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Exome-wide analyses identify low-frequency variant in CYP26B1 and additional coding variants associated with esophageal squamous cell carcinoma

Nature Genetics volume 50, pages 338–343 (2018)Cite this article

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Abstract

Genome-wide association studies have identified common variants associated with risk of esophageal squamous cell carcinoma (ESCC). However, these common variants cannot explain all heritability of ESCC. Here we report an exome-wide interrogation of 3,714 individuals with ESCC and 3,880 controls for low-frequency susceptibility loci, with two independent replication samples comprising 7,002 cases and 8,757 controls. We found six new susceptibility loci in CCHCR1, TCN2, TNXB, LTA, CYP26B1 and FASN (P = 7.77 × 10−24 to P = 1.49 × 10−11), and three low-frequency variants had relatively high effect size (odds ratio > 1.5). Individuals with the rs138478634-GA genotype had significantly lower levels of serum all-trans retinoic acid, an anticancer nutrient, than those with the rs138478634-GG genotype (P = 0.0004), most likely due to an enhanced capacity of variant CYP26B1 to catabolize this agent. These findings emphasize the important role of rare coding variants in the development of ESCC.

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Fig. 1: Manhattan plot for associations between genetic variants and ESCC risk.
Fig. 2: Significant gene–lifestyle interaction for the CYP26B1 rs138478634 variant.
Fig. 3: CYP26B1 rs138478634 variant influences ESCC risk by altering the catabolic activity of the enzyme.

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Acknowledgements

This work was supported by the National Key Research and Development Plan Program (2016YFC1302702 to X.M., 2016YFC1302701 to C.W. and 2016YFC1302703 to R.Z.); the National Program for Support of Top-notch Young Professionals, National Natural Science Foundation of China (81171878, 81222038 to X.M.); the Fok Ying Tung Foundation for Young Teachers in the Higher Education Institutions of China (131038 to X.M.); and the Program for HUST Academic Frontier Youth Team (to X.M.).

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  1. Jiang Chang, Rong Zhong, Jianbo Tian and Jiaoyuan Li contributed equally to this work.

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China

    Jiang Chang, Rong Zhong, Jianbo Tian, Jiaoyuan Li, Juntao Ke, Jiao Lou, Wei Chen, Beibei Zhu, Na Shen, Yi Zhang, Ying Zhu, Yajie Gong, Yang Yang, Danyi Zou, Xiating Peng & Xiaoping Miao

  2. Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Kan Zhai, Chen Wu & Dongxin Lin

  3. Medical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China

    Kan Zhai

  4. Department of Chemotherapy and Radiotherapy, Tangshan Gongren Hospital, Tangshan, China

    Zhi Zhang

  5. Department of Molecular Genetics, College of Life Science, North China University of Science and Technology, Tangshan, China

    Xuemei Zhang

  6. Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China

    Kun Huang

  7. Department of Occupational and Environmental Health, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China

    Tangchun Wu

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Contributions

X.M. and C.W. were the overall principal investigators of this study, who conceived the study and obtained financial support, were responsible for study design and oversaw the entire study, and synthesized the paper. J.C. performed statistical analyses, interpreted the results and drafted the initial manuscript. J.C., R.Z., J.T., J. Li, K.Z., J.K., J. Lou, W.C., B.Z., N.S., Y. Zhang, Y.G., Y.Y., Y. Zhu, D.Z. and X.P. performed laboratory analyses. Z.Z. and X.Z. were responsible for patient recruitment and sample preparation from Hebei province. K.H., T.W. and D.L. reviewed the manuscript. All authors have approved the final report for publication.

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Correspondence to Chen Wu or Xiaoping Miao.

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Supplementary Figure 1

Summary of the study design and work flow

Supplementary Figure 2 Plots for genetic matching of three principal components derived from the PCA of 3,714 cases with ESCC and 3,880 controls, and 206 HapMap individuals without relationships

(a) PC1 versus PC2 for 3,714 cases, 3,880 controls and 206 HapMap individuals, including 57 YRIs, 60 CEUs, 44 JPTs, and 45 CHBs. (b) PC1 versus PC2 for 3,714 ESCC cases and 3,880 controls. (c) PC1 versus PC3 for 3,714 ESCC cases and 3,880 controls. (d) PC2 versus PC3 for 3,714 ESCC cases and 3,880 controls. The case-control matching suggested minimal evidence of population stratification.

Supplementary Figure 3 Quantile-quantile plot and genomic inflation factor lambda for associations with ESCC risk

The results were based on 3,714 ESCC cases and 3,880 controls in the discovery stage of this study. The red circles represent the distribution of P values for the association in the discovery stage. The observed versus expected χ2 test statistics shows no evidence for inflation of χ2 tests (inflation factor λ = 1.032).

Supplementary Figure 4 Regional plots of association results and recombination rates within the four significant susceptibility loci

(a-f) rs130079 (a), rs117353193 (b), rs204900 (c), rs1041981 (d), rs138478634 (e) and rs17848945 (f). The association results were based on imputation results of 3,714 ESCC cases and 3,880 controls in the discovery stage of this study. P values are two sided and were calculated by an additive model in logistic regression analysis adjusted for sex, age, smoking status, drinking status and the first three principle components. For each plot, the −log10P values (y-axis) of the SNPs are presentedaccording to their chromosomal positions (x-axis). The genetic recombination rates (cM/Mb) estimated using the 1000 Genomes June2014 ASN samples are shown with ablue line; we annotated the genes within the region of interest, and these genes are shown as arrows. The LD r2 values were calculated using pairwise linkage disequilibrium analyses. The top genotyped SNP is labeled by rs ID, and the r2 values of the rest of the SNPs with the top genotyped SNP are indicated by different colors.

Supplementary Figure 5 Linkage disequilibrium plot of rs117353193

(a) Regional plot of LD r2 and recombination rates in a 1-Mb region centered by rs117353193. The LD r2 was calculated based on the 1000 Genomes phase 3 ASN population. (b) The LD block plot of variants with LD r2 > 0.1 for rs117353193. The LD r2 was calculated using pairwise linkage disequilibrium analyses in PLINK based on 504 individuals from the 1000 Genomes phase 3 ASN population.

Supplementary Figure 6 Linkage disequilibrium plot of rs17848945

(a) Regional plot of LD r2 and recombination rates in a 1-Mb region centered by rs17848945. The LD r2 was calculated based on the 1000 Genomes phase 3 ASN population. (b) The LD block plot of variants with LD r2 > 0.1 for rs17848945. The LD r2 was calculated using pairwise linkage disequilibrium analyses in PLINK based on 504 individuals from the 1000 Genomes phase 3 ASN population.

Supplementary Figure 7 Linkage disequilibrium plot of rs138478634

(a) Regional plot of LD r2 and recombination rates in a 1-Mb region centered by rs138478634. The LD r2 was calculated based on the 1000 Genomes phase 3 ASN population. (b) The LD block plot of variants with LD r2 > 0.1 for rs138478634. The LD r2 was calculated using pairwise linkage disequilibrium analyses in PLINK based on 504 individuals from the 1000 Genomes phase 3 ASN population.

Supplementary Figure 8 Stratification analysis of the association between risk of ESCC and the six identified SNPs

(a-f) rs130079 (a), rs117353193 (b), rs204900 (c), rs1041981 (d), rs138478634 (e) and rs17848945 (f). Each box and horizontal line represent the OR point estimate and 95% CI derived from the additive model. The analyses were based on 10,716 ESCC cases and 12,637 controls in this study. The area of each box is proportional to the statistical weight of the study. The heterogeneity P values are shown in the right side of the plots.

Supplementary Figure 9 Histogram distribution of minor allele frequencies of variants interrogated in this study in controls

The y-axis shows number of variants. The x-axis shows range of minor allele frequencies.

Supplementary Figure 10 Result of the test of transfection efficiency

(a-f) Relative expression levels of CYP26B1 are shown as determined by western blot (a-d) or qRT–PCR (e,f). The western blot experiment was repeated independently three times with similar results. KYSE30 and KYSE150 cells were transfected with CYP26B1[G], CYP26B1[A] and control vector (a,c,e) or targeting siRNAs and siControl (b,d,f). (a,b) Cropped western blot are shown. (c,d) Full scans of western blots are shown. (e,f) Results present means ± s.e.m. from three independent experiments and each had three replications. P values were compared with control by two-sided unpaired Student’s t-test.

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Chang, J., Zhong, R., Tian, J. et al. Exome-wide analyses identify low-frequency variant in CYP26B1 and additional coding variants associated with esophageal squamous cell carcinoma. Nat Genet 50, 338–343 (2018). https://doi.org/10.1038/s41588-018-0045-8

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