Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2014 Jul 1.
Published in final edited form as: Genes Chromosomes Cancer. 2013 Apr 30;52(7):10.1002/gcc.22056. doi: 10.1002/gcc.22056

Telomere Length, Telomere-Related Genes, and Breast Cancer Risk: The Breast Cancer Health Disparities Study

Andrew J Pellatt 1, Roger K Wolff 1, Gabriela Torres-Mejia 2, Esther M John 3,4, Jennifer S Herrick 1, Abbie Lundgreen 1, Kathy B Baumgartner 5, Anna R Giuliano 6, Lisa M Hines 7, Laura Fejerman 8, Richard Cawthon 9, Martha L Slattery 1,*
PMCID: PMC3807250  NIHMSID: NIHMS516590  PMID: 23629941

Abstract

Telomeres are involved in maintaining genomic stability. Previous studies have linked both telomere length (TL) and telomere-related genes with cancer. We evaluated associations between telomere-related genes, TL, and breast cancer risk in an admixed population of US non-Hispanic white (1,481 cases, 1,586 controls) and U.S. Hispanic and Mexican women (2,111 cases, 2,597 controls) from the Breast Cancer Health Disparities Study. TL was assessed in 1,500 women based on their genetic ancestry. TL-related genes assessed were MEN1, MRE11A, RECQL5, TEP1, TERC, TERF2, TERT, TNKS, and TNKS2. Longer TL was associated with increased breast cancer risk [odds ratio (OR) 1.87, 95% confidence interval (CI) 1.38, 2.55], with the highest risk (OR 3.11, 95% CI 1.74, 5.67 p interaction 0.02) among women with high Indigenous American ancestry. Several TL-related single nucleotide polymorphisms had modest association with breast cancer risk overall, including TEP1 rs93886 (OR 0.82, 95% CI 0.70,0.95); TERF2 rs3785074 (OR 1.13, 95% CI 1.03,1.24); TERT rs4246742 (OR 0.85, 95% CI 0.77,0.93); TERT rs10069690 (OR 1.13, 95% CI 1.03,1.24); TERT rs2242652 (OR 1.51, 95% CI 1.11,2.04); and TNKS rs6990300 (OR 0.89, 95% CI 0.81,0.97). Several differences in association were detected by hormone receptor status of tumors. Most notable were associations with TERT rs2736118 (ORadj 6.18, 95% CI 2.90, 13.19) with estrogen receptor negative/progesterone receptor positive (ER−/PR+) tumors and TERT rs2735940 (ORadj 0.73, 95% CI 0.59, 0.91) with ER−/PR− tumors. These data provide support for an association between TL and TL-related genes and risk of breast cancer. The association may be modified by hormone receptor status and genetic ancestry.

Introduction

Telomeres cap the ends of linear chromosomes and play a role in maintaining genomic stability (Mirabello et al., 2010a,b). They prevent chromosomes from shortening during DNA replication, by precluding chromosome ends from being recognized as double strand breaks (DSBs) that are targeted for repair, resulting in the improper joining of chromosome ends (Cassidy et al., 2010). Telomeres consist of tandem (TTAGGG)n nucleotide repeats that shorten with aging (Lin et al., 2010) and are necessary for the maintenance of chromosome length because DNA polymerase is unable to replicate the 3I ends of chromosomes. Telomere length (TL) has been linked to a number of diseases, including cardiovascular diseases, diabetes, osteosarcoma, and multiple types of cancer (Mirabello et al., 2009; Njajou et al., 2009; Mirabello et al., 2010a,b; Pooley et al., 2010; Hoen et al., 2011; Mirabello et al., 2011; O'Donovan et al., 2011). Recent studies have suggested an association between TL and breast cancer risk, however, relatively few studies examined the association between genetic variants in telomere-related genes and breast cancer susceptibility (Savage et al., 2007; Martinez-Delgado et al., 2011; Shen et al., 2012).

TL is maintained by a number of telomere-related factors. One such factor is the telomerase enzyme. Telomerase is composed of telomerase reverse transcriptase (TERT) and telomere RNA component (TERC). TERT uses TERC as a template for the synthesis of single stranded DNA within the telomere, thereby preventing the chromosome from shortening during chromosomal replication. However, telomerase activity is usually absent in differentiated cells presumably causing telomeres to shorten with age; when TL reaches a critical limit the cell undergoes senescence (Cassidy et al., 2010; Mirabello et al., 2010a,b; Shammas, 2011; Wolkowitz et al., 2011).

In addition to TERT and TERC activity, recent studies have begun to examine the effects of single nucleotide polymorphisms (SNPs) in other telomere-related genes. Multiple endocrine neoplasia type I (MEN1), RECQ Protein-Like 5 (RECQL5), Meiotic Recombination 11 Homolog A (MRE11A), TRF1-interacting ankyrin-related ADP-ribose polymerase (TNKS), TRF1-interacting ankyrin-related ADP-ribose polymerase 2 (TNKS2), Telomerase-associated protein 1 (TEP1), and Telomeric repeat-binding factor 2 (TERF2) have been reported as being associated with TL (Chiang et al., 2006; Mirabello et al., 2010a,b). MEN1 is associated with the TERT promoter. RECQL5 is a helicase associated with protecting genomic integrity. MRE11A modulates t-loop formation; t-loops prevent telomeres from being recognized as DSBs and being targeted for repair. TNKS is a polymerase believed to positively regulate TL. TNKS2 inhibits telomere elongation. TEP1 and TERF2 are components of the ribonucleoprotein complex responsible for telomerase activity (Chiang et al., 2006).

Some reports have linked these genes to breast cancer. TERT (Savage et al., 2007; Shen et al., 2012), TEP1 (Salhab et al., 2008), TERF2 (Varadi et al., 2009; Shen et al., 2012), RECQL5 (Islam et al., 2012), TNKS2 (Varadi et al., 2009), and MEN1 (Imachi et al., 2010) have been shown to be associated with breast cancer susceptibility and/or survival. Although MRE11A and TNKS have been shown to interact with BRCA1 and BRCA2, evaluation of specific SNPs within these genes has been inconclusive regarding their effect on breast cancer risk overall (Gelmini et al., 2004; McCabe et al., 2009; Loizidou et al., 2010; Rebbeck et al., 2011).

In this study, we examined associations between breast cancer risk and TL and telomere-related SNPs. We evaluated SNPs in TERT and candidate SNPs in TERC, MEN1, RECQL5, MRE11A, TEP1, TERF2, TNKS, and TNKS2 which have previously been associated with TL. We added to the previous work by examining TL and telomere-related genes in an admixed population of non-Hispanic white (NHW), Hispanic and Native American women from the U.S and Mexico. Breast cancer risk has been shown to vary significantly in these racial and ethnic groups, with higher incidence rates in NHW than Hispanic and Native American women. We also evaluated breast cancer associations by menopausal status and estrogen receptor (ER) and progesterone receptor (PR) tumor status.

Methods

The Breast Cancer Health Disparities Study includes participants from three population-based case–control studies: the 4-Corner's Breast Cancer Study, the Mexico Breast Cancer Study, and the San Francisco Bay Area Breast Cancer Study (Slattery et al., 2012). All participants signed informed written consent prior to participation and each study was approved by their Institutional Review Board for Human Subjects.

The 4-Corner's Breast Cancer Study participants were NHW, Hispanic, or Native American women living in nonreservation areas in the states of Arizona, Colorado, New Mexico, or Utah at the time of diagnosis or selection (Slattery et al., 2007). The present analyses included female cases between 25 and 79 years of age with a histologically confirmed diagnosis of in situ or invasive breast cancer between October 1999 and May 2004. Controls were selected to match the age and ethnicity of the cases. A total of 1,833 cases and 2,057 controls with interview data collected by in-person interview and DNA were included.

Participants from the Mexico Breast Cancer Study were between 28 and 74 years of age, living in Monterrey, Veracruz and Mexico City for the past five years (Seinost et al., 2000). Eligible cases were diagnosed with either a new histologically confirmed in situ or invasive breast cancer between January 2004 and December 2007 at 12 participating hospitals from three main health care systems in Mexico. Controls were randomly selected from the catchment area of the 12 participating hospitals using a probabilistic multistage design. Interview data and DNA were available for 816 cases and 994 controls.

The San Francisco Bay Area Breast Cancer Study included women aged 35 to 79 years from the San Francisco Bay Area diagnosed with a first primary histologically confirmed invasive breast cancer; controls were identified by random-digit dialing and frequency-matched to cases based on the expected race/ethnicity and 5-year age distribution (John et al., 2003, 2005). This analysis included subjects with data collected by in-person interview and DNA (cases diagnosed between April 1997 and April 2002 and their matched controls) including 1,105 cases and 1,318 controls.

Data Harmonization

Data were harmonized across all study centers and questionnaires (Slattery et al., 2012). Adjustment variables used in the analyses included body mass index (BMI) calculated as weight (kg) divided by height squared [meters squared (m2)] which was based on measured height (or self-reported height if the measurement was declined) and self-reported weight during the referent year. The referent year was defined as the year prior to diagnosis for cases or selection for controls. Parity was defined as the number of full-term pregnancies, age at first birth was defined as age at first live birth or still birth, race/ethnicity in U.S. studies was based on self-report (all women in Mexico were classified as Hispanic since race/ethnicity was not asked), and education was based on the highest level of reported education. Women were classified as either premenopausal or postmenopausal based on responses to questions on menstrual history. Women who reported still having periods during the referent year were classified as premenopausal. Center-specific definitions were used to define postmenopausal women. Within each study, women were classified as postmenopausal if they were taking menopausal hormone therapy and still having periods and were at or above the 95th percentile of age for race/ethnicity of those who reported having a natural menopause (i.e., ≥12 months since their last period). This age was 58 years for NHW and 56 for Hispanics from the 4-Corner's Breast Cancer Study, 54 for the Mexico Breast Cancer Study, and 55 for NHW and 56 for Hispanics from the San Francisco Bay Area Breast Cancer Study. Average alcohol intake (grams per day) consumed over the lifetime was available for all but about 600 cases and controls from California. For those women, we used alcohol consumption during the referent year as an adjustment variable. Physical activity was harmonized as hours per week of vigorous intensity activity performed during the referent year and analyzed using center-specific cut-points to accommodate the level of inquiry of each study questionnaire

Genetic Data

DNA was derived from either whole blood or mouthwash samples obtained from study participants. A total of 7,287 blood-derived and 634 mouthwash-derived samples were examined. Candidate SNPs were selected based on previous data that suggested an association with TL, except for TERT and TNKS where a tagSNP approach was used to identify SNPs. TagSNPs were selected based on linkage disequilibrium (LD) blocks defined using a Caucasian LD map and an r2 = 0.8; minor allele frequency >0.1; range = −1,500 bps from the initiation codon to +1,500 bps from the termination codon; and 1 SNP/LD bin. Genetic admixture data were obtained by evaluation of 104 Ancestral Informative Markers (AIMs) to distinguish European and Indigenous American (IA) ancestry in the study population (Slattery et al., 2012). All markers were genotyped using a multiplexed bead array assay format based on GoldenGate chemistry (Illumina, San Diego, California). A genotyping call rate of 99.93% was attained (99.65% for whole genome amplified samples). We included 132 internal replicates that were blinded representing 1.6% of the sample set. The duplicate concordance rate was 99.996% as determined by 193,297 matching genotypes among sample pairs. Table 1 shows SNPs assessed in this study. Online Supporting Information Table S1 details the LD between SNPs within a gene.

Table 1. Description of Genes Included in Study.

Gene Chromosome Location dbSNP ID Major/Minor Allele MAF FDR HWE P
NHW HISP/NA NHW HISP/NA
MEN1 11q13.1 rs670358a G/A 0.09 0.34 0.97 0.00
MRE11A 11q21 rs12270338 C/A 0.21 0.12 0.96 0.50
rs13447720 A/G 0.22 0.12 0.97 0.37
RECQL5 17q25.1 rs820152b T/C 0.38 0.27 0.96 0.49
TEP1 14q11.2 rs938886 G/C 0.20 0.37 0.89 0.01
TERC 3q26 rs12696304 C/G 0.27 0.53 0.83 0.002
TERF2 16q22.1 rs3785074 A/G 0.26 0.18 0.96 0.66
TERT 5p15.33 rs2736118 A/G 0.26 0.15 1.00 0.42
rs4246742 A/T 0.17 0.29 1.00 0.79
rs10069690 C/T 0.25 0.18 0.96 0.82
rs2242652 C/T 0.19 0.14 0.65 0.79
rs2736100 T/G 0.50 0.36 0.96 0.85
rs2853676 G/A 0.26 0.19 0.96 0.64
rs2735940 C/T 0.50 0.36 0.97 0.93
TNKS 8p23.1 rs11991621 C/T 0.19 0.08 0.82 0.72
rs6990097 T/C 0.27 0.18 0.93 0.47
rs12549064 A/C 0.18 0.09 0.86 0.59
rs10903314 C/T 0.26 0.17 0.96 0.34
rs6990300 A/G 0.32 0.42 0.97 0.73
rs11249943 A/C 0.20 0.09 0.93 0.81
rs17150478 A/G 0.20 0.18 1.00 0.76
TNKS2 10q23.3 rs10509637 A/G 0.15 0.09 0.96 0.65
Open in a new tab
a

This SNP is located within the CDC42BPG gene.

b

This SNP is located in MYO15B gene.

TL

TL was measured using a multiplexed quantitative PCR (qPCR) method previously described by Cawthon (2009). This method modified earlier qPCR methods in which telomere signals were measured separately from single copy gene (scg) signals in order to produce a T/S ratio corresponding to TL. The multiplexed PCR analysis uses a single dye and measures both the telomere signals and scg signals in the same well. This is achieved by using CG clamps to stabilize the scg giving it a higher melting point. The telomere amplification signal is collected early in the thermal cycling, while the scg amplification signal is still at baseline; and the scg amplification signal is collected at later cycles at a high temperature that completely melts the telomere amplification product, sending its fluorescent signal to baseline. This design allows a single qPCR to determine the T/S ratio. DNA from blood was used to measure TL on a subset of the entire study population that was selected to include 250 cases and 250 controls from each of the three genetic admixture groups evaluated in this study. Thirty-two individuals were excluded from analysis because of failed sample or results being outside of the boundaries of the standard curve.

Tumor Characteristics

Cancer registries in Utah, Colorado, Arizona, New Mexico, and California provided information on stage at diagnosis, ER and PR status. Information on ER and PR status was available for 1,019 (69%) NHW cases and 977 (75%) Hispanic cases. Information on tumor characteristics was not available for cases from Mexico.

Statistical Methods

The program STRUCTURE was used to compute individual ancestry for each study participant assuming two founding populations (Pritchard et al., 2000; Falush et al., 2003; Slattery et al., 2012). A two-population structure model best fit the population and was used in analysis. Genetic ancestry was used as a continuous variable to adjust for possible confounding. Genes were assessed for their association with breast cancer risk by strata of genetic ancestry, ER/PR status, and menopausal status. For these later analyses, genetic ancestry was categorized based on the distribution of percent IA ancestry in the control population. Three strata, 0–28%, 29–70%, and 71–100%, were used to evaluate potential differences in association by level of IA ancestry. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). Logistic regression models were used to estimate the age and study center-adjusted odds ratios (OR) for breast cancer risk associated with SNPs. Additionally, we adjusted for potential confounding variables of BMI, parity, age at first birth, hours of vigorous-intensity physical activity, alcohol consumption, and genetic admixture when not stratifying by it. Associations with SNPs were assessed assuming a co-dominant model. Based on the initial assessment, SNPs which appeared to have a dominant or recessive mode of inheritance were evaluated with those inheritance models in subsequent analyses. Interactions between genetic variants, genetic ancestry and menopausal status were assessed using P values from a likelihood-ratio test comparing a full model that included an ordinal interaction term with a reduced model without an interaction term.

P values corresponding to Wilcoxon rank-sum tests are presented alongside TL medians and interquartile ranges, stratified by self-reported race/ethnicity and genetic admixture group. Associations with breast cancer risk and TL categorized into quartiles for the overall population and for the three genetic admixture groups were estimated using ORs from logistic regression models. Interaction between TL and genetic ancestry was assessed using P values from a 1-df likelihood-ratio test (as previously mentioned). Generalized linear models (GLMs) were used to estimate the log transformed TL means by genotype among the control population. Logistic regression and GLM analyses were adjusted for age, study center, genetic admixture, and BMI.

As TERT and TNKs were evaluated using a tagSNP approach we adjusted for multiple comparisons. We used P values based on 1-df Wald test statistics for adjustment of multiple comparisons taking into account tagSNPs within the gene using the step-down Bonferroni correction (i.e., Holm method) based on the effective number of independent SNPs as determined using the SNP spectral decomposition method proposed by Nyholt (Suarez et al., 2007) and modified by Li and Ji (2005).

Results

Among controls, 31.5% of NHWs and 40.7% of Hispanics were premenopausal (Table 2). Among cases, ER+/PR+ was the most common tumor phenotype for both NHW and Hispanic women. ER−/PR− tumors were observed in 18.4% of NHW women and 23.4% of Hispanic women. TL in cases was generally longer than that observed for controls. This trend in TL was observed for all self-reported race/ethnicity groups and for all strata of genetic ancestry. TL was significantly inversely associated with age for all women combined and for women in each ancestry group (P < 0.001).

Table 2. Description of Study Population by Self-Reported Race/Ethnicitya.

Non-Hispanic white (NHW) P valueb Hispanic P value
Controls Cases Controls Cases
N % N % N % N %
Study site
 4 Corner's study 1321 83.3 1227 82.8 NA 723 27.8 597 28.3 NA
 Mexico 0 0.0 0 0.0 994 38.3 816 38.7
 California 264 16.7 254 17.2 880 33.9 698 33.1
Age (years)
 <40 116 7.3 89 6.0 NA 311 12.0 200 9.5 NA
 40–49 408 25.7 409 27.6 831 32.0 713 33.8
 50–59 409 25.8 413 27.9 756 29.1 617 29.2
 60–69 349 22.0 361 24.4 526 20.3 430 20.4
 70+ 303 19.1 209 14.1 173 6.7 151 7.2
Menopausal status
 Premenopausal 494 31.5 489 33.5 NA 1027 40.7 836 40.9
 Postmenopausal 1075 68.5 970 66.5 1499 59.3 1210 59.1
Estimated indigenous American (IA) ancestryc
 Low IA (0–28%) 1577 99.5 1472 99.4 NA 278 10.7 275 13.0 NA
 Intermediate IA (29–70%) 7 0.4 7 0.5 1686 64.9 1393 66.0
 High 1A (71–100%) 1 0.1 2 0.1 633 24.4 443 21.0
ER/PR statusd
 ER+/PR+ 695 68.2 NA 605 61.9 NA
 ER+/PR− 121 11.9 115 11.8
 ER−/PR+ 15 1.5 28 2.9
 ER−/PR− 188 18.4 229 23.4
Telomere Length Median (Q1,Q3) Median (Q1,Q3) Median (Q1,Q3) Median (Q1,Q3)
 Overall 232 1.26 (1.08,1.51) 237 1.30 (1.10,1.55) 0.418 494 1.27 (1.11,1.43) 500 1.35 (1.17,1.53) <0.00l
 Low IA (0–28%) 231 1.25 (1.08,1.51) 236 1.30 (1.10,1.55) 0.396 16 1.27 (1.04,1.57) 8 1.27 (1.19,1.53) 0.540
 Intermediate IA (29–70%) 1 1.26 1 1.05 NA 241 1.29 (1.08,1.49) 246 1.35 (1.18,1.52) 0.049
 High 1A (71–100%) 0 NA 0 NA NA 237 1.26 (1.12,1.40) 246 1.35 (1.17,1.53) <0.00l
Open in a new tab
a

AII women from Mexico are classified as Hispanic since race/ethnicity was not asked.

b

P value for Wilcoxon rank-sum test.

c

Assessment of indigenous American ancestry (IA) is based on 104 AIMs markers.

d

Tumor information unavailable for the Mexico study site.

The association between TL and breast cancer risk for all women and by genetic ancestry is shown in Table 3. Longer TL was associated with increased breast cancer risk (OR 1.87, 95% CI 1.38, 2.55). The TL and breast cancer association was stronger in women with greater IA ancestry, with ORs of 3.11 (95% CI 1.74, 5.67) for women with IA ancestry >70%, 2.15 (95% CI 1.22, 3.82) for those with IA ancestry 29–70%, and 1.22 (95% CI 0.75, 1.96) for women with IA ancestry <29%; the difference in associations between TL and breast cancer risk between admixture groups was statistically significant (P interaction = 0.02). Associations between TL and breast cancer risk did not differ by menopausal status (data not shown).

Table 3. Associations Between TL and Breast Cancer Risk Overall and by PERCENT Indigenous American (IA) Ancestry.

Overall IA Ancestry
Low (0–28%) Intermediate (29–70%) High (71–100%)
Quartile Controls Cases ORa (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI)
1 183 148 1.00 68 64 1.00 62 44 1.00 53 40 1.00
2 186 150 0.96 (0.70,1.31) 58 50 0.90 (0.54,1.50) 52 46 1.20 (0.66,2.17) 76 54 0.98 (0.57,1.68)
3 180 192 1.27 (0.94,1.73) 44 50 1.24 (0.73,2.12) 65 77 1.60 (0.93,2.77) 71 65 1.22 (0.71,2.11)
4 171 238 1.87 (1.38,2.55) 74 80 1.22 (0.75,1.96) 62 76 2.15 (1.22,3.82) 35 82 3.11 (1.74,5.67)
Open in a new tab
a

Odds ratios (OR) and 95% confidence intervals (CI) adjusted for age, study center, genetic admixture, and BMI in the referent year; P interaction between ancestry groups was 0.02.

Assessment of the relationship between SNPs and TL among controls showed some associations (see online Supporting Information Table S2 for TL by genotype and by menopausal status). TERT rs2736118 was associated with TL overall (P = 0.0007), with longer TL in individuals with the GG genotype after adjusting for age, BMI, genetic ancestry, and study; this association was strongest for postmenopausal women (P = 0.003). TNKS rs10509637 also was associated with TL among controls after adjustment, where individuals with the GG genotype had significantly longer TL relative to those carrying other genotypes (P = 0.019). Among premenopausal women the variant genotypes of MRE11A rs12270338 and rs13447720 were associated with longer TL (both P = 0.028) and the variant genotype of TERT rs2736100 was marginally associated with longer TL (P = 0.055). Among postmenopausal women the variant genotypes of TNKS rs11249943 and TNKS rs11991621 were associated with shorter TL (P = 0.04 and 0.054 respectively).

Five TERT SNPs: rs4246742, rs10069690, rs2242652, rs2736100, and rs2853676, were associated with overall breast cancer risk, although most associations were modest (Table 4). The variant allele in TERT rs4246742 was inversely associated with breast cancer risk among all women (OR 0.85, 95% CI 0.77, 0.93). Positive associations were found for TERT rs10069690 (OR 1.13, 95% CI 1.03, 1.24), TERT rs2242652 (OR 1.51, 95% CI 1.11, 2.04), and TERT rs2853676 (OR 1.23, 95% CI 1.00, 1.51). The variant allele in TERT rs2242652 exhibited a slightly stronger association among postmenopausal women (OR 1.86, 95% CI 1.27, 2.74) and TERT rs2736100 was associated with risk only in postmenopausal women (OR 1.20, 95% CI 1.01, 1.42).

Table 4. Associations Between Telomere-Associated SNPs for All Breast Cancers Combined and by Menopausal Status.

All women Premenopausal women Postmenopausal wonen
Controls N Cases N ORb (95% CI) Controls N Cases N OR (95% CI) Controls N Cases N OR (95% CI) Interactiona P value
TEPI (rs938886)
 GG/GC 3645 3221 1.00 1295 1181 1.00 2270 1963 1.00 0.38
 CC 453 311 0.82 (0.70, 0.95) 182 119 0.75 (0.59, 0.97) 266 182 0.82 (0.67, 1.01)
TERF2 (rs3785074)
 AA 2561 2088 1.00 932 781 1.00 1577 1256 1.00 0.74
 AG/GG 1542 1447 1.13 (1.03, 1.24) 547 521 1.09 (0.93, 1.28) 962 890 1.16 (1.03, 1.30)
TERT (rs4246742)
 AA 2323 2177 1.00 798 782 1.00 1479 1343 1.00 0.38
 AT/TT 1766 1349 0.85 (0.77, 0.93) 677 515 0.82 (0.70, 0.96) 1050 799 0.85 (0.76, 0.96)
TERT (rs10069690)
 CC 2568 2093 1.00 956 777 1.00 1558 1262 1.00 0.36
 CT/TT 1518 1429 1.13 (1.03, 1.24) 520 523 1.20 (1.02, 1.40) 968 873 1.11 (0.98, 1.25)
TERT (rs2242652)
 CC/CT 3680 3098 1.00 1290 1143 1.00 2326 1893 1.00 0.13
 TT 78 102 1.51 (1.11, 2.04) 30 33 1.08 (0.65, 1.80) 45 68 1.86 (1.27, 2.74)
TERT (rs2736100)
 TT 1450 1179 1.00 558 467 1.00 863 686 1.00 0.47
 TG 1934 1674 1.03 (0.93, 1.15) 676 606 1.00 (0.85, 1.19) 1219 1023 1.05 (0.92, 1.20)
 GG 714 681 1.10 (0.97, 1.26) 244 228 0.99 (0.79, 1.24) 454 437 1.20 (1.01, 1.42)
TERT (rs2853676)
 GG/GA 3916 3327 1.00 1419 1231 1.00 2417 2015 1.00 0.98
 AA 186 206 1.23 (1.00, 1.51) 59 69 1.19 (0.83, 1.71) 122 131 1.26 (0.98, 1.63)
TNKS (rs699097)
 TT 2560 2211 1.00 906 831 1.00 1608 1325 1.00 0.05
 TC/CC 1538 1320 0.96 (0.87, 1.05) 570 470 0.83 (0.71, 0.98) 929 819 1.06 (0.94, 1.20)
TNKS (rs6990100)
 AA 1545 1462 1.00 520 549 1.00 992 876 1.00 0.03
 AG/GG 2557 2073 0.89 (0.81, 0.97) 959 753 0.76 (0.65, 0.89) 1546 1270 0.94 (0.84, 1.06)
Open in a new tab
a

P value for difference by menopausal status.

b

OR and 95% CI adjusted for age, study, BMI in referent year, vigorous activity in referent year, parity, age at first birth, alcohol consumption, and genetic admixture; Bold text indicates significant at the 0.05 level after adjustment for multiple comparisons; italics indicates adjusted p value between 0.05 and 0.15.

Associations with variants in TEP1 (rs938886), TERF2 (rs3785074), and TNKS (rs6990097 and rs6990300) are shown in Table 4. TEP1 rs938886 was inversely associated with breast cancer risk (OR 0.82, 95% CI 0.70, 0.95), whereas TERF2 rs3785074 was positively associated with risk (OR 1.13, 95% CI 1.03, 1.24). For both TNKS SNPs we found inverse associations, however, for TNKS rs6990097 the association was statistically significant for premenopausal women only (OR 0.83, 95% CI 0.71, 0.98). For TNKS rs6990300, the OR was 0.89 (95% CI 0.81, 0.97) for all women combined.

We also examined associations with breast cancer risk within genetic admixture groups (data in online Supporting Information Table 3). Some associations were significantly different across the admixture groups. MEN1 rs670358 showed slightly greater risk for the AA genotype among women with greater European ancestry (OR 1.39, 95% CI 0.73, 2.64), whereas those with more IA ancestry were at a nonsignificant reduced risk (OR 0.93, 95% CI 0.65, 1.35) (P interaction 0.03). Likewise MRE11A rs13447720 showed a slightly greater nonsignificant risk for those with higher IA ancestry (OR 1.21, 95% CI 0.85, 1.74), whereas for women with higher European ancestry the OR was 0.94 (95% CI 0.82, 1.07) (P interaction 0.04). Other SNPs showed significant associations within strata of admixture, although no significant interactions between admixture groups were noted. Noteworthy associations were observed for TEP1 rs938886 CC versus GG/GC genotypes for intermediate and high IA ancestry groups (OR 0.79, 95% CI 0.63, 0.98; and OR 0.73, 95% CI 0.52, 1.05, respectively), TERT rs4246742 dominant model for higher European ancestry (OR 0.79, 95% CI 0.68, 0.91) and higher IA ancestry (OR 0.76, 95% CI 0.58, 0.99), and TNKS rs6990300 dominant model for those with the highest level of European ancestry (OR 0.86,95% CI 0.75, 0.98) or IA ancestry (OR 0.73, 95% CI 0.54, 0.98).

We observed differences in risk by menopausal status and IA ancestry (Table 5). Among premenopausal women we observed a significant interaction with TERC rs12696394, with a slightly increased risk among those with more European ancestry (OR 1.50, 95% CI 0.95, 2.32) and a reduced risk among those with the most IA ancestry (OR 0.59, 95% CI 0.27, 1.11) (p interaction 0.03).

Table 5. Associations by Admixture Among Premenopausal and Postmenopausal Women Where Significant Differences Between Admixture Groups Were Observed.

Low IA ancestry (0–23%) Intermediate IA ancestry (29–70%) High IA ancestry (71–100%) 2-way
Controls N Cases N OR (95% CI) Controls N Cases N OR (95% CI) Controls N Cases N OR (95% CI) Interaction P value Holms P value
Premenopausal women
MRE11A (rs12270338)
 CC 361 387 1.00 494 380 1.00 235 163 1.00 0.03 0.03
 CA/AA 204 189 0.85 (0.67, 1.09) 147 149 1.30 (0.99, 1.71) 37 32 1.23 (0.71, 2.15)
MREI IA (rs13447720)
 AA 356 372 1.00 505 385 1.00 236 163 1.00 0.04 0.04
 AG/GG 209 204 0.92 (0.72, 1.18) 137 146 1.38 (1.05, 1.82) 36 32 1.27 (0.73, 2.23)
TERC (rs12696304)
 CC 285 276 1.00 158 134 1.00 22 24 1.00 0.03 0.03
 CG 234 235 1.02 (0.80, 1.31) 312 263 1.01 (0.75, 1.35) 122 86 0.63 (0.31, 1.28)
 GG 41 58 1.50 (0.96, 2.32) 169 132 0.97 (0.69, 1.36) 127 84 0.55 (0.27, 1.11)
TNKS (rs1199162I)
 CC 375 408 1.00 527 447 1.00 251 169 1.00 0.03 0.06
 CT/TT 190 168 0.80 (0.62, 1.04) 113 82 0.87 (0.63, 1.20) 21 26 1.57 (0.80, 3.10)
TNKS (rs699097)
 TT 296 336 1.00 411 364 1.00 199 131 1.00 0.05 0.06
 TC/CC 269 239 0.77 (0.61, 0.98) 228 167 0.80 (0.62, 1.03) 73 64 1.25 (0.80, 1.96)
TNKS (rs12549064) 2
 AA 369 411 1.00 528 433 1.00 240 163 1.00 0.01 0.03
 AC/CC 196 165 0.75 (0.58, 0.97) 113 98 1.05 (0.77, 1.43) 32 32 1.38 (0.77, 2.47)
INKS (rs17150478)
 AA 363 410 1.00 412 354 1.00 192 121 1.00 0.003 0.01
 AG/GG 202 166 0.73 (0.57, 0.94) 230 175 0.89 (0.70, 1.15) 80 74 1.59 (1.04, 2.42)
Post-Menopausal Women
MEN1 (rs670358)
 GG 994 880 1.00 462 402 1.00 86 55 1.00 0.02 0.02
 GA 222 221 1.13 (0.92, 1.40) 411 325 0.87 (0.71, 1.06) 163 118 1.24 (0.80, 1.92)
 AA 10 15 1.63 (0.72, 3.67) 94 73 0.82 (0.58, 1.16) 97 55 0.86 (0.52, 1.42)
Open in a new tab

Adjusted for age, study, BMI in referent year, vigorous activity in referent year, parity, age at first birth and alcohol consumption; Bold text indicates significant at the 0.05 level after adjustment for multiple comparisons; italics indicates significant between 0.05 and 0.15

Multiple SNPs were found to be associated with joint ER/PR status of breast cancer tumors (Table 6). RECQL5 rs821052, TERT rs2736118, TERT rs2736100, TERT rs4246742, TERT rs10069690, TERT rs2735940, TNKS rs6990097, TNKS rs10903314, and TNKS rs17150478 were all associated with ER/PR status. TERT rs10069690 was found to be associated with ER+/PR+ tumors (OR 1.17, 95% CI 1.03, 1.34). RECQL5 rs821052 was associated with ER+/PR− tumors (OR 1.46, 95% CI 1.08, 1.98) and TERT rs2736118 with ER−/PR+ tumors (OR 6.18, 95% CI 2.90, 13.19). TERT rs4246742 (OR 0.80, 95% CI 0.64, 0.99), TERT rs2736100 (OR 0.79 95% CI 0.63, 0.98), TERT rs27359400 (OR 0.73 95% CI 0.59, 0.91), TNKS rs6990097 (OR 0.78, 95% CI 0.63, 0.98), TNKS rs10903314 (OR 0.79, 95% CI 0.64, 0.99), and TNKS rs17150478 (OR 0.76, 95% CI 0.61, 0.96) were all associated with ER−/PR− tumors only. Although data for TL and ER/PR status of breast cancer tumors were only available for Utah and thus we had limited power to detect an association, we did not see an association between TL and ER/PR status.

Table 6. Associations Between ER and PR Status of Tumors and Telomere-Related Genes.

Controls N Cases N ER+PR+ Cases N ER+/PR− Cases N ER−/PR+ Cases N ER−/PR−
ORa (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
RECQL5 (rs821052)
 TT 1270 503 1.00 69 1.00 18 1.00 143 1.00
 TC/CC 1611 648 1.00 (0.87, 1.15) 131 1.46 (1.08, 1.98) 17 0.76 (0.39, 1.49) 198 1.09 (0.87, 1.37)
TERT (rs2736118)
 AA 1909 787 1.00 138 1.00 9 1.00 236 1.00
 AG/GG 1207 505 0.98 (0.85, 1.12) 97 1.11 (0.84, 1.46) 31 6.18 (2.90, 13.19) 174 1.21 (0.97, 1.49)
TERT (rs2736100)
 TT 986 371 1.00 70 1.00 9 1.00 154 1.00
 TG/GG 2134 921 1.12 (0.97, 1.29) 165 1.09 (0.81, 1.47) 32 1.90 (0.89, 4.04) 256 0.79 (0.63, 0.98)
TERT (rs2735940)
 CC 958 379 1.00 62 1.00 11 1.00 157 1.00
 CT/TT 2159 912 1.03 (0.89, 1.19) 173 1.23 (0.91, 1.67) 30 1.38 (0.68, 2.80) 254 0.73 (0.59, 0.91)
TNKS (rs6990097)
 TT 1866 781 1.00 147 1.00 26 1.00 268 1.00
 TC/CC 1254 510 0.95 (0.83, 1.08) 87 0.88 (0.66, 1.16) 15 0.85 (0.44, 1.62) 142 0.78 (0.63, 0.98)
TNKS (rs10903314)
 CC 1900 804 1.00 152 1.00 28 1.00 273 1.00
 CT/TT 1224 488 0.93 (0.81, 1.06) 83 0.84 (0.64, 1.11) 13 0.73 (0.37, 1.42) 138 0.79 (0.64, 0.99)
TNKS (rsl7150478)
 AA 2102 902 1.00 157 1.00 28 1.00 298 1.00
 AG/GG 1021 388 0.89 (0.77, 1.02) 78 1.03 (0.78, 1.37) 13 0.94 (0.48, 1.83) 111 0.76 (0.61, 0.96)
Open in a new tab
a

OR and 95% CI adjusted for age, study, BMI in referent year, vigorous activity in referent year, parity, age at first birth, genetic admixture, and alcohol consumption; Bold text indicates significant at the 0.05 level after adjustment for multiple comparisons; italics indicates adjusted P value between 0.05 and 0.10.

Discussion

In this study, we found that longer TL was associated with increased breast cancer risk. The magnitude of this association was dependent on level of IA ancestry, with risk being highest among those with more IA ancestry. We also observed multiple associations between SNPs in telomere biology-related genes and breast cancer. SNPs in TERT, TEP1, TERF2, and TNKS were positively associated with risk. Additionally, we found that SNPs in MEN1 and MRE11A had significantly different associations with breast cancer risk across admixture groups. TEP1, TERT, TERF2, and TNKS were observed to have significant associations within strata of admixture groups, although no interaction between specific groups was observed. Several SNPs were also found to be associated with ER/PR tumor status, with the majority of these SNPs showing associations with ER−/PR− tumors.

Earlier reports on the relationship between TL and breast cancer have been mixed. Some studies failed to observe an association between TL and sporadic breast cancer risk, or between TL and hereditary breast cancer (Zheng et al., 2010; Kim et al., 2011; Yanowsky et al., 2012). One study reported an association between shorter TL and breast cancer risk in families carrying mutations in BRCA1 or BRCA2 but not among families without these inherited mutations (Martinez-Delgado et al., 2011). Consistent with our results, other studies found that longer TL is associated with both sporadic and familial breast cancer (Svenson et al., 2008; Gramatges et al., 2010). Meta-analyses of TL data and cancer further support the inconsistency of data on associations between TL and cancer, although they have summarized that shortened TL in most often associated with increased cancer risk (Ma et al., 2011; Wentzensen et al., 2011). Importantly, we found the association with TL was strongest among those women with greater IA ancestry. This is the first report on TL and breast cancer risk evaluating IA ancestry, which appears to be an important modifier of risk in this study. Further evaluation of factors that influence TL among those with more IA ancestry may shed additional light on this association.

TL in both cancer tissue and in blood leukocytes has been associated with cancer (Meeker, 2006; Han et al., 2009; Lan et al., 2009; Mirabello et al., 2010a,b; Nan et al., 2011; Cui et al., 2012; Pellatt et al., 2012), although it appears that the direction of association between TL and cancer may be cancer-specific. For instance shorter leukocyte telomeres appear to be associated with risk of colon, ovarian, and prostate cancer, whereas longer telomeres have been associated with breast, melanoma, and hepatocellular cancer, melanoma, and non-Hodgkin's lymphoma. It has been proposed that TL may influence cancer by maintaining genomic stability. Genetic instability has been linked to a variety of cancers, including breast cancer (Miki, 2012; Sens-Abuazar et al., 2012). Telomeres maintain genomic stability and integrity by ensuring that DNA is not lost during the replication of chromosomes and by protecting the ends of chromosomes from being recognized by repair enzymes as DSBs which are targeted for repair. Telomeres are also responsible for preventing the improper joining of chromosomes. When telomeres reach a critical length, the cell undergoes apoptosis or senescence. However, alterations that result in telomeres lengthening could prevent cells from undergoing senescence or apoptosis and allows cells to continuously regenerate, which could ultimately result in cancer development. In this scenario, increased number of cell cycles associated with longer telomeres could result in greater mutation potential. Some studies have suggested that both long and short TL may alter risk of colorectal cancer (Cui et al., 2012; Pellatt et al., 2012), implying that mechanisms that involve both genomic instability and increased mutation potential are viable. Data also have found increased telomerase activity, often associated with survival in multiple types of cancer tissue (Meeker and Argani, 2004; Bautista et al., 2007; Lu et al., 2011), further suggesting a role for telomere-related factors in the etiology and progression of cancer. Leukocyte TL may indicate susceptibility to cancer risk, that is defined in the biology of the tumors themselves. Studies have shown that an individual's TL is tissue independent and that TL in blood is a surrogate for relative TL in other tissues (Friedrich et al., 2000).

Previous data found that TERT, TERC, MEN1, MRE11A, RECQL5, TNKS, TNKS2, TEP1 and TERF2 are all associated with TL (Chiang et al., 2006; Varadi et al., 2009; Mirabello et al., 2010a,b; Jones et al., 2012). We were only able to confirm an association between TL and TERT rs2736118 and TNKS rs10509637 when evaluating associations among controls. We did not observe unique differences in TL by genotype and by admixture group. Our inability to confirm previously reported associations could stem from the younger age range of our population, whereas most other studies included women who were predominately over age 60 years. We did observe differences in TL for TERT, MRE11A, and TNKS by menopausal status after adjustment for age, lending some support for this hypothesis. We also adjusted for potential confounding factors, such as BMI, which could alter associations. It is possible that other lifestyle factors that are associated with TL could differ between our study population and those previously reported and influence associations between TL-related SNPs and TL.

Variants in TERT (Savage et al., 2007; Shen et al., 2012), TEP1 (Salhab et al., 2008), TERF2 (Savage et al., 2007; Varadi et al., 2009), RECQL5 (Islam et al., 2012), TNKS2 (Varadi et al., 2009), MEN1 (Imachi et al., 2010) have previously been associated with breast cancer, whereas associations between variants in MRE11A, and TNKS and breast cancer are inconclusive. We replicated earlier reports of associations between TERT, TEP1, and TERF2 and breast cancer. Additionally, we found an association between TNKS and breast cancer risk. However, we failed to replicate earlier findings for RECQL5, TNKS2, and MEN1.

The associations between these SNPs and breast cancer have genetic plausibility. TERT is a functional unit of the telomerase enzyme, which is responsible for the maintenance of TL. Telomerase activity is usually absent from differentiated cells; however, it has been previously observed in multiple types of cancer cells, including breast cancer (Bautista et al., 2007; Hofer et al., 2011; Mohajeri et al., 2011; Jones et al., 2012). We found that multiple TERT SNPs are directly associated with breast cancer risk, indicating that these polymorphisms might be involved in the activation of telomerase. However, one TERT SNP (rs4246742) showed an inverse association with breast cancer, suggesting that this SNP might inhibit telomerase expression in differentiated cells. TEP1 is another component of the telomerase ribonucleoprotein complex; it is responsible for catalyzing the addition of new telomeres to chromosomes. We found that TEP1 rs938886 is inversely associated with breast cancer, suggesting that this SNP might inhibit telomerase activity in differentiated cells. TERF2 plays a role in the prevention of end to end ligation of chromosomes by telomeres. This allows TERF2 to protect the integrity of the telomere, and therefore, genomic stability. Polymorphisms within this gene could lead to genomic instability, which has been linked to breast cancer (Miki, 2012; Sens-Abuazar et al., 2012). TNKS enhances the access of telomerase to telomeres (Gao et al., 2011), thereby playing a role in telomere maintenance. We found an inverse association with two TNKS SNPs (rs6990097 and rs6990300), suggesting that certain polymorphisms can inhibit its ability to increase the access of telomerase to telomeres and therefore prevents telomerase from maintaining chromosomes in differentiated somatic cells. However, the associations observed between SNPs and TL may stem from non-telomere related mechanisms (Epel et al., 2004; Wolkowitz et al., 2011; Ghosh et al., 2012). For instance, Ghosh has demonstrated that telomerase expression is associated with signaling via NFκB, a regulator of inflammation (Ghosh et al., 2012). Others have documented that hTERT can enhance cell proliferation, decrease apoptosis, regulate DNA damage response, and alter cell proliferation lifespan (Mukherjee et al., 2011). These functions of hTERT provide mechanisms for telomere-related genes to alter cancer risk that is non-telomere related.

To our knowledge this is the first study to examine the effects of telomere-biology related SNPs on breast cancer within genetic admixture groups. We have previously reported higher risk of breast cancer associated with more Europeans ancestry and a lower risk associated with more IA ancestry (Akman et al., 2009; Lurje et al., 2009; Slattery et al., 2012). Moreover, these earlier studies have found heterogeneity in the magnitude and the direction of genetic associations with breast cancer among Hispanic women. We found a few modest associations with breast cancer risk when evaluating telomere-related genes by genetic admixture groups. MEN1 rs670358 was associated with a slightly greater risk in NHW women which is consistent with earlier findings of increased breast cancer risk among women of higher European ancestry. We also found that MRE11A rs1344720 was associated with increased risk among individuals with higher IA ancestry. Other associations were observed for overall breast cancer risk; however, no significant interactions between admixture groups were noted for these SNPs.

Seven SNPs were associated with ER/PR status, and of these SNPs five were associated with the ER−/PR− subtype. This is the first study to suggest that the ER−/PR− tumor subtype might be influenced by TL. This is significant because previous studies have found that women with ER−/PR− tumors have lower survival rates (Onitilo et al., 2009). We had limited power to assess TL with ER/PR status.

Our study has several strengths, including the large admixed population, which allowed us to evaluate breast cancer risk among Hispanic women with a wide range of IA American ancestry. Additionally, we evaluated associations with breast cancer risk overall as well as by tumor phenotype while adjusting for potentially confounding variables. Although the study was population-based, not all eligible women participated. However, it is unlikely that genotypes of telomere-related genes influenced participation.

In addressing our findings, it is important to consider the timing of the blood draw for the case-control studies in which we examined TL. Blood was drawn from the Mexico samples prior to treatment; in Utah blood was drawn after treatment. Associations between TL and breast cancer were strongest among those with higher Native American ancestry, that is, Mexico. This finding would suggest that differences in associations are not from treatment as has been hypothesized by Pooley as one possible explanation for differences in association observed between their cohort and case–control study participants (Pooley et al., 2010). The lack of association between treatment and TL is supported by Pooley that shows no association between any one type of treatment and TL (Pooley's online Supporting Information). Differences in association between prospective and retrospective studies could stem from changes in lifestyle factors that influence TL between the time that blood was drawn and TL assessment since lifestyle factors have been associated with TL (Mirabello et al., 2009; Puterman et al., 2010; Pellatt et al., 2012).

In summary, our study strongly suggests that both TL and telomere-related genes influence breast cancer risk in NHW and Hispanic women. The association with TL appears to be modified by IA ancestry. ER/PR tumor status seems to be an important modifier of the associations with telomere-related genes and breast cancer risk. Further exploration of factors that may be associated with both breast cancer and TL among admixed populations such as this may provide further insight into breast cancer etiology.

Supplementary Material

online supplement

Acknowledgments

The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute or endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors. The authors thank the following individuals for their contributions to the study: Sandra Edwards for data harmonization oversight; Erica Wolff and Michael Hoffman for laboratory support; Carolina Ortega for her assistance with data management for the Mexico Breast Cancer Study, Jocelyn Koo for data management for the San Francisco Bay Area Breast Cancer Study. Dr. Tim Byers for his contribution to the 4-Corner's Breast Cancer Study, Dr. Josh Galanter for assistance in selection of AIMs markers for the study, Dr. Elad Ziv for his input into the study, and Drs. Sue Ingles and Wei Wang for contribution to the study.

Supported by: National Cancer Institute, Grant numbers: CA14002; National Cancer Institute, Grant numbers: CA63446 and CA77305; U.S. Department of Defense, Grant number: DAMD17-96-1-6071; California Breast Cancer Research Program, Grant number: 7PB-0068; California Department of Public Health (part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885); National Cancer Institute's Surveillance, Epidemiology and End Results Program, Grant number: HHSN261201000036C; Centers for Disease Control and Prevention's National Program of Cancer Registries, Grant number: 1U58 DP000807-01; National Cancer Institute, Grant numbers: CA078682, CA078762, CA078552, and CA078802; Utah Cancer Registry (National Cancer Institute), Grant number: N01-PC-67000; State of Utah Department of Health; New Mexico Tumor Registry; Arizona and Colorado cancer registries; Centers for Disease Control and Prevention National Program of Cancer Registries (and additional state support); Consejo Nacional de Ciencia y Tecnología (CONACyT), Grant number: SALUD-2002-C01-7462.

Footnotes

Additional Supporting Information may be found in the online version of this article.

References

  1. Akman A, Yilmaz E, Mutlu H, Ozdogan M. Complete remission of psoriasis following bevacizumab therapy for colon cancer. Clin Exp Dermatol. 2009;34:e202–e204. doi: 10.1111/j.1365-2230.2008.02991.x. [DOI] [PubMed] [Google Scholar]
  2. Bautista CV, Felis CP, Espinet JM, Garcia JB, Salas JV. Telomerase activity is a prognostic factor for recurrence and survival in rectal cancer. Dis Colon Rectum. 2007;50:611–620. doi: 10.1007/s10350-006-0820-y. [DOI] [PubMed] [Google Scholar]
  3. Cassidy A, De Vivo I, Liu Y, Han J, Prescott J, Hunter DJ, Rimm EB. Associations between diet, lifestyle factors, and telomere length in women. Am J Clin Nutr. 2010;91:1273–1280. doi: 10.3945/ajcn.2009.28947. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. 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]
  5. Chiang YJ, Nguyen ML, Gurunathan S, Kaminker P, Tessarollo L, Campisi J, Hodes RJ. Generation and characterization of telomere length maintenance in tankyrase 2-deficient mice. Mol Cell Biol. 2006;26:2037–2043. doi: 10.1128/MCB.26.6.2037-2043.2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cui Y, Cai Q, Qu S, Chow WH, Wen W, Xiang YB, Wu J, Rothman N, Yang G, Shu XO, Gao YT, Zheng W. Association of leukocyte telomere length with colorectal cancer risk: nested case-control findings from the shanghai women's health study. Cancer Epidemiol Biomarkers Prev. 2012;21:1807–1813. doi: 10.1158/1055-9965.EPI-12-0657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Epel ES, Blackburn EH, Lin J, Dhabhar FS, Adler NE, Morrow JD, Cawthon RM. Accelerated telomere shortening in response to life stress. Proc Natl Acad Sci USA. 2004;101:17312–17315. doi: 10.1073/pnas.0407162101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics. 2003;164:1567–1587. doi: 10.1093/genetics/164.4.1567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Friedrich U, Griese E, Schwab M, Fritz P, Thon K, Klotz U. Telomere length in different tissues of elderly patients. Mech Ageing Dev. 2000;119:89–99. doi: 10.1016/s0047-6374(00)00173-1. [DOI] [PubMed] [Google Scholar]
  10. Gao J, Zhang J, Long Y, Tian Y, Lu X. Expression of tank yrase 1 in gastric cancer and its correlation with telomerase activity. Pathol Oncol Res. 2011;17:685–690. doi: 10.1007/s12253-011-9369-8. [DOI] [PubMed] [Google Scholar]
  11. Gelmini S, Poggesi M, Distante V, Bianchi S, Simi L, Luconi M, Raggi CC, Cataliotti L, Pazzagli M, Orlando C. Tankyrase, a positive regulator of telomere elongation, is over expressed in human breast cancer. Cancer Lett. 2004;216:81–87. doi: 10.1016/j.canlet.2004.05.010. [DOI] [PubMed] [Google Scholar]
  12. Ghosh A, Saginc G, Leow SC, Khattar E, Shin EM, Yan TD, Wong M, Zhang Z, Li G, Sung WK, Zhou J, Chng WJ, Li S, Liu E, Tergaonkar V. Telomerase directly regulates NF-kappaB-dependent transcription. Nat Cell Biol. 2012;14:1270–1281. doi: 10.1038/ncb2621. [DOI] [PubMed] [Google Scholar]
  13. Gramatges MM, Telli ML, Balise R, Ford JM. Longer relative telomere length in blood from women with sporadic and familial breast cancer compared with healthy controls. Cancer Epidemiol Biomarkers Prev. 2010;19:605–613. doi: 10.1158/1055-9965.EPI-09-0896. [DOI] [PubMed] [Google Scholar]
  14. Han J, Qureshi AA, Prescott J, Guo Q, Ye L, Hunter DJ, De Vivo I. A prospective study of telomere length and the risk of skin cancer. J Invest Dermatol. 2009;129:415–421. doi: 10.1038/jid.2008.238. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hoen PW, de Jonge P, Na BY, Farzaneh-Far R, Epel E, Lin J, Blackburn E, Whooley MA. Depression and leukocyte telomere length in patients with coronary heart disease: Data from the heart and soul study. Psychosom Med. 2011;73:541–547. doi: 10.1097/PSY.0b013e31821b1f6e. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hofer P, Baierl A, Feik E, Fuhrlinger G, Leeb G, Mach K, Holzmann K, Micksche M, Gsur A. MNS16A tandem repeats minisatellite of human telomerase gene: A risk factor for colorectal cancer. Carcinogenesis. 2011;32:866–871. doi: 10.1093/carcin/bgr053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Imachi H, Murao K, Dobashi H, Bhuyan MM, Cao X, Kontani K, Niki S, Murazawa C, Nakajima H, Kohno N, Yamashita H, Iwase H, Hayashi S, Ishida T, Yamauchi A. Menin, a product of the MENI gene, binds to estrogen receptor to enhance its activity in breast cancer cells: Possibility of a novel predictive factor for tamoxifen resistance. Breast Cancer Res Treat. 2010;122:395–407. doi: 10.1007/s10549-009-0581-0. [DOI] [PubMed] [Google Scholar]
  18. Islam MN, Paquet N, Fox D, Dray E, Zheng XF, Klein H, Sung P, Wang W. A variant of the breast cancer type 2 susceptibility protein (BRC) repeat is essential for the RECQL5 helicase to interact with RAD51 recombinase for genome stabilization. J Biol Chem. 2012;287:23808–23818. doi: 10.1074/jbc.M112.375014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. John EM, Horn-Ross PL, Koo J. Lifetime physical activity and breast cancer risk in a multiethnic population: The San Francisco Bay area breast cancer study. Cancer Epidemiol Biomarkers Prev. 2003;12:1143–1152. [PubMed] [Google Scholar]
  20. John EM, Phipps AI, Davis A, Koo J. Migration history, acculturation, and breast cancer risk in Hispanic women. Cancer Epidemiol Biomarkers Prev. 2005;14:2905–2913. doi: 10.1158/1055-9965.EPI-05-0483. [DOI] [PubMed] [Google Scholar]
  21. Jones AM, Beggs AD, Carvajal-Carmona L, Farrington S, Tenesa A, Walker M, Howarth K, Ballereau S, Hodgson SV, Zauber A, Bertagnolli M, Midgley R, Campbell H, Kerr D, Dunlop MG, Tomlinson IP. TERC polymorphisms are associated both with susceptibility to colorectal cancer and with longer telomeres. Gut. 2012;61:248–254. doi: 10.1136/gut.2011.239772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kim S, Sandler DP, Carswell G, De Roo LA, Parks CG, Cawthon R, Weinberg CR, Taylor JA. Telomere length in peripheral blood and breast cancer risk in a prospective case-cohort analysis: Results from the Sister Study. Cancer Causes Control. 2011;22:1061–1066. doi: 10.1007/s10552-011-9778-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Lan Q, Cawthon R, Shen M, Weinstein SJ, Virtamo J, Lim U, Hosgood HD, 3rd, Albanes D, Rothman N. A prospective study of telomere length measured by monochrome multiplex quantitative PCR and risk of non-Hodgkin lymphoma. Clin Cancer Res. 2009;15:7429–7433. doi: 10.1158/1078-0432.CCR-09-0845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity. 2005;95:221–227. doi: 10.1038/sj.hdy.6800717. [DOI] [PubMed] [Google Scholar]
  25. Lin J, Epel E, Cheon J, Kroenke C, Sinclair E, Bigos M, Wolkowitz O, Mellon S, Blackburn E. Analyses and comparisons of telomerase activity and telomere length in human T and B cells: Insights for epidemiology of telomere maintenance. J Immunol Methods. 2010;352:71–80. doi: 10.1016/j.jim.2009.09.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Loizidou MA, Cariolou MA, Neuhausen SL, Newbold RF, Bashiardes E, Marcou Y, Michael T, Daniel M, Kakouri E, Papadopoulos P, Malas S, Hadjisavvas A, Kyriacou K. Genetic variation in genes interacting with BRCA1/2 and risk of breast cancer in the Cypriot population. Breast Cancer Res Treat. 2010;121:147–156. doi: 10.1007/s10549-009-0518-7. [DOI] [PubMed] [Google Scholar]
  27. Lu L, Zhang C, Zhu G, Irwin M, Risch H, Menato G, Mitidieri M, Katsaros D, Yu H. Telomerase expression and telomere length in breast cancer and their associations with adjuvant treatment and disease outcome. Breast Cancer Res. 2011;13:R56. doi: 10.1186/bcr2893. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lurje G, Hendifar AE, Schultheis AM, Pohl A, Husain H, Yang D, Manegold PC, Ning Y, Zhang W, Lenz HJ. Polymorphisms in interleukin 1 beta and interleukin 1 receptor antagonist associated with tumor recurrence in stage II colon cancer. Pharmacogenet Genom. 2009;19:95–102. doi: 10.1097/FPC.0b013e32831a9ad1. [DOI] [PubMed] [Google Scholar]
  29. Ma H, Zhou Z, Wei S, Liu Z, Pooley KA, Dunning AM, Svenson U, Roos G, Hosgood HD, 3rd, Shen M, Wei Q. Shortened telomere length is associated with increased risk of cancer: A meta-analysis. PLoS One. 2011;6:e20466. doi: 10.1371/journal.pone.0020466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Martinez-Delgado B, Yanowsky K, Inglada-Perez L, Domingo S, Urioste M, Osorio A, Benitez J. Genetic anticipation is associated with telomere shortening in hereditary breast cancer. PLoS Genet. 2011;7:e1002182. doi: 10.1371/journal.pgen.1002182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. McCabe N, Cerone MA, Ohishi T, Seimiya H, Lord CJ, Ashworth A. Targeting Tankyrase 1 as a therapeutic strategy for BRCA-associated cancer. Oncogene. 2009;28:1465–1470. doi: 10.1038/onc.2008.483. [DOI] [PubMed] [Google Scholar]
  32. Meeker AK. Telomeres and telomerase in prostatic intraepithelial neoplasia and prostate cancer biology. Urol Oncol. 2006;24:122–130. doi: 10.1016/j.urolonc.2005.11.002. [DOI] [PubMed] [Google Scholar]
  33. Meeker AK, Argani P. Telomere shortening occurs early during breast tumorigenesis: A cause of chromosome destabilization underlying malignant transformation? J Mammary Gland Biol Neoplasia. 2004;9:285–296. doi: 10.1023/B:JOMG.0000048775.04140.92. [DOI] [PubMed] [Google Scholar]
  34. Miki Y. Cellular functions of BRCA genes - from basic science to therapeutics. Gan To Kagaku Ryoho. 2012;39:498–501. [PubMed] [Google Scholar]
  35. Mirabello L, Huang WY, Wong JY, Chatterjee N, Reding D, Crawford ED, De Vivo I, Hayes RB, Savage SA. The association between leukocyte telomere length and cigarette smoking, dietary and physical variables, and risk of prostate cancer. Aging Cell. 2009;8:405–413. doi: 10.1111/j.1474-9726.2009.00485.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Mirabello L, Garcia-Closas M, Cawthon R, Lissowska J, Brinton LA, Peplonska B, Sherman ME, Savage SA. Leukocyte telomere length in a population-based case-control study of ovarian cancer: A pilot study. Cancer Causes Control. 2010a;21:77–82. doi: 10.1007/s10552-009-9436-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Mirabello L, Yu K, Kraft P, De Vivo I, Hunter DJ, Prescott J, Wong JY, Chatterjee N, Hayes RB, Savage SA. The association of telomere length and genetic variation in telomere biology genes. Hum Mutat. 2010b;31:1050–1058. doi: 10.1002/humu.21314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Mirabello L, Richards EG, Duong LM, Yu K, Wang Z, Cawthon R, Berndt SI, Burdett L, Chowdhury S, Teshome K, Douglass C, Savage SA. Telomere length and variation in telomere biology genes in individuals with osteosarcoma. Int J Mol Epidemiol Genet. 2011;2:19–29. [PMC free article] [PubMed] [Google Scholar]
  39. Mohajeri A, Zarghami N, Pourhasan Moghadam M, Alani B, Montazeri V, Baiat A, Fekhrjou A. Prostate-specific antigen gene expression and telomerase activity in breast cancer patients: Possible relationship to steroid hormone receptors. Oncol Res. 2011;19:375–380. doi: 10.3727/096504011x13123323849636. [DOI] [PubMed] [Google Scholar]
  40. Mukherjee S, Firpo EJ, Wang Y, Roberts JM. Separation of telomerase functions by reverse genetics. Proc Natl Acad Sci USA. 2011;108:E1363–E1371. doi: 10.1073/pnas.1112414108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Nan H, Du M, De Vivo I, Manson JE, Liu S, McTiernan A, Curb JD, Lessin LS, Bonner MR, Guo Q, Qureshi AA, Hunter DJ, Han J. Shorter telomeres associate with a reduced risk of melanoma development. Cancer Res. 2011;71:6758–6763. doi: 10.1158/0008-5472.CAN-11-1988. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Njajou OT, Hsueh WC, Blackburn EH, Newman AB, Wu SH, Li R, Simonsick EM, Harris TM, Cummings SR, Cawthon RM. Association between telomere length, specific causes of death, and years of healthy life in health, aging, and body composition, a population-based cohort study. J Gerontol A Biol Sci Med Sci. 2009;64:860–864. doi: 10.1093/gerona/glp061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. O'Donovan A, Pantell MS, Puterman E, Dhabhar FS, Blackburn EH, Yaffe K, Cawthon RM, Opresko PL, Hsueh WC, Satterfield S, Newman AB, Ayonayon HN, Rubin SM, Harris TB, Epel ES. Cumulative inflammatory load is associated with short leukocyte telomere length in the health, aging and body composition study. PLoS One. 2011;6:e19687. doi: 10.1371/journal.pone.0019687. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Onitilo AA, Engel JM, Greenlee RT, Mukesh BN. Breast cancer subtypes based on ER/PR and Her2 expression: Comparison of clinicopathologic features and survival. Clin Med Res. 2009;7:4–13. doi: 10.3121/cmr.2009.825. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Pellatt AJ, Wolff RK, Lundgreen A, Cawthon R, Slattery ML. Genetic and lifestyle influence on telomere length and subsequent risk of colon cancer in a case control study. Int J Mol Epidemiol Genet. 2012;3:184–194. [PMC free article] [PubMed] [Google Scholar]
  46. Pooley KA, Sandhu MS, Tyrer J, Shah M, Driver KE, Luben RN, Bingham SA, Ponder BA, Pharoah PD, Khaw KT, Easton DF, Dunning AM. 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]
  47. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–959. doi: 10.1093/genetics/155.2.945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Puterman E, Lin J, Blackburn E, O'Donovan A, Adler N, Epel E. The power of exercise: Buffering the effect of chronic stress on telomere length. PLoS One. 2010;5:e10837. doi: 10.1371/journal.pone.0010837. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Rebbeck TR, Mitra N, Domchek SM, Wan F, Friebel TM, Tran TV, Singer CF, Tea MK, Blum JL, Tung N, Olopade OI, Weitzel JN, Lynch HT, Snyder CL, Garber JE, Antoniou AC, Peock S, Evans DG, Paterson J, Kennedy MJ, Donaldson A, Dorkins H, Easton DF, Rubinstein WS, Daly MB, Isaacs C, Nevanlinna H, Couch FJ, Andrulis IL, Freidman E, Laitman Y, Ganz PA, Tomlinson GE, Neuhausen SL, Narod SA, Phelan CM, Greenberg R, Nathanson KL. Modification of BRCA1-associated breast and ovarian cancer risk by BRCA1-interacting genes. Cancer Res. 2011;71:5792–5805. doi: 10.1158/0008-5472.CAN-11-0773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Salhab M, Jiang WG, Newbold RF, Mokbel K. The expression of gene transcripts of telomere-associated genes in human breast cancer: Correlation with clinico-pathological parameters and clinical outcome. Breast Cancer Res Treat. 2008;109:35–46. doi: 10.1007/s10549-007-9622-8. [DOI] [PubMed] [Google Scholar]
  51. Savage SA, Chanock SJ, Lissowska J, Brinton LA, Richesson D, Peplonska B, Bardin-Mikolajczak A, Zatonski W, Szeszenia-Dab-rowska N, Garcia-Closas M. Genetic variation in five genes important in telomere biology and risk for breast cancer. Br J Cancer. 2007;97:832–836. doi: 10.1038/sj.bjc.6603934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Seinost G, Renner W, Brodmann M, Winkler M, Koppel H, Pilger E. C677T mutation in the methylene tetrahydrofolate reductase gene as a risk factor for venous thrombotic disease in Austrian patients. Thromb Res. 2000;100:405–407. doi: 10.1016/s0049-3848(00)00341-8. [DOI] [PubMed] [Google Scholar]
  53. Sens-Abuazar C, Napolitano EFE, Osorio CA, Krepischi AC, Ricca TI, Castro NP, da Cunha IW, Maciel Mdo S, Rosenberg C, Brentani MM, Soares FA, Rocha RM, Carraro DM. Down-regulation of ANAPC13 and CLTCL1: Early events in the progression of preinvasive ductal carcinoma of the Breast. Transl Oncol. 2012;5:113–123. doi: 10.1593/tlo.11280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Shammas MA. Telomeres, lifestyle, cancer, and aging. Curr Opin Clin Nutr Metab Care. 2011;14:28–34. doi: 10.1097/MCO.0b013e32834121b1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Shen J, Gammon MD, Terry MB, Bradshaw PT, Wang Q, Teitelbaum SL, Neugut AI, Santella RM. Genetic polymorphisms in telomere pathway genes, telomere length, and breast cancer survival. Breast Cancer Res Treat. 2012;134:393–400. doi: 10.1007/s10549-012-2058-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Slattery ML, Sweeney C, Edwards S, Herrick J, Baumgartner K, Wolff R, Murtaugh M, Baumgartner R, Giuliano A, Byers T. Body size, weight change, fat distribution and breast cancer risk in Hispanic and non-Hispanic white women. Breast Cancer Res Treat. 2007;102:85–101. doi: 10.1007/s10549-006-9292-y. [DOI] [PubMed] [Google Scholar]
  57. Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Herrick JS, Baumgartner KB, Hines LM, Stern MC, Wolff RK. Genetic variation in genes involved in hormones, inflammation and energetic factors and breast cancer risk in an admixed population. Carcinogenesis. 2012;33:1512–1521. doi: 10.1093/carcin/bgs163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Suarez MM, Bautista RM, Almela M, Soriano A, Marco F, Bosch J, Martinez JA, Bove A, Trilla A, Mensa J. Listeria monocytogenes bacteremia: Analysis of 110 episodes. Med Clin. 2007;129:218–221. doi: 10.1157/13107920. [DOI] [PubMed] [Google Scholar]
  59. Svenson U, Nordfjall K, Stegmayr B, Manjer J, Nilsson P, Tavelin B, Henriksson R, Lenner P, Roos G. Breast cancer survival is associated with telomere length in peripheral blood cells. Cancer Res. 2008;68:3618–3623. doi: 10.1158/0008-5472.CAN-07-6497. [DOI] [PubMed] [Google Scholar]
  60. Varadi V, Brendle A, Brandt A, Johansson R, Enquist K, Henriksson R, Svenson U, Tavelin B, Roos G, Hemminki K, Lenner P, Forsti A. Polymorphisms in telomere-associated genes, breast cancer susceptibility and prognosis. Eur J Cancer. 2009;45:3008–3016. doi: 10.1016/j.ejca.2009.08.012. [DOI] [PubMed] [Google Scholar]
  61. Wentzensen IM, Mirabello L, Pfeiffer RM, Savage SA. 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]
  62. Wolkowitz OM, Mellon SH, Epel ES, Lin J, Dhabhar FS, Su Y, Reus VI, Rosser R, Burke HM, Kupferman E, Compagnone M, Nelson JC, Blackburn EH. Leukocyte telomere length in major depression: Correlations with chronicity, inflammation and oxidative stress--preliminary findings. PLoS One. 2011;6:e17837. doi: 10.1371/journal.pone.0017837. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Yanowsky K, Barroso A, Osorio A, Urioste M, Benitez J, Martinez-Delgado B. Mutational analysis of telomere genes in BRCA1/2-negative breast cancer families with very short telomeres. Breast Cancer Res Treat. 2012;134:1337–1343. doi: 10.1007/s10549-012-2141-2. [DOI] [PubMed] [Google Scholar]
  64. Zheng YL, Ambrosone C, Byrne C, Davis W, Nesline M, McCann SE. Telomere length in blood cells and breast cancer risk: Investigations in two case-control studies. Breast Cancer Res Treat. 2010;120:769–775. doi: 10.1007/s10549-009-0440-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

online supplement
NIHMS516590-supplement-online_supplement.doc (384.5KB, doc)

RESOURCES