Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Whole exome sequencing of TSCC (tongue squamous cell carcinoma)

ABSTRACT: Of the multiple anatomical sites represented in oral cancer, squamous cell carcinoma of the tongue (TSCC) shows the highest incidence among younger age group. Chewing betel leaf, areca nut & slaked lime and smoking tobacco are common practises in India which have direct clinical implication in TSCC carcinogenesis. Here, for the first time we define the landscape of genomic alterations in TSCC from the Indian diaspora which would help to identify novel therapeutic targets for clinical intervention and define the genetic basis for TSCC. We performed high throughput sequencing of fifty four tongue samples using whole exome sequencing (n=47, 23 paired normal tumor and 1 unpaired) and transcriptome sequencing (n=17, 10 tumor and 5 normal). Mutation, copy number analysis were carried out using exome sequencing data and transcriptome analysis provided expressed genes and transcript fusions in tongue cancer patients. Further, integrated analysis were performed to identify biologically relevant alterations. Our preliminary analysis revealed presence of most frequently altered mutations in TSCC which includes mutations in TP53, NOTCH1, CDKN2A, USP6, KMT2D etc, consistent with literature. We observed high frequency of CG/T(GC/A) transversions in non-CpG islands, a signature associated with tobacco exposure. Somatic copy number analysis revealed copy number gain in known hallmarks such as CCND1, MYC, ORAOV1 genes along with copy number alteration in novel genes. Significant positive correlation was observed in the genes harbouring copy number gains and showing increased expression.

INSTRUMENT(S): Illumina HiSeq 1500

ORGANISM(S): Homo sapiens

SUBMITTER: Amit Dutt

PROVIDER: E-MTAB-4653 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. DNA copy number profiles generated with a new tool, ENCODER, were compared to DNA copy number profiles from SNP6, NimbleGen and low-coverage Whole Genome Sequencing.

Dataset Information

Whole exome sequencing of TSCC (tongue squamous cell carcinoma)

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure