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British Journal of Cancer logoLink to British Journal of Cancer
. 2016 Apr 12;114(10):1165–1174. doi: 10.1038/bjc.2016.94

Common genetic variation associated with increased susceptibility to prostate cancer does not increase risk of radiotherapy toxicity

Mahbubl Ahmed 1,13,*, Leila Dorling 2,13, Sarah Kerns 3, Laura Fachal 2,4, Rebecca Elliott 5, Matt Partliament 6, Barry S Rosenstein 7, Ana Vega 8, Antonio Gómez-Caamaño 9, Gill Barnett 2, David P Dearnaley 1, Emma Hall 10, Matt Sydes 11, Neil Burnet 12, Paul D P Pharoah 2, Ros Eeles 1, Catharine M L West 5
PMCID: PMC4865979  PMID: 27070714

Abstract

Background:

Numerous germline single-nucleotide polymorphisms increase susceptibility to prostate cancer, some lying near genes involved in cellular radiation response. This study investigated whether prostate cancer patients with a high genetic risk have increased toxicity following radiotherapy.

Methods:

The study included 1560 prostate cancer patients from four radiotherapy cohorts: RAPPER (n=533), RADIOGEN (n=597), GenePARE (n=290) and CCI (n=150). Data from genome-wide association studies were imputed with the 1000 Genomes reference panel. Individuals were genetically similar with a European ancestry based on principal component analysis. Genetic risks were quantified using polygenic risk scores. Regression models tested associations between risk scores and 2-year toxicity (overall, urinary frequency, decreased stream, rectal bleeding). Results were combined across studies using standard inverse-variance fixed effects meta-analysis methods.

Results:

A total of 75 variants were genotyped/imputed successfully. Neither non-weighted nor weighted polygenic risk scores were associated with late radiation toxicity in individual studies (P>0.11) or after meta-analysis (P>0.24). No individual variant was associated with 2-year toxicity.

Conclusion:

Patients with a high polygenic susceptibility for prostate cancer have no increased risk for developing late radiotherapy toxicity. These findings suggest that patients with a genetic predisposition for prostate cancer, inferred by common variants, can be safely treated using current standard radiotherapy regimens.

Keywords: prostate cancer, genetic variants, radiotherapy, late toxicity

Prostate carcinoma accounts for a quarter of cancer diagnoses in men in the United Kingdom and is the fourth most common cancer worldwide with an estimated 1.1 million men diagnosed in 2012 (Globocan, 2012; Cancer Research UK, 2014). It is estimated that approximately a third of patients with localised or locally advanced prostate cancer undergo external beam radiotherapy (RT) with curative intent (Foroudi et al, 2003). The use of RT in combination with androgen-deprivation prolongs survival (Martin and D'Amico, 2014), and has contributed to the increase in 5-year survival rate from 30% in the 1970s to 80% in 2009 (Cancer Research UK, 2014). Because of increased cure rates, cancer survivorship and late treatment toxicity have become increasingly important issues in health-care provision (England, 2013).

Late toxicity following irradiation for prostate cancer includes damage to the bladder, bowel and erectile function (Peeters et al, 2005). The median rates of late gastrointestinal (GI) and genitourinary (GU) toxicity are reported to be 15% and 17% respectively (Ohri et al, 2012). The rates of severe GI and GU toxicity are reported to be 2% and 3% respectively (Ohri et al, 2012). There is now supporting evidence that new techniques such as intensity-modulated radiotherapy (IMRT) reduce rates of long-term GI and GU side effects compared with 3D conformal RT, even with dose escalation (Dolezel et al, 2015; Wilkins et al, 2015). Despite these advances, approximately one in five patients will experience some degree of late radiation toxicity (Dolezel et al, 2015).

Studies are attempting to identify the genetic variants that increase an individual's risk of radiation toxicity (Kerns et al, 2013; Barnett et al, 2014; Fachal et al, 2014). This work has highlighted the need to increase the statistical power to identify individual common variants with small effects (Barnett et al, 2012a). To address this need, the Radiogenomics Consortium (RGC) was established in 2009 to facilitate large-scale collaborative research with sufficient power to detect genetic variants that predict a patient's risk of radiation toxicity (West et al, 2010). The RGC groups have undertaken genome-wide association studies (GWASs) and are starting to identify replicated variants that increase a prostate cancer patient's risk of toxicity (Fachal et al, 2014).

In the cancer predisposition field, GWASs have identified 76 common single-nucleotide polymorphisms (SNPs) associated with prostate cancer susceptibility (Eeles et al, 2014). Although the biologic role of these SNPs in the development of prostate cancer is an area of on-going investigation, their proximity to genes that are involved in DNA repair processes suggests that disruption of DNA damage response and repair mechanisms may have a key role (Eeles et al, 2014; Hazelett et al, 2014). If a patient has an inherent compromised ability to repair DNA damage, they may be predisposed to both prostate cancer and toxicity following RT, as the same DNA repair pathways play a central role in cellular response to radiation. In addition, recent epidemiological evidence suggests that radiation exposure increases the risk of developing prostate cancer (Myles et al, 2008; Schmitz-Feuerhake and Pflugbeil, 2011; Kondo et al, 2013). Therefore, the hypothesis underlying this study was that common genetic variants involved in cancer predisposition may have roles in both tumour formation and in the response of normal tissues to radiation-induced DNA damage. The aim of this study was to investigate the association between prostate cancer germline risk SNPs and likelihood of developing late radiation toxicity.

Materials and methods

Patients

This prospective study involved four prostate cancer radiotherapy cohorts: RAPPER (N=533), RADIOGEN (N=597), GenePARE (N=290) and CCI (N=150). Informed consent was obtained from all patients. RAPPER was approved by the Cambridge South Research Ethics Committee (05/Q0108/365). RADIOGEN was approved by the Galician Ethical Committee. GenePARE was approved by the Mount Sinai Medical Center Institutional Review Board. The CCI study was approved by the Health Research Ethics Board of Alberta (Cancer).

The UK RAPPER study (UKCRN1471) recruited patients who received neoadjuvant androgen suppression and external beam radiotherapy (EBRT) from two clinical trials RT01 (ISRCTN47772397) and CHHiP (ISRCTN97182923). A full description of the cohort is available elsewhere (Barnett et al, 2014). The RTO1 study was a randomised dose escalation study using 3D conformal radiotherapy comparing 64 and 74 Gy in the treatment of localised prostate cancer (Dearnaley et al, 2014). The CHHiP study randomised between standard (74 Gy in 37 fractions) and hypofractionated (60 Gy in 20 fractions or 57 Gy in 19 fractions) IMRT (Dearnaley et al, 2012).

RADIOGEN comprised patients who received 3D conformal radical or post-prostatectomy EBRT at the Clinical University Hospital of Santiago de Compostela, Spain. A total of 473 patients had adjuvant hormone therapy. Patients received radical EBRT using doses of between 70 and 76 Gy in 2 Gy per fraction. The adjuvant EBRT doses used were 60–66 Gy in 2 Gy per fraction. A full description of the cohort can be found elsewhere (Fachal et al, 2012).

GenePARE patients received brachytherapy with/without EBRT at the Mount Sinai Hospital, New York. Of the ∼800 patients included in the initial GenePARE study, 290 individuals of European ancestry had high-quality genome-wide SNP data available and were included in the present study. Of these individuals, 147 received adjuvant hormone therapy. The 125I (160 Gy; TG-43) was used in patients undergoing brachytherapy alone and 103Pd (100 Gy) in patients also receiving EBRT. The EBRT regimen was delivered using 3D conformal technique using 24–50 Gy. External beam radiotherapy alone was delivered using IMRT using 66.6–81 Gy, and further full details can be found elsewhere (Kerns et al, 2014b).

The CCI cohort recruited patients from the Cross Cancer Institute in Edmonton and the Tom Baker Cancer Centre in Alberta, Canada. Patients underwent EBRT using a hypofractionated (68 Gy in 25 fractions or 55 Gy in 16 fractions) or conventional (72–82 Gy delivered in 2 Gy per fraction) schedule. Approximately 50% of patients received androgen suppression. Further treatment details can be found elsewhere (Kerns et al, 2013).

Assessment of late radiotherapy toxicity

Late toxicity data were collected prospectively and assessed using standardised scoring systems (Supplementary Table 1). Data collected at 2 years were used as in other RGC studies (Andreassen et al, 2012; Dearnaley et al, 2012; Kerns et al, 2013). For rectal bleeding in GenePARE, a 1–5-year window was allowed, because the scoring system assigns grades based on whether rectal bleeding occurs as a single incident or intermittent symptoms over time.

Decreased stream, urinary frequency and rectal bleeding data were harmonised across the four cohorts to create comparable end points (see Supplementary Table 2). Toxicity end points were analysed as change from baseline rather than actual recorded grade such that the toxicity captured was due to radiotherapy only. Because of the low number of high-grade toxicities (⩾2) it was only possible to analyse toxicities as grade 0 vs ⩾1 (Table 1). Scale-independent Standardised Total Average Toxicity (STAT) scores were derived, as described previously (Barnett et al, 2012b), from a range of individual toxicity end points to provide an overall measure of 2-year toxicity that was comparable across the four cohorts.

Table 1. Distributions of patient characteristics and toxicity.

  RAPPER (N=533) RADIOGEN (N=597) GenePARE (N=290) CCI (N=150) P-valuea
Age
Mean (s.d.) 67.2 (5.7) 71.0 (6.5) 64.0 (7.5) 66.7 (7.4) P<0.00005
Diabetes
Yes, n (%) 39 (7.3) 144 (24.1) 16 (5.5) 24 (16.0) P<0.00005
No, n (%) 493 (92.5) 453 (75.9) 274 (94.5) 122 (81.3)  
Missing, n (%) 1 (0.2) 0 0 4 (2.7)  
Prior TURP
Yes, n (%) 56 (10.5) 45 (7.5) 6 (2.1) 6 (4.0) P=0.0002
No, n (%) 472 ((88.6) 552 (92.5) 284 (97.9) 144 (96.0)  
Missing, n (%) 5 (0.9) 0 0 0  
BED
Mean (s.d.) 120.5 (6.2) 120.5 (5.6) 191.9 (22.4) 125.5 (6.2) P<0.00005
STAT 2 years
Mean (s.d.) −0.01 (0.5) 0.02 (0.8) 0.12 (0.7) −0.01 (0.7) 0.06
Decreased stream
Grade 0, n (%) 483 (90.6) 472 (79.1) 189 (65.2) NA P<0.00005
Grade ⩾1, n (%) 29 (5.5) 6 (1.0) 66 (22.7) NA  
Missing, n (%) 21 (3.9) 119 (19.9) 35 (12.1) NA  
Urine frequency
Grade 0, n (%) 482 (90.5) 423 (70.9) 179 (61.7) 120 (80) P<0.00005
Grade ⩾1, n (%) 45 (8.4) 54 (9.0) 76 (26.2) 30 (20)  
Missing, n (%) 6 (1.1) 120 (20.1) 35 (12.1) 0  
Rectal bleeding
Grade 0, n (%) 446 (83.7) 522 (87.4) 208 (71.7) 110 (73.3)  
Grade ⩾1, n (%) 81 (15.2) 74 (12.4) 82 (28.3) 40 (26.7) P<0.00005
Missing, n (%) 6 (1.1) 1 (0.2) 0 0  
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Abbreviations: BED=biologically effective dose; NA=not available; STAT=Standardised Total Average Toxicity; TURP=transurethral resection of the prostate.

a

P-value for test of heterogeneity across cohorts.

Genotyping, quality control and imputation

Samples were genotyped as part of previously completed GWAS (Kerns et al, 2013; Barnett et al, 2014; Fachal et al, 2014). Standard quality control procedures were applied to remove variants that were missing in >5% of samples, had a minor allele frequency (MAF) <1% or displayed genotype frequencies deviating from those expected under Hardy–Weinberg equilibrium (P-value <10−6). Samples that had >3% of all variants missing were removed. Allele frequencies are known to vary by ancestral background, and hence principle component analysis (PCA) was used to identify and exclude individuals with non-European ancestry in order to avoid false positive associations arising from population substructure because of the small number of participants with other ethnicities. Comparable sets of variants were produced through imputation using SHAPEIT (Delaneau et al, 2012) and IMPUTE2 (Howie et al, 2011) with the 1000 Genomes Phase I reference panel (Abecasis et al, 2010). Supplementary Table 3 lists the 76 known prostate cancer susceptibility SNPs. Genotype dosages for the prostate cancer risk alleles were extracted from the imputed data.

Statistical analysis

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

Non-weighted, for patient i: Inline graphic

Weighted, for patient i: Inline graphic

where j=variants 1.76

βj=the per-allele log-odds ratio for risk of prostate cancer associated with variant j

G= risk allele dosage

The log-odds ratios used to weight the risk score were taken from the review paper by Eeles et al (2014).

Within each cohort, logistic regression was used to test the association between each individual toxicity end point and polygenic risk score, adjusted for important nongenetic factors identified by QUANTEC (Bentzen et al, 2010). Total biologically effective dose (BED) was calculated for individuals in all four studies as a measure of radiation dose exposure using an α/β=3. Other nongenetic risk factors included were age at treatment, diabetes (rectal bleeding only), rectal volume (rectal bleeding only), transurethral resection of the prostate (TURP) before radiotherapy (urinary end points only) and baseline toxicity (Table 1). Linear regression was used to test the association between STAT score and polygenic risk score, adjusted for all the nongenetic factors above. Logistic and linear regression was also used to test each genetic variant individually. Regression coefficients and their standard errors were then meta-analysed using standard inverse-variance weighted fixed effects meta-analysis methods.

Power calculations

This study was well powered to detect significant associations between prostate cancer polygenic risk scores and common radiotherapy toxicity end points. Assuming a moderate difference of 0.34 in mean polygenic risk score between prostate cancer patients who experience toxicity and those who do not, with a significance level of α=0.05, the power to detect an association between toxicity (grade ⩾1) and polygenic risk score would be 99% for a toxicity end point with 15% prevalence (grade ⩾1) and 96% for a toxicity end point with 6% prevalence (grade ⩾1). This difference in mean risk would be equivalent to a relative risk of toxicity of 1.4 for the subset of patients with a higher mean polygenic risk of prostate cancer.

Results

The distributions of patient characteristics, toxicity end points and STAT scores are summarised in Table 1. Of the 76 germline prostate cancer risk SNPs, 75 were genotyped or imputed successfully (R2>0.3; Table 2). Histograms of the polygenic risk scores show an approximate normal distribution within each cohort (Figure 1). Brachytherapy slightly increases urinary toxicity compared with EBRT alone, explaining the higher urinary toxicity in GenePARE (Sutani et al, 2015).

Table 2. SNPs associated with prostate cancer.

        RAPPER
 RADIOGEN
 GenePARE
 CCI
SNP Chromosome Position Alleles major/minor MAF R2 a MAF R2 a MAF R2 a MAF R2 a
rs1218582 1 154834183 A/G 0.45 0.94 0.50 0.87 0.43 0.96 0.48 0.96
rs4245739 1 204518842 A/C 0.24 0.99 0.30 0.99 0.28 1 0.23 1
rs11902236 2 10117868 G/A 0.27 0.93 0.29 1 0.27 0.91 0.28 1
rs13385191 2 20888265 G/Ab 0.25 0.99 0.24 1 0.27 1 0.26 1
rs1465618 2 43553949 G/A 0.22 0.98 0.26 0.98 0.23 0.98 0.22 1
rs721048 2 63131731 G/A 0.22 1 0.22 1 0.18 0.94 0.17 0.93
rs10187424 2 85794297 A/G 0.37 1 0.44 0.99 0.42 0.99 0.42 1
rs12621278 2 173311553 A/G 0.04 0.97 0.04 1 — — — —
rs2292884 2 238443226 A/G 0.28 1 0.23 0.97 0.24 0.99 0.22 0.99
rs3771570 2 242382864 G/A 0.18 0.99 0.16 0.96 0.14 1 0.15 1
rs2660753 3 87110674 C/T 0.11 1 0.16 1 0.19 0.96 0.08 0.98
rs2055109 3 87467332 C/Tb 0.26 1 0.28 0.99 0.24 0.86 0.22 0.99
rs7611694 3 113275624 A/C 0.41 1 0.37 0.98 0.36 1 0.39 1
rs10934853 3 128038373 C/A 0.29 0.98 0.29 0.94 0.32 1 0.34 1
rs6763931 3 141102833 C/T 0.45 0.98 0.39 1 0.40 0.96 0.39 1
rs10936632 3 170130102 A/C 0.49 0.94 0.45 0.95 0.46 0.94 0.47 0.95
rs1894292 4 74349158 G/A 0.47 0.86 0.47 1 0.40 0.91 0.44 1
rs12500426 4 95514609 C/A 0.48 0.96 0.48 0.98 0.49 0.98 0.49 0.99
rs17021918 4 95562877 C/T 0.31 0.99 0.31 0.99 0.35 1 0.37 1
rs7679673 4 106061534 C/A 0.34 1 0.38 0.99 0.49 0.99 0.38 1
rs2242652 5 1280028 G/A 0.19 0.91 0.15 0.45 0.22 0.62 0.19 0.62
rs12653946 5 1895829 C/T 0.45 1 0.46 0.58 0.48 0.90 0.44 0.90
rs2121875 5 44365545 T/G 0.35 1 0.37 1 0.41 0.99 0.34 1
rs6869841 5 172939426 G/A 0.23 0.98 0.21 0.99 0.24 0.99 0.23 0.99
rs130067c 6 31118511 T/G 0.21 1 0.19 1 NA NA NA NA
rs3096702d 6 32192331 G/A 0.42 0.97 0.28 1 NA NA NA NA
rs1983891 6 41536427 C/T 0.29 0.95 0.35 0.98 0.31 1 0.31 1
rs2273669 6 109285189 A/G 0.15 0.99 0.16 0.97 0.12 0.97 0.14 0.99
rs339331 6 117210052 T/Cb 0.27 0.97 0.25 1 0.19 1 0.32 1
rs1933488 6 153441079 A/G 0.38 0.99 0.41 1 0.44 1 0.41 1
rs9364554 6 160833664 C/T 0.33 0.99 0.25 1 0.25 1 0.29 1
rs12155172 7 20994491 G/A 0.22 0.82 0.21 1 0.21 0.97 0.22 0.97
rs10486567 7 27976563 G/Ab 0.19 0.97 0.20 1 0.25 0.99 0.21 0.98
rs6465657 7 97816327 T/C 0.49 1 0.47 1 0.41 1 0.49 1
rs2928679 8 23438975 C/T 0.48 1 0.45 0.99 0.49 0.97 0.46 1
rs1512268 8 23526463 G/A 0.45 0.99 0.50 0.98 0.49 0.99 0.42 1
rs11135910 8 25892142 G/A 0.2 0.93 0.15 1 0.16 0.97 0.18 0.99
rs12543663 8 127924659 A/C 0.35 0.94 0.26 0.97 0.31 0.94 0.33 0.94
rs10086908 8 128011937 T/C 0.27 1 0.31 1 0.26 1 0.26 0.99
rs16901979 8 128124916 C/A 0.05 0.99 0.05 1 0.05 1 0.05 1
rs620861 8 128335673 C/T 0.34 1 0.36 0.99 0.36 0.99 0.31 0.99
rs6983267 8 128413305 G/Tb 0.43 0.95 0.40 1 0.48 0.98 0.45 1
rs1447295 8 128485038 C/A 0.14 0.97 0.07 1 0.09 0.99 0.10 0.99
rs817826 9 110156300 T/C 0.17 0.64 0.17 0.94 0.21 1 0.12 1
rs1571801 9 124427373 C/A 0.28 0.76 0.23 1 0.22 1 0.32 1
rs10993994 10 51549496 C/T 0.46 0.79 0.43 1 0.47 0.92 0.43 0.92
rs3850699 10 104414221 A/G 0.28 0.99 0.28 0.96 0.29 0.97 0.26 0.97
rs2252004 10 122844709 G/Tb 0.09 0.99 0.09 0.99 0.11 1 0.09 1
rs4962416 10 126696872 T/C 0.28 0.93 0.31 1 0.31 0.98 0.24 0.98
rs7127900 11 2233574 G/A 0.22 0.98 0.23 0.91 0.27 0.59 0.22 0.96
rs1938781 11 58915110 T/C 0.21 1 0.21 1 0.20 0.99 0.19 1
rs7931342 11 68994497 G/T 0.46 1 0.41 1 0.36 1 0.41 1
rs11568818 11 102401661 A/G 0.44 0.89 0.46 0.99 0.42 0.91 0.42 0.93
rs902774 12 53273904 G/A 0.16 0.98 0.14 1 0.17 0.99 0.18 1
rs1270884 12 114685571 G/A 0.48 0.93 0.49 0.98 0.48 0.98 0.48 1
rs10875943 12 49676010 T/C 0.32 0.86 0.28 1 0.32 0.93 0.29 0.95
rs9600079 13 73728139 G/T 0.46 0.86 0.47 1 0.43 0.89 0.46 0.95
rs8008270 14 53372330 G/A 0.15 1 0.19 0.99 0.19 1 0.16 1
rs7141529 14 69126744 G/Ab 0.47 1 0.45 1 0.46 0.99 0.49 0.99
rs4430796 17 36098040 G/A 0.47 0.94 0.49 0.88 0.49 0.90 0.45 0.93
rs7210100e 17 47436749 A/G — — — — — — — —
rs11649743 17 36074979 G/Ab 0.2 1 0.19 1 0.15 1 0.16 1
rs11650494 17 47345186 G/A 0.09 0.99 0.08 0.93 0.13 0.99 0.09 0.99
rs684232 17 618965 A/G 0.35 0.99 0.33 0.98 0.34 0.98 0.39 0.99
rs1859962 17 69108753 T/G 0.5 0.99 0.45 1 0.43 1 0.47 1
rs7241993 18 76773973 G/A 0.28 0.94 0.28 0.91 0.32 0.51 0.32 0.56
rs8102476 19 38735613 C/Tb 0.42 0.99 0.34 0.96 0.38 0.97 0.43 0.97
rs11672691 19 41985587 A/Gb 0.24 0.87 0.21 0.93 0.23 0.93 0.27 0.93
rs103294f 19 54797848 T/C 0.23 1 0.20 1 0.22 0.30 — —
rs2735839 19 51364623 G/A 0.12 1 0.15 1 0.17 0.93 0.16 0.97
rs2427345 20 61015611 G/A 0.35 0.99 0.35 0.37 0.39 0.91 0.40 0.96
rs6062509 20 62362563 A/C 0.32 1 0.26 0.99 0.27 0.98 0.28 0.98
rs5759167 22 43500212 G/T 0.47 1 0.45 0.90 0.46 0.80 0.47 1
rs2405942 23 9814135 A/G 0.17 1 0.20 0.95 0.21 0.94 0.23 0.93
rs5919432 23 67021550 A/G 0.14 0.98 0.18 1 0.21 1 0.18 1
rs5945619 23 51241672 T/C 0.41 0.92 0.44 1 0.38 1 0.33 1
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Abbreviations: MAF=minor allele frequency; NA=not available; SNP=single-nucleotide polymorphism.

a

R2 refers to the ‘imputation info' metric produced by IMPUTE2 that 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).

b

Major allele is associated with increased risk of prostate cancer.

c

Merged SNP rs115664826.

d

Merged SNP rs114376585.

e

The rs7210100 MAF=0, R2=0, excluded from RAPPER analyses; RADIOGEN R2=0.005; not imputed in GenePARE or CCI data sets.

f

rs103294 poorly imputed in CCI.

Figure 1.

Figure 1

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Histograms showing the approximate normal distributions for the non-weighted and the weighted polygenic risk scores in (A) the RAPPER cohort, (B) the RADIOGEN cohort, (C) the GenePARE cohort and (D) the CCI cohort.

The results of the association analyses are shown in Table 3 and Supplementary Tables 4–7. Neither the non-weighted nor the weighted polygenic risk score was associated with any late radiotherapy individual toxicity end points or STAT score in any of the individual studies or on meta-analysis (meta-analysis P>0.35 and P>0.33 for non-weighted and weighted scores respectively; Table 3). None of the individual SNPs were associated with late radiation toxicity at 2 years at the prespecified significance level of P-value <5 × 10−4 in any of the individual studies or on meta-analysis (Supplementary Tables 4–7). There was no statistical evidence of heterogeneity between studies for any individual SNPs or the polygenic risk score.

Table 3. Polygenic risk score analysis results.

  RAPPER
 RADIOGEN
 GenePARE
 CCI
 Meta-analysis
  β (95% CI) Pa β (95% CI) P β (95% CI) P β (95% CI) P β (95% CI) P Q, P-hetb
STAT score
Non-weighted risk score 0.003 (−0.006, 0.01) 0.49 0.003 (−0.008, 0.01) 0.61 −0.01 (−0.03, 0.01) 0.15 −0.01 (−0.04, 0.01) 0.25 0.00002 (−0.01, 0.01) 0.99 3.88, 0.28
Weighted risk score 0.04 (−0.02, 0.10) 0.19 0.03 (−0.05, 0.10) 0.44 −0.06 (−0.17, 0.05) 0.28 −0.09 (−0.26, 0.08) 0.29 0.01 (−0.03, 0.06) 0.49 4.15, 0.25
Decreased stream
Non-weighted risk score 0.01 (−0.06, 0.08) 0.78 0001 (−0.001, 0.003) 0.36 0.004 (−0.06, 0.06) 0.91 NA NA 0.001 (−0.001, 0.003) 0.35 0.08, 0.96
Weighted risk score 0.34 (−0.15, 0.83) 0.17 0.01 (−0.01, 0.02) 0.36 0.04 (−0.35, 0.43) 0.86 NA NA 0.01 (−0.01, 0.02) 0.33 1.84, 0.40
Urine frequency
Non-weighted risk score −0.004 (−0.06, 0.06) 0.90 −0.001 (−0.01, 0.004) 0.66 −0.04 (−0.10, 0.02) 0.18 −0.03 (−0.12, 0.05) 0.45 −0.002 (−0.007, 0.004) 0.54 2.19, 0.53
Weighted risk score −0.003 (−0.40, 0.40) 0.99 −0.01 (−0.05, 0.02) 0.50 −0.31 (−0.72, 0.08) 0.12 0.04 (−0.56, 0.62) 0.89 −0.01 (−0.05, 0.02) 0.42 2.18, 0.54
Rectal bleeding
Non-weighted risk score 0.04 (−0.009, 0.08) 0.11 −0.002 (−0.01, 0.003) 0.47 −0.02 (−0.08, 0.04) 0.48 −0.0003 (−0.07, 0.07) 0.99 −0.002 (−0.01, 0.003) 0.55 3.22, 0.36
Weighted risk score 0.28 (−0.03, 0.59) 0.08 −0.01 (−0.04, 0.03) 0.78 −0.06 (−0.45, 0.32) 0.75 −0.05 (−0.57, 0.47) 0.86 −0.002 (−0.04, 0.03) 0.90 3.32, 0.34
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Abbreviations: CI=confidence interval; NA=not available; STAT=Standardised Total Average Toxicity.

a

P-value corresponding to β-estimate.

b

P-value for test of heterogeneity between studies.

Discussion

This study found no evidence that prostate cancer patients with a high polygenic risk score for susceptibility to the disease have an increased risk of developing late toxicity following RT. The study was well powered to detect an association between prostate cancer polygenic risk and radiotherapy toxicity end points with a prevalence ⩾6% and a moderate effect of RR=1.4. There was also no evidence for individual SNPs to be associated with risk of toxicity, although the study was not sufficiently powered to detect associations with individual SNPs that are each likely to carry a very small risk for radiotherapy toxicity. Rare, highly penetrant variants like BRCA1, BRCA2 and HOXB13 were not included in this analysis as they require sequencing in a much larger number of patients and different statistical analysis methods.

The biggest nongenetic determinant of radiotherapy toxicity is known to be dose (Kerns et al, 2015). In this study we calculated BED to allow comparison across cohorts receiving external beam therapy only (RAPPER, RADIOGEN and CCI) and those receiving brachytherapy as well (GenePARE). Other important nongenetic factors such as age and comorbidities were also adjusted for. Tests for heterogeneity in these factors across the cohorts were highly significant, suggesting that the cohorts are not homogeneous. However, none of the meta-analysis P-values for heterogeneity were statistically significant. Thus, although the heterogeneity of the cohorts may have reduced the power of the meta-analysis, it is unlikely to have biased the results for the SNPs.

The prostate cancer risk SNPs are mostly located in intronic regions and the functional target genes through which they increase prostate cancer risk are not known. However, some SNPs associated with prostate cancer risk reside near genes that may influence the DNA repair process. The SNP rs817826, identified in a Han Chinese population, lies in an intergenic region between RAD23B and KLF4 (Xu et al, 2012). RAD23B is a key protein involved in the nucleotide excision repair pathway that functions to repair single-strand DNA breaks from ionising radiation (Clement et al, 2010). Defects in this pathway have been associated with photosensitive conditions such as xeroderma pigmentosa (XP) and increase the likelihood of double-strand breaks and late radiation toxicity (Feltes and Bonatto, 2015). Another SNP, rs1938781, found on chromosome 11q12 lies very close to FAM111A and FAM111B (Akamatsu et al, 2012). Mutations in FAM111B have been associated with the development of hereditary fibrosing poililoderma with pulmonary fibrosis, tendon contracture and myopathy (Mercier et al, 2013). The underlying mechanism in which FAM111B causes the above abnormalities is not known. One of the most interesting SNPs, rs7141529 on chromosome 14q24, is an intronic SNP in the DNA repair gene RAD51B (Eeles et al, 2013). Though the functional effect of this SNP is unknown, RAD51B is involved in homologous recombination repair induced by double-strand DNA breaks such as those caused by RT. The SNPs in the TERT locus of 5p15 have been shown to affect prostate cancer risk by interfering with TERT expression (Amin Al Olama et al, 2013). The TERT gene functions by adding telomere repeat sequences at the end of chromosomes that prevent cells undergoing telomere-dependent senescence (Kote-Jarai et al, 2013). A number of proteins have been identified that are involved in telomere maintenance as well as being involved in repair of DNA double-strand breaks by homologous recombination (Huda et al, 2009). Another SNP that has an association with aggressive prostate cancer is rs4245739 that is located in the 3′ untranslated region (UTR) of MDM4 on chromosome 1q32 (Eeles et al, 2013). When functioning normally, MDM4 is a critical negative regulator of the tumour suppressor gene TP53. MDM4 is frequently overexpressed in many cancers that have wild-type TP53 (Wynendaele et al, 2010). TP53 is involved in DNA repair (Merino and Malkin, 2014).

Studies investigating genetic variation in relation to risk of radiotherapy toxicity focused initially on ATM, because individuals with homozygote mutations are extremely sensitive to radiation. The first SNP studies were reported at the start of twenty-first century, and the most widely studied genes encoded proteins associated with DNA repair (e.g., ATM), the development of fibrosis (e.g., TGFB1) and scavenging of reactive oxygen species (e.g., SOD2). Although significant associations were reported, replication was often unsuccessful (Andreassen et al, 2012; Barnett et al, 2012a). Since the establishment of the RGC, replicated associations have been found in both large candidate gene (Talbot et al, 2012; Seibold et al, 2015) and genome-wide association (Fachal et al, 2014) studies. It is interesting to note that the SNPs being identified through GWAS fall in or near genes associated with the function of the tissue irradiated (Fachal et al, 2014; Kerns et al, 2014a, 2014c, 2015; Rosenstein et al, 2014). Although DNA damage response gene products have a clear role in cancer eradication, other pathways are clearly important in the pathogenesis of late radiotherapy toxicity (Bentzen, 2006).

The study reported here had a number of limitations. First, the findings are limited to prostate cancer risk conferred by common variants only – many thousands of participants will need to be studied to assess a role for rare variants. Second, our analysis was limited to men who were genetically of European ancestry and therefore the conclusions may not be generalisable to men of other ethnicities. Third, many genes that predispose to prostate cancer have not yet been identified. Fourth, there are likely to be unrecorded toxicities in patients because underreporting is a known issue of data collection in radiotherapy studies (Bentzen et al, 2010). For example, it was not possible to analyse sexual dysfunction as no data were available for two of the cohorts.

Only 33% of common germline variants that predict the familial risk of developing prostate cancer have so far been discovered (Eeles et al, 2014). The top 1% of the risk distribution have a 4.7 times increased risk of developing prostate cancer than the average population being profiled (Eeles et al, 2013). The National Institute of Health-funded GAME-ON initiative is a cross-cancer genotyping project that will include 100 000 prostate cancer patient samples on a genotyping array of 500 000 SNPs. Through this expanded genotyping effort, additional risk SNPs for prostate cancer susceptibility are expected to be identified. Approximately 5000 samples from the RGC are included in the OncoArray genotyping initiative, and can be used to test associations between SNPs and radiotherapy toxicity in a future larger study with more SNPs covering a larger percentage of the familial risk. The larger sample size should allow for better testing of individual SNPs.

In summary, this work showed that there is no association between genetic susceptibility to developing prostate cancer and the development of late radiation toxicity. The implication of this finding is that standard RT for prostate cancer can be given to patients with an increased genetic burden for prostate cancer without the risk of increased late radiotherapy toxicity.

Acknowledgments

This work was supported by Cancer Research UK (C1094/A11728 to CMLW and Neil Burnet for the RAPPER study, C26900/A8740 to Gil Barnett and C5047A17528 to Ros Eeles), the Royal College of Radiologists (to Gil Barnett), Prostate Cancer UK (P2012148 to Ros Eeles), The ELLIPSE Consortium on behalf of the GAME-ON Network, The National Institute for Health Research (to Gil Barnett), Addenbrooke's Charitable Trust (to Gil Barnett), NIHR support to the Biomedical Research Centre at The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, The National Institute for Health Research Cambridge Biomedical Research Centre (to Neil Burnet), UK Medical Research Council (LD), the Experimental Cancer Medicine Centre (CMLW), the Royal Marsden NHS Foundation Trust (to DPD), the United States National Institutes of Health (1R01CA134444 to BSR), the American Cancer Society (RSGT-05-200-01-CCE to BSR), the United States Department of Defense (PC074201 to BSR), Mount Sinai Tisch Cancer Institute Developmental Fund Award (to BSR), the Instituto de Salud Carlos III (FIS PI10/00164 and PI13/02030 to AV), Fondo Europeo de Desarrollo Regional (FEDER 2007-2013 to AV), Xunta de Galicia and the European Social Fund (POS-A/2013/034 to LF), and the Alberta Cancer Board Research Initiative Program (103.0393.71760001404 to MP). Laboratory infrastructure for the RAPPER study was funded by Cancer Research UK (C8197/A10123). DPD acknowledges support from the National Institute for Health Research RM/ICR Biomedical Research Centre and all the researchers at the Royal Marsden Hospital and the Institute of Cancer Research. The RAPPER cohort comprises patients and data recruited into the RT01 and CHHiP UK radiotherapy trials. The RT01 trial was supported by the UK Medical Research Council. The CHHiP trial (CRUK/06/016) was supported by the Department of Health and Cancer Research UK (C8262/A7253); trial recruitment was facilitated within centers by the National Institute for Health Research Cancer Research Network. We acknowledge the participants who agreed to be a part of this study.

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies this paper on British Journal of Cancer website (http://www.nature.com/bjc)

Supplementary Material

Supplementary Information
Click here for additional data file. (302KB, docx)

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