Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer (original) (raw)

. Author manuscript; available in PMC: 2013 Oct 1.

Published in final edited form as: Nat Genet. 2013 Apr;45(4):371–384e2. doi: 10.1038/ng.2566

Abstract

_TERT_-locus single nucleotide polymorphisms (SNPs) and leucocyte telomere measures are reportedly associated with risks of multiple cancers. Using the iCOGs chip, we analysed ~480 _TERT_-locus SNPs in breast (n=103,991), ovarian (n=39,774) and BRCA1 mutation carrier (11,705) cancer cases and controls. 53,724 participants have leucocyte telomere measures. Most associations cluster into three independent peaks. Peak 1 SNP rs2736108 minor allele associates with longer telomeres (_P_=5.8×10−7), reduced estrogen receptor negative (ER-negative) (_P_=1.0×10−8) and BRCA1 mutation carrier (_P_=1.1×10−5) breast cancer risks, and altered promoter-assay signal. Peak 2 SNP rs7705526 minor allele associates with longer telomeres (_P_=2.3×10−14), increased low malignant potential ovarian cancer risk (_P_=1.3×10−15) and increased promoter activity. Peak 3 SNPs rs10069690 and rs2242652 minor alleles increase ER-negative (_P_=1.2×10−12) and BRCA1 mutation carrier (_P_=1.6×10−14) breast and invasive ovarian (_P_=1.3×10−11) cancer risks, but not via altered telomere length. The cancer-risk alleles of rs2242652 and rs10069690 respectively increase silencing and generate a truncated TERT splice-variant.

Introduction

Chromosome ends are capped by telomeres, which protect them from inappropriate DNA repair and maintain genomic integrity1. Telomeres consist of structural proteins2 combined with many hundreds of hexanucleotide DNA repeats3,4, which are progressively shortened by normal cell division5–7. Shortening restricts proliferation of normal somatic cells, but not of cancer cells, which can maintain long telomeres, usually via telomerase8–10, and may divide indefinitely. The TERT gene at 5p15.33 (see URLs) encodes the catalytic subunit of telomerase reverse transcriptase, an important component of telomerase. Germline mutations in TERT cause dyskeratosis congenita, a cancer susceptibility disorder characterized by exceedingly short telomeres11. Although up to 80% of the variation of telomere length is estimated to be due to heritable factors12,13, association studies on TERT SNPs and differences in leucocyte telomere length have, to date, been inconclusive14–17. Furthermore, it is unclear whether telomere length, measured in leucocyte DNA, is predictive of cancer risk: retrospective studies report that cancer patients, after diagnosis, have shorter telomeres than unaffected controls18–21, but prospective studies, with DNA taken prior to diagnosis, have been inconclusive19,22,23. SNPs at 5p15.33 are reported to be associated with risks of several human cancers14–16,24–32, including certain subtypes of both ovarian33 and breast cancers34.

Due to a common interest, SNPs surrounding the TERT locus were nominated by members of each of the constituent COGS consortia. Consequently, the iCOGS chip design included a combination of individual TERT gene candidate SNPs, as well as a more comprehensive set to fine-scale map the entire locus, for shared use by all consortia. This study had three aims: to assess SNPs across the _TERT_-locus for all detectable associations with mean telomere length and breast and ovarian cancer subtypes; to fine-scale map this locus to identify potentially-causal variants for the observed associations; and to evaluate the functional effects of the strongest candidate causative variants.

Results

One hundred and ten SNPs at the 5p15.33 locus (Build 37 positions 1,227,693 – 1,361,969) passed quality control tests (QC) in 103,991 breast cancer cases and controls from 52 Breast Cancer Association Consortium (BCAC) studies, of which 41 studies (89,050 individuals) were of European, nine were of Asian (12,893 individuals) and two were of African-American ancestry (2,048 individuals). The same 110 SNPs passed QC in 11,705 BRCA1 mutation carriers of European ancestry, recruited by 45 studies from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), while 108 SNPs passed QC in 44,308 ovarian cancer cases and controls from 43 Ovarian Cancer Association Consortium (OCAC) studies. For OCAC, analysis was confined to the 39,774 European ancestry participants, of whom 8,371 cases had invasive epithelial ovarian- and 986 had serous low malignant potential (LMP) neoplasia. For all study participants, genotype-imputation, using the 110 genotyped SNPs together with the January 2012 release of the 1000 Genome Project (1000GP)35–38 was used to increase coverage to ~480 SNPs (imputation r2>0.3, minor allele frequency (MAF)>0.02) for each phenotype. Telomere length was initially measured in control subjects from two BCAC studies (SEARCH and CCHS, combined n= 15,567) (see Supplementary Information).

Figure 1 shows Manhattan plots of the genotyped and well-imputed SNPs for the seven phenotypes analyzed: mean telomere length (a), overall breast cancer (b), breast cancer in BRCA1 carriers (c), estrogen receptor negative (ER-negative) breast cancer (d), estrogen receptor positive (ER-positive) breast cancer (e), serous LMP ovarian cancer (f) and serous invasive ovarian cancer (g). Conditional analyses within each of these phenotypes revealed multiple independent SNP associations each for telomere length, overall breast cancer, ER-negative breast cancer and overall breast cancer risk in BRCA1 mutation carriers, but only one peak each for ER-positive breast cancer, serous LMP and invasive ovarian cancer (Table 1). Full results of all these SNP analyses are given in Supplementary Tables 1–3. All associations are consistent with a log-additive model.

Figure 1.

Association results for all SNPs for seven phenotypes including: (a) telomere length, (b) overall breast cancer, (c) breast cancer risk in BRCA1 mutation carriers, (d) ER-negative breast cancer, (e) ER-positive breast cancer, (f) serous low malignant potential (LMP) ovarian cancer and (g) serous invasive ovarian cancer. Directly-genotyped SNPs are represented by solid black circles and imputed SNPs (r2>0.3, minor allele frequency (MAF)>0.02) as open red circles, plotted as the negative log of the _P_-value against relative position across the locus. Schematics of the gene structures are shown above panel a. Association peaks, as described in the text are labeled at the top of the figure, and are shown as grey regions, when appropriate.

Table 1. Independently-associated SNPs for each phenotype.

The independently-associated SNPs are shown for each phenotype following the forward conditional stepwise logistic regression analysis of overall breast cancer and estrogen receptor subgroups in European individuals in BCAC, invasive and LMP subgroups in OCAC, relative change of telomere length (RTL) in the SEARCH and CCHS combined data following the forward stepwise linear regression analysis, and CIMBA BRCA1 mutation carriers, following the forward stepwise Cox regression. These analyses were performed on all SNPs with MAF>0.02 and P<10−4 in the single SNP analyses.

Associations with telomere length

SNPs in two distinct regions (hereafter denoted Peaks 1 and 2) are strongly associated with telomere length (Tables 1 and 2; Fig.1, panel a; Supplementary Fig.1, panel a). Imputed SNP rs7705526 (Peak 2, position 1285974, TERT intron 2) has the largest effect with a change in relative telomere length of 1.026-fold per-allele (95%CI 1.019–1.033, _P_=2.3×10−14, conditional _P_=2.5×10−11). We confirmed this finding in an additional 20,512 women and 17,645 men from a third study (CGPS) genotyped for rs7726159 (the best directly-genotyped SNP; r2=0.83 with rs7705526). From a joint analysis of all 53,724 individuals, the change in relative telomere length is 1.020-fold per-allele (95%CI 1.016–1.023, _P_=7.5×10−28). A second, independent association was observed with rs2736108 (Peak 1, position 1297488, TERT promoter) with a per-allele change in relative telomere length of 1.017-fold (95%CI 1.010–1.024, _P_=5.8×10−7, conditional _P_=4.0×10−4) (Fig.1, panel a; Supplementary Fig.1, panel a; Tables 1 and 2). SNPs rs7705526 and rs2736108 are only weakly correlated (r2=0.04 in Europeans). Weak associations between Peak 3 SNPs and telomere length became non-significant after adjustment for Peak 2 SNP rs7705526 (data not shown).

Table 2. Association between TERT SNPs and the seven studied phenotypes.

Single SNP estimates for the most significant SNPs per peak per phenotype in the TERT fine mapping region of Chromosome 5 from position 1227014 to 1361964 (genome build 37). The major and minor alleles are from the genotyping assay and not necessarily from the coding strand. The change in telomere length per minor allele is given as fold-change (RTL) with respect to the estimated telomere length for the common homozygote per SNP, both with 95%CI (see Supplementary Methods). All breast and ovarian cancer risk results are given as odds ratios (OR) with 95% confidence intervals (CI) and per-allele _P_-trend, and the BRCA1 carrier risk results are given as hazard ratios (HR) with 95%CI. All SNPs that cannot be excluded as >100× worse than the top hit in the block for any phenotype are listed. The independent Peaks 1–3 were localized using forward conditional analyses described in Table 1. The most significant hit in each region for each phenotype is emboldened for each peak. The results of the SNPs emerging as most significant in each block in the forward conditional analyses, and described further in Table 1, are marked with an asterisk. Genotyped SNPs are marked with a hash tag; all three peaks contain at least one directly genotyped variant.

Associations with breast cancer risk

We identified SNPs associated with breast cancer risk (P<10−4) in three distinct regions in BCAC studies and two in CIMBA BRCA1 mutation carriers. No significant (P<10−4) evidence for heterogeneity among odds ratios (OR) or hazard ratios (HR) between studies for any of the top SNPs was observed (Supplementary Fig.6). The strongest association with overall breast cancer risk in BCAC is with Peak 1 SNP rs3215401 (Fig.1, panel b; Supplementary Fig.1, panel b; Tables 1 and 2). There is also good evidence for an association with SNPs in Peak 2 and weaker evidence for an additional SNP, outside the three main association peaks, to be independently associated with breast cancer risk (Supplementary Table 1; Table 1). The most strongly-associated SNPs in BRCA1 mutation carriers are located in introns 2–4 (hereafter denoted Peak 3) including rs10069690 (Fig.1, panel c; Supplementary Fig.2, panel c; Tables 1 and 2) and rs2242652 (correlation with rs10069690, r2= 0.70). The latter SNP also exhibits the strongest association with ER-negative breast cancer in BCAC (Fig. 1, panel d; Supplementary Fig. 1, panel d; Tables 1 and 2), but shows little evidence of association with ER-positive breast cancer (Table 2). Stepwise regression analysis in CIMBA studies indicated two independent associations with breast cancer risk in BRCA1 mutation carriers (conditional _P_=5×10−5 for rs2736108 in Peak 1; _P_=4.8×10−13 for rs10069690 in Peak 3). A very similar pattern was observed for ER-negative breast cancer in BCAC (conditional _P_=6×10−6 for rs3215401 in Peak 1 and _P_=4.3×10−9 for rs2242652 in Peak 3; Table 1). The most strongly associated SNP with ER-positive breast cancer is rs2736107 in Peak 1 (Fig.1, panel e; Supplementary Fig.2, panel e; Tables 1 and 2). Weak associations between the key SNPs and risk for BRCA2 mutation carriers are also observed, but the sample size is too small to draw definitive conclusions (data not shown).

Associations with ovarian cancer risk

The strongest association observed for risk of LMP ovarian cancer is with Peak 2 SNP rs7705526 and this is the only SNP retained in the stepwise regression analysis (Fig.1, panel f; Supplementary Fig.1, panel f; Table 1 and 2). The strongest observed association for serous invasive ovarian cancer is with Peak 3 SNP rs10069690 (Fig.1, panel g; Supplementary Fig.1, panel g; Tables 1 and 2). No other independent associations were observed for serous invasive ovarian cancer (Table 1). We also analysed SNP associations with endometrioid, mucinous, clear cell invasive and mucinous LMP ovarian cancers but found no associations at P<10−4 (Supplementary Table 4). We attempted analysis of invasive serous ovarian cancer stratified by grade, but again statistical power was low (Supplementary Fig.2).

Three main peaks of association within the TERT locus

The above results indicate that the majority of observed associations with all seven tested phenotypes fall into association Peaks 1–3. Correlated SNPs in the TERT promoter (Peak 1) are associated with telomere length, ER-positive breast cancer, ER-negative breast cancer and breast cancer in BRCA1 mutation carriers. SNPs in Peak 2, spanning TERT introns 2–4, are independently associated with telomere length, overall breast cancer and serous LMP ovarian cancer. Finally, SNPs in Peak 3, also spanning TERT introns 2–4, display strong associations with ER-negative breast cancer, breast cancer risk for BRCA1 mutation carriers and serous invasive ovarian cancer, but not with telomere length (Tables 1 and 2). Although Peaks 2 and 3 overlap physically, they define distinct sets of SNPs that are only partially correlated (e.g. correlation between rs10069690 and rs7705526; r2=0.33; Fig.2). Some SNP-phenotype associations in Peak 2 are clearly weaker than those in Peak 3 (e.g. with ER-negative breast cancer) and become non- significant after adjustment for SNP rs2242652 in Peak 3. Conversely, the associations with telomere length and serous LMP ovarian cancer are stronger for SNPs in Peak 2, indicating that the associations in Peaks 2 and 3 are not being driven by the same causal variants.

Figure 2.

Associated signals within the TERT gene. Peak regions described in the text are labeled: Peaks 2 and 3 overlap around introns 2–4, and Peak 1 encompasses the promoter. The positions of associated SNPs are shown as black and red ticks representing genotyped and imputed SNPs, respectively. The TERT gene structure is depicted with exons (boxes) joined by introns (lines). The positions of all analyzed iCOGS SNPs are marked. Data from the UCSC genome browser including epigenetic marks for H3K4Me1 and H3K4Me3, ESPERR regulatory potential, and vertebrate conservation tracks are shown. Regions cloned into reporter constructs are depicted as the green bar (TERT promoter) or blue bars (PRE-A and PRE-B). The pattern of linkage disequilibrium based on the BCAC population is shown where white represents r2=0 and black r2=1.

The strongest candidates for causation within each peak were identified by computing likelihood ratios; the SNPs listed in Tables 1 and 2 are those that cannot be excluded at a likelihood ratio of >1:100 fold compared to the top hit in the peak. The power to exclude SNPs differs between phenotypes; in Peak 1, all but seven SNPs can be excluded from being causal for relative telomere length, breast cancer risk in BRCA1 mutation carriers and ER-negative risk, but an additional SNP can be excluded for ER-positive breast cancer risk (Table 2). In Peak 2, the greatest power is for the telomere length phenotype, where all but three SNPs can be excluded, whilst five or six remain for cancer risk. For Peak 3, three putative causal SNPs remain for ER-negative breast cancer risk, two for serous invasive ovarian cancer risk and just one for breast cancer risk in BRCA1 carriers. Results in each peak are compatible with a single causative variant being responsible for the multiple phenotype associations (n.b. in Peak 3, SNPs rs2242652 and rs10069690 are equally compatible with being the single causal variant). However the possibilities of different causal variants being responsible for different phenotypes, or of the associations being due to haplotype effects, cannot be ruled out.

Asian and African studies

We tested all SNPs (n=341) with MAF >0.02 and imputation r2>0.3 for association with breast cancer in the nine BCAC Asian studies (comprising 6,269 cases and 6,624 controls) but none reached formal levels of significance. Furthermore none of the European top SNPs displayed more than borderline levels of significance in Asians (Supplementary Table 5). Peak 3 SNP rs10069690 was directly genotyped in two BCAC African-American studies (1,116 cases and 932 controls), as well as the above mentioned Asian studies and has estimated effects on ER-negative breast cancer similar to those in European populations: per-allele OR=1.19, 95%CI 1.06–1.31, _P_=0.009 in African-Americans and OR=1.09, 95%CI 1.00–1.19, _P_=0.07 in Asian women. Within OCAC there were too few women of Asian and African ethnicity to draw meaningful conclusions. (Supplementary Table 6).

Functional and in silico analyses

Chromatin analysis

Analysis of the ENCyclopedia Of DNA Elements (ENCODE) data39 revealed no evidence of regulatory elements or open chromatin coinciding with any risk-associated SNPs in normal breast epithelial cells or the other represented tissues (Supplementary Fig.3) (Data for ovarian tissues are not included in ENCODE). We therefore performed site-specific formaldehyde-assisted isolation of regulatory elements (FAIRE40) in ovarian cancer precursor tissues to identify regulatory elements in a 1Mb region centred on Peak 3. In fallopian tube secretory- and ovarian surface- epithelial cells, we detected FAIRE peaks coinciding with the CLPTM1L promoter but not the TERT promoter (Supplementary Fig.3). In silico analyses additionally indicated that TERT introns 4 and 5 (within and beyond Peak 3) contain regions showing regulatory potential and vertebrate sequence conservation41. We used site-specific FAIRE analyses of a ~1 kb region centered on the Peak 3 SNP rs10069690 in normal tissue samples from breast reduction mammoplasty (n=4), ovarian cancer precursor tissues (n=4) and ovarian cancer cell-lines (n=4). Breast cells from each woman were sorted into four enriched fractions based on differential expression of cell surface markers42 (myoepithelial/stem, luminal progenitor, mature luminal and stromal) and assays were performed on each fraction (Fig.3). Chromatin was closed in all the ovarian, breast luminal progenitor and mature luminal fractions. However, in 2/4 stromal cell fractions, we detected ~600bp open chromatin of varying amplitude, covering the position of SNP rs10069690, but not of rs2242652, and in 3/4 myoepithelial/stem cell fractions, we detected ~800bp open chromatin, covering the positions of both SNPs rs10069690 and rs2242652.

Figure 3.

Open chromatin signatures around rs10069690. (A) Map of the PCR amplicon sites A-D used to annotate a 1 kilobase region surrounding rs10069690 and rs2442652. Primer sequences are listed in Supplementary Table 11. (B) PCR analysis of FAIRE-processed chromatin from FACS-sorted myoepithelial/stem, luminal progenitor, mature luminal and stromal cell enriched fractions derived from breast tissues of four subjects Q695, Q723, Q706, and Q674. Error bars represent standard errors of triplicate PCR reactions.

Luciferase reporter assays

The regulatory capabilities of the DNA in each of the three peaks, and the effects of most of the strongest candidate causative variants in each one, were examined in luciferase reporter assays, using a construct containing 3915bp of the TERT promoter sequence43. Effects of Peak 1 TERT promoter variants were examined via five haplotype constructs differing at rs2736107, rs2736108 and rs273610925 (Fig.4a): one with all three major alleles (TERT wt), another with all three minor alleles (rs2736107, rs2736108, and rs2736109), and three with minor alleles of each SNP individually. Relative promoter activity was determined in ER-positive (MCF7), ER-negative (MDA-MB-468) breast and ovarian (A2780) cancer cell-lines. The construct containing all three minor alleles consistently generated the lowest luciferase signals, close to baseline. To determine whether the risk-associated variants in Peaks 2 and 3 fall within putative _cis_-acting regulatory elements (PREs), we cloned ~3 kb of sequence surrounding each SNP. Constructs of PRE-A (Peak 2) had no significant effect on the activity of either promoter construct (Fig.4b). However, inclusion of the minor allele of rs7705526 increased TERT promoter activity by ~30% in all three cell-lines, suggesting that it can act as a transcriptional enhancer. This increase in promoter activity was also observed with the construct in A2780 ovarian cells, but not in the two breast cancer cell-lines. Constructs of PRE-B (Peak 3) consistently act as strong transcriptional silencers, leading to a 40–50% decrease in activity, specifically in constructs containing the TERT wt promoter. Notably, inclusion of the minor allele of rs2242652 in PRE-B constructs decreased relative TERT wt promoter activity by a further ~20% compared to the silencer containing the major alleles, but highly-correlated SNP rs10069690 did not generate this effect (Fig.4b).

Figure 4.

TERT promoter and putative regulatory element (PRE) activity. Luciferase reporter assays following transient transfection of ER-negative breast cancer cell line (MDA-MB-468), ER-positive breast cancer cell line (MCF7), and ovarian cancer cell line (A2780). Error bars represent standard error between at least three separate experiments. (a-c) Luciferase reporter assays following transient transfection with pGL2-control (SV40 promoter and enhancer), pGL2-basic (lacks promoter and enhancer), and the TERT reporter vectors TERT wildtype (wt) (3.9kb of TERT promoter), the minor (T) alleles of rs2736107, rs2736108, rs2736109, and rs2736107/8/9 (T-alleles at all sites). Comparisons with TERT wt performed using one-way ANOVA with post hoc Dunnett's tests are represented by ** (_P_-value <0.001) and * (_P_-value 0.005).

(d–f) PRE-A or PRE-B were cloned downstream of either the TERT wt or TERT rs2736107/8/9 (TERTh) promoter-driven reporters with and without SNPs rs10069690, rs2242652 and rs7705526. Comparisons with TERT wt or TERTh performed using one way ANOVA with post hoc Dunnett's tests are represented by *** (_P_-value <0.0001) and ** (_P_-value <0.001).

Alternative splicing of TERT

Several TERT alternative splice variants have been found to impact on telomerase activity44,45. To determine the role of PRE-B (Peak 3) SNPs in TERT alternative splicing, we inserted intron 4 sequence into a full length TERT cDNA mini-gene construct, and confirmed accurate splicing. Cancer risk-associated alleles for rs10069690 and rs2242652 were generated individually and in combination within the mini-gene. RT-PCR, using primers spanning intron 4, revealed that all SNP permutations in all cell-lines produced comparable levels of both wild-type and an INS1 alternative splice variant, which includes the first 38bp of TERT intron 446,47 (Supplementary Fig.5a). We also identified a novel TERT splice variant, specifically associated with the minor allele of rs10069690 (termed INS1b; Supplementary Fig.5a). Sequence analysis confirmed that INS1b includes the first 480bp of intron 4 and results from an alternative splice donor created by the minor allele of rs1006969048. INS1b has a premature stop codon 16 amino acids into intron 4 and is predicted to generate a severely truncated protein product, likely to impact on telomerase activity (Supplementary Fig.5b).

Gene expression and methylation analyses in ovarian tissue

We used The Cancer Genome Atlas (TCGA)49 data to examine gene expression of the 11 protein-coding genes and one microRNA (MIR4457) located within 1Mb of Peak 3 SNP rs10069690. Most genes showed higher expression in ovarian tumors compared with normal tissues (Supplementary Fig.3; Supplementary Table 7). We observed no association between rs10069690 and expression levels of any of the genes in any of the cells tested (Supplementary Table 7; Supplementary Table 8; Supplementary Fig.5). There is some evidence of association between rs10069690 and tumor methylation with probes cg23827991 (TERT CpG island, _P_=1.3×10−6) and cg06550200 (CLPTM1L, _P_=6.9×10−4) among 935 probes tested. Both showed reduced methylation with the minor, cancer-risk allele (Supplementary Table 9) but this did not correlate with changes in expression.

Discussion

Our comprehensive examination of the TERT locus has answered some long-standing questions and raised several new ones. We have identified two independent regions associated with telomere length in leucocyte DNA; these provide definitive evidence for genetic control of telomere length by common TERT variants. For rs2736108, the most significant SNP in promoter Peak 1, the minor allele is associated with a 1.7%-fold increase in telomere length. This equates to a telomere length change of ~60bp and, since telomere length decreases by approximately 19bp per year of age50, this is equivalent in magnitude to an age difference of 3.1 years. We estimate that rs2736108 explains 0.08% of the variance in telomere length in men and 0.06% in women. SNPs in Peak 2 have a stronger effect on telomere length with each additional A (minor) allele of rs7705526 associated with a 2.6%-fold increase. This equates to a ~90bp change in telomere length and, correspondingly, to 4.7 years of age. We estimate that rs7705526 explains 0.31% of the variance in telomere length in men and 0.16% in women. The only other reported associations with telomere length, reaching genome-wide significance, involve _TERC_-locus SNP rs126930451 and _OBFC1_-locus SNP rs438728752, which have similar effects on telomere length (75bp and 115bp per-allele, respectively).

Our only findings consistent with the hypothesis that shorter telomeres predispose to increased cancer risk53 (equivalent to longer telomeres being protective) are those from the Peak 1 SNPs. However, a regulatory-element construct containing the longer telomere associated alleles of three highly correlated SNPs, rs2736108, rs2736107 and rs2736109 (reconstructing a haplotype with 25% frequency in Europeans35) virtually abolished promoter activity in a reporter assay. This finding leaves an apparently paradoxical association between decreased enhancer activity and increased telomere length (Figure 4). Control of telomerase activity is currently poorly understood and this clearly merits further investigation.

SNPs within Peak 3 (TERT introns 2–4) exhibit strong associations with hormone-related cancers: Peak 3 SNP rs10069690 is associated with risk of ER-negative breast cancer34 and breast cancer in BRCA1 mutation carriers, consistent with the observation that the majority of breast cancers arising in BRCA1 mutation carriers are ER-negative. This variant has been reported to be associated with prostate cancer26,54 and we find it associated with serous invasive ovarian cancer. Although SNPs in Peaks 2 and 3 overlap on a physical map, the SNPs most strongly associated with cancer risk or telomere length were not highly correlated with each other [r2 between rs10069690 and rs7705526 = 0.33 (Fig.2, panel b)]. These observations suggest that either the associations observed with multiple cancers and SNPs in Peak 3 are mediated via a mechanism distinct from control of telomere length, or that telomere length in breast, prostate and ovarian cells is under the control of a different set of SNPs to those controlling telomere length in leucocytes. Luciferase reporter assays show that Peak 3 contains a silencer of the TERT promoter and that the minor allele of Peak 3 SNP rs2242652 further reduces expression. Consistent with this finding, Kote-Jarai Z. et al.54, report that the minor, risk allele of this SNP is associated with reduced TERT expression in benign prostate tissue. However, our search for comparable associations in ovarian or breast tumor tissue has been negative, possibly because TERT expression is severely disregulated in most tumors. Taken together, our luciferase assays indicate that either reduced signal from regulatory elements in Peaks 1 and 3, or increased signal from Peak 2, increases risk of specific cancer types.

We have additionally shown that the minor allele of rs10069690 affects splicing and is associated with transcription of a novel, truncated isoform resulting from a premature stop codon (Supplementary Fig.4). We do not yet know whether this isoform affects canonical telomerase activity, or how it changes activity. We further identified novel, open chromatin signatures overlapping rs10069690 in breast stromal and myoepithelial/stem cell fractions but not in progenitor or differentiated luminal epithelial cell fractions. Senescent stromal cells can stimulate malignant transformation of epithelial cells in in vitro and in vivo models55,56, and these SNP mechanisms merit investigation in future studies.

The SNPs originally reported to be associated with risk of lung (rs402710)57 and breast cancer (rs3816659)58 (Supplementary Table 10) were not associated with any cancer in this study. Moreover SNP rs2736100, in Peak 2, has been reported to be associated with glioma, lung and testicular cancer27,28,31,57,59–62 while nearby SNP rs2853677 was reported to be associated with glioma in the Chinese Han population63. Despite their physical proximity, these are not highly correlated with rs7705526 (r2=0.52 and 0.18 respectively), nor do they display independent associations with telomere length after adjustment for rs7705526. Thus, variants underlying susceptibility to different cancer types are different from the set of variants in the TERT region mediating changes in telomere length.

One limitation of this study is the incomplete representation of all SNPs at 5p15.33 on the iCOGS chip, which was designed in March 2010 using SNPs catalogued in HapMap3, together with those from the pilot study of the 1000GP35. To help fill known gaps on the iCOGS chip, additional SNPs were genotyped from the October 2010 1000GP data release, and imputation was based on the most recent, January 2012 release. However, several gaps remain across the TERT locus and this, coupled with the strikingly low linkage disequilibrium across the region (Fig.2), raises the possibility that there could be more independent associations that we have not yet detected. Furthermore, the incomplete SNP catalogue at the time of study design means that we cannot assume with certainty that the true causal variants, directly responsible for the observed association peaks, were captured in our analysis. It is also possible that more rare variants, not specifically investigated in this study, could have additional functional effects within this locus. Further re-sequencing of this region is needed to uncover the full spectrum of variation and phenotype associations. Another limitation is that telomere length was measured in DNA from leucocytes rather than from breast or ovarian tissue. Whilst we obtained suitable blood DNA for measurements in >53,000 subjects (a necessary sample size for adequate statistical power), obtaining comparable qualities and quantities of DNA from normal breast or ovary cells would be almost impossible. Telomere lengths, measured in different tissues within one individual have been shown to be highly correlated64–66 meaning that leukocyte telomere lengths are likely to be good surrogates for other tissues. Furthermore, one of our aims was to investigate whether the previously reported associations between mean telomere length and cancer risk might be mediated through TERT variants, and such studies have been based on telomere length measured in blood cell DNA. Another limitation was that we were unable to stratify OCAC ovarian cancer cases by BRCA1 and BRCA2 mutation status because this information was not available; nor was there sufficient power to evaluate ovarian cancer risk in mutation carriers in CIMBA.

Our findings provide evidence relevant to the hypothesis that shorter telomeres increase cancer risks: associations in the TERT promoter (Peak 1) fit this hypothesis best, while those in Peaks 2 and 3 (introns 2–4) and other reported 5p15.33 SNP-cancer associations (Supplementary Table 10) do not. Thus, it would appear that the majority of cancer associations within the TERT locus are mediated via alternative mechanisms involving the TERT gene. The protein product of TERT has functions beyond the telomerase-mediated extension of telomeres67. These non-canonical functions of TERT strongly resemble those mediated by MYC and WNT68, which are upstream regulators of proliferation, differentiation and migration. TERT also modulates WNT/β-catenin signaling69, and ectopic TERT expression induces increased cell division and decreased apoptosis in primary mammary cells, independently of telomere elongation70.

In conclusion, this study provides definitive evidence for genetic control of telomere length by common genetic variants in the TERT locus. Additionally, we report multiple, independent TERT SNP associations with breast cancer risk, confirming previously-reported associations and identifying new associations in both the general population and in BRCA1 mutation carriers. We also provide, for the first time, highly significant evidence for the association of distinct TERT SNPs with serous LMP and invasive ovarian cancer risk. Our results demonstrate that the relationships between TERT genotype, telomere length and cancer risk are complex, and that the TERT locus may influence cancer risk through multiple mechanisms.

Online Methods

SNP selection and genotyping

Most SNPs were genotyped on the iCOGS custom array36,37,71. SNPs at 5p15.33 (Build 36 positions 1280000–1415000; Build 37 positions 1227693 to 1361969) were selected, based on published cancer associations, from the March 2010 release of the 1000 Genomes Project (1000GP)35. These included all known SNPs with minor allele frequency (MAF)>0.02 in Europeans and r2>0.1 with the then-known cancer associated SNPs (rs40271057 and/or rs381665958), plus a tagging set for all known SNPs in the linkage disequilibrium blocks encompassing the genes in the region (SLC6A18, TERT and CLPTM1L). An additional 30 SNPs in TERT were selected through a telomere length candidate gene approach. In total, 134 SNPs were selected, 121 of which were successfully manufactured; 110 of those passed quality control (QC)36 in BCAC and CIMBA, and 108 in OCAC (Supplementary Tables 1–3). After genotyping, these SNPs were complemented with 22 SNPs, selected from the October 2010 release of 1000GP, to improve coverage. These were genotyped in two BCAC studies, SEARCH72 and CCHS73, using a Fluidigm™ array according to the manufacturer's instructions. To improve SNP density further, comprehensive genotype data for the locus were imputed for all subjects based on the January 2012 1000GP release. The genotype imputation process is described in36–38.

Samples and Quality Control

Study characteristics, iCOGS methodology and quality control for cancer-risk analyses are detailed elsewhere36–38. We measured telomere length in 6,766 control samples from the SEARCH study; 1,569 of these were accrued by SEARCH itself36, 793 were collected as part of the Sisters in Breast Screening (SIBS) study15 and 4,404 were cancer-free participants in the EPIC-Norfolk study19. We also measured telomere length in 8,841 participants in the Copenhagen City Heart Study (CCHS)73,74 and in 38,145 participants in the Copenhagen General Population Study (CGPS)75,76. Genotype clusters were visually inspected for the most strongly associated SNPs (Supplementary Fig.6). For all studies, ethnicity was assigned using HapMap (release 22) genotype data for European, African and Asian populations as reference (for BCAC and CIMBA using multidimensional scaling, for OCAC using LAMP77). All CIMBA analyses were restricted to individuals of European ancestry. For BCAC, separate estimates for individuals of East Asian and African-American ancestry were also derived. For OCAC, limited analyses of non-European ancestry groups were also performed. A subset of BCAC and OCAC were utilised in previous breast and ovarian cancer association studies of individual SNPs78. However, the associations with the key SNPs (rs10069690, rs2736108 and rs7705526) remained significant after excluding these studies, demonstrating similar ORs.

Telomere length measurement

Telomere length was measured in SEARCH using a modified version of the protocol as described elsewhere19,79. Twelve percent of samples were run in duplicate. Failed PCR reactions were not repeated. Telomere length was measured in CCHS and CGPS with a modified version of the protocol as described elsewhere50,80. Each individual was measured in quadruplicate. After exclusion of outliers, average cycle threshold (Ct) values of the remaining samples were calculated. Failed measurements were repeated up to twice. For meta-analysis, telomere length measurements from SEARCH were converted to the same scale as the CCHS and CGPS measurements, based on parameters from the linear regression between corresponding CCHS and SEARCH 10-year-age and 5-percentile groups in women only (Supplementary Fig.7). This measure of telomere length was used for all the analyses and then converted into fold changes (RTL) to aid interpretation (Supplementary Fig.7).

Statistical analyses

SNP associations with telomere length were evaluated using linear regression to model the fold-change in telomere length per-minor-allele, adjusted for age, 384-well plate, sex, seven principal components and study. The SNP was coded as number of minor alleles (0, 1, 2 for genotyped and the inferred genotype for imputed SNPs). The test of association was based on the 1 degree of freedom (1_df_) trend-test statistic. We also performed separate analyses (SEARCH, CCHS females, CCHS males, CGPS females, CGPS males) and combined the parameter estimates in a fixed-effect meta-analysis81. Associations with breast and ovarian cancer risk in BCAC and OCAC were evaluated by comparing genotype frequencies in cases and controls using unconditional logistic regression. Analyses were adjusted for study and seven principal components in BCAC36 and five in OCAC37. Nine OCAC studies with case-only genotype data were paired with case-control studies from similar geographic regions, resulting in 34 analysis study-strata. The principal analysis fitted the SNP as an allelic dose and tested for association using a 1_df_ trend-test, but genotype-specific risks were also obtained. Associations between genotypes and breast cancer risk in CIMBA studies (BRCA1 carriers) were evaluated using a 1_df_ per-allele trend-score test, based on modeling the retrospective likelihood of the observed genotypes conditional on breast cancer phenotypes82. To allow for non-independence among related individuals, an adjusted version of the score-test was used in which the variance of the score was derived, taking into account the correlation between the genotypes by estimating the kinship coefficient for each pair of individuals using the available genotype data83. Per-allele Hazard Ratio (HR) estimates were obtained by maximizing the retrospective likelihood. All analyses were stratified by country of residence. USA and Canada strata were further stratified on the basis of reported Ashkenazi Jewish ancestry.

Conditional analyses were performed to identify SNPs independently associated with each phenotype. To identify the most parsimonious model, all SNPs with marginal _P_-value<0.001 were included in forward-selection regression analyses with a threshold for inclusion of _P_-value<10−4, and including terms for age (for telomere length only), principal components and study. Similarly, forward-selection Cox-regression analysis was performed for BRCA1 carriers, stratified by country of residence, using the same _P_-value thresholds. This approach provides valid association tests, although the estimates can be biased82,84. Parameter estimates for the most parsimonious model were obtained using the retrospective likelihood approach.

FACS-sorting

Normal breast tissue was donated by women undergoing reduction mammoplasty surgery. Patients provided written consent and all work was performed with full local institutional human ethics approval. Tissue was dissociated as described previously85. Cells were prepared for flow cytometry as described previously42 by staining with a cocktail of Lin+ markers (CD31-PE, CD45-PE and CD235a-PE), EpCAM-FITC, CD49f-PE-Cy5 and Sytox Blue. Cells were then processed by a BD FACSAria II Cell Sorter and live cells immuno-negative for Lin+ markers were sorted into four subpopulations on the basis of their EpCAM-FITC and CD49f-PE-Cy5 fluorescence.

FAIRE analysis

Cell pellets derived from FACS-sorting of breast tissue samples were cross-linked in 1% formaldehyde, then lysed in 200μl Tris-buffered 1% SDS lysis buffer containing protease inhibitors. Lysates were sonicated using a QSONICA Model Q125 Ultra Sonic Processor to shear chromatin to 200bp-1kb fragments. Insoluble cell material was removed through centrifugation, and supernatants equally divided into 100μl INPUT and FAIRE samples. INPUTs were incubated overnight at 65°C to reverse cross-linking. All samples were purified through two rounds of phenol-chloroform extraction and DNA recovered through ethanol precipitation and re-suspended in water for use as PCR templates.

Plasmid construction and luciferase assays

TERT promoter variants were introduced into pGL3-TERT-391543 by site-directed mutagenesis (Agilent Technologies). TERT Putative Regulatory Elements PRE-A (hg19; chr5:1,284,900–1,287,087) and PRE-B (chr5:1,279,401–1,282,763), were PCR amplified using KAPAHiFi DNA polymerase (Geneworks) and cloned into pGL3-TERT-3915 or rs2736107/8/9 vectors. Individual SNPs were incorporated using overlap extension PCR. Cells were transfected with equimolar amounts of luciferase reporter plasmids and 50ng of pRLTK using siPORT NeoFX Transfection Agent (Ambion), according to the manufacturer's instructions, and harvested after 48h. Luminescence was measured with a Wallac Victor3 1420 multilabel counter and data (n=3) was analysed by one-way ANOVA with post-hoc Dunnett's tests.

Mini-gene construction and qRT-PCR analysis

TERT intron 4 was synthesised by GenScript and subcloned into pIRES-TERT44. The minor alleles at rs10069690 and rs2242652 were introduced by site-directed mutagenesis (Agilent Technologies). The resultant plasmids, designated pIRES-TERTint4-WT (wild-type intron 4), pIRES-TERTint4-rs10069690, pIRES-TERTint4-rs2242652 and pIRES-TERTint4-DM (minor alleles at both sites), were transfected using siPORT NeoFX Transfection Agent (Ambion) and cells harvested after 24h. Total RNA was extracted using the RNeasy Mini Kit (Qiagen) and digested with DNaseI (Invitrogen). cDNA was synthesised from 1μg RNA by random priming using SuperScript III reverse transcriptase (Invitrogen). Samples were screened for the presence of TERT splice variants by RT-PCR.

Molecular correlations at the 5p15.33 locus

For each gene within 1Mb we performed the following assays: (1) gene expression analysis in ovarian cancer cell lines (N=50) compared to ovarian surface epithelial and fallopian tube secretory cell lines (N=73) and tissues from high-grade serous ovarian cancers; (2) methylation analysis in high-grade serous ovarian cancers compared to normal tissues, and methylation quantitative trait locus (mQTL) analysis; (3) expression quantitative trait locus (eQTL) analysis to evaluate genotype-gene expression associations in normal high-grade serous ovarian cancers precursor tissues. We also evaluated these genes in silico in the somatic data from The Cancer Genome Atlas (TCGA49). We also profiled the spectrum of non-coding regulatory elements in ovarian surface epithelial and fallopian tube secretory cell lines using a combination of formaldehyde-assisted isolation of regulatory elements sequencing (FAIRE-seq40) and RNA sequencing (RNA-seq).

Supplementary Material

Part 1

Part 2

Acknowledgements

COGS is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 - HEALTH-F2-2009-223175). BCAC is funded by CR-UK (C1287/A10118 and C1287/A12014). BCAC meetings have been funded by the European Union COST programme (BM0606). Telomere length measurement and analysis was funded by CR-UK project grant C1287/A9540 and Chief Physician Johan Boserup and Lise Boserup's Fund. CIMBA data management and analysis were supported by Cancer Research – UK grants C12292/A11174 and C1287/A10118. OCAC is supported by a grant from the Ovarian Cancer Research Fund thanks to the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). Genotyping of the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710), the Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program (J.S. & D.E.), and the Ministry of Economic Development, Innovation and Export Trade of Quebec – grant # PSR-SIIRI-701 (J.S. & D.E, P.Hall). Scientific development and funding of the OCAC portion of this project were supported by the Genetic Associations and Mechanisms in Oncology (GAME-ON; U19-CA148112). CIMBA genotyping was supported by NIH grant CA128978, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a U.S. Department of Defense Ovarian Cancer Idea award (W81XWH-10-1-0341), grants from the Breast Cancer Research Foundation and the Komen Foundation for the Cure. This study made use of data generated by: The Wellcome Trust Case Control Consortium (A full list of WTCCC investigators is available from http://www.wtccc.org.uk/; funding was provided by Wellcome Trust award 076113) and The Cancer Genome Atlas (TCGA) Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (The investigators and institutions constituting the TCGA research network, can be found at http://cancergenome.nih.gov/.)

We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out.

Footnotes

References

• 1.McEachern MJ, Krauskopf A, Blackburn EH. Telomeres and their control. Annu. Rev. Genet. 2000;34:331–358. doi: 10.1146/annurev.genet.34.1.331. [DOI] [PubMed] [Google Scholar]
• 2.Palm W, de LT. How shelterin protects mammalian telomeres. Annu. Rev. Genet. 2008;42:301–334. doi: 10.1146/annurev.genet.41.110306.130350. [DOI] [PubMed] [Google Scholar]
• 3.Baird DM. Telomeres. Exp. Gerontol. 2006;41:1223–1227. doi: 10.1016/j.exger.2006.09.009. [DOI] [PubMed] [Google Scholar]
• 4.Moyzis RK, et al. A highly conserved repetitive DNA sequence, (TTAGGG)n, present at the telomeres of human chromosomes. Proc. Natl. Acad. Sci. U. S. A. 1988;85:6622–6626. doi: 10.1073/pnas.85.18.6622. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 5.Allsopp RC, et al. Telomere length predicts replicative capacity of human fibroblasts. Proc. Natl. Acad. Sci. U. S. A. 1992;89:10114–10118. doi: 10.1073/pnas.89.21.10114. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 6.Harley CB. Telomere loss: mitotic clock or genetic time bomb? Mutat. Res. 1991;256:271–282. doi: 10.1016/0921-8734(91)90018-7. [DOI] [PubMed] [Google Scholar]
• 7.Levy MZ, et al. Telomere end-replication problem and cell aging. J. Mol. Biol. 1992;225:951–960. doi: 10.1016/0022-2836(92)90096-3. [DOI] [PubMed] [Google Scholar]
• 8.Counter CM, et al. Telomere shortening associated with chromosome instability is arrested in immortal cells which express telomerase activity. EMBO J. 1992;11:1921–1929. doi: 10.1002/j.1460-2075.1992.tb05245.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 9.Hiyama E, et al. Telomerase activity in human breast tumors. J. Natl. Cancer Inst. 1996;88:116–122. doi: 10.1093/jnci/88.2.116. [DOI] [PubMed] [Google Scholar]
• 10.Stampfer MR, Yaswen P. Human epithelial cell immortalization as a step in carcinogenesis. Cancer Lett. 2003;194:199–208. doi: 10.1016/s0304-3835(02)00707-3. [DOI] [PubMed] [Google Scholar]
• 11.Alter BP, et al. Telomere length is associated with disease severity and declines with age in dyskeratosis congenita. Haematologica. 2012;97:353–359. doi: 10.3324/haematol.2011.055269. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 12.Njajou OT, et al. Telomere length is paternally inherited and is associated with parental lifespan. Proc. Natl. Acad. Sci. U. S. A. 2007;104:12135–12139. doi: 10.1073/pnas.0702703104. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 13.Slagboom PE, Droog S, Boomsma DI. Genetic determination of telomere size in humans: a twin study of three age groups. Am. J. Hum. Genet. 1994;55:876–882. [PMC free article] [PubMed] [Google Scholar]
• 14.Mirabello L, et al. The association of telomere length and genetic variation in telomere biology genes. Hum. Mutat. 2010;31:1050–1058. doi: 10.1002/humu.21314. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 15.Pooley KA, et al. No association between TERT-CLPTM1L single nucleotide polymorphism rs401681 and mean telomere length or cancer risk. Cancer Epidemiol. Biomarkers Prev. 2010;19:1862–1865. doi: 10.1158/1055-9965.EPI-10-0281. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 16.Mocellin S, et al. Telomerase reverse transcriptase locus polymorphisms and cancer risk: a field synopsis and meta-Analysis. J. Natl. Cancer Inst. 2012 doi: 10.1093/jnci/djs222. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 17.Sørensen M, et al. Genetic variation in TERT and TERC and human leukocyte telomere length and longevity: a cross-sectional and longitudinal analysis. Aging Cell. 2012;11:223–227. doi: 10.1111/j.1474-9726.2011.00775.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 18.McGrath M, et al. Telomere length, cigarette smoking, and bladder cancer risk in men and women. Cancer Epidemiol. Biomarkers Prev. 2007;16:815–819. doi: 10.1158/1055-9965.EPI-06-0961. [DOI] [PubMed] [Google Scholar]
• 19.Pooley KA, et al. Telomere length in prospective and retrospective cancer case-control studies. Cancer Res. 2010;70:3170–3176. doi: 10.1158/0008-5472.CAN-09-4595. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 20.Shen J, et al. Telomere length, oxidative damage, antioxidants and breast cancer risk. Int. J. Cancer. 2009;124:1637–1643. doi: 10.1002/ijc.24105. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 21.Wentzensen IM, et al. The association of telomere length and cancer: a meta-analysis. Cancer Epidemiol. Biomarkers Prev. 2011;20:1238–1250. doi: 10.1158/1055-9965.EPI-11-0005. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 22.de Vivo I, et al. A prospective study of relative telomere length and postmenopausal breast cancer risk. Cancer Epidemiol. Biomarkers Prev. 2009;18:1152–1156. doi: 10.1158/1055-9965.EPI-08-0998. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 23.Zee RY, et al. Mean telomere length and risk of incident colorectal carcinoma: a prospective, nested case-control approach. Cancer Epidemiol. Biomarkers Prev. 2009;18:2280–2282. doi: 10.1158/1055-9965.EPI-09-0360. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 24.Baird DM. Variation at the TERT locus and predisposition for cancer. Expert. Rev. Mol. Med. 2010;12:e16. doi: 10.1017/S146239941000147X. [DOI] [PubMed] [Google Scholar]
• 25.Beesley J, et al. Functional polymorphisms in the TERT promoter are associated with risk of serous epithelial ovarian and breast cancers. PLoS. One. 2011;6:e24987. doi: 10.1371/journal.pone.0024987. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 26.Kote-Jarai Z, et al. Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study. Nat. Genet. 2011;43:785–791. doi: 10.1038/ng.882. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 27.Landi MT, et al. A genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma. Am. J. Hum. Genet. 2009;85:679–691. doi: 10.1016/j.ajhg.2009.09.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 28.Rafnar T, et al. Sequence variants at the TERT-CLPTM1L locus associate with many cancer types. Nat. Genet. 2009;41:221–227. doi: 10.1038/ng.296. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 29.Shete S, et al. Genome-wide association study identifies five susceptibility loci for glioma. Nat. Genet. 2009;41:899–904. doi: 10.1038/ng.407. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 30.Stacey SN, et al. New common variants affecting susceptibility to basal cell carcinoma. Nat. Genet. 2009;41:909–914. doi: 10.1038/ng.412. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 31.Turnbull C, et al. Variants near DMRT1, TERT and ATF7IP are associated with testicular germ cell cancer. Nat. Genet. 2010;42:604–607. doi: 10.1038/ng.607. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 32.Wang Y, et al. Common 5p15.33 and 6p21.33 variants influence lung cancer risk. Nat. Genet. 2008;40:1407–1409. doi: 10.1038/ng.273. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 33.Johnatty SE, et al. Evaluation of candidate stromal epithelial cross-talk genes identifies association between risk of serous ovarian cancer and TERT, a cancer susceptibility “hot-spot”. PLoS. Genet. 2010;6:e1001016. doi: 10.1371/journal.pgen.1001016. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 34.Haiman CA, et al. A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat. Genet. 2011;43:1210–1214. doi: 10.1038/ng.985. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 35.Durbin RM, et al. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–1073. doi: 10.1038/nature09534. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 36.Michailidou K, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat. Genet. 2013 doi: 10.1038/ng.2563. Accepted. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 37.Pharoah P, et al. GWAS meta-analysis and replication identifies three new common susceptibility loci for ovarian cancer. Nat. Genet. 2013 doi: 10.1038/ng.2564. Accepted. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 38.Garcia-Closas M, et al. Genome-wide association studies identify four ER-negative specific breast cancer risk loci. Nat. Genet. 2013 doi: 10.1038/ng.2561. Accepted. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 39.Rosenbloom KR, et al. ENCODE whole-genome data in the UCSC Genome Browser: update 2012. Nucleic Acids Res. 2012;40:D912–D917. doi: 10.1093/nar/gkr1012. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 40.Giresi PG, et al. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 2007;17:877–885. doi: 10.1101/gr.5533506. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 41.Taylor J, et al. ESPERR: learning strong and weak signals in genomic sequence alignments to identify functional elements. Genome Res. 2006;16:1596–1604. doi: 10.1101/gr.4537706. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 42.Lim E, et al. Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers. Nat. Med. 2009;15:907–913. doi: 10.1038/nm.2000. [DOI] [PubMed] [Google Scholar]
• 43.Chen YJ, et al. PAX8 regulates telomerase reverse transcriptase and telomerase RNA component in glioma. Cancer Res. 2008;68:5724–5732. doi: 10.1158/0008-5472.CAN-08-0058. [DOI] [PubMed] [Google Scholar]
• 44.Colgin LM, et al. The hTERTalpha splice variant is a dominant negative inhibitor of telomerase activity. Neoplasia. 2000;2:426–432. doi: 10.1038/sj.neo.7900112. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 45.Saeboe-Larssen S, Fossberg E, Gaudernack G. Characterization of novel alternative splicing sites in human telomerase reverse transcriptase (hTERT): analysis of expression and mutual correlation in mRNA isoforms from normal and tumour tissues. BMC. Mol. Biol. 2006;7:26. doi: 10.1186/1471-2199-7-26. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 46.Kilian A, et al. Isolation of a candidate human telomerase catalytic subunit gene, which reveals complex splicing patterns in different cell types. Hum. Mol. Genet. 1997;6:2011–2019. doi: 10.1093/hmg/6.12.2011. [DOI] [PubMed] [Google Scholar]
• 47.Wick M, Zubov D, Hagen G. Genomic organization and promoter characterization of the gene encoding the human telomerase reverse transcriptase (hTERT) Gene. 1999;232:97–106. doi: 10.1016/s0378-1119(99)00108-0. [DOI] [PubMed] [Google Scholar]
• 48.Desmet FO, et al. Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res. 2009;37:e67. doi: 10.1093/nar/gkp215. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 49.Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474:609–615. doi: 10.1038/nature10166. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 50.Weischer M, et al. Short telomere length, myocardial infarction, ischemic heart disease, and early death. Arterioscler. Thromb. Vasc. Biol. 2012;32:822–829. doi: 10.1161/ATVBAHA.111.237271. [DOI] [PubMed] [Google Scholar]
• 51.Codd V, et al. Common variants near TERC are associated with mean telomere length. Nat. Genet. 2010;42:197–199. doi: 10.1038/ng.532. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 52.Levy D, et al. Genome-wide association identifies OBFC1 as a locus involved in human leukocyte telomere biology. Proc. Natl. Acad. Sci. U. S. A. 2010;107:9293–9298. doi: 10.1073/pnas.0911494107. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 53.Willeit P, et al. Telomere length and risk of incident cancer and cancer mortality. JAMA. 2010;304:69–75. doi: 10.1001/jama.2010.897. [DOI] [PubMed] [Google Scholar]
• 54.Kote-Jarai Z, et al. Fine-mapping identifies multiple prostate cancer risk loci on 5p15, one of which associates with TERT expression. Hum. Mol. Genet. 2013 doi: 10.1093/hmg/ddt086. Submitted. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 55.Krtolica A, et al. Senescent fibroblasts promote epithelial cell growth and tumorigenesis: a link between cancer and aging. Proc. Natl. Acad. Sci. U. S. A. 2001;98:12072–12077. doi: 10.1073/pnas.211053698. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 56.Lawrenson K, et al. Senescent fibroblasts promote neoplastic transformation of partially transformed ovarian epithelial cells in a three-dimensional model of early stage ovarian cancer. Neoplasia. 2010;12:317–325. doi: 10.1593/neo.91948. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 57.McKay JD, et al. Lung cancer susceptibility locus at 5p15.33. Nat. Genet. 2008;40:1404–1406. doi: 10.1038/ng.254. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 58.Shen J, et al. Multiple genetic variants in telomere pathway genes and breast cancer risk. Cancer Epidemiol. Biomarkers Prev. 2010;19:219–228. doi: 10.1158/1055-9965.EPI-09-0771. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 59.Hu Z, et al. A genome-wide association study identifies two new lung cancer susceptibility loci at 13q12.12 and 22q12.2 in Han Chinese. Nat. Genet. 2011;43:792–796. doi: 10.1038/ng.875. [DOI] [PubMed] [Google Scholar]
• 60.Mushiroda T, et al. A genome-wide association study identifies an association of a common variant in TERT with susceptibility to idiopathic pulmonary fibrosis. J. Med. Genet. 2008;45:654–656. doi: 10.1136/jmg.2008.057356. [DOI] [PubMed] [Google Scholar]
• 61.Truong T, et al. Replication of lung cancer susceptibility loci at chromosomes 15q25, 5p15, and 6p21: a pooled analysis from the International Lung Cancer Consortium. J. Natl. Cancer Inst. 2010;102:959–971. doi: 10.1093/jnci/djq178. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 62.Zou P, et al. The TERT rs2736100 Polymorphism and Cancer Risk: A Meta-analysis Based on 25 Case-Control Studies. BMC. Cancer. 2012;12:7. doi: 10.1186/1471-2407-12-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 63.Zhao Y, et al. Fine-mapping of a region of chromosome 5p15.33 (TERT-CLPTM1L) suggests a novel locus in TERT and a CLPTM1L haplotype are associated with glioma susceptibility in a Chinese population. Int. J. Cancer. 2011 doi: 10.1002/ijc.27417. [DOI] [PubMed] [Google Scholar]
• 64.Takubo K, et al. Telomere lengths are characteristic in each human individual. Exp. Gerontol. 2002;37:523–531. doi: 10.1016/s0531-5565(01)00218-2. [DOI] [PubMed] [Google Scholar]
• 65.Gadalla SM, et al. Telomere length in blood, buccal cells, and fibroblasts from patients with inherited bone marrow failure syndromes. Aging (Albany. NY) 2010;2:867–874. doi: 10.18632/aging.100235. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 66.Thomas P, O' Callaghan NJ, Fenech M. Telomere length in white blood cells, buccal cells and brain tissue and its variation with ageing and Alzheimer's disease. Mech. Ageing Dev. 2008;129:183–190. doi: 10.1016/j.mad.2007.12.004. [DOI] [PubMed] [Google Scholar]
• 67.Greider CW, Blackburn EH. Identification of a specific telomere terminal transferase activity in Tetrahymena extracts. Cell. 1985;43:405–413. doi: 10.1016/0092-8674(85)90170-9. [DOI] [PubMed] [Google Scholar]
• 68.Choi J, et al. TERT promotes epithelial proliferation through transcriptional control of a Myc- and Wnt-related developmental program. PLoS Genet. 2008;4:e10. doi: 10.1371/journal.pgen.0040010. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 69.Park JI, et al. Telomerase modulates Wnt signalling by association with target gene chromatin. Nature. 2009;460:66–72. doi: 10.1038/nature08137. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 70.Mukherjee S, et al. Separation of telomerase functions by reverse genetics. Proc. Natl. Acad. Sci. U. S. A. 2011;108:E1363–E1371. doi: 10.1073/pnas.1112414108. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 71.Gaudet M, et al. Identification of a BRCA2-specific Modifier Locus at 6p24 Related to Breast Cancer Risk. PLoS Genet. 2013 doi: 10.1371/journal.pgen.1003173. Accepted. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 72.Azzato EM, et al. Association between a germline OCA2 polymorphism at chromosome 15q13.1 and estrogen receptor-negative breast cancer survival. J. Natl. Cancer Inst. 2010;102:650–662. doi: 10.1093/jnci/djq057. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 73.Bojesen SE, Tybjaerg-Hansen A, Nordestgaard BG. Integrin beta3 Leu33Pro homozygosity and risk of cancer. J. Natl. Cancer Inst. 2003;95:1150–1157. doi: 10.1093/jnci/djg005. [DOI] [PubMed] [Google Scholar]
• 74.Allin KH, Bojesen SE, Nordestgaard BG. Baseline C-reactive protein is associated with incident cancer and survival in patients with cancer. J. Clin. Oncol. 2009;27:2217–2224. doi: 10.1200/JCO.2008.19.8440. [DOI] [PubMed] [Google Scholar]
• 75.Allin KH, et al. C-reactive protein and the risk of cancer: a mendelian randomization study. J. Natl. Cancer Inst. 2010;102:202–206. doi: 10.1093/jnci/djp459. [DOI] [PubMed] [Google Scholar]
• 76.Zacho J, et al. Genetically elevated C-reactive protein and ischemic vascular disease. N. Engl. J. Med. 2008;359:1897–1908. doi: 10.1056/NEJMoa0707402. [DOI] [PubMed] [Google Scholar]
• 77.Sankararaman S, et al. Estimating local ancestry in admixed populations. Am. J. Hum. Genet. 2008;82:290–303. doi: 10.1016/j.ajhg.2007.09.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 78.Terry KL, et al. Telomere length and genetic variation in telomere maintenance genes in relation to ovarian cancer risk. Cancer Epidemiol. Biomarkers Prev. 2012;21:504–512. doi: 10.1158/1055-9965.EPI-11-0867. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 79.Cawthon RM. Telomere measurement by quantitative PCR. Nucleic Acids Res. 2002;30:e47. doi: 10.1093/nar/30.10.e47. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 80.Cawthon RM. Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res. 2009;37:e21. doi: 10.1093/nar/gkn1027. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 81.STATA SE. Windows. StataCorp.; College Station, TX: 2012. [Google Scholar]
• 82.Barnes DR, et al. Evaluation of association methods for analysing modifiers of disease risk in carriers of high-risk mutations. Genet. Epidemiol. 2012;36:274–291. doi: 10.1002/gepi.21620. [DOI] [PubMed] [Google Scholar]
• 83.Antoniou AC, et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat. Genet. 2010;42:885–892. doi: 10.1038/ng.669. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 84.Antoniou AC, et al. A weighted cohort approach for analysing factors modifying disease risks in carriers of high-risk susceptibility genes. Genet. Epidemiol. 2005;29:1–11. doi: 10.1002/gepi.20074. [DOI] [PubMed] [Google Scholar]
• 85.Eirew P, et al. A method for quantifying normal human mammary epithelial stem cells with in vivo regenerative ability. Nat. Med. 2008;14:1384–1389. doi: 10.1038/nm.1791. [DOI] [PubMed] [Google Scholar]

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