Telomere Content and Risk of Second Malignant Neoplasm in Survivors of Childhood Cancer: A Report from the Childhood Cancer Survivor Study

Abstract

PURPOSE

Shorter constitutional telomere length has been associated with increased cancer incidence. Furthermore, telomere shortening is observed in response to intensive chemotherapy and/or ionizing radiation exposure. We aimed to determine if less telomere content was associated with treatment-related second malignant neoplasms (SMNs) in childhood cancer survivors.

EXPERIMENTAL DESIGN

Using a nested case-control design, 147 cancer survivors with breast cancer, thyroid cancer, or sarcoma developing after treatment for childhood cancer (cases) were matched (1:1) with childhood cancer survivors without a SMN (controls). Cases and controls were matched by primary cancer diagnosis, years since diagnosis, age at time of sample collection, years of follow up from childhood cancer diagnosis, exposure to specific chemotherapy agents, and to specific radiation fields. We performed conditional logistic regression using telomere content (TC) as a continuous variable to estimate odds ratios (ORs) with corresponding 95% confidence intervals (CI) for development of SMN. ORs were also estimated for specific SMN types, i.e., breast cancer, thyroid cancer, and sarcoma.

RESULTS

There was an inverse relationship between TC and SMN, with an adjusted OR of 0.3 per unit change in telomere length to single copy gene ratio (95% CI, 0.09–1.02, p=0.05). Patients with thyroid cancer SMN were less likely to have more telomere content (OR 0.04, 95% CI, 0.00–0.55, p=0.01), but statistically significant associations could not be demonstrated for breast cancer or sarcoma.

CONCLUSIONS

A relation between less telomere content and treatment-related thyroid cancer was observed, suggesting that shorter telomeres may contribute to certain SMNs in childhood cancer survivors.

INTRODUCTION

Second malignant neoplasms (SMNs) are a well-recognized late-effect of cancer therapy, and have emerged as the leading cause of non-relapse-related late mortality after childhood cancer(1–3). The cumulative incidence of SMNs (excluding non-melanoma skin cancers) approaches 8% at 30 years from primary cancer diagnosis, and childhood cancer survivors are at a 3- to 6-fold increased risk of SMNs when compared with an age- and sex-matched general population(3–5). Exposure to radiation and specific chemotherapeutic agents is associated with an increased risk of developing SMNs(6, 7). Evidence that 28% of Childhood Cancer Survivor Study (CCSS) participants with a history of a second neoplasm will subsequently develop additional neoplasms(8), coupled with an increased risk of cancer observed in siblings and other family members of childhood cancer survivors with SMNs(9, 10), may suggest that, in addition to exogenous exposures, underlying genetic factors that increase genomic instability may contribute to increased SMN risk.

Telomeres are repetitive DNA-protein structures localized to chromosome ends that protect chromosome integrity by preventing end-to-end fusions as well as loss of proximal terminal coding regions during DNA replication. Telomere length is determined by associated telomeric proteins, i.e. telomerase, the telomeric environment, and genetic factors (11, 12). In somatic cells where sufficient telomerase is lacking, telomeric DNA shortens progressively with each cell division until a critically short length is reached. Critically short telomeres are recognized as DNA damage resulting in cellular senescence or apoptosis(13); thus, progressive telomere shortening serves as a molecular clock for cellular replicative aging. In the absence of cellular senescence or apoptosis, short telomeres lose their protective capacity, permitting chromosomal fusions and breakage-fusion-breakage cycles that may result in genomic instability and a potential for malignant transformation(12). Therefore, telomere shortening that naturally occurs with age has been proposed as a mechanism for the increased cancer risk observed in older populations(14). Indeed, the relation between risk for de novo cancers and estimates of average constitutional telomere length suggest that shortened telomeres may be a marker for cancer susceptibility(15–17).

In addition to the effect of aging upon telomere length, chemotherapy and ionizing radiation can significantly impair telomere maintenance and function in human cells(18). In fact, exposure to ionizing radiation has been shown to result in persistent irreparable damage to telomeric DNA(19). Thus, cancer therapy-induced telomere dysfunction may predispose cancer survivors to development of additional cancers. In the current study, we tested the hypothesis that less telomere content would be associated with SMNs among childhood cancer survivors.

MATERIALS AND METHODS

Participants

The study population was drawn from the CCSS: a multi-institutional retrospective cohort of 5+ year survivors of leukemia, brain tumor, Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL), Wilms tumor, neuroblastoma, soft tissue sarcoma, or bone tumor diagnosed before the age of 21 years and between 1970 and 1986(20). All participating institutions obtained local institutional review board approval for the CCSS protocol and participants provided informed consent for data collection, medical record abstraction, and banking of a biologic specimen. The current study was approved by the Baylor College of Medicine Institutional Review Board, where the qPCR analysis was performed.

Study Design

Utilizing a nested case-control design, CCSS participants with a confirmed SMN (cases) were compared to survivors who had not developed a SMN (controls). We restricted this analysis to 3 SMN types – breast cancer, thyroid cancer and sarcomas, thus capturing the most common SMNs observed in this population. To be eligible for the current study, subjects must have had a buccal cell DNA sample available in the CCSS biorepository and should not have received hematopoietic cell transplantation (HCT). Subjects with buccal cell samples procured prior to the development of their first SMN (n=41) were prioritized. However, in order to ensure adequate sample size, we did include subjects with samples procured after first (n=96) and second SMN diagnosis (n=10). Among the 10 subjects with a second SMN, those with breast cancer as a first SMN (n=5) had developed breast (n=4) and thyroid (n=1) as second SMN’s; those with sarcoma as first SMN (n=3) had developed sarcoma (n=2) and thyroid (n=1) second SMN’s; those with thyroid cancer as first SMN (n=2) had developed melanoma (n=1) and sarcoma (n=1) second SMN’s.

In selecting the appropriate control group for this study, our goal was to ensure that both cases and controls had equal opportunity for developing a SMN. Accordingly, controls were comprised of CCSS participants matched (1:1) to cases by primary diagnosis, age at time of sample collection (+/−10 years, with 87 [59.2%] pairs falling within ±2 years, 33 pairs [22.4%] falling between 2+ and 5 years, and 27 pairs [18.4%] falling within 5+ years), number of years between primary cancer diagnosis and sample collection (exceeded the latency between primary cancer diagnosis and development of SMN for the index case by a mean of 4.4 ± 6.7 years (with 79 [53.7%] pairs falling within ±2 years, 56 pairs [38.1%] falling between 2+ and 5 years, and 12 pairs [8.2%] falling within 5+ years), exposure (yes/no) to specific classes of chemotherapeutic agents (anthracyclines, alkylators, epipodophyllotoxins, other, none), and exposure (yes/no) to specific radiation fields (chest/spine, brain/neck/head, abdomen/pelvis, other, none). The case/control pairs were generated by density sampling and, as with cases, HCT recipients were excluded from consideration as controls.

Source of DNA and Methods of Isolation

Estimations of constitutional telomere length may be made using DNA extracted from blood, buccal cell samples, or fibroblasts as a representative cell population for the organism as a whole(21, 22). In individuals with an underlying defect of telomere biology, these three cell types have demonstrated significant intra-individual correlation when telomere content (TC) is measured by quantitative PCR(21). However, because telomere length may vary between cell populations, only subjects with DNA from mouthwash samples were included in this study, in order to minimize potential intra-individual variability (see Supplementary Methods and Supplementary Figures S1 and S2). Samples were collected using methods described previously(23), and genomic DNA was isolated from buccal cells using Qiagen kits. DNA was quantified using a NanoDrop spectrophotometer (ThermoScientific) after vortexing to ensure accurate and uniform concentration. Samples were stored at −80° C until time of use.

Quantitative PCR for Telomere Content

We used a quantitative PCR (qPCR) technique to measure TC in buccal cell DNA. The data generated are a relative rather than absolute quantification, with the value corresponding to the amount of telomere DNA amplified relative to a single copy gene (36B4). Because this technique estimates telomere DNA quantity relative to the quantity of a single copy gene, we refer to the output of this measure as ‘telomere content,’ or TC, rather than ‘telomere length.’ The qPCR technique, as first described by Cawthon et al(24), does correlate with telomere length measured by Southern blot of telomere restriction fragments(25), and was selected to accommodate the large number of samples and the limited quantity and quality of DNA available (see Supplementary Methods). Following PCR amplification, wells with threshold cycle (Ct) values greater than 0.5 from the middle value of each triplicate or those wells failing to amplify were excluded, so that the Ct values ascribed to the remaining two wells were averaged for the TC calculation. Case and control samples were intermixed at random so that the investigators were blinded to the case/control status at the time of TC measurement and calculation.

Statistical Analysis

To account for matching, we compared cases and controls with respect to the distributions of potential confounders using the Generalized Estimating Equation(26) when the outcomes were continuous or binary, and the bootstrap method(27) when the outcomes were from three or more categories (Tables 1 and 2). The Generalized Estimating Equation(26) was also used to compare cases and controls with respect to mean TC (Table 3). Odds ratios (ORs) and corresponding 95% confidence intervals (CI) for developing an SMN per unit change in TC (a continuous variable) were estimated by conditional logistic regression, adjusting for age at diagnosis of primary cancer, sex, race/ethnicity, smoking status, and family history of cancer in a first-degree relative. ORs were first estimated for all SMNs, and then by specific SMN type (Table 3). The same analyses were repeated using only the case/control pairs with case sample drawn prior to SMN diagnosis (Table 4), and then only pairs whose cases were exposed to ionizing radiation. All tests were two-sided, with a two-sided p value of < 0.05 considered statistically significant. All statistical analyses were performed using SAS software version 9.2 (SAS Institute, Cary, NC).

Table 1.

Distribution of Childhood Cancer Survivors with and without SMN

	Survivors with SMN (n = 147)		Survivor without SMN (n = 147)
	n	%	n	%
Primary Diagnosis
Leukemia	22	15	22	15
Central Nervous System Cancer	12	8	12	8
Hodgkin Lymphoma	63	43	63	43
Non-Hodgkin Lymphoma	9	6	9	6
Kidney (Wilms Tumor)	6	4	6	4
Neuroblastoma	2	1	2	1
Soft tissue sarcoma	17	12	17	12
Bone cancer	16	11	16	11
Time between primary diagnosis and specimen collection (years)
11–20	40	27	40	27
21–30	100	68	97	66
31–40	7	5	10	7
Age at time of specimen collection (years)
10–19	3	2	3	2
20–29	27	18	27	18
30–29	70	48	68	46
40+	47	32	49	33
Chemotherapy exposure
Alkylating agents (non-platinum)	81	55	81	55
Anthracyclines	49	33	49	33
Epipodophyllotoxins	4	3	4	3
Other agents	102	69	102	69
None of the above	38	26	37	25
Total (any drug)	103	70	104	71
Radiation field exposure
Head/brain/neck	92	63	92	63
Chest/spine	86	59	86	59
Abdomen/pelvis	71	48	71	48
Other location	9	6	1	1
Total (any radiation)	116	79	111	76
No radiation	31	21	36	24

Table 2.

Distribution of Childhood Cancer Survivors with and without SMN for Criteria Not Used in Matching

	Survivors with SMN (n = 147)		Survivor without SMN (n = 147)		P value
	n	%	n	%
Sex of patient					<0.001
Male	32	22	75	51
Female	115	78	72	49
Race					0.49
Non-Hispanic white	138	94	135	92
Other	9	6	12	8
Family history of cancer in a 1st degree relative					0.78
Yes	37	25	35	24
No	110	75	112	76
Smoking exposure by baseline					0.02
>1 pack/week × 5+ yrs	21	14	44	30
Had smoked, but ≤ 1 pack/week × 5+ yrs	12	8	10	7
Never	114	78	93	63
	Mean	SD	Mean	SD
Age at diagnosis of primary disease (yrs)	12.5	5.5	12.0	5.6	0.15

Table 3.

Association between TC and SMN, Cases vs. Controls

SMN	Number of case/control pairs	Mean TC ± SD			Unadjusted Odds Ratio (OR)		Adjusted Odds Ratio (OR)*
		Cases	Controls	p value	OR (95% CI)	p value	OR (95% CI)	p value
All SMN	147	0.56 ± 0.21	0.58 ± 0.26	0.64	0.80 (0.31 – 2.05)	0.64	0.30 (0.09 – 1.02)	0.05
Breast Cancer	68	0.55 ± 0.18	0.55 ± 0.25	0.87	0.87 (0.17 – 4.40)	0.87	0.91 (0.08 – 9.90)	0.94
Thyroid Cancer	48	0.54 ± 0.21	0.63 ± 0.30	0.09	0.24 (0.04 – 1.42)	0.12	0.04 (0.00 – 0.55)	0.01
Sarcoma	31	0.63 ± 0.27	0.54 ± 0.23	0.20	3.42 (0.47 – 25.08)	0.23	5.08 (0.17 – 155.92)	0.35

^*Adjusted for sex, race, family history, smoking status, and age at diagnosis of primary disease

Table 4.

Association between TC and SMN, Cases vs. Controls, with Case sample drawn prior to SMN diagnosis

SMN	Number of case/control pairs	Mean TC ± SD			Unadjusted Odds Ratio (OR)		Adjusted Odds Ratio (OR)*
		Cases	Controls	p value	OR (95% CI)	p value	OR (95% CI)	p value
All SMN	41	0.56 ± 0.20	0.65 ± 0.33	0.15	0.32 (0.06 – 1.72)	0.18	0.08 (0.01 – 1.11)	0.06
Breast Cancer	22	0.57 ± 0.15	0.65 ± 0.29	0.30	0.24 (0.01 – 5.02)	0.36	0.97 (0.03 – 36.98)	0.98
Thyroid Cancer	13	0.53 ± 0.28	0.69 ± 0.41	0.27	NA†		NA†
Sarcoma	6	0.57 ± 0.17	0.58 ± 0.28	0.97	NA†		NA†

^*Adjusted for sex, race, family history, smoking status, and age at the time of diagnosis of primary disease,

^†Unable to estimate ORs due to the small sample size.

RESULTS

A total of 159 SMN cases (breast cancer: n=75, thyroid cancer: n=49, and sarcoma: n=35) and 153 matched controls were identified. Two cases and two controls were excluded due to failure to amplify in all three wells or because the difference between all three values in the triplicate was greater than 0.5, leaving 157 cases and 151 controls. Exclusion of cases without matched controls resulted in a final analysis set of 147 case/control pairs. The 147 cases included 68 cases with breast cancer, 48 with thyroid cancer, and 31 with sarcoma. The clinical characteristics of the cases and controls are summarized in Table 1, and the distribution of potential confounding factors not included in the matching criteria are shown in Table 2, with corresponding p values. Significant differences between cases and controls were noted in the sex distribution, primarily reflective of the number of women developing breast cancer SMN, and smoking status, with more ‘>1 pack/week for 5+ years’ smokers among controls than cases.

TC was obtained for the 294 samples. All unknown Ct values were within the range of the standard curve, with correlation coefficients of ≥0.98. The average coefficient of variation for telomere Ct values was 0.58% (0.02% - 1.86%), and for 36B4 was 0.39% (0.004% - 1.28%). Paired analysis of TC indicated no significant difference in mean TC between the cases and controls for all SMNs (p=0.71), or when analysis was conducted by type of SMN (breast cancer, thyroid cancer, or sarcoma) (Table 3).

We conducted a multivariable analysis with TC as a continuous explanatory variable, adjusting for age at diagnosis of primary disease, sex, race, family history of cancer in a first-degree relative, and smoking status. For all SMNs, cases had less TC than controls (adjusted OR 0.3, 95% CI 0.09–1.02, p=0.05), a finding that appeared to be primarily driven by observations in cases with thyroid cancer (OR=0.04, 95% CI 0.00–0.55, p=0.01). This effect was not observed in the sarcoma and breast cancer SMN subgroups (Table 3).

We then examined only pairs where the case sample was taken before their SMN diagnosis (41 pairs) (Table 4). In this group, for all SMNs, the adjusted OR was 0.08 (95% CI 0.01–1.11, p=0.06), suggesting that SMN risk associated with less TC may not have been the result of additional treatment exposure for the SMN. For this sub-analysis, the population was too small to conduct the analysis for individual SMN types.

In order to understand the relation between TC and SMN among patients exposed to radiation, we restricted the analysis to case/control pairs with a history of exposure to radiation (116 pairs). In this group, for all SMNs, the adjusted OR was 0.38 (95% CI 0.09–1.70, p=0.21). Within the individual SMN types, the observed difference between cases and controls exposed to radiation was only significant for those pairs including cases with thyroid cancer as a SMN (OR 0.06, 95% CI 0.00–0.92, p=0.04). A similar analysis restricted to case/control pairs exposed only to chemotherapy was uninformative due to small sample size.

DISCUSSION

In this study, we demonstrated an association between less TC and SMNs in childhood cancer survivors, an observation primarily driven by subjects with secondary thyroid cancer. The three SMN types included in the current study, breast cancer, thyroid cancer, and sarcoma, are among the most prevalent SMNs in childhood cancer survivors and are associated primarily with exposure to radiation, and to a lesser extent chemotherapy(28–33), factors that are known to affect telomere maintenance. By selecting controls matched to cases by primary diagnosis and specific therapeutic exposures, we were able to account for the risk associated with these variables in the development of SMN.

Evidence exists for telomere shortening and down-regulation of telomerase activity after chemotherapy or radiation exposure in vitro(18), with effects that are irreversible(19). These results have also been noted in vivo, as both telomere shortening and reduced telomerase activity were noted in individuals who had received chemotherapy, compared with samples taken before the exposure(34). In addition, when compared with age-matched healthy controls, those who received chemotherapy had significantly shorter telomeres, suggesting that chemotherapeutic agents may accelerate the natural shortening that occurs with the aging process(35). Exposure to ionizing radiation also impairs telomere maintenance in vivo, as evidenced in Chernobyl workers exposed to low-dose ionizing radiation where telomeric shortening was seen even 20 years after exposure, further suggesting that the effect of radiation-induced telomere loss is prolonged(36). In a prospective study of HL patients, those with shorter telomeres, more complex chromosome rearrangements, and in vitro radiation sensitivity were more likely to develop SMN compared with those who did not demonstrate those characteristics(37). Therefore, individuals with constitutionally short telomeres or telomere damage after chemotherapy or radiation exposure may be at increased risk for developing SMN.

Our study suggests a relation between SMN and TC for all SMNs. Cases and controls were closely matched by age, diagnosis, and therapeutic considerations to allow each group equal opportunity to develop an SMN. Of note, significant differences in both sex distribution and tobacco exposure between the two groups may have acted as potential confounders, though adjustments for these factors were included in our statistical analyses. In considering specific SMN diagnoses, the association between TC and thyroid cancer, breast cancer and sarcoma varied among the three subtypes examined. A statistically significant association was observed between less TC and thyroid cancer. Risk for secondary thyroid cancer (in particular the papillary variant) has been associated primarily with radiation exposure, although associations with chemotherapy have also been described(28, 32). This risk exhibits a dose-response relationship, as demonstrated previously in the larger CCSS cohort, of which this study is a subset(28). Interestingly, constitutional telomere shortening has been described in subjects with familial papillary thyroid carcinoma, when compared with those with sporadic thyroid cancers(38, 39). Moreover, evidence of damage to telomeric DNA, such as increased number of telomeric fusions and spontaneous telomeric interactions, was observed in familial papillary thyroid cancer subjects when compared with healthy subjects and spontaneous thyroid cancer patients, suggesting an underlying genomic instability predisposing these patients to cancer(40). These findings support the existence of abnormalities in telomere maintenance specific to thyroid cancer with hereditary predisposition, which may be a contributing factor to the significant association between secondary thyroid cancer and shortened telomeres observed in our study population.

Our results fail to demonstrate a relation between TC and secondary breast cancer. Findings with regard to telomere length and risk for de novo breast cancer have been inconsistent. Though most retrospective studies show an association with shorter telomeres, this association loses significance when the question is examined prospectively suggesting that treatment effect may play a role in observed telomere shortening(41, 42). Therefore, the absence of an association with regard to secondary breast cancer is perhaps not surprising. We observed a statistically non-significant positive association between TC and secondary sarcoma. Of the three SMN types considered in the current study, the sarcoma population was the most limited, both by sample size and heterogeneity, as samples represented both soft tissue sarcomas and bone sarcomas such as Ewing’s sarcoma or osteosarcoma. Recently, a case-control study showed longer leukocyte telomeres to be associated with an increased risk for soft tissue sarcomas(43). In contrast, another case-control study examining constitutional telomere length and risk for de novo osteosarcoma demonstrated a statistically significant association with shortened telomeres among females(44). Osteosarcoma is often associated with chromosomal aneuploidy(45), suggesting a predisposing underlying chromosomal instability. However, no significant correlations with polymorphisms in telomere biology genes have been found thus far(44). In addition, the matched controls for our sarcoma SMN group consisted of a disproportionately high number of heavy smokers compared with the study population as a whole, which may have confounded the analysis, as smoking has been shown to shorten telomere length with each pack-year smoked(46). Although attempts were made to adjust for smoking status, an imbalance of tobacco exposure may in part explain the observation of less mean TC among the sarcoma cases. Furthermore, restricting the sarcoma population to those whose sample was collected prior to SMN reversed the direction of the association to what was observed in the breast and thyroid cancer populations.

The association between TC and the subsequent development of SMN is most reliable when the sample is taken prior to the diagnosis of the SMN. One limitation to our study was that a significant proportion of our SMN cases had samples taken after their SMN diagnosis, raising the concern for further reduction in telomere content as a result of additional chemotherapy rather than as an observation that preceded SMN diagnosis. We examined this possibility by conducting a sub-analysis among cases that had samples collected prior to SMN diagnosis, demonstrating a maintained association between less TC and SMN that was concordant with our hypothesis. Such an association would be further strengthened if there were a measurable trajectory of telomere attrition prior to the development of SMN, as was shown by Chakraborty et al to occur preceding development of therapy-related myelodysplasia(47).

Additional limitations include the relatively small cohort size, and the small amount of DNA available for analysis, which precluded our ability to validate our qPCR findings by Southern blot by probing for telomere DNA. Similarly, though it is the shortest telomere end, rather than average telomere length, that contributes to chromosomal instability(48), the limited amount of DNA available also prohibited performance of single telomere length analysis. There may be additional confounders potentially affecting telomere length, such as presence of gingival inflammation at the time of sample procurement, that we were unable to control for in our analysis due to lack of information. For example, we were unable to determine the influence of familial cancer syndromes on our data set, as information with regard to p53 or BRCA status, for example, was also not available. However, we did control for family history of cancer in a first-degree relative; furthermore, previous estimates of the proportion of familial cancer syndromes within the CCSS cohort, based upon self-reported family history, are low (9).

To our knowledge, this study represents the only investigation of an association between TC and SMNs in childhood cancer survivors. Our findings in this unique cohort suggest value in further study of shortened telomeres as a predisposing factor to development of SMN. Prospective validation of our results may elucidate whether subjects with SMN had short telomeres preceding their first diagnosis of cancer or had inappropriately rapid germline telomere attrition in response to chemotherapy or radiation. It is worth noting that the CCSS cohort is still relatively young, with the oldest subjects now in their fifth decade of life. Therefore, the effects of further age-related telomere shortening may yet be forthcoming, so that re-visiting this question in 5–10 years may provide a larger sample size and a stronger statistical comparison. Current recommendations for SMN surveillance are based upon known risk factors, such as patient demographics and therapeutic exposures(49). Methods for predicting risk for SMN in childhood cancer survivors have been proposed based upon statistical modeling that incorporate clinical and demographic variables(50). Prospective confirmation of our findings may lead to incorporation of telomere length measurements into such predictive algorithms in the future, thus targeting surveillance to those at highest risk for developing SMNs.

STATEMENT OF TRANSLATIONAL RELEVANCE.

Shortened constitutional telomeres are a well-established risk factor for development of primary malignancies. This association is thought to derive from genomic instability due to excessive telomere shortening, predisposing cells toward malignant transformation. In this study, we hypothesized that second cancers may also be associated with shortened telomeres. To investigate this hypothesis, we obtained biological specimens from the Childhood Cancer Survivor Study and performed a matched case-control analysis of telomere content between survivors with and without second malignant neoplasm (SMN). Our results suggest an association between shortened telomeres and SMN that is primarily driven by thyroid SMN. This study represents the first report of telomere content analysis in childhood cancer survivors. As approximately 1/3 of cancer survivors with SMN develop additional cancers, our results suggest a genetic predisposition for cancer related to telomere biology, whether due to inherited short telomeres, therapy-related telomere shortening, or other factors affecting telomere homeostasis.