A two-stage genome-wide association study to identify novel genetic loci associated with acute radiotherapy toxicity in nasopharyngeal carcinoma

Clinical characteristics

This two-stage genome-wide association study involved a total of 1084 subjects. Among them, a total of 319 patients from Jiangxi Province Tumor Hospital were recruited into the discovery stage cohort, and 765 patients from Hunan Cancer Hospital were used to validate the results. Table 1 summarizes the demographic characteristics of all patients. The whole-genome microarray analysis was performed on peripheral blood DNA samples from 319 patients in the discovery stage. After quality control and genotype imputation, a total of 4,112,760 SNPs passed quality screening and were included in the association analysis.

Table 1 Characteristics of all patients

In the discovery stage of this study, five acute radiotherapy toxicities of skin reaction, dysphagia, oral mucositis, salivary gland toxicity, and myelosuppression were investigated. The characteristics of NPC patients involved in these association analyses are summarized in Tables S1, S2, S3, S4 and S5. For each phenotype, the association of clinical characteristics of age, gender, BMI, smoking status, EBV infection, clinical stage, and treatment scheme between different subgroups was analyzed. All significant clinical factors were considered as covariates and adjusted in subsequent GWAS analysis.

GWAS analysis for five toxicities

For GWAS analysis, a quantile-quantile plot was created to evaluate the data quality (Fig. S3). Deviation from the expected P value distribution was evident only in the tail region. For the results of the association analysis, the genomic inflation factor λ was 0.971, 1.017, 0.989, 0.976, and 0.981 for skin reaction, dysphagia, oral mucositis, salivary gland toxicity, and myelosuppression, respectively. In addition, PCA results showed that no stray samples appeared in any toxicity (Fig. S2). The results also indicated that the patient population was ethnically homogeneous, and the observed associations were not driven by potential population substructure. Thus, no principal components (PC) were incorporated as covariates in the following association analysis. The Manhattan plot in Fig. 1B-E shows the results of five toxicities in discovery stage. Variations with P < 1 × 10− 5 (dysphagia, oral mucositis, salivary gland toxicity, and myelosuppression) or P < 1 × 10–5.5 (skin reaction) were selected for MassArray genotyping in the validation stage samples. As indicated in Table 2, a total of 16 SNPs were finally successfully genotyped. For myelosuppression, no SNPs satisfied the quality control criteria in the validation cohort, rendering this phenotype incompletely assessed. For oral mucositis and salivary gland toxicity, no SNPs were successfully replicated as being associated with the specific toxicity in the validation cohort. Finally, five SNPs were successfully validated by showing statistical significance.

Table 2 The SNPs associated with acute radiotherapy toxicity in nasopharyngeal carcinoma patients

Rs6711678, rs4848597, rs4848598, and rs2091255 correlated with skin reaction. These four SNPs were all located on the chromosome 2q14.2 and showed high LD with each other. They showed similar P and OR value in both the discovery (P value ranged from 4.82 × 10− 7 to 9.83 × 10− 7, OR value ranged from 1.23 to 1.40) and validation cohort (P value ranged from 0.007 to 0.014, OR value ranged from 1.42 to 1.48) for the minor allele. In addition, rs584547 was identified to be associated with high occurrence of dysphagia for A allele in both the discovery (P = 1.27 × 10− 6, OR = 1.55) and validation (P = 0.002, OR = 4.20) cohort. Thus, these five SNPs underwent further analysis.

Stratified and meta-analysis of five associated variants

As previously reported, clinical factors may also contribute to drug or radiotherapy toxicities [18, 19]. Therefore, we conducted stratified analysis for these five SNPs in the combined cohort. Clinical characteristics only showed significant influence on the genetic correlation of rs6711678, rs4848597, rs4848598, and rs2091255. Thus, these SNPs were further analyzed. As indicated in Table S6, for smoking status, the association existed in both smokers and non-smokers. However, for EBV infection, clinical stage, and treatment scheme, the association only existed in subgroups of EBV positive, late stage (III and IV), and patients receiving both concurrent chemoradiotherapy and induction/adjuvant chemotherapy. This result indicated that clinical factors might affect the results of radiotherapy toxicity analysis. For example, half of EBV positive patients who were homozygous mutant for these four SNPs also experienced grade 2 or 3 skin toxicity after receiving radiotherapy. In contrast, this ratio was less than 25% in the wild-type patients.

Based on these findings, we considered that rs6711678, rs4848597, rs4848598, and rs2091255 were more associated with skin reaction in NPC patients who were EBV positive, late stage (III and IV), and receiving both concurrent chemoradiotherapy and induction/adjuvant chemotherapy. We thus further conducted the association analysis in this subgroup. As indicated in Table 3, the results are consistent in three different models for all SNPs. Minor allele carriers showed higher risk of skin reaction with an OR value increasing to more than 2. Figure 2 supports this result more intuitively by calculating the patients with different grades of toxicities in groups of three genotypes. For all SNPs, occurrence of grade 2 and 3 skin reaction toxicities remarkably increased in minor allele carriers. In contrast, mild toxicity (grade 1) dramatically decreased. We next performed therapeutic response analysis in these patients and observed that there were more non-responders in patients with minor alleles for all four SNPs, with an OR value ranging from 1.92 to 2.66 (Table S7).

Table 3 Association between skin reaction and chromosome 2q14.2 loci in the stratified patientsFig. 2figure 2

Patient distribution in different genotypes according to skin reaction toxicity (A) and radiotherapeutic response (B). For 2q14.2 loci, all patients were EBV positive, late stage (III and IV), and receiving both concurrent chemoradiotherapy and induction/adjuvant chemotherapy. For rs584547, AA genotypes were combined in analysis with AG heterozygote patients due to the limited number

For rs584547, no clinical characteristics were found to significantly influence the genetic correlation. Thus, it was analyzed in the combined cohort without any stratification. Due to the limited number, AA genotypes were combined in analysis with AG heterozygote patients. As indicated in Fig. 2, grade 3 toxicity occurred more in the A allele carriers, while all other grade toxicities were decreased. Higher resistance to the radiotherapy was also found in these patients.

Toxicity prediction models

Based on above analysis, the toxicity was increased, and response was decreased in minor allele carriers for all five SNPs, which could be considered as potential biomarkers for precision radiotherapy of NPC patients. We thus evaluated their toxicity prediction performance. For skin reaction toxicity and dysphagia, the combined cohort patients were first randomly divided into two groups, which were used to establish and test models, respectively. The sample size of each group accounted for 50% of all patients. Three multivariable logistic regression models were established: with genetic factors only, clinical factors only, and a combination of both genetic and clinical factors. For skin reaction toxicity, four SNPs of rs6711678, rs4848597, rs4848598, and rs2091255 were combined as polygenic risk scores in the genetic-only model after controlling clinical parameters. As indicated in Fig. S4, the area under curve (AUC) value of receiver operating characteristic (ROC) curves of the genetic-only model was larger than that of clinical-only model in both groups for all phenotypes. This result suggested that the genetic model integrating four SNPs was more accurate in classifying skin reaction occurrence patients compared with estimating using only clinical characteristics. After further integrating clinical factors, the ROC AUC of the combined model further increased. The similar performance of each model in both establishing and testing groups supported the reliability of our models. Therefore, we established the final prediction models for two toxicities in the combined cohort. As indicated in Fig. 3A, the ROC AUCs of genetic only, clinical only, and combined factors for skin reaction toxicity were 0.632, 0.577, and 0.657, respectively. The performance was better for dysphagia, where the corresponding values were 0.721, 0.688, and 0.788, respectively (Fig. 3B). These results together showed that these five genetic variants could potentially be used to predict skin reaction and dysphagia toxicities in NPC patients receiving radiotherapy.

Fig. 3figure 3

Receiver operating characteristic curves of three models for skin reaction (A) and dysphagia (B). All models were generated by using multivariable logistic regression. The genetic model only involved genetic factors: rs6711678, rs4848597, rs4848598, and rs2091255 for skin reaction, and rs584547 for dysphagia. During the calculation, rs6711678, rs4848597, rs4848598, and rs2091255 were combined as polygenic risk scores. The clinical model involved clinical factors only, which include age, sex, BMI, smoking status, stage, EBV infection, and radiotherapeutic regimen. The combined model integrated both genetic and clinical factors. BMI: body mass index, EBV: Epstein-Barr virus, AUC: area under curve

Gene mapping

To learn the functional consequences of the five risk loci, we mapped them to genes. As indicated in Fig. 4, rs6711678, rs4848597, rs4848598, and rs2091255 were closely located on the chromosome 2q14.2, spanning only 1690 bp. Further analysis showed that all four SNPs were in strong LD in the genome. Although they were closer to a long non-coding RNA (LncRNA) of LINC01101, eQTL analysis implicated genes of inhibin subunit beta B (INHBB). In addition, 3D chromatin interaction indicated that they interacted with transcription factor CP2 like 1 (TFCP2L1) and protein tyrosine phosphatase non-receptor type 4 (PTPN4).

Fig. 4figure 4

Gene mapping and LD analysis of rs6711678, rs4848597, rs4848598, rs2091255, and rs584547. A The regional plot for rs6711678, rs4848597, rs4848598, rs2091255, and rs584547. The regional plots were constructed using LocusZoom. P values (−log10(P values); y axis) were plotted against the respective chromosomal position of each SNP (x axis). Colors indicate LD (r2) with four SNPs in 1000 Genomes Project East Asian populations. B The haplotype block of four SNPs in chromosome 2. The genomic positions were indicated. C 3D chromatin interaction and eQTL analysis of rs6711678, rs4848597, rs4848598, and rs2091255. Circos plot showing genes on chromosome 2 interacted with four SNPs by 3D chromatin interaction (orange line) and eQTL mapping (green line). The outer layer showed the –log10(P values) of four SNP in the GWAS analysis

The SNP rs584547 localized very closely upstream of synaptotagmin-8 (SYT8). However, LD analysis showed that it was not in linkage with any proximal SNPs (Fig. 4D). 3D chromatin interaction and eQTL analysis also showed no implicated genes. Considering 5′ upstream SNPs may affect gene expression by altering promoter activity, we evaluated its transcription regulation function using ENCODE database. The result suggested that rs584547 showed a proximal transcriptional regulation function on SYT8 gene.

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