (0.23)	0.93 (0.90 – 0.95) [42]
6q25; ESR1	rs9383938	1.20 [34]
6q25; ESR1	rs2046210 (0.34)	1.08 (1.06 – 1.10) [28], [35]
6q25; ESR1	rs3757318 (0.07)	1.16 (1.12 – 1.21) [30], [35]
7p15.3; DNAH11, CDCA7L	rs7971 (0.35)	0.96 (0.94 – 0.98) [41]
7p15.1; CUX1	rs17156577 (0.11)	1.05 (1.02 – 1.08) [41]
7q21.3	rs17268829 (0.28)	1.05 (1.03 – 1.07) [41]
7q22.1; CUX1	rs71559437 (0.12)	0.93 (0.91 – 0.96) [41]
7q32.3; FLJ43663	rs4593472 (0.35)	0.95 (0.94 – 0.97) [39]
7q35; ARHGEF5, NOBOX	rs720475 (0.25)	0.94 (0.92 – 0.96) [35]
8p23.3; RPL23AP53	rs66823261 (0.23)	1.09 (1.06 – 1.12) [42]
8p21.1	rs9693444 (0.32)	1.07 (1.05 – 1.09) [35]
8p11.23; LOC102723593	rs13365225 (0.17)	0.95 (0.93 – 0.98) [39]
8q21.11	rs6472903 (0.18)	0.91 (0.89 – 0.93) [35]
8q21.13; HNF4G	rs2943559 (0.07)	1.13 (1.09 – 1.17) [35]
8q22.3	rs514192 (0.32)	1.05 (1.03 – 1.07) [41]
8q23.1; ZFPM3	rs12546444 (0.10)	0.93 (0.91 – 0.96) [41]
8q23.3; LINC00536	rs13267382 (0.36)	1.05 (1.03 – 1.07) [39]
8q24	rs13281615 (0.41)	1.09 (1.07 – 1.12) [22], [35]
8q24.13; ANXA13	rs17350191 (0.34)	1.07 (1.04 – 1.09) [42]
8q24.13	rs58847541 (0.15)	1.08 (1.05 – 1.10) [41]
8q24.21; MIR1208	rs11780156 (0.16)	1.07 (1.04 – 1.10) [35]
9p21.3; CDKN2A/B	rs1011970 (0.17)	1.06 (1.03 – 1.08) [30], [35]
9q31; LOC105376214	rs865686 (0.38)	0.89 (0.88 – 0.91) [31], [35]
9q31.2; TP63	rs10759243 (0.39)	1.06 (1.03 – 1.08) [35]
9q33.1; ASTN2	rs1895062 (0.41)	0.94 (0.92 – 0.95) [41]
9q33.3; LMX1B	rs10760444 (0.43)	1.03 (1.02 – 1.05) [41]
9q34.2; ABO	rs8176636 (0.20)	1.03 (1.01 – 1.06) [41]
10p15.1; ANKRD16	rs2380205 (0.44)	0.98 (0.96 – 1.00) [30], [35]
10p14	rs67958007 (0.12)	1.09 (1.06 – 1.12) [41]
10p12.31; DNAJC1	rs11814448 (0.02)	1.26 (1.18 – 1.35) [35]
10p12.31; DNAJC1	rs7072776 (0.29)	1.07 (1.05 – 1.09) [35]
10q21.2; ZNF365	rs10995190 (0.16)	0.86 (0.84 – 0.88) [30], [35]
10q22.3; ZMIZ1	rs704010 (0.38)	1.08 (1.06 – 1.10) [30], [35]
10q23.33	rs140936696 (0.18)	1.04 (1.02 – 1.07) [41]
10q25.2; TCF7L2	rs7904519 (0.46)	1.06 (1.04 – 1.08) [35]
10q26.12	rs11199914 (0.32)	0.95 (0.93 – 0.96) [35]
10q26.13; FGFR2	rs2981579 (0.40)	1.27 (1.24 – 1.29) [30], [35]
10q26.13; FGFR2	rs2981582 (0.40)	1.27 (1.24 – 1.29) [22], [35]
11p15.5; LSP1	rs3817198 (0.31)	1.07 (1.05 – 1.09) [22], [35]
11p15; PIDD1	rs6597981 (0.48)	0.96 (0.94 – 0.97) [41]
11q13.1	rs3903072 (0.47)	0.95 (0.93 – 0.96) [35]
11q13.3; CCND1	rs554219 (0.12)	1.33 (1.28 – 1.37) [37]
11q13.3; CCND1	rs614367 (0.15)	1.21 (1.18 – 1.24) [30], [35]
11q13.3; CCND1	rs75915166 (0.06)	1.38 (1.32 – 1.44) [37]
11q22.3; KDELC2	rs11374964 (0.42)	0.94 (0.92 – 0.96) [42]
11q22.3; KDELC2	rs74911261 (0.02)	0.82 (0.75 – 0.89) [42]
11q24.3	rs11820646 (0.41)	0.95 (0.93 – 0.97) [35]
12p13.1	rs12422552 (0.26)	1.05 (1.03 – 1.07) [35]
12p11.22; PTHLH	rs10771399 (0.12)	0.86 (0.83 – 0.88) [33], [35]
12p11.22; PTHLH	rs1975930	1.22 [34]
12q21.31	rs202049448 (0.34)	0.95 (0.93 – 0.97) [41]
12q22 NTN4	rs17356907 0.30)	0.91 (0.89 – 0.93) [35]
12q24; LOC105370003	rs1292011 (0.42)	0.92 (0.90 – 0.94) [33], [35]
12q24.31	rs206966 (0.16)	1.05 (1.02 – 1.07) [41]
13q13.1; BRCA2	rs11571833 (0.01)	1.26 (1.14 – 1.39) [35]
14q13.3; PAX9	rs2236007 (0.21)	0.93 (0.91 – 0.95) [35]
14q24.1; RAD51B	rs999737 (0.23)	0.92 (0.90 – 0.94) [27], [35]
14q24.1; RAD51B	rs2588809 (0.16)	1.08 (1.05 – 1.11) [35]
14q32.12; RIN3	rs11627032 (0.26)	0.94 (0.92 – 0.96) [39]
14q32.12; CCDC88C	rs941764 (0.34)	1.06 (1.04 – 1.09) [35]
14q32.33; ADSSL1	rs10623258 (0.45)	1.04 (1.02 – 1.06) [41]
16p13.3; ADCY9	rs11076805 (0.25)	0.92 (0.90 – 0.95) [42]
16q12.1; TOX3	rs3803662 (0.26)	1.24 (1.21 – 1.27) [22], [35]
16q12.2; FTO	rs11075995 (0.24)	1.04 (1.02 – 1.06) [38]
16q12.2; FTO	rs17817449 (0.40)	0.93 (0.91 – 0.95) [35]
16q12.2	rs28539243 (0.49)	1.05 (1.03 – 1.07) [41]
16q13; AMFR	rs2432539 (0.40)	1.03 (1.02 – 1.05) [41]
16q23.2; CDYL2	rs13329835 (0.22)	1.08 (1.05 – 1.10) [35]
16q24.2	rs4496150 (0.25)	0.96 (0.94 – 0.98) [41]
17q11.2; ATAD5	rs29230520 (0.20)	0.93 (0.91 – 0.96) [39]
17q21.2; CNTNAP1	rs72826962 (0.01)	1.20 (1.11 – 1.30) [41]
17q21.31; KANSL1	rs2532263 (0.19)	0.95 (0.93 – 0.97) [41]
17q22; COX11	rs6504950 (0.28)	0.94 (0.92 – 0.96) [26], [35]
17q25.3	rs745570 (0.50)	0.95 (0.93 – 0.97) [39]
18q11.2	rs527616 (0.38)	0.95 (0.93 – 0.97) [35]
18q11.2; CHST9	rs1436904 (0.40)	0.96 (0.94 – 0.98) [35]
18q12.1; CDH2	rs36194942 (0.30)	0.94 (0.91 – 0.96) [42]
18q12.1; GAREM1	rs117618124 (0.05)	0.89 (0.85 – 0.92) [41]
18q12.3; SETBP1	rs6507583 (0.07)	0.91 (0.88 – 0.95) [39]
19p13.31; SMG9, KCNN4, LYPD5, ZNF283	rs3760982 (0.46)	1.06 (1.04 – 1.08) [35]
19p13.13; NFIX1	rs78269692 (0.05)	1.09 (1.04 – 1.13) [41]
19p13.12	rs2594714 (0.23)	0.97 (0.95 – 0.99) [41]
19p13.11; SSBP4	rs4808801 (0.35)	0.93 (0.91 – 0.95) [35]
19p13.11; GATAD2A, MIR640	rs2965183 (0.35)	1.04 (1.02 – 1.06) [41]
19p13.11; MERIT40	rs2363956 (0.50)	1.01 (0.98 – 1.04) [110]
19p13.11; MERIT40	rs8170 (0.19)	1.04 (1.01 – 1.06) [29], [35]
19p13.2; TSPAN16	rs322144 (0.47)	0.95 (0.93 – 0.97) [42]
19q12; CCNE	rs113701136 (0.32)	1.07 (1.04 – 1.09) [42]
19q13.22; GIPR	rs71338792 (0.23)	1.05 (1.03 – 1.07) [41]
20p12.3; MCM8	rs16991615 (0.06)	1.10 (1.06 – 1.14) [41]
20q11	rs2284378	1.08 (1.05 – 1.12) [34]
20q13.13	rs6122906 (0.18)	1.05 (1.03 – 1.07) [41]
21q21.1; NRIP1	rs2823093 (0.27)	0.92 (0.90 – 0.94) [33], [35]
22q12.2; EMID1, RHBDD3, EWSR1	rs132390 (0.04)	1.12 (1.07 – 1.18) [35]
22q13.1; PLA2G6	rs738321 (0.38)	0.95 (0.93 – 0.97) [41]
22q13.2; MKL1	rs6001930 (0.11)	1.12 (1.09 – 1.16) [35]
22q13.2; XRCC6	rs73161324 (0.06)	1.06 (1.02 – 1.09) [41]
22q13.31	rs28512361 (0.11)	1.05 (1.02 – 1.08) [41]

The existing data is a plentiful resource to investigate further questions related to breast cancer with regard to therapy efficacy, prognosis, pathway analyses and gene environment interactions. The influence of common genetic variants on therapy efficacy and prognosis has previously been shown in several breast cancer studies [43] – [49]. Data from large international consortia additionally contribute to these questions [50], [51], [52], [53], [54], [55], [56], [57], [58]. The relation of common variants to well-known environmental risk factors as well as their interaction is of special interest as individuals who are at a higher risk could be identified. Data on this, however, is scarce [59], [60], [61], [62], [63], so that future analyses with a focus on this field of research are necessary.

 

MissingLinkText

Risk Prediction Tools

With increasing knowledge about genetic and non-genetic risk factors, several risk assessment tools have been developed, validated with clinical data and continuously up-dated over the last decades. Their functionality is shown in [Table 3]. Each testing tool features different aspects of breast and/or ovarian cancer risk and is more or less accurate in risk prediction depending on different risk situations [64]. To improve their performance many models have included different genetic and non-genetic risk factors such as age, body mass index (BMI), menarche and menopause status, hormone replacement therapy, mammographic density, histological characteristics, familial cancer background, ethnicity and others.

Table 3 Breast cancer risk assessment tools.

Risk Factor, Reference	NCI model [111], [112]	Claus model [113]	Tyrer-Cuzick model [65], [79], [114], [115]	BRCAPRO [67], [73]	BOADICEA [66], [71], [72]	Tice [116]	Darabi [117]	Eriksson [118]
Abbreviations: NCI: National Cancer Institute; BOADICEA: Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm.
Age	+	+	+	+	+	+	+	+
Age at menarche	+		+				+
Age at menopause			+					+
Body mass index			+				+	+
Age at first birth	+		+				+
Mammographic density			+			+	+	+
Suspicious mammographic findings								+
History of breast biopsies	+		+			+	+
History of premalignant lesions	+		+				+
Hormone replacement therapy			+					+
Family history of breast cancer	+	+	+	+	+		+	+
Family history of ovarian cancer			+	+	+
Family history of prostate cancer					+
Family history of pancreatic cancer					+
Contralateral breast cancer			+	+	+
Histology of breast cancer				+	+
BRCA1/2 mutation	+		+	+	+
Low penetrant genetic variants			(+)				+
Ethnicity/Ashkenazi Jewish ancestry	+		+	+	+	+	+
Mastectomy				+
Oophorectomy				+

Many of these models have lately been developed forward with up-dates and more simplified versions [65] – [69]. In the light of demographic change, assessment tools have also been tested in older people such as the NCI tool for people older than 75 years [70].

Two of the most commonly used risk models are BOADICEA [66], [71], [72] and BRCAPRO [67], [73]. Besides from predicting age-specific breast and ovarian cancer risks, both models are also capable of predicting the probability of carrying a BRCA1/2 mutation. Both include a refinement of histopathological features as triple-negativity or estrogen receptor-negativity that increase the risk of a genetic background [66], [67]. Moreover, BRCAPRO considers mastectomy and oophorectomy and imputes age if it is not available from family history [67].

One persisting challenge is the over- and under-estimation of the individual risk by different risk tools. This leads to the issue how to find the right genetic risk tool for a patient. A recent web-based support tool, called iPrevent, can help finding the adequate risk tool for patients. Collins et al. designed a new algorithm for the selection of either BOADICEA or IBIS (= Tyrer-Cuzick model). The Tyrer-Cuzick model performs better at family constellations with fewer family members and is restricted to breast and ovarian cancer. It also includes non-genetic risk factor data like BMI, reproductive factors and personal history of high-risk breast lesions such as atypical hyperplasia and lobular carcinoma in situ. The BOADICEA model performs better at family constellations with more family members and also includes the histology of breast cancer and other cancer types such as pancreatic or prostate cancer. With that question algorithm patients are guided to the more appropriate testing tool and are divided into groups at average, intermediate and high risk [74].

 

MissingLinkText

Polygenic Risk Scores

As mentioned above, although the effects on breast cancer risk are rather small, common genetic variants can explain up to 18% of the familial breast cancer risk. Therefore it is reasonable to explore in how far this information can be used for an individual risk prediction and breast cancer prevention. The developed models are usually referred to as polygenic risk scores (PRS). For breast cancer a first PRS based on a comprehensive dataset was developed after the availability of the data from the iCOGS chip and was based on 77 validated breast cancer SNPs [75].

Combining these 77 SNPs into a risk prediction model, lifetime risks and 10-year disease risks for different ages could be provided for both estrogen receptor (ER)-positive and ER-negative disease. For ER-positive disease 20% of the population with the highest risk have a lifetime risk of over 15%, and 20% of the population with the lowest risk have a lifetime risk of under 5% according to this model ([Fig. 1]) [75]. Regarding ER-negative disease, the lifetime risks are much lower with around 3% and 1%, respectively ([Fig. 2]). The 10-year disease risk was highest at age 60 and was about 10% for all breast cancer types in the top 1% of the population with the highest risk based on the PRS [75].

Fig. 1 Cumulative lifetime risk of developing estrogen receptor (ER)-positive breast cancer for women of European origin by percentiles of the polygenic risk score (PRS). Figure from [75] under the terms of the Creative Commons Attribution License.

Fig. 2 Cumulative lifetime risk of developing estrogen receptor (ER)-negative breast cancer for women of European origin by percentiles of the polygenic risk score (PRS). Figure from [75] under the terms of the Creative Commons Attribution License.

Subsequently, several attempts have been made to combine the PRS with non-genetic risk factors and mammographic density [76], [77], [78], [79], [80], [81], [82]. The inclusion of the most comprehensive number of SNPs into a breast cancer risk model (Tyrer-Cuzick) showed that risk prediction could be improved. Nevertheless, the prediction by non-genetic risk factors and common variants was independent from each other [79]. Similar results were seen when combining the PRS with the risk factor mammographic density. Risk prediction could be improved, however, genetic factors and mammographic density also predicted risk independently from each other [78]. Mammographic density is of special interest with regard to individualized screening programs and individual accuracy of the mammography.

 

MissingLinkText

Screening for Different Risk Populations

It is known that screening programs are not equally effective and equally necessary for all women. Breast cancer screening might be less effective in a population with a low breast cancer risk. Recently, it has also been discussed whether screening programs can effectively reduce mortality because aggressive forms of cancer are missed [83], [84], [85]. So the question arises, whether the risk for aggressive forms of breast cancer is high enough in the screened population [86].

Women could possibly benefit from individualized screening methods as mammographic density, diagnostic accuracy and genetic risk factors interact with each other. Several studies have underlined the correlation between certain common variants and mammographic breast density [61], [87] – [89]. Both, mammographic density and the PRS, contribute to breast cancer risk prediction [78], and from several studies it is known that a high mammographic density reduces the sensitivity of mammography in breast cancer detection [90], [91]. Therefore an individualized algorithm might be helpful in directing individualized screening programs ([Fig. 3]). With technical advances like the fusion of several imaging methods [92], [93], automated assessment of mammographic density [94] and diagnostic accuracy of mammography [95] as well as the integration of big data and machine learning into patient and tumor assessments [96], [97] such individualized screening strategies seem to be feasible and several studies are already ongoing [98], [99], [100], [101], [102].

Fig. 3 Possible integration of automated mammography assessment and genetic risk assessment into individualized diagnostics for breast cancer.

 

MissingLinkText

Conclusion

As genetic information on breast cancer is increasing, it is important to interpret all data in a concerted way and to provide healthy women as well as breast cancer patients with sufficient information to facilitate understanding of their individual risk, decision making regarding the appropriate individual prevention strategy and choosing the right treatment option. Risk prediction programs include a growing number of parameters and are getting more precise. In addition, more data on moderate and low risk genes are available. The challenge of the next years will be to translate this knowledge into clinical routine. To provide greater numbers of breast cancer patients with relevant genetic information, it is necessary to further lower the thresholds for genetic testing and to reduce its costs. Furthermore, the integration of germline and somatic genetic data, the expression profile of the tumor as well as clinical data might provide the best treatment for the individual patient. These factors are still being investigated in research settings.

 

MissingLinkText

 

MissingLinkText

Conflict of Interest

Naiba Nabieva received honoraria from Janssen-Cilag and travel support from Novartis. Michael P. Lux received honoraria from Pfizer, Roche, MSD, Hexal, Novartis, Lilly, AstraZeneca, Celgene, Eisai, medac and Thieme for advisory boards, lectures and travel support. Peter A. Fasching received honoraria from Roche, Pfizer, Novartis and Celgene. His institution conducts research for Novartis. All other authors have declared no conflicts of interest.

• 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
• 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
• 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
• 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
• 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
• 6 Fasching PA. Breast cancer in young women: do BRCA1 or BRCA2 mutations matter?. Lancet Oncol 2018; 19: 150-151
• 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
• 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
• 9 Exome Aggregation Consortium. ExAC Browser (Beta) (2018). Online: http://exac.broadinstitute.org/ last access: 01.04.2018
• 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
• 11 Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490: 61-70
• 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
• 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;
• 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
• 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
• 16 The ENIGMA consortium. Evidence-based Network for the Interpretation of Germline Mutant Alleles (2018). Online: https://enigmaconsortium.org/ last access: 01.04.2018
• 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
• 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
• 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
• 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
• 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
• 22 Easton DF, Pooley KA, Dunning AM. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007; 447: 1087-1093
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 41 Michailidou K, Lindstrom S, Dennis J. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 2017; 551: 92-94
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 55 Pirie A, Guo Q, Kraft P. et al. Common germline polymorphisms associated with breast cancer-specific survival. Breast Cancer Res 2015; 17: 58
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 65 Cuzick J. Version 8 of Tyrer-Cuzick model (2017). Online: http://www.ems-trials.org/riskevaluator/ last access: 01.04.2018
• 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
• 67 Mazzola E, Blackford A, Parmigiani G. et al. Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO. Cancer Inform 2015; 14: 147-157
• 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
• 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
• 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;
• 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
• 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
• 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
• 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
• 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
• 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
• 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;
• 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
• 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
• 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
• 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
• 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
• 83 Heywang-Koebrunner S, Bock K, Heindel W. et al. Mammography Screening – as of 2013. Geburtsh Frauenheilk 2013; 73: 1007-1016
• 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
• 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
• 86 Schulz-Wendtland R, Wittenberg T, Michel T. et al. [Future of mammography-based imaging]. Radiologe 2014; 54: 217-223
• 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
• 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
• 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
• 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
• 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
• 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;
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 111 Gail MH, Benichou J. Validation studies on a model for breast cancer risk. J Natl Cancer Inst 1994; 86: 573-575
• 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
• 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
• 114 Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004; 23: 1111-1130
• 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
• 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
• 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
• 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