Genetic Variation in IGF2 and HTRA1 and Breast Cancer Risk among BRCA1 and BRCA2 Carriers

Genetic Variation in IGF2 and HTRA1 and Breast Cancer Riskamong BRCA1 and BRCA2 Carriers

Susan L. Neuhausen1, Sean Brummel2, Yuan Chun Ding1, Linda Steele1, Katherine L.Nathanson5,6, Susan Domchek5,6, Timothy R. Rebbeck5,7, Christian F. Singer9, GeorgPfeiler9, Henry T. Lynch10, Judy E. Garber3, Fergus Couch11, Jeffrey N. Weitzel1, AndrewGodwin12, Steven A. Narod13, Patricia A. Ganz14, Mary B. Daly8, Claudine Isaacs15,Olufunmilayo I. Olopade16, Gail E. Tomlinson17, Wendy S. Rubinstein18, Nadine Tung4,Joanne L. Blum19, and Daniel L. Gillen20

1Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte,California2Center for Biostatistics in AIDS Research, Harvard School of Public Health3Department of Medicine, Harvard Medical School and Dana Farber Cancer Institute4Beth Israel Deaconess Medical Center, Boston, Massachusetts5Abramson Cancer Center, University of Pennsylvania School of Medicine6Department of Medicine, University of Pennsylvania School of Medicine7Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine8Fox Chase Cancer Center, Philadelphia, Pennsylvania9Division of Special Gynecology, Department of Obstetrics and Gynaecology, Medical Universityof Vienna, Vienna, Austria10Departments of Medicine, and Preventive Medicine and Public Health, Creighton University,Omaha, Nebraska11Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota12Department of Pathology and Laboratory Medicine, University of Kansas Medical Center,Kansas City, Kansas13Women's College Hospital, Toronto, Ontario14Jonsson Comprehensive Cancer Center at the University of California, Los Angeles, California15Lombardi Cancer Center, Georgetown University, Washington, District of Columbia16Departments of Medicine and Human Genetics, University of Chicago, Chicago, Illinois

© 2011 American Association for Cancer Research.

Corresponding Author: Susan L. Neuhausen, Department of Population Sciences, the Beckman Research Institute of the City ofHope, 1500 E. Duarte, Duarte, CA 91010. Phone: 626-471-9261; Fax: 626-471-9269; [email protected].

Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers and Prevention Online (http://cebp.aacrjournals.org/).

Disclosure of Potential Conflicts of InterestT.R. Rebbeck is the Editor-in-Chief of Cancer Epidemiology, Biomarkers & Prevention. In keeping with the AACR’s Editorial policy,the manuscript was peer reviewed and a member of the AACR’s Publications Committee rendered the decision concerningacceptability. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the State ofNebraska or the Nebraska Department of Health and Human Services.

NIH Public AccessAuthor ManuscriptCancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2012 August 01.

Published in final edited form as:Cancer Epidemiol Biomarkers Prev. 2011 August ; 20(8): 1690–1702. doi:10.1158/1055-9965.EPI-10-1336.

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17Division of Pediatric Hematology Oncology, The University of Texas Health Science Center atSan Antonio and University of Texas Southwestern Medical Center, Dallas, Texas18NorthShore University Health System, Center for Medical Genetics, Evanston, and University ofChicago Pritzker School of Medicine, Chicago, Illinois19Baylor-Charles A. Sammons Cancer Center, Dallas, Texas20Department of Statistics and Department of Epidemiology, The University of California Irvine,Irvine, California

AbstractBackground—BRCA1 and BRCA2 mutation carriers have a lifetime breast cancer risk of 40%to 80%, suggesting the presence of risk modifiers. We previously identified significantassociations in genetic variants in the insulin-like growth factor (IGF) signaling pathway. Here, weinvestigate additional IGF signaling genes as risk modifiers for breast cancer development inBRCA carriers.

Methods—A cohort of 1,019 BRCA1 and 500 BRCA2 mutation carriers were genotyped for 99single-nucleotide polymorphisms (SNP) in 13 genes. Proportional hazards regression was used tomodel time from birth to diagnosis of breast cancer for BRCA1 and BRCA2 carriers separately.For linkage disequilibrium (LD) blocks with multiple SNPs, an additive genetic model was used.For an SNP analysis, no additivity assumptions were made.

Results—Significant associations were found between risk of breast cancer and LD blocks inIGF2 for BRCA1 and BRCA2 mutation carriers (global P values of 0.009 for BRCA1 and 0.007for BRCA2), HTRA1 for BRCA1 carriers (global P value of 0.005), and MMP3 for BRCA2carriers (global P = 0.0000007 for BRCA2).

Conclusions—We identified significant associations of genetic variants involved in IGFsignaling. With the known interaction of BRCA1 and IGF signaling and the loss of PTEN in amajority of BRCA1 tumors, this suggests that signaling through AKT may modify breast cancerrisk in BRCA1 carriers.

Impact—These results suggest potential avenues for future research targeting the IGF signalingpathway in modifying risk in BRCA1and BRCA2 mutation carriers.

IntroductionWomen who carry mutations in the BRCA1 or BRCA2 (BRCA) genes have a substantiallyincreased risk of developing breast and ovarian cancers as compared with the generalpopulation. Estimates of the age-specific risk attributable to mutations at these loci varydepending on the ascertainment scheme. The cumulative risks of breast cancer by age 70years have been estimated between 40% and 87% for BRCA1 and BRCA2 mutation carriers(1–5). Among BRCA mutation carriers, there is considerable variability in both age atdiagnosis and incidence of breast and ovarian cancers, even among women who carry thesame BRCA mutation (6–8). These observations of large variation in risk are consistent withthe hypothesis that the breast cancer risk in mutation carriers is modified by other geneticand/or environmental factors. To test this hypothesis, we have focused on genetic variationin the insulin-like growth factor (IGF) signaling pathway, as it regulates both cellularproliferation and apoptosis (reviewed in refs. 9–11). We previously reported significantassociations of single-nucleotide polymorphisms (SNP) in insulin receptor substrate (IRS) 1and IGF1R in BRCA1 mutation carriers and in IGF1R and IGFBP2 in BRCA2 mutationcarriers (12).

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For this study, we investigated the association of variants in additional genes involved inIGF signaling. We focused on 13 genes in this pathway, which were closely related to geneswith significant associations with breast cancer risk in our previous work (12). These genesincluded the IGF1 receptor (IGF1R) ligands IGF2 and insulin (INS); the IGF bindingproteins (IGBFP) IGFBP3 (and its partners in binding IGF, IGFALS), IGFBP4, andIGFBP6; the IGFBP proteases kallikrein–related peptidase 3 (KLK3, aka prostate-specificantigen; PSA), cathepsin D (CTSD), matrix metalloproteinases 3 and 7 (MMP3 and MMP7),HTRA serine peptidase 1 (HTRA1, aka PRSS11); the IGF1R docking protein IRS2; and itsphosphorylation target phosphoinositide 3-kinase 110-kb catalytic subunit B (PIK3CB), aspotential disease modifiers in deleterious mutation carriers of BRCA1 and BRCA2.

Materials and MethodsParticipants

For the primary investigation, women with germline, deleterious mutations in BRCA1 orBRCA2 were identified in 14 centers in the United States, 1 center in Canada, and 1 centerin Austria, including Baylor University Medical Center-Dallas, Dallas, TX, Beth Israel inBoston, City of Hope, Creighton University, Omaha, NE, Dana Farber, Boston, MA, FoxChase Cancer Center, Philadelphia, PA, Georgetown University, Washington DC, the MayoClinic, Scottsdale, AZ, Medical University of Vienna, Vienna, Austria, North ShoreUniversity Health-System, Chicago, IL University of California, Los Angeles, CA,University of California, Irvine, CA, University of Chicago, Chicago, IL, University ofPennsylvania, Philadelphia, PA, and Women’s College Hospital, Toronto, Ontario. Themajority of subjects were recruited from the Medical University in Vienna, CreightonUniversity in Nebraska, the University of Pennsylvania, and the University of CaliforniaIrvine (previously at the University of Utah and now at the Beckman Research Institute ofthe City of Hope). All centers are part of the Modifiers and Genetics in Cancer (MAGIC)Consortium. All participants were enrolled under Institutional Review Boards or ethicscommittee approval at each participating site. Women were identified through either clinicalor research high-risk programs, generally because of a family history of breast and/orovarian cancer. The current study uses data on a total of 1,519 adult, female non-HispanicCaucasian mutation carriers, including 1,019 BRCA1 carriers (381 cases and 638 unaffectedcontrols) and 500 BRCA2 carriers (219 cases and 281 unaffected controls). The BRCAmutation status of all subjects was confirmed by direct mutation testing, with full informedconsent under protocols approved by the human subjects review boards at each institution.Women were eligible for entry into the study cohort if they tested positive for a knowndeleterious mutation in BRCA1 or BRCA2. Women with deleterious mutations in bothBRCA1 and BRCA2 were excluded. Women were excluded if they were missinginformation on year of birth, parity, menopausal status, and oral contraceptive use or had adiagnosis of breast or ovarian cancer more than 3 years prior to study entry. Informationabout breast cancer, ovarian cancer, prophylactic mastectomy, and prophylacticoophorectomy was obtained from medical records, and information on reproductive historyand lifestyle habits was obtained by questionnaire.

SNP genotypingThe Genome Variation Server (GVS; ref. 13) at the University of Washington was used toidentify SNPs in the genes and select haplotype tagging SNPs (htSNP) using the programLD Select (14), which is integrated with the GVS. A total of 99 SNPs were selected from 13genes to mark the common genetic variation in each gene and minimize the genotypingcosts. However, because some of the genes had SNPs from the pooled HapMap race/ethnicity samples, we could not discern race and so we recalculated linkage disequilibrium(LD) blocks specifically for the European (EU) population in this article as described later.



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Genotyping was done using the SNPlex assay from Applied Biosystems, Inc. (ABI), for allbut 3 SNPs in IGFBP3 for which alleles were genotyped by ABI TaqMan exonucleaseassays. Genotyping by SNPlex was done according to ABI’s protocols. The SNPlex assay isbased on an oligonucleotide ligation assay (OLA), followed by multiplex PCR amplificationusing a universal primer pair, hybridization of reporter probes to amplicons, and capillaryelectrophoresis to rapidly identify eluted reporter probes (ABI). On the basis of the list ofSNPs sent to ABI, 3 SNPlex pools were designed and ordered for genotyping. We obtainedgenotype data for 99 SNPs following the manufacturer’s protocol (ABI). Briefly, 120 nggenomic DNA for each sample was fragmented by denaturing at 99°C for 10 minutes.Phosphorylation of probes and allele-specific ligation between fragmented DNA and probeswere done by running an OLA program on a PTC225 thermal cycler. After purification ofligation product by exonuclease digestion, PCR was carried out using universal primers withthe reverse primer biotinylated. During hybridization, the captured biotinylated PCR strandswere hybridized with fluorescently labeled ZipChute probes, with a unique sequencecorresponding to the unique Zip code sequences at the 5′ end of the allelic-specific probesused in OLA reactions. The Zip-Chute probes were separated by the ABI 3130xl capillarysequencer, and genotypes were called by ABI Genemapper 4.0 program. The laboratorytechnician was blinded as to whether samples were duplicates, cases, or controls. Each 96-well plate contained 87 unique samples, 2 positive controls, 1 negative control, and 6ladders. Genotyping call rates ranged from 67% to 99%. SNPs that were monomorphic orwith minor allele frequencies (MAF) less than 0.03 (n = 10 SNPs) or with genotyping callrates less than 80% (n = 5 SNPs) were excluded from further analysis. Because the duplicateconcordance rates of the 40 duplicated samples were higher than 99.9%, we felt confidentthat the data were accurate even for the 10 SNPs with 82% to 89% call rates.

Determination of LD blocksTo define LD blocks relative to the current study population, all LD blocks were computedusing the current analytic data set. Because of the relatively small proportion of non-EUsubjects within the study sample, and the possibility that LD structure may differ betweenEU and non-EU ancestries, we restricted the analysis to only non-Hispanic Caucasians.SNPs were grouped according to their adjacent pairwise LD coefficient, D′, which wascomputed between all adjacent marker pairs within each candidate gene. To account forwithin-family correlation, multiple outputation (15) was used to estimate D′. In this case, asingle member from each family was randomly sampled to create a single bootstrap sample,from which D′ was computed. This process was repeated to obtain 200 bootstrap samples,yielding an empirical distribution of D′. An LD block was defined as a set of contiguousSNPs having D′ values exceeding 0.90 between each contiguous pair of SNPs. Theboundary of an LD block would be defined by a marker pair with D′ ≥ 0.9. Given thatindividuals of Ashkenazi Jewish ancestry make up approximately 24% of the analyticsample, we further explored differential LD structure between Ashkenazi Jewish ancestryand non-Ashkenazi Jewish ancestry. No statistically significant differences were found (asdefined by nonoverlapping 95% CIs for D′ between ancestry strata). However, to guardagainst grouping SNPs within an ancestry that may or may not be in linkage, we used theminimum D′ between the non-Jewish and the Jewish groups when defining the LD blocks.The LD blocks for the SNPs within each gene are shown in Supplementary Table S1 alongwith the MAFs.

Statistical analysisFor all analyses, subjects were considered at risk for breast cancer from birth until the firstoccurrence of breast cancer diagnosis, death, or loss to follow-up. Subjects were censored inthe event that they underwent a bilateral prophylactic surgery of the breasts more than 1 yearpreceding the diagnosis of breast cancer. Bilateral prophylactic surgery of the breasts



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occurring within a year of breast cancer was considered an event to avoid potential biasesresulting from informative censoring. Descriptive breast cancer rates were calculated as theobserved number of breast cancers per total patient time at risk and standardized to the agedistribution of the study cohort at the time of interview (16). Covariates that vary with time(ovarian cancer and prophylactic ovarian surgery) were treated as time-dependent in thecalculation of rates. Thus, a subject who had a diagnosis of ovarian cancer contributed timeat risk in the nonovarian cancer group prior to the diagnosis and then time at risk in theovarian cancer group following the diagnosis. Because subjects were ascertained primarilyfrom high-risk clinics, selection bias is likely to result in an overrepresentation of cases inthe analysis sample relative to the general population. To account for potential bias incumulative risk estimates due to nonrandom sampling from the general population, Kaplan–Meier estimates of the cumulative probability of breast cancer diagnosis were computedusing age-specific sampling weights for cases and controls as obtained from Antoniou andcolleagues (17).

Cox proportional hazards regression was used to estimate the association between time frombirth to diagnosis of breast cancer and haplotypes in genes within the IGF pathway. In thismodel, the hazard or instantaneous rate of breast cancer diagnosis is modeled as a functionof the predictor covariates. The relative risk or HR is then interpreted for each covariate asthe proportionate change in the instantaneous rate of diagnosis for 2 individuals differing bya single unit of that covariate. When analyzing LD blocks with multiple SNPs, an additivehaplotype effect was assumed where the haplotype composed of the major allele for eachindividual SNP within the LD block was used as the referent group for comparisons (i.e.,0000). However, when an LD block consisted of an SNP, a general genetic model makingno additivity assumption was used. To account for phase uncertainty in haplotype analysis,we used a 2-step approximation to the semiparametric maximum likelihood estimator of Linand Zeng (18). This method used the expectation–maximization algorithm to computeposterior estimates of the probability of all potential haplotypes for a subject given theirknown genotype, and these probabilities were used to weight the individual’s contribution tothe partial likelihood. A similar approach has previously been applied to logistic regressionmodels for analyzing case–control data and shown to provide robust inference for relativelycommon haplotypes with little phase ambiguity (19). To account for hierarchical clusteringat the individual level (multiple records per individual were analyzed according to thenumber of potential diplotypes consistent with the individual’s genotype) and the familylevel (matched controls were often selected from the family of a case), the sandwichestimator of Lin and Wei (20) was used in combination with multiple outputation (15) toobtain robust inference about haplotype associations. All estimates were adjusted for birthcohort (to account for frequency matching of cases and controls), ancestry, age at firstpregnancy, and region of center [North American (US) vs. EU]. Ashkenazi Jewishindividuals were considered a separate group because the carriers had only 1 of 3 foundermutations. Age at first pregnancy, prophylactic oophorectomy, and ovarian cancer statuswere treated as time-dependent covariates in the analysis. To account for potential selectionbias through recruitment from high-risk clinics, all regression models employed age-specificsampling probability weights for cases and controls as obtained from Antoniou andcolleagues (17). For LD blocks exhibiting significant associations with the time to breastcancer diagnosis, secondary analyses of individual SNPs making up the LD block wereconducted. In addition, we considered sensitivity analyses to assess the assumption ofadditivity for haplotype models and found no quantifiable differences.

In total, the current analysis involved testing of 83 SNPs in 41 LD blocks across the 13genes, which is likely to result in an inflation of the family-wise type I error rate for thestudy if unadjusted critical values are used for assessing LD block significance. Noting thatthis analysis represents a first stage in identifying variants in the IGF pathway that are



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associated with time to breast cancer diagnosis, we sought to control the family-wise typeerror rate at 10% to minimize the type II error rate, limiting the possibility of ruling outpotentially important LD blocks from future investigation. Simulation was used to estimatethe family-wise type I error rate, assuming a correlation of 0.75 across tests. On the basis of100,000 simulations, it was estimated that an adjusted P value of 0.010 on any individualLD block test would result in a family-wise type I error rate of 10% for the study. Thus, anadjusted P value less than 0.010 was interpreted as a significant association.

ResultsThe characteristics of study participants, study sites, and the observed incidence rate (per1,000 women per year) of breast cancer diagnosis stratified by BRCA status were previouslydescribed (12). Briefly, the age-standardized incidence rate of breast cancer diagnosis wasestimated to be 26.94 per 1,000 per year in BRCA1 carriers (95% CI: 19.79, 34.10)compared with 25.03 per 1,000 per year in BRCA2 carriers (95% CI: 18.71–31.36). Thecharacteristics of the study participants, limited to non-Hispanic Caucasians, are shown inTable 1. The majority of study subjects in both strata were of non-Jewish ancestry (76%). Ofthe study subjects, 9% underwent bilateral prophylactic mastectomy and 41% underwentprophylactic bilateral salpingo-oophorectomy. Median age at diagnosis was estimated to be57.0 years (95% CI: 54.1–62.2) among BRCA1 carriers and was 70.5 years (95% CI: 67.7–INF) among BRCA2 carriers. The results are presented grouped by genes in components ofthe IGF signaling pathway. As described in Materials and Methods, only P values of lessthan 0.010 for individual LD blocks were considered significant.

IGFIR ligands: IGF2 and INSIn Figure 1, we present the estimated HR for time to diagnosis by LD block and BRCAstatus after adjustment for covariates (described in Materials and Methods). For bothBRCA1 and BRCA2 mutation carriers, significant associations of LD blocks in IGF2 wereobserved. Among BRCA1 carriers, for IGF2 LD block 3 (defined by SNP rs1065687),women with at least 1 variant allele were estimated to experience a 76% higher relative riskof diagnosis than did women with no variant allele (perallele HR 1.76; 95% CI: 1.15–2.70; P= 0.009). Among BRCA2 carriers, in LD block 5, composed of 4 SNPs, the global P valuewas significant for LD block 4 (P = 0.003) and LD block 5 (P = 0.0007). In LD block 4,women with haplotype 10 had a more than 2-fold increased risk of breast cancer (HR = 2.20;95% CI: 1.4–3.5; P = 0.0008). In LD block 5, women with haplotypes 0001 (HR = 0.48;95% CI: 0.30–0.76; P = 0.0017), 0100 (HR = 0.40; 95% CI: 0.26–0.61; P = 0.00002), and0101 (HR = 0.39; 95% CI: 0.20–0.79; P = 0.009) were estimated to experience asignificantly decreased risk of diagnosis when compared with the reference haplotype.Given the significant HRs for the haplotypes in IGF2 LD blocks 4 and 5, we investigated theHRs for the 6 individual SNPs (rs3213232, rs3213229, rs3213223, rs3213221, rs3213217,and rs3213216) within the LD blocks to examine whether the observed haplotype effectcould be attributed to any one SNP. Of the 6 SNPs, there was only an effect of carrying 1copy of the variant allele of rs3213221 (HR = 0.60; 95% CI: 0.40–0.92; P = 0.018). Therewas no significant association of INS and breast cancer risk among BRCA1 or BRCA2carriers after adjusting for multiple testing.

IGFBPs: IGFALS (in the complex with IGFBP3), IGFBP3, IGFBP4, and IGFBP6Estimated HRs for the haplotypes of the IGBFPs are shown in Figure 2. No significantglobal tests of associations were observed for any haplotypes of the 3 IGFBP genes. TheSNP in IGFALS was also not associated with risk (see Supplementary Table S1).



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IGFBP proteases: HTRA1, MMP3, MMP7, KLK3, and CTSDEstimated HRs for the haplotypes of the IGFBP proteases are shown in Figures 3 and 4. Ahighly significant association in HTRA1 LD block 3, composed of 3 SNPs, was observed inBRCA1 carriers (global P = 0.005) with no association in BRCA2 carriers (global P =0.147). Within LD block 3, the rare haplotypes grouped together were significantlyassociated with a 2-fold risk to develop breast cancer (HR = 2.12; 95% CI: 1.30–3.46; P =0.0028). When looking at individual SNPs within HTRA1, no SNP within LD block 3 wasassociated with risk. There were no significant associations of LD blocks in HTRA1 and riskamong BRCA2 carriers. However, among BRCA2 carriers, carrying the variant allele inrs12571363 in LD block 1 conferred a 2-fold increased risk (per allele HR = 2.03; 95% CI:1.29–3.21; P = 0.002). Haplotypes in KLK3 and CTSD were not associated with anymodification of risk for developing breast cancer. For MMP3 in BRCA2 carriers, a highlysignificant association (global P = 0.0000003) in LD block 3 was observed because of acombination of rare haplotypes associated with a 5.1-fold higher risk (HR: 5.08; CI: 2.67–9.67; P = 0.0000007; Fig. 4A). None of the 3 SNPs constituting this haplotype, rs476762,rs679620, or rs3025058, could explain the association. For MMP7, after adjusting formultiple testing, there were no significant associations of any LD blocks with risk inBRCA1 or BRCA2 carriers.

IGF1R downstream docking protein and phosphorylation target: IRS2 and PIK3CBThere was no significant association of LD blocks in either IRS2 or PIK3CB and breastcancer risk (Fig. 5).

DiscussionThe IGF pathway signals cells to grow, differentiate, and survive through activation of thephosphoinositol-3-kinase/protein kinase AKT (PI3K/AKT) pathway and the RAS/mitogen-activated protein kinase (RAS/MAPK) pathway. The role of the IGF pathway in breastcancer was reviewed, and a figure of the IGF system and its downstream effectors waspresented by Hamelers and Steenbergh (21). This study is a continuation of our investigationof the role of genetic variants in IGF signaling as modifiers of breast cancer risk in womenwho carry deleterious mutations in BRCA1 and BRCA2. We previously investigated only asmall number of the genes involved in IGF signaling and reported significant HRsassociated with genetic variants in IGF1R and IRS1 in BRCA1 mutation carriers and inIGFBP2 and IGFBP5 in BRCA2 mutation carriers (12). In this investigation of an additional13 genes in the pathway, there were significant associations of variants in IGF2 in bothBRCA1 and BRCA2 mutation carriers and HTRA1 in BRCA1 mutation carriers and MMP3in BRCA2 carriers.

There have been a limited number of epidemiologic studies of the association of sporadicbreast cancer risk and genetic variation in the genes in the IGF pathway investigated in thisstudy. In a large study in the NCI breast and prostate cancer cohort consortium of 6,292breast cancer cases and 8,135 unaffected controls, they reported no significant associations(P < 0.00005) of SNPs in IGF2, IGFALS, IGFBP3, IGFBP4, IGFBP6, and IRS2 and breastcancer risk (22). Their results were inconsistent with our observation of significantassociations with haplotypes in IGF2, which may reflect different SNPs and differentpopulation characteristics including ages at diagnosis and BRCA1 and BRCA2 carrierstatus. There have been several studies of the −202 A>C SNP in the IGFBP3 promoter andbreast cancer risk, with most being negative (reviewed in ref. 23). In association studies ofSNPs and breast cancer risk in the Cancer Prevention Study II, 3 of 11 SNPs in IRS2 wereassociated with breast cancer risk (haplotype OR = 0.76; 95% CI: 0.63–0.92; ref. 24) and theINS rs3842752 SNP was not associated with breast cancer risk (25). The INS SNP was the



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same, whereas none of the 11 IRS2 SNPs were the same as investigated in our study. Twostudies have reported significant associations of SNPs in MMP7 and breast cancer survival,although with different SNPs than investigated here (26, 27) As yet, there are no reportedassociation studies in breast cancer investigating SNPs in HTRA1, PIK3CB, or MMP3.

IGF1 and IGF2 are the primary ligands for binding to the IGF1R, and IGF2 levels are higherthan IGF1 levels in cells. Interestingly, in a study of women of 43 women with localizedbreast cancer and 38 unaffected controls, there were significantly elevated circulating freeIGF2 and IGF1 levels yet significantly reduced total IGF2 levels and reduced total IGF1levels in the breast cancer cases as compared with controls (28). They also found thatIGFBP2 levels were significantly reduced in the cases with no differences in IGFBP1, 3, 4or 6 levels and suggested that the increase in free IGF levels was because there was lessIGFBP2 to bind IGF2. IGFBP2 and IGFBP5 preferentially bind IGF2 over IGF1. Withhigher amounts of free IGFs, there can be more signaling activity through IGF1R. Thesignificant association with variants in IGF2 fits with our previous results in which we hadobserved a significant association with variants at the IGFBP2_IGFBP5 locus in BRCA2carriers and at IGF1R in BRCA1 carriers.

Proteolysis of IGFBPs occurs through the action of IGFBP proteases that include serineproteases (KLKs and HTRA1), MMPs, and CTS (29). With proteolysis of the IGFBPs, thereis then more bioavailability of the IGFs for signaling through IGF1R. In BRCA1 mutationcarriers, an HTRA1 haplotype in LD block 3 was significantly associated with breast cancer(global P = 0.0005). HTRA1, located at 10q25.3-q26.2, was identified in 1996 as a serineprotease specific for IGFBPs (30). HTRA1 is also now thought to function as a tumorsuppressor gene (31), possibly through regulation of TGF-B signaling (32). None of the 4SNPs in the LD block were significantly associated with breast cancer, suggesting that theassociated haplotype only marks the causal variant in the region. Further investigation ofthis region is needed to understand how it may be modifying risk of developing breastcancer in BRCA1 mutation carriers. A group of rare haplotypes (combined frequency of2%) in MMP3, another IGFBP protease, was significantly associated with breast cancer inBRCA2 carriers (HR = 4.33). For MMP3, many more carriers need to be investigated todetermine the actual associated haplotype within the grouped haplotypes, as neither of the 2SNPs in the LD block showed association.

PIK3CB is a 110-kDa catalytic subunit of PI3K and is activated through the IGF1R asfollows: Binding of IGF induces receptor autophosphorylation of the β-subunit of IGF1R,resulting in activation of the tyrosine kinase activity and subsequent tyrosinephosphorylation of IRS1. The activated IRS1 binds the p85 α-subunit PI3K, which, in turn,activates PIK3CB for downstream signaling. PTEN regulates PI3K signaling bydephosphorylating PI3K, so in the absence of PTEN, the pathway is activated. In cellswithout PTEN, experiments have shown that PIK3CB is necessary for continued PI3Ksignaling (33). PIK3CB may be particularly relevant to cancer in BRCA1 carriers because amajority of BRCA1 and other basal-like breast cancers have loss of PTEN expression (34).Furthermore, this suggests a possible therapeutic target in BRCA1 carriers. Clinical trials areongoing with agents targeting the inhibition of the IGF1R and mTOR, although none aretargeted to BRCA mutation carriers. In BRCA1 carriers, there was a marginal association ofgenetic variation in PIK3CB and breast cancer. The intronic SNP rs9878820 was associatedwith breast cancer (per allele HR = 0.83, CI: 0.70–0.99, P = 0.04; homozygous recessive HR= 0.69, P = 0.04), and was more strongly associated in the larger data set includingHispanics, African Americans, and others (P = 0.008).

Although this study provides an important continued step in identifying potential geneticmodifiers of risk among BRCA1 and BRCA2 mutation carriers, there are some limitations.



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First, the sample size was limited, particularly for BRCA2 carriers, so that there may beassociations that we could not detect and the point estimates for the associations we diddetect may be imprecise, particularly for SNPs and haplotypes of low frequency. Second,the loci that we identified in this study that had significant HRs for risk were not previouslyidentified as the top candidates in the genome-wide association study (GWAS) of BRCA1(35) or BRCA2 mutation carriers (36). However, even known associations can be missed inGWAS, as only the top hits are further investigated through replication and imputation ofadditional SNPs in those regions. One example is the previous finding that a promoter SNPin RAD51C (37) is a modifier of risk in BRCA2 carriers with an HR of 3.2 among the rarehomozygotes, yet it was not identified in the GWAS (36). Therefore, it is possible that oneor more of the associations identified here were missed in the GWAS. Third, although theIGF pathway was a priori hypothesized as a source for potential modifiers, multiple LDblocks were considered for association testing and such testing could lead to inflation of theoverall type I error rate for the study. With this being said, we studied only a relatively smallnumber of the genes in IGF signaling that we a priori deemed would potentially play a rolein the time to diagnosis and considered significant only those associations that decreasedbeyond a multiple comparison-adjusted critical value. In addition, we view the goal of thecurrent research as hypothesis refinement where, based upon the results from this well-defined set of genes, we will further validate the results using an independent sample. Aswith all observational studies, there is the potential for selection bias and unmeasuredconfounding. However, we have adjusted for those factors that are most likely to influencethe risk of breast cancer diagnosis within this cohort, thus lowering the potential forunadjusted confounding.

We and others have investigated putative risk factors, and a number of published studieshave implicated loci as modifiers of breast or ovarian cancer penetrance in women whocarry germline BRCA1 or BRCA2 mutations (12, 35–42). The majority of modifiers ofBRCA1 cancer risk identified to date have been genetic variants in proteins that interactwith BRCA1 in DNA repair. Our results suggest that IGF signaling also modifies breastcancer penetrance in BRCA1 and BRCA2 mutation carriers. IGF signaling and BRCA1directly interact with multiple lines of evidence, including that IGF-1 enhances BRCA1activity (43), BRCA1 regulates IGF1R and IRS1 activity (44, 45), and BRCA1 proteinexpression and localization are dependent on AKT activation (46).

In conclusion, this is the second study to investigate the role of genetic variation in IGFsignaling and breast cancer risk in women carrying deleterious mutations in BRCA1 andBRCA2. In the previous study, we identified significant associations in variants in IGF1Rand IRS1 for BRCA1 carriers and in IGF1R, IGFBP2, and IGFBP5 in BRCA2 carriers. Onthe basis of those results, and given the known interaction of BRCA1 and IGF signaling, weinvestigated additional variants in genes in this pathway and identified significant breastcancer risk modifiers with haplotypes in IGF2 for both BRCA1 and BRCA2 mutationcarriers and in HTRA1 and MMP3 for BRCA1 and BRCA2 mutation carriers, respectively.The identification of causal mechanisms within these loci is needed to validate and betterunderstand these associations. We hope that with further identification of genetic factors thatmodulate age at diagnosis and overall incidence of breast cancer, and better elucidation ofthe actual mechanisms of modifying risk, these data can be used in the future to providemore precise risk estimates to develop cancer and design targeted therapeutics for carriers.

Supplementary MaterialRefer to Web version on PubMed Central for supplementary material.



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AcknowledgmentsThe MAGIC Consortium includes the following centers and individuals: Baylor-Charles A. Sammons CancerCenter (J.L. Blum, MD, PhD; Estelle Brothers, RN; G. Ethington), Beckman Research Institute of the City of Hope(formerly at the University of California, Irvine, S.L. Neuhausen, PhD; L. Steele; J.N. Weitzel, MD; S. Sand), BethIsrael Deaconess Medical Center (N. Tung, MD), Creighton University (C. Snyder, BA; H.T. Lynch, MD; P.Watson, PhD), Dana Farber Cancer Institute (K. Stoeckert; J.E. Garber, MD MPH), Fox Chase Cancer Center(M.B. Daly, MD, PhD; A.K. Godwin, PhD), Georgetown University (C. Isaacs, MD), Jonsson ComprehensiveCancer Center at the University of California, Los Angeles (J. Seldon, MSGC; P.A. Ganz, MD), Mayo Clinic (F.Couch, PhD), North shore University Health System Center for Medical Genetics (C. Selkirk, S.M. Weissman,W.S. Rubinstein, MD, PhD), University of Chicago (S. Cummings; O. Olopade, MD), University of PennsylvaniaHealth System: (S. Domchek, MD; K. Nathanson, MD; T. Friebel, MPH; T. Rebbeck, PhD), University of Texas,San Antonio (G. Tomlinson, MD), University of Vienna (C. Singer, MD), Women’s College Hospital (S. A. Narod,MD).

Grant Support

Support was received from NIH grants 5UO1 CA86389 (to H.T. Lynch), R01-CA083855 and R01-CA74415 (toS.L. Neuhausen), R03-CA135691 (to D.L. Gillen), R01-CA102776 and R01-CA083855 (to T.R. Rebbeck), RC4-CA153828 (to J.N. Weitzel), R01CA140323 and 5U01CA113916 (to A. Godwin), R01-CA128978 and R01-CA116167 (to F. Couch), and U01CA69631 (to M.B. Daly) and a grant from the Breast Cancer ResearchFoundation (to F. Couch). This publication was supported in part by revenue from Nebraska cigarette taxesawarded to Creighton University by the Nebraska Department of Health and Human Services. S.L. Neuhausen ispartially supported by the Morris and Horowitz Families Endowment.

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Figure 1.Estimated HRs associated with haplotype presence for IGF2 (A) and INS (B). Linkageblocks (C) were defined as pairwise D′ ≥ 0.90. Estimates were stratified by BRCA1/2mutation status (BRCA1 in left column and BRCA2 in right column) and adjusted for birthcohort and ancestry (non-Jewish or Jewish) as well as first pregnancy, prophylacticoophorectomy, and diagnosis of ovarian cancer as time-dependent covariates. C, SNPswithin each of the LD blocks are shown for each gene.



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Figure 2.Estimated HRs associated with haplotype presence for IGFBP3 (A), IGFBP4 (B), andIGFBP6 (C). Linkage blocks (D) were defined as pairwise D′ ≥ 0.90. Estimates werestratified by BRCA1/2 mutation status (BRCA1 in left column and BRCA2 in right column)and adjusted for birth cohort and ancestry as well as first pregnancy, prophylacticoophorectomy, and diagnosis of ovarian cancer as time-dependent covariates.



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Figure 3.Estimated HRs associated with haplotype presence for HTRA1 (A), KLK3 (B), and CTSD(C). Linkage blocks (D) were defined as pairwise D′ ≥ 0.90. Estimates were stratified byBRCA1/2 mutation status (BRCA1 in left column and BRCA2 in right column) and adjustedfor birth cohort and ancestry as well as first pregnancy, prophylactic oophorectomy, anddiagnosis of ovarian cancer as time-dependent covariates.



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Figure 4.Estimated HRs associated with haplotype presence for MMP3 (A) and MMP7 (B). Linkageblocks (C) were defined as pairwise D′ ≥ 0.90. Estimates were stratified by BRCA1/2mutation status (BRCA1 in left column and BRCA2 in right column) and adjusted for birthcohort and ethnicity as well as first pregnancy, prophylactic oophorectomy, and diagnosis ofovarian cancer as time-dependent covariates.



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Figure 5.Estimated HRs associated with haplotype presence for IRS2 (A) and PIK3CB (B). Linkageblocks (C) were defined as pairwise D′ ≥ 0.90. Estimates were stratified by BRCA1/2mutation status (BRCA1 in left column and BRCA2 in right column) and adjusted for birthcohort and ancestry as well as first pregnancy, prophylactic oophorectomy, and diagnosis ofovarian cancer as time-dependent covariates.



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Table 1

Characteristics of study participants by BRCA gene

Characteristic BRCA1 carriers BRCA2 carriers

Total, n (%) Cases, n (%) Total, n (%) Cases, n (%)

Total 1,019 381 500 219

Ethnicity

Caucasian (non-Jewish, non-Hispanic) 774 (76) 283 (74) 381 (76) 176 (80)

Caucasian (Jewish, non-Hispanic) 245 (24) 98 (26) 119 (24) 43 (20)

Ovarian cancer

Yes 111 (11) 26 (7) 30 (6) 5 (2)

No 908 (89) 407 (93) 513 (94) 233 (98)

Bilateral BPO

Yes, before breast cancer diagnosis 270 42 103 17

Yes, after breast cancer diagnosis 154 154 94 94

No BPO 594 184 303 108

Prophylactic bilateral mastectomy

Yes 99 37

No 920 463

Abbreviation: BPO, prophylactic oophorectomy.


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Genetic Variation in IGF2 and HTRA1 and Breast Cancer Risk among BRCA1 and BRCA2 Carriers