Genetic Modifiers of Menopausal Hormone Replacement Therapy And

A Rudolph et al. Genome–wide interaction study 20:6 875–887 Research of MHT and BC

Genetic modiﬁers of menopausal hormone replacement therapy and breast cancer risk: a genome–wide interaction study

Anja Rudolph1, Rebecca Hein1,2, Sara Lindstro¨m3,4, Lars Beckmann1,5, Sabine Behrens1, Jianjun Liu6, Hugues Aschard3,4, Manjeet K Bolla7, Jean Wang7, The´re`se Truong8,9, Emilie Cordina-Duverger8,9, Florence Menegaux8,9, Thomas Bru¨ning10, Volker Harth10,11, The GENICA Network10,11,12,13,14,15, Gianluca Severi16,17, Laura Baglietto16,17, Melissa Southey18, Stephen J Chanock19, Jolanta Lissowska20, Jonine D Figueroa19, Mikael Eriksson21, Keith Humpreys21, Hatef Darabi21, Janet E Olson22, Kristen N Stevens22, Celine M Vachon22, Julia A Knight23,24, Gord Glendon25, Anna Marie Mulligan26, Alan Ashworth27,28, Nicholas Orr27,28, Minouk Schoemaker27,28, Penny M Webb29, kConFab Investigators30, AOCS Management Group29,30, Pascal Gue´nel8,9, Hiltrud Brauch15, Graham Giles16,17, Montserrat Garcı´a-Closas19,31, Kamila Czene21, Georgia Chenevix-Trench29, Fergus J Couch32, Irene L Andrulis33,34, Anthony Swerdlow28,35, David J Hunter3, Dieter Flesch-Janys36, Douglas F Easton7,37, Per Hall21, Heli Nevanlinna38, Peter Kraft3,4,39, and Jenny Chang-Claude1, on behalf of the Breast Cancer Association Consortium

1Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Endocrine-Related Cancer Heidelberg, Germany 2PMV Research Group at the Department of Child and Adolescent Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany 3Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA 4Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA 5Foundation for Quality and Efﬁciency in Health Care (IQWIG), Cologne, Germany 6Human Genetics, Genome Institute of Singapore, Singapore, Singapore 7Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK 8INSERM (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, Villejuif, France 9Unite´ Mixte de Recherche Scientiﬁque (UMRS) 1018, University Paris-Sud, Villejuif, France 10Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr-Universita¨ t Bochum (IPA), Bochum, Germany 11Institute for Occupational Medicine and Maritime Medicine, University Medical Center, Hamburg-Eppendorf, Hamburg, Germany 12Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany 13Institute of Pathology, University of Bonn, Bonn, Germany 14Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany 15Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tu¨ bingen, Stuttgart, Germany 16Cancer Epidemiology Centre, The Cancer Council Victoria, Melbourne, Victoria, Australia 17Centre for Molecular, Environmental, Genetic, and Analytic Epidemiology, The University of Melbourne, Melbourne, Victoria, Australia 18Department of Pathology, The University of Melbourne, Melbourne, Victoria, Australia 19Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA 20Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland 21Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden 22Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA 23Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada 24Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada 25Ontario Cancer Genetics Network, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada

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26Laboratory Medicine Program, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada 27Breakthrough Breast Cancer Research Centre, London, The Institute of Cancer Research, UK 28Division of Breast Cancer Research, The Institute of Cancer Research, Sutton, Surrey, UK 29Queensland Institute of Medical Research, Brisbane, Queensland, Australia 30Peter MacCallum Cancer Center, Melbourne, Victoria, Australia 31Sections of Epidemiology and Genetics, Institute of Cancer Research and Breakthrough Breast Cancer Research Centre, London, UK 32Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA 33Ontario Cancer Genetics Network, Fred A. Litwin Center for Cancer Genetics, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada 34Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada 35Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, UK 36Department of Cancer Epidemiology/Clinical Cancer Registry and Institute for Medical Biometrics and Epidemiology, University Clinic Hamburg-Eppendorf, Hamburg, Germany 37Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK 38Department of Correspondence should be addressed Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland to J Chang-Claude 39 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA Email [email protected]

Abstract

Women using menopausal hormone therapy (MHT) are at increased risk of developing Key Words breast cancer (BC). To detect genetic modiﬁers of the association between current use of " breast cancer MHT and BC risk, we conducted a meta-analysis of four genome-wide case-only studies " genetic variation followed by replication in 11 case–control studies. We used a case-only design to assess " menopausal hormone interactions between single-nucleotide polymorphisms (SNPs) and current MHT use on risk therapy of overall and lobular BC. The discovery stage included 2920 cases (541 lobular) from four " genome-wide

genome-wide association studies. The top 1391 SNPs showing P values for interaction (Pint) K !3.0!10 3 were selected for replication using pooled case–control data from 11 studies of

Endocrine-Related Cancer the Breast Cancer Association Consortium, including 7689 cases (676 lobular) and 9266

controls. Fixed-effects meta-analysis was used to derive combined Pint. No SNP reached genome-wide significance in either the discovery or combined stage. We observed effect modification of current MHT use on overall BC risk by two SNPs on chr13 near POMP K6 K5 (combined Pint%8.9!10 ), two SNPs in SLC25A21 (combined Pint%4.8!10 ), and three K5 SNPs in PLCG2 (combined Pint%4.5!10 ). The association between lobular BC risk was K5 potentially modified by one SNP in TMEFF2 (combined Pint%2.7!10 ), one SNP in CD80 K6 (combined Pint%8.2!10 ), three SNPs on chr17 near TMEM132E (combined Pint%2.2! K6 K5 10 ), and two SNPs on chr18 near SLC25A52 (combined Pint%4.6!10 ). In conclusion, polymorphisms in genes related to solute transportation in mitochondria, transmembrane signaling, and immune cell activation are potentially modifying BC risk associated with

current use of MHT. These ﬁndings warrant replication in independent studies. Endocrine-Related Cancer (2013) 20, 875–887

Introduction

Menopausal hormone therapy (MHT) is prescribed to risk and the elevated risk dissipates within 2 years after women in order to alleviate climacteric symptoms and cessation of use (Narod 2011). Furthermore, the associated it is still commonly used despite evidence of associations risk varies with the type of MHT preparation and is greater with increased risk of cardiovascular diseases and breast for the use of combined estrogen–progestogen therapy cancer (BC; Farquhar et al. 2009, Tsai et al. 2011, Sprague than for the use of estrogen-monotherapy (Narod 2011, et al. 2012). Regarding BC, only recent use of MHT increases Chlebowski & Anderson 2012). A meta-analysis conducted

in 2005 reported an odds ratio (OR) of 1.39 (95% CI 1.12– Study population of case-only genome-wide studies 1.72) for the association between the use of combined Under the assumption that the genetic and environmental estrogen–progestogen therapy and BC risk, whereas the factors are not associated in the population from which respective OR for use of estrogen-monotherapy was 1.16 the cases were drawn, case-only studies provide a powerful (95% CI 1.06–1.28) (Shah et al. 2005). Also, differences in and efficient way to detect gene–environment interactions histology have been observed, with a stronger increase in (Piegorsch et al. 1994). We conducted a meta-analysis of the risk for lobular and tubular BCs compared with ductal four studies with quality control-checked genome-wide BC (Flesch-Janys et al. 2008, Bakken et al. 2011). data and information on current MHT use: the Mammary Understanding of the role of female sex hormones in Carcinoma Risk Factor Investigation (MARIE) from breast carcinogenesis has already led to the development Germany (Flesch-Janys et al. 2008), the Singapore and of therapeutic strategies such as the adjuvant endocrine Sweden Breast Cancer Study (SASBAC; Wedren et al. 2004), therapy for estrogen-receptor-positive BC (Smith & the Helsinki Breast Cancer Study (HEBCS; Kilpivaara et al. Dowsett 2003). By investigating genetic modifiers of 2004), and the Nurses’ Health Study (NHS) from the USA MHT-associated BC, the underlying mechanisms could (Hunter et al. 2007). Details on all studies included in the be further elucidated. The detection of genes involved in discovery as well as replication stage can be found in hormone-related breast carcinogenesis could lead to new strategies for BC prevention and treatment. Knowledge Supplementary Table 1, see section on supplementary data of genetic modifiers could also contribute to safer use of given at the end of this article. In total, these studies MHT, as the individual risk of developing BC when using contributed 2920 cases (541 cases with lobular tumors) to MHT may vary depending on the genetic background. the meta-analysis. Previous studies investigating the interactions Briefly, the MARIE study is a population-based case– between single-nucleotide polymorphisms (SNPs) and use control study of postmenopausal women aged 50–74 of MHT regarding BC risk predominantly pursued a years, carried out in two regions of Germany with the candidate-gene approach. Most of the reported incident cases diagnosed during 2001–2005 and controls interactions have not been followed up in further studies matched by birth year and study region (Flesch-Janys (Lee et al. 2011, Hein et al. 2012, Justenhoven et al. 2012). et al. 2008). Initially, 800 MARIE cases with a known age The possible interaction with the variants of the known at menopause were randomly selected for genotyping, genetic susceptibility loci for BC in FGFR2 has not been with lobular cases oversampled (Hein et al. 2013). After Endocrine-Related Cancer clearly confirmed in subsequent studies (Kawase et al. 2009, quality control checks, a total of 742 MARIE cases were Prentice et al. 2009, Rebbeck et al. 2009, Travis et al. included in the case-only genome-wide association 2010, Campa et al. 2011, Nickels et al. 2013). We previously analysis, of which 279 were lobular cases. SASBAC is a failed to replicate the most significant interaction with subset of a Swedish nationwide population-based case– MHT use observed for 2q36.3 in a genome–wide inter- control study (Wedren et al.2004). The cases were action study using a case-only approach (Hein et al. 2013). incident BC cases diagnosed during 1993–1995 identified We here have expanded our previous work (Hein et al. via the six regional cancer registries in Sweden, to which 2013) and conducted a meta-analysis of four case-only reporting is mandatory. Overall, 773 cases (36 lobular genome-wide gene–environment interaction studies for tumors) were included in the case-only genome-wide overall as well as for lobular BC risk. We then evaluated the analyses (Li et al. 2011). A further 344 postmenopausal top 1391 SNPs by case–control analyses using data from cases (88 lobular) were contributed by the hospital-based 11 studies by researchers participating in the Breast Cancer Finnish study HEBCS. In HEBCS, cases included both Association Consortium (BCAC; http://ccge.medschl.cam. unselected BC and familial BC patients recruited at the ac.uk/consortia/bcac/index.html). Helsinki University Central Hospital, 1997–2004 (Kilpivaara et al. 2004). The NHS cohort was established in 1976 and comprised 121 700 female registered nurses. Subjects and methods In 1989–1990, 32 826 participants donated a blood An overview of the included studies at each stage with sample. Of this sub-cohort 1061 participants of European respective numbers of cases and controls as well as the descent with incident postmenopausal invasive BC number of SNPs analyzed is displayed in Fig. 1. All studies (138 lobular) were included in the case-only genome- were approved by the relevant ethics committees and all wide association analysis (Hunter et al. 2007). All subjects participants gave informed consent. were of European ancestry.

HEBCS MARIE NHS SA SBAC 344 cases 742 cases 1061 cases 773 cases (88 lobular) (279 lobular) (138 lobular) (36 lobular) 510 067 SNPs 318 237 SNPs 540 000 SNPs 540 007 SNPs

Imputation to HapMap phase ll, release 24

HEBCS MARIE NHS SA SBAC 2 388 007 SNPs 2 417 876 SNPs 2 840 000 SNPs 2 438 810 SNPs

Case-only SNP × MHT analysis on overall and lobular breast cancer

Detection: fixed effects meta-analysis 2920 overall breast cancer cases, 541 lobular cases, 2.5 million SNPs

Merging of overall and lobular breast cancer results to select top SNPs

QC filter: MAF <0.05, Cochran’s Q P value <0.05, l 2>30%, imputed/genotyped SNP not available in at least two studies, r 2 <0.3 for imputed SNPs

1 391 SNPs genotyped, of those 57 excluded due to iCOGS QC, nine excluded due to MAF <0.05 in replication stage

Replication: Case–control SNP × MHT analysis in pooled BCAC data 11 studies, in total 7 689 cases (676 lobular) and 9 266 controls, 1 325 SNPs

Fixed effects meta-analysis of case-only estimate and case–control estimate for SNPs with Pinteraction <0.01 in replication stage Endocrine-Related Cancer

Figure 1 Diagram describing numbers of participants, investigated SNPs, and analyses conducted at each stage. QC, quality control; MAF, minor allele frequency; iCOGS, custom Illumina iSelect genotyping array designed as part of the Collaborative Oncological Gene-Environment Study.

Study populations used in the replication stage was missing. Participants in SASBAC and MARIE were excluded if they had contributed already to the respective SNPs selected from the case-only genome-wide association case-only genome-wide association studies. Additionally, studies for replication were evaluated using seven popu- cases in MCCS and pKARMA with prevalent BC at time of lation-based studies (five case–control studies (CECILE, enrollment were excluded. The reference date for controls GENICA, MARIE, PBCS, SASBAC), one case–cohort was date of enrollment (MCCS) or date of interview (case– (MCCS), and one nested case–control study (UKBGS)), and four non-population-based studies (MCBCS, kConFab/ control studies). The reference date for cases was the date AOCS, OFBCR, pKARMA), including in total 7689 cases of BC diagnosis. The reference age was accordingly the (676 lobular) and 9266 controls participating in the BCAC. age at reference date. In total, 7698 cases (676 lobular) and Studies from BCAC were included if participants were of 9266 controls contributed to the replication analysis. European descent and if genotype information, information on MHT use, and information on reference age MHT exposure definition were available for at least 200 postmenopausal cases and 200 postmenopausal controls. A reference age of R54 years Any type of MHT was taken into account when defining was used as surrogate for defining the postmenopausal ever use of MHT. Only women using MHT more than status if study-derived information on menopausal status 3 months were considered to be ever users. We defined

current use of MHT as use within the last 6 months before Statistical analysis reference date. Harmonization and plausibility checks of We tested for multiplicative SNP!MHT interactions on MHT information were conducted centrally for all studies the genome-wide level (2.5 million SNPs) in case-only participating in BCAC (Nickels et al. 2013). analysis using logistic regression with MHT use (current use codes as 1, never/past use coded as 0) as the outcome Genotyping and quality control variable and the SNP as the explanatory variable. The SNP was assessed according to a log-additive genetic model, i.e. Genotyping was performed using the Illumina a 1 df test for trend by number of minor alleles (0, 1, or 2). Humancnv370-duo chip (318 237 SNPs) in the MARIE Uncertainty of imputed SNPs was accounted for by using study and the Illumina HumanHap550 chip I in SASBAC estimated genotype probabilities for imputed SNPs in the (500 007 SNPs), HEBCS (510 067 SNPs), and NHS (540 000 regression model. Covariates were not considered in the SNPs). Genotyping in NHS was part of the Cancer Genetic case-only analyses. These analyses were performed with Markers of Susceptibility (CGEMS) project. All studies the software ProbABEL version 0.1-2-plus (Aulchenko et al. provided quality control-checked genotype data. 2010). Only genotyped SNPs that were also contained in SNPs selected for replication were genotyped on a the HapMap reference data and imputed SNPs were custom Illumina iSelect genotyping array (iCOGS) that included in the case-only analyses. Analyses were per- was designed by BCAC in collaboration with three formed for all cases as well as separately for lobular cases. other consortia (the Collaborative Oncological Gene– As only individuals of European descent were included, environment Study, COGS) (Michailidou et al. 2013). the genomic inflation factor lambda was close to one After genotyping, the iCOGS data were centrally quality (HEBCS lZ1.016; MARIE lZ1.014; SASBAC lZ1.009) controlled, which led to exclusion of 56 SNPs and, in case of NHS, there was also no indication of selected for replication. We additionally excluded nine population stratification (Hunter et al. 2007). Therefore, ! SNPs with minor allele frequency (MAF) 0.05 in the the analyses were not corrected for population stratifica- replication dataset. tion. Combined results based on the four case-only analyses were obtained from a meta-analysis assuming a Imputation fixed effects model, using the software PLINK, version 1.07 (Purcell et al. 2007). Heterogeneity between studies was Endocrine-Related Cancer All SNPs genotyped in the genome-wide studies, which assessed using Cochran’s Q statistic and I2 (Higgins & were also contained in the HapMap phase II release 24 data, Thompson 2002). were used for imputation of additional genotypes using For the replication analysis, data from 11 studies were the software MACH 1.0.16 (Li et al. 2010). Employing the pooled and analyzed using case–control logistic ‘autoflip’ option, the alleles are coded according to a regression. These analyses were performed using SAS unique reference scheme, so that the same allele was coded software, version 9.2. SNP!MHT interactions were eval- as reference in all four genome-wide case-only studies. For uated by means of a log-likelihood ratio test, comparing quality control of imputed data, imputed SNPs with MAF models with and without a multiplicative interaction term 2 !0.01 or r !0.3 were excluded from the analysis. between SNP (coded according to log-additive mode of inheritance) and current use of MHT. The models were SNP selection for replication adjusted for study, reference age, former use of MHT, and six principal components to account for population A list of 1391 SNPs was generated based on the lowest Pint stratification. The models included also interaction terms ! ! K3 (cutoff Pint 3.0 10 ) derived from the analysis of between study design (non-population-based vs popu- multiplicative interaction between MHT and BC risk, lation based) and current use of MHT as well as former use after merging the results for overall and lobular BC. of MHT. These interaction terms were included to account A total of 3277 SNPs were selected based on the interaction for possible differences in the estimates of the MHT effect with overall BC and 1723 selected based on their according to study design. association with lobular BC. These SNPs were filtered Results from the case-only meta-analysis and the according to the criteria of MAF R0.05, P value R0.05 for replication were combined in a meta-analysis assuming a Cochran’s Q or I2 !30% and the availability of the fixed effects model, using the package ‘meta’, version 2.1-2 respective SNP data in at least two case-only studies. (Schwarzer 2012) within the R software, version 2.15.0

(R Development Core Team 2012). Linkage disequilibrium BC and 1.83 (95% CI 1.34–2.47) for lobular BC, as (LD) between selected SNPs was estimated in the control displayed in Supplementary Figure 1. population of population-based studies using SNP_Tools, The meta-analysis of the case-only genome-wide version 1.70 (Chen et al. 2009a), and Haploview, version studies did not identify any SNP!MHT interaction at 4.2 (Barrett et al. 2005). genome-wide signiﬁcance level (see Supplementary The association between current MHT use and SNPs Figures 2 and 3, respectively, for quantile-quantile (QQ) was assessed using data from all studies of the replication and Manhattan plots of the results). The strongest stage as well as solely population-based studies. We ﬁtted a association was observed for rs3824418 located in an Z ! K7 logistic regression model adjusted for study with current intronic region of TLE1 on 9q21.3 (Pint 6.7 10 ). MHT use as the outcome. Of the 1391 SNPs carried forward for replication, 944

To illustrate the modiﬁcation of overall as well as were selected based on their Pint for modiﬁcation of overall

lobular BC risk associated with current use of MHT by BC risk associated with MHT, and 447 based on their Pint SNPs, the effect of current use of MHT was assessed in for modification of lobular BC risk. Overall, seven SNPs strata defined by the SNP genotype in pooled case–control showed effect modification of overall BC with combined ! ! K5 data of the replication stage. The models were adjusted for Pint 5.0 10 , as well as seven SNPs with respect to study, reference age, former use of MHT, and the two lobular BC risk (Table 1). There was no strong association previously described interaction terms to account for between the SNPs selected for follow-up and current use possible differences in the estimates of the MHT effect of MHT, as can be seen from QQ-plots in Supplementary according to study design. Figure 4, see section on supplementary data given at To further evaluate the effect of modification of the end of this article. When analyzing the whole sample current use of MHT by multiple modifying loci, a polygenic of the replication stage, the SNPs showing strongest score was built for each individual. For the genetic score, association with current use of MHT were rs12538442, we included the genetic loci found to modify the located in an intronic region of DOCK4 on chromosome 1 K association with current use of MHT at a significance (PZ3.1!10 4), and rs17738984, located on chromosome K5 K4 level of Pint!5.0!10 and selected one SNP per region 17q22 (PZ8.8!10 ). The SNPs showing the strongest based on the effect estimate. The allele increasing the association with MHT use when restricting the sample effect of MHT on BC risk was used as the risk allele and to population-based studies were rs10924245 in KIF26B K K the polygenic score was derived by summing up the risk (PZ1.6!10 4) and rs2102354 in KCNN3 (PZ7.7!10 4), Endocrine-Related Cancer alleles (0, 1 or 2) for each SNP. Separate scores were both located on chromosome 1. constructed for overall and lobular BC risk. To demonstrate Further information on SNPs, including their associ- the polygenic modifying effect, associations of current ation with overall BC risk in the replication dataset and MHT use with BC risk were calculated stratified by MAFs in the different study populations, can be found in score categories (roughly quintiles for all BCs and tertiles Supplementary Table 4, see section on supplementary data for lobular tumors). The respective logistic regression given at the end of this article. The identified SNPs on each models were adjusted for study, reference age, former use of the chromosomes 13, 14, 16, 17, and 18 are located in of MHT, an interaction term between former use of MHT close proximity to each other and do not represent and study design (non-population-based vs population- independent signals of genetic modification of the MHT based), and an interaction term between current use of effect. The respective LD plots can be found in Supple- MHT and study design. mentary Figure 5. For overall BC risk, two SNPs (rs9578047 and POMP Results rs9579199) near on chromosome 13 showed an interaction with current use of MHT with combined K6 K6 Characteristics of the patients participating in the studies PintZ7.9!10 and 7.6!10 . SNP!MHT interactions

included in the discovery stage and the replication stage with Pint of similar magnitude were also observed with are shown in Supplementary Tables 2 and 3, see section on rs7148646 and rs848694 in SLC25A21 on chromosome Z ! K5 ! K5 supplementary data given at the end of this article. The 14 (combined Pint 1.2 10 and 4.6 10 )and prevalence of current use of MHT varied between 10 and three SNPs (rs7192724, rs17202296, and rs4888190) in Z ! K6 60%. The estimated OR for the marginal effect of current PLCG2 on chromosome 16 (combined Pint 3.3 10 , K K use of MHT in the population-based studies of the 2.8!10 5,and4.5!10 5)(Table 1). Associations replication stage was 1.51 (95% CI 1.21–1.88) for overall between current use of MHT and overall BC risk stratiﬁed

http://erc.endocrinology-journals.org q 2013 Society for Endocrinology Published by Bioscientiﬁca Ltd. DOI: 10.1530/ERC-13-0349 Printed in Great Britain Downloaded from Bioscientifica.com at 09/25/2021 05:31:16AM via free access Endocrine-Related Cancer O:1.50EC1-39Pitdi ra Britain Great in Printed 10.1530/ERC-13-0349 DOI: http://erc.endocrinology-journals.org Research

Table 1 Odds ratios of multiplicative interaction between current menopausal hormone therapy (MHT) use and SNPs for overall and lobular breast cancer risk

GWASa Replication Combinedb Position c SNP Chr (build 37) RefSeq gene Feature OR (95% CI) Pint OR (95% CI) Pint OR (95% CI) Pint Overall breast cancer K K study interaction Genome–wide K al. et Rudolph A rs9578047 13 29164731 68 kb 50 of – 0.81 (0.72–0.91) 6.3!10 4 0.84 (0.75–0.95) 3.6!10 3 0.83 (0.76–0.90) 7.9!10 6 q

03SceyfrEndocrinology for Society 2013 POMP K K K rs9579199 13 29164783 68 kb 50 of – 0.81 (0.72–0.91) 6.3!10 4 0.84 (0.75–0.95) 3.4!10 3 0.83 (0.76–0.90) 7.6!10 6 POMP K K K rs7148646 14 37407036 SLC25A21 Intronic 0.80 (0.70–0.90) 3.7!10 4 0.85 (0.76–0.96) 7.4!10 3 0.83 (0.76–0.90) 1.2!10 5 K K K rs848694 14 37371724 SLC25A21 Intronic 0.80 (0.70–0.91) 9.4!10 4 0.86 (0.76–0.97) 1.3!10 2 0.83 (0.76–0.91) 4.6!10 5 K K K rs7192724 16 81958298 PLCG2 Intronic 1.24 (1.09–1.42) 1.3!10 3 1.24 (1.09–1.40) 7.8!10 4 1.24 (1.13–1.36) 3.3!10 6 K K K rs17202296 16 81959191 PLCG2 Intronic 1.28 (1.13–1.46) 1.4!10 4 1.14 (1.01–1.29) 2.9!10 2 1.21 (1.10–1.32) 2.8!10 5 K K K rs4888190 16 81963618 PLCG2 Intronic 1.32 (1.16–1.5) 3.8!10 5 1.11 (0.99–1.26) 8.0!10 2 1.20 (1.10–1.31) 4.5!10 5 Lobular breast cancer K K K rs11680872 2 192830249 TMEFF2 Intronic 2.01 (1.40–2.87) 1.5!10 4 1.40 (1.05–1.87) 1.9!10 2 1.61 (1.29–2.01) 2.9!10 5 K K K rs7648642 3 119261375 CD80 Intronic 0.58 (0.43–0.79) 4.6!10 4 0.68 (0.54–0.87) 2.3!10 3 0.64 (0.53–0.78) 5.1!10 6 BC and MHT of ulse yBocetﬁaLtd. Bioscientiﬁca by Published K K K rs11654964 17 32989538 23 kb 30 of 1.76 (1.30–2.38) 2.7!10 4 1.58 (1.17–2.14) 2.4!10 3 1.67 (1.35–2.06) 2.7!10 6 TMEM132E K K K rs16970162 17 32989786 23 kb 30 of 1.85 (1.34–2.54) 1.5!10 4 1.49 (1.09–2.05) 1.1!10 2 1.66 (1.33–2.07) 8.6!10 6 TMEM132E K K K rs11080292 17 32998577 32 kb 30 of 1.77 (1.29–2.44) 4.6!10 4 1.57 (1.14–2.15) 4.9!10 3 1.66 (1.33–2.08) 9.3!10 6 TMEM132E K K K rs6506940 18 29333635 6 kb 30 of 2.06 (1.35–3.13) 7.2!10 4 1.80 (1.19–2.71) 4.0!10 3 1.92 (1.43–2.58) 1.2!10 5 SLC25A52 K K K rs594334 18 29364523 24 kb 50 of 1.92 (1.25–2.94) 3.0!10 3 1.85 (1.22–2.81) 3.0!10 3 1.88 (1.40–2.54) 3.4!10 5 SLC25A52 Downloaded fromBioscientifica.com at09/25/202105:31:16AM

aFixed effects meta-analysis of results from four case-only GWAS. bFixed effects meta-analysis of results from GWAS and replication analysis. cAdjusted for study, reference age, former use of MHT, interaction terms between study design (population-bases vs non-population-based), and former use and current of MHT, as well as genetic principal components. 20 :6 881 via freeaccess Research A Rudolph et al. Genome–wide interaction study 20:6 882 of MHT and BC

K5 by genotypes of these SNPs are displayed in Table 2.We PintZ2.9!10 (Table 1). Also, rs7648642 in CD80 on did not observe significantly heterogeneous SNP!MHT chromosome 3 modified MHT-associated lobular BC risk interactions between studies that were pooled in the in both case-only and case–control analysis (combined K6 replication stage (Phet ranging from 0.16 to 0.91). The PintZ5.1!10 ). The variants rs11654964, rs16970162, respective forest plots are shown in Supplementary and rs11080292 located near TMEM132E on chromosome K6 K6 Figure 6, see section on supplementary data given at the 17 yielded combined Pint of 2.7!10 , 8.6!10 , and K end of this article. 9.3!10 6 respectively. Further SNP!MHT interactions We combined rs7148646_G, rs9579199_G, and were observed for rs6506940 and rs594334 near SLC25A52 K5 rs7192724_G and constructed a polygenic score to on chromosome 18 (combined PintZ1.2!10 and K assess BC risk associated with current use of MHT 3.4!10 5 respectively) (Table 1). Table 2 shows the depending on the modifying genetic risk (number of risk associations between current use of MHT and lobular BC modifying alleles) (Fig. 2A). For women with a low risk in strata defined by genotypes of these SNPs. There polygenic score of two or fewer risk-modifying alleles was no significant heterogeneity by study regarding the

(16.7% of women), current use of MHT was not associated estimates for SNP!MHT interactions (Phet ranging from with an increased BC risk (ORZ1.04, 95% CI 0.87–1.25). 0.22 to 0.94). The respective forest plots are shown in Among women carrying three (30.9% of women) or four Supplementary Figure 7, see section on supplementary (34.2% of women) risk-modifying alleles, current use of data given at the end of this article. MHT was associated with a significantly increased BC For lobular BC risk, a polygenic score was con- risk (ORZ1.25, 95% CI 1.08–1.45 and ORZ1.57, 95% CI structed by combining rs11680872_A, rs7648642_C, 1.36–1.80 respectively). The strongest association between rs11654964_A, and rs6506940_A (Fig. 2B). Current use of current use of MHT and BC risk was observed for women MHT was not associated with increased lobular BC risk in with a polygenic score of five or six (18.2% of women) women carrying zero to four risk-modifying alleles (15.2% (ORZ1.86, 95% CI 1.56–2.22), as expected. of women, ORZ0.61, 95% CI 0.35–1.07), while the OR for With respect to lobular BC, the variant rs11680872 lobular BC risk was 1.64 (95% CI 1.18–2.26) in the located in an intronic region of TMEFF2 on chromo- subgroup (25.5% of women) carrying five risk-modifying some 2 showed a SNP!MHT interaction with combined alleles. The association with current MHT use increased Endocrine-Related Cancer Table 2 Association between current use of menopausal hormone therapy (MHT) and overall as well as lobular breast cancer risk stratified by genotype

Allelesa Homozygous reference Heterozygous Homozygous coded

SNP Reference Coded OR (95% CI)b P OR (95% CI)b P OR (95% CI)b P

Overall breast cancer rs9578047 A G 1.91 (1.46–2.50) 2.0!10K6 1.50 (1.30–1.72) 1.3!10K8 1.30 (1.14–1.48) 6.4!10K5 rs9579199 G A 1.92 (1.47–2.51) 1.8!10K6 1.50 (1.30–1.72) 1.4!10K8 1.30 (1.14–1.48) 6.5!10K5 K K K rs7148646 G A 1.58 (1.39–1.80) 1.9!10 12 1.24 (1.08–1.43) 2.6!10 3 1.31 (1.10–1.73) 5.2!10 2 rs848694 G A 1.55 (1.37–1.75) 3.2!10K12 1.23 (1.06–1.43) 5.8!10K3 1.34 (0.97–1.85) 7.5!10K2 rs7192724 C G 1.10 (0.78–1.54) 5.9!10K1 1.25 (1.09–1.44) 1.9!10K3 1.58 (1.40–1.80) 7.8!10K13 K K K rs17202296 G T 1.19 (0.88–1.62) 2.5!10 1 1.32 (1.15–1.51) 1.1!10 4 1.53 (1.35–1.74) 7.0!10 11 rs4888190 C G 1.22 (0.89–1.66) 2.1!10K1 1.36 (1.18–1.57) 1.5!10K5 1.52 (1.34–1.73) 1.3!10K10 Lobular breast cancer rs11680872 G A 1.33 (0.64–2.76) 4.5!10K1 1.42 (1.04–1.94) 2.6!10K2 2.19 (1.70–2.82) 1.3!10K9 rs7648642 C A 3.05 (2.10–4.42) 4.1!10K9 1.62 (1.23–2.14) 6.0!10K4 1.43 (1.01–2.02) 4.3!10K2 K K K rs11654964 C A 0.24 (0.06–1.04) 5.7!10 2 1.61 (1.18–2.19) 2.8!10 3 2.15 (1.68–2.76) 1.7!10 9 rs16970162 C G 0.33 (0.07–1.47) 1.5!10K1 1.57 (1.14–2.17) 6.3!10K3 2.09 (1.64–2.66) 3.0!10K9 rs11080292 T C 0.38 (0.08–1.70) 2.0!10K1 1.47 (1.06–2.03) 2.1!10K2 2.15 (1.69–2.74) 6.5!10K10 K K K rs6506940 G A 0.94 (0.10–8.90) 9.6!10 1 1.08 (0.72–1.61) 7.2!10 1 2.05 (1.63–2.58) 7.4!10 10 rs594334 C T 1.40 (0.24–7.98) 7.1!10K1 1.03 (0.68–1.58) 8.7!10K1 2.09 (1.67–2.61) 1.6!10K10

aThe coded allele was not necessarily the minor allele. bAdjusted for study, reference age, former use of MHT, interaction between former use of MHT and study design (non-population-based vs population- based), and interaction between current use of MHT and study design.

A Category Odds ratio OR 95% CI lobular BC risk. Given the observed interactions are

Score_0_2 1.04 (0.87; 1.25) conﬁrmed, the polygenic score illustrates that even Score_3 1.25 (1.08; 1.45) though the single detected interaction might be modest, Score_4 1.57 (1.36; 1.80) Score_5_6 1.86 (1.56; 2.22) the associated BC risk for a woman using MHT may be HRT_overall 1.41 (1.27; 1.57) appreciably increased if she carries a large number of adverse risk-modifying alleles. 0.45 12 The loci of the identiﬁed polymorphisms provide B Category Odds ratio OR 95% CI indication of possible biological relevance for breast

Score_0_4 0.61 (0.35; 1.07) carcinogenesis. Two variants rs9579199 and rs9578047 Score_5 1.64 (1.18; 2.26) close to FLT1 and POMP on chromosome 13 showed Score_6 2.20 (1.66; 2.92) Score_7_8 2.38 (1.78; 3.18) modifying effects on MHT-associated BC risk. FLT1 is a HRT_overall 1.85 (1.51; 2.27) vascular endothelial growth factor receptor and involved in tumor angiogenesis (Fischer et al. 2008). So far, no 0.31 0.5 1 2 3.18 association has been reported between tumor development and POMP, a proteasome maturation protein. The Figure 2 Odd ratios for (A) overall breast cancer and (B) lobular breast cancer risk variants on chromosome 14 (rs7148646, and rs848694) lie associated with current use of menopausal hormone therapy in categories in an intronic region of SLC25A21. SLC25A21 encodes an defined by polygenic scores. oxodicarboxylate carrier, which transports C5–C7 oxodi- carboxylates across the inner membranes of mitochondria with the polygenic score and the OR for lobular BC was (Fiermonte et al. 2001). Interestingly, the two SNPs, 2.20 (95% CI 1.66–2.92) in women with a polygenic score rs6506940 and rs594334, found to modify the risk of of six (30.9% of women) and 2.38, 95% CI 1.78– 3.18) lobular BC are located near another mitochondrial carrier in those carrying seven or eight risk-modifying alleles gene, SLC25A52, on chromosome 18. Estrogen has been (25.8% of women). reported to be an important regulator of mitochondrial function (Chen et al. 2009b) and results of this study suggest that mitochondria-related mechanisms may play a Discussion role in MHT-associated breast carcinogenesis. The associ- Using a two-stage approach consisting of a meta-analysis ation between MHT and BC was also modified by Endocrine-Related Cancer of four case-only genome-wide association studies and rs7192724, rs17202296, and rs4888190 located in intronic a replication analysis of independent data from ten case– regions of PLCG2. PLCG2 is a member of the phospho- control studies, we attempted to identify SNPs that modify inositide-specific phospholipase C family and is involved the effect of MHT use on BC risk. Despite our large sample in transmitting activation signals across the cell mem- size in both discovery and replication stages, we did not branes predominantly of the B cells (Wang et al. 2000)as identify any SNPs that reached genome-wide significance. well as the natural killer cells (Tassi et al. 2005). We observed three loci on chromosomes 13, 14, and 16 With respect to lobular BC, genetic variants of two that modified MHT-associated overall BC risk in both transmembrane proteins were implicated. rs11680872 is the discovery and replication stage, with combined located in an intron of TMEFF2, whose biological function K5 Pint!5.0!10 . Additionally, four genomic loci on is unclear but its promoter region has been commonly chromosomes 2, 3, 17, and 18 modified lobular BC risk found to be hypermethylated in various cancers, associated with MHT use in both study stages at the same including BC (Lin et al. 2011, Park et al. 2011). Three significance level. variants (rs11654964, rs16970162, and rs11080292) are When combining variants that modify the effect of near (23–32 kb 30) TMEM132E, another transmembrane current use of MHT in a polygenic score, current MHT use protein. We observed also an interaction with rs7648642, was associated with an 86% increased BC risk among which lies in an intron of CD80 on chromosome 3. women with five to six risk-modifying alleles (18.2% of all CD80 is known to play an important role in T cell women included in the study) and was not associated with activation (Bhatia et al. 2006), and its expression has BC risk among women carrying two or fewer (16.7% of been found to be decreased in peripheral blood of BC women). Women with a polygenic score of three or four patients (Gong et al. 2012). who were currently using MHT were at an intermediate To account for differences by country with respect to increased risk of BC. Similar results were observed for types of preparations and dosages as well as different

genotype platforms and laboratories, we used a meta- variants in more recent studies (Campa et al. 2011, analysis to combine the results in the discovery stage and Andersen et al. 2012, Nickels et al. 2013). adjusted for study in the replication stage. Heterogeneity Genome-wide studies of gene–environment inter- of estimates regarding MHT use between studies due to actions present challenges, as the required sample differences in the study design was in part accounted for size may be inflated due to misclassification of environ- by adding an interaction term between MHT use and mental exposures and additional factors involved includ- study design in the regression models (Supplementary ing effect size of gene–environment interaction and Figure 1). The sensitivity analysis restricted to solely prevalence of environmental exposure(s) (Zondervan & population-based studies of the replication stage for Cardon 2004, Dempfle et al. 2008). To optimize power, we selected SNP!MHT interactions did not yield substan- used the case-only approach in the discovery stage, which tially altered estimates for interaction (!7.5% for overall offers greater precision in estimating the interaction term, BC and !11.5% for lobular BC, Supplementary Table 5, and the case–control approach in the replication stage to see section on supplementary data given at the end of account for false positive results due to correlation of the this article). Using the total study population for the environmental exposure with the genetic marker in the replication stage, the study had 80% power to detect an population (Piegorsch et al. 1994). There was no indication interaction effect of 1.20, assuming an allele frequency of of strong associations between SNPs selected for follow-up 20%, a marginal genetic effect of 1.15, and a marginal and current use of MHT (Supplementary Figure 4), effect of current MHT use of 1.35. The power was reduced supporting the assumption of gene–environment inde- to 55% when restricting the sample to population-based pendence. We minimized possible spurious associations studies. Furthermore, although the associations between due to differences in allele frequencies in the underlying current MHT use and BC risk observed in the single studies populations by restriction to solely individuals of were heterogeneous, this was not the case for the European descent. The observed genomic inflation in the SNP!MHT interactions (Supplementary Figures 6 and 7). case-only studies was close to one and the case–control In general, estimates for gene–environment interaction analyses were controlled for population stratification by are unlikely to be affected by selection bias (Morimoto including genetic principal components. et al. 2003) and more likely to be underestimated in In conclusion, the association between current use of the presence of non-differential or differential misclassi- MHT and risk of overall and lobular BC is potentially

Endocrine-Related Cancer fication (Garcia-Closas et al. 1999). modified by genetic variants of genes related to mitochondrial solute carriers, and transmembrane signaling as Most of the reported genetic modifiers of MHT- well as immune cell activation. These findings need associated BC risk have so far not been followed up in replication in independent studies of adequate power. further studies (Justenhoven et al. 2012). One exception The identified modest interaction effects are presently is with respect to variants in FGFR2, as it is also a unlikely to be of clinical significance, but provide valuable known BC susceptibility loci. Rebbeck et al. (2009) insights into potential mechanisms of BC development. reported that the association between combined estrogen–progestogen therapy and BC risk was modified by rs1219648 in postmenopausal women of European Z Supplementary data descent (Pint 0.010). A study conducted in participants This is linked to the online version of the paper at http://dx.doi.org/10.1530/ of the Women’s Health Initiative trial could not replicate ERC-13-0349.

this ﬁnding (PintZ0.661) but observed an interaction Z with rs3750817 in FGFR2 (Pint 0.033) (Prentice et al. 2009). A similar modifying effect of this SNP was also Declaration of interest observed for estrogen-monotherapy (P Z0.046). The The authors declare that there is no conﬂict of interest that could be int perceived as prejudicing the impartiality of the research reported. variants rs1219648 and rs3750817 are in moderate LD (r2Z0.44, D0Z1.00). However, we did not observe an

interaction regarding BC risk with current use of any MHT Funding and rs1219648 (PintZ0.15) or rs3750817 (PintZ0.23) in Funding for the iCOGS infrastructure came from: the European Commu- the genome-wide association study and the variants nity’s Seventh Framework Program under grant agreement n8 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, were not followed up in the replication stage. Similarly, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, no signiﬁcant interactions were observed with FGRR2 C5047/A15007, C5047/A10692), the National Institutes of Health

(CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 Innovation Centre, Stig E Bojesen, Sune F Nielsen, Borge G Nordestgaard, and CA148065, and 1U19 CA148112 – the GAME-ON initiative), the Department the staff of the Copenhagen DNA laboratory, and Julie M Cunningham, of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Sharon A Windebank, Christopher A Hilker, Jeffrey Meyer and the staff of Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Mayo Clinic Genotyping Core Facility. We thank all the individuals who took Komen Foundation for the Cure, the Breast Cancer Research Foundation, part in these studies and all the researchers, clinicians, technicians, and and the Ovarian Cancer Research Fund. Meetings of the BCAC have been administrative staff who have enabled this work to be carried out. In funded by the European Union COST program (BM0606). D F Easton is a particular, we thank: Ursula Eilber, Muhabbet Celik, Teresa Selander, Nayana Principal Research Fellow of CR-UK. The CECILE study was funded by the Weerasooriya, Louise Brinton, Neonila Szeszenia-Dabrowska, Beata Fondation de France; the French National Institute of Cancer (INCa); The Peplonska, Witold Zatonski, Pei Chao, Michael Stagner, The GENICA network: National League against Cancer; the National Agency for Environmental Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, and and Occupational Health and Food Safety (ANSES), the National Agency for University of Tu¨ bingen, Germany (Wing-Yee Lo, HB); Department of Internal Research (ANR), and the Association for Research against Cancer (ARC). Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, GENICA was funded by the Federal Ministry of Education and Research Bonn, Germany (Yon-Dschun Ko, Christian Baisch); Institute of Pathology, (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0, and University of Bonn, Bonn, Germany (Hans-Peter Fischer); Molecular Genetics 01KW0114 as well as 01KH0401, 01KH0410, 01KH0411, the Robert Bosch of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidel- Germany (Ute Hamann); Institute for Prevention and Occupational Medicine berg, Institute for Prevention and Occupational Medicine of the German of the German Social Accident Insurance (IPA), Bochum, Germany (TB, Beate Social Accident Insurance (IPA), Bochum, as well as the Department of Pesch, Sylvia Rabstein, Anne Lotz) and Institute for Occupational Medicine Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Krankenhaus, Bonn, Germany. The MARIE study was supported by the Germany (VH), Heather Thorne, Eveline Niedermayr and the kConFab Clinical Deutsche Krebshilfe e.V. (70-2892-BR I), the Hamburg Cancer Society, the Follow-Up Study, the AOCS Management Group (D Bowtell, G Chenevix- German Cancer Research Center and genotype work in part by the Federal Trench, A deFazio, D Gertig, A Green, P Webb), the ACS Management Group Ministry of Education and Research (BMBF) Germany (01KH0402). The (A Green, P Parsons, N Hayward, P Webb, D Whiteman); The Australian Cancer MCBCS was supported by the NIH grants (CA122340, CA128978) and a Study Management Group (A Green, P Parsons, N Hayward, P M Webb, and D Specialized Program of Research Excellence (SPORE) in Breast Cancer Whiteman), project staff, collaborating institutions, clinical, and scientific (CA116201). MCCS cohort recruitment was funded by VicHealth and Cancer collaborators and study participants. Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 251553, and 504711 and by infrastructure provided by Cancer Council Victoria. The Nurses’ Health Studies are supported by US NIH (National Institute of Health) grants CA65725, CA87969, CA49449, CA67262, CA50385, and 5UO1CA098233. OFBCR was supported by grant References UM1 CA164920 from the National Cancer Institute. The content of this Andersen SW, Trentham-Dietz A, Figueroa JD, Titus LJ, Cai Q, Long J, manuscript does not necessarily reflect the views or policies of the National Hampton JM, Egan KM & Newcomb PA 2013 Breast cancer suscep- Cancer Institute or any of the collaborating centers in the Breast Cancer tibility associated with rs1219648 (fibroblast growth factor receptor 2) Family Registry (BCFR), nor does mention of trade names, commercial and postmenopausal hormone therapy use in a population-based products, or organizations imply endorsement by the US Government or United States study. Menopause 20 354–358. (doi:10.1097/GME. the BCFR. The PBCS was funded by Intramural Research Funds of the Endocrine-Related Cancer 0b013e318268ca46) National Cancer Institute, Department of Health and Human Services, USA. Aulchenko YS, Struchalin MV & van Duijn CM 2010 ProbABEL package for kConFab is supported by grants from the National Breast Cancer genome-wide association analysis of imputed data. BMC Bioinformatics Foundation, the NHMRC, the Queensland Cancer Fund, the Cancer Councils 11 134. (doi:10.1186/1471-2105-11-134) of New South Wales, Victoria, Tasmania and South Australia and the Cancer Bakken K, Fournier A, Lund E, Waaseth M, Dumeaux V, Clavel-Chapelon F, Foundation of Western Australia. The kConFab Clinical Follow-Up Study Fabre A, Hemon B, Rinaldi S, Chajes V et al. 2011 Menopausal hormone was funded by the NHMRC (145684, 288704, 454508). 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Received in ﬁnal form 13 September 2013 Accepted 26 September 2013 Made available online as an Accepted Preprint 30 September 2013 Endocrine-Related Cancer