• breast cancer;
  • ionizing radiation;
  • single nucleotide polymorphisms;
  • DNA repair genes


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

High-dose ionizing radiation exposure to the breast and rare autosomal dominant genes have been linked with increased breast cancer risk, but the role of low-to-moderate doses from protracted radiation exposure in breast cancer risk and its potential modification by polymorphisms in DNA repair genes has not been previously investigated among large numbers of radiation-exposed women with detailed exposure data. Using carefully reconstructed estimates of cumulative breast doses from occupational and personal diagnostic ionizing radiation, we investigated the potential modification of radiation-related breast cancer risk by 55 candidate single nucleotide polymorphisms in 17 genes involved in base excision or DNA double-strand break repair among 859 cases and 1083 controls from the United States Radiologic Technologists (USRT) cohort. In multivariable analyses, WRN V114I (rs2230009) significantly modified the association between cumulative occupational breast dose and risk of breast cancer (adjusted for personal diagnostic exposure) (p = 0.04) and BRCA1 D652N (rs4986850), PRKDC IVS15 + 6C > T (rs1231202), PRKDC IVS34 + 39T > C (rs8178097) and PRKDC IVS31 − 634C > A (rs10109984) significantly altered the personal diagnostic radiation exposure-response relationship (adjusted for occupational dose) (p ≤ 0.05). None of the remaining 50 SNPs significantly modified breast cancer radiation dose-response relationships. The USRT genetic study provided a unique opportunity to examine the joint effects of common genetic variation and ionizing radiation exposure on breast cancer risk using detailed occupational and personal diagnostic exposure data. The suggestive evidence found for modification of radiation-related breast cancer risk for 5 of the 55 SNPs evaluated requires confirmation in larger studies of women with quantified radiation breast doses in the low-to-moderate range. © 2007 Wiley-Liss, Inc.

Breast cancer is the most common malignancy among women in the United States,1 but known risk factors (e.g. single or fractionated high-dose ionizing radiation exposure, certain heritable mutations, parity, age at first birth, obesity, oral contraceptive use) account for only 40% of breast cancer cases.2 Certain types of diagnostic radiographic procedures (particularly mammograms) have been postulated to be linked with increased risk of breast cancer, although epidemiological studies examining this relationship have been inconclusive.3 It has also been hypothesized that women with certain polymorphic variants in DNA repair genes, such as single nucleotide polymorphisms (SNPs), might have increased susceptibility of developing breast cancer, but the findings from recent pooled and meta-analyses have been largely null.4, 5, 6

Quantitative assessment of breast cancer risks associated with low-to-moderate doses of radiation in the general population is complicated by multiple requirements including the need for long-term and complete follow-up of large numbers of exposed and unexposed women in epidemiologic studies that include carefully performed reconstruction of lifetime estimated doses.3 Among female atomic bomb survivors, the excess relative risk (ERR) of breast cancer per Seivert (Sv) of radiation dose to breast tissue was estimated at 1.6, with doses among cases ranging from 0 to 6 Sv.7 This relationship was no longer detectable when the analysis was restricted to individuals with breast doses below 0.2 Sv.7

Dose-response relationships may be more apparent in subsets of women who are potentially more susceptible to radiation induced breast cancer as a result of certain genetic variants in DNA repair genes. If higher radiation-related breast cancer risks were found to be associated with specific genetic variants, the priority should be given to efforts to modify diagnostic and therapeutic radiation protocols for especially radiosensitive subgroups of women. Four previous studies have examined the joint effects of SNPs in DNA repair pathways and exposure to ionizing radiation on risk of breast cancer, but the exposure variables were either dichotomous (ever/never) or counts of radiologic procedures, and not all potentially relevant diagnostic procedures were included.8, 9, 10, 11

We undertook one of the first studies to evaluate the potential modification of the relation between low-to-moderate protracted radiation breast doses from occupational and personal diagnostic radiation sources and breast cancer risk by SNPs in DNA repair genes. We investigated 6 candidate SNPs in 3 base excision repair (BER) genes and 49 candidate SNPs in 14 DNA double strand break (DSB) repair genes. We chose to focus on BER and DNA DSB pathways because these are the primary pathways involved in the repair of the types of DNA damage induced by ionizing radiation.12

Among the unique features of this study were the extensive genotyping of major DNA repair genes, the carefully reconstructed cumulative occupational radiation breast dose estimates, the questionnaire-based cumulative diagnostic radiation breast exposure estimates and the availability of detailed information about reproductive, demographic and lifestyle factors derived from interviews of all subjects.

Material and methods

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Study population

In 1982, the U.S. National Cancer Institute, in collaboration with the University of Minnesota and the American Registry of Radiologic Technologists, initiated a study of cancer incidence and mortality among 146,022 (106,953 female) U.S. radiologic technologists who were certified for at least 2 years between 1926 and 1982. The cohort members are predominantly white (95%) and their current mean age is 55 years. During 1984–1989 and during 1993–1998, postal surveys were conducted that included detailed questions related to work history as a radiologic technologist, family history of cancer, reproductive history, height, weight, other cancer risk factors (such as alcohol and tobacco use), and information regarding health outcomes, including breast cancer. 69,524 of 98,233 (71%) and 69,998 of 94,508 (74%) known living female technologists responded to the first and second surveys, respectively (see13 for study participation details). This study has been approved by the human subjects review boards of the National Cancer Institute and the University of Minnesota.

Cancer confirmation and recruitment

All living female technologists reporting a primary breast cancer (ductal carcinoma in situ or invasive breast cancer) that was subsequently confirmed based on pathology or medical records were eligible for inclusion. In December 1999, when biospecimen collection began, there were 1402 living breast cancer cases with diagnosis years ranging from 1955 to 1998. By the end of December 2003, 871 (62%) breast cancer cases had provided informed consent, a blood sample, and responded to a telephone interview that collected updated cancer risk factor and family cancer history information and selected work history data. Another 84 cases could not be located or had an unlisted telephone number and did not respond to repeated correspondence inviting participation, 22 were too ill, 376 refused, and 49 could not arrange a blood draw or the draw was unsuccessful. Fifty-eight percent of the 871 cases were diagnosed with breast cancer after completing a questionnaire. The 369 cases with post-diagnostic questionnaire data were older at time of recruitment into the case-control study, began working earlier and worked for longer than cases with prediagnostic questionnaire data. The cases with postdiagnostic questionnaire data had greater occupational ionizing radiation breast doses (0.04 Gy versus 0.03 Gy), but there were no significant differences between these groups when comparing personal diagnostic ionizing radiation exposure, race, education, marital status, cigarette smoking, alcohol consumption, age at menarche, menopausal status, age at first live birth and number of live births.

Control selection

Controls were frequency matched to cases (ratio 1.5:1) by year of birth in 5 year strata. There were 2,268 living controls; 1,093 (48%) provided informed consent, a blood sample, and responded to the telephone interview. There were 250 controls who could not be located or had an unlisted telephone number and did not respond to repeated correspondence inviting participation, 36 were too ill, 839 refused, and 50 could not arrange a blood draw or the draw was unsuccessful.

Nonparticipants and decedents

Demographic and risk factor characteristics were compared among participant and nonparticipant cases and controls. For both cases and controls the proportion of African-Americans was lower among participants than nonparticipants, slightly more participants than nonparticipants used birth control pills, and participants were more likely to be from the Midwest than the Northeast US. Decedents who reported a breast cancer on the first or second survey but died before blood collection (N = 352) were older at breast cancer diagnosis, began working earlier, included more African-Americans, and smoked cigarettes longer than responders. Because the decedents began working earlier, they also had greater occupational ionizing radiation breast doses than study participants. Nonparticipants and decedents did not differ with participants when comparing education, marital status, personal diagnostic ionizing radiation exposure, alcohol consumption, age at menarche, menopausal status, age at first live birth and number of live births.


After venipuncture, whole blood samples were shipped overnight with an ice pack to the processing laboratory in Frederick, MD. Blood components were separated and DNA was extracted using Qiagen Kits (Qiagen, Valencia, CA). The samples were tracked by a unique ID code, and laboratory investigators were blinded to case-control status. Because of biospecimen contamination (n = 12), inadequate biospecimen quantity (n = 9) and incomplete survey data (n = 2), the final sample size consisted of 859 cases and 1,083 controls.

Sample genotyping

Candidate SNP selection methods for several variants evaluated here have been previously described in detail.14 In brief, SNPs with likely functional effects based on potential protein changes, evolutionary conservation and location in putative functional regions were identified. Using data from the Environmental Genome Project ( and the International HapMap Project (release 16) (, we selected 12 additional SNPs from PRKDC to capture the overall genetic diversity of the locus, irrespective of their possible functional relevance.15 A total of 55 SNPs (6 SNPs in 3 BER genes and 49 SNPs in 14 DSB repair genes) were selected for analysis (Supplementary Tables SII and SIII).

Samples were genotyped for SNPs using standard TaqMan or MGB Eclipse assays. Genotyping methods for various SNPs have been described previously16 and methods for specific genotype assays can be found at There were 115 quality control samples embedded randomly in the sample trays, comprised of between 9 and 14 replicate samples from the same individuals; laboratory personnel performed the genotyping blinded to the location of the quality control samples. Of 4,600 replicated samples for the 55 assays, there were 2 discrepancies resulting in a genotyping error of 0.04%. For the various SNP assays, genotypes could not be determined for 1 (0.05%) to 52 (2.7%) study subjects.

Occupational ionizing radiation exposure

The retrospective dose reconstruction effort for occupational radiation exposures to estimate absorbed doses to the breast has been described in detail elsewhere18 although there were some significant subsequent improvements.19 Briefly, the present version incorporates new dose factors [i.e., gray (Gy) to breast per sievert (Sv) of badge dose] that reflect temporal changes in X-ray machine tube potentials and filtration, more reliable estimates of photon transmission through protective aprons and shields, as well as substantially more badge dose readings for cohort members during the period before 1977.

In brief, the current occupational dosimetry system provides individual annual probability density functions that represent a distribution of possible breast doses. Monte Carlo methods were used to simulate 100 dose realizations from each annual dose density, and the mean of the 100 annual doses was taken to be the annual mean breast dose. For analysis in this study, we summed the means of each subject's annual distributions to derive a mean estimate of cumulative breast dose up to 10 years prior to breast cancer diagnosis for cases and an equivalent time point for controls. The “age at diagnosis” equivalent for controls was calculated by dividing cases and controls into year of birth tertiles and assigning controls the mean age at diagnosis of cases in that tertile. A 10-year lag for exposure was chosen because this is a generally accepted latency period for solid cancers, including breast cancer.20, 21, 22

Personal medical radiation exposure

From the 2 mailed surveys administered to the cohort, we used the self-reported numbers and calendar time periods of various diagnostic X-ray procedures to calculate a cumulative breast dose score. The score was calculated by multiplying the number of procedures by nominal estimates of breast doses from these procedures over time from previous publications23, 24, 25 ( and expert judgment by 2 medical radiation dosimetrists (M.S. and M.R.) (Supplementary Table SI). While the breast dose score is an approximation of Gy, because of the uncertainties in recall of various procedures and uncertainties with the nominal per procedure dose estimates, we prefer the term “cumulative breast dose score” rather than breast dose per se. Procedures that occurred within 10 years prior to breast cancer diagnosis for cases and an equivalent time point for controls were not included in the cumulative score; a 10-year lag also minimizes potential bias from procedures performed due to symptoms from preclinical disease.26

For radionuclide procedures we created an “ever/never” variable because information on the number of procedures subjects underwent was not available. Calendar year was available, so if the reported year was during the 10 years before breast cancer diagnosis (or equivalent year in controls) the subject was assigned to the “never” category.

We also created an “ever/never” variable for radiation therapy. Subjects were considered to be ever exposed if they received any cancer therapy or therapy for benign conditions in which breast tissue was likely to be located within the treatment field 10 years before breast cancer diagnosis (or equivalent year in controls). We assumed that radiation therapies for benign conditions in which breast tissue was not likely to be located within the treatment field would have been minor, and these procedures were not included. However, cancer therapy for sites more distant from breast tissue could still result in significant scatter radiation to the breast because of higher treatment doses, so these procedures were included in the “ever” exposed category.

Statistical analysis

For each SNP, the rare allele among controls was considered the variant allele. When <2% of the controls were homozygous variant, homozygous variant and heterozygous subjects were combined in 1 category. We assessed Hardy–Weinberg equilibrium (HWE) among controls using chi-square or Fisher's exact tests.

Associations between SNPs and breast cancer were evaluated using unconditional univariate and multivariable logistic regression. Main effects of occupational breast dose and personal diagnostic radiation breast dose score were assessed by modeling the odds ratio as a linear function in logistic regression models.27

  • equation image

D is continuous radiation dose and β is the excess odds ratio (EOR) per unit dose or dose score.

Occupational radiation dose and personal diagnostic radiation dose score were adjusted for each other. Adjusting for exposure from radiation and radionuclide therapies had little effect on the estimated risks (<10%) from occupational and personal diagnostic X-ray exposures.

To evaluate whether SNPs modified the relation between radiation and breast cancer risk, we allowed the radiation-related EOR to vary by genotype while adjusting for the genotype effect. Heterogeneity of the EOR across genotype categories was assessed using likelihood ratio tests (LRT). Because of the small numbers of individuals in some genotype categories, dose-response relationships were sometimes estimated as being below zero. In these instances the estimates were denoted as “<0.”

All regression models were adjusted for year of birth. Occupational radiation main effect estimates as well as occupational radiation effect estimates stratified by genotype were adjusted for personal diagnostic radiation dose score (categorically as seen in Table I) and vice versa. Adjustment for menopausal status, age at menarche, number of live births, age at first birth, family history of breast cancer, history of benign breast disease, oral contraceptive use, hormonal replacement therapy, body mass index, height, alcohol consumption and smoking did not substantially change genotype or radiation main effect estimates as well as radiation effect estimates stratified by genotype (<10%). So, these variables were not included in the final models.

Table I. Demographic and Ionizing Radiation Exposure Variable Distributions Among Cases and Controls, U.S. Radiologic Technologists Study
CharacteristicCases (%) (n = 859)Controls (%) (n = 1083)Odds ratio195% CIp-trend2
  • NA, not applicable.

  • 1

    All odds ratios adjusted for year of birth categories; occupational dose and personal diagnostic dose score analyses mutually adjusted (categorically); radionuclide procedure analysis adjusted for occupational dose categories, personal diagnostic dose score categories and radiation therapy categories; radiation therapy analysis adjusted for occupational dose categories, personal diagnostic dose score categories and radionuclide procedures categories.

  • 2

    Trend test with categories of interest modeled as continuous variables in logistic regression analyses; adjusted for applicable covariates as indicated above.

 Caucasian842 (98)1048 (97)1.0ReferentNA
 African American9 (1)18 (2) 
 Other8 (1)17 (2) 
Year of birth
 ≤1925120 (14)138 (13)1.0Referent0.7
 1926–1935195 (23)249 (23) 
 1936–1945292 (34)382 (35) 
 >1945252 (29)314 (29) 
Occupational ionizing radiation breast dose (Gy)
 0–0.05665 (77)859 (79)1.0Referent0.6
 >0.05–0.1108 (13)121 (11) 
 >0.1–0.265 (8)83 (8) 
 >0.221 (2)20 (2) 
Personal diagnostic X-ray breast dose score
 0–0.05686 (80)908 (84)1.0Referent0.05
 >0.05–0.1106 (12)104 (10) 
 >0.1–0.246 (5)51 (5) 
 >0.221 (2)20 (2) 
Radionuclide procedures
 Never721 (84)937 (87)1.0ReferentNA
 Ever65 (8)71 (7) 
 Unknown73 (9)75 (7)N/A   
Radiation therapy
 Never803 (93)1021 (94)1.0ReferentNA
 Ever24 (3)14 (1) 
 Unknown32 (4)48 (4)N/A   

Confidence intervals for genotype risk estimates were Wald-based while confidence intervals for radiation risk estimates were derived from the profile likelihood method. We used EPICURE software (Hirosoft, Seattle, WA) for analyses involving radiation exposure and SAS software (SAS Institute, Cary, North Carolina, Release 8.02) for all other analyses.


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Distributions of demographic and ionizing radiation exposure variables are presented in Table I. Increasing categories of personal diagnostic radiation breast dose score levels and a history of ever undergoing radiation therapy appear to be associated with breast cancer risk. In contrast, increasing occupational radiation breast dose levels and a history of ever undergoing radionuclide procedures do not demonstrate any significant relationships.

Genotype distributions are shown in Supplementary Table SII, with 3 of the variants deviating from HWE (p ≤ 0.05). The results of multivariable regression analyses of SNP genotype associations with breast cancer risks are presented in Supplementary Table SII. In multivariable analyses, PRKDC IVS27 + 542G > A and BRCA2 N289H heterozygotes were associated with statistically significantly decreased risks of breast cancer (OR = 0.8, 95% CI = 0.7–1.0 and OR = 0.7, 95% CI = 0.5–0.9, respectively).

Overall, cumulative occupational radiation breast dose (EOR/Gy = 1.4, 95% CI = <0, 4.0) and personal diagnostic radiation breast dose score (EOR/unit dose score = 1.3, 95% CI = <0, 4.0) were not statistically significantly associated with breast cancer risk in multivariable regression.

Table II and Supplementary Table SIII present results from tests of heterogeneity of dose-response relationships by genotype, including dose-response estimates by genotype and 95% confidence intervals. The WRN V114I variant statistically significantly modified the association between occupational dose and breast cancer risk (LRT p-value = 0.04) (Table II). BRCA1 D652N, PRKDC IVS15 + 6C > T, PRKDC IVS34 + 39T > C and PRKDC IVS31634C > T significantly altered the relationship between personal diagnostic dose score and breast cancer risk (LRT p-value = 0.02, 0.03, 0.05 and 0.002, respectively) (Table II). None of the remaining 50 SNPs significantly modified the occupational or personal diagnostic radiation dose relationships with breast cancer risk (Supplementary Table SIII).

Table II. Effect Modification of Occupational and Personal Radiographic Dose Response Relationships With Breast Cancer by DNA Repair Polymorphisms, U.S. Radiologic Technologists Study
GeneEntrez SNP ID1AA or nt variant ID2GenotypeCases (%) (n = 859)Controls (%) (n = 1083)Occupational radiation effect modificationDiagnostic radiation effect modification
EOR/Gy395% Confidence intervalp-value4EOR/unit breast dose score395% Confidence intervalp-value4
  • 1
  • 2

    Amino acid sequence variation (regular font), nucleotide sequence variation (italics).

  • 3

    Excess odds ratio (OR = 1 + EOR), adjusted for year of birth and occupational or personal diagnostic radiation dose.

  • 4

    Likelihood ratio test comparing the genotype specific EOR.

  • 5

    Because of the small numbers of individuals in some genotype categories, dose-response relationships were sometimes estimated as being below 0.

BRCA1rs4986850D652NGG698 (85)916 (85)1.2<05.1>0.50.1<02.70.02
GA/AA122 (15)165 (15)2.3<
 PRKDCrs1231202IVS15 + 6C > TCC753 (91)971 (90)1.6<
CT/TT77 (9)105 (10)<05<04.4<0<01.5
 PRKDCrs8178097IVS34 + 39T > CTT738 (89)964 (90)1.6<
TC/CC89 (11)110 (10)<0<08.2<0<02.1
 PRKDCrs10109984IVS31 − 634T > CTT305 (37)375 (35)1.2<07.2>
CT383 (46)522 (49)2.5<08.2<0<01.8
CC145 (17)177 (16)<0<05.2<0<04.5
 WRNrs2230009V114IGG720 (87)960 (89)0.6<<04.1>0.5
GA/AA109 (13)119 (11)<013.4


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

We found evidence of modification of the relation between low-to-moderate cumulative breast dose from protracted occupational radiation exposure and breast cancer risk by genotypes of WRN V141I. In our study population, we also found evidence of modification of the relation between cumulative personal diagnostic breast dose score and breast cancer risk by genotypes of BRCA1 D652N, PRKDC IVS15 + 6C > T, PRKDC IVS34 + 39T > C and PRKDC IVS31 − 634C > A. We found no evidence of effect modification by any of the remaining 50 candidate SNPs in the 17 genes studied.

We did observe statistically significant associations with decreased risk of breast cancer for BRCA2 N289H and PRKDC IVS27 + 542G > A (p ≤ 0.05). However, BRCA2 N289H was not associated with breast cancer risk in a large U.S. case-control study.5, 6 For PRKDC IVS27 + 542G > A, the decreased risk of breast cancer was restricted to heterozygotes, with homozygote variants demonstrating a nonsignificantly increased risk of breast cancer. Based on these findings, there was no compelling evidence of direct associations with BER and DNA DSB repair SNPs and breast cancer risk in our study of radiologic technologists. For many of the remaining SNPs, our findings were generally consistent with recent meta-analyses and other large studies of XRCC1 R194W, Q339R and R280H.6 Similarly, we found no association with ATM D1853N and breast cancer.28 In a study of U.S. Caucasians, no statistically significant associations were observed for BRCA2 N289H, BRIP1 S919P, LIGASEIV T9I, XRCC4 IVS7 − 1A > G, ZNF350 L66 and ZNF350 R501S.5 A recent large pooled analysis that included data from our study for 7 DNA repair SNPs did not demonstrate significant associations with breast cancer for any of these SNPs.4

Spurdle et al. (2002) found no differential effects of 2 ATM SNPs on breast cancer risk and self-reported X-ray exposure or radiation treatments to the chest.11 Figueiredo et al. (2004) found no evidence of effect modification between XRCC3 T241M or XRCC1 R339Q and self-reported ionizing radiation exposure during adolescence on risk of breast cancer.9 Both studies only considered ever/never exposures to radiation, and neither study considered diagnostic procedures to anatomic sites more distant from the chest that also expose the breast to radiation doses as great as or greater than a routine chest X-ray (Table SI).

Duell et al.8 observed slightly stronger associations between occupational and medical radiation exposures (ever/never) and breast cancer risk among women that were XRCC1 R339Q homozygote wildtype as compared to women carrying the variant allele. While formal tests were not reported, this difference did not appear to be statistically significant. Millikan et al.10 observed an exposure-response relationship between numbers of mammograms and breast cancer risk among women carrying variants at 2 or more loci in 4 DNA DSB repair genes (XRCC3 T241M, NBS1 E185Q, XRCC2 R188H and BRCA2 N372H) but did not observe this relationship in women carrying 0 or 1 variant.10 In contrast to the 10-year lag in our analysis, Millikan et al. used a 2-year lag, which may have been insufficient to entirely exclude mammograms performed for suspicion of breast cancer. The analysis was presented as an example of “pathway genotypes.”29 Ideally, pathway genotypes would use biological evidence or predictive algorithms to take into account various combinations of SNP genotypes that would have equivalent repair capacities.29 The assumption that the impact of variation at any 2 to 4 loci was equal may not always be biologically justifiable.

In the subset of U.S. radiological technologists who participated in this study, we observed breast cancer risk estimates for occupational and personal diagnostic X-ray exposures that were not statistically significant but were consistent with the estimate for female atomic bomb survivors aged 20–39 at the time of exposure.30 It is difficult to assign an age at exposure for protracted or episodic radiation from occupational or diagnostic sources because one could use the age first exposed, the age(s) when substantial radiation exposure occurred or the midpoint of the working lifetime. Nevertheless, the similarity in risk estimates provides support to our findings. Although cancer risks among populations exposed to low-to-moderate radiation doses are estimated to be quite low20 based on extrapolations from high-dose studies, it has been postulated that particular subgroups may be more susceptible to radiation-induced cancers at lower doses. In this study, we investigated the potential of DNA repair polymorphisms to increase the risk of radiation-realted breast cancer and found some suggestive results. Additional biological plausibility of our findings was provided by the relative consistency of the dose-response pattern by genotypes across exposures. For example, WRN V114I carriers demonstrated steeper dose response relationships for both occupational and personal diagnostic exposures (Table II).

Although Werner's syndrome, a rare recessive genetic disorder associated with a mutation in the WRN gene that disrupts its normal DNA helicase activity, has not generally been associated with increased radiation sensitivity,31 a recent study demonstrated increased radiosensitivity in human cell lines expressing truncated, yet functional, WRN protein.32 Similarly, in vitro and in vivo studies of individuals carrying familial BRCA1 mutations have not demonstrated increased radiosensitivity,33 but subtle changes in BRCA1 function rather than complete loss of function may result in increased radiation sensitivity. Our finding of effect modification with PRKDC IVS31 − 634C > A is supported by studies in the BALB/c mouse attributing increased susceptibility to radiation induced mammary tumors to a rare, variant form of the PRKDC gene.20, 34, 35

Our evaluation of effect modification was based on multiple tests, i.e., 1 for occupational and 1 for diagnostic exposure for each of the 55 SNPs. Although chance may explain our findings, we believe our interpretation of the results is appropriate because radiation exposure is an established breast cancer risk factor, and the magnitude of the differences in radiation-related risk observed for several SNPs was considerable (e.g., EOR/Gy = 10.9 for carriers of at least 1 variant allele of the WRN V114I SNP compared to EOR/Gy = 0.6 among noncarriers or EOR/unit dose score = 9.4 for carriers of at least one variant allele of the BRCA1 D652N SNP compared to EOR/unit dose score = 0.1 among noncarriers).

The breast doses from occupational radiation doses were derived from a comprehensive dose reconstruction system, and were a major strength of this study. Another strength of the study was the use of detailed questionnaire-based data on the numbers and timing of personal diagnostic procedures for study subjects to estimate a cumulative breast radiation dose score based on published estimates of breast doses for diagnostic procedures and the expert judgment of 2 specialists in medical radiation dosimetry. Given their profession, radiologic technologists may have better recall of the radiologic procedures performed on them than the general population. As with most case-control studies, response bias is a concern, but there was no significant difference in personal diagnostic dose score between cases with pre and postdiagnostic questionnaire data.

Limitations of this study include the less than ideal participation rates (62% and 48%, respectively), the use of prevalent rather than incident breast cancer cases, the inability to validate self-reported personal medical radiation exposure and the low power to detect effect modification when the variant was rare. However, comparisons of various characteristics between participants and nonparticipants did not reveal any significant differences and a recent analysis did not demonstrate significant differences in SNP genotype frequencies by levels of participation in control groups from 3 different studies.36 Also, prevalence of genotype frequencies by survival time between breast cancer diagnosis and blood collection did not yield any significant differences (results not shown). A similar analysis considering occupational and personal diagnostic ionizing radiation exposures was not possible because increased survival time was associated with greater age, which is associated with greater cumulative exposure among our study subjects. However, an analysis considering all types of cancers among atomic bomb survivors demonstrated no association between survival time and radiation dose.37

The USRT genetic study of breast cancer is the first to evaluate the joint effects of genotypes and occupational and personal diagnostic ionizing radiation breast dose on breast cancer risk. We report effect modification in specific DNA repair genes that require replication and must be followed up in studies with substantially larger numbers of women with quantified radiation doses in the low-to-moderate range.


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

We are grateful to the radiologic technologists who participated in the USRT Study; Jerry Reid of the American Registry of Radiologic Technologists for continued support of this study; Diane Kampa of the University of Minnesota for data collection and study coordination; Chris McClure of Research Triangle International, Inc. for tracing and data management.


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information
  • 1
    Jemal A, Siegel R, Ward E, Murray T, Xu J, Smigal C, Thun MJ. Cancer statistics, 2006. CA Cancer J Clin 2006; 56: 10630.
  • 2
    Madigan MP, Ziegler RG, Benichou J, Byrne C, Hoover RN. Proportion of breast cancer cases in the United States explained by well-established risk factors. J Natl Cancer Inst 1995; 87: 16815.
  • 3
    Ron E. Cancer risks from medical radiation. Health Phys 2003; 85: 4759.
  • 4
    Breast Cancer Association C. Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium. J Natl Cancer Inst 2006; 98: 138296.
  • 5
    Garcia-Closas M, Egan KM, Newcomb PA, Brinton LA, Titus-Ernstoff L, Chanock S, Welch R, Lissowska J, Peplonska B, Szeszenia-Dabrowska N, Zatonski W, Bardin-Mikolajczak A, et al. Polymorphisms in DNA double-strand break repair genes and risk of breast cancer: two population-based studies in USA and Poland, and meta-analyses. Hum Genet 2006; 119: 37688.
  • 6
    Zhang Y, Newcomb PA, Egan KM, Titus-Ernstoff L, Chanock S, Welch R, Brinton LA, Lissowska J, Bardin-Mikolajczak A, Peplonska B, Szeszenia-Dabrowska N, Zatonski W, et al. Genetic polymorphisms in base-excision repair pathway genes and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 2006; 15: 3538.
  • 7
    Lake SL, Lyon H, Tantisira K, Silverman EK, Weiss ST, Laird NM, Schaid DJ. Estimation and tests of haplotype-environment interaction when linkage phase is ambiguous. Hum Hered 2003; 55: 5665.
  • 8
    Duell EJ, Millikan RC, Pittman GS, Winkel S, Lunn RM, Tse CK, Eaton A, Mohrenweiser HW, Newman B, Bell DA. Polymorphisms in the DNA repair gene XRCC1 and breast cancer. Cancer Epidemiol Biomarkers Prev 2001; 10: 21722.
  • 9
    Figueiredo JC, Knight JA, Briollais L, Andrulis IL, Ozcelik H. Polymorphisms XRCC1-R399Q and XRCC3-T241M and the risk of breast cancer at the Ontario site of the breast cancer family registry. Cancer Epidemiol Biomarkers Prev 2004; 13: 58391.
  • 10
    Millikan RC, Player JS, Decotret AR, Tse CK, Keku T. Polymorphisms in DNA repair genes, medical exposure to ionizing radiation, and breast cancer risk. Cancer Epidemiol Biomarkers Prev 2005; 14: 232634.
  • 11
    Spurdle AB, Hopper JL, Chen X, McCredie MR, Giles GG, Newman B, Chenevix-Trench G, Khanna K. No evidence for association of ataxia-telangiectasia mutated gene T2119C and C3161G amino acid substitution variants with risk of breast cancer. Breast Cancer Res 2002; 4: R15.
  • 12
    Hall EJ, Giaccia AJ. Radiobiology for the radiologist, 6th edn. Philadelphia: Lippincott Williams & Wilkins, 2006.
  • 13
    Sigurdson AJ, Doody MM, Rao RS, Freedman DM, Alexander BH, Hauptmann M, Mohan AK, Yoshinaga S, Hill DA, Tarone R, Mabuchi K, Ron E, et al. Cancer incidence in the US radiologic technologists health study, 1983–1998. Cancer. 2003; 97: 30809.
  • 14
    Bhatti P, Church DM, Rutter JL, Struewing JP, Sigurdson AJ. Candidate single nucleotide polymorphism selection using publicly available tools: a guide for epidemiologists. Am J Epidemiol 2006; 164: 794804.
  • 15
    Carlson CS, Eberle MA, Rieder MJ, Smith JD, Kruglyak L, Nickerson DA. Additional SNPs and linkage-disequilibrium analyses are necessary for whole-genome association studies in humans. Nat Genet 2003; 33: 51821.
  • 16
    Stredrick DL, Garcia-Closas M, Pineda MA, Bhatti P, Alexander BH, Doody MM, Lissowska J, Peplonska B, Brinton LA, Chanock SJ, Struewing JP, Sigurdson AJ. The ATM missense mutation p.Ser49Cys (c.146C > G) and the risk of breast cancer. Hum Mutat 2006; 27: 53844.
  • 17
    Packer BR, Yeager M, Staats B, Welch R, Crenshaw A, Kiley M, Eckert A, Beerman M, Miller E, Bergen A, Rothman N, Strausberg R, et al. SNP500Cancer: a public resource for sequence validation and assay development for genetic variation in candidate genes. Nucleic Acids Res 2004; 32: D528D32. Database issue.
  • 18
    Simon SL, Weinstock RM, Doody MM, Neton J, Wenzl T, Stewart P, Mohan AK, Yoder RC, Hauptmann M, Freedman DM, Cardarelli J, Feng HA, et al. Estimating historical radiation doses to a cohort of U.S. radiologic technologists. Radiat Res 2006; 166: 17492.
  • 19
    Bhatti P, Preston DL, Doody MM, Hauptmann M, Kampa D, Alexander BH, Petibone D, Simon SL, Weinstock RM, Bouville A, Yong LC, Freedman DM, et al. Retrospective biodosimetry among United States radiologic technologists. Radiat Res 2007; 167: 72734.
  • 20
    National Research Council, Committee on the Biological Effects of Ionizing Radiations. BEIR VII: health risks from exposure to low levels of ionizing radiation, Washington D.C.: National Academies Press 1990.
  • 21
    United Nations Scientific Committee on the Effects of Atomic Radiation. Sources and effects of ionizing radiation: UNSCEAR 2000 report to the General Assembly, with scientific annexes, United Nations, 2000.
  • 22
    Land CE. Temporal distributions of risk for radiation-induced cancers. J Chronic Dis 1987; 40( Suppl. 2): 45S57S.
  • 23
    Betsou S, Efstathopoulos EP, Katritsis D, Faulkner K, Panayiotakis G. Patient radiation doses during cardiac catheterization procedures. Br J Radiol 1998; 71: 6349.
  • 24
    Kereiakes JG, Rosenstein M. Handbook of radiation doses in nuclear medicine and diagnostic X-rayed. Boca Raton, Florida: CRC, 1980.
  • 25
    Shapiro S, Strax P, Venet L. Evaluation of periodic breast cancer screening with mammography. Methodology and early observations. JAMA 1966; 195: 7318.
  • 26
    Boice JD,Jr, Morin MM, Glass AG, Friedman GD, Stovall M, Hoover RN, Fraumeni JF,Jr. Diagnostic X-ray procedures and risk of leukemia, lymphoma, and multiple myeloma. JAMA 1991; 265: 12904.
  • 27
    Preston DL, Mattsson A, Holmberg E, Shore R, Hildreth NG, Boice JD,Jr. Radiation effects on breast cancer risk: a pooled analysis of eight cohorts. Radiat Res 2002; 158: 22035.
  • 28
    Angele S, Romestaing P, Moullan N, Vuillaume M, Chapot B, Friesen M, Jongmans W, Cox DG, Pisani P, Gerard JP, Hall J. ATM haplotypes and cellular response to DNA damage: association with breast cancer risk and clinical radiosensitivity. Cancer Res 2003; 63: 871725.
  • 29
    Mohrenweiser HW, Wilson DM,III, Jones IM. Challenges and complexities in estimating both the functional impact and the disease risk associated with the extensive genetic variation in human DNA repair genes. Mutat Res 2003; 526: 93125.
  • 30
    Land CE, Tokunaga M, Koyama K, Soda M, Preston DL, Nishimori I, Tokuoka S. Incidence of female breast cancer among atomic bomb survivors, Hiroshima and Nagasaki, 1950–1990. Radiat Res 2003; 160: 70717.
  • 31
    Thompson LH, Schild D. Recombinational DNA repair and human disease. Mutat Res 2002; 509: 4978.
  • 32
    Kashino G, Kodama S, Suzuki K, Matsumoto T, Watanabe M. Exogenous expression of exonuclease domain-deleted WRN interferes with the repair of radiation-induced DNA damages. J Radiat Res (Tokyo) 2005; 46: 40714.
  • 33
    Baeyens A, Thierens H, Claes K, Poppe B, de Ridder L, Vral A. Chromosomal radiosensitivity in BRCA1 and BRCA2 mutation carriers. Int J Radiat Biol 2004; 80: 74556.
  • 34
    Ponnaiya B, Cornforth MN, Ullrich RL. Induction of chromosomal instability in human mammary cells by neutrons and gamma rays. Radiat Res 1997; 147: 28894.
  • 35
    Ullrich RL, Ponnaiya B. Radiation-induced instability and its relation to radiation carcinogenesis. Int J Radiat Biol 1998; 74: 74754.
  • 36
    Bhatti P, Sigurdson AJ, Wang SS, Chen J, Rothman N, Hartge P, Bergen AW, Landi MT. Genetic variation and willingness to participate in epidemiologic research: data from three studies. Cancer Epidemiol Biomarkers Prev 2005; 14: 244953.
  • 37
    Radiation Effects Research Foundation Department of Statistics. Effect of AHS participation on cancer survival. Radiat Effects Res Found News Views 2002; 13: 28.

Supporting Information

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

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