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. Author manuscript; available in PMC: 2010 Oct 15.
Published in final edited form as: Cancer Res. 2010 Apr 15;70(8):3170–3176. doi: 10.1158/0008-5472.CAN-09-4595

Telomere length in prospective and retrospective cancer case-control studies

Karen A Pooley 1,*, Manjinder S Sandhu 2,4, Jonathan Tyrer 3, Mitul Shah 3, Kristy E Driver 3, Robert N Luben 2, Sheila A Bingham 2,‡, Bruce AJ Ponder 5, Paul DP Pharoah 3, Kay-Tee Khaw 2, Douglas F Easton 1, Alison M Dunning 3
PMCID: PMC2855947  EMSID: UKMS28790  PMID: 20395204

Abstract

Previous studies have reported that shorter mean telomere length in lymphocytes is associated with increased susceptibility to common diseases of aging, and may be predictive of cancer risk. However, most analyses have examined retrospectively-collected case-control studies.

Mean telomere length was measured using high-throughput quantitative Real Time PCR. Blood for DNA extraction was collected after cancer diagnosis in the East Anglian SEARCH Breast (2243 cases, 2181 controls) and SEARCH Colorectal (2249 cases, 2161 controls) studies. Prospective case-control studies were conducted for breast cancer (199 cases) and colorectal cancer (185 cases), nested within the EPIC-Norfolk cohort. Blood has been collected at least 6 months prior to diagnosis, and was matched to DNA from two cancer-free controls per case.

In the retrospective, SEARCH studies, the age-adjusted Odds Ratios for shortest (Q4) vs. longest (Q1) quartile of mean telomere length was 15.5 (95%CI 11.6–20.8), p-het=5.7×10−75; with a ‘per quartile’ p-trend=2.1×10−80 for breast cancer, and 2.14 (95%CI 1.77–2.59), p-het=7.3×10−15; with a ‘per quartile’ p-trend=1.8×10−13 for colorectal cancer. In the prospective, EPIC study, the comparable Odds Ratios [Q4 vs. Q1] were 1.58 (95%CI 0.75–3.31), p-het=0.23 for breast cancer, and 1.13 (95%CI 0.54–2.36), p-het=0.75 for colorectal cancer risk.

Mean telomere length was shorter in retrospectively-collected cases than in controls but the equivalent association was markedly weaker in the prospective studies. This suggests that telomere shortening largely occurs after diagnosis, and may not, therefore, be of value in cancer prediction.

Introduction

Human chromosomes are capped and stabilised by telomeres, which are predominantly formed from several thousand (TTAGGG)n repeats (1-3) and are heterogeneous in length, varying between chromosomes and individuals (4-8). Telomeres prevent chromosome ends from being recognised as damaged DNA in need of double-strand break repair and, as a result, protect against chromosome-chromosome fusions and rearrangements, helping maintain genomic integrity (9-12).

Telomeric DNA is inefficiently copied. This is referred to as ‘the end replication problem’ (1, 13, 14) and leads to a progressive loss in mean telomere length estimated at 15-60bp per year (15, 16) with this rate increasing over the age of 50 years (5, 17). There are reports that telomere attrition may be accelerated by environmental cofactors such as smoking (18-20), oxidative stress (21-23), poor physical health (24-26) and obesity (18). Thus, mean telomere length has been suggested as a measure of the ‘biological age’ of both the cell and the organism (27, 28).

Previous experimental studies have indicated that individuals with relatively short mean telomere lengths may have an increased risk of mortality from multiple diseases (20, 29-38). In particular, five studies have examined associations between breast cancer risk and mean telomere length (35-39). The first, by Shen et al. (35), reported an increased risk of breast cancer of borderline statistical significance associated with shorter telomere length (287 cases and 350 sister controls); Odds Ratio [shortest Q4 vs. longest Q1 (referent)] = 1.55 (95% Confidence Intervals 0.88–2.73), p=0.14. A second, larger study from the same group (36) reported a significant association with breast cancer risk in women under 50 years of age; OR [Q4 vs. Q1] = 1.78 (95%CI 1.15–2.76), p=0.01. No association was seen between mean telomere length and breast cancer in women of 50 years of age or older (36). In both of these studies, blood samples for DNA extraction from the case individuals were collected retrospectively - after cancer diagnosis.

Zheng et al. (37) investigated mean telomere length and breast cancer risk in two small, retrospectively-collected studies. The combined Odds Ratio for both studies (292 cases, 335 controls) associated with a telomere length ‘below’ verses ‘above’ the median length was 1.23 (95%CI 0.89–1.71), p=0.13.

Another study (Svenson et al., 38) has looked directly at mean telomere length and both cancer risk and survival. In contrast to the other reports, this reported an increased risk of breast cancer with increasing telomere length; [longest Q1 vs. shortest Q4 (referent)] OR = 5.17 (95%CI 3.09–8.64), p=0.001, and demonstrated consistently longer telomeres in cases than controls for all age groups.

Recently, De Vivo et al. (39) published the largest and only prospectively-designed breast cancer case-control study to date, with blood extracted from individuals prior to disease diagnosis or development, in 1122 postmenopausal women and 1147 matched controls. Using Real Time PCR to measure mean telomere length, they estimated an Odds Ratio for telomere length ‘below’ verses ‘above’ the median length of 1.23 (95%CI 0.94–1.60), p=0.20.

Thus far, there have been two studies published looking at the relationship between mean telomere length and colorectal cancer (40, 41). These prospective, nested case-control comparisons in men (191 cases, 306 controls) and women (134 cases, 357 controls) showed no association between telomere length and disease status.

Here, we have measured mean telomere length in prospectively- and retrospectively-collected breast and colorectal cancer case-control series to evaluate more precisely the association between telomere length and cancer status, and to test whether mean telomere length has predictive value in cancer susceptibility.

Materials and Methods

Retrospective SEARCH Breast Cancer case-control study

The SEARCH Breast study (42) is an ongoing population based study, recruiting cases ascertained through the Eastern Cancer Registration and Information Centre (ECRIC, http://www.ecric.org.uk/), a population-based cancer registry covering the counties of Cambridgeshire, Norfolk, Suffolk, Bedfordshire, Hertfordshire and Essex. Women aged under 70 years who were diagnosed from 1996 onwards (incident cases, median age 55 years) were eligible for inclusion. Approximately 64% of eligible patients have enrolled in the study. Study participants were asked to provide a 20 ml blood sample for DNA analysis and to complete a comprehensive epidemiological questionnaire. Eligible patients who did not take part in the study were similar to participants except the proportion of clinical stage III/IV cases was somewhat higher among non-participants.

For this report, the most recently accrued, extracted and normalized DNA samples were used, consisting of 2243 incident cases diagnosed between 2004 and 2007, and 2181 controls. The majority (n=1524) of the controls were collected as cancer-history-free control participants in SEARCH study, recruited over the period 2003 to 2007. The remainder (n=657) were cancer-free women selected from the Norfolk component of EPIC (European Prospective Investigation into Cancer). Controls are broadly similar in age to the cases (median age of SEARCH controls 53 years, median age of EPIC controls 54 years) and SEARCH controls were additionally matched to the area of residence and the age group frequencies of the SEARCH cases.

Further study characteristics are presented in Supplementary Table 1a.

Retrospective SEARCH Colorectal Cancer case-control study

Cases were selected from the SEARCH Colorectal Study, ascertained through ECRIC, as described for the breast cancer study (above). Eligible patients for recruitment were diagnosed with invasive colorectal cancer or anal adenocarcinoma, and aged 18-69 years at diagnosis, between March 2001 and February 2004. Approximately 63% of eligible patients enrolled in the study. Eligible patients who did not take part in the study were similar to participants except the proportion of clinical stage III/IV cases was somewhat higher among non-participants.

2249 SEARCH colorectal cases and 2161 controls were analyzed. All controls were recruited as cancer-history-free control participants in SEARCH study, recruited from 2002 to 2005. Controls were frequency matched with respect to gender, age in five-year bands and the area of residence of the cases.

Further study characteristics are presented in Supplementary Table 1a.

Prospective EPIC Breast and Colorectal nested case-control studies

Cases and controls were sampled from the Norfolk cohort of the European Prospective Investigation into Cancer (EPIC) (http://www.srl.cam.ac.uk/epic/). EPIC is an ongoing prospective study of diet and cancer being carried out in nine European countries. The EPIC-Norfolk cohort comprises over 30,000 individuals, aged 45–75 at recruitment, resident in Norfolk, East Anglia, and recruited from general practice registers between 1993 and 1997. It is an ethnically homogeneous population with >99% reported as white European. A total of 25,639 participants completed an initial health examination. In January 1998, the cohort was invited for a second health examination, and 15,786 people took part in this second phase. All participants gave informed consent and were matched to the East Anglian Cancer Registry and the United Kingdom Office of National Statistics Register. These provided notification of all cancer registrations, deaths, and emigrations for the entire cohort, thus, loss to follow-up was <0.1%.

Eligible cases for this analysis were study participants who were cancer-free at the baseline assessments and who were subsequently diagnosed with incident breast or colorectal cancer at least six months after blood draw and up to the end of December 2003. Full data was available for telomere length analysis on 199 and 185 cases of incident breast and colorectal cancer, respectively. Eligible controls were study participants who remained free of cancer during this time. Two controls were matched to each case by gender, age (within one year) and date of blood draw (within three months). In total, 384 cases of incident breast or colorectal cancer and 826 matched control participants were available for analysis. The mean time from blood collection to date of diagnosis was 3.3 years (range 1.4 to 4.6 years). Further study characteristics are presented in Supplementary Table 1b.

Ethical approval was obtained from The Norwich District Health Authority Ethics Committee for all the studies used.

Real Time PCR method

Relative mean telomere length was ascertained by SYBR® Green Real Time PCR measurement of the ratio of telomere repeat units (TEL) to a single copy gene (CON); (TEL/CON) (20, 43). The TEL assay amplifies a 78bp telomeric repeat unit and detected fluorescence is proportional to the number of telomeric repeats in the genome available for primer binding and, thus, the mean telomere length in the cells from which the DNA was extracted. The CON assay amplifies a segment of the single-copy human β-Globin gene sequence and is used to correct for sample-to-sample variation in template DNA added to the reaction.

For each assay, the fractional PCR cycle at which each reaction crossed a predefined fluorescence threshold was determined (‘Ct value’). The amount of starting template is expected to be proportional to 2−Ct. In order to correct for variation in genomic DNA concentration, the CON Ct value was subtracted from the TEL Ct value (ΔCt, ‘delta Ct’). The relative ‘telomere copy number’ per genome for each sample should then be proportional to 2−ΔCt. Ten nanograms of genomic DNA, dried down in a 384-well plate format, was resuspended in 10μl of either the CON or TEL PCR reaction mix for 2h at 4°C prior to thermal cycling.

The PCR primer sequences for both PCR reactions are as previously published (20). Each 10μl CON reaction contained 300nM and 700nM of the forward and reverse primers (Sigma Aldrich), respectively, and 5μl of 2 × Power SYBR® Green PCR Master Mix (Applied Biosystems). The PCR profile was: initial denaturation step of 95°C for 10 min, followed by 40 cycles of 95°C for 15s, 58°C for 20s, and 72°C for 28s. Each 10μl TEL reaction contained 100nM and 900nM of the forward and reverse primers (Sigma Aldrich), respectively, 5μl of 2 × Power SYBR® Green PCR Master Mix (Applied Biosystems) and 0.3μl of DMSO PCR Reagent (Sigma Aldrich). The PCR profile was: initial denaturation step of 95°C for 10 min, followed by 40 cycles of 95°C for 15s, and 54°C for 2min.

All reactions were performed using an ABI7900 thermal cycler, in 384-well format. Each plate was composed equally of interdigitated cases and controls, and included negative control wells (containing no DNA). 32% (n=382) of the prospective EPIC study and 12% (n=526) of the retrospective SEARCH studies were run in duplicate, with repeated samples assayed in a secondary run during the experiment, using a separately-prepared mix of PCR reagents.

A standard plate of ‘test’ samples was additionally assayed. This plate consisted of 94 high yield DNA samples and was assayed in each PCR batch performed, i.e. in triplicate, as a method of inter-experiment quality control.

Failed PCR reactions were not repeated (as defined below).

Statistical Methods

Analyses were based primarily on the ΔCt variable, rather than the relative copy number (2−ΔCt), as the copy number data was positively skewed, whilst the ΔCt values fitted a normal distribution well, making it unnecessary to transform the raw ΔCt data twice. The inter-experimental quality control comparisons of repeated samples were assessed using Pearson correlation calculations. The intra-experimental comparison of standard ‘test’ plates, for assurance of study-to-study quality control, was assessed using Spearman rank correlations. For all analyses, ‘outlier’ samples were removed if the CON PCR Ct value was more than two standard deviations away from the mean, and these reactions were considered ‘fails’.

The association between ΔCt and age at blood draw was evaluated using unconditional linear regression, adjusting for study, gender and individual 384-well sample plate.

In each case-control study, we used logistic regression to assess the association between mean telomere length and cancer status (breast or colorectal). Subjects were categorized into quartiles for telomere length, the boundaries of which were defined by the distribution of ΔCt in the control sample population of each study. Adjustments are detailed in Tables 1. and 2.

Table 1. Telomere length in prospective and retrospective breast cancer case control studies.

Subjects were categorized into quartiles of telomere length, defined by the continuous distribution of ΔCt in the respective study's control sample population. The Q1 referent quartile group had the longest mean telomere length and the Q4 quartile group had the shortest.

Relative
telomere length -
adjusted for covariates* 		Breast Cancer
OR (95% CI), p-het
Prospective EPIC
199 cases, 420 controls		 Retrospective SEARCH
2243 cases, 2181 controls
Q1 longest 1.00 ref 1.00 ref
Q2 1.04 (0.57 - 1.89), 0.91 2.03 (1.63 - 2.53), 2.3×10−10
Q3 1.54 (0.80 - 2.98), 0.20 4.59 (3.51 - 6.00), 6.4×10−29
Q4 shortest 1.58 (0.75 - 3.31), 0.23 15.5 (11.6 - 20.8), 5.7×10−75
Per quartile 1.18 (0.93 - 1.50)
p-trend = 0.18		 2.56 (2.32 - 2.82)
p-trend = 2.1 × 10−80
Median Split$ 1.52 (0.90 - 2.58)
p-het = 0.12		 4.29 (3.52 - 5.23)
p-het = 9.2 × 10−47
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*

The prospective EPIC study was matched for age and gender, and analysis adjusted for study plate. The retrospective SEARCH study analysis was adjusted for study plate and age.

$

The median split refers to the division of the samples into those ‘above’ verses ‘below’ the median length i.e. samples in Q1 and Q2 (referent) vs. Q3 and Q4.

Table 2. Telomere length in prospective and retrospective colorectal cancer case control studies.

Subjects were categorized as detailed in the Table 1 legend.

Relative
telomere length -
adjusted for covariates* 		Colorectal Cancer
OR (95% CI), p-het
Prospective EPIC
185 cases, 406 controls		 Retrospective SEARCH
2249 cases, 2161 controls
Q1 longest 1.00 ref 1.00 ref
Q2 1.09 (0.56 - 2.10), 0.81 1.51 (1.24 - 1.84), 4.7×10−5
Q3 0.71 (0.34 - 1.47), 0.35 1.47 (1.21 - 1.79), 1.3×10−4
Q4 shortest 1.13 (0.54 - 2.36), 0.75 2.14 (1.77 - 2.59), 7.3×10−15
Per quartile 1.03 (0.81 - 1.30)
p-trend = 0.82		 1.25 (1.18 - 1.33)
p-trend = 1.8 × 10−13
Median Split$ 0.85 (0.51 - 1.40)
p-het = 0.52		 1.43 (1.25 - 1.63)
p-het = 1.4 × 10−7
Open in a new tab
*

The prospective EPIC study was matched and analyzed as in Table 1. The retrospective SEARCH study analysis was adjusted for study plate, gender and age.

$

The median split is defined in the Table 1 legend.

All analyses were performed using Intercooled Stata 10.1 statistical package (Stata, College Station, TX).

Results

Quality Control

As a measure of assay quality assurance across the three case-control series', the correlation between repeated measurements of the same samples, assayed in separate PCR batches, was calculated. This was ≥0.83 for the ΔCt values (≥0.92 for the CON PCR, ≥0.93 for the TEL PCR) in each study. Greater than 97% of the samples attempted gave results. The Spearman rank order correlations across the triplicate ‘test’ plate assays were ≥0.71 for the ΔCt values (≥0.72 for the CON PCR, ≥0.87 for the TEL PCR). As a further validation of the assay used, the known association of mean telomere length with age in the unaffected controls in the sample sets studied (n=5256) was examined. As expected, there was a significant decrease in mean telomere length with age (after adjustment for study, gender and 384-well plate); increase in ΔCt ‘per annum’ = 0.0045 (95%CI 0.003–0.006), p-trend=1.5 × 10−9.

Mean telomere length and breast cancer

The estimated Odds Ratios by quartile of ΔCt, adjusted for age and plate, in the retrospective and prospective breast cancer studies are shown in Table 1. A stronger association was observed in the retrospective study compared to the prospective study. In the retrospective study, there is a trend across decreasing quartiles of telomere length, with a ‘per quartile’, plate and age-adjusted OR = 2.56 (95%CI 2.32–5.23), p-trend=2.1×10−80. The Odds Ratio for shortest (Q4) vs. longest (Q1) quartile of ΔCt was 15.5 (95%CI 11.6–20.8), p-het=5.7×10−75. Although cases had been matched to controls from two different sources in this study, there was little difference in the effect size between those matched to SEARCH controls [OR ‘per quartile’ = 2.44(95%CI 2.21-2.70), p-trend=8.5×10−69] and those matched to controls ascertained through EPIC-Norfolk [2.18 (95%CI 1.90-2.51), p-trend=2.5×10−28]. There was no significant difference between the effect sizes in subjects under 50 years old or 50 years and older, a division approximating pre- and post- menopausal status (data not shown).

The distributions of ΔCt in cases and controls, for each study, are further illustrated in Supplementary Figure 1. In all four panels, the case distribution is skewed to the right of the control distribution, but this is most marked in the retrospective breast cancer study (bottom left), where almost half of the cases (45% - 1013 of 2243) lie in the shortest quartile for length, Q4.

In the prospective breast cancer study, the association with ΔCt was in the same direction as the retrospective breast cancer study, but was of a much smaller magnitude and not statistically significant; plate and age-adjusted OR ‘per quartile’ = 1.18 (95%CI 0.93–1.50), p-trend=0.18; OR [Q4 vs. Q1] = 1.58 (0.75–3.31), p-het=0.23.

Mean telomere length and colorectal cancer

The colorectal cancer case-control results followed a similar pattern to those of the breast cancer studies, albeit with smaller point estimates of risk (Table 2). In the retrospective colorectal study, the plate and age-adjusted OR ‘per quartile’ = 1.25 (95%CI 1.18 – 1.33), p-trend=1.18×10−13; OR [Q4 vs. Q1] = 2.14 (95%CI 1.77–2.59), p-het=7.3×10−15. There was no evidence for an association in the prospective colorectal cancer study. The plate and age-adjusted OR ‘per quartile’ = 1.03 (95%CI 0.81 – 1.30), p-trend=0.82; OR [Q4 vs. Q1] = 1.13 (0.54–2.36), p-het=0.75.

Discussion

We have adapted a quantitative PCR assay (20, 43) to evaluate telomere length in lymphocytes in large-scale epidemiological studies. We have demonstrated that this methodology is reproducible in multiply-repeated assays, and shows the expected shortening of telomere length known to occur with increasing age.

We found a strong association between shorter telomere length and breast cancer status in the retrospective study, with an OR of ~15 for women in the bottom quartile of telomere length compared to the top quartile. The prospective breast cancer study showed some evidence of an association in the same direction, but the effect was not statistically significant. Thus, the hypothesis that mean telomere length in lymphocytes is a predictor of cancer risk appears to have been overstated. All but one of the previously-published studies looking at associations between breast cancer and mean telomere length, showed an association in the same direction as we report here. However, most of these studies have effect sizes of a similar magnitude to that seen in the prospective study, even though these were predominantly retrospective in collection. The only other truly prospective study of breast cancer risk and mean telomere length (39) gave a similar risk estimate to ours; OR (39) [‘below’ verses ‘above’ the median length] = 1.23 (95%CI 0.94–1.60), p-trend=0.20 vs. OR = 1.52 (95%CI 0.90–2.58), p-het=0.12, respectively.

It is possible that the results from the prospective studies have underestimated the true size of any association, perhaps due to the comparatively small size of these studies, or simply random measurement error. It is unlikely that study size has led to us missing a true association of the magnitude of the SEARCH retrospective study. In our breast cancer studies, there is very little overlap in the confidence intervals of the prospective and retrospective studies, and the work published by De Vivo et al. (39) shows similar point estimates and confidence intervals using a study twice the size of ours.

The effect we have seen in our substantial retrospective breast cancer study is much larger than previous reports. The level of statistical significance may, in part, be due to the increased study size, but the large Odds Ratio raises the possibility that this association may be artefactual, due to differences in DNA collection, storage, quality or other biases that retrospectively-collected studies are prone to. This Real Time PCR assay is very dependant on uniform DNA quality. However, we found little difference in mean telomere length between controls obtained from different sources. Furthermore, there was no correlation between DNA extraction yield, which we hypothesized may be related to DNA quality, and ΔCt value in the retrospective samples. No plate bias was apparent, when using the original, whole study ΔCt boundaries for quartile calculation, or when the quartiles of ΔCt were recalculated on a plate-by-plate basis (Supplementary Figure 2.). However, what is apparent from the ΔCt values along the x axes in Supplementary Figure 1. is that the ranges are different from experiment to experiment (n.b. the two prospective studies were assayed together in a single experiment). It should be noted that the mean telomere lengths generated from experiment to experiment are relative to that experiment, rather than absolute. They can vary according PCR efficacy and reagent batch, but these factors have little effect on rank or quartile assignment.

Risk of colorectal cancer was not significantly affected by mean telomere length in our prospective study of 185 cases and 406 controls (Table 2). This agrees with the other, similarly-sized studies published to date (40, 41) looking directly at colorectal cancer risk and mean telomere length. In the retrospective colorectal study, we found a significant association between mean telomere length and cancer status, but the effect was much weaker than that seen in the retrospective breast study; ‘per quartile’ OR = 1.25 (95%CI 1.18–1.33), p-het=1.8×10−13. It is possible that in a larger prospective colorectal cancer study, we may see a significant association, as there is a larger confidence interval overlap between our colorectal studies than that seen between the breast cancer studies, but we would not anticipate a large effect size.

The mean telomere length differences observed in these retrospective studies could be an effect of cancer treatment. Both radiotherapy and chemotherapy can cause DNA damage, but it is unclear whether this could have a significant effect in lymphocytes, or particular lymphocyte subpopulations. We found no evidence for any difference in ΔCt in the retrospective cases according to whether the recorded treatment involved chemotherapy, hormone therapy or radiotherapy (Supplementary Table 2). However, as we were not able to evaluate in detail the combinations and duration of treatment, it remains possible that telomere attrition is a response to a particular aspect of treatment or treatment regime. Alternatively, changes in telomere length may occur systemically during disease development. If so, mean telomere length could be an important screening marker, though not causally related to risk.

It could be that a more relevant measure of telomere length attenuation, with respect to cancer risk, is at the level of the individual chromosome, rather than the cellular mean. It may also be important to evaluate mean telomere length in solid tumor DNA and it would be interesting to investigate the relationship between this measurement and cancer development and treatment. These experiments are, however, beyond the scope of this particular study.

We believe we have excluded most artefactual bias as the cause for the difference between the results of our prospectively and retrospectively-collected studies, and so our data suggests that the majority of telomere attrition occurs after cancer diagnosis rather than before or during cancer development.

Supplementary Material

1
2

Acknowledgements

We would like to thank all the patients and control subjects who participated in both the SEARCH and EPIC studies. We would also like to thank the SEARCH team: Hannah Munday, Barbara Perkins, Patricia Harrington, Rebecca Mayes, Bridget Curzon, Clare Jordan, Judy West, Anabel Simpson, Anne Stafford and Sue Irvine; the local general practices and nurses, and the East Anglian Cancer Registry and EPIC-Norfolk investigators and management team for recruitment of the subjects for these studies. We also wish to thank Don Conroy, Craig Luccarini and Caroline Baynes for their technical assistance.

Funding: This work was supported by Cancer Research UK. K.A.P. is funded by Cancer Research UK project grant C1287/A9540. EPIC-Norfolk is supported by the Medical Research Council UK and Cancer Research UK, with additional support from the European Union, Stroke Association, British Heart Foundation, Department of Health and the Wellcome Trust. D. F. Easton is a Cancer Research UK principal research fellow, P. D. P. Pharoah is a Cancer Research UK senior clinical research fellow, and B. A. J. Ponder is a Gibb fellow of CRUK.

Footnotes

Disclosure of Potential Conflicts of interest: No potential conflicts of interest were disclosed.

Publisher's Disclaimer: 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.

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Supplementary Materials

1
NIHMS28790-supplement-1.doc (236KB, doc)
2
NIHMS28790-supplement-2.doc (67KB, doc)

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