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• W2923926791 abstract "Survivors of childhood cancer are at increased risk for a variety of treatment-related complications and premature mortality (Armstrong et al., 2016Armstrong G.T. Chen Y. Yasui Y. Leisenring W. Gibson T.M. Mertens A.C. et al.Reduction in late mortality among 5-year survivors of childhood cancer.New Engl J Med. 2016; 374: 833-842Crossref PubMed Scopus (371) Google Scholar), including subsequent neoplasms (SNs) (Bhatia and Sklar, 2002Bhatia S. Sklar C. Second cancers in survivors of childhood cancer.Nat Rev Cancer. 2002; 2: 124-132Crossref PubMed Scopus (197) Google Scholar, Hudson et al., 2013Hudson M.M. Ness K.K. Gurney J.G. Mulrooney D.A. Chemaitilly W. Krull K.R. et al.Clinical ascertainment of health outcomes among adults treated for childhood cancer.JAMA. 2013; 309: 2371-2381Crossref PubMed Scopus (767) Google Scholar, Olsen et al., 2009Olsen J.H. Moller T. Anderson H. Langmark F. Sankila R. Tryggvadottir L. et al.Lifelong cancer incidence in 47,697 patients treated for childhood cancer in the Nordic countries.J Natl Cancer Inst. 2009; 101: 806-813Crossref PubMed Scopus (157) Google Scholar, Reulen et al., 2011Reulen R.C. Frobisher C. Winter D.L. Kelly J. Lancashire E.R. Stiller C.A. et al.Long-term risks of subsequent primary neoplasms among survivors of childhood cancer.JAMA. 2011; 305: 2311-2319Crossref PubMed Scopus (247) Google Scholar). The Childhood Cancer Survivor Study (CCSS) (Friedman et al., 2010Friedman D.L. Whitton J. Leisenring W. Mertens A.C. Hammond S. Stovall M. et al.Subsequent neoplasms in 5-year survivors of childhood cancer: the Childhood Cancer Survivor Study.J Natl Cancer Inst. 2010; 102: 1083-1095Crossref PubMed Scopus (511) Google Scholar, Turcotte et al., 2017Turcotte L.M. Liu Q. Yasui Y. Arnold M.A. Hammond S. Howell R.M. et al.Temporal trends in treatment and subsequent neoplasm risk among 5-year survivors of childhood cancer, 1970–2015.JAMA. 2017; 317: 814-824Crossref PubMed Scopus (147) Google Scholar) previously showed that the risk of SNs increases with time from primary cancer diagnosis, yielding a 30-year cumulative incidence of 7.9% for subsequent malignant neoplasms and 9.1% for nonmelanoma skin cancer. Basal cell carcinoma (BCC) accounts for more than 80% of the nonmelanoma skin cancer cases (Watt et al., 2012Watt T.C. Inskip P.D. Stratton K. Smith S.A. Kry S.F. Sigurdson A.J. et al.Radiation-related risk of basal cell carcinoma: a report from the childhood cancer survivor study.J Natl Cancer Inst. 2012; 104: 1240-1250Crossref PubMed Scopus (76) Google Scholar), contributing to increased morbidity, impaired quality of life, and increased risk of subsequent cancer (Song et al., 2013Song F. Qureshi A.A. Giovannucci E.L. Fuchs C.S. Chen W.Y. Stampfer M.J. et al.Risk of a second primary cancer after non-melanoma skin cancer in white men and women: a prospective cohort study.PLoS Med. 2013; 10: e1001433Crossref PubMed Scopus (62) Google Scholar). Therapeutic irradiation is the most significant risk factor for BCC as a subsequent malignancy in childhood cancer survivors, with a well-established dose-response relationship (Watt et al., 2012Watt T.C. Inskip P.D. Stratton K. Smith S.A. Kry S.F. Sigurdson A.J. et al.Radiation-related risk of basal cell carcinoma: a report from the childhood cancer survivor study.J Natl Cancer Inst. 2012; 104: 1240-1250Crossref PubMed Scopus (76) Google Scholar). In contrast, incident BCC cases in the general population are strongly associated with sun exposure and skin color (Koh et al., 1996Koh H.K. Geller A.C. Miller D.R. Grossbart T.A. Lew R.A. Prevention and early detection strategies for melanoma and skin cancer. Current status.Arch Dermatol. 1996; 132: 436-443Crossref PubMed Google Scholar). Although it is clear that irradiated survivors of childhood cancer are at increased risk of BCC, many are exposed to radiotherapy but do not develop BCC. This interindividual variability despite the common risk-elevating exposure suggests a potential role of genetic susceptibility to subsequent BCC among survivors. To this end, we performed a genome-wide association (GWA) study of subsequent BCC among irradiated survivors of European ancestry in the CCSS discovery cohort (401 case patients and 2,330 control individuals) and evaluated genome-wide significant (P < 5 × 10–8) single-nucleotide polymorphisms (SNPs) in an independent cohort of irradiated long-term childhood cancer survivors (97 case patients and 1,082 control individuals) from the St. Jude Lifetime Cohort (i.e., SJLIFE). The institutional review boards at St. Jude Children’s Research Hospital and at each of the CCSS participating centers approved the study, and participants provided written informed consent. Demographic characteristics of both cohorts are summarized in Supplementary Table S1 online. We first examined associations between relevant nongenetic risk factors including sex, age at cancer diagnosis, attained age, primary cancer type and radiation exposures, and first BCC in the CCSS discovery cohort (see Supplementary Materials). The results of the multivariable clinical model (without any genetic variables) showed lower BCC rates among survivors who were older than 10 years at childhood cancer diagnosis, relative to those who were 5 years or younger at diagnosis (see Supplementary Table S2 online). Likewise, follow-up time periods corresponding to attained age of younger than 40 years showed lower BCC rates than those corresponding to attained age of 50 years or older. These data suggested low-risk subgroup/periods of BCC consisting of the follow-up time periods corresponding to attained age of less than 40 years among survivors treated at older than 10 years of age. Assuming an additive model, GWA analysis of common (minor allele frequency [MAF] ≥ 0.05) SNPs imputed by using the Haplotype Reference Consortium r1.1 (McCarthy et al., 2016McCarthy S. Das S. Kretzschmar W. Delaneau O. Wood A.R. Teumer A. et al.A reference panel of 64,976 haplotypes for genotype imputation.Nat Genet. 2016; 48: 1279-1283Crossref PubMed Scopus (1402) Google Scholar) was performed with Cox regression, with attained age as a time scale, adjusting for the discussed nongenetic risk factors and the top 20 principal components. Results from the entire CCSS cohort did not identify any variants reaching genome-wide significance; however, 14 SNPs on HTR2A showed the strongest associations (adjusted hazard ratios [HRs] of approximately 1.50; P < 1 × 10–6) (see Supplementary Figure S1 and Supplementary Table S3 online). Motivated by these results, we further examined the HTR2A locus among these low-risk subgroup/periods, and the results showed genome-wide significance for 11 out of the 14 HTR2A SNPs (Table 1). Notably, all 11 SNPs produced larger effect sizes (HR > 2.18) than those based on the entire cohort (HRs of approximately 1.50), with the strongest association observed for rs633737 (HR = 2.25; P = 5.99 × 10–9, P-value based on 100 million permutations [Pperm] < 1 × 10–8) (Table 1 and Figure 1). Among leukemia and Hodgkin lymphoma survivors, rs633737 showed HR = 4.93 (P = 0.009) and HR = 2.20 (P = 6.61 × 10–5), respectively. Association of the HTR2A locus with BCC was attenuated (HRs = 1.30–1.36) and/or not statistically significant (P > 0.51) among survivors not included in the low-risk subgroup/periods (see Supplementary Tables S4 and S5 online). We replicated all 11 HTR2A SNPs in the independent SJLIFE cohort, including 23 BCC case patients and 469 control individuals in the low-risk subgroup/periods (HR = 3.38; P = 0.01, Pperm = 3 × 10–4) (Table 1). These results suggest that the HTR2A-BCC association among irradiated childhood cancer survivors may be more pronounced in younger age periods (<40 years old) of those treated at an older age (≥10 years old). This is possibly because the key nongenetic factors, (radiotherapy exposure, years of sun exposure, and aging) are less influential relative to the older ages of survivors treated at a younger age (≤10 years old).Table 1HTR2A SNP Associations among Irradiated Survivors in the Low-Risk Subgroup/Periods in the CCSS Discovery and SJLIFE Replication CohortsChrSNPBP1Genomic position is shown relative to GRCh37 (hg19).RAOACCSS (118 cases; 1051 controls)SJLIFE (23 cases; 469 controls)RsqRAFcasesRAFcontrolsHR (95% CI)2Low-risk subgroup/periods (survivors who were >10 years of age at cancer diagnosis with their follow-up time period corresponding to age under 40 years).PRAFcasesRAFcontrolsHR (95% CI)2Low-risk subgroup/periods (survivors who were >10 years of age at cancer diagnosis with their follow-up time period corresponding to age under 40 years).P13rs63373747427344AT1.000.440.262.25 (1.71–2.94)5.99 × 10–90.390.293.38 (1.29–8.83)0.0113rs62233747427626GA1.000.440.262.25 (1.71–2.94)5.99 × 10–90.390.293.38 (1.29–8.83)0.0113rs63290347427512GT1.000.440.262.25 (1.71–2.94)5.99 × 10–90.390.293.38 (1.29–8.83)0.0113rs62149447427791GA1.000.440.262.25 (1.71–2.94)5.99 × 10–90.390.293.38 (1.29–8.83)0.0113rs192388847424385AG0.990.440.262.23 (1.70–2.93)9.40 × 10–90.390.293.38 (1.29–8.83)0.0113rs277030047427045CT1.000.440.262.20 (1.68–2.89)1.35 × 10–80.390.293.38 (1.29–8.83)0.0113rs374227947426265GA0.990.440.262.20 (1.68–2.89)1.42 × 10–80.390.293.38 (1.29–8.83)0.0113rs229697247428471AC1.000.440.272.20 (1.68–2.88)1.49 × 10–80.390.293.38 (1.29–8.83)0.0113rs64362747428611CT0.990.440.272.18 (1.67–2.86)1.67 × 10–80.390.293.38 (1.29–8.83)0.0113rs65585447428200TC1.000.440.272.18 (1.67–2.86)1.67 × 10–80.390.293.38 (1.29–8.83)0.0113rs65588847428181CT1.000.440.272.18 (1.67–2.86)1.67 × 10–80.390.293.38 (1.29–8.83)0.01Abbreviations: BP, base pairs; CCSS, Childhood Cancer Survivor Study; Chr, chromosome; CI; confidence interval; HR, hazard ratio; OA, other allele; RA, risk allele; RAF, risk allele frequency; Rsq, imputation quality metric; SJLIFE, St. Jude Lifetime Cohort; SNP, single nucleotide polymorphism.1 Genomic position is shown relative to GRCh37 (hg19).2 Low-risk subgroup/periods (survivors who were >10 years of age at cancer diagnosis with their follow-up time period corresponding to age under 40 years). Open table in a new tab Abbreviations: BP, base pairs; CCSS, Childhood Cancer Survivor Study; Chr, chromosome; CI; confidence interval; HR, hazard ratio; OA, other allele; RA, risk allele; RAF, risk allele frequency; Rsq, imputation quality metric; SJLIFE, St. Jude Lifetime Cohort; SNP, single nucleotide polymorphism. Of the 32 SNPs reported in the most recent GWA meta-analysis for BCC in the general population (Chahal et al., 2016Chahal H.S. Wu W.T. Ransohoff K.J. Yang L.Y. Hedlin H. Desai M. et al.Genome-wide association study identifies 14 novel risk alleles associated with basal cell carcinoma.Nat Commun. 2016; 7: 12510Crossref PubMed Scopus (32) Google Scholar), which did not include the HTR2A locus, data were available for 26 SNPs. Of these, six showed nominal significance (Pperm < 0.05) with the same directions of effect, suggesting significant commonality (P = 0.002, binomial test) (see Supplementary Table S6 online). Although there may be both shared and unique genetic mechanisms underlying BCC in the general population and irradiated childhood cancer survivors, additional larger studies would provide a more conclusive interpretation about this generalizability. The HTR2A association signal is marked by 11 SNPs in very high linkage disequilibrium (r2 > 0.96) occurring with an MAF of approximately 0.28 in Europeans. The top SNP rs633737 overlaps a genomic region containing histone marks representing a promoter/enhancer chromatin state in various tissues including skin, peripheral blood, breast, and rectum, and alters the DNA binding motif of CTCF in human breast adenocarcinoma cell line (Ward and Kellis, 2012Ward L.D. Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants.Nucleic Acids Res. 2012; 40: D930-D934Crossref PubMed Scopus (1664) Google Scholar), suggesting an important regulatory role (see Supplementary Figure S2 online). HTR2A encodes one of the receptors for serotonin, which is a multifunctional neurotransmitter with an important role in many physiologic processes such as sleep, hormone secretion, and appetite. Recently, an HTR2A SNP rs6311 was shown to be associated with UV-induced BCC in women, by a multifactor dimensionality reduction analysis (Welsh et al., 2011Welsh M.M. Karagas M.R. Kuriger J.K. Houseman A. Spencer S.K. Perry A.E. et al.Genetic determinants of UV-susceptibility in non-melanoma skin cancer.PLoS One. 2011; 6: e20019Google Scholar). The rs6311 is completely independent of the rs633737 (linkage disequilibrium r2 = 0.0 in the 1000 Genomes Project Europeans), suggesting that the HTR2A association signal identified in this study is previously unreported, to our knowledge, and specific to radiation-related BCC among childhood cancer survivors. In summary, we conducted a GWA study for subsequent BCC among irradiated childhood cancer survivors and identified a risk locus in HTR2A that was specific to survivors with reduced nongenetic risk. Additional research efforts are warranted to gain important insights underlying the observed genetic association and pathophysiology of subsequent BCC. If the association is confirmed further, it could also be useful for identifying survivors at increased risk of developing subsequent BCC. Data sets related to this article can be found at dbGaP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001327.v1.p1)(Mailman et al., 2007Mailman M.D. Feolo M. Jin Y. Kimura M. Tryka K. Bagoutdinov R. et al.The NCBI dbGaP database of genotypes and phenotypes.Nature Genetics. 2007; 39: 1181-1186Crossref PubMed Scopus (807) Google Scholar). The authors state no conflict of interest. The Childhood Cancer Survivor Study (CCSS) genome-wide association study and pooled analyses were supported by the Division of Cancer Epidemiology and Genetics, Intramural Research Program of the National Cancer Institute, National Institutes of Health. This work used the computational resources of the National Institutes of Health High-Performance Computing Biowulf cluster (http://hpc.nih.gov). CCSS is supported by the National Cancer Institute (CA55727: GTA, principal investigator). A portion of the CCSS genotyping also was supported by the Leukemia and Lymphoma Society (K. Kamdar, principal investigator). The St. Jude Lifetime Cohort is supported by the National Cancer Institute (U01 CA195547: MMH and LLR, principal investigators; Cancer Center Support CORE grant CA21765: C. Roberts, principal investigator) and the American Lebanese Syrian Associated Charities (Memphis, TN). This study is also supported by R01 CA216354 (YY and JZ, principal investigators) from the National Cancer Institute. Conceptualization: YS, LLR, LMM, SB, YY; Data Curation: YS, LMT, MJE, RMH, MAA, CLW, WL, ZW, JS, CLD, EK, SAL, BDH, RW, SAS, KS, QL, MAT, SJC, JZ, MMH, JPN, GTA, LLR, LM, SB, YY; Formal Analysis: YS, LMT, JPN, MJE, MAA, JS, CLD, EK, SAL, BDH, MMH, GTA, LLR, LMM, YS, QL, YY; Investigation: YS, LMT, MJE, RMH, MAA, CLW, WL, ZW, JS, CLD, EK, SAL, BDH, RW, SAS, KS, QL, MAT, SJC, JZ, MMH, JPN, GTA, LLR, LM, SB, YY; Methodology: YS, LLR, LMM, SB, YY; Project Administration: YS, CLW, ZW, CLD, EK, SAL, BDH, MAT, SJC, MMH, GTA, LLR, LMM, SB, YY; Supervision: YS, LLR, LMM, SB, YY; Validation: YS, QL; Writing - Original Draft Preparation: YS, SB, YY; Writing - Review and Editing: YS, LMT, MJE, RMH, MAA, CLW, WL, ZW, JS, CLD, EK, SAL, BDH, RW, SAS, KS, QL, MAT, SJC, JZ, MMH, JPN, GTA, LLR, LM, SB, YY The CCSS is a retrospective cohort with prospective follow-up of 5-year survivors of childhood cancer (Robison et al., 2002Robison L.L. Mertens A.C. Boice J.D. Breslow N.E. Donaldson S.S. Green D.M. et al.Study design and cohort characteristics of the childhood cancer survivor study: a multi-institutional collaborative project.Med Pediatr Oncol. 2002; 38: 229-239Crossref PubMed Scopus (582) Google Scholar, Robison et al., 2009Robison L.L. Armstrong G.T. Boice J.D. Chow E.J. Davies S.M. Donaldson S.S. et al.The Childhood Cancer Survivor Study: a National Cancer Institute-supported resource for outcome and intervention research.J Clin Oncol. 2009; 27: 2308-2318Crossref PubMed Scopus (477) Google Scholar). CCSS participants were younger than 21 years at diagnosis between January 1, 1970, and December 31, 1986, and were treated at one of the 26 participating institutions in the United States or Canada. The details of the study design and description of the cohort have been published elsewhere (Leisenring et al., 2009Leisenring W.M. Mertens A.C. Armstrong G.T. Stovall M.A. Neglia J.P. Lanctot J.Q. et al.Pediatric cancer survivorship research: experience of the Childhood Cancer Survivor Study.J Clin Oncol. 2009; 27: 2319-2327Crossref PubMed Scopus (208) Google Scholar, Robison et al., 2002Robison L.L. Mertens A.C. Boice J.D. Breslow N.E. Donaldson S.S. Green D.M. et al.Study design and cohort characteristics of the childhood cancer survivor study: a multi-institutional collaborative project.Med Pediatr Oncol. 2002; 38: 229-239Crossref PubMed Scopus (582) Google Scholar). The institutional review boards at St. Jude Children’s Research Hospital and at each of the participating centers approved the study, and participants provided informed consent. Ascertainment of BCC diagnosis was performed through self- or proxy-reports of BCC on CCSS questionnaires and confirmed by review of pathology reports and/or medical records by the CCSS Pathology Center. Relevant demographic data were extracted from the CCSS questionnaires. Radiation dose was estimated for seven regions of the body (head, neck, chest, abdomen, pelvis, legs, and arms) for each patient in the cohort by using data from review of individual radiation therapy records. The prescribed dose within each body region was taken as the total prescribed dose from all overlapping fields within that region. Because the dose to the skin is generally accepted to be 40%–60% of the dose prescribed to the primary tumor (Kry et al., 2012Kry S.F. Smith S.A. Weathers R. Stovall M. Skin dose during radiotherapy: a summary and general estimation technique.J Appl Clin Med Phys. 2012; 13: 20-34Crossref Scopus (60) Google Scholar), we halved the radiation doses to body regions if this dose exceeded 5 Gy to estimate the maximum treatment dose to the skin surface of a body region, taking into account only in-beam contributions. Body regions not directly treated but adjacent to the primary site of treatment were estimated to have received 2 Gy. For regions more distant from the primary treatment site, radiation dose was estimated at 0.2 Gy. For this study, data for GWA analysis were obtained from a larger effort to genotype all CCSS participants with available DNA regardless of race or sex (Morton et al., 2017Morton L.M. Sampson J.N. Armstrong G.T. Chen T.H. Hudson M.M. Karlins E. 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: djx058Crossref Scopus (51) Google Scholar). Genomic DNA was extracted from whole blood, saliva, and buccal samples and was genotyped using the Illumina (San Diego, CA) HumanOmni5Exome array at the Cancer Genomics Research Laboratory of the National Cancer Institute (Bethesda, MD). Genotypes were called by using Genotyping Module, version 1.9, implemented in the Illumina GenomeStudio software, version 2011.1. Exclusion criteria for samples included 8% or greater missingness, per-sample heterozygosity of less than 0.11 or greater than 0.16, sex discordance (X chromosome heterozygosity > 5.0% for males or < 20.0% for females), and first-degree relatives (defined as having identity-by-descent sharing > 0.70) (Morton et al., 2017Morton L.M. Sampson J.N. Armstrong G.T. Chen T.H. Hudson M.M. Karlins E. 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: djx058Crossref Scopus (51) Google Scholar). Variants with MAF of less than 1%, missingness of 5% across samples, and with Hardy-Weinberg equilibrium test with P less than 1 × 10–10 were also excluded. A total of 5,739 samples passed this quality control, of which 5,156 were of European ancestry based on principal component analysis (see Supplementary Figure S3). Genotype data passing the quality control criteria were imputed up to the Haplotype Reference Consortium r1.1 (2016) reference haplotypes with Minimac3 (Das et al., 2016Das S. Forer L. Schonherr S. Sidore C. Locke A.E. Kwong A. et al.Next-generation genotype imputation service and methods.Nat Genet. 2016; 48: 1284-1287Crossref PubMed Scopus (1515) Google Scholar), implemented in the Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html#!pages/home; University of Michigan, Chicago, IL). For this study, we additionally excluded 84 survivors with cryptic relatedness (PI_HAT ≥ 0.2) and greater than 5% missingness across samples. For participants with multiple BCCs, we analyzed the first BCC based on diagnosis date. A total of 886 individuals were excluded (also participated in the SJLIFE study [n = 646]; radiation therapy dose was not available [n = 198]; had hematopoietic cell transplantation [n = 39]; or had an SN that was basosquamous [n = 3]), leaving 401 BCC patients and 2,330 survivors without BCC for analysis. We performed another round of quality control among the 2,731 survivors and excluded SNPs with MAF less than 5%, missingness greater than 5%, and Hardy-Weinberg equilibrium test of P < 1 × 10–6 in survivors without BCC. We also restricted to high quality (imputation r2 ≥ 0.8) imputed variants, which resulted in approximately 5.38 million common SNPs for GWA analysis. Assuming a multiplicative model, our discovery sample had 82% and 74% power to detect associations with genome-wide significance (P < 5 × 10–8) between SNPs with MAFs of 0.05 and 0.30 and genotype relative risks of 2 and 1.5, respectively (Purcell et al., 2003Purcell S. Cherny S.S. Sham P.C. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits.Bioinformatics. 2003; 19: 149-150Crossref PubMed Scopus (1777) Google Scholar). Cox regression was used to assess the association between common SNPs and risk of first BCC. Follow-up of survivors at risk of BCC started at 5 years from diagnosis of childhood cancer and ended at death or last date of contact. Each survivor’s follow-up time was divided into single-year segments, used in a counting-process format with start and end ages of the segments. The baseline hazard of BCC by age was modeled nonparametrically in Cox regression and multiplicatively modified by explanatory variables, including age at cancer diagnosis, sex, primary cancer type, radiation dosimetry variables for seven body regions (head, neck, arm, chest, abdomen, pelvis, and leg) and top 20 principal components. To estimate specific age segments’ effects on BCC rates in nongenetic models before evaluating the SNP-BCC associations, piecewise-exponential models corresponding to the Cox proportional hazards models described were used to model the baseline hazard of BCC parametrically with a piecewise-constant step function of age. Common SNPs were added (one at time) to the nongenetic model, and their associations were estimated in Cox regression as HRs with 95% confidence intervals, and P values were based on the standard large-sample inference. SNPs showing associations with P less than 5 × 10–8 were considered as statistically significant at the genome-wide level. The quantile-quantile plot (see Supplementary Figure S4) and the genomic inflation factor lambda (λ) of 1.03 indicate no or little influence of population stratification on the association results. For the SNPs showing genome-wide significance, we also calculated P-values based on 100 million permutations by permuting genotype data. Our replication sample was derived from the SJLIFE cohort, a clinically assessed cohort of survivors who were treated for childhood cancer at the St. Jude Children’s Research Hospital between 1962 and 2005 (Hudson et al., 2017Hudson M.M. Ehrhardt M.J. Bhakta N. Baassiri M. Eissa H. Chemaitilly W. et al.Approach for classification and severity grading of long-term and late-onset health events among childhood cancer survivors in the St. Jude Lifetime Cohort.Cancer Epidemiol Biomarkers Prev. 2017; 26: 666-674Crossref PubMed Scopus (117) Google Scholar). Participants in SJLIFE survived for 5 years or longer after diagnosis. To be eligible for the current analysis, participants had returned to St. Jude Children’s Research Hospital for clinical evaluation and provided a blood sample. The methodologies for radiation dosimetry and estimated body region-specific radiation dose calculation were identical to those used in the CCSS cohort. The methods for genotyping of the SJLIFE cohort have been previously published (Wang et al., 2018Wang Z. Wilson C.L. Easton J. Thrasher A. Mulder H. Liu Q. et al.Genetic risk for subsequent neoplasms among long-term survivors of childhood cancer.J Clin Oncol. 2018; 36: 2078-2087Crossref PubMed Scopus (75) Google Scholar). Using the same exclusion criteria as in the CCSS cohort, we identified 1,179 survivors of European ancestry with genomic data (97 survivors with and 1,082 without BCC). SNPs achieving genome-wide significance in the discovery GWA analysis were examined in the SJLIFE replication cohort for their associations with BCC, with the same statistical model as in the CCSS discovery analysis with adjustment of the same nongenetic risk factors. We also calculated P-values of the top replicated SNPs using 10,000 permutations. To assess generalizability of the BCC risk loci in the general population among survivors of childhood cancers, we looked at the CCSS GWA association results for the 32 genome-wide significant SNPs reported in the most recent GWA meta-analysis for BCC in the general population (Chahal et al., 2016Chahal H.S. Wu W.T. Ransohoff K.J. Yang L.Y. Hedlin H. Desai M. et al.Genome-wide association study identifies 14 novel risk alleles associated with basal cell carcinoma.Nat Commun. 2016; 7: 12510Crossref PubMed Scopus (32) Google Scholar). P-values of these SNPs were also calculated with 10,000 permutations.Supplementary Figure S2UCSC Genome Browser snapshot showing the 11 HTR2A genome-wide significant SNPs. Tracks displayed from top to bottom include genomic base position (hg19), 11 HTR2A genome-wide significant SNPs, UCSC genes, layered H3K4Me1 mark on seven cell lines from the ENCODE, layered H3K4Me3 mark on seven cell lines from the ENCODE, layered H3K27Ac mark on seven cell lines from ENCODE, DNaseI hypersensitivity clusters in 125 cell types from ENCODE, and transcription factor chromatin immunoprecipitation sequencing (CHIP-seq) (161 factors) from ENCODE, respectively. SNP rs633737 is highlighted in yellow. SNP, single nucleotide polymorphism; UCSC, University of California–Santa Cruz.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Figure S3Principal component analysis of ancestry of survivors from the CCSS discovery cohort included in the study. To identify non-European survivors, the CCSS data were combined with five global populations from the 1,000 Genomes Phase 3 project, and we performed EIGENSTRAT-based principal component analysis to obtain the first two principal components (PC1 and PC2). Population codes and sample sizes are as follows: EUR, European: n = 503; AMR, Admixed American: n = 347; SAS, South Asian: n = 489; EAS, East Asian: n = 504; and AFR, African: n = 661. The mean PC1 and PC2 scores of the European populations were used as a reference point, and any CCSS survivor more than three standard deviations from these along the PC1 and PC2 (shown by red and blue dotted lines) was considered a non-European. Global populations and the CCSS survivors are color coded, and only survivors with European ancestry are shown in the plot.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Figure S4Quantile-quantile plot for the GWA analysis of subsequent BCC in the entire CCSS discovery cohort. The plot was constructed by ranking P-values from smallest to largest (the “order” statistics) and plotting them against their expected values under the null hypothesis of no association (samples from the known chi-square distribution). Deviations above the line of equality (drawn in white) indicate a preponderance of smaller P-values. To aid interpretation, we have also calculated 95% confidence envelopes (shaded grey). These are formed by calculating, for reach order statistic, the 2.5th and 97.5th percentiles of the distribution of the order statistic under random sampling and the null hypothesis. The genomic inflation factor (λ) is also shown, defined as the ratio of the median of the empirically observed distribution of the test statistic to the expected median, thus quantifying the extent of the bulk inflation.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Table S1Demographic Characteristics of Survivors with and without BCC from Both CCSS and SJLI" @default.
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• W2923926791 title "Genome-Wide Association Study in Irradiated Childhood Cancer Survivors Identifies HTR2A for Subsequent Basal Cell Carcinoma" @default.
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