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CCGD-ESCC: A Comprehensive Database for Genetic Variants Associated with Esophageal Squamous Cell Carcinoma in Chinese Population

Esophageal squamous-cell carcinoma (ESCC) is one of the most lethal malignancies in the world and occurs at particularly higher frequency in China. While several genome-wide association studies (GWAS) of germline variants and whole-genome or whole-exome sequencing studies of somatic mutations in ESCC have been published, there is no comprehensive database publically available for this cancer. Here, we developed the Chinese Cancer Genomic Database-Esophageal Squamous Cell Carcinoma (CCGD-ESCC) database, which contains the associations of 69,593 single nucleotide polymorphisms (SNPs) with ESCC risk in 2022 cases and 2039 controls, survival time of 1006 ESCC patients (survival GWAS) and gene expression (expression quantitative trait loci, eQTL) in 94 ESCC patients. Moreover, this database also provides the associations between 8833 somatic mutations and survival time in 675 ESCC patients. Our user-friendly database is a resource useful for biologists and oncologists not only in identifying the associations of genetic variants or somatic mutations with the development and progression of ESCC but also in studying the underlying mechanisms for tumorigenesis of the cancer. CCGD-ESCC is freely accessible at http://db.cbi.pku.edu.cn/ccgd/ESCCdb.


Introduction
The studies of cancer genetics and genomics powered by rapidly developing high-throughput biochip and sequencing technologies have promoted our understanding of the roles of genetic variants in carcinogenesis and cancer progression. However, most literature only reports the most significant association results due to the space limitation of the paper. Although many well-known genomic databases are available, the information on esophageal squamous-cell carcinoma (ESCC) is scarce due to the limited number of studies and its relatively low prevalence in Caucasian populations [1]. However, ESCC is the fourth most common leading cause of cancer-related deaths and kills $250,000 people in China annually [2]. Therefore, there is an increasing demand to establish a comprehensive genetic and genomic database for ESCC, which is currently absent.
The genetic etiology of ESCC remains largely unknown. However, several studies indicate that individuals' genetic makeup alone with some environmental factors, such as alcohol intake and cigarette smoking, play an important role in esophageal carcinogenesis in China [3]. Using genome-wide association studies (GWAS), a powerful and successful tool in identification of common disease alleles, 14 susceptible genes or loci including PLCE1, ADH and ALDH2 have been shown to be associated with ESCC risk [4][5][6][7][8][9]. However, the use of stringent P value threshold (P 10 À7 ) might miss some true ESCC-associated loci with moderate effect sizes. Moreover, accumulating evidence has demonstrated that genetic variants associated with complex diseases or phenotypes are often located in non-coding regions and could regulate the gene expression. Therefore, expression quantitative trait loci (eQTL) analysis might be an effective method in identification of SNPs with potential regulatory functions [10][11][12]. However, the genome-wide eQTL analysis for ESCC based on tumors and the paired normal esophageal tissues has not yet been performed, limiting its power to identify functional genes or variants associated with ESCC.
Currently, surgery, chemotherapy and radiotherapy are commonly used to treat ESCC. Unfortunately, the long-term outcome in patients is still dismal, with 5-year survival rate around 30% [13]. Previous studies have suggested that germline genetic variability may provide important prognostic information for ESCC [14,15], while recent whole-exome sequencing (WES) or whole-genome sequencing (WGS) studies have identified the landscape of somatic mutations as another important genomic factor in ESCC [16][17][18][19][20][21][22]. However, most previous GWAS, WGS and WES analyses suffer from the lack of patients' clinical information such as tumor staging, treatment outcome and survival time. Such lack of the clinical information would hinder the exploration and identification of the genomic changes that are associated with phenotypes and affect treatment outcomes, which are critical in translational and precision medicine.
In the present study, we integrated the genetic and genomic data derived from the studies in our laboratory [6,7,9,14,22] and other groups [16][17][18][19][20][21] to establish the Chinese Cancer Genomic Database-Esophageal Squamous Cell Carcinoma (CCGD-ESCC) database. Our endeavor is not only to provide comprehensive information on the associations of genetic variants or somatic mutations with ESCC risk and prognosis but also to dissect their potential regulatory functions based on eQTL analysis in ESCC tumor and normal esophageal tissues. The CCGD-ESCC database has an intuitive, well-organized and easy-to-use interface for search and display. We believe that this database can enhance our understanding of genetics, genomics and their biological functions in ESCC and facilitate the generations of new genetic and genomic markers or potential therapeutic targets by integrating multidimensional information.  Table 1). The criteria of enrollment of study subjects were described in the previous publications [6,7,9,14,22]. Genetic variants were annotated using the ANNOVAR with the reference cluster identifier (rsID) from the NCBI dbSNP build 146 [23].

First section: GWAS
Germline genotype data of 2022 cases and 2039 controls were obtained using Affymetrix GeneChip Human Mapping 6.0 set (Affymetrix). To increase power and coverage for association analyses, we used MACH software to impute untyped SNPs in whole genome using the 1000 Genomes Project Phase 3 ASN (Asian) as the reference panel [24]. We then filtered out SNPs with low imputation quality (estimated R-squared, i.e., Rsq, <0.3), low minor allele frequency (MAF < 0.01) and divergence from Hardy-Weinberg equilibrium (P < 1.0 Â 10 À6 ). After the quality control (QC) process, 8,252,518 SNPs on autosomes were used in the final GWAS analysis. For each SNP, we carried out association analysis with ESCC risk using an additive model in a logistic regression (1-degree-of-freedom) adjusted for age, sex, smoking status and drinking status as covariates. The odds ratios (ORs) are presented for the minor allele of each SNP. Consequently, 16,544 SNPs associated with ESCC risk with P < 1.0 Â 10 À5 were retained in the CCGD-ESCC.

Second section: survival GWAS
We included 1006 ESCC patients with the information on survival outcome available to identify variants associated with length of survival time. The overall survival time of 1006 ESCC patients was calculated from the date of diagnosis to the date of last follow-up or death, with median follow-up time of 101 months. As a result, 8,279,620 SNPs on autosomes passed the same QC criteria as for the GWAS analysis in the first section. Log-rank test was used to calculate the associations between SNPs and survival time in ESCC patients. The additive model was used for survival analysis if the homozygous genotype for the minor allele was >1%; otherwise, the survival analysis was performed using the dominant model. Consequently, a total of 1652 SNPs with P < 1.0 Â 10 À4 was retained in the CCGD-ESCC.

Third section: eQTL
We performed eQTL analysis to evaluate the associations between genotypes extracted from WGS data and gene expression based on RNA-seq data in paired tumor and normal tissues obtained from 94 individuals with ESCC. The sequencing data production and processing were described in detail elsewhere [22]. A total of 15,075,079 SNPs was called using Freebayes based on the WGS of blood samples from 94 individuals [25]. SNPs were excluded if their minor allele frequency (MAF) is <5% or the genotype distributions were deviated from those expected according to the Hardy-Weinberg equilibrium (P < 1.0 Â 10 À6 ). We also excluded genes that have absent call in over 90% of the samples. After QC, 6,092,313 SNPs and 18,085 genes on autosomes were selected for final cis-eQTL analysis using linear regression with additive model in the R package Matrix eQTL [26]. The effect size was defined as the slope coefficient of linear regression. Only SNPs within a ±100-kb window of the transcription start site (TSS) of a given gene on the same chromosome were tested. Consequently, a total of 7,676,942 gene-cis-SNP pairs were tested, and we found 16,984 cis-eQTLs in tumor tissues and 54,445 cis-eQTLs in paired normal tissues, which were significantly associated with gene expression with a false discovery rate (FDR) of <0.05.

Fourth section: somatic mutations and their associations with patients' survival
We combined our WGS data in 94 individuals with ESCC [22] and WES data in 581 individuals with ESCC obtained from published studies [16][17][18][19][20][21] to analyze the effects of somatic mutations on survival time in these patients, whose survival time information is available. After QC, 31,673 SNVs or indels in the protein-coding regions and 2203 genes on autosomes that contained mutations in >1% of all samples were analyzed. Kaplan-Meier method was used to estimate the median survival time and log-rank test was used to test the significant difference between patients with or without somatic mutations (P < 0.05 as significant threshold). Finally, there are 662 genes retained in the CCGD-ESCC.

Web interface
We designed the CCGD-ESCC to enable users to explore the associations between genetic variants, somatic mutations and genes of interest and risk, survival time or gene expression for ESCC. The overview of the database can be found at the upper part of the homepage. A quick search box is provided in the middle part of the homepage for searching by the SNP identifier, gene symbol or chromosomal region ( Figure 1A). The

The variant page
To acquire the information of a SNP, users can search the database by providing its rsID from dbSNP (build 146).
The variant page provides the basic information about the SNP, including its chromosomal location, genomic region, reference allele, alternative allele, as well as the hyperlinks to dbSNP, UCSC, ClinVar, and GWASdb. The GWAS section shows the association results with ESCC risk including MAF in controls, OR, 95% confidence interval (CI), P value, and its functional annotations by ANNOVAR (Figure 1B). In the Survival GWAS section, users can obtain the association results of SNPs and patients' overall survival, including P value and the functional annotations by ANNO-VAR ( Figure 1C). The eQTL section provides the genes affected by SNPs with effect size and P value obtained in ESCC tumor tissues or normal esophageal tissues (Figure 1D). Users can obtain the boxplot figure of eQTL after clicking on the eQTL results. In addition, users can also reach our related publications in PubMed by clicking the links for more information.

The gene page
To explore a specific gene region, users can search the database using gene symbol. The gene page shows the primary information of the gene, including the full gene name, Entrez ID, brief gene description by NCBI, as well as hyperlinks to GeneCards, HUGO, COSMIC, Ensembl, UCSC and GWASdb. We provide the association results of all SNPs located in and around the gene annotated by ANNO-VAR in the GWAS, Survival GWAS and eQTL sections ( Figure 2A). The gene page and the variant page are also linked with each other. Users can hyperlink respective variant pages by clicking the SNP column on the gene page and vice versa. In addition, the gene page also provides the results of Somatic Mutation section, which shows all somatic mutations in the protein-coding region of the query gene with genomic location, reference allele, mutant allele, amino acid change, mutation type and the association of somatic mutations with ESCC survival time ( Figure 2B).

Other pages
Users can also search using a particular chromosomal region (written as ''chrN: start-end") to obtain a table of all SNPs within the specified genome region. The corresponding variant page of a SNP is shown when the SNP is clicked. To improve the usability, we provide an About page with detailed description of the database, a FAQ page that gives answers to some common questions and a Contact page for help and feedback.

Discussion
We established the CCGD-ESCC to share data on the associations of genetic variants and somatic mutations with risk or survival of ESCC for cancer researchers worldwide. To the best of our knowledge, the CCGD-ESCC is the first comprehensive database that systematically integrated association results of genetic variants and somatic mutations at the genome-wide level. Another important strength of this database is that it contains patients' survival data and the correlations between the genetic variants or somatic mutations and survival time of patients with ESCC, which provide cancer researchers and clinicians with a precious resource. The majority of genetic variants potentially associated with ESCC that are identified by GWAS are located in non-coding sequences, suggesting that their functions, if any, may be exerted through regulating the expression of genes producing proteins or non-coding RNAs [27]. Thus, integration of GWAS and eQTL data is essential for elucidating the underlying mechanisms for genetic variants in diseases [28]. The currently available databases, such as the GTEx Project and the NHGRI GWAS Catalog, provide only one-sided association data of genetic variants for gene expression or for cancer risk. The Cancer Genome Atlas (TCGA), which provides the greatest amount of cancer omics data, mainly focuses on somatic mutations not germline variations. Conversely, our CCGD-ESCC database provides integrative data, from which users can explore the functional variants in both ESCC tumor tissues and normal esophageal tissues. More information given in our database, such as systematic analysis of the cis-regulatory variants in matched tumor and normal samples, might help users to identify the variants specific for the development of ESCC and perhaps other squamous cell carcinomas as well. However, CCGD-ESCC only provides significant associations that are above the corresponding thresholds as defined in the four sections. Users can send us emails to access to the associations with the SNPs of their interest. We hope that the CCGD-ESCC database can be a one-stop portal for seeking potential functional SNPs and related genes for research on ESCC and other cancers. While the database currently covers ESCC only, we are actively working to update and extend the portal, aiming to generate a comprehensive genomic database for cancers in Chinese populations.

Authors' contributions
CW, GG, and DL conceptualized and supervised this study. CW, GG, LP, SC, and Y Lin contributed to the study design. LP, QC, Y Luo, JC, MS, WF, YC, AL, YX, YS, LZ, CZ, and WT performed genotyping assays, imputation analysis and association analysis. SC, LP, and Y Lin prepared the CCGD-ESCC web server. DL, CW, GG, LP, SC, and Y Lin prepared the manuscript. All authors read and approved the final manuscript. The association results for genes in CCGD-ESCC A. Summary of basic information of the gene and hyperlinks of external public resources, as well as association data of ACAD10 from GWAS, Survival GWAS or eQTL sections. Results of the association data are tailored since the actual web page is too large to fit. B. The results of Somatic Mutation section are displayed for the genes with somatic mutations significantly affecting ESCC patients' survival. MAF, minor allele frequency in controls; OR, odds ratio for the minor allele; eQTL, expression quantitative trait loci; SNV, single nucleotide variation.