Background: Both shorter and longer telomeres in peripheral blood leukocyte (PBL) DNA have been associated with cancer risk. However, associations remain inconsistent across studies of the same cancer type. This study compares DNA preparation methods to determine telomere length from patients with colorectal cancer.

Methods: We examined PBL relative telomere length (RTL) measured by quantitative PCR (qPCR) in 1,033 patients with colorectal cancer and 2,952 healthy controls. DNA was extracted with phenol/chloroform, PureGene, or QIAamp.

Results: We observed differences in RTL depending on DNA extraction method (P < 0.001). Phenol/chloroform-extracted DNA had a mean RTL (T/S ratio) of 0.78 (range 0.01–6.54) compared with PureGene-extracted DNA (mean RTL of 0.75; range 0.00–12.33). DNA extracted by QIAamp yielded a mean RTL of 0.38 (range 0.02–3.69). We subsequently compared RTL measured by qPCR from an independent set of 20 colorectal cancer cases and 24 normal controls in PBL DNA extracted by each of the three extraction methods. The range of RTL measured by qPCR from QIAamp-extracted DNA (0.17–0.58) was less than from either PureGene or phenol/chloroform (ranges, 0.04–2.67 and 0.32–2.81, respectively).

Conclusions: RTL measured by qPCR from QIAamp-extracted DNA was less than from either PureGene or phenol/chloroform (P < 0.001).

Impact: Differences in DNA extraction method may contribute to the discrepancies between studies seeking to find an association between the risk of cancer or other diseases and RTL. Cancer Epidemiol Biomarkers Prev; 22(11); 2047–54. ©2013 AACR.

Constitutional telomere length and its association with cancer risk has been the focus of multiple studies seeking to identify predictive and prognostic biomarkers for malignancy, targets for prevention and treatment, and a better understanding of the mechanisms underlying carcinogenesis. Telomeres are tandem repeated DNA sequences (TTAGGG) that cap linear chromosomes and protect them from unraveling; telomeres shorten with each cell division and with oxidative damage (1). In healthy cells, telomere-shortening results in regulated cell senescence and apoptosis. Conversely, aberrant telomere length homeostasis may disrupt the normal process of senescence and programmed cell death. Evidence is accruing that variation in constitutional telomere length plays an important role in the etiology of many diseases. Extremes of telomere length, either short or long, have been associated with cancer risk (2–24). Yet, across studies, peripheral blood leukocyte (PBL) telomere length has not been consistently associated with cancer risk, even within cancers of the same histopathology (25).

In meta-analyses of the association of telomere length and cancer risk (25), shorter telomere lengths have consistently been associated with the risk of bladder, esophageal, gastric, head and neck, ovarian, and renal cell cancers in all published studies. Results have not been consistent for non-Hodgkin lymphoma and breast, lung, and colorectal cancers. Differences in telomere measurement methods, interlaboratory variation, and influences of DNA extraction and storage procedures were identified as potential sources underlying inconsistent results (25).

The most frequently used technique to determine telomere length in cancer-risk association studies is quantitative PCR (qPCR), which measures the canonical region of the telomeres, the same region measured by the less common quantitative FISH (Q-FISH). In contrast, Southern blot analysis measurement includes the noncanonical (subtelomeric) portion of the telomeres. Reproducibility in reported studies remains an issue (26), although interassay variation (coefficient of variance, CV) may be improved with good quality control (27). Interassay variability for Q-FISH has been reported to be more than 5% (28), more than 6% for qPCR, and more than 2% for Southern blot analysis (26). Unlike Q-FISH, qPCR and Southern blot analysis require DNA extraction. PBL DNA is often extracted for using one of three methods: a column method, salting-out of the DNA or organic extraction with phenol/chloroform.

In our case–control study, assessing the association between PBL telomere length and the presence of colorectal cancer, we observed that relative telomere length (RTL) measured by qPCR was shorter in samples from two of six centers that comprise the Colon Cancer Cooperative Family Registry (Colon CFR) than for samples from the other four centers. Because each of the centers had used one or two of three different DNA extraction methods, we sought to determine, whether the DNA extraction methods might be driving the differences in the telomere lengths in both our cases and healthy controls.

Study population

All cases and controls were collected through Institutional Review Board (IRB)–approved protocols. The use of these specimens for this study was approved by the Mayo Clinic, IRB (Rochester, MN).

Colorectal cancer cases.

Colorectal cancer cases were selected from two prospective collections. The Biobank for Gastrointestinal Health Research (BGHR) is an ongoing IRB-approved collection of biospecimens from participants with normal colonoscopic examinations, colon polyps or colorectal cancer seen at Mayo Clinic, from the year 2000 to the present. The second group of cases originated from the Colon CFR, an international consortium of six centers in North America and Australia described in more detail by Newcomb and colleagues (29). This collaborative resource collected colorectal cancer cases from family clinics; population-based sampling from incident cases of colorectal cancer; and population-based recruitment of patients with younger age of cancer onset (29). Overall, there were 1,033 cases available for telomere length assessment from two sources: BGHR (n = 243; mean age ± SD = 46.74 ± 8.73; 54% male); and Colon CFR (n = 790; mean age ± SD = 51.13 ± 8.05; 50% male).

Controls.

Controls were collected from the (i) BGHR; (ii) Colon CFR; (iii) Mayo Clinic Biobank; and (iv) Mayo Clinic SPORE in Pancreatic Cancer (MCCSPC). Controls from the BGHR are consented subjects with normal colonoscopies and no prior polyp or cancer history and those from the Colon CFR are nonblood relative and/or spousal controls. Initiated in 2009, the Mayo Clinic Biobank controls are subjects from Olmsted County, MN, found to be healthy during a medical examination in the Department of Medicine divisions of Community Internal Medicine, Family Medicine and General Internal Medicine. A total of 962 healthy Caucasian controls recruited through the MCCSPC were from a Mayo Clinic–based control sample of patients with primary care having routine check-up visits (general medical exam) between May 1, 2004 and August 31, 2006.

All control subjects were consented for recruitment into one of these registries; registry participation included collection of peripheral blood samples, and/or tissue samples, and/or completion of a self-administered clinical and family history questionnaire and food-frequency questionnaire. Controls had no previous diagnosis of cancer (except possibly nonmelanoma skin cancer) at the time of enrollment. Control subjects with a family history of Lynch Syndrome, FAP, Peutz-Jeghers syndrome, or Juvenile polyposis were excluded.

Overall, 2,952 controls were available for telomere length assessment from the four sources: BGHR (n = 56; mean age ± SD = 57.14 ± 2.73; 34% male); Colon CFR (n = 1,400; mean age ± SD = 50.85 ± 8.18; 47% male); Mayo Biobank (n = 534; mean age ± SD = 49.22 ± 8.84; 58% male); and MCCSPC (n = 962; mean age ± SD = 66.37 ± 9.78; 55% male). Controls were frequency matched to the cases' age (±5 years), gender, and race/ethnicity.

DNA extraction from PBLs for cases and controls

DNA extraction was conducted on all BGHR cases and controls, Mayo Biobank controls and MCCSPC controls using PureGene chemistries (a salting-out extraction) via the Gentra Autopure platform and quantified by UV absorbance; DNA quality was assessed by 260/280 optical density ratio. DNA extraction methods for the Colon CFR samples varied according to the origin site. Colon CFR sample DNA was extracted using one of three different methods: PureGene, phenol/chloroform, or QIAamp (Qiagen) column extraction (Table 1).

Table 1.

Test set: extraction methods by study center

Center no.Extraction method (column %)
Study(Site location)Phenol/chloroformQiagen/QIAampPureGene
Colon CFR 11 (Ontario) 233 (43%) 252 (21%)  
 13 (Australia) 303 (57%)   
 14 (Hawaii)  101 (9%)  
 15 (Minnesota)   479 (21%) 
 16 (Seattle)  822 (70%)  
Pancreas spore 21   962 (43% 
BGHR 19   299 (13%) 
Mayo Biobank 20   534 (24%) 
Total  536 1,175 2,274 
Center no.Extraction method (column %)
Study(Site location)Phenol/chloroformQiagen/QIAampPureGene
Colon CFR 11 (Ontario) 233 (43%) 252 (21%)  
 13 (Australia) 303 (57%)   
 14 (Hawaii)  101 (9%)  
 15 (Minnesota)   479 (21%) 
 16 (Seattle)  822 (70%)  
Pancreas spore 21   962 (43% 
BGHR 19   299 (13%) 
Mayo Biobank 20   534 (24%) 
Total  536 1,175 2,274 

Assessment of telomere length by qPCR

DNA was quantitated with PicoGreen to ensure that the same amount of DNA was used for each PCR reaction. This PCR-based assay uses a set of primers to the telomeric hexamer repeats, thus amplifying the telomere (30). An average RTL for a sample is measured by determining the sample's telomere to single-copy gene ratio (T/S ratio) in comparison with the T/S ratio of a reference DNA sample.

Validation dataset

To determine whether the variation in RTL might be attributable to the specific DNA extraction method used, we extracted DNA on three samples from each of 25 new cases and 25 controls using each of the three extraction methods (PureGene, phenol/chloroform, and QIAamp from Qiagen). For six of the samples, RTL for one measurement was more than two SDs from the mean; these outliers were dropped, resulting in a final sample of 20 cases and 24 controls.

Systematic literature review

To evaluate whether the method of DNA extraction may be associated with a detected relationship between telomere length and cancer risk, we reviewed all case–control studies published through November 2012 that were designed to assess an association between cancer risk and peripheral blood or buccal DNA RTL measured by qPCR. We searched Medline via PubMed (U.S. Library of Medicine, Bethesda, MD) using the terms “cancer and telomeres” and “cancer and telomere length”, and we cross-referenced these articles with those reported in two published meta-analyses of telomere length and cancer risk (25, 31). Reported ORs (below the median telomere length vs. above), 95% confidence intervals (CI), and DNA extraction methods were then sought from review of the primary article or calculated from the article as needed (ref. 2; Supplementary Table S1). In many studies, DNA had been used from long-standing registries and previously reported cohorts. Thus, the DNA extraction methods were identified in previously referenced articles; in situations in which the extraction method was still not clear, we contacted the originating site for the DNA used in those articles to determine the DNA extraction method.

Statistical analysis

T/S ratio was used for the comparison of telomere length among the three DNA extraction methods. As the comparison of arithmetic means of the T/S ratio may be misleading because of violation of the normality assumption, the T/S ratio was log-transformed to compare geometric means of the T/S ratio, which enables meaningful statistical evaluations. RTL was also summarized for a descriptive purpose with mean, SD, and range for each DNA extraction method in cases and controls. For the case–control data (test set), an analysis of covariance (ANCOVA) was used for the comparison of the log (T/S ratio) among independent samples after controlling for age at blood draw. For the validation set, a mixed-effect model was used for the comparison among correlated samples.

RTL by DNA extraction methods in our case–control study was summarized in Table 2 (test set) and the interassay CV was 6%. A shorter RTL was observed in QIAamp, compared with phenol/chloroform and PureGene (Fig. 1A). This remained similar in the comparison of the T/S ratio in a fixed effects model: after controlling for age differences, DNA extracted using QIAmp still had significantly shorter telomeres than DNA extracted using phenol/chloroform or PureGene (P < 0.001).

Figure 1.

Distribution of telomere length by extraction method in the test and validation samples. A, in a test sample, telomere length was measured in 1,033 cases and 2,952 controls whose DNA had been extracted using either phenol/chloroform, PureGene, or QIAamp. B, in a validation sample, DNA was extracted from frozen buffy coats of 20 cases and 24 controls using all three extraction methods (phenol/chloroform, PureGene, and QIAamp). RTL was measured for each of the DNAs extracted by these different methods. The y-axis shows the telomere length in terms of the log-scale of the T/S.

Figure 1.

Distribution of telomere length by extraction method in the test and validation samples. A, in a test sample, telomere length was measured in 1,033 cases and 2,952 controls whose DNA had been extracted using either phenol/chloroform, PureGene, or QIAamp. B, in a validation sample, DNA was extracted from frozen buffy coats of 20 cases and 24 controls using all three extraction methods (phenol/chloroform, PureGene, and QIAamp). RTL was measured for each of the DNAs extracted by these different methods. The y-axis shows the telomere length in terms of the log-scale of the T/S.

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Table 2.

Test set: differences in peripheral blood RTL by DNA extraction method

T/S ratioLog-transformed T/S ratio
SampleExtraction methodnMean (SD)MinMaxEstimated difference (95% CI)P
All Phenol/chloroform 536 0.78 (0.73) 0.01 6.54 0.52 (0.44–0.60) <0.001 
 PureGene 2,274 0.75 (0.79) 0.00 12.33 0.58 (0.52–0.63) <0.001 
 QIAamp 1,175 0.38 (0.31) 0.02 3.69 Reference 
Cases Phenol/chloroform 190 1.09 (0.88) 0.04 6.26 0.86 (0.73–0.98) <0.001 
 PureGene 408 0.76 (0.53) 0.02 3.19 0.52 (0.42–0.62) <0.001 
 QIAamp 435 0.41 (0.37) 0.05 3.33 Reference 
Controls Phenol/chloroform 346 0.61 (0.57) 0.01 6.54 0.33 (0.23–0.43) <0.001 
 PureGene 1,866 0.75 (0.84) 0.00 12.33 0.62 (0.55–0.69) <0.001 
 QIAamp 740 0.37 (0.27) 0.02 3.69 Reference 
T/S ratioLog-transformed T/S ratio
SampleExtraction methodnMean (SD)MinMaxEstimated difference (95% CI)P
All Phenol/chloroform 536 0.78 (0.73) 0.01 6.54 0.52 (0.44–0.60) <0.001 
 PureGene 2,274 0.75 (0.79) 0.00 12.33 0.58 (0.52–0.63) <0.001 
 QIAamp 1,175 0.38 (0.31) 0.02 3.69 Reference 
Cases Phenol/chloroform 190 1.09 (0.88) 0.04 6.26 0.86 (0.73–0.98) <0.001 
 PureGene 408 0.76 (0.53) 0.02 3.19 0.52 (0.42–0.62) <0.001 
 QIAamp 435 0.41 (0.37) 0.05 3.33 Reference 
Controls Phenol/chloroform 346 0.61 (0.57) 0.01 6.54 0.33 (0.23–0.43) <0.001 
 PureGene 1,866 0.75 (0.84) 0.00 12.33 0.62 (0.55–0.69) <0.001 
 QIAamp 740 0.37 (0.27) 0.02 3.69 Reference 

Abbreviation: n, number of samples tested.

However, there was uncertainty as to whether the difference was mainly due to different patient populations rather than DNA extraction methods. To reduce confounding effects from different patient characteristics and verify our findings from the test set, we selected an additional set of 20 colorectal cancer cases and 24 noncancer controls, and compared their RTL as measured using the three DNA extraction methods (phenol/chloroform, PureGene, and QIAamp; Fig. 1B). These samples were extracted in the same laboratory by all three methods. Consistent with the original test set of cases and controls, the range of RTL measured by qPCR from PBL DNA extracted by the QIAamp column method was less than that detected by either the PureGene or phenol/chloroform extraction methods (T/S ratio ranges, 0.17–0.58, 0.04–2.67, and 0.32–2.81, respectively). Table 3 shows the results of the mixed effects model to compare the T/S ratio among the three extraction methods for the same patients. Telomere length from DNA extracted by the phenol/chloroform and PureGene methods was significantly more than telomere length from DNA extracted by QIAamp (P < 0.0001 and P = 0.0016, respectively). Statistical significance was not found in the difference in telomere length between PureGene and phenol/chloroform extraction methods (data not shown). The residual variances indicate that RTL from QIAamp was less variable than either PureGene or phenol/chloroform (likelihood ratio test, P < 0.0001).

Table 3.

Validation set: fixed effects and variance estimates of telomere length (log T/S ratio) by extraction method

Extraction methodFixed effects (SE)PResidual variance
Intercept −1.23 (0.04)  — 
Phenol/chloroform 0.80 (0.08) <0.0001 0.261 
PureGene 0.49 (0.15) 0.0016 0.982 
QIAamp Reference  0.077 
Extraction methodFixed effects (SE)PResidual variance
Intercept −1.23 (0.04)  — 
Phenol/chloroform 0.80 (0.08) <0.0001 0.261 
PureGene 0.49 (0.15) 0.0016 0.982 
QIAamp Reference  0.077 

Telomeres protect eukaryotic chromosomes from degradation and DNA repair activity. In most normal cells, there is no continually compensating elongation mechanism and telomeres become shorter with each cell division. Telomere length has been widely studied in relation to disease risk and progression, particularly in cancer (25, 32–38). qPCR is the most common method used to estimate telomere length in studies with high sample numbers, as it is rapid, sensitive, and requires relatively small amounts of DNA. Although there is considerable variation in reproducibility with CV ranging between 0.37% and 28% (6% in our study), there are a growing number of reports using this approach (2, 3, 5, 6, 12–17, 19, 21, 23, 24, 34, 39–48).

A recent article by Olsen and colleagues (27) indicated that quality control is critical in qPCR telomere length studies. In this report, we note that the methods used to extract DNA influence measurements of RTL. While DNA isolated by salting-out (PureGene) or organic extraction (phenol/chloroform) produced similar RTL results, DNA extracted using the silica-gel membrane QIAamp column technology rendered shorter estimates for RTL as well as narrowed the range of RTL variance in the 44 subjects who had DNA extracted by all three methods. The method of DNA isolation may explain these differences because column methods have been associated with shearing of DNA leading to lower molecular weight DNA. Column-based techniques involve more mixing and vigorous vortexing with a greater potential for shearing DNA as it passes through the column, allowing longer strands that cannot stick to the column media to be spun off and not retrieved.

It is possible that some factor other than the average size of the DNA fragments is responsible for the differences in RTL we observed by extraction method. Measurement by qPCR is susceptible to the influence of residual inhibitors if organic solvents such as phenol or inorganic residual salts are not completely removed from the DNA before qPCR amplification. However, even when telomere length was assessed by Southern blot analysis conducted on unamplified DNA extracted from the same subjects by PureGene and by QIAamp (supplementary Tables S2 and S3), telomere lengths of the column based–extracted DNA were significantly less than those extracted by the PureGene method, indicating that the telomere length may be related to the DNA quality and not related to a qPCR inhibitor that is the sequela of the salting-out DNA extraction method.

A possible consequence of the apparent alteration in measured RTL using QIAamp extraction may increase false-negative (type II error) rates. Indeed, a systematic review of the literature indicates that DNA extraction method may, in part, contribute to the discrepancies between studies evaluating associations between telomere length and the risk of cancer or other diseases. Figure 2 presents a Forest Plot of the ORs and CIs from the studies identified in the systematic review grouped by DNA extraction method. The majority of studies used column methods (column, n = 20; phenol/chloroform, n = 8; salting-out, n = 7). Overall, studies that used QIAamp-extracted DNA were the least likely to find an association between PBL telomere length and cancer risk (column, 20%; phenol/chloroform, 50%; salting-out, 71%). We note that Q-FISH does not require a DNA extraction step, and most studies that used Q-FISH–reported associations between telomere length and cancer risk. This suggests that studies using QIAamp for DNA extraction may fail to find an association when one may truly exist, whereas studies using phenol/chloroform or salting-out methods, or those using methods that do not require DNA extraction (i.e., Q-FISH) may be better able to detect such associations.

Figure 2.

Association between RTL and cancer risk: forest plot clustered by DNA extraction methods. Colored bars indicate the ORs for studies using qPCR to measure RTL. The blue bars represent RTL results from DNA extracted using column methods, green bars from DNA extracted via salting-out methods, and red bars represent RTL from DNA extracted by phenol/chloroform. The black bar represents OR for studies using Q-FISH to measure telomere length directly and does not require DNA extraction. Br, breast cancer; CRC, colorectal cancer; Bl, urinary bladder cancer; Mel, melanoma; SCC, squamous cell skin carcinoma; BCC, basal cell carcinoma; Lng, lung cancer; Gas, gastric cancer; Eso, esophageal cancer; NHL, non-Hodgkin's lymphoma; Ren, renal cancer; Pan, pancreatic cancer; Osteo, osteosarcoma; Pros, prostate cancer; Ov, ovarian cancer; Endo, endometrial cancer. *, Compared quintiles; **, compared tertiles; and ***, compared dichotomized telomere length.

Figure 2.

Association between RTL and cancer risk: forest plot clustered by DNA extraction methods. Colored bars indicate the ORs for studies using qPCR to measure RTL. The blue bars represent RTL results from DNA extracted using column methods, green bars from DNA extracted via salting-out methods, and red bars represent RTL from DNA extracted by phenol/chloroform. The black bar represents OR for studies using Q-FISH to measure telomere length directly and does not require DNA extraction. Br, breast cancer; CRC, colorectal cancer; Bl, urinary bladder cancer; Mel, melanoma; SCC, squamous cell skin carcinoma; BCC, basal cell carcinoma; Lng, lung cancer; Gas, gastric cancer; Eso, esophageal cancer; NHL, non-Hodgkin's lymphoma; Ren, renal cancer; Pan, pancreatic cancer; Osteo, osteosarcoma; Pros, prostate cancer; Ov, ovarian cancer; Endo, endometrial cancer. *, Compared quintiles; **, compared tertiles; and ***, compared dichotomized telomere length.

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Although other factors including the sample size, specific white blood cell subgroup source for DNA, DNA quality, storage and qPCR quality control may also affect telomere length, our results indicate that DNA extraction methods may be crucial to the proper assessment of telomere length. All three extractions methods use a proteinase K digestion but phenol/chloroform and salting-out techniques, which are both liquid-to-liquid phase methods differ significantly from a column extraction. Both liquid-to-liquid phase extraction methods typically have less protein contamination and typically result in higher molecular weight DNA. Deproteinization to enhance DNA purity occurs in the phenol/chloroform process because proteins have both hydrophobic and hydrophilic cores that draw them toward the organic phenol phase for precipitation, whereas the negatively charged nucleotides of DNA are preferentially attracted to the aqueous phase. Similarly, in salting-out methods, deproteinization results by dehydrating and precipitating out protein, leaving a supernatant of DNA that is then precipitated in ethanol. In the column-based methods, deproteinization occurs because only nucleic acids adsorb to a solid-phase component such as silica or resin beads in the face of adjustments of the salt and pH of the buffer. These changes in salt concentration and pH increase the negative charge of the solid-phase causing the DNA to stick and the other molecules including proteins to pass through the column. Column-based methods tend to shear DNA more than liquid-to-liquid phase extractions, likely because of vortexing differences. Column methods also result in lower molecular weight DNA during multiple washes to clean the column and these fragments are discarded in the filtrate that results from each wash. Only DNA that has remained on the column is finally removed during elution. Presumably, a portion of these low-molecular weight DNA fragments would include portions of the telomeres that cap every chromosome and the measurement of average telomere lengths would thus, be lower as portions of telomere base pairs are lost, along with other low-molecular weight DNA.

In our case–control study, including multiple covariates such as age, gender, and family history, we detected an association between colorectal cancer risk and RTL. This association was more evident in samples extracted by PureGene or phenol/chloroform than in samples extracted by QIAamp, where the range of RTL might be limited because the starting DNA itself has been truncated during extraction. An inability to detect the full range of RTL would presumably diminish the ability to reliably assess longer telomeres, leading to misclassification and less sensitivity for detecting associations between telomere length and cancer risk. We found that DNA extracted with either phenol/chloroform or PureGene methods yielded more accurate measurement of telomere length by qPCR than DNA extracted with QIAamp.

Accurate assessment of telomere length is critical to the recognition of the impact of telomere length in disease and health. Here, we report that the method of DNA extraction may influence the ability to properly measure telomere length and should be considered in epidemiologic studies of telomere length.

No potential conflicts of interest were disclosed.

The content of this article does not necessarily reflect the views or policies of the NCI or any of the collaborating centers in the CFRs, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government or the CFR.

Conception and design: H.G. Skinner, S.N. Thibodeau, G.M. Petersen, L.A. Boardman

Development of methodology: R.A. Johnson, H.G. Skinner, L.A. Boardman

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): R.A. Johnson, D.L. Riegert-Johnson, R. Haile, M.A. Jenkins, L.A. Boardman

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): K. Litzelman, H.G. Skinner, S. Seo, C.D. Engelman, R.E. Gangnon, G.M. Petersen

Writing, review, and/or revision of the manuscript: J.M. Cunningham, K. Litzelman, H.G. Skinner, S. Seo, C.D. Engelman, R.J. Vanderboom, G.W. Kimmel, R.E. Gangnon, J.A. Baron, J.D. Potter, R. Haile, D.D. Buchanan, M.A. Jenkins, D.N. Rider, S.N. Thibodeau, G.M. Petersen, L.A. Boardman

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): K. Litzelman, H.G. Skinner

Study supervision: H.G. Skinner

The authors thank Karen Lazar for her outstanding technical assistance.

The study was supported by RO-1 CA132718 through the National Cancer Institute (NCI; to L.A. Boardman, J.M. Cunningham, R.A. Johnson, K. Litzelman, H.G. Skinner, S. Seo, C.D. Engelman, D.N. Rider, S.N. Thibodeau, and G.M. Petersen); L.A. Boardman is supported by P30 DK084567 (Mayo Clinic Center for Cell Signaling in Gastroenterology) through National Institutes of Diabetes, Digestive and Kidney Diseases (NIDDK); G.M. Petersen has been awarded an NCI P50 CA102701 (Mayo Clinic SPORE in Pancreatic Cancer) and the Lustgarten Foundation for Pancreatic Cancer Research; the Mayo Clinic Center for Individualized Medicine and by the NCI, NIH under RFA # CA-96-011 and through cooperative agreements with the Australasian Colorectal Cancer Family Registry (U01 CA097735); Familial Colorectal Neoplasia Collaborative Group (U01 CA074799; USC); Mayo Clinic Cooperative Family Registry for Colon Cancer Studies (U01 CA074800); Ontario Registry for Studies of Familial Colorectal Cancer (U01 CA074783); Seattle Colorectal Cancer Family Registry (U01 CA074794); and University of Hawaii Colorectal Cancer Family Registry (U01 CA074806).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Aviv
A
. 
Telomeres, sex, reactive oxygen species, and human cardiovascular aging
.
J Mol Med
2002
;
80
:
689
95
.
2.
Broberg
K
,
Bjork
J
,
Paulsson
K
,
Hoglund
M
,
Albin
M
. 
Constitutional short telomeres are strong genetic susceptibility markers for bladder cancer
.
Carcinogenesis
2005
;
26
:
1263
71
.
3.
McGrath
M
,
Wong
JY
,
Michaud
D
,
Hunter
DJ
,
De Vivo
I
. 
Telomere length, cigarette smoking, and bladder cancer risk in men and women
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
815
9
.
4.
Wu
G
,
Jiang
X
,
Lee
WH
,
Chen
PL
. 
Assembly of functional ALT-associated promyelocytic leukemia bodies requires Nijmegen Breakage Syndrome 1
.
Cancer Res
2003
;
63
:
2589
95
.
5.
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
13
.
6.
Pooley
KA
,
Sandhu
MS
,
Tyrer
J
,
Shah
M
,
Driver
KE
,
Luben
RN
, et al
Telomere length in prospective and retrospective cancer case–control studies
.
Cancer Res
2010
;
70
:
3170
6
.
7.
Shen
J
,
Terry
MB
,
Gurvich
I
,
Liao
Y
,
Senie
RT
,
Santella
RM
. 
Short telomere length and breast cancer risk: a study in sister sets
.
Cancer Res
2007
;
67
:
5538
44
.
8.
Svenson
U
,
Nordfjall
K
,
Stegmayr
B
,
Manjer
J
,
Nilsson
P
,
Tavelin
B
, et al
Breast cancer survival is associated with telomere length in peripheral blood cells
.
Cancer Res
2008
;
68
:
3618
23
.
9.
Levy
T
,
Agoulnik
I
,
Atkinson
EN
,
Tong
XW
,
Gause
HM
,
Hasenburg
A
, et al
Telomere length in human white blood cells remains constant with age and is shorter in breast cancer patients
.
Anticancer Res
1998
;
18
:
1345
9
.
10.
Martinez
P
,
Blasco
MA
. 
Telomeric and extra-telomeric roles for telomerase and the telomere-binding proteins
.
Nat Rev Cancer
2011
;
11
:
161
76
.
11.
Jones
AM
,
Beggs
AD
,
Carvajal-Carmona
L
,
Farrington
S
,
Tenesa
A
,
Walker
M
, et al
TERC polymorphisms are associated both with susceptibility to colorectal cancer and with longer telomeres
.
Gut
2012
;
61
:
248
54
.
12.
Cui
Y
,
Cai
Q
,
Qu
S
,
Chow
WH
,
Wen
W
,
Xiang
YB
, et al
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
13
.
13.
Risques
RA
,
Vaughan
TL
,
Li
X
,
Odze
RD
,
Blount
PL
,
Ayub
K
, et al
Leukocyte telomere length predicts cancer risk in Barrett's esophagus
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2649
55
.
14.
Hou
L
,
Savage
SA
,
Blaser
MJ
,
Perez-Perez
G
,
Hoxha
M
,
Dioni
L
, et al
Telomere length in peripheral leukocyte DNA and gastric cancer risk
.
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
3103
9
.
15.
Liu
X
,
Bao
G
,
Huo
T
,
Wang
Z
,
He
X
,
Dong
G
. 
Constitutive telomere length and gastric cancer risk: case–control analysis in Chinese Han population
.
Cancer Sci
2009
;
100
:
1300
5
.
16.
Willeit
P
,
Willeit
J
,
Mayr
A
,
Weger
S
,
Oberhollenzer
F
,
Brandstatter
A
, et al
Telomere length and risk of incident cancer and cancer mortality
.
JAMA
2010
;
304
:
69
75
.
17.
Jang
JS
,
Choi
YY
,
Lee
WK
,
Choi
JE
,
Cha
SI
,
Kim
YJ
, et al
Telomere length and the risk of lung cancer
.
Cancer Sci
2008
;
99
:
1385
9
.
18.
Widmann
TA
,
Herrmann
M
,
Taha
N
,
Konig
J
,
Pfreundschuh
M
. 
Short telomeres in aggressive non-Hodgkin's lymphoma as a risk factor in lymphomagenesis
.
Exp Hematol
2007
;
35
:
939
46
.
19.
Mirabello
L
,
Richards
EG
,
Duong
LM
,
Yu
K
,
Wang
Z
,
Cawthon
R
, et al
Telomere length and variation in telomere biology genes in individuals with osteosarcoma
.
Int J Mol Epidemiol Genet
2011
;
2
:
19
29
.
20.
Mirabello
L
,
Yu
K
,
Kraft
P
,
De Vivo
I
,
Hunter
DJ
,
Prescott
J
, et al
The association of telomere length and genetic variation in telomere biology genes
.
Hum Mutat
2010
;
31
:
1050
8
.
21.
Skinner
HG
,
Gangnon
RE
,
Litzelman
K
,
Johnson
RA
,
Chari
ST
,
Petersen
GM
, et al
Telomere length and pancreatic cancer: a case-control study
.
Cancer Epidemiol Biomarkers Prev
2012
;
21
:
2095
100
.
22.
Shao
L
,
Wood
CG
,
Zhang
D
,
Tannir
NM
,
Matin
S
,
Dinney
CP
, et al
Telomere dysfunction in peripheral lymphocytes as a potential predisposition factor for renal cancer
.
J Urol
2007
;
178
:
1492
6
.
23.
Han
J
,
Qureshi
AA
,
Prescott
J
,
Guo
Q
,
Ye
L
,
Hunter
DJ
, et al
A prospective study of telomere length and the risk of skin cancer
.
J Invest Dermatol
2009
;
129
:
415
21
.
24.
Nan
H
,
Qureshi
AA
,
Prescott
J
,
De Vivo
I
,
Han
J
. 
Genetic variants in telomere-maintaining genes and skin cancer risk
.
Hum Genet
2011
;
129
:
247
53
.
25.
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
50
.
26.
Aviv
A
,
Hunt
SC
,
Lin
J
,
Cao
X
,
Kimura
M
,
Blackburn
E
. 
Impartial comparative analysis of measurement of leukocyte telomere length/DNA content by southern blots and qPCR
.
Nucleic Acids Res
2011
;
39
:
e134
.
27.
Olsen
MT
,
Berube
M
,
Robbins
J
,
Palsboll
PJ
. 
Empirical evaluation of humpback whale telomere length estimates; quality control and factors causing variability in the singleplex and multiplex qPCR methods
.
BMC Genet
2012
;
13
:
77
.
28.
Rufer
N
,
Dragowska
W
,
Thornbury
G
,
Roosnek
E
,
Lansdorp
P
. 
Telomere length dynamics in human lymphocyte subpopulations measured by flow cytometry
.
Nat Biotechnol
1998
;
16
:
743
.
29.
Newcomb
PA
,
Baron
J
,
Cotterchio
M
,
Gallinger
S
,
Grove
J
,
Haile
R
, et al
Colon Cancer Family Registry: an international resource for studies of the genetic epidemiology of colon cancer
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2331
43
.
30.
Cawthon
RM
. 
Telomere measurement by quantitative PCR
.
Nucleic Acids Res
2002
;
30
:
e47
.
31.
Ma
H
,
Zhou
Z
,
Wei
S
,
Liu
Z
,
Pooley
KA
,
Dunning
AM
, et al
Shortened telomere length is associated with increased risk of cancer: a meta-analysis
.
PLoS ONE
2011
;
6
:
e20466
.
32.
Martinez-Delgado
B
,
Yanowsky
K
,
Inglada-Perez
L
,
de la Hoya
M
,
Caldes
T
,
Vega
A
, et al
Shorter telomere length is associated with increased ovarian cancer risk in both familial and sporadic cases
.
J Med Genet
2012
;
49
:
341
4
.
33.
Odagiri
E
,
Kanada
N
,
Jibiki
K
,
Demura
R
,
Aikawa
E
,
Demura
H
. 
Reduction of telomeric length and c-erbB-2 gene amplification in human breast cancer, fibroadenoma, and gynecomastia. Relationship to histologic grade and clinical parameters
.
Cancer
1994
;
73
:
2978
84
.
34.
Mirabello
L
,
Garcia-Closas
M
,
Cawthon
R
,
Lissowska
J
,
Brinton
LA
,
Peplonska
B
, et al
Leukocyte telomere length in a population-based case–control study of ovarian cancer: a pilot study
.
Cancer Causes Control
2010
;
21
:
77
82
.
35.
Kunifuji
Y
,
Gotoh
S
,
Abe
T
,
Miura
M
,
Karasaki
Y
. 
Downregulation of telomerase activity by anticancer drugs in human ovarian cancer cells
.
Anticancer Drugs
2002
;
13
:
595
8
.
36.
Alter
BP
,
Rosenberg
PS
,
Giri
N
,
Baerlocher
GM
,
Lansdorp
PM
,
Savage
SA
. 
Telomere length is associated with disease severity and declines with age in dyskeratosis congenita
.
Haematologica
2012
;
97
:
353
9
.
37.
Agrawal
A
,
Dang
S
,
Gabrani
R
. 
Recent patents on anti-telomerase cancer therapy
.
Recent Pat Anticancer Drug Discov
2012
;
7
:
102
17
.
38.
Butlin
R
,
Debelle
A
,
Kerth
C
,
Snook
RR
,
Beukeboom
LW
,
Castillo Cajas
RF
, et al
What do we need to know about speciation?
Trends Ecol Evol
2012
;
27
:
27
39
.
39.
De Vivo
I
,
Prescott
J
,
Wong
JY
,
Kraft
P
,
Hankinson
SE
,
Hunter
DJ
. 
A prospective study of relative telomere length and postmenopausal breast cancer risk
.
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
1152
6
.
40.
Martinez-Delgado
B
,
Yanowsky
K
,
Inglada-Perez
L
,
Domingo
S
,
Urioste
M
,
Osorio
A
, et al
Genetic anticipation is associated with telomere shortening in hereditary breast cancer
.
PLoS Genet
2011
;
7
:
e1002182
.
41.
Jones
CH
,
Pepper
C
,
Baird
DM
. 
Telomere dysfunction and its role in haematological cancer
.
Br J Haematol
2012
;
156
:
573
87
.
42.
Zee
RY
,
Castonguay
AJ
,
Barton
NS
,
Buring
JE
. 
Mean telomere length and risk of incident colorectal carcinoma: a prospective, nested case–control approach
.
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
2280
2
.
43.
Prescott
J
,
McGrath
M
,
Lee
IM
,
Buring
JE
,
De Vivo
I
. 
Telomere length and genetic analyses in population-based studies of endometrial cancer risk
.
Cancer
2010
;
116
:
4275
82
.
44.
Xing
J
,
Ajani
JA
,
Chen
M
,
Izzo
J
,
Lin
J
,
Chen
Z
, et al
Constitutive short telomere length of chromosome 17p and 12q but not 11q and 2p is associated with an increased risk for esophageal cancer
.
Cancer Prev Res
2009
;
2
:
459
65
.
45.
Hosgood
HD
 III
,
Cawthon
R
,
He
X
,
Chanock
S
,
Lan
Q
. 
Genetic variation in telomere maintenance genes, telomere length, and lung cancer susceptibility
.
Lung Cancer
2009
;
66
:
157
61
.
46.
Mirabello
L
,
Huang
WY
,
Wong
JY
,
Chatterjee
N
,
Reding
D
,
Crawford
ED
, et al
The association between leukocyte telomere length and cigarette smoking, dietary and physical variables, and risk of prostate cancer
.
Aging Cell
2009
;
8
:
405
13
.
47.
Hofmann
JN
,
Baccarelli
A
,
Schwartz
K
,
Davis
FG
,
Ruterbusch
JJ
,
Hoxha
M
, et al
Risk of renal cell carcinoma in relation to blood telomere length a population-cased case–control study
.
Br J Cancer
2011
;
105
:
1772
5
.
48.
Liang
G
,
Qureshi
AA
,
Guo
Q
,
De Vivo
I
,
Han
J
. 
No association between telomere length in peripheral blood leukocytes and the risk of nonmelanoma skin cancer
.
Cancer Epidemiol Biomarkers Prev
2011
;
20
:
1043
5
.