Purpose: It has been hypothesized that increased predisposition to breast cancer may correlate with radiosensitivity, and thus increased risk of toxicity following breast irradiation. This study investigated the relationship between common breast cancer risk variants and radiotherapy toxicity.

Experimental Design: SNP genotypes were determined in female breast cancer patients from the RAPPER (Radiogenomics: Assessment of polymorphisms for predicting the effects of radiotherapy) study using the Illumina CytoSNP12 genome-wide array. A further 15,582,449 genotypes were imputed using the 1000 Genomes Project reference panel. Patient (n = 1,160) polygenic risk scores were generated by summing risk-allele dosages, both unweighted and weighted by published effect sizes for breast cancer risk. Regression models were used to test associations of individual variants and polygenic risk scores with acute and late toxicity phenotypes (telangiectasia, breast edema, photographically assessed shrinkage, induration, pigmentation, breast pain, breast sensitivity, and overall toxicity).

Results: Genotypes of 90 confirmed breast cancer risk variants were accurately determined and polygenic risk scores were approximately normally distributed. Variant rs6964587 was associated with increased breast edema 5 years following radiotherapy (Beta, 0.22; 95% confidence interval, 0.09–0.34; P = 7 × 10−4). No other associations were found between individual variants or the unweighted (P > 0.17) or weighted (P > 0.13) polygenic risk score and radiotherapy toxicity. This study had >87% power to detect an association between the polygenic risk score (relative risk > 1.1) and toxicity.

Conclusions: Cancer patients with a high polygenic predisposition to breast cancer do not have an increased risk of radiotherapy toxicity up to 5 years following radiotherapy but individual variants may increase risk. Clin Cancer Res; 22(6); 1413–20. ©2015 AACR.

Translational Relevance

Common germline genetic variation impacts on the development of cancer and 94 common variants have now been identified, which together explain almost 20% of the variance in breast cancer risk. Up to 75% of breast cancer patients are likely to have radiotherapy and, depending on the country of treatment, up to 80% of patients survive breast cancer. For over 20 years clinicians have been concerned that the biologic mechanisms underlying genetic predisposition to breast cancer risk may also increase radiosensitivity, such that some patients receiving breast tumor radiotherapy may be at increased risk of normal tissue toxicity. We have tested the breast cancer risk variants, both individually and as a multi-SNP risk profile, for association with acute and late radiotherapy toxicity phenotypes in 1,160 breast cancer patients. Our study showed that those with a high polygenic risk of breast cancer do not have increased risk of radiotherapy toxicity.

To date, 94 common breast cancer susceptibility loci have been confirmed (P < 5 × 10−8) via meta-analyses of 11 genome wide association studies (GWAS; 15,748 cases and 18,084 controls) and 41 studies in the Breast Cancer Association Consortium (BCAC; 46,785 cases and 42,892 controls; refs. 1–3). The effect sizes of each locus are generally modest with ORs ≤1.34. It is estimated that 16% of the familial risk of breast cancer can be explained by common genetic variation (3). In a recent study, SNP markers of 77 confirmed breast cancer susceptibility loci were combined to create a polygenic risk score for breast cancer (4). Women in the highest 1% of the risk score were 3.6 times more likely to develop breast cancer than women in the middle quintile (4).

Radiation exposure is a known risk factor for the development of breast cancer (5) and individuals with the disease have been reported to have increased radiosensitivity (6) postulated as due to inherited defects in cellular responses to DNA damage from either endogenous or exogenous sources (7). It has also been suggested that elevated radiosensitivity may be a marker of low penetrance predisposition to cancer (8). Because radiosensitive individuals are more likely to experience toxicity following radiotherapy (9), it has been hypothesized that genetic variants that predispose to breast cancer may also increase the risk of radiation side-effects. This hypothesis was based on the assumption that gene variants involved in cancer predisposition may have roles in both tumor formation and in the response to radiation-induced DNA damage.

We have tested this hypothesis using data from the UK RAPPER (Radiogenomics: Assessment of polymorphisms for predicting the effects of radiotherapy) study (10). Common genetic variants individually have small effects on risk of breast cancer and are likely to have similarly small effects on radiotherapy toxicity. Thus, to search for evidence of associations with radiosensitivity, we tested the currently known breast cancer risk variants, both individually and within a more statistically powerful polygenic risk score.

Patients

The UK RAPPER (UKCRN1471) study recruits patients from clinical trials and observational studies involving radiotherapy given with curative intent. This study comprised 1,160 female breast cancer patients who were included in a GWAS described elsewhere, which provides details on the cohorts included (11): 937 patients from the Cambridge Breast IMRT Trial, 167 from the RACE (Radiation Complications and Epidemiology) study and 56 from a prospective study at the Christie Hospital, Manchester. Briefly, all patients underwent conservative surgery followed by adjuvant radiotherapy with doses of: 40 Gy in 15 fractions over 3 weeks (Cambridge Breast IMRT trial, Christie Hospital); 50 Gy in 25 fractions or 39.0 Gy/42.9 Gy in 13 fractions over five weeks (RACE study). An alpha/beta ratio of 3.4 (12) was used to calculate equivalent dose in 2Gy fraction for these schedules for inclusion in multivariable analyses. Patient characteristics and treatment factors are summarized in Table 1.

Table 1.

Demographic and treatment-related factors of patients in the RAPPER cohort

CharacteristicRAPPER (N = 1,160)
Age N; mean (SD) 1,160; 58 (9.2) 
 Missing (%) 
Diabetesa Yes (%) 42 (3.6) 
 No (%) 925 (79.8) 
 Missing (%) 193 (16.6) 
Smokera Yes (%) 138 (11.9) 
 No (%) 845 (72.8) 
 Missing (%) 177 (15.3) 
Tumor size (mm)a N; mean (SD) 968; 15.4 (7.6) 
 Missing (%) 192 (16.6) 
Adjuvant chemotherapy Yes (%) 198 (17.1) 
 No (%) 935 (80.6) 
 Missing (%) 27 (2.3) 
Tamoxifen use Yes (%) 785 (67.7) 
 No (%) 334 (28.8) 
 Missing (%) 41 (3.5) 
Postoperative breast infectiona Yes (%) 182 (15.7) 
 No (%) 789 (68.0) 
 Missing (%) 189 (16.3) 
Postoperative hematomaa Yes (%) 69 (6.0) 
 No (%) 759 (65.4) 
 Missing (%) 332 (28.6) 
Breast boost Yes (%) 735 (63.4) 
 No (%) 424 (36.5) 
 Missing (%) 1 (0.1) 
Actual volume of breast receiving >107% of radiation dosea N; mean (SD) 932; 19.1 (51.5) 
 Missing (%) 228 (19.7) 
CharacteristicRAPPER (N = 1,160)
Age N; mean (SD) 1,160; 58 (9.2) 
 Missing (%) 
Diabetesa Yes (%) 42 (3.6) 
 No (%) 925 (79.8) 
 Missing (%) 193 (16.6) 
Smokera Yes (%) 138 (11.9) 
 No (%) 845 (72.8) 
 Missing (%) 177 (15.3) 
Tumor size (mm)a N; mean (SD) 968; 15.4 (7.6) 
 Missing (%) 192 (16.6) 
Adjuvant chemotherapy Yes (%) 198 (17.1) 
 No (%) 935 (80.6) 
 Missing (%) 27 (2.3) 
Tamoxifen use Yes (%) 785 (67.7) 
 No (%) 334 (28.8) 
 Missing (%) 41 (3.5) 
Postoperative breast infectiona Yes (%) 182 (15.7) 
 No (%) 789 (68.0) 
 Missing (%) 189 (16.3) 
Postoperative hematomaa Yes (%) 69 (6.0) 
 No (%) 759 (65.4) 
 Missing (%) 332 (28.6) 
Breast boost Yes (%) 735 (63.4) 
 No (%) 424 (36.5) 
 Missing (%) 1 (0.1) 
Actual volume of breast receiving >107% of radiation dosea N; mean (SD) 932; 19.1 (51.5) 
 Missing (%) 228 (19.7) 

aNot available in RACE.

Assessment of radiotherapy toxicity

Late toxicity data were collected prospectively using standardized scoring systems (Supplementary Table S1). This study used toxicity data collected at two specific time points: 2 years and 5 years following radiotherapy. Two-year telangiectasia, breast edema, photographically assessed shrinkage, induration, pigmentation, breast pain, and breast sensitivity was recorded for 1,134 patients. Five-year telangiectasia, breast edema, photographically assessed shrinkage and induration was collected for 594 patients in the Cambridge Breast IMRT trial. Scale-independent standardized total average toxicity (STAT) scores were derived from the individual endpoints for overall measures of 2-year and 5-year toxicity (13). Acute toxicity was recorded for 920 patients in the Cambridge Breast IMRT Trial at 3 weeks following radiotherapy, using the Radiation Therapy Oncology Group (RTOG) scoring system (see Supplementary Table S2).

Genotyping, quality control, and imputation

Genotyping and imputation across the whole genome was performed for the RAPPER GWAS. Samples were genotyped using the Illumina CytoSNP12 (Illumina). Standard quality control procedures were applied to remove variants that were missing in >5% of samples; had a minor allele frequency (MAF) < 1%; or had MAF<5% and were also missing in >1% samples. Variants were also removed if their genotype frequencies deviated from those expected under Hardy–Weinberg equilibrium (P < 10−6). Samples were removed that had >3% of all variants missing. Principle components analysis (PCA) was used to identify and exclude individuals with non-European ancestry. Genome coverage was increased by imputation using SHAPEIT (14) and IMPUTE2 (15) with the 1000 Genomes Phase III reference panel (16). In this study, expected dosages of the alleles of the 94 breast cancer risk variants were extracted from the imputed data.

Statistical analysis

Polygenic risk scores were created to quantify the patients' genetic risk of breast cancer. For each patient, genotype dosages for the 94 breast cancer risk-increasing alleles were calculated and then summed across all the variants. Two types of risk score were calculated:

Unweighted, for patient i: |${\rm risk}\;{\rm score}_i \, = \,\sum\nolimits_1^j {G_{ij\,}}$|⁠.

Weighted, for patient i: |${\rm weighted}\;{\rm risk}\;{\rm score}_i \, = \,\sum\nolimits_1^j {\beta _j G_{ij}}$|⁠,

where j = variants 1 to 94.

βj = the per-allele log OR for risk of breast cancer associated with variant j.

G = risk allele dosage.

The log ORs used to weight the risk score were taken directly from the report by Mavaddat and colleagues (4), and were estimated by testing the association of each variant with breast cancer risk while adjusting for the effect of other variants. Seventeen variants were identified after Mavaddat and colleagues performed their analysis, so the values of log (OR) used for these variants were estimates from BCAC (3).

For univariable analysis, ordinal logistic regression was used to test the association between the acute or individual late toxicity endpoints and polygenic risk score. Linear regression was used to test the association between STAT and risk score. Each genetic variant was also tested individually. For multivariable analysis, a residual analysis was performed (17). Residuals from multivariable analyses of the 2-year toxicity endpoints with nongenetic covariates, produced as part of the original RAPPER GWAS of late toxicity (11), were used to analyze 2-year toxicity. These residuals estimate the risk of toxicity not explained by available patient- and treatment-related factors, including baseline symptoms that resulted from surgery and were present before radiotherapy. Residuals for acute toxicity and 5-year toxicity were calculated from multivariable regression models, including significant nongenetic factors as part of this study. Linear regression was used to evaluate the association between the residuals and both the genetic risk scores and the individual variants. The association between polygenic risk and baseline symptoms was assessed separately to confirm that adjustment for baseline symptoms would not substantially change the results. For the primary analysis, the polygenic risk scores were considered statistically significant if P < 0.05. To reduce the chance of a false-positive association due to multiple testing, the individual genetic variants were deemed statistically significant if P < 5 × 10−4.

The study was well powered to detect significant associations between breast cancer polygenic risk score and many radiotherapy toxicity endpoints. Assuming a 20% prevalence of moderate/severe toxicity (grade≥2) in breast cancer patients, the power to detect an association between toxicity (grade≥2) and polygenic risk score at P < 0.05 would be 87% for a small difference in mean risk score of 0.1. This difference in mean risk would be equivalent to a relative risk of toxicity of 1.1 for the subset of patients with a higher mean polygenic risk of breast cancer. All analyses were performed using statistical software package STATA version 13.1. STrengthening the Reporting of Genetic Association studies in Radiogenomics (STROGAR) guidelines were followed throughout (18).

Of the 94 genetic variants known to increase the risk of breast cancer, 90 were genotyped or imputed successfully (r2 > 0.66) in the 1,160 patients in the RAPPER cohort—details in Table 2. Figure 1 shows the distributions of the unweighted and weighted polygenic risk scores among all patients with late toxicity data available (N = 1,134), which approximate to a normal distribution in these samples.

Figure 1.

Distribution of polygenic risk scores in the 1134 RAPPER patients for whom late radiotherapy toxicity data were available. A, unweighted polygenic risk score. B, weighted polygenic risk score.

Figure 1.

Distribution of polygenic risk scores in the 1134 RAPPER patients for whom late radiotherapy toxicity data were available. A, unweighted polygenic risk score. B, weighted polygenic risk score.

Close modal
Table 2.

Genetic variants known to influence risk of breast cancer

VariantNearest geneChromfPosition (build 37g)Breast cancer risk allelePublished ORaRAPPER risk allele frequencyRAPPER imputation r2c
rs616488 PEX14 10566215 1.06 0.67 
rs11552449 PTPN22-BCL2L15-AP4B1-DCLRE1B-HIPK1 114448389 1.08 0.20 0.98 
rs11249433 None 121280613 1.10 0.38 0.67 
rs12405132 RNF115 145644984 1.05b 0.64 
rs12048493 OTUD7B 149927034 1.07b 0.39 0.66 
rs6678914 LGR6 202187176 1.01 0.59 0.99 
rs4245739 MDM4 204518842 1.03 0.28 0.99 
rs72755295 EXO1 242034263 1.15b 0.03 0.69 
rs12710696 OSR1 19320803 1.04 0.38 0.99 
rs4849887 INHBB 121245122 1.09 0.90 0.99 
rs2016394 METAP1D-DLX1-DLX2 172972971 1.05 0.54 
rs1550623 CDCA7 174212894 1.06 0.83 0.98 
rs1045485 CASP8d 202149589 1.04 0.86 0.98 
rs13387042 IGFBP5d 217905832 1.14 0.55 
rs16857609 DIRC3 218296508 1.07 0.29 0.98 
rs6762644 ITPR1-EGOT 4742276 1.07 0.40 0.99 
rs4973768 SLC4A7 27416013 1.09 0.50 
rs12493607 TGFBR2 30682939 1.05 0.33 0.99 
rs6796502 PRSS42 46866866 1.09b 0.90 0.96 
rs1053338 ATXN7 63967900 1.08b 0.15 0.98 
rs9790517 TET2 106084778 1.05 0.20 0.99 
rs6828523 ADAM29 175846426 1.10 0.89 0.99 
rs10069690 TERTd 1279790 1.02 0.27 
rs7726159 TERTd 1282319 1.04 0.33 0.85 
rs2736108 TERTd 1297488 1.07 0.72 0.90 
rs13162653 MARCH11 16187528 1.05b 0.55 0.89 
rs2012709 SUB1 32567732 1.05b 0.49 
rs10941679 None 44706498 1.12 0.30 0.98 
rs889312 MAP3K1d 56031884 1.12 0.31 0.99 
rs10472076 RAB3C 58184061 1.04 0.39 0.96 
rs1353747 PDE4D 58337481 1.09 0.90 0.98 
rs7707921 ATG10 81538046 1.08b 0.76 0.99 
rs1432679 EBF1 158244083 1.07 0.46 0.99 
rs11242675 FOXQ1 1318878 1.06 0.63 0.94 
rs204247 RANBP9 13722523 1.05 0.45 0.99 
rs9257408 None 28926220 1.05b 0.39 0.99 
rs17529111 None 82128386 1.05 0.21 0.99 
rs12662670 ESR1e 151918856 1.14 0.09 0.99 
rs2046210 ESR1e 151948366 1.05 0.37 0.99 
rs6964587 AKAP9 91630620 1.05b 0.40 0.98 
rs4593472 LINC-PINT 130667121 1.05b 0.66 0.92 
rs720475 ARHGEF5-NOBOX 144074929 1.06 0.74 0.95 
rs9693444 None 29509616 1.07 0.34 0.99 
rs13365225 KCNU1 36858483 1.05b 0.85 0.98 
rs6472903 CASC9 76230301 1.10 0.84 0.94 
rs2943559 HNF4G 76417937 1.13 0.09 0.98 
rs13267382 None 117209548 1.05b 0.36 0.96 
rs13281615 MYCd 128355618 1.10 0.44 0.99 
rs11780156 MYCd 129194641 1.07 0.17 0.99 
rs1011970 CDKN2A/B 22062134 1.05 0.18 
rs10759243 KLF4d 110306115 1.05 0.29 0.75 
rs865686 KLF4d 110888478 1.11 0.62 0.99 
rs2380205 ANKRD16 10 5886734 1.02 0.59 0.99 
rs7072776 MLLT10-DNAJC1 10 22032942 1.06 0.27 0.91 
rs11814448 DNAJC1 10 22315843 1.22 0.02 0.84 
rs10995190 NRBF2d 10 64278682 1.17 0.87 0.99 
rs704010 ZMIZ1 10 80841148 1.07 0.43 0.97 
rs7904519 TCF7L2 10 114773927 1.06 0.48 0.99 
rs11199914 FGFR2d 10 123093901 1.06 0.70 0.73 
rs2981579 FGFR2d 10 123337335 1.25 0.47 0.99 
rs3817198 LSP1 11 1909006 1.07 0.33 0.92 
rs3903072 DKFZp761e198-OVOLI-SNX32-CFL1-MUS81 11 65583066 1.06 0.55 0.99 
rs78540526 CCND1d 11 69331418 1.18 0.08 0.95 
rs554219 CCND1d 11 69331642 1.12 — — 
rs75915166 CCND1d 11 69379161 1.024 0.06 0.86 
rs11820646 BARX2 11 129461171 1.05 0.60 0.98 
rs12422552 None 12 14413931 1.03 — — 
rs10771399 PTHLH 12 28155080 1.16 — — 
rs17356907 NTN4 12 96027759 1.10 — — 
rs1292011 None 12 115836522 1.08 0.59 
rs11571833 BRCA2-N4BP2LI-N4BP2L2 13 32972626 1.26 0.01 0.87 
rs2236007 PAX9-SLO25A21 14 37132769 1.09 0.82 
rs2588809 RAD51L1 14 68660428 1.07 0.17 0.97 
rs999737 RAD51L1 14 69034682 1.08 0.77 0.97 
rs941764 CCDC88C 14 91841069 1.06 0.36 0.95 
rs11627032 RIN3 14 93104072 1.06b 0.74 0.93 
rs3803662 TOX3 16 52586341 1.23 0.29 
rs17817449 MIRI972-2-FTO 16 53813367 1.08 0.60 0.99 
rs11075995 FTO 16 53855291 1.04 0.76 0.99 
rs13329835 CDYL2 16 80650805 1.08 0.24 0.98 
rs146699004 TEFM 17 29230520 GGT 1.08b 0.74 0.99 
rs6504950 COX11e 17 53056471 1.07 0.72 0.97 
rs745570 CBX8 17 77781725 1.05b 0.50 0.97 
rs527616 None 18 24337424 1.04 0.66 0.98 
rs1436904 CHST9 18 24570667 1.06 0.63 0.99 
rs6507583 SETBP1 18 42399590 1.10b 0.93 0.99 
rs8170 ABHD8/ANKLE1e 19 17389704 1.03 0.20 0.99 
rs2363956 ABHD8/ANKLE1e 19 17394124 1.03 0.51 0.98 
rs4808801 SSBP4-ISYNA1-ELL 19 18571141 1.07 0.67 0.99 
rs3760982 C19orf61-KCNN4-LYPD5-ZNF283 19 44286513 1.06 0.49 
rs2823093 NRIP1 21 16520832 1.08 0.75 0.96 
rs17879961 CHEK2 22 29121087 1.36 0.001 0.69 
rs132390 EMID1-RHBDD3-EWSR1 22 29621477 1.11 0.04 
rs6001930 MKL1 22 40876234 1.13 0.11 0.99 
VariantNearest geneChromfPosition (build 37g)Breast cancer risk allelePublished ORaRAPPER risk allele frequencyRAPPER imputation r2c
rs616488 PEX14 10566215 1.06 0.67 
rs11552449 PTPN22-BCL2L15-AP4B1-DCLRE1B-HIPK1 114448389 1.08 0.20 0.98 
rs11249433 None 121280613 1.10 0.38 0.67 
rs12405132 RNF115 145644984 1.05b 0.64 
rs12048493 OTUD7B 149927034 1.07b 0.39 0.66 
rs6678914 LGR6 202187176 1.01 0.59 0.99 
rs4245739 MDM4 204518842 1.03 0.28 0.99 
rs72755295 EXO1 242034263 1.15b 0.03 0.69 
rs12710696 OSR1 19320803 1.04 0.38 0.99 
rs4849887 INHBB 121245122 1.09 0.90 0.99 
rs2016394 METAP1D-DLX1-DLX2 172972971 1.05 0.54 
rs1550623 CDCA7 174212894 1.06 0.83 0.98 
rs1045485 CASP8d 202149589 1.04 0.86 0.98 
rs13387042 IGFBP5d 217905832 1.14 0.55 
rs16857609 DIRC3 218296508 1.07 0.29 0.98 
rs6762644 ITPR1-EGOT 4742276 1.07 0.40 0.99 
rs4973768 SLC4A7 27416013 1.09 0.50 
rs12493607 TGFBR2 30682939 1.05 0.33 0.99 
rs6796502 PRSS42 46866866 1.09b 0.90 0.96 
rs1053338 ATXN7 63967900 1.08b 0.15 0.98 
rs9790517 TET2 106084778 1.05 0.20 0.99 
rs6828523 ADAM29 175846426 1.10 0.89 0.99 
rs10069690 TERTd 1279790 1.02 0.27 
rs7726159 TERTd 1282319 1.04 0.33 0.85 
rs2736108 TERTd 1297488 1.07 0.72 0.90 
rs13162653 MARCH11 16187528 1.05b 0.55 0.89 
rs2012709 SUB1 32567732 1.05b 0.49 
rs10941679 None 44706498 1.12 0.30 0.98 
rs889312 MAP3K1d 56031884 1.12 0.31 0.99 
rs10472076 RAB3C 58184061 1.04 0.39 0.96 
rs1353747 PDE4D 58337481 1.09 0.90 0.98 
rs7707921 ATG10 81538046 1.08b 0.76 0.99 
rs1432679 EBF1 158244083 1.07 0.46 0.99 
rs11242675 FOXQ1 1318878 1.06 0.63 0.94 
rs204247 RANBP9 13722523 1.05 0.45 0.99 
rs9257408 None 28926220 1.05b 0.39 0.99 
rs17529111 None 82128386 1.05 0.21 0.99 
rs12662670 ESR1e 151918856 1.14 0.09 0.99 
rs2046210 ESR1e 151948366 1.05 0.37 0.99 
rs6964587 AKAP9 91630620 1.05b 0.40 0.98 
rs4593472 LINC-PINT 130667121 1.05b 0.66 0.92 
rs720475 ARHGEF5-NOBOX 144074929 1.06 0.74 0.95 
rs9693444 None 29509616 1.07 0.34 0.99 
rs13365225 KCNU1 36858483 1.05b 0.85 0.98 
rs6472903 CASC9 76230301 1.10 0.84 0.94 
rs2943559 HNF4G 76417937 1.13 0.09 0.98 
rs13267382 None 117209548 1.05b 0.36 0.96 
rs13281615 MYCd 128355618 1.10 0.44 0.99 
rs11780156 MYCd 129194641 1.07 0.17 0.99 
rs1011970 CDKN2A/B 22062134 1.05 0.18 
rs10759243 KLF4d 110306115 1.05 0.29 0.75 
rs865686 KLF4d 110888478 1.11 0.62 0.99 
rs2380205 ANKRD16 10 5886734 1.02 0.59 0.99 
rs7072776 MLLT10-DNAJC1 10 22032942 1.06 0.27 0.91 
rs11814448 DNAJC1 10 22315843 1.22 0.02 0.84 
rs10995190 NRBF2d 10 64278682 1.17 0.87 0.99 
rs704010 ZMIZ1 10 80841148 1.07 0.43 0.97 
rs7904519 TCF7L2 10 114773927 1.06 0.48 0.99 
rs11199914 FGFR2d 10 123093901 1.06 0.70 0.73 
rs2981579 FGFR2d 10 123337335 1.25 0.47 0.99 
rs3817198 LSP1 11 1909006 1.07 0.33 0.92 
rs3903072 DKFZp761e198-OVOLI-SNX32-CFL1-MUS81 11 65583066 1.06 0.55 0.99 
rs78540526 CCND1d 11 69331418 1.18 0.08 0.95 
rs554219 CCND1d 11 69331642 1.12 — — 
rs75915166 CCND1d 11 69379161 1.024 0.06 0.86 
rs11820646 BARX2 11 129461171 1.05 0.60 0.98 
rs12422552 None 12 14413931 1.03 — — 
rs10771399 PTHLH 12 28155080 1.16 — — 
rs17356907 NTN4 12 96027759 1.10 — — 
rs1292011 None 12 115836522 1.08 0.59 
rs11571833 BRCA2-N4BP2LI-N4BP2L2 13 32972626 1.26 0.01 0.87 
rs2236007 PAX9-SLO25A21 14 37132769 1.09 0.82 
rs2588809 RAD51L1 14 68660428 1.07 0.17 0.97 
rs999737 RAD51L1 14 69034682 1.08 0.77 0.97 
rs941764 CCDC88C 14 91841069 1.06 0.36 0.95 
rs11627032 RIN3 14 93104072 1.06b 0.74 0.93 
rs3803662 TOX3 16 52586341 1.23 0.29 
rs17817449 MIRI972-2-FTO 16 53813367 1.08 0.60 0.99 
rs11075995 FTO 16 53855291 1.04 0.76 0.99 
rs13329835 CDYL2 16 80650805 1.08 0.24 0.98 
rs146699004 TEFM 17 29230520 GGT 1.08b 0.74 0.99 
rs6504950 COX11e 17 53056471 1.07 0.72 0.97 
rs745570 CBX8 17 77781725 1.05b 0.50 0.97 
rs527616 None 18 24337424 1.04 0.66 0.98 
rs1436904 CHST9 18 24570667 1.06 0.63 0.99 
rs6507583 SETBP1 18 42399590 1.10b 0.93 0.99 
rs8170 ABHD8/ANKLE1e 19 17389704 1.03 0.20 0.99 
rs2363956 ABHD8/ANKLE1e 19 17394124 1.03 0.51 0.98 
rs4808801 SSBP4-ISYNA1-ELL 19 18571141 1.07 0.67 0.99 
rs3760982 C19orf61-KCNN4-LYPD5-ZNF283 19 44286513 1.06 0.49 
rs2823093 NRIP1 21 16520832 1.08 0.75 0.96 
rs17879961 CHEK2 22 29121087 1.36 0.001 0.69 
rs132390 EMID1-RHBDD3-EWSR1 22 29621477 1.11 0.04 
rs6001930 MKL1 22 40876234 1.13 0.11 0.99 

aAdjusted ORs from Mavaddat and colleagues (4).

bUnadjusted ORs from Michailidou and colleagues (1) or Michailidou and colleagues (3).

cImputation r2 calculated by the mean info score from IMPUTE2. The info score represents the certainty with which the SNP has been imputed and lies between 0 (no certainty) and 1 (high certainty; r2 = 1 for genotyped SNPs).

dPublished target gene.

eKnown target gene, not yet published.

fChromosome.

gHuman genome build 37 (GRCh37) by the Genome Reference Consortium, released on March 3, 2009. Available from: http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/human/.

The distributions of the nine different acute and late-arising toxicity endpoints (acute toxicity, telangiectasia, breast edema, photographically assessed shrinkage, induration, pigmentation, breast pain, breast sensitivity, and a standardized total average toxicity (STAT) score) measured in this study are shown in Table 3. Baseline symptoms that resulted from surgery and were present before radiotherapy were not associated with any of the genetic variants either individually or in the polygenic risk score. The significant nongenetic factors associated with acute toxicity and the 5-year toxicity endpoints are shown in Supplementary Table S3.

Table 3.

Distributions of toxicity endpoints in RAPPER patient sample

Number of patients (%)
Acute toxicity Grade 0 Grade 1 Grade 2a Grade 2b Grade 3 
RTOG acute toxicity 30 (3.3) 549 (59.7) 328 (35.7) 12 (1.3) 1 (0.1) 
2 year toxicity Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 
 Telangiectasia 738 (79.7) 116 (12.5) 53 (5.7) 18 (1.9) 1 (0.1) 
 Breast edema 522 (56.4) 261 (28.2) 110 (11.9) 33 (3.6) 
 Shrinkage assessed photographically NA 593 (63.5) 285 (30.5) 56 (6.0) NA 
 Induration 177 (18.3) 444 (45.8) 276 (28.5) 70 (7.2) 3 (0.3) 
 Pigmentation 629 (83.0) 62 (8.2) 67 (8.8) 
 Breast pain NA 416 (49.9) 362 (43.4) 45 (5.4) 11 (1.3) 
 Breast sensitivity NA 507 (60.7) 278 (33.3) 41 (4.9) 10 (1.2) 
 Overall toxicity (STATaN = 1134, mean (standard deviation) = 0.003 (0.66) 
5 year toxicity Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 
 Telangiectasia 493 (83.1) 48 (8.1) 30 (5.1) 22 (3.7) 
 Breast edema 487 (82.1) 73 (12.3) 27 (4.6) 6 (1.1) 
 Shrinkage assessed photographically NA 337 (58.7) 201 (35.0) 36 (6.3) NA 
 Induration 174 (29.3) 267 (45.0) 130 (21.9) 22 (3.7) 
 Overall toxicity (STATaN = 593, mean (SD) = 0.01 (0.64) 
Number of patients (%)
Acute toxicity Grade 0 Grade 1 Grade 2a Grade 2b Grade 3 
RTOG acute toxicity 30 (3.3) 549 (59.7) 328 (35.7) 12 (1.3) 1 (0.1) 
2 year toxicity Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 
 Telangiectasia 738 (79.7) 116 (12.5) 53 (5.7) 18 (1.9) 1 (0.1) 
 Breast edema 522 (56.4) 261 (28.2) 110 (11.9) 33 (3.6) 
 Shrinkage assessed photographically NA 593 (63.5) 285 (30.5) 56 (6.0) NA 
 Induration 177 (18.3) 444 (45.8) 276 (28.5) 70 (7.2) 3 (0.3) 
 Pigmentation 629 (83.0) 62 (8.2) 67 (8.8) 
 Breast pain NA 416 (49.9) 362 (43.4) 45 (5.4) 11 (1.3) 
 Breast sensitivity NA 507 (60.7) 278 (33.3) 41 (4.9) 10 (1.2) 
 Overall toxicity (STATaN = 1134, mean (standard deviation) = 0.003 (0.66) 
5 year toxicity Grade 0 Grade 1 Grade 2 Grade 3 Grade 4 
 Telangiectasia 493 (83.1) 48 (8.1) 30 (5.1) 22 (3.7) 
 Breast edema 487 (82.1) 73 (12.3) 27 (4.6) 6 (1.1) 
 Shrinkage assessed photographically NA 337 (58.7) 201 (35.0) 36 (6.3) NA 
 Induration 174 (29.3) 267 (45.0) 130 (21.9) 22 (3.7) 
 Overall toxicity (STATaN = 593, mean (SD) = 0.01 (0.64) 

aStandardized total average toxicity.

None of the individual genetic variants were associated, at the pre-specified significance level of P < 5 × 10−4, with acute toxicity or 2-year toxicity in either univariable or multivariable analyses (Supplementary Table S4 and Supplementary Figs. S1–S9). Univariable analysis of 5-year toxicity identified one SNP, rs6964587, to be significantly associated with increased risk of breast edema 5 years following radiotherapy [OR, 1.75; 95% confidence interval (CI), 1.30–2.49; P = 4 × 10−5). The association remained strong after adjustment for nongenetic factors (residual beta, 0.22; 95% CI, 0.09–0.34; P = 7 × 10−4). No other individual variants were associated with 5-year toxicity on univariable or multivariable analysis (Supplementary Table S5).

Scatter plots comparing each variant's effect on breast cancer risk with their effects on STAT (overall toxicity score) at 2 years and 5 years are shown in Fig. 2. Scatter plots for all other toxicity endpoints can be found in Supplementary Figs. S10–S17. Points in the top right hand quarters of the plots represent alleles associated with both increased breast cancer and radiation toxicity risk. Conversely for points in the bottom right hand quarter, the allelic effects are opposite for breast cancer and radiation toxicity risk. The plots reveal no correlations between these confirmed breast cancer risk alleles and increased radiotherapy toxicity risk in this RAPPER cohort.

Figure 2.

Scatter plot of individual breast cancer risk variants comparing their effect on breast cancer risk with their effect on STAT score. A, STAT at 2 years. B, STAT at 5 years.

Figure 2.

Scatter plot of individual breast cancer risk variants comparing their effect on breast cancer risk with their effect on STAT score. A, STAT at 2 years. B, STAT at 5 years.

Close modal

When the more statistically powerful polygenic risk scores were tested, there were no associations with acute toxicity (weighted P > 0.30; unweighted P > 0.39) or any of the 2-year toxicity endpoints (weighted P ≥ 0.13; unweighted P ≥ 0.17) or 5-year endpoints (weighted P ≥ 0.19; unweighted P ≥ 0.39; Table 4).

Table 4.

Association of risk scores with toxicity endpoints from residual analyses

Betaa (95% CI) and P
ToxicityUnweighted risk scoreWeighted risk score
Acute toxicity 
 RTOG grade −0.01 (−0.02–0.01) P = 0.39 −0.08 (−0.23–0.07) P = 0.30 
2 year toxicity 
 Telangiectasia −0.01 (−0.02–0.01) P = 0.40 −0.05 (−0.20–0.10) P = 0.54 
 Breast edema 0.001 (−0.01–0.01) P = 0.80 0.02 (−0.13–0.17) P = 0.80 
 Shrinkage assessed photographically −0.01 (−0.02–0.01) P = 0.41 −0.08 (−0.22–0.07) P = 0.30 
 Induration −0.003 (−0.01–0.01) P = 0.64 −0.05 (−0.20–0.10) P = 0.51 
 Pigmentation 0.01 (−0.004–0.02) P = 0.17 0.13 (−0.04–0.29) P = 0.13 
 Breast pain 0.003 (−0.01–0.01) P = 0.67 0.01 (−0.14–0.17) P = 0.85 
 Oversensitivity −0.001 (−0.01–0.01) P = 0.85 −0.05 (−0.21–0.10) P = 0.49 
 Overall toxicity (STATb0.002 (−0.01–0.01) P = 0.72 0.02 (−0.13–0.16) P = 0.81 
5 year toxicity 
 Telangiectasia −0.0004 (−0.02–0.01) P = 0.96 −0.05 (−0.24–0.14) P = 0.61 
 Breast edema −0.003 (−0.02–0.01) P = 0.72 −0.05 (−0.23–0.13) P = 0.58 
 Shrinkage assessed photographically 0.001 (−0.01–0.02) P = 0.92 0.05 (−0.14–0.25) P = 0.58 
 Induration −0.01 (-0.03–0.01) P = 0.19 −0.09 (−0.28–0.11) P = 0.39 
 Overall toxicity (STATb−0.01 (−0.02–0.01) P = 0.47 −0.07 (−0.26–0.12) P = 0.48 
Betaa (95% CI) and P
ToxicityUnweighted risk scoreWeighted risk score
Acute toxicity 
 RTOG grade −0.01 (−0.02–0.01) P = 0.39 −0.08 (−0.23–0.07) P = 0.30 
2 year toxicity 
 Telangiectasia −0.01 (−0.02–0.01) P = 0.40 −0.05 (−0.20–0.10) P = 0.54 
 Breast edema 0.001 (−0.01–0.01) P = 0.80 0.02 (−0.13–0.17) P = 0.80 
 Shrinkage assessed photographically −0.01 (−0.02–0.01) P = 0.41 −0.08 (−0.22–0.07) P = 0.30 
 Induration −0.003 (−0.01–0.01) P = 0.64 −0.05 (−0.20–0.10) P = 0.51 
 Pigmentation 0.01 (−0.004–0.02) P = 0.17 0.13 (−0.04–0.29) P = 0.13 
 Breast pain 0.003 (−0.01–0.01) P = 0.67 0.01 (−0.14–0.17) P = 0.85 
 Oversensitivity −0.001 (−0.01–0.01) P = 0.85 −0.05 (−0.21–0.10) P = 0.49 
 Overall toxicity (STATb0.002 (−0.01–0.01) P = 0.72 0.02 (−0.13–0.16) P = 0.81 
5 year toxicity 
 Telangiectasia −0.0004 (−0.02–0.01) P = 0.96 −0.05 (−0.24–0.14) P = 0.61 
 Breast edema −0.003 (−0.02–0.01) P = 0.72 −0.05 (−0.23–0.13) P = 0.58 
 Shrinkage assessed photographically 0.001 (−0.01–0.02) P = 0.92 0.05 (−0.14–0.25) P = 0.58 
 Induration −0.01 (-0.03–0.01) P = 0.19 −0.09 (−0.28–0.11) P = 0.39 
 Overall toxicity (STATb−0.01 (−0.02–0.01) P = 0.47 −0.07 (−0.26–0.12) P = 0.48 

aBeta estimates are for the association between polygenic risk score and change in residual toxicity; a positive beta represents an increased risk of toxicity and a negative beta represents reduced risk of toxicity.

bStandardized total average toxicity.

The hypothesis behind this study was that common genetic variants known to increase the risk of breast cancer may also increase the likelihood of developing toxicity following radiotherapy for breast cancer. We found no conclusive evidence supporting this hypothesis. One SNP, rs6964587, was identified as associated with increased breast edema at 5 years (OR, 1.75; 95% CI, 1.30–2.49). This SNP is a missense SNP lying in a coding region of the AKAP9 gene, which encodes the A kinase (PRKA) anchor protein 9—a key component of signal transduction that contributes to carcinogenesis (2, 19, 20). The SNP is predicted to generate an M643I substitution in the AKAP9 protein sequence. Of note, a study by Han and colleagues (20) suggested that breast tumor cells secrete the AKAP9 protein in response to ionizing radiation. This SNP association merits further investigation in independent radiotherapy toxicity cohorts.

The nearest gene to each individual breast cancer risk locus studied is listed in Table 2. For some of these (marked with d or e in Table 2), the target gene and mode of action of the causal variant has been elucidated, but for the majority this process is still on-going. A few putative target breast cancer genes on the list (e.g., CHEK2 and RAD51B) have known roles in DNA damage repair, but it is unlikely that the majority will prove to have similar functions. In this study of common low-penetrance breast cancer loci, the rare, more highly penetrant mutations in genes such as ATM, BRCA1, BRCA2, and TP53 were not genotyped and could not be included in this analysis. Investigation of these genes requires complete sequencing of the relevant genes and different statistical methods and is on-going. The link between radiosensitivity and cancer risk has been exemplified by ATM gene mutations, causing ataxia-telangiectasia, a cancer predisposing immunodeficiency syndrome characterized by extreme sensitivity to radiation and life-threatening radiotherapy toxicity (21). Carriers of ATM gene mutations, including relatives of people with ataxia-telangiectasia, have an increased risk of breast cancer (22). An in vitro radiosensitivity assay revealed that around 40% of breast cancer patients tested displayed evidence of increased radiosensitivity (7, 8, 23). Another study demonstrated the heritability of enhanced chromosomal radiosensitivity in breast cancer families (6).

This RAPPER study had a relatively small sample size. The original studies confirming the associations of the 94 genetic variants with increased breast cancer risk were performed in BCAC with over 100,000 study participants. In contrast, the RAPPER breast cohort comprises just fewer than 1,200 patients and the study had low power to detect a true association between individual variants and radiotherapy toxicity. Despite this, a single variant associated with an approximately 75% increased odds of breast edema 5 years following radiotherapy has been detected at a stringent statistical significance threshold. Future larger studies with greater power will be able to identify other individual SNPs that may be associated with radiotherapy toxicity. These studies could also explore whether the effect sizes of individual variants on breast cancer risk differ from those for radiation toxicity risk. Although this study was generally underpowered to detect individual variants, the study was well powered (87%) to detect an association between radiotherapy toxicity and a polygenic risk score. The 90 confirmed breast cancer variants, used in this study, have been estimated to explain 16% of the familial risk of breast cancer (3) and an earlier polygenic risk profile, which included a subset of 77 of these SNPs, demonstrated that women in the highest 1% of the risk score were 3.6 times more likely to develop breast cancer than women in the middle quintile (4). We have extended the polygenic risk profile to include 90 currently assayable breast cancer risk loci and have shown that they have no detectable predictive value for risk of radiation toxicity in women being treated for breast cancer. Our analysis was limited to women of European ancestry only, and therefore the findings may not be generalizable to women of other ancestries.

In summary, our study found no evidence to support a suggestion that patients with a high polygenic risk of breast cancer have an increased likelihood of developing acute or late radiotherapy toxicity. The conclusion from this work is that it appears to be safe for breast cancer patients with a high polygenic risk of breast cancer to receive standard radiotherapy for breast cancer.

No potential conflicts of interest were disclosed.

Conception and design: L. Dorling, G.C. Barnett, C.E. Coles, A.M. Dunning, P.D.P. Pharoah, C.M. West

Development of methodology: L. Dorling, G.C. Barnett, C.E. Coles, N.G. Burnet, A.M. Dunning

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): C.E. Coles, J. Yarnold, R.M. Elliott, P.D.P. Pharoah, C.M. West

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): L. Dorling, G.C. Barnett, K. Michailidou, A.M. Dunning

Writing, review, and/or revision of the manuscript: L. Dorling, G.C. Barnett, K. Michailidou, C.E. Coles, N.G. Burnet, A.M. Dunning, P.D.P. Pharoah, C.M. West

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): L. Dorling, R.M. Elliott

Study supervision: A.M. Dunning, C.M. West

The authors acknowledge the Cambridge Centre for Cancer Genetic Epidemiology (CCGE) computing cluster at Strangeways Research Laboratory at the University of Cambridge for providing supercomputing infrastructure and Michael Lush for his technical support. The authors thank Nasim Mavaddat for early access to prepublished details of the breast cancer risk variants. The authors also acknowledge NHS funding to the NIHR Biomedical Research Centre at The Royal Marsden and the ICR.

L. Dorling is funded by the Medical Research Council (UK). C.E. Coles and N.G. Burnet are supported by the Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre. J. Yarnold is supported by the Institute of Cancer Research & Royal Marsden NIHR Biomedical Research Centre. P.D.P. Pharoah is funded by Cancer Research UK (C490/A16561). C.M. West is supported by Experimental Cancer Medicine Centre (ECMC) funding. The collaborative group (RAPPER) is funded by Cancer Research UK (C1094/A18504). EU FP7 Health-F2-2009-223175 “COGS” funded the discovery or confirmation of the breast cancer risk variants used in this study.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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