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
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.
Genetic Variants of Low Penetrance
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.
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 ].
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.
Screening for Different Risk Populations
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.
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.