Inherited Genetic Susceptibility to Breast Cancer

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The American Journal of Pathology, Vol. -, No. -, - 2013

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Review Breast Cancer Theme Issue 69707172737475767778798081

REVIEWInherited Genetic Susceptibility to Breast Cancer

The Beginning of the End or the End of the Beginning?Maya Ghoussaini,* Paul D.P. Pharoah,*y and Douglas F. Easton*

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From the Departments of Oncology* and Public Health and Primary Care,y Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge,United Kingdom

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Accepted for publication
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July 22, 2013.

Address correspondence toMaya Ghoussaini, Departmentof Oncology, Centre for CancerGenetic Epidemiology, Univer-sity of Cambridge, WortsCauseway, Cambridge CB18RN, United Kingdom.E-mail: [email protected].

opyright ª 2013 American Society for Inve

ublished by Elsevier Inc. All rights reserved

ttp://dx.doi.org/10.1016/j.ajpath.2013.07.003

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Genome-wide association studies have identified 72 loci associated with breast cancer susceptibility.Seventeen of these are known to predispose to other cancers. High-penetrance susceptibility loci forbreast cancer usually result from coding alterations, principally in genes involved in DNA repair,whereas almost all of the associations identified through genome-wide association studies are found innoncoding regions of the genome and are likely to involve regulation of genes in multiple pathways.However, the genes underlying most associations are not yet known. In this review, we summarize thefindings from genome-wide association studies in breast cancer and describe the genes and mechanismsthat are likely to be involved in the tumorigenesis process. We also discuss approaches to fine-scalemapping of susceptibility regions used to identify the likely causal variant(s) underlying the associa-tions, a major challenge in genetic epidemiology. Finally, we discuss the potential impact of suchfindings on personalized medicine and future avenues for screening, prediction, and preventionprograms. (Am J Pathol 2013, -: 1e14; http://dx.doi.org/10.1016/j.ajpath.2013.07.003)

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Supported by the European Community’s Seventh Framework Programmegrant 223175 (HEALTH-F2-2009-223175) and Cancer Research UK Q14.

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Breast cancer is the most common cancer among women inboth developing and developed regions in the world,accounting for 23% of all malignancies in women world-wide.1 In the United States, the lifetime risk of breast canceris approximately one in eight.2 It is also the most commoncause of cancer-related deaths worldwide (nearly 400,000a year).1

The incidence of breast cancer is approximately twofoldhigher in first-degree relatives of breast cancer patients. Thisrisk increases with the number of affected relatives and ismuch greater for women with relatives affected at a youngage.3e6 The higher prevalence of breast cancer amongmonozygotic twins of patients compared with dizygotic twinsor siblings suggests strongly that genetic, rather than envi-ronmental, factors account for most of the familial aggrega-tion. Molecular genetic studies during the past 20 years havedemonstrated that the genetic basis of susceptibility to breastcancer is complex, and is the result of germ-line variation atmany different loci. The susceptibility alleles at these loci havewidely different frequencies, and confer different disease risks(Figure 1). Genetic variants for breast cancer can be broadly

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categorized into high-, moderate-, and low-penetrance alleles.The high-penetrance alleles typically confer lifetime risks ofbreast cancer of >50%, sufficient to warrant intervention toreduce risk (eg, prophylactic surgery or magnetic resonanceimaging screening), even in the absence of any other factors.Moderate-penetrance alleles confer risks of greater thantwofold, corresponding to lifetime risks of �20% in Westernpopulations, whereas low-penetrance alleles confer smallerincreases in risk (ie, 10% to 20% lifetime risk).

Rare High-Penetrance Alleles

Family-based linkage analysis and positional cloning wereused to identify high-penetrance alleles in BRCA1 andBRCA2, two tumor-suppressor genes involved in DNArepair.7,8 Deleterious alleles of these genes are rare (cumu-lative frequency, approximately 1 in 800 for BRCA1 muta-tions and 1 in 500 for BRCA2mutations9) and confer a 10- to

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Figure 1 Genetic loci identified for breast cancer by risk allelefrequency and risk conferred.

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30-fold increase in the risk of breast cancer in carrierscompared with noncarriers.10 These alleles also predispose toother cancer types, including ovarian and prostate cancers.11

Deleterious alleles in other genes conferring a similar riskto BRCA1 and BRCA2 may exist, but they are likely to berare, because approximately 84% of families with four ormore cases of breast cancer diagnosed at younger than 60years can be accounted for by mutations in BRCA1 orBRCA2.10 A few high-penetrance alleles have been identi-fied as part of inherited cancer symptoms, such as TP53mutations found in Li-Fraumeni cancer syndrome12 andSTK11/LKB1 mutations found in Peutz-Jegher syndrome.13

Rare Moderate-Penetrance Alleles

Moderate-penetrance variants have been found by rese-quencing candidate genes, such as those interacting withBRCA1 and BRCA2, or acting in the same DNA repairpathway. Protein truncating variants in ATM,14 CHEK2,15,16

PALB2,17 and BRIP118 genes have been shown to confera twofold to fourfold risk of breast cancer. Other moderate-penetrance genes have been identified through linkagestudies in families with rare syndromes involving breast andother malignancies. Deleterious alleles of PTEN (Cowdendisease)19 and CDH1 (hereditary diffuse gastric cancer)20

confer relative risks of two to eight. It is likely that othergenetic variations in this category exist, but their identifica-tion will require resequencing in large case-control studies.

The susceptibility alleles previously mentioned are rareand do not reach a frequency of >1% even when combined,suggesting a selective disadvantage, possibly as a result ofhomozygotes being nonviable or seriously disadvantaged.Altogether, they account for <25% of the familial risk ofbreast cancer.21

Common Low-Penetrance Variation

Susceptibility alleles that are common in the general pop-ulation (minor allele frequency of >5%) are associated witha modest increase in breast cancer risk (relative risk of <1.5;no alleles with a relative risk between 1.5 and 2 have beenidentified). Such alleles are generally identified by case-control, association studies. Early association studies usinga candidate gene approach focused on polymorphisms ingenes thought to be relevant to cancer biology throughmechanisms such as cell cycle regulation, apoptosis, andDNA repair. Many positive associations were reported, butfew have been convincingly replicated.22 The mostconvincing reported evidence is for a coding polymorphismin CASP8, D302H, the minor allele of which has beenassociated with a reduced risk of breast cancer.23

During the past 5 years, improvements in genotypingtechnology have made genome-wide association studies(GWASs) possible. In GWASs, a few 100,000 variants thatreport on the most common variation in the genome are

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genotyped, allowing associations to be detected withoutprior knowledge of function or location. GWASs have beenextremely successful in identifying susceptibility loci formany common diseases and phenotypes. To date, 72 breastcancer susceptibility loci have been identified throughGWASs and large-scale replication studies.24e36 The asso-ciated single-nucleotide polymorphisms (SNPs) and thenearest genes to each of the loci are summarized in Table 1.These loci, however, explain approximately 14% of theinherited genetic component of breast cancer. It is likely thatthere are many more common alleles with weak effects.

Association of GWAS Loci by Tumor Subtypes

Breast cancer is heterogeneous, and different types of tumorscan vary etiologically, histologically, and clinically.37 Morethan 90% of breast tumors that develop in carriers of BRCA1mutations are estrogen receptor negative (ER�),37 whereasthe common breast cancer susceptibility alleles identified aremore strongly associated with ERþ disease (FGFR2,MAP3K1, 8q24, 5p12, 11q13, 12q24, and 21q21) and lessstrongly, if at all, with ER� disease. Some loci, such as 16q12(TOX3), 12p11 (PTHLH ), and 10q21 (ZNF365), are asso-ciated with both disease types.26,30,31,38e41 Seven loci withcommon susceptibility alleles for ER� breast cancer havebeen identified: 6q25 (near ESR1) was first identified ina GWAS conducted in Chinese women33; 19p13 was initiallyidentified as a modifier of the BRCA1 breast cancer,25 and5p12 was identified through a GWAS in ER� and triple-negative (ER�/progesterone receptor�/HER� Q) breast can-cer.28 Four loci (1q32, MDM4; 1q32, LGR6; 2p24, gene Q

desert; 16q12, FTO) were recently identified after combiningthree GWASs comprising 4193 ER-negative breast cancercases and 35,194 controls.34 Most GWAS loci also modifythe penetrance of breast cancer in BRCA2 carriers, to a similarrelative extent, but only the subset of SNPs associated withER� disease appear to modify the risk in BRCA1 carriers,consistent with the distinct pathological characteristics of thedisease in BRCA1 carriers.

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Genetic Susceptibility to Breast Cancer

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With the exception of the FGFR2 locus, which alsoshows the strongest association with breast cancer, themechanisms and the genes behind these associations arelargely unknown. However, knowledge about gene functionwithin the regions suggests that a complex and diverse rangeof biological pathways are involved in the commonsusceptibility to breast carcinogenesis. Almost all of the lociare in regions that contain genes that could be involved inbreast cancer development (Figure 2), and it is notable thatseveral of these genes have been shown to be somaticallymutated in breast tumors (MAP3K1, TBX3, NOTCH2, MYC,CASP8, CCND1, and TET2).42 However, in almost allcases, there is no direct evidence that these genes arecausally related to the observed susceptibility.

From Association to Causation and Function

A Major Challenge for Molecular Genetics

There are two key steps to understanding the functional mech-anisms bywhich the risk-associated variant causes breast cancerat a susceptibility locus. The first is to identify the true causalvariant at the locus, and the second is to identify the molecularmechanism by which the risk allele could cause cancer.

Fine-Scale Mapping

Fine-scale mapping aims to identify the causal variant(s) ata susceptibility locus. The set of SNPs genotyped in a GWASare designed to tag common variation across the genome.Thus, most variants found to be associated with disease will becorrelated with multiple other variants in the surroundingregion [called linkage disequilibrium (LD) block or haplotypeblock], for which the boundaries are delimited by recombi-nation hot spots. Haplotype blocks vary substantially in size,the number of SNPs, and the LD structure of those SNPs.

Several steps are required to narrow down a locus ofinterest and pinpoint the causal variant(s). The first step is toobtain a complete list of all variants in the LD block ofinterest. The availability of the 1000 Genomes project(http://www.1000genomes.org, last accessed July 29, 2013)has accelerated this process, and resequencing of the regionis usually no longer needed to search for common variants.Ideally, all of the SNPs correlated with the associatedmarker are then genotyped in a large case-control set, andthe strength of association is compared. The variant with thestrongest statistical evidence for association is the strongestcandidate for being the causal variant. However, because ofthe high correlation among SNPs within a block, multipleSNPs may have equally strong evidence and the true causalvariant may not be definitively identified. Because thepattern of LD often differs among populations, case-controlstudies from multiple populations (notably populations ofEast Asian or African ancestries, in addition to Europeanancestry) can assist with the fine mapping, provided that thesame causal variant segregates in each population

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(Figure 3). However, complementary approaches are stilllikely to be required to identify a single causal variant.

Because the causative variant is expected to be morestrongly associated with breast cancer risk than other SNPs,finding the causal variant in each of the GWAS loci will alsoimprove the accuracy of disease risk prediction. Bio-informatic analysis SNPs may have a variety of functionaleffects, by either directly altering protein structure (missensevariants or splice site variants) or altering the transcriptionalregulation of nearby genes. In silico bioinformatics analysiscan help identify SNPs more or less likely to have a func-tional effect.43 For example, SNPs in genomic regions thatare highly conserved between species are more likely to befunctionally important, particularly those in noncodingregions. Genome data on many species, including 28vertebrate species, are publicly available to enable suchanalyses to be routine. The ENCyclopedia Of DNA Elements(National Human Genome Research Institute, NIH, http://www.genome.gov/10005107, last accessed July 29, 2013)project has recently provided functional annotation across thehuman genome [histone modification and regulatoryelements, DNA methylation, DNase-hypersensitive sites andfootprints, chromosome-interacting regions, transcriptionfactor (TF) binding sites, regulatory elements, and codingand noncoding RNAs], showing that 80% of the noncodinggenome could be functional.44 Putative causal SNPs can bemapped to see if they lie in specific functional elements, suchas enhancers, silencers, or TF binding sites.

When the LD block of interest contains several genes, itmay be difficult to predict what gene is being targeted by theassociated variant. Because most associations are thought tobe regulatory, the target gene may be identified by evalu-ating whether the SNP genotype is associated with geneexpression. For example, a gene expression database, GENeExpression VARiation, in which the expression of geneswas quantified in immortalized B lymphocytes extractedfrom the 270 HapMap individuals of European, Asian, andAfrican ancestries, has been made publicly available. Thisapproach may be limited by the size of the association withexpression levels, which may be modest. More important,expression may be tissue specific, in which case availabledata in, for example, blood will be of limited value.Emerging data on expression in tumors, through projectssuch as The Cancer Genome Atlas (National Cancer Insti-tute, http://www.cancergenome.nih.gov, last accessed July29, 2013) and the International Cancer Genome ConsortiumCancer Genome Projects (http://www.icgc.org, last accessedJuly 29, 2013), may be more valuable for analysis of genesexpressed in the breast, but ultimately studies in normalbreast tissue may be required.

Functional Experiments

Experiments to determine the functional effect of candidatecausal variants are beyond the scope of this review. Briefly,if an SNP falls in the noncoding region of the genome

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Table 1 List of the 72 Breast Cancer Susceptibility Loci Identified to Date.

Region SNP Nearest plausible genes Year Per-allele OR P value MAF

1p11 rs11249433 NOTCH2/FCGR1B 2009 1.16 (1.09e1.24) 7 � 10�10 0.391p13 rs11552449 TPN22/BCL2L15 2013 1.07 (1.04e1.09) 1.8 � 10�8 0.171p36 rs616488 PEX14 2013 0.94 (0.92e0.96) 2.0 � 10�10 0.331q32 rs4245739 MDM4 2013 1.14 (1.10e1.18) 2.1 � 10�12 0.261q32 rs6678914 LGR6 2013 1.10 (1.06e1.13) 1.4 � 10�8 0.592p24 rs12710696 desert 2013 1.10 (1.06e1.13) 0.362q14 rs4849887 None 2013 0.91 (0.88e0.94) 3.7 � 10�11 0.102q31 rs2016394 METAP1D 2013 0.95 (0.93e0.97) 1.2 � 10�8 0.482q31 rs1550623 CDCA7 2013 0.94 (0.92e0.97) 3.0 � 10�8 0.162q33 rs1045485 CASP8 2007 0.88 (0.84e0.92) 1.1 � 10�7 0.13

rs10931936 0.88 (0.82e0.94) 0.262q35 rs13387042 IGFBP2, IGFBP5, TPN2 2007 1.20 (1.14e1.26) 1 � 10�13 0.492q35 rs16857609 DIRC3 2013 1.08 (1.06e1.10) 1.1 � 10�15 0.263p24 rs4973768 SLC4A7/NEK10 2009 1.11 (1.08e1.13) 4.1 � 10�23 0.463p24 rs12493607 TGFBR2 2013 1.06 (1.03e1.08) 2.3 � 10�8 0.353p26 rs6762644 ITPR1/EGOT 2013 1.07 (1.04e1.09) 2.2 � 10�12 0.404q24 rs9790517 TET2 2013 1.05 (1.03e1.08) 4.2 � 10�8 0.234q34 rs6828523 ADAM29 2013 0.90 (0.87e0.92) 3.5 � 10�16 0.135p12 rs10941679 MRPS30/HCN1 2008 1.19 (1.13e1.26) 1 � 10�11 0.25

rs9790879 1.10 (1.03e1.17) 0.405p15* rs10069690 TERT/CLPTM1L 2011 1.18 (1.13e1.25) 1.0 � 10�10 0.305q11 rs889312 MAP3K1/MEIR3 2007 1.13 (1.10e1.16) 1 � 10�15 0.285q11 rs10472076 RAB3C 2013 1.05 (1.03e1.07) 2.9 � 10�8 0.385q11 rs1353747 PDE4D 0.92 (0.89e0.95) 2.5 � 10�8 0.105q33 rs1432679 EBF1 2013 1.07 (1.05e1.09) 2.0 � 10�14 0.436p23 rs204247 RANBP9 2013 1.05 (1.03e1.07) 8.3 � 10�9 0.436p25 rs11242675 FOXQ1 2013 0.94 (0.92e0.96) 7.1 � 10�9 0.396q14 rs17530068 None 2012 1.12 (1.08e1.16) 1.1 � 10-9 0.226q25 rs3757318 ESR1 2009 1.21 (1.13e1.31) 2 � 10�15 0.076q25 rs2046210 ESR1 2009 1.11 (1.07e1.16) 3.7 � 10�9 0.347q35 rs720475 ARHGEF5/NOBOX 2013 0.94 (0.92e0.96) 7.0 � 10�11 0.258p12 rs9693444 None 2013 1.07 (1.05e1.09) 9.2 � 10�14 0.328q21 rs6472903 None 2013 0.91 (0.89e0.93) 1.7 � 10�17 0.188q21 rs2943559 HNF4G 2013 1.13 (1.09e1.17) 5.7 � 10�15 0.078q24 rs13281615 MYC 2007 1.08 (1.05e1.11) 5 � 10�12 0.408q24 rs1562430 MYC 2010 1.17 (1.10e1.25) 5 � 10�12 0.408q24 rs11780156 MIR1208 2013 1.07 (1.04e1.10) 3.4 � 10�11 0.169p21 rs1011970 CDKN2A/B 2010 1.09 (1.04e1.14) 2.5 � 10�8 0.179q31 rs865686 KLF4/RAD23B 2011 0.89 (0.85� 0.92) 1.7 � 10�10 0.399q31 rs10759243 None 2013 1.06 (1.03e1.08) 1.2 � 10�8 0.3910p12 rs7072776 MLLT10/DNAJC1 2013 1.07 (1.05e1.09) 4.3 � 10�14 0.2910p12 rs11814448 DNAJC1 2013 1.26 (1.18e1.35) 9.3 � 10�16 0.0210p15 rs2380205 ANKRD16 2010 0.94 (0.91e0.98) 4.6 � 10�7 0.4310q21 rs10995190 ZNF365 2010 0.86 (0.82e0.91) 5.1 � 10�15 0.1510q22 rs704010 ZMIZ1 2010 1.07 (1.03e1.11) 3 � 10�8 0.3910q25 rs7904519 TCF7L2 2013 1.06 (1.04e1.08) 3.1 � 10�8 0.4610q26 rs2981582 FGFR2 2007 1.26 (1.23e1.30) 2 � 10�76 0.3810q26 rs2981579 FGFR2 2010 1.43 (1.35e1.53) 2 � 10�76 0.4210q26 rs11199914 None 2013 0.95 (0.93e0.97) 1.9 � 10�8 0.3211p15 rs3817198 LSP1/H19 2007 1.07 (1.04e1.11) 1 � 10�9 0.3011p15 rs909116 LSP1/H19 2007 1.17 (1.10e1.24) 1 � 10�9 0.3011q13* rs614367 CCND1/FGFs 2010 1.15 (1.10e1.20) 3.2 � 10�15 0.1511q13 rs3903072 OVOL1 2013 0.95 (0.93e0.96) 8.6 � 10�12 0.4711q24 rs11820646 None 2013 0.95 (0.93e0.97) 1.1 � 10�9 0.4112p11 rs10771399 PTHLH 2011 0.79 (0.71e0.87) 4.3 � 10�35 0.1012p13 rs12422552 None 2013 1.05 (1.03e1.07) 3.7 � 10�8 0.2612q22 rs17356907 NTN4 2013 0.91 (0.89e0.93) 1.8 � 10�22 0.30

(table continues)

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Table 1 (continued )

Region SNP Nearest plausible genes Year Per-allele OR P value MAF

12q24 rs1292011 TBX3/MAPKAP5 2011 0.92 (0.91e0.94) 5.9 � 10�19 0.4113q13 rs11571833 BRCA2 2013 1.26 (1.14e1.39) 4.9 � 10�8 0.0114q13 rs2236007 PAX9/SLC25A21 2013 0.93 (0.91e0.95) 1.7 � 10�13 0.2114q24 rs999737 RAD51B 2009 0.94 (0.88e0.99) 2 � 10�7 0.2414q24 rs8009944 RAD51B 2009 0.88 (0.82e0.95) 2 � 10�7 0.2414q24 rs2588809 RAD51L1 2013 1.08 (1.05e1.11) 1.4 � 10�10 0.1614q32 rs941764 CCDC88C 2013 1.06 (1.04e1.09) 3.7 � 10�10 0.3416q12 rs12443621 TOX3/LOC643714 2007 1.11 (1.08e1.14) 1 � 10�36 0.4616q12 rs3803662 TOX3/LOC643714 2010 1.20 (1.16e1.24) 1 � 10�36 0.2616q12 rs17817449 MIR1972-2-FTO 2013 0.93 (0.91e0.95) 6.4 � 10�14 0.4016q23 rs13329835 CDYL2 2013 1.08 (1.05e1.10) 2.1 � 10�16 0.2216q22 rs11075995 FTO 2013 1.07 (1.11e1.15) 4.0 � 10�8 0.2417q23 rs6504950 STXBP4/COX11 2008 0.95 (0.92e0.97) 1.4 � 10�8 0.2718q11 rs527616 None 2013 0.95 (0.93e0.97) 1.6 � 10�10 0.3818q11 rs1436904 CHST9 2013 0.96 (0.94e0.98) 3.2 � 10�8 0.4019p13 rs8170 MERIT40 2010 1.26 (1.17e1.35) 2.3 � 10�9 0.1819p13 rs2363956 MERIT40 2010 0.84 (0.80e0.89) 5.5 � 0�9 0.5019p13 rs4808801 SSBP4/ISYNA1/ELL 2013 0.93 (0.91e0.95) 4.6 � 10�15 0.3519q13 rs3760982 KCNN4/ZNF283 2013 1.06 (1.04e1.08) 2.1 � 10�10 0.4620q11 rs2284378 RALY 2012 1.16 (1.01e1.10) 1.1 � 10�8 0.3521q21 rs2823093 NRIP1 2011 0.94 (0.92e0.96) 1.1 � 10�10 0.2722q12 rs132390 EMID1/RHBDD3 2013 1.12 (1.07e1.18) 3.1 � 10�9 0.0422q13 rs6001930 MKL1 2013 1.12 (1.09e1.16) 8.8 � 10�19 0.11

MAF, minor allele frequency.*For each of the 5p15/TERT locus and 11q13/CCND1 locus, three independent SNPs are associated with breast cancer.


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(promoter, enhancer, or intron) and is predicted to affect thebinding of a TF, experiments such as electrophoreticmobility shift assays can be used to test for a differentialprotein binding pattern. Luciferase transfection assays canbe used to measure the activity of a promoter or theexpression of target genes. Chromatin immunoprecipitationcan be used to establish whether the site of a candidatecausal variant is occupied by a TF in vivo. This approachwas recently used to determine the SNPs that are likely tohave a functional effect in FGFR2.45 Chromosome confor-mation capture can be used to test long-range interactionsthat influence gene expression (eg, between a regulatoryelement containing the causal SNP and the promoter of thesuspected targeted gene). This technique was used for thefine mapping of the 11q13 locus, where the enhancer andsilencer in which the causal variants lie were found tointeract with the promoter and terminator of the CCND1gene.46

In summary, a combination of genetic epidemiologicalcharacteristics, in silico bioinformatics, and functionalexperiments may enable the causal variant (or variants) to beidentified with some certainty.

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Fine Mapping in Breast Cancer

To date, only four of the 72 known breast cancer suscepti-bility loci have been thoroughly fine mapped in European,Asian, and African American populations.26,45e49 In


addition, 19 of the breast cancer susceptibility loci havebeen fine mapped in African Americans.50

FGFR2 Locus

Two variants in intron 2 of FGFR2 on the 10q26 locus werefound to be associated with breast cancer in two differentstudies.26,29 Fine-scale mapping in Europeans, Asians, andAfrican Americans identified eight SNPs as the most likelycandidates for being the causal variant.26,47 The moststrongly associated SNP, rs2981578, lies in an open chro-matin conformation region, where DNA is highly conservedamong mammalian species and was found to mediatedifferential TF binding, leading to an increase in FGFR2expression levels.47 The same SNP was associated withincreased FGFR2 signaling activity in stromal fibroblasts.51

These data altogether suggest that rs2981578 is likely to befunctional. Another correlated SNP, rs7895676, was alsofound to increase FGFR2 expression.47 With the availabilityof a more complete catalog of common variants released bythe 1000 Genome project and fine mapping in larger case-control studies, it is possible that additional causal vari-ant(s) may be identified.

TOX3 Locus

The second strongest GWAS locus lies in a region onchromosome 16q12 containing TOX3 (formerly calledTNRC9) and LOC643714.26,52 The number of candidate

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Figure 2 Mechanisms that are potentially involved in breast cancer susceptibility. Genes in red represent high- and moderate-penetrance mutations. Genesin blue are cancer candidates found in/near common breast cancer susceptibility loci. With the exception of FGFR2, MYC, CASP8, and CCND1, it is unclear ifthose genes are the ones driving the association. Follow-up of the associated loci and functional analysis will confirm or exclude the involvement of thesegenes.

Causative SNP Tag SNP

Haplotype 1

Hap 1 Hap 2 Hap 3Population 2

Population 1

Figure 3 Schematic diagram of the LD pattern in a different pop-ulation. This diagram illustrates the value of using populations from otherethnicities to refine the signal of association. Haplotype 1 in population 1is split into three haplotypes in population 2. In population 1, the causalvariant and the tag SNPs are both in the same haplotype block, in highcorrelation with each other, and are hence both associated with thedisease. In population 2, the causal variant is no longer on the samehaplotype as the tag SNP because they were separated by a recombinationevent. Therefore, if we were to genotype both SNPs in population 2, thecausal variant will remain associated with the disease but not the tag SNP.Assuming that the LD structure is different between populations for thelocus of interest, fine mapping in other ethnicities can help to reduce thenumber of candidate causal variants.

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causal variants was reduced to 13 using data from Europeanand Asian studies, with eight clustering in a highlyconserved region of open chromatin conformation located inthe intergenic region.26,52 Based on these data, it wasimpossible to discriminate which one of the 13 was thecausal variant. In African American studies, these variantshad completely opposite effects, suggesting the existence ofan additional risk variant, not present in Europeans andAsians, although this hypothesis requires confirmation.52

The expression of TOX3 did not vary by the genotypes ofthe original risk variant, rs3803662, in normal and tumorbreast samples.52 Long et al53 found the strongest associa-tion with one of the 13 variants, rs4784227, in Asian andEuropean case-control studies, and demonstrated that thisvariant is functional in luciferase and electrophoreticmobility shift assays. However, unlike the variants previ-ously mentioned, it lies in a region of closed chromatin,emphasizing the difficulty in interpreting functional exper-iments in this context.53 More recently, rs478227 wasassociated with increased FOXA1 binding and a repressiveeffect on TOX3.54

CCND1 Locus (11q13)

The 11q13 locus (rs614367) was among the loci with thestrongest association with breast cancer.32 An extensivefine-scale mapping of this locus using 4405 genotyped andimputed variants was performed in 89,050 Europeansubjects from the Breast Cancer Association Consortium


(BCAC).46 Three independent association signals weredetected and were strictly confined to ERþ tumors;rs554219, in the strongest peak, lies in an enhancer element,increases breast cancer risk, induces differential binding ofa TF, reduces the enhancer activity, and is associated witha decrease in cyclin D1 (CCND1) levels in breast tumors. Asecond, independent SNP in the same peak, rs78540526,lies in the same enhancer element and also appears to befunctional. A third SNP, rs75915166, generates a GATA3


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binding site within a silencer element. A long-range inter-action confined to ERþ breast cancer cells was identifiedbetween the enhancer and silencer elements and withCCND1. These associations were replicated in Asian pop-ulations despite their low frequency (<2%), providingfurther support that these SNPs may be causative. The effectsizes of those newly identified SNPs are larger than anypreviously reported breast cancer GWAS loci.46

TERT Locus (5p15)

Fine-scale mapping of the TERT locus was performed inapproximately 103,000 breast cancer cases and controls(European, Asian, and African American ancestries) fromBCAC, approximately 12,000 BRCA1 carriers, and approx-imately 40,000 ovarian cancer cases and their matchingcontrols.49 Three independent association signals wereidentified. SNPs in peak 1 (TERT promoter) were associatedwith telomere length, ERþ and ER� breast cancer, and breastcancer in BRCA1 carriers. SNPs in peak 2 (introns 2 to 4)were associated with telomere length and all breast andovarian cancers. SNPs in peak 3 (introns 2 to 4) were asso-ciated with ER� breast cancer, breast cancer in BRCA1carriers, and ovarian cancer, but not with telomere length.The findings provide evidence for genetic control of telomerelength by common variants across TERT. However, only thedirection of the findings in peak 1 supports the hypothesisthat shorter telomere length increases the risk of breastcancer, suggesting that most of cancer associations identifiedin the TERT locus are likely to be mediated through analternative mechanism involving the TERT gene.49

Biological Characteristics of Known Low-Penetrance Breast Cancer Loci

Insights into Genes and Pathways

For most of the known susceptibility loci, neither the causalvariant nor the gene through which the functional effects ofthat variant are mediated is known. Nevertheless, byconsidering the known functions of all of the genes neara susceptibility locus, which are thus candidates for beingthe functionally relevant gene, some patterns begin toemerge.

Mammary Gland Development

Three of the breast cancer susceptibility loci are in regionswith genes that are candidates because they are involved inmammary gland development: FGFR2 at 10q26, PTHLH at12p11, and TBX3 (T-BoX3) at 12q24. FGFR2, a tyrosinekinase receptor, plays a key role in normal mammary glanddevelopment and stem cell function.55 It is overexpressed andamplified in 5% to 10% of breast tumors. Several missensesomatic mutations, inducing FGFR2 overexpression, havebeen found in multiple cancers.56,57 PTHLH, an oncoprotein


expressed in approximately 60% of breast tumors, playsa key role in lactation.58 It has also been suggested thatpatients with PTHLH-positive breast carcinoma are morelikely to develop bone metastasis.58 TBX3 is a downstreamtarget of the WNT/b-catenin pathway (playing a key role instem cell regulation and cell differentiation and prolifera-tion), is amplified and overexpressed in several cancers,including breast cancer, and is found at high levels in plasmafrom breast cancer patients.59 Germ-line mutations thatdisrupt TBX3 are responsible for Ulnar-Mammary syndrome,a condition characterized by defects of several organs,including the mammary glands and the heart.60 Expansion ofbreast cancer stem cells by estrogen has been found to bemediated by FGF/FGFR/TBX3 pathways.59

DNA Repair Pathway

DNA repair processes play a key role in the maintenance ofgenomic integrity. Five susceptibility loci are near genesinvolved in the DNA repair pathway: RAD51B at 14q24,RAD23B at 9q31, MUS81 at 11q13, SSBP4 at 14q13, andBABAM1 at 19p13. RAD51B is involved in the homologousrecombination repair pathway. Overexpression of this genecauses cell cycle G1 delay and cell apoptosis,61 and copynumber variation at 14q24 has been observed consistently infamilies with Li-Fraumeni syndrome.62 RAD23B also hasa key function in recognizing DNA lesions and in the nucle-otide excision repair pathway, as well as in targeting ubiq-uitylated proteins for proteasomal degradation.63 MUS81 isimportant in DNA repair mechanism and in maintaininggenome stability.64 SSBP4 is also involved in DNA repair andrecombination and acts as a tumor-suppressor gene.65

BABAM1 interacts with BRCA1 in the DNA repair processand enforces the BRCA1-dependent DNA damage response.66

Cell Cycle, Differentiation, and Apoptosis

Cell division, differentiation, and apoptosis are similar inboth normal and tumor cells, except that these processes areaberrantly regulated in cancer, leading the cancer cell and itsprogeny into uncontrolled expansion and invasion. Mostbreast cancer susceptibility loci contain genes with a centralfunction in these pathways. These include MDM4 at 1q32,CDKN2A and CDKN2B (CDK Qinhibitors 2A and 2B,respectively) at 9p21, MAP3K1 at 5q11, MRPS30 at 5p12,MYC at 8q24, TCF7L2 at 10q25, MAPKAP/MK5 at 12q24,NOTCH2 at 1p11, CCND1 and FGFs at 11q13, and KRE-MEN1 at 22q12. MDM4 is an oncogene that represses thetranscription of TP53 and TP73 and therefore plays a keyrole in cell cycle regulation and apoptosis.67 CDKN2A andCDKN2B are tumor-suppressor genes that lie adjacent toeach other. The p16 protein encoded by CDKN2A plays animportant role in cell cycle arrest, and its inactivation isa crucial event in the development of several types of humantumors.68 CDKN2B is another cell growth regulator thatcontrols cell cycle G1 progression. Its expression is

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dramatically induced by transforming growth factor-b,suggesting its involvement in transforming growth factor-beinduced growth inhibition.69,70 MAP3K1 encodes acomponent of the mitogen-activated protein kinase (MAPK)signaling pathway, known for its key role in cell differen-tiation, proliferation, and apoptosis.71 MAP3K1 regulatescell migration and survival, as well as immune systemfunction. MAP3K1-deficient retinas exhibit increased pro-liferation and apoptosis in mice.72 MRPS30 (programed celldeath protein 9/PDCP9) is a key component of the apoptoticsignals in cells.73 MYC is a proto-oncogene that drivesseveral mitogenic signals through activation of the MAPK/ERK pathway.74 MYC is overexpressed in different tumors,including breast and prostate cancers,74 and reducing itsexpression levels using RNA interference inhibits tumorgrowth both in vivo and in vitro.75 The TCF7L2 is a proto-oncogene, an important component of the WNT pathway,and well known for its association with type 2 diabetes.76

The MAPKAPKs have a pivotal role in the cell mitogenicprocesses and survival and are directly regulated byMAPKs.77 MK5/MAPKAP5 was found to regulate thetranslation of MYC and vice versa by a negative feedbackloop.78 NOTCH2 belongs to a family of transmembranereceptors and plays a key role in cell differentiation, pro-liferation, and apoptosis.79 Decreases in NOTCH2 expres-sion are associated with increasing grade of human breastcancer,80 and its signaling is required for anti-tumor im-munity in vivo.81 CCND1 is a key cell cycle regulator, andits amplification and overexpression were documented ina variety of tumors.82 FGFs have diverse roles in regulatingcell proliferation, migration, and differentiation, and theyplay a role in cancer pathogenesis.83 KREMEN1 suppressesthe WNT/b-catenin pathway, and its expression is low orabsent in tumor cells compared with normal tissues. Lack ofKREMEN1 and therefore activation of the WNT/b-cateninpathway is likely to be one of the mechanisms by whichtumorigenesis is enhanced.84

Of these regions, only 8q24 has been the focus ofmultiple functional experiments. Several independent loci,clustered in a gene desert a few hundred kb upstream anddownstream of MYC, have been found to be associated withan increased risk of prostate, breast, colorectal, ovarian,glioblastoma, and bladder cancers, as well as lymphocyticleukemia (National Humane Genome Research Institute,NIH, http://www.genome.gov/gwastudies, last accessed July29, 2013). Functional assessment both in vivo and in vitroidentified enhancer elements in each of the loci, includingmammary and prostate enhancers that interact physicallywith the MYC and PVT-1 promoters through a long-rangechromatin loop and regulate its expression levels.85e87

Growth Factors and Tumor Aggressiveness

Although departure from tissue homeostasis and initiation ofcancer are mainly initiated by oncogenic mutations, growthfactors play a major role in the subsequent steps of tumor


progression, particularly clonal expansion, invasiveness,and colonization of distant organs. Several genes near themost recently identified loci encode a growth factor (NTN4at 12q22) or are involved in tumor aggressiveness andmetastasis (PTH1R at 3p21), FOXQ1 at 6p25, ARHGEF5 at7q35, PAX9 at 14q13, and MKL1 at 22q13. NTN4 encodesa growth factor crucial for the regulation of tumor growth.High expression levels of NTN4 were detected in ERþ

breast cancer but not in ER�, and its use was suggested asa prognosis marker.88 PTH1R is the receptor of PTHLH(also identified in a GWAS). Up-regulation of PTH1R wasfound in patients with invasive breast ductal carcinoma.Silencing PTH1R induced suppression of cell prolifera-tion.89 Forkhead TF (FOXQ1) plays a key role in regulatingepithelial-mesenchymal transition (EMT; crucial formetastasis) in breast cancer cells. Its expression is correlatedwith high-grade breast cancers and is associated with poorprognosis. Silencing FOXQ1 reversed EMT in invasivehuman breast cancer cell lines and decreased their inva-siveness. Conversely, overexpression of FOXQ1 in humanmammary epithelial cells induced an EMT and the acqui-sition of resistance to apoptosis after chemotherapy.90

ARHGEF5 is an oncogene and plays a pivotal role incancer development and breast metastasis through activationof the ARHGEF5/TIM pathway.91 PAX9 encodes a TF thatregulates cell proliferation, invasion, and resistance toapoptosis.92 MLK1 is also important in cell invasion andmetastasis, particularly in breast carcinomas.93

ER Pathway

Estrogen plays a key role in growth and in the maintenanceof reproductive functions. Two breast cancer susceptibilityloci are near genes with pivotal roles in this pathway. One at6q25 is close to ESR1 and the other, at 21q21, is close toNRIP1/RIP140. ESR1 encodes the ERa protein, which isactivated by bound estrogen. NRIP1 is a cofactor of ERaand can inhibit its mitogenic effects. This gene plays animportant role in the regulation of breast cancer cell growth,notably in ER-positive breast tumors.94

Telomere Length

Telomeres are regions of repetitive nucleotides at the end ofchromosomes. They are crucial in maintaining genomicstability by protecting chromosomes from degradation,recombination, and end-to-end fusion.95 Human telomerestend to progressively shorten with each cell division, andthis shortening process is associated with ageing andincreased risk of cancer.96 One of the identified breastcancer susceptibility loci lies at 5p15 and is near TERT,which encodes telomerase reverse transcriptase, the catalyticsubunit of telomerase.28 Abnormal expression of TERT hasbeen reported in several types of cancer, and the region isfrequently amplified in many types of cancer, such as lungand cervical cancers.97,98


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Other Biological Pathways

Many genes near the known susceptibility loci are either ofunknown function or have functions that do not have a clearconnection to breast cancer development (a notable examplebeing the TOX3 locus). These genes could simply not be theones being targeted, but alternatively might be involved insusceptibility through a previously unsuspected mechanism.Future functional analysis should shed some light on thecausal variants within each of these loci and the mechanismsby which they are driving the association.

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Common Pathways for Multiple Phenotypes

Although most breast cancer susceptibility loci are diseasespecific, at least 17 loci (at 1q32, 2p24, 2q31, 4q24, 5p12,5p15, 6q25, 8q24, 9p21, 9q31, 10p12, 19p13, 10q26,11p15, 11q13, 12q24, and 14q24) occur in regions associ-ated with other cancers and complex diseases/phenotypes,suggesting a shared mechanism in disease development(Figure 4). Four of these have been intensively discussed.The 5p15 region containing TERT is also associated withglioma, basal cell carcinoma, and lung and pancreaticcancers (http://www.genome.gov/gwastudies). The 8q24region is associated with at least six cancers and one of theloci, rs6983267, has a pleiotropic effect on prostate, colo-rectal, and possibly ovarian cancers (http://www.genome.gov/gwastudies). The 9p21 region containing the twotumor-suppressor genes, CDKN2A and CDKN2B, is

Figure 4 Location of the common breast cancer susceptibility loci together4q24, 5p12, 5p15, 6q25, 8q24, 9p21, 9q31, 10p12, 10q26, 11p15, 11q13, 12qcancers.


associated with at least six cancers (breast and pancreaticcancers, lymphoblastic leukemia, melanoma, glioma, basalcell carcinoma, and nasopharyngeal carcinoma), myocardialinfarction, type 2 diabetes, intracranial aneurysm, cutaneousnevi, and sporadic amyotrophic lateral sclerosis (http://www.genome.gov/gwastudies). The 11q13 region containsCCND1 and several FGFs and is associated with breast,prostate, and renal cancers (http://www.genome.gov/gwastudies. The nearest genes to the 12q24 region areTBX3, important for mammary gland development, andMAPKAP, a component of the MAPK pathway that inter-acts with MYC. SNPs in this region are associated withbreast cancer, squamous esophageal carcinoma, liveradenoma, renal cell carcinoma, heart diseases, type 1 dia-betes, blood pressure, and PSA Qlevels (http://www.genome.gov/gwastudies).

Clinical Implications

Although the clinical utility of testing for high-penetrancemutations such as BRCA1 and BRCA2, is well established,the potential clinical use of polygenic prediction for breastcancer is extensively debated.99e102 Taken separately, theSNP associations are too weak to be useful on an individualbasis. However, because their effects seem to combinemultiplicatively, there is potential for the predictive powerto improve substantially as more variants are found.101 Ithas been argued that polygenic risk profiles have limiteddiscrimination for breast cancer, but because the clinical

with the most plausible nearby genes. At least 17 loci (1q32, 2p24, 2q31,24, 14q24, and 19q13) are associated with breast, prostate, and ovarian

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utility of a risk prediction model is not restricted todiscrimination, these should be further evaluated in thecontext of risk stratification after combining other riskfactors, such as lifestyle elements and family history.

In a recent study, Pashayan et al103 showed that person-alized screening based on age and polygenic risk factors (18common low-penetrance loci) is more efficient than a stan-dard approach based solely on age. Personalized screeningcould reduce the number of people eligible for screening by24% while detecting most cancers (86%) compared witha program based on age alone.103 Individual risk stratifica-tion would increase the efficiency of the screening program,by intensifying screening in high-risk individuals andsparing unnecessary and even potentially harmful screeningin those with lower risk levels. This increase in efficiencywould be even greater for risk stratification based on the 72loci identified.104

Future Directions

Whole Genome Resequencing

The emerging results from GWASs suggest that a substan-tial fraction, perhaps the majority, of the familial risk ofbreast cancer can be explained by common variants, most ofwhich are yet to be identified. It is highly likely that rarer,higher-risk alleles (such as risk alleles of PALB2) alsoremain to be identified. Unraveling the full architecture ofbreast cancer susceptibility will therefore require a compre-hensive search for rarer variants. Technological advancesover the past few years have rendered whole-exome orwhole-genome resequencing of large cohorts of cases andcontrols increasingly more practical.

Consortia and Collaborations

Studies with large sample sizes are critical to enhancepower, achieve genome-wide significance levels, and yieldadditional insights in terms of replication of the results, newgenetic discoveries, and key biological findings.

The past few years witnessed the formation of multipleinternational consortia and substantial widespread collabo-rations of many multidisciplinary investigators, includingepidemiologists, clinicians, statisticians, bioinformaticians,and biologists. Groups with their individual case-control setsjoined efforts to increase sample size through meta-analysis,and these collaborations resulted in an explosion of newassociations. One of the most successful examples in canceris the Collaborative Oncological Gene-Environment Study,a collaborative project funded by the European Commission,which combined data from four different consortia: BCAC,Prostate Cancer Association Group to Investigate CancerAssociated Alterations in the Genome, the Ovarian CancerAssociation Consortium, and the Consortium of Investigatorsof Modifiers of BRCA1/2. This collaboration has developeda genotyping array (Collaborative Oncological Gene-


Environment Study) of approximately 200,000 SNPs thathas been genotyped in approximately 200,000 cases withbreast, prostate, and ovarian cancers and approximately200,000 controls from around the world. Results from thisproject identified 74 novel breast, prostate, and ovariancancer loci (41 of which are for breast cancer), nearlydoubling the number of the previously established ones. Inaddition, those results provided an accurate assessment of therisk related to each locus and will offer a comprehensive finemapping of the known loci. It will also be possible to evaluategenetic associations with disease survival, clinical outcome,and tumor subsets and to evaluate the combined effect ofgenetic variants and lifestyle risk factors on disease risk.More findings emerging from this project should add greatlyto our understanding of the genetic architecture of breast andother cancers.

GWASs in Other Populations

Breast cancer GWASs have primarily been conducted inwomen of European descent, with the exception of one studyperformed in Chinese women, which indicated importantdifferences in the pattern of genetic loci between ethnicities.These may result from differences in allele frequencies,patterns of LD, or interactions with genetic background orlifestyle risk factors. Further GWASs in non-Europeanpopulations should therefore be extremely fruitful.

Conclusions

In the future, more breast cancer susceptibility alleles arelikely to be identified, and these should, in turn, lead tofurther functional studies and expand our understanding ofthe underlying biological characteristics of breast cancer. Thecandidate genes in susceptibility regions identified thus farappear to cluster into a few biological pathways: mammarygland development, DNA repair, cell cycle control, andestrogen receptor signaling. Genes with unknown function atthese loci will need further work, but their characterizationmight reveal unexpected and important insights into cancerbiology. Loci that are associated with breast cancer and othercancers and traits will be important in revealing biologicalmechanisms that connect pathways and diseases. In additionto the common variants, rare genetic variations identifiedthrough whole-exome and whole-genome sequencing strat-egies will likely expand the catalog of breast cancersusceptibility variations. Further studies to examine struc-tural variation and epigenomics may also uncover moresusceptibility loci. Integration of the germ-line susceptibilitydata with emerging somatic sequence data may be importantto both provide a more comprehensive analysis of suscepti-bility by disease subtype and interpret better the underlyingbiological characteristics. Finally, large-scale populationstudies (preferably prospective) will be required to define thecombined effects of common and rare variants, and lifestyle


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and other risk factors, on disease risk. This should lead tomore rational strategies of disease prevention, while theknowledge of breast cancer etiology gained through thefunctional understanding of disease susceptibility may leadto novel therapeutic approaches for breast cancer patients.

Acknowledgments

We thank Dr. David Meyre and Mel Maranian for com-menting on an early draft of the manuscript and RogerMilne for generating Figure 4.

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