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Clinical Cancer Research 13, 7037, December 1, 2007. doi: 10.1158/1078-0432.CCR-07-0432
© 2007 American Association for Cancer Research
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Imaging, Diagnosis, Prognosis

Telomere DNA Content Predicts Breast Cancer–Free Survival Interval

Christopher M. Heaphy1, Kathy B. Baumgartner2, Marco Bisoffi1,2, Richard N. Baumgartner2 and Jeffrey K. Griffith1,2

Authors' Affiliations: 1 Department of Biochemistry and Molecular Biology and 2 Cancer Research and Treatment Center, University of New Mexico School of Medicine, Albuquerque, New Mexico

Requests for reprints: Jeffrey K. Griffith, Department of Biochemistry and Molecular Biology, MSC08 4670, 1 University of New Mexico, Albuquerque, NM 87131-0001. Phone: 505-272-8432; Fax: 505-272-6587; E-mail: jkgriffith{at}salud.unm.edu.


   Abstract
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 Abstract
Materials and Methods
 Results
 Discussion
 References
 
Background: Telomeres are nucleoprotein complexes that protect chromosome ends from degradation and recombination. Critically shortened telomeres generate genomic instability. It has been postulated that the extent of telomere DNA loss is related to the degree of genomic instability within a tumor and therefore may presage clinical outcome. The objective of this investigation was to evaluate the hypothesis that telomere DNA content (TC) in breast tumor tissues predicts breast cancer–free survival interval.

Materials and Methods: Slot blot titration assay was used to quantitate TC in 530 archival breast tumor tissues in a population-based cohort. The relationships between TC, 12 risk factors for breast cancer adverse events (i.e., death due to breast cancer, breast cancer recurrence, or development of a new primary breast tumor), and breast cancer–free survival interval were evaluated by Fisher's exact test, log-rank analysis, and univariate and multivariate Cox proportional hazards models.

Results: TC was independent of each of the 12 risk factors. Ethnicity, tumor-node-metastasis stage, estrogen receptor, progesterone receptor, and p53 status, chemotherapy sequence, adjuvant therapy, and TC each conferred significant relative hazards. The best overall multivariate Cox proportional hazards model included TC, p53 status, tumor-node-metastasis stage, and estrogen receptor status as independent predictors of breast cancer–free survival interval (P < 0.00005). Low TC (≤200% of standard), relative to the high-TC group (>200% of standard), conferred an adjusted relative hazard of 2.88 (95% confidence interval, 1.16-7.15; P = 0.022) for breast cancer–related adverse events.

Conclusions: TC in breast cancer tissue is an independent predictor in this group of breast cancer–free survival interval.

Therapeutic management of breast cancer is complicated by the reality that conventional prognostic markers, such as patient age, tumor-node-metastasis (TNM) stage, and hormone receptor status, often do not identify women who will have a local or distant recurrence (1–3). Hence, many women are unintentionally overtreated or undertreated. For example, approximately one-third of women with breast cancer are node-negative at the time of diagnosis, of whom ~80% and 70% will survive for 5 and 10 years, respectively, if treated with surgery and radiotherapy alone (1). Adjuvant polychemotherapy in node-negative patients with ages <50 years improves 10-year survival from 71% to 78%, whereas in patients with ages 50 to 70 years, adjuvant therapy improves 10-year survival from 67% to only 69% (1). However, because currently available staging and prognostic markers cannot reliably identify the minority of women who will benefit from adjuvant therapy, the NIH/National Cancer Institute and St. Gallen guidelines each recommend adjuvant polychemotherapy for all women with moderate-risk to high-risk breast cancer (2, 3). Consequently, the majority of women with localized tumors have therapy-related side effects and reduced quality of life while gaining no therapeutic benefit (4). Thus, there is a pressing need for new markers that accurately predict the likelihood of breast cancer recurrence.

Tumorigenesis in humans is a multistep process in which successive genetic alterations, each conferring a selective advantage, drives the progressive transformation of normal cells into highly malignant cancer cells (5). Due to incomplete replication, telomeres, the nucleoprotein complexes that protect the ends of eukaryotic chromosomes from degradation and recombination, are shortened during each round of cellular replication (6), resulting in a reduction in telomere length with each cycle of chromosome replication (7, 8). Consequently, there is a limit to the number of doublings somatic cells can undergo before telomeres are critically shortened, become dysfunctional, and trigger successive rounds of chromosome breakage-bridge-fusion cycles, thus driving chromosome amplification, loss or structural rearrangement, and, consequently, tumorigenesis (5, 9–12).

The relationship between dysfunctional telomeres, genomic instability, and altered gene expression implies that tumors with the shortest telomeres have the most unstable genomes and, consequently, the greatest probability of aberrant gene expression. Likewise, tumors with the longest telomeres would be expected to have fewer genomic alterations and, therefore, lower probability of containing cells with the phenotypes associated with disease recurrence. Accordingly, several recent studies suggest telomere length may provide independent prognostic information for several solid tumors, including breast cancers (reviewed in ref. 13). However, measurement of telomere length in formalin-fixed, paraffin-embedded (FFPE) tissues that are typically available for retrospective studies is problematic due to the limited quantity and poor quality of the DNA that is recovered. Methods that are not affected by these limitations, such as telomere fluorescence in situ hybridization, are not well suited for the high-throughput analyses needed for large sample sets (14).

To circumvent these problems, we previously described a method for measuring telomere length in genomic DNA obtained from fresh, frozen, and, most importantly, FFPE tissues (15, 16). The content of telomere DNA sequences (TC) in a DNA sample is titrated by hybridization on a slot blot with a telomere-specific probe and then normalized to the quantity of total genomic DNA in the same sample, thus controlling for the differences in DNA ploidy that are frequent in solid tumors. TC is particularly well-suited for use with DNA from archival tissues: TC is directly proportional to telomere length measured by Southern blot (r = 0.904), can be measured with as little as 5 ng of genomic DNA, is insensitive to fragmentation of the DNA to <1 kb in length, and can be measured successfully in DNA from FFPE tissues stored for up to 20 years at room temperature (15–18).

Using this method, we have recently shown that TC is associated with breast cancer–free survival interval [relative hazard, 4.43; 95% confidence interval (95% CI), 1.44-13.64; P = 0.009], controlling for age at diagnosis and TNM stage (17). This study and other investigations (reviewed in ref. 13) provide strong evidence that TC predicts clinical outcome. However, our previous study had a retrospective design (which is more open to bias than the current prospective study), included a limited number (n = 77) of specimens collected in the mid 1980s and early 1990s, and was not controlled for the effects of adjuvant treatments and other clinical and prognostic variables. Therefore, it is unknown how TC would perform as a prognostic marker in a contemporary, population-based cohort, in which most tumors are detected by screening at earlier stages and many women elect breast-sparing surgery with adjuvant radiation, chemotherapy, or hormonal therapy.

In the current investigation, we addressed these questions by assessing the relationship between TC and breast cancer–free survival interval in FFPE tumor specimens obtained from 530 members of the New Mexico subset of the National Cancer Institute/Surveillance, Epidemiology, and End Results Health, Eating, Activity and Lifestyle (HEAL) prospective, population-based cohort (19).


   Materials and Methods
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 Abstract
 Materials and Methods
Results
 Discussion
 References
 
Tissue samples. The HEAL study is an ongoing population-based, multicenter prospective cohort study of women diagnosed with breast cancer designed to evaluate the association between body composition, hormones, diet, physical activity, and prognosis over time for non-Hispanic White, Hispanic, and African-American women ascertained through the Surveillance, Epidemiology, and End Results registries3 (19). In New Mexico, incident cases were ascertained by the New Mexico Tumor Registry. Eligibility was based on a first primary breast cancer diagnosis with in situ or stages I to IIIA breast cancer (based on the revised 2002 American Joint Committee on Cancer stage groupings; ref. 20) between July 1, 1996 and March 31, 1999, with ages 18 years or more, and residence in one of five centrally located New Mexico counties (Bernalillo, Santa Fe, Sandoval, Valencia, Taos). Women completed a postdiagnosis interview, blood draw, and anthropometric measurements. A total of 998 eligible first primary breast cancer cases were ascertained. Of the eligible cases, 615 patients (61%) chose to participate in the study. Participation rates were 55% for Hispanics and 64% for non-Hispanic Whites. Reasons for nonparticipation or exclusion included physician refusal (3%), unable to locate or interview subject (12%), and subject refusal (24%). Of the 615 total eligible patients for the study, 530 cases (86%) had slides retrieved for subsequent TC analysis, and there was no statistically significant difference in the block retrieval rates between cases with invasive and in situ disease. Lymph node status, tumor size, age, chemotherapy, adjuvant therapy, hormonal therapy, and menopausal status were based on medical record abstraction. Lymph node status was based on whether nodes were examined, and the number was identified as positive or negative for cancer. Ethnicity and family history were based on self-report at the time of interview. Coded data, stripped of all personal identifiers (Table 1 ), were provided by the HEAL investigators (R.N.B. and K.B.B.) and the New Mexico Tumor Registry, as approved by the University of New Mexico Human Research Review Committee. The mean age and follow-up of cohort members were 59.1 (range, 29-89; SD, 12.5) and 6.7 (range, 0.45-9.16; SD, 1.6) years, respectively. At the time of analysis, 83% of the cohort members were alive. Additionally, 85% of the cohort members were free of disease, either at time of analysis or at time of their non–breast cancer–related deaths.


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  		Table 1. Relative hazards of risk factors for breast cancer–related adverse events in the HEAL patient cohort by TC level

 
Histologic review. FFPE tissue sections were obtained from the original diagnostic material, stained with H&E and examined microscopically by a surgical breast pathologist. Tissue sections were not microdissected and typically contained from 75% to 100% tumor cells.

Determination of TC. DNA was extracted from four 10-µm FFPE tissue sections, and TC was measured in known masses, typically 5 to 10 ng, by slot blot titration assay, as previously described (17, 18). TC is expressed as a percentage of the TC in a placental DNA standard measured in parallel. Each measurement was repeated independently thrice and the coefficient of variation for each sample was <10%.

Immunohistochemistry. Immunohistochemistry was done on FFPE breast tumor sections to determine hormone receptor, p53, and HER2/neu status. Hormone receptor assays were conducted in laboratories associated with the hospitals, wherein cases were diagnosed. p53 protein expression was evaluated using the anti-p53 monoclonal antibody DO-7 (Santa Cruz Biotechnology), which recognizes both the mutated and wild-type protein (21). p53 tumor suppressor gene mutations occur in 20% to 50% of breast carcinomas (22) and have been reported to be associated with poor prognosis (23). Mutations in p53 are predominantly missense and lead to conformational alterations of the protein and accumulation in tumor cell nuclei (24, 25). The cutoff levels for staining for p53 are negative (no staining), focal (<5% staining), low (5-39% staining), and high (40-100% staining). HER2/neu protein expression was evaluated using the anti-HER2/neu monoclonal antibody CB11 (Santa Cruz Biotechnology). The cutoff levels for staining for HER2/neu are negative (no staining observed or membrane staining observed in <10% of tumor cells), focal (faint/barely perceptible membrane staining detected in >10% of tumor cells and cells only stained in part of their membrane), low (weak to moderate complete membrane staining observed in >10% of tumor cells), and high (moderate to strong complete membrane staining observed in >10% of tumor cells). The negative and focal groups are considered clinically negative; whereas, the low and high groups are considered clinically positive.

Statistical methods. The distribution of risk factors in the high-TC and low-TC groups (Table 1) was evaluated by the Fischer's exact test. Missing data for each risk factor was evaluated categorically in the analysis, but these data were not reported. The associations between TC and both overall survival interval and breast cancer–free survival interval were evaluated using log-rank Kaplan-Meier survival analyses. Univariate and multivariate Cox proportional hazards analysis was used to compute the relative hazards for breast cancer–related adverse events (i.e., death due to breast cancer, breast cancer recurrence, or development of a new primary breast tumor), and the best overall model, defined as the lowest overall model fit P value, is reported. Covariate-adjusted estimates of the survival function by level of TC (≤200% versus >200%) are the baseline survival estimates from a stratified proportional hazards model and were computed at the mean level of the covariates. Subjects were censored at the time lost to follow-up. P values of <0.05 were considered significant for all tests.


   Results
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 Results
Discussion
 References
 
TCs predict overall survival. To confirm prior associations observed between TC and overall survival interval, the cohort was initially divided into sixths, the survival interval for each group was calculated, and the results were evaluated for statistical significance by log-rank analysis. Groups with statistically indistinguishable survival intervals were combined, and the process was repeated until only groups with significantly different survival intervals remained. Using this process, the cohort was stratified into two TC groups: low TC was defined as ≤200% of the placental DNA control (n = 444), and high TC was defined as >200% of TC in the placental DNA control (n = 86). Log-rank analysis showed a significant relationship between TC group and overall survival interval (P = 0.025), with low TC predicting a shorter survival interval. The results are plotted by the method of Kaplan and Meier and shown in Fig. 1A . A univariate Cox proportional hazards model showed low TC had an unadjusted relative hazard of 2.25 (95% CI, 1.09-4.64; P = 0.029) relative to high TC (not shown). The relationship between TC group and overall survival interval in the subset of invasive tumors (i.e., without the 97 ductal carcinoma in situ cases) was also evaluated. In this subset, log-rank analysis also showed a significant relationship between TC group and overall survival interval (P = 0.046). The results are plotted by the method of Kaplan and Meier and shown in Fig. 1B. A univariate Cox proportional hazards model showed low TC had an unadjusted relative hazard of 2.06 (95% CI, 1.00-4.26; P = 0.05) relative to high TC (not shown).


Figure 1
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  		Fig. 1. Overall survival interval by TC in breast tumors. The set of all tumors (A) or invasive tumors only (B) was divided into two groups based on the low-TC and high-TC cutoff (200% of standard). Overall survival interval (in y) is shown on the x axis, and the surviving fraction is shown on the y axis. Subjects were censored at the time lost to follow-up. The log-rank test was used to test the significance (P) of the differences in the group's survival intervals. n, number of subjects in each group.

 
TCs predict breast cancer–free survival. Next, we refined our criteria to evaluate the prognostic value of TC in predicting breast cancer–related, adverse event–free survival interval. An adverse event was defined as death due to breast cancer, breast cancer recurrence, or development of a new primary breast tumor. Seventy-nine breast cancer–related adverse events had occurred by the time of the analysis, including 46 deaths, 15 recurrences, and 18 new primary breast tumors. A Kaplan-Meier plot and log-rank test (Fig. 2A ) showed significant differences in the groups' survival intervals (P = 0.009) with low TC, again predicting a shorter survival interval. A univariate Cox proportional hazards model showed low TC had an unadjusted relative hazard of 3.14 (95% CI, 1.27-7.76; P = 0.013) relative to high TC (Table 1). The relationship between TC group and breast cancer–free survival in the subset of invasive tumors was also evaluated. In this subset, log-rank analysis also showed a significant relationship between TC group and breast cancer–free survival interval (P = 0.032). The results are plotted by the method of Kaplan and Meier and shown in Fig. 2B. A univariate Cox proportional hazards model showed low TC had an unadjusted relative hazard of 2.61 (95% CI, 1.05-6.48; P = 0.039) relative to high TC (not shown). Similarly, although not statistically significant, results were shown in the subset of ductal carcinoma in situ cases (not shown).


Figure 2
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  		Fig. 2. Breast cancer–free survival interval by TC in breast tumors. The set of all tumors (A) or invasive tumors only (B) was divided into two groups based on the low-TC and high-TC cutoff (200% of standard). Breast cancer–free survival interval (in y) is shown on the x axis, and the recurrence-free fraction is shown on the y axis. See Fig. 1 for additional details.

 
TC is an independent predictor of breast cancer–free survival. The relative hazards for breast cancer–related adverse events associated with 12 categorical risk factors were evaluated individually by Cox proportional hazards analysis (Table 1). Ethnicity, TNM stage, estrogen receptor, progesterone receptor, and p53 status, chemotherapy sequence, adjuvant therapy, and TC each conferred significant (P < 0.05) relative hazards. There was no significant hazard associated with age at diagnosis, family history of breast cancer, HER2/neu, or postmenopausal status or hormonal therapy. Pair-wise analysis using Fisher's exact test showed no significant difference in the distribution of any of the risk factors in the low-TC and high-TC groups (Table 1).

Multivariate Cox proportional hazards models were developed using TC and all combinations of the covariates that conferred significant relative hazards (ethnicity, TNM stage, estrogen receptor, progesterone receptor, and p53 status, chemotherapy sequence, adjuvant therapy, and TC). The best overall model (Table 2 ), defined as the lowest overall model fit P value, included TC, p53 and estrogen receptor status, and TNM stage (P < 0.00005). Relative to the high-TC group, low TC conferred an adjusted relative hazard of 2.88 (95% CI, 1.16-7.15; P = 0.022). The chemotherapy, adjuvant therapy, and hormonal therapy covariates were strongly associated with TNM stage and with each other (P < 0.0001). Therefore, additional multivariate Cox proportional hazards models were developed using TC and chemotherapy, adjuvant therapy, and hormonal therapy as covariates, either alone or in combinations. The best overall models, defined as the lowest overall model fit P value, included TC and either chemotherapy or adjuvant therapy (P = 0.002); the addition of the hormonal therapy covariate had no effect. In the second model, low TC conferred an adjusted relative hazard of 2.84 (95% CI, 1.14-7.05; P = 0.025), relative to the high-TC group (not shown).


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  		Table 2. Relative hazards and 95% CIs from a multivariate Cox proportional hazards model of breast cancer–free survival interval from date of diagnosis of breast cancer

 

   Discussion
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 Abstract
 Materials and Methods
 Results
 Discussion
References
 
TC is a convenient proxy for telomere length that is particularly well-suited for the analysis of samples where DNA is degraded or scant, such as sections from archival, FFPE tissues (15, 16). We used this method to determine TC values in tumor tissue collected in a prospective, population-based cohort composed of 530 women and evaluated the associations of TC with clinical variables and end points, including overall and breast cancer–free survival intervals.

The principal conclusion from this investigation is that TC predicts breast cancer–free survival interval, independent of 12 clinical factors, prognostic markers, and adjuvant therapies. Tumors with TC of ≤200% of placental DNA standard conferred an adjusted hazard for breast cancer recurrence of 2.88 (95% CI, 1.16-7.15; P = 0.022). These results, obtained from a large population-based cohort, are in accord with our recent study (17) of breast tumors (predominantly TNM stage IIA and above) that also showed highly significant associations between TC and overall 5-year survival (P < 0.0001) and breast cancer–free survival interval (relative hazard, 4.43; 95% CI, 1.44-13.64; P = 0.009). Likewise, our previous investigation of prostate cancer (18) revealed that TC was also associated with time to prostate cancer recurrence (relative hazard, 5.02; 95% CI, 1.40-17.96; P = 0.013), controlling for age at diagnosis, Gleason sum, and pelvic node involvement. Similar results were obtained when analyses were done using the subset of invasive tumors, and a similar trend was observed in the subset of ductal carcinoma in situ cases. These data suggest that TC may be able to predict clinical outcome in both invasive tumors and ductal carcinoma in situ cases. As discussed above, adjuvant polychemotherapy in node-negative patients with ages <50 years improves 10-year survival from 71% to 78% (a 24% increase, i.e., seven per 29%), whereas in patients with ages 50 to 70 years, adjuvant therapy improves 10-year survival from 67% to only 69% (a 6% increase, i.e., two per 33%). A TC threshold of >200% of the standard defines a subgroup comprising of ~17% of the population-based cohort that have a significantly reduced risk of disease recurrence (7% at 8 years) that would be potential candidates for less aggressive adjuvant therapy. However, subsequent experiments in larger cohorts are needed to extend these findings.

The point estimate of the relative hazard for breast cancer recurrence associated with "low" TC was lower than in our prior investigation (2.88 versus 4.43), although the confidence intervals overlap. One possibility is that the discrepancy in the point estimates reflects the difference in the length of follow-up in the two studies. The mean, maximum, and interquartile ranges for follow-up in the HEAL cohort were 6.7, 9.2, and 1.5 years, respectively, versus 9.1, 23, and 11.2 years, respectively, in the prior study (17). The ongoing follow-up of the HEAL cohort will resolve this question. It is also important to consider that HEAL is a prospective study in which FFPE tissue samples were collected for participants at multiple independent sites at the time of diagnosis before the start of follow-up, rather than a retrospective study of archival tissues from a single facility, which is more open to inadvertent selection bias.

Another important difference between these two studies is that the TC threshold used to discriminate women at risk for breast cancer recurrence, >123% and >200% in the prior and present studies, respectively. This difference may also reflect the differences in the lengths of follow-up, in which case we would expect that the threshold will decrease as more deaths and adverse effects occur. Alternatively, the discrepancies in threshold, as well as the point estimates for the relative hazard ratios, could reflect either the larger number of specimens (530 versus 77) or the larger fraction of localized tumors (stages 0 and I) in the HEAL cohort and prior cohort (67% versus 14%).

Here, using the HEAL cohort, we have shown that TC predicts breast cancer–free survival interval independent of other risk factors. It is important to note that these other established risk factors, such as ethnicity, TNM stage, estrogen receptor, progesterone receptor, and p53 status, chemotherapy, and adjuvant therapy also conferred significant univariate relative hazards for breast cancer–related adverse events, confirming a representative population cohort. However, this population was not selected for TC (or any other biomarker) analysis and, thus, represents an unbiased assessment of TC as a prognostic factor. Telomere shortening has been associated with age in normal tissues (26); however, in this study, there was no association between TC and patient age, which is consistent with our previous results (17, 18). This indicates that telomere attrition due to tumorigenesis far exceeds the shortening contributed to age alone. Additionally, it must be noted that the cutoff established in this study, >200% of the placental DNA standard, exceeds the 95% CI for TC in several normal tissues (75-143% of standard), including breast (17). Speculatively, these longer telomeres may result from the early up-regulation of telomerase during tumor progression.

In summary, TC in tissues from breast tumors is an independent predictor in this group of breast cancer–free survival interval. In the future, TC, in combination with extant prognostic markers, could provide women and their physicians new information to guide therapeutic decisions. However, the assay in its current format, due to the relatively complex experimental procedure, is more suitable for use in a research rather than clinical setting. Therefore, development of a platform for TC determination that is simple and readily adaptable to a clinical laboratory is necessary before these findings can be validated in independent laboratories with independent cohorts.


   Acknowledgments
 
We thank William Hunt and Sharon Wayne for their help with the statistical analysis and Dr. Melanie Royce for her critical evaluation of the manuscript.


   Footnotes
 
Grant support: Department of Defense Breast Cancer Research Program grants DAMD17-01-1-0572, W81XWH-05-1-0226, W81XWH-05-1-0273, National Cancer Institute/Surveillance, Epidemiology, and End Results grant NO-1-CN-65034-29 and NCI-PC-05016-20, and NIH grant RR0164880.

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.

Note: Current address for K.B. Baumgartner and R.N. Baumgartner: Department of Epidemiology and Population Health, School of Public Health and Information Science, University of Louisville, Louisville, KY.

3 http://appliedresearch.cancer.gov/surveys/heal/ Back

Received 2/20/07; revised 7/18/07; accepted 8/23/07.


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Molecular Cancer Research Cancer Prevention Research
Cancer Prevention Journals Portal Cancer Reviews Online
Annual Meeting Education Book Meeting Abstracts Online