Dataset Information

Interpreting whole genome and exome sequencing data of individual gastric cancer samples.

ABSTRACT: Gastric cancer is the fourth most common cancer and the second leading cause of cancer death worldwide. In order to understand the genetic background, we sequenced the whole exome and the whole genome of one microsatellite stable as well as one microsatellite unstable tumor and the matched healthy tissue on two different NGS platforms. We here aimed to provide a comparative approach for individual clinical tumor sequencing and annotation using different sequencing technologies and mutation calling algorithms.We applied a population-based whole genome resource as a novel pathway-based filter for interpretation of genomic alterations from single nucleotide variations (SNV), indels, and large structural variations. In addition to a comparison with tumor genome database resources and a filtering approach using data from the 1000 Genomes Project, we performed pyrosequencing analysis and immunohistochemistry in a large cohort of 428 independent gastric cancer cases.We here provide an example comparing the usefulness and potential pitfalls of different technologies for a clinical interpretation of genomic sequence data of individual gastric cancer samples. Using different filtering approaches, we identified a multitude of novel potentially damaging mutations and could show a validated association between a mutation in GNAS and gastric cancer.

SUBMITTER: Esser D

PROVIDER: S-EPMC5501078 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Interpreting whole genome and exome sequencing data of individual gastric cancer samples.

Esser Daniela D Holze Niklas N Haag Jochen J Schreiber Stefan S Krüger Sandra S Warneke Viktoria V Rosenstiel Philip P Röcken Christoph C

BMC genomics 20170706 1

<h4>Background</h4>Gastric cancer is the fourth most common cancer and the second leading cause of cancer death worldwide. In order to understand the genetic background, we sequenced the whole exome and the whole genome of one microsatellite stable as well as one microsatellite unstable tumor and the matched healthy tissue on two different NGS platforms. We here aimed to provide a comparative approach for individual clinical tumor sequencing and annotation using different sequencing technologies ...[more]

PMID: 28683819

Similar Datasets

Project description:ImportanceThe E-cadherin gene, CDH1, and the α-E-catenin gene, CTNNA1, were previously identified as hereditary diffuse gastric cancer (HDGC) susceptibility genes, explaining 25% to 50% of HDGC cases. The genetic basis underlying disease susceptibility in the remaining 50% to 75% of patients with HDGC is still unknown.ObjectiveTo assess the incidence rate of CDH1 germline alterations in HDGC, identify new susceptibility genes that can be used for screening of HDGC, and provide a genetic landscape for HDGC.Design, setting, and participantsThis cohort study conducted retrospective whole-exome and targeted sequencing of 284 leukocyte samples and 186 paired tumor samples from Chinese patients with HDGC over a long follow-up period (median, 21.7 [range, 0.6-185.9] months). Among 10 431 patients diagnosed with gastric cancer between January 1, 2002, and August 31, 2018, 284 patients who met the criteria for HDGC were included. Data were analyzed from August 1 to 30, 2020.Main outcomes and measuresIncidence rate of CDH1 germline alterations, identification of new HDGC susceptibility genes, and genetic landscape of HDGC.ResultsAmong 284 Chinese patients, 161 (56.7%) were female, and the median age was 35 (range, 20-75) years. The frequency of CDH1 germline alterations was 2.8%, whereas the frequency of CDH1 somatic alterations was 25.3%. The genes with the highest incidence (>10%) of private germline alterations (including insertions and deletions) in the HDGC cohort were MUC4, ABCA13, ZNF469, FCGBP, IGFN1, RNF213, and SSPO, whereas previously reported germline alterations of CTNNA1, BRCA2, STK11, PRSS1, ATM, MSR1, PALB2, BRCA1, and RAD51C were observed at low frequencies (median, 4 [range, 1-12] cases). Furthermore, enrichment of the somatic variant signature of exposure to aflatoxin suggested potential interaction between genetics and environment in HDGC. Double-hit events in genes such as CACNA1D were observed, which suggested that these events might serve as important mechanisms for HDGC tumorigenesis. In addition, germline variants of FSIP2, HSPG2, and NCKAP5 and somatic alterations of FGFR3, ASPSCR1, CIC, DGCR8, and LZTR1 were associated with poor overall survival among patients with HDGC.Conclusions and relevanceThis study provided a genetic landscape for HDGC. The study's findings challenged the previously reported high germline alteration rate of CDH1 in HDGC and identified new potential susceptibility genes. Analyses of variant signatures and double-hit events revealed potentially important mechanisms for HDGC tumorigenesis. Findings from the present study may provide helpful information for further investigations of HDGC.

Project description:BackgroundINDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.MethodsWe characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low-quality INDELs (7% vs. 51%).ResultsSimulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (53%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (84% vs. 57%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.ConclusionsOverall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (for example, capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Dataset Information

Interpreting whole genome and exome sequencing data of individual gastric cancer samples.

Publications

Interpreting whole genome and exome sequencing data of individual gastric cancer samples.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets