[Skip to Navigation]
Sign In
Table 1.  Characteristics of Women With Contralateral Breast Cancer and Women With Unilateral Breast Cancer From the WECARE Study With Known Radiation Therapy Status
Characteristics of Women With Contralateral Breast Cancer and Women With Unilateral Breast Cancer From the WECARE Study With Known Radiation Therapy Status
Table 2.  Exposure to Radiation Therapy for First Primary Breast Cancer, Radiation Dose to the Contralateral Breast, and Risk of Contralateral Breast Cancer in the Women’s Environmental Cancer and Radiation Epidemiology Study
Exposure to Radiation Therapy for First Primary Breast Cancer, Radiation Dose to the Contralateral Breast, and Risk of Contralateral Breast Cancer in the Women’s Environmental Cancer and Radiation Epidemiology Study
Table 3.  Radiation Dose to the Contralateral Breast During Treatment for First Primary Breast Cancer and Risk of Contralateral Breast Cancer in the Women’s Environmental Cancer and Radiation Epidemiology Study
Radiation Dose to the Contralateral Breast During Treatment for First Primary Breast Cancer and Risk of Contralateral Breast Cancer in the Women’s Environmental Cancer and Radiation Epidemiology Study
Table 4.  NHEJ GRS, Location-Specific Radiation Dose, and Risk of Contralateral Breast Cancer in the Women’s Environmental Cancer and Radiation Epidemiology Studya
NHEJ GRS, Location-Specific Radiation Dose, and Risk of Contralateral Breast Cancer in the Women’s Environmental Cancer and Radiation Epidemiology Studya
1.
Chen  Y, Thompson  W, Semenciw  R, Mao  Y.  Epidemiology of contralateral breast cancer.  Cancer Epidemiol Biomarkers Prev. 1999;8(10):855-861.PubMedGoogle Scholar
2.
Lizarraga  IM, Sugg  SL, Weigel  RJ, Scott-Conner  CE.  Review of risk factors for the development of contralateral breast cancer.  Am J Surg. 2013;206(5):704-708. doi:10.1016/j.amjsurg.2013.08.002PubMedGoogle ScholarCrossref
3.
Bernstein  JL, Thompson  WD, Risch  N, Holford  TR.  Risk factors predicting the incidence of second primary breast cancer among women diagnosed with a first primary breast cancer.  Am J Epidemiol. 1992;136(8):925-936. doi:10.1093/oxfordjournals.aje.a116565PubMedGoogle ScholarCrossref
4.
van den Broek  AJ, van ’t Veer  LJ, Hooning  MJ,  et al.  Impact of age at primary breast cancer on contralateral breast cancer risk in BRCA1/2 mutation carriers.  J Clin Oncol. 2016;34(5):409-418. doi:10.1200/JCO.2015.62.3942PubMedGoogle ScholarCrossref
5.
Reiner  AS, John  EM, Brooks  JD,  et al.  Risk of asynchronous contralateral breast cancer in noncarriers of BRCA1 and BRCA2 mutations with a family history of breast cancer: a report from the Women’s Environmental Cancer and Radiation Epidemiology Study.  J Clin Oncol. 2013;31(4):433-439. doi:10.1200/JCO.2012.43.2013PubMedGoogle ScholarCrossref
6.
Shankar  A, Roy  S, Malik  A,  et al.  Contralateral breast cancer: a clinicopathological study of second primaries in opposite breasts after treatment of breast malignancy.  Asian Pac J Cancer Prev. 2015;16(3):1207-1211. doi:10.7314/APJCP.2015.16.3.1207PubMedGoogle ScholarCrossref
7.
Broeks  A, Braaf  LM, Huseinovic  A,  et al.  Identification of women with an increased risk of developing radiation-induced breast cancer: a case only study.  Breast Cancer Res. 2007;9(2):R26. doi:10.1186/bcr1668PubMedGoogle ScholarCrossref
8.
Graeser  MK, Engel  C, Rhiem  K,  et al.  Contralateral breast cancer risk in BRCA1 and BRCA2 mutation carriers.  J Clin Oncol. 2009;27(35):5887-5892. doi:10.1200/JCO.2008.19.9430PubMedGoogle ScholarCrossref
9.
Malone  KE, Begg  CB, Haile  RW,  et al.  Population-based study of the risk of second primary contralateral breast cancer associated with carrying a mutation in BRCA1 or BRCA2 J Clin Oncol. 2010;28(14):2404-2410. doi:10.1200/JCO.2009.24.2495PubMedGoogle ScholarCrossref
10.
Mellemkjær  L, Steding-Jessen  M, Frederiksen  K,  et al.  Risk of contralateral breast cancer after tamoxifen use among Danish women.  Ann Epidemiol. 2014;24(11):843-848. doi:10.1016/j.annepidem.2014.08.003PubMedGoogle ScholarCrossref
11.
Aihara  T, Tanaka  S, Sagara  Y,  et al.  Incidence of contralateral breast cancer in Japanese patients with unilateral minimum-risk primary breast cancer, and the benefits of endocrine therapy and radiotherapy.  Breast Cancer. 2014;21(3):284-291. doi:10.1007/s12282-012-0396-4PubMedGoogle ScholarCrossref
12.
Bertelsen  L, Bernstein  L, Olsen  JH,  et al; Women’s Environmental Cancer and Radiation Epidemiology Study Collaborative Group.  Effect of systemic adjuvant treatment on risk for contralateral breast cancer in the Women’s Environment, Cancer and Radiation Epidemiology Study.  J Natl Cancer Inst. 2008;100(1):32-40. doi:10.1093/jnci/djm267PubMedGoogle ScholarCrossref
13.
Kramer  I, Schaapveld  M, Oldenburg  HSA,  et al.  The influence of adjuvant systemic regimens on contralateral breast cancer risk and receptor subtype.  J Natl Cancer Inst. 2019. doi:10.1093/jnci/djz010PubMedGoogle Scholar
14.
Stovall  M, Smith  SA, Langholz  BM,  et al; Women’s Environmental Cancer and Radiation Epidemiology Study Collaborative Group.  Dose to the contralateral breast from radiotherapy and risk of second primary breast cancer in the WECARE Study.  Int J Radiat Oncol Biol Phys. 2008;72(4):1021-1030. doi:10.1016/j.ijrobp.2008.02.040PubMedGoogle ScholarCrossref
15.
Borrego-Soto  G, Ortiz-López  R, Rojas-Martínez  A.  Ionizing radiation-induced DNA injury and damage detection in patients with breast cancer.  Genet Mol Biol. 2015;38(4):420-432. doi:10.1590/S1415-475738420150019PubMedGoogle ScholarCrossref
16.
Pollard  JM, Gatti  RA.  Clinical radiation sensitivity with DNA repair disorders: an overview.  Int J Radiat Oncol Biol Phys. 2009;74(5):1323-1331. doi:10.1016/j.ijrobp.2009.02.057PubMedGoogle ScholarCrossref
17.
Lo  Y-L, Yu  J-C, Chen  S-T,  et al.  Breast cancer risk associated with genotypic polymorphism of the mitotic checkpoint genes: a multigenic study on cancer susceptibility.  Carcinogenesis. 2007;28(5):1079-1086. doi:10.1093/carcin/bgl256PubMedGoogle ScholarCrossref
18.
Siegel  RL, Miller  KD, Jemal  A.  Cancer statistics, 2018.  CA Cancer J Clin. 2018;68(1):7-30. doi:10.3322/caac.21442PubMedGoogle ScholarCrossref
19.
Xiong  Z, Yang  L, Deng  G,  et al.  Patterns of occurrence and outcomes of contralateral breast cancer: analysis of SEER data.  J Clin Med. 2018;7(6):E133. doi:10.3390/jcm7060133PubMedGoogle ScholarCrossref
20.
Bernstein  JL, Haile  RW, Stovall  M,  et al; WECARE Study Collaborative Group.  Radiation exposure, the ATM gene, and contralateral breast cancer in the women’s environmental cancer and radiation epidemiology study.  J Natl Cancer Inst. 2010;102(7):475-483. doi:10.1093/jnci/djq055PubMedGoogle ScholarCrossref
21.
Bernstein  JL, Thomas  DC, Shore  RE,  et al; WECARE Study Collaborative Group.  Contralateral breast cancer after radiotherapy among BRCA1 and BRCA2 mutation carriers: a WECARE Study report.  Eur J Cancer. 2013;49(14):2979-2985. doi:10.1016/j.ejca.2013.04.028PubMedGoogle ScholarCrossref
22.
Weischer  M, Nordestgaard  BG, Pharoah  P,  et al.  CHEK2*1100delC heterozygosity in women with breast cancer associated with early death, breast cancer-specific death, and increased risk of a second breast cancer.  J Clin Oncol. 2012;30(35):4308-4316. doi:10.1200/JCO.2012.42.7336PubMedGoogle ScholarCrossref
23.
Morton  LM, Sampson  JN, Armstrong  GT,  et al.  Genome-wide association study to identify susceptibility loci that modify radiation-related risk for breast cancer after childhood cancer.  J Natl Cancer Inst. 2017;109(11):djx058-djx058. doi:10.1093/jnci/djx058PubMedGoogle ScholarCrossref
24.
Robson  ME, Reiner  AS, Brooks  JD,  et al; WECARE Study Collaborative Group.  Association of common genetic variants with contralateral breast cancer risk in the WECARE Study.  J Natl Cancer Inst. 2017;109(10). doi:10.1093/jnci/djx051PubMedGoogle Scholar
25.
Bernstein  JL, Langholz  B, Haile  RW,  et al.  Study design: evaluating gene-environment interactions in the etiology of breast cancer: the WECARE study.  Breast Cancer Res. 2004;6(3):R199-R214. doi:10.1186/bcr771PubMedGoogle ScholarCrossref
26.
Langballe  R, Mellemkjær  L, Malone  KE,  et al; WECARE Study Collaborative Group.  Systemic therapy for breast cancer and risk of subsequent contralateral breast cancer in the WECARE Study.  Breast Cancer Res. 2016;18(1):65. doi:10.1186/s13058-016-0726-0PubMedGoogle ScholarCrossref
27.
Langholz  B, Goldstein  L.  Risk set sampling in epidemiologic cohort studies.  Stat Sci. 1996;11(1):35-53. doi:10.1214/ss/1032209663Google ScholarCrossref
28.
Reiner  AS, Sisti  J, John  EM,  et al; WECARE Study Collaborative Group.  Breast cancer family history and contralateral breast cancer risk in young women: an update from the Women’s Environmental Cancer and Radiation Epidemiology Study.  J Clin Oncol. 2018;36(15):1513-1520. doi:10.1200/JCO.2017.77.3424PubMedGoogle ScholarCrossref
29.
Huberman  M, Langholz  B.  Application of the missing-indicator method in matched case-control studies with incomplete data.  Am J Epidemiol. 1999;150(12):1340-1345. doi:10.1093/oxfordjournals.aje.a009966PubMedGoogle ScholarCrossref
30.
Boice  JD  Jr, Preston  D, Davis  FG, Monson  RR.  Frequent chest x-ray fluoroscopy and breast cancer incidence among tuberculosis patients in Massachusetts.  Radiat Res. 1991;125(2):214-222. doi:10.2307/3577890PubMedGoogle ScholarCrossref
31.
Boice  JD  Jr, Harvey  EB, Blettner  M, Stovall  M, Flannery  JT.  Cancer in the contralateral breast after radiotherapy for breast cancer.  N Engl J Med. 1992;326(12):781-785. doi:10.1056/NEJM199203193261201PubMedGoogle ScholarCrossref
32.
Bruzzi  P, Green  SB, Byar  DP, Brinton  LA, Schairer  C.  Estimating the population attributable risk for multiple risk factors using case-control data.  Am J Epidemiol. 1985;122(5):904-914. doi:10.1093/oxfordjournals.aje.a114174PubMedGoogle ScholarCrossref
33.
Carithers  LJ, Ardlie  K, Barcus  M,  et al; GTEx Consortium.  A novel approach to high-quality postmortem tissue procurement: the GTEx project.  Biopreserv Biobank. 2015;13(5):311-319. doi:10.1089/bio.2015.0032PubMedGoogle ScholarCrossref
34.
Jayasekera  J, Li  Y, Schechter  CB,  et al; CISNET-BOLD Collaborative Group.  Simulation modeling of cancer clinical trials: application to omitting radiotherapy in low-risk breast cancer.  J Natl Cancer Inst. 2018;110(12):1360-1369. doi:10.1093/jnci/djy059PubMedGoogle ScholarCrossref
35.
Winzer  KJ, Sauerbrei  W, Braun  M,  et al; German Breast Cancer Study Group.  Radiation therapy and tamoxifen after breast-conserving surgery: updated results of a 2 × 2 randomised clinical trial in patients with low risk of recurrence.  Eur J Cancer. 2010;46(1):95-101. doi:10.1016/j.ejca.2009.10.007PubMedGoogle ScholarCrossref
36.
Hughes  KS, Schnaper  LA, Bellon  JR,  et al.  Lumpectomy plus tamoxifen with or without irradiation in women age 70 years or older with early breast cancer: long-term follow-up of CALGB 9343.  J Clin Oncol. 2013;31(19):2382-2387. doi:10.1200/JCO.2012.45.2615PubMedGoogle ScholarCrossref
Original Investigation
Oncology
September 27, 2019

Association of a Pathway-Specific Genetic Risk Score With Risk of Radiation-Associated Contralateral Breast Cancer

Author Affiliations
  • 1Memorial Sloan Kettering Cancer Center, New York, New York
  • 2Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston
  • 3Department of Preventive Medicine, University of Southern California, Los Angeles
  • 4Fred Hutchinson Cancer Research Center, Seattle, Washington
  • 5Department of Epidemiology, University of Iowa, Iowa City
  • 6Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California
  • 7Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
  • 8Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
  • 9Danish Cancer Society Research Center, Copenhagen, Denmark
  • 10Beckman Research Institute, City of Hope National Medical Center, Duarte, California
  • 11Cedars-Sinai Medical Center, Los Angeles, California
  • 12Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, Arizona
  • 13National Council on Radiation Protection and Measurements, Bethesda, Maryland
  • 14Vanderbilt University, Nashville, Tennessee
  • 15New York University School of Medicine, New York
  • 16University of Cambridge, Cambridge, England
  • 17Genetics Institute, University of Florida, Gainesville
JAMA Netw Open. 2019;2(9):e1912259. doi:10.1001/jamanetworkopen.2019.12259
Key Points español 中文 (chinese)

Question  Is a genetic risk score comprising variants in a DNA repair pathway associated with risk of developing a second primary contralateral breast cancer among women who underwent radiation therapy for a primary breast cancer?

Findings  In this case-control study including 3732 women who received a diagnosis for a first invasive local or regional breast cancer when they were younger than 55 years, a genetic risk score comprising variants in a DNA repair pathway was associated with increased risk of a subsequent radiation-associated contralateral breast cancer. Among younger women with a high genetic risk score, the attributable increased risk for contralateral breast cancer associated with stray radiation dose was 28%.

Meaning  This genetic risk score may be a helpful tool to guide treatment for young women with breast cancer.

Abstract

Importance  Radiation therapy for breast cancer is associated with increased risk of a second primary contralateral breast cancer, but the genetic factors modifying this association are not well understood.

Objective  To determine whether a genetic risk score comprising single nucleotide polymorphisms in the nonhomologous end-joining DNA repair pathway is associated with radiation-associated contralateral breast cancer.

Design, Setting, and Participants  This case-control study included a case group of women with contralateral breast cancer that was diagnosed at least 1 year after a first primary breast cancer who were individually matched to a control group of women with unilateral breast cancer. Inclusion criteria were receiving a first invasive breast cancer diagnosis prior to age 55 years between 1985 and 2008. Women were recruited through 8 population-based cancer registries in the United States, Canada, and Denmark as part of the Women’s Environment, Cancer, and Radiation Epidemiology Studies I (November 2000 to August 2004) and II (March 2010 to December 2012). Data analysis was conducted from July 2017 to August 2019.

Exposures  Stray radiation dose to the contralateral breast during radiation therapy for the first breast cancer. A novel genetic risk score comprised of genetic variants in the nonhomologous end-joining DNA repair pathway was considered the potential effect modifier, dichotomized as high risk if the score was above the median of 74 and low risk if the score was at or below the median.

Main Outcomes and Measures  The main outcome was risk of contralateral breast cancer associated with stray radiation dose stratified by genetic risk score, age, and latency.

Results  A total of 5953 women were approached for study participation, and 3732 women (62.7%) agreed to participate. The median (range) age at first diagnosis was 46 (23-54) years. After 5 years of latency or more, among women who received the first diagnosis when they were younger than 40 years, exposure to 1.0 Gy (to convert to rad, multiply by 100) or more of stray radiation was associated with a 2-fold increased risk of contralateral breast cancer compared with women who were not exposed (rate ratio, 2.0 [95% CI, 1.1-3.6]). The risk was higher among women with a genetic risk score above the median (rate ratio, 3.0 [95% CI, 1.1-8.1]), and there was no association among women with a genetic risk score below the median (rate ratio, 1.3 [95% CI, 0.5-3.7]). Among younger women with a high genetic risk score, the attributable increased risk for contralateral breast cancer associated with stray radiation dose was 28%.

Conclusions and Relevance  This study found an increased risk of contralateral breast cancer that was attributable to stray radiation exposure among women with a high genetic risk score and who received a first breast cancer diagnosis when they were younger than 40 years after 5 years or more of latency. This genetic risk score may help guide treatment and surveillance for women with breast cancer.

Introduction

Survivors of invasive breast cancer have a high risk of developing asynchronous contralateral breast cancer.1,2 This risk is increased among women who are relatively young when they receive the first diagnosis,3,4 have a family history of breast cancer,5,6 or have high-penetrance mutations.7-9 Treatment with tamoxifen, aromatase inhibitors, or chemotherapy is associated with reduced risk of contralateral breast cancer,10-13 while stray radiation doses received to the contralateral breast during radiation therapy of the first primary tumor are associated with increased risk.14 Although radiation therapy is an effective cancer treatment, stray radiation during radiation therapy produces potentially carcinogenic DNA damage in unaffected tissue. Radiation exposure induces a variety of lesions in DNA, the most dangerous of which are DNA double strand breaks. In humans, double strand breaks are repaired primarily by the relatively error-prone nonhomologous end-joining (NHEJ) pathway, whereas the less error-prone homologous recombination pathway is active mainly during the G2 phase of actively cycling cells.15 Research has shown that mutations in DNA damage response genes are associated with hypersensitivity to ionizing radiation and a high incidence of cancer.16 Additionally, a 2007 study17 among Taiwanese women found that variants in 2 genes within the NHEJ pathway were associated with breast cancer risk. As breast cancer is the most common malignant neoplasm among women in the United States18 and radiation therapy is used to treat more than half of women with breast cancer,19 identifying risk factors for radiation-associated contralateral breast cancer is an important issue.

To date, only rare, high-penetrance genetic mutations have been evaluated in radiation-associated contralateral breast cancer, to our knowledge.7,20-22 A recent genome-wide association study of survivors of childhood cancers treated with radiation therapy identified several loci that may interact with radiation exposure to increase risk of subsequent breast cancer,23 but it is uncertain whether risk of cancer after radiation therapy among children can be generalized to risk of cancer after radiation therapy among adults. Genetic risk scores (GRSs), which aggregate the associations of many genetic variants, are a promising method to identify associations that are not detectable for individual variants.24 Therefore, we genotyped single nucleotide polymorphisms (SNPs) in women with contralateral breast cancer (case group) and individually matched women with unilateral breast cancer (control group) in the Women’s Environmental Cancer and Radiation Epidemiology (WECARE) Study to develop a GRS that captures variation in risk of contralateral breast cancer associated with stray radiation exposure. We considered several approaches to develop this GRS, including aggregating SNPs associated with contralateral breast cancer in the WECARE Study, aggregating SNPs associated with contralateral breast cancer in the literature,24 and aggregating SNPs located in or near genes in specific DNA damage response pathways.

In this study, we focused on a GRS comprising genes in the NHEJ DNA damage response pathway, which is the most common pathway for DNA double strand break response in humans in all phases of the cell cycle. The rationale for focusing on the NHEJ pathway is that genetic variation in genes involved in DNA damage response is associated with cancer risk16,20; we hypothesized that having a greater number of alleles associated with cancer risk in the NHEJ pathway would be associated with a greater risk of contralateral breast cancer associated with exposure to stray radiation. By aggregating SNPs in and adjacent to the 7 genes encoding the NHEJ pathway, we assessed whether a high score on this pathway-specific NHEJ GRS was associated with a high risk of contralateral breast cancer subsequent to stray radiation exposure in the WECARE Study.

Methods
Study Population

The WECARE Study is a multicenter, population-based, case-control study of women with contralateral breast cancer as the case group and individually-matched women with unilateral breast cancer as the control group. Participants were recruited through 8 population-based cancer registries in the United States, Canada, and Denmark in 2 phases: the WECARE Study I,25 which recruited from November 2000 to August 2004, and the WECARE Study II,26 which recruited from March 2010 to December 2012. Participants included women who had a first invasive local or regional breast cancer that was diagnosed between 1985 and 2008 and a subsequent primary invasive cancer or carcinoma in situ in the contralateral breast (≥1 year after the first primary diagnosis for the WECARE Study I,25 and ≥2 years after the first primary diagnosis in the WECARE Study II26) with no cancers in the intervening period. Breast cancer primary status was further confirmed by the Surveillance, Epidemiology, and End Results program for participants in the United States and by medical record review after identification in the Danish Cancer Registry for women in Denmark. The control group included women who had received a first primary local or regional breast cancer and no subsequent contralateral breast cancer. Women in the control group were individually matched with women in the case group at a ratio of 2 to 1 in the WECARE Study I25 and a ratio of 1 to 1 in the WECARE Study II26 by age when they received the first breast cancer diagnosis (5-year strata), diagnosis year (4-year strata), cancer registry region, and self-reported race/ethnicity. The at-risk period for a woman with contralateral breast cancer was the time between when she received the first breast cancer diagnosis to when she received the second breast cancer diagnoses. Women in the control group were randomly selected from women in the risk set who were living and had not been diagnosed with any cancer during the interval after they received the first diagnosis corresponding to the length of the at-risk period of the matching woman with contralateral breast cancer. In the WECARE Study I,25 case-control sets were counter-matched such that exactly 2 members of the triad had cancer registry–reported radiation treatment for the primary breast cancer, which increased power to detect gene-environment interactions for contralateral breast cancer. A summary of enrollment criteria for each phase of the WECARE Study is provided in eTable 1 in the Supplement.

The study was approved at each participating research center by its institutional review board and, in Denmark, additionally by the ethics committee system. Participants gave verbal informed consent for a telephone interview that collected sociodemographic and clinical data, including a detailed breast cancer risk-factor history. After obtaining written informed consent, biological specimens were collected for DNA extraction and genotyping (peripheral blood cells in WECARE Study I25 and saliva in WECARE Study II26). Data on tumor characteristics and treatment were obtained from cancer registries and by abstraction of medical records. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Quantification of Radiation Dose to the Contralateral Breast

Radiation dose estimation methods have been detailed previously.25 Briefly, radiation dose to the contralateral breast was quantified for 4 quadrants (ie, upper left, upper right, lower left, lower right) and the areola region. The dose estimates were based on individual radiation parameters (eg, field locations and sizes, energy of the radiation beam, and radiation therapy dose delivered) from available information, including radiation therapy records, summary notes, and abstracted physician correspondence from medical records. Stray doses to the contralateral breast quadrants and areola were measured using lithium fluoride powder thermoluminescent dosimeters placed in tissue-equivalent phantoms, molded on women in treatment position. All reported doses are location-specific to estimate the radiation dose received by the affected quadrants or areola of the woman’s contralateral breast during treatment for the first breast cancer and the corresponding region in the unaffected breast of her matched control, as previously described.14

SNP Genotyping

In the WECARE Study I,25 SNPs were genotyped using the HumanOmni1-Quad BeadChip platform (Illumina). In the WECARE Study II,26 SNP genotyping was conducted using 2 custom oligonucleotide probe panels using the Infinium iSelect HD Custom BeadChip (Illumina). Quality control methods have been detailed previously.24 Self-reported race/ethnicity was confirmed by principal components analysis. To reduce the probability of confounding by population stratification, final analyses using genetic data were restricted to non-Hispanic white women.

Development of the NHEJ GRS

The NHEJ GRS is comprised of SNPs in or near the 7 genes in the NHEJ pathway: DCLRE1C (OMIM 602450), LIG4 (OMIM 606593), NHEJ1 (OMIM 611291), PRKDC (OMIM 600899), XRCC4 (OMIM 194363), XRCC5 (OMIM 194364), and XRCC6 (OMIM 152690). Ninety-three SNPs in the pathway passed quality control, and 24 SNPs were excluded owing to strong linkage disequilibrium (r2 > 0.5) with others in the pathway. To our knowledge, there are no reported significant associations of SNPs in the NHEJ pathway with radiation-associated second cancers. Therefore, for the final 69 SNPs, we determined the alleles associated with contralateral breast cancer risk by the directionality of the main association with contralateral breast cancer risk among all women in the WECARE Study. We chose to use the direction of association from the entire study population rather than only among those exposed to radiation to ensure that the NHEJ GRS was independent of the interaction between the score and radiation exposure. The final NHEJ GRS is the sum of risk alleles (0, 1, or 2) across all 69 NHEJ SNPs. Women in the case group and the control group were classified as high NHEJ GRS if their score was greater than the median score of the all participants, 74 (range, 57-93) alleles, and low NHEJ GRS if their score was less than or equal to the median score.

Statistical Analysis

Multivariable-adjusted rate ratios (RRs) and corresponding 95% CIs were estimated using conditional logistic regression. Women in the control group were sampled from the failure time risk sets of the women in the case group, so the RR estimates are equivalent to those obtained in a proportional hazards model of cohort data.27 To account for the counter-matched WECARE Study I25 design, statistical models included a log-weight offset term. The WECARE Study II26 participants, who were matched in pairs without counter-matching on radiation therapy, were assigned an offset equal to 1. All statistical models were adjusted for known and suspected contralateral breast cancer risk factors.28 Several important covariates had missing values for women in the case and control groups. To address missing data, we used the missing indicator method for a matched case-control study, which improves statistical efficiency relative to a complete-case analysis and is preferred to an unmatched analysis of incomplete pairs, which is vulnerable to additional confounding.29

First, we estimated the association of radiation exposure with contralateral breast cancer risk. Models were developed separately for radiation therapy ever vs never exposure and for location-specific dose received to the contralateral breast (categorized as 0 Gy [to convert to rads, multiply by 100], >0 to <1.0 Gy, and ≥1.0 Gy). Stratified analyses were defined a priori by age at first diagnosis (<40 years vs ≥40 years) and latency between first and second cancers (<5 years vs ≥5 years), based on previous WECARE Study results.30,31 For each model of radiation dose and contralateral breast cancer risk, a single stratified model was developed to obtain stratum-specific estimates while avoiding overfitting within subgroups. Statistical tests for trend were performed across the dose categories.

For the primary analysis, we examined the joint associations of the NHEJ GRS and radiation dose with contralateral breast cancer risk, fitting conditional logistic regression models with stratification as described and with additional adjustment for 3 eigenvectors obtained in principal components analysis. We estimated the population-attributable risk fraction of contralateral breast cancer associated with radiation exposure from these results.32

We assessed the robustness of the NHEJ GRS associations by adjusting for a GRS by Robson et al24 that captures common genetic variation in known breast cancer susceptibility loci. This GRS was developed by aggregating SNPs associated with first breast cancer risk and was also associated with contralateral breast cancer risk in the WECARE Study.

All analyses were performed in SAS/STAT software version 9.4 (SAS Institute). All statistical tests were 2-sided, and statistical significance was set at less than .05.

Results
Characteristics of the Study Population

A total of 5953 women were approached for enrollment in the WECARE Study I25 or WECARE Study II,26 of whom 3732 (63%) gave informed consent and participated. Participant characteristics are presented in Table 1. Most participants (88%) reported non-Hispanic white race/ethnicity. Location-specific dose reconstruction was not possible for 600 women (262 women in the case group and 338 women in the control group) owing to multifocal tumors or incomplete treatment or location data, leaving 3132 women available for analyses of radiation dose and contralateral breast cancer risk. For the primary analysis involving the NHEJ GRS, we excluded 278 women with genotyping quality control issues and 347 women who were not white or were Hispanic, leaving 2507 women with location-specific dose data and successful genotyping.

Overall Association of Radiation Exposure With Contralateral Breast Cancer Risk

All models were stratified by age at first diagnosis (<40 years vs ≥40 years) and latency (<5 years vs ≥5 years). Ever having received radiation therapy was associated with increased contralateral breast cancer risk among women younger than 40 years when they received the first diagnosis after a latency of 5 years or more (RR, 1.7 [95% CI, 1.1-2.6]) (Table 2). The risk of contralateral breast cancer was not associated with radiation therapy among women younger than 40 years with less than 5 years of latency (RR, 1.3 [95% CI, 0.8-2.1]) or among women who received the first breast cancer diagnosis 40 years or older with less than 5 years of latency (RR, 0.9 [95% CI, 0.7-1.2]) or with more than 5 years of latency (RR, 1.0 [95% CI, 0.8-1.2]).

Exposure to location-specific stray radiation dose of 1.0 Gy or more was associated with contralateral breast cancer risk among women who received their first breast cancer diagnosis when they were younger than 40 years and with 5 years or more of latency compared with those who were not exposed to stray radiation (RR, 2.0 [95% CI, 1.1-3.6]). There was a statistically significant dose trend (P = .03). No association was observed between location-specific stray radiation dose and contralateral breast cancer risk in the overall study population (RR, 1.1 [95% CI, 0.9-1.3]), or other age or latency subgroups (Table 3).

Joint Associations of NHEJ GRS and Radiation Dose on Risk of Contralateral Breast Cancer

Contralateral breast cancer risk was not associated with the interaction between individual SNPs in the NHEJ GRS and radiation dose after Bonferroni correction for multiple comparisons (corrected α-level of statistical significance = 7.3 × 10−4) (eTable 2 in the Supplement). The NHEJ GRS was approximately normally distributed (eFigure in the Supplement) and was dichotomized at the overall median for analysis; the median (range) GRS in the case group was 75 (57-93) alleles and the median (range) GRS in the control group was 74 (57-90) alleles, and the score was independent of radiation dose. In the high NHEJ GRS group, among women who received the first diagnosis when they were younger than 40 years with a latency of 5 years or more, a stray radiation dose of 1.0 Gy or more was associated with 3-fold greater contralateral breast cancer risk compared with no radiation exposure (RR, 3.0 [95% CI, 1.1-8.1]) (Table 4); the test for trend across dose categories was statistically significant (P = .03). In contrast, for women with an NHEJ GRS of 74 alleles or fewer in the same age and latency group, there was no association between radiation dose and contralateral breast cancer risk (RR, 1.3 [95% CI, 0.5-3.7]). No associations were found for women who received their first breast cancer diagnosis when they were 40 years or older. Based on these results, after a latency of 5 years or longer among women who received their first breast cancer diagnosis when they were younger than 40 years with a high NHEJ GRS, the population attributable risk fraction of contralateral breast cancer attributable to stray radiation exposure to the contralateral breast was 28%. The corresponding population attributable risk fraction among women who received their first diagnosis when they were younger than 40 years after a latency of 5 years or more with a low NHEJ GRS was 18%.

After further adjustment for known breast cancer susceptibility variants, results were not significantly changed; the association of radiation exposure with contralateral breast cancer risk among women who received their first diagnosis when they were younger than 40 years of age after a latency of 5 years or more in the high NHEJ GRS group remained statistically significant (RR, 2.9 [95% CI, 1.0-8.0]) (eTable 3 in the Supplement). In addition, to identify genes that might be driving the overall NHEJ GRS association with contralateral breast cancer risk, we repeated the modeling for each gene individually in the high-risk subgroup (<40 years of age when they received the first diagnosis and ≥5 years latency of effect). Stratifying by the median number of risk alleles within each, we found that the associations of radiation dose with contralateral breast cancer risk varied depending on the gene analyzed (eTable 4 in the Supplement). However, for women with greater than the median number of risk alleles for any given gene within the NHEJ pathway, there was no statistically significant increased risk of contralateral breast cancer risk with exposure to radiation dose of 1.0 Gy or more.

Post-hoc Analysis of Gene Expression in the NHEJ Pathway

To explore the mechanism whereby variation in the NHEJ GRS is associated with risk of contralateral breast cancer associated with radiation exposure, we searched the NHEJ GRS SNPs against the Genotype-Tissue Expression (GTEx) database33 for significant expression quantitative trait loci (eQTLs) affecting the genes encoding NHEJ components. For 5 of the 7 genes in the NHEJ pathway (LIG4, NHEJ1, XRCC4, XRCC5 and XRCC6), we identified significant eQTLs associated with either increased or decreased expression of the corresponding NHEJ gene in multiple tissues. Notably, within each gene, significant eQTLs were always associated with a single direction of association (i.e., all risk alleles in XRCC4 were associated with decreased expression and all risk alleles in LIG4 were associated with increased expression).

Discussion

This study found that a substantial proportion of the risk of radiation-associated contralateral breast cancer was attributable to common genetic variants in the NHEJ pathway among younger women. Our pathway-specific GRS approach, based on our a priori hypothesis that variation in a DNA damage response pathway would be associated with radiation-associated contralateral breast cancer, allowed a valid and statistically powered evaluation of genetic variation and radiation-associated contralateral breast cancer risk. Radiation-associated contralateral breast cancer risk is characterized by an inverse association with age at exposure, a latency of effect, and a proportional association with radiation dose,14,31 which we confirmed in the present study. We also found that women who received the first diagnosis when they were younger than 40 years and were exposed to at least 1.0 Gy of stray radiation had an increased risk of developing contralateral breast cancer after 5 years, but only if they carried many NHEJ GRS risk alleles, whereas those with a low NHEJ GRS had no significantly increased risk of contralateral breast cancer associated with radiation dose. For women who were 40 years or older when they received the first diagnosis, the risk of radiation-associated contralateral breast cancer was low regardless of latency time or NHEJ GRS. We estimated that, after a latency of 5 years or more among women with a high genetic risk who were younger than 40 years when they received the first diagnosis, 28% of the increased risk of contralateral breast cancer was attributable to stray radiation exposure. For younger women with breast cancer, these findings could influence decision-making for locoregional therapy. For example, young women with a high NHEJ GRS may consider partial-breast radiation therapy (rather than whole-breast radiation therapy when appropriate), opt for radiation therapy techniques that reduce integral dose (eg, proton-beam), or decide for non-radiation therapy–based locoregional management (eg, mastectomy).34-36 These findings may be especially important in younger women with medially located breast cancers, where the scatter dose to the contralateral breast is likely to be higher.34-36 Therefore, focusing on SNPs in the NHEJ pathway may prove useful for individualizing breast cancer treatment, particularly for women treated with radiation therapy when they are younger than 40 years.

The choice of age and latency cutoffs were based on evidence from previous studies suggesting the associations between cancer and radiation exposure are strongest for women exposed when younger than 40 years and that associations with radiation exposure are greatest after at least 5 years.30,31 We observed no associations between radiation exposure and contralateral breast cancer risk in the overall study population or among women who received the first diagnosis after age 40 years, affirming the importance of age- and latency-stratified analysis to identify women at increased risk of radiation-associated contralateral breast cancer.31

Even after accounting for known breast cancer susceptibility SNPs (eTable 3 in the Supplement), after 5 years or more of latency, women with a high NHEJ GRS who received the first diagnosis when they were younger than 40 years maintained a statistically significantly increased risk of contralateral breast cancer associated with radiation dose. The association was similar to the primary analysis, suggesting that the NHEJ GRS captures a distinct component of radiation-associated contralateral breast cancer risk that is independent of overall predisposition to contralateral breast cancer. For women undergoing treatment for their first primary breast cancer, it may be prudent to estimate both overall genetic predisposition to contralateral breast cancer and radiation-specific risk.

In our gene-by-gene analysis in the high-risk subgroup (eTable 4 in the Supplement), risk estimates for each gene were not precisely consistent with the primary NHEJ GRS results, indicating that the NHEJ GRS effectively aggregates positive but variable associations of NHEJ SNPs with radiation-associated contralateral breast cancer risk. Further, our analysis of GTEx data indicated that the SNPs comprising the NHEJ GRS include eQTLs with significant associations with the expression of NHEJ genes. The predicted direction of association with contralateral breast cancer risk was uniformly increased for the LIG4, NHEJ1, and XRCC5 genes while uniformly decreased for the XRCC4 and XRCC6 genes. The consistent association of multiple NHEJ GRS risk alleles with eQTLs in a single direction suggests that the NHEJ GRS may be capturing the effect of SNP alleles on the transcription of 1 or more genes in the NHEJ pathway. This supports the hypothesis that the variation in this pathway may alter double-stranded DNA damage response, thereby increasing the risk of tumor development. However, the results from GTEx are drawn from multiple tissues that may not be appropriate proxies for breast tissue and the impact of genetic variation in the overall NHEJ pathway is likely to be complex.

Limitations and Strengths

This study has limitations. First, although a large population of participants had quantified location-specific radiation dose, accurate dosimetry was not possible for 600 women with incomplete location or clinical data or multifocal tumors. Second, the NHEJ GRS was developed based on the directionality of SNP associations among non-Hispanic white women in the WECARE Study, which may limit generalizability to other populations. However, this does not reduce the validity of the score for this population. Third, it is not currently possible to replicate our NHEJ GRS findings in another data set, as no other study of contralateral breast cancer is large enough with the necessary biospecimens, long-term follow-up, and dosimetry and questionnaire data, to our knowledge. Strengths of the study include the large study population that allowed for subgroup analyses, individual matching to reduce confounding, and individualized dosimetry. Future studies should assess whether these results are consistent for other radiation-sensitive second primary cancers.

Conclusions

In conclusion, this study found that as much as 28% of the risk of contralateral breast cancer was attributable to stray radiation exposure among women who received their first breast cancer diagnosis when they were younger than 40 years and who were at high genetic risk, after a latency of 5 years or more. These findings may support clinical decision-making related to radiation treatment, particularly among women for whom other modalities may be considered. The NHEJ GRS may be useful for individualizing both treatment and surveillance plans for young women who have received a first primary breast cancer diagnosis.

Back to top
Article Information

Accepted for Publication: August 9, 2019.

Published: September 27, 2019. doi:10.1001/jamanetworkopen.2019.12259

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Watt GP et al. JAMA Network Open.

Corresponding Author: Jonine L. Bernstein, PhD, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY 10065 (bernstej@mskcc.org).

Author Contributions: Ms Reiner and Dr J. L. Bernstein had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Reiner, Stram, L. Bernstein, Robson, Boice, Tischkowitz, Thomas, J. L. Bernstein.

Acquisition, analysis, or interpretation of data: Watt, Reiner, Smith, Capanu, Malone, Lynch, John, Knight, Mellemkjær, L. Bernstein, Brooks, Woods, Liang, Haile, Riaz, Conti, Duggan, Shore, Orlow, Thomas, Concannon, J. L. Bernstein.

Drafting of the manuscript: Watt, Brooks, Tischkowitz, Concannon, J. L. Bernstein.

Critical revision of the manuscript for important intellectual content: Watt, Reiner, Smith, Stram, Capanu, Malone, Lynch, John, Knight, Mellemkjær, L. Bernstein, Brooks, Woods, Liang, Haile, Riaz, Conti, Robson, Duggan, Boice, Shore, Orlow, Thomas, J. L. Bernstein.

Statistical analysis: Watt, Reiner, Stram, Capanu, Knight, Liang, Conti, Shore, Thomas, J. L. Bernstein.

Obtained funding: Stram, Malone, Duggan, Thomas, J. L. Bernstein.

Administrative, technical, or material support: Lynch, L. Bernstein, Woods, Boice, Orlow, Concannon, J. L. Bernstein.

Supervision: John, L. Bernstein, Orlow, J. L. Bernstein.

Conflict of Interest Disclosures: Ms Smith reported receiving grants from Memorial Sloan Kettering and the Women’s Environment, Cancer, and Radiation Epidemiology Study during the conduct of the study and from the National Cancer Institute Radiation Epidemiology Branch outside the submitted work. Dr Lynch reported receiving grants from Duke University (paid to the University of Iowa) outside the submitted work. Dr Riaz reported receiving personal fees from Illumina during the conduct of the study and grants from Pfizer, Bristol-Myers Squib, and AstraZeneca outside the submitted work. Dr Boice reported receiving grants from Memorial Sloan Kettering Cancer Center through NIH during the conduct of the study. Dr Concannon reported receiving grants from the NIH and the Defense Threat Reduction Agency, serving on the Scientific Ethics and Oversight Committee for the Vietnam-Era Twin Registry from the Veterans Administration, and other support from Illumina and Canon (paid to the University of Florida) outside the submitted work. No other disclosures were reported.

Funding/Support: This research was funded by the National Institutes of Health National Cancer Institute (grants CA114236 [R01], CA097397 [R01], CA129639 [R01], CA168339 [R01], and CA008748 [P30]).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: Jørgen H. Olsen, MD, DMSc (Danish Cancer Society), provided assistance in the design of the WECARE Study. Rikke Langballe, MPH. (Danish Cancer Society), Kristina Blackmore, MSc (Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital), Judy Goldstein, BA (Cancer Prevention Institute of California, retired), Rita Weathers, MS (MD Anderson Cancer Center), Irene Harris, BS (MD Anderson Cancer Center), Michele West, PhD, and Cecilia O’Brien, BS (Fred Hutchinson Cancer Research Center), provided support in collecting participant data. They were not compensated for their contributions.

References
1.
Chen  Y, Thompson  W, Semenciw  R, Mao  Y.  Epidemiology of contralateral breast cancer.  Cancer Epidemiol Biomarkers Prev. 1999;8(10):855-861.PubMedGoogle Scholar
2.
Lizarraga  IM, Sugg  SL, Weigel  RJ, Scott-Conner  CE.  Review of risk factors for the development of contralateral breast cancer.  Am J Surg. 2013;206(5):704-708. doi:10.1016/j.amjsurg.2013.08.002PubMedGoogle ScholarCrossref
3.
Bernstein  JL, Thompson  WD, Risch  N, Holford  TR.  Risk factors predicting the incidence of second primary breast cancer among women diagnosed with a first primary breast cancer.  Am J Epidemiol. 1992;136(8):925-936. doi:10.1093/oxfordjournals.aje.a116565PubMedGoogle ScholarCrossref
4.
van den Broek  AJ, van ’t Veer  LJ, Hooning  MJ,  et al.  Impact of age at primary breast cancer on contralateral breast cancer risk in BRCA1/2 mutation carriers.  J Clin Oncol. 2016;34(5):409-418. doi:10.1200/JCO.2015.62.3942PubMedGoogle ScholarCrossref
5.
Reiner  AS, John  EM, Brooks  JD,  et al.  Risk of asynchronous contralateral breast cancer in noncarriers of BRCA1 and BRCA2 mutations with a family history of breast cancer: a report from the Women’s Environmental Cancer and Radiation Epidemiology Study.  J Clin Oncol. 2013;31(4):433-439. doi:10.1200/JCO.2012.43.2013PubMedGoogle ScholarCrossref
6.
Shankar  A, Roy  S, Malik  A,  et al.  Contralateral breast cancer: a clinicopathological study of second primaries in opposite breasts after treatment of breast malignancy.  Asian Pac J Cancer Prev. 2015;16(3):1207-1211. doi:10.7314/APJCP.2015.16.3.1207PubMedGoogle ScholarCrossref
7.
Broeks  A, Braaf  LM, Huseinovic  A,  et al.  Identification of women with an increased risk of developing radiation-induced breast cancer: a case only study.  Breast Cancer Res. 2007;9(2):R26. doi:10.1186/bcr1668PubMedGoogle ScholarCrossref
8.
Graeser  MK, Engel  C, Rhiem  K,  et al.  Contralateral breast cancer risk in BRCA1 and BRCA2 mutation carriers.  J Clin Oncol. 2009;27(35):5887-5892. doi:10.1200/JCO.2008.19.9430PubMedGoogle ScholarCrossref
9.
Malone  KE, Begg  CB, Haile  RW,  et al.  Population-based study of the risk of second primary contralateral breast cancer associated with carrying a mutation in BRCA1 or BRCA2 J Clin Oncol. 2010;28(14):2404-2410. doi:10.1200/JCO.2009.24.2495PubMedGoogle ScholarCrossref
10.
Mellemkjær  L, Steding-Jessen  M, Frederiksen  K,  et al.  Risk of contralateral breast cancer after tamoxifen use among Danish women.  Ann Epidemiol. 2014;24(11):843-848. doi:10.1016/j.annepidem.2014.08.003PubMedGoogle ScholarCrossref
11.
Aihara  T, Tanaka  S, Sagara  Y,  et al.  Incidence of contralateral breast cancer in Japanese patients with unilateral minimum-risk primary breast cancer, and the benefits of endocrine therapy and radiotherapy.  Breast Cancer. 2014;21(3):284-291. doi:10.1007/s12282-012-0396-4PubMedGoogle ScholarCrossref
12.
Bertelsen  L, Bernstein  L, Olsen  JH,  et al; Women’s Environmental Cancer and Radiation Epidemiology Study Collaborative Group.  Effect of systemic adjuvant treatment on risk for contralateral breast cancer in the Women’s Environment, Cancer and Radiation Epidemiology Study.  J Natl Cancer Inst. 2008;100(1):32-40. doi:10.1093/jnci/djm267PubMedGoogle ScholarCrossref
13.
Kramer  I, Schaapveld  M, Oldenburg  HSA,  et al.  The influence of adjuvant systemic regimens on contralateral breast cancer risk and receptor subtype.  J Natl Cancer Inst. 2019. doi:10.1093/jnci/djz010PubMedGoogle Scholar
14.
Stovall  M, Smith  SA, Langholz  BM,  et al; Women’s Environmental Cancer and Radiation Epidemiology Study Collaborative Group.  Dose to the contralateral breast from radiotherapy and risk of second primary breast cancer in the WECARE Study.  Int J Radiat Oncol Biol Phys. 2008;72(4):1021-1030. doi:10.1016/j.ijrobp.2008.02.040PubMedGoogle ScholarCrossref
15.
Borrego-Soto  G, Ortiz-López  R, Rojas-Martínez  A.  Ionizing radiation-induced DNA injury and damage detection in patients with breast cancer.  Genet Mol Biol. 2015;38(4):420-432. doi:10.1590/S1415-475738420150019PubMedGoogle ScholarCrossref
16.
Pollard  JM, Gatti  RA.  Clinical radiation sensitivity with DNA repair disorders: an overview.  Int J Radiat Oncol Biol Phys. 2009;74(5):1323-1331. doi:10.1016/j.ijrobp.2009.02.057PubMedGoogle ScholarCrossref
17.
Lo  Y-L, Yu  J-C, Chen  S-T,  et al.  Breast cancer risk associated with genotypic polymorphism of the mitotic checkpoint genes: a multigenic study on cancer susceptibility.  Carcinogenesis. 2007;28(5):1079-1086. doi:10.1093/carcin/bgl256PubMedGoogle ScholarCrossref
18.
Siegel  RL, Miller  KD, Jemal  A.  Cancer statistics, 2018.  CA Cancer J Clin. 2018;68(1):7-30. doi:10.3322/caac.21442PubMedGoogle ScholarCrossref
19.
Xiong  Z, Yang  L, Deng  G,  et al.  Patterns of occurrence and outcomes of contralateral breast cancer: analysis of SEER data.  J Clin Med. 2018;7(6):E133. doi:10.3390/jcm7060133PubMedGoogle ScholarCrossref
20.
Bernstein  JL, Haile  RW, Stovall  M,  et al; WECARE Study Collaborative Group.  Radiation exposure, the ATM gene, and contralateral breast cancer in the women’s environmental cancer and radiation epidemiology study.  J Natl Cancer Inst. 2010;102(7):475-483. doi:10.1093/jnci/djq055PubMedGoogle ScholarCrossref
21.
Bernstein  JL, Thomas  DC, Shore  RE,  et al; WECARE Study Collaborative Group.  Contralateral breast cancer after radiotherapy among BRCA1 and BRCA2 mutation carriers: a WECARE Study report.  Eur J Cancer. 2013;49(14):2979-2985. doi:10.1016/j.ejca.2013.04.028PubMedGoogle ScholarCrossref
22.
Weischer  M, Nordestgaard  BG, Pharoah  P,  et al.  CHEK2*1100delC heterozygosity in women with breast cancer associated with early death, breast cancer-specific death, and increased risk of a second breast cancer.  J Clin Oncol. 2012;30(35):4308-4316. doi:10.1200/JCO.2012.42.7336PubMedGoogle ScholarCrossref
23.
Morton  LM, Sampson  JN, Armstrong  GT,  et al.  Genome-wide association study to identify susceptibility loci that modify radiation-related risk for breast cancer after childhood cancer.  J Natl Cancer Inst. 2017;109(11):djx058-djx058. doi:10.1093/jnci/djx058PubMedGoogle ScholarCrossref
24.
Robson  ME, Reiner  AS, Brooks  JD,  et al; WECARE Study Collaborative Group.  Association of common genetic variants with contralateral breast cancer risk in the WECARE Study.  J Natl Cancer Inst. 2017;109(10). doi:10.1093/jnci/djx051PubMedGoogle Scholar
25.
Bernstein  JL, Langholz  B, Haile  RW,  et al.  Study design: evaluating gene-environment interactions in the etiology of breast cancer: the WECARE study.  Breast Cancer Res. 2004;6(3):R199-R214. doi:10.1186/bcr771PubMedGoogle ScholarCrossref
26.
Langballe  R, Mellemkjær  L, Malone  KE,  et al; WECARE Study Collaborative Group.  Systemic therapy for breast cancer and risk of subsequent contralateral breast cancer in the WECARE Study.  Breast Cancer Res. 2016;18(1):65. doi:10.1186/s13058-016-0726-0PubMedGoogle ScholarCrossref
27.
Langholz  B, Goldstein  L.  Risk set sampling in epidemiologic cohort studies.  Stat Sci. 1996;11(1):35-53. doi:10.1214/ss/1032209663Google ScholarCrossref
28.
Reiner  AS, Sisti  J, John  EM,  et al; WECARE Study Collaborative Group.  Breast cancer family history and contralateral breast cancer risk in young women: an update from the Women’s Environmental Cancer and Radiation Epidemiology Study.  J Clin Oncol. 2018;36(15):1513-1520. doi:10.1200/JCO.2017.77.3424PubMedGoogle ScholarCrossref
29.
Huberman  M, Langholz  B.  Application of the missing-indicator method in matched case-control studies with incomplete data.  Am J Epidemiol. 1999;150(12):1340-1345. doi:10.1093/oxfordjournals.aje.a009966PubMedGoogle ScholarCrossref
30.
Boice  JD  Jr, Preston  D, Davis  FG, Monson  RR.  Frequent chest x-ray fluoroscopy and breast cancer incidence among tuberculosis patients in Massachusetts.  Radiat Res. 1991;125(2):214-222. doi:10.2307/3577890PubMedGoogle ScholarCrossref
31.
Boice  JD  Jr, Harvey  EB, Blettner  M, Stovall  M, Flannery  JT.  Cancer in the contralateral breast after radiotherapy for breast cancer.  N Engl J Med. 1992;326(12):781-785. doi:10.1056/NEJM199203193261201PubMedGoogle ScholarCrossref
32.
Bruzzi  P, Green  SB, Byar  DP, Brinton  LA, Schairer  C.  Estimating the population attributable risk for multiple risk factors using case-control data.  Am J Epidemiol. 1985;122(5):904-914. doi:10.1093/oxfordjournals.aje.a114174PubMedGoogle ScholarCrossref
33.
Carithers  LJ, Ardlie  K, Barcus  M,  et al; GTEx Consortium.  A novel approach to high-quality postmortem tissue procurement: the GTEx project.  Biopreserv Biobank. 2015;13(5):311-319. doi:10.1089/bio.2015.0032PubMedGoogle ScholarCrossref
34.
Jayasekera  J, Li  Y, Schechter  CB,  et al; CISNET-BOLD Collaborative Group.  Simulation modeling of cancer clinical trials: application to omitting radiotherapy in low-risk breast cancer.  J Natl Cancer Inst. 2018;110(12):1360-1369. doi:10.1093/jnci/djy059PubMedGoogle ScholarCrossref
35.
Winzer  KJ, Sauerbrei  W, Braun  M,  et al; German Breast Cancer Study Group.  Radiation therapy and tamoxifen after breast-conserving surgery: updated results of a 2 × 2 randomised clinical trial in patients with low risk of recurrence.  Eur J Cancer. 2010;46(1):95-101. doi:10.1016/j.ejca.2009.10.007PubMedGoogle ScholarCrossref
36.
Hughes  KS, Schnaper  LA, Bellon  JR,  et al.  Lumpectomy plus tamoxifen with or without irradiation in women age 70 years or older with early breast cancer: long-term follow-up of CALGB 9343.  J Clin Oncol. 2013;31(19):2382-2387. doi:10.1200/JCO.2012.45.2615PubMedGoogle ScholarCrossref
×