Dataset Information

Identification of cancer-driver genes in focal genomic alterations from whole genome sequencing data.

ABSTRACT: DNA copy number alterations (CNAs) are the main genomic events that occur during the initiation and development of cancer. Distinguishing driver aberrant regions from passenger regions, which might contain candidate target genes for cancer therapies, is an important issue. Several methods for identifying cancer-driver genes from multiple cancer patients have been developed for single nucleotide polymorphism (SNP) arrays. However, for NGS data, methods for the SNP array cannot be directly applied because of different characteristics of NGS such as higher resolutions of data without predefined probes and incorrectly mapped reads to reference genomes. In this study, we developed a wavelet-based method for identification of focal genomic alterations for sequencing data (WIFA-Seq). We applied WIFA-Seq to whole genome sequencing data from glioblastoma multiforme, ovarian serous cystadenocarcinoma and lung adenocarcinoma, and identified focal genomic alterations, which contain candidate cancer-related genes as well as previously known cancer-driver genes.

SUBMITTER: Jang H

PROVIDER: S-EPMC4860638 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Identification of cancer-driver genes in focal genomic alterations from whole genome sequencing data.

Jang Ho H Hur Youngmi Y Lee Hyunju H

Scientific reports 20160509

DNA copy number alterations (CNAs) are the main genomic events that occur during the initiation and development of cancer. Distinguishing driver aberrant regions from passenger regions, which might contain candidate target genes for cancer therapies, is an important issue. Several methods for identifying cancer-driver genes from multiple cancer patients have been developed for single nucleotide polymorphism (SNP) arrays. However, for NGS data, methods for the SNP array cannot be directly applied ...[more]

PMID: 27156852

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Project description:Hodgkin lymphoma (HL) is a lymphoproliferative malignancy of B-cell origin that accounts for 10% of all lymphomas. Despite evidence suggesting strong familial clustering of HL, there is no clear understanding of the contribution of genes predisposing to HL. In this study, whole genome sequencing (WGS) was performed on 7 affected and 9 unaffected family members from three HL-prone families and variants were prioritized using our Familial Cancer Variant Prioritization Pipeline (FCVPPv2). WGS identified a total of 98,564, 170,550, and 113,654 variants which were reduced by pedigree-based filtering to 18,158, 465, and 26,465 in families I, II, and III, respectively. In addition to variants affecting amino acid sequences, variants in promoters, enhancers, transcription factors binding sites, and microRNA seed sequences were identified from upstream, downstream, 5' and 3' untranslated regions. A panel of 565 cancer predisposing and other cancer-related genes and of 2,383 potential candidate HL genes were also screened in these families to aid further prioritization. Pathway analysis of segregating genes with Combined Annotation Dependent Depletion Tool (CADD) scores >20 was performed using Ingenuity Pathway Analysis software which implicated several candidate genes in pathways involved in B-cell activation and proliferation and in the network of "Cancer, Hematological disease and Immunological Disease." We used the FCVPPv2 for further in silico analyses and prioritized 45 coding and 79 non-coding variants from the three families. Further literature-based analysis allowed us to constrict this list to one rare germline variant each in families I and II and two in family III. Functional studies were conducted on the candidate from family I in a previous study, resulting in the identification and functional validation of a novel heterozygous missense variant in the tumor suppressor gene DICER1 as potential HL predisposition factor. We aim to identify the individual genes responsible for predisposition in the remaining two families and will functionally validate these in further studies.

Dataset Information

Identification of cancer-driver genes in focal genomic alterations from whole genome sequencing data.

Publications

Identification of cancer-driver genes in focal genomic alterations from whole genome sequencing data.

Similar Datasets

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