Dataset Information

A viral discovery methodology for clinical biopsy samples utilising massively parallel next generation sequencing.

ABSTRACT: Here we describe a virus discovery protocol for a range of different virus genera, that can be applied to biopsy-sized tissue samples. Our viral enrichment procedure, validated using canine and human liver samples, significantly improves viral read copy number and increases the length of viral contigs that can be generated by de novo assembly. This in turn enables the Illumina next generation sequencing (NGS) platform to be used as an effective tool for viral discovery from tissue samples.

SUBMITTER: Daly GM

PROVIDER: S-EPMC3244418 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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A viral discovery methodology for clinical biopsy samples utilising massively parallel next generation sequencing.

Daly Gordon M GM Bexfield Nick N Heaney Judith J Stubbs Sam S Mayer Antonia P AP Palser Anne A Kellam Paul P Drou Nizar N Caccamo Mario M Tiley Laurence L Alexander Graeme J M GJ Bernal William W Heeney Jonathan L JL

PloS one 20111221 12

Here we describe a virus discovery protocol for a range of different virus genera, that can be applied to biopsy-sized tissue samples. Our viral enrichment procedure, validated using canine and human liver samples, significantly improves viral read copy number and increases the length of viral contigs that can be generated by de novo assembly. This in turn enables the Illumina next generation sequencing (NGS) platform to be used as an effective tool for viral discovery from tissue samples. ...[more]

PMID: 22216131

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Project description:Gastric cancer (GC) remains the second tumor caused death threat worldwide, and personalized medicine for GC is far from expectation. Finding novel, recurrently mutated genes through next-generation sequencing (NGS) is a powerful and productive approach. However, previous genomic data for GC are based on surgical resected samples while a large proportion of advanced gastric cancer (AGC) patients have already missed the chance for operation. The aim of this study is to assess frequent genomic alteration in AGC via biopsy samples. Here we performed targeted genomic sequencing of 78 AGC patients' tumor biopsies along with matched lymphocyte samples based on a 118 cancer related gene panel. In total, we observed 301 somatic nonsynonymous genomic alterations in 92 different genes, as well as 37 copy number gain events among 15 different genes (fold change 2-12), and validated the fold changes of ERBB2 copy number gains with IHC and FISH test showed an accuracy of 81.8%. Previously reported driver genes for gastric cancer (TP53, KMT2D, KMT2B, EGFR, PIK3CA, GNAQ, and ARID1A), and several unreported mutations (TGFBR2, RNF213, NF1, NSD1, and LRP2) showed high non-silent mutation prevalence (7.7%-34.6%). When comparing intestinal-type gastric cancer (IGC) with diffuse-type gastric cancer (DGC), TP53 and GNAQ appear to be more frequently mutated in IGC (P=0.028 and P=0.023, respectively), whereas LRP2, BRCA2 and FGFR3 mutations are not observed in IGC, but have 12.8%, 7.7% and 7.7% mutation rates, respectively, in DGC patients. Patients with one or more mutations in adherens junction pathway (CREBBP, EP300, CDH1, CTNNB1, EGFR, MET, TGFBR2 and ERBB2) or TGF-β signaling pathway (CREBBP, EP300, MYST4, KRAS and TGFBR2) showed significantly better overall survival (P=0.007 and P=0.014, respectively), consistent with The Cancer Genome Atlas (TCGA) cohort data. Importantly, 57 (73.1%) patients harbored at least one genomic alteration with potential treatments, making NGS-based drug target screening a viable option for AGC patients. Our study established a comprehensive genomic portrait of AGC, and identified several mutation signatures highly associated with clinical features, survival outcomes, which may be used to design future personalized treatments.

Dataset Information

A viral discovery methodology for clinical biopsy samples utilising massively parallel next generation sequencing.

Publications

A viral discovery methodology for clinical biopsy samples utilising massively parallel next generation sequencing.

Similar Datasets

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