Dataset Information

VCF2CNA: A tool for efficiently detecting copy-number alterations in VCF genotype data and tumor purity.

ABSTRACT: VCF2CNA is a tool (Linux commandline or web-interface) for copy-number alteration (CNA) analysis and tumor purity estimation of paired tumor-normal VCF variant file formats. It operates on whole genome and whole exome datasets. To benchmark its performance, we applied it to 46 adult glioblastoma and 146 pediatric neuroblastoma samples sequenced by Illumina and Complete Genomics (CGI) platforms respectively. VCF2CNA was highly consistent with a state-of-the-art algorithm using raw sequencing data (mean F1-score?=?0.994) in high-quality whole genome glioblastoma samples and was robust to uneven coverage introduced by library artifacts. In the whole genome neuroblastoma set, VCF2CNA identified MYCN high-level amplifications in 31 of 32 clinically validated samples compared to 15 found by CGI's HMM-based CNA model. Moreover, VCF2CNA achieved highly consistent CNA profiles between WGS and WXS platforms (mean F1 score 0.97 on a set of 15 rhabdomyosarcoma samples). In addition, VCF2CNA provides accurate tumor purity estimates for samples with sufficient CNAs. These results suggest that VCF2CNA is an accurate, efficient and platform-independent tool for CNA and tumor purity analyses without accessing raw sequence data.

SUBMITTER: Putnam DK

PROVIDER: S-EPMC6637131 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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VCF2CNA: A tool for efficiently detecting copy-number alterations in VCF genotype data and tumor purity.

Putnam Daniel K DK Ma Xiaotu X Rice Stephen V SV Liu Yu Y Newman Scott S Zhang Jinghui J Chen Xiang X

Scientific reports 20190717 1

VCF2CNA is a tool (Linux commandline or web-interface) for copy-number alteration (CNA) analysis and tumor purity estimation of paired tumor-normal VCF variant file formats. It operates on whole genome and whole exome datasets. To benchmark its performance, we applied it to 46 adult glioblastoma and 146 pediatric neuroblastoma samples sequenced by Illumina and Complete Genomics (CGI) platforms respectively. VCF2CNA was highly consistent with a state-of-the-art algorithm using raw sequencing data ...[more]

PMID: 31316100

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Project description:BackgroundGenomic aberrations can be used to determine cancer diagnosis and prognosis. Clinically relevant novel aberrations can be discovered using high-throughput assays such as Single Nucleotide Polymorphism (SNP) arrays and next-generation sequencing, which typically provide aggregate signals of many cells at once. However, heterogeneity of tumor subclones dramatically complicates the task of detecting aberrations.ResultsThe aggregate signal of a population of subclones can be described as a linear system of equations. We employed a measure of allelic imbalance and total amount of DNA to characterize each locus by the copy number status (gain, loss or neither) of the strongest subclonal component. We designed simulated data to compare our measure to existing approaches and we analyzed SNP-arrays from 30 melanoma samples and transcriptome sequencing (RNA-Seq) from one melanoma sample.We showed that any system describing aggregate subclonal signals is underdetermined, leading to non-unique solutions for the exact copy number profile of subclones. For this reason, our illustrative measure was more robust than existing Hidden Markov Model (HMM) based tools in inferring the aberration status, as indicated by tests on simulated data. This higher robustness contributed in identifying numerous aberrations in several loci of melanoma samples. We validated the heterogeneity and aberration status within single biopsies by fluorescent in situ hybridization of four affected and transcriptionally up-regulated genes E2F8, ETV4, EZH2 and FAM84B in 11 melanoma cell lines. Heterogeneity was further demonstrated in the analysis of allelic imbalance changes along single exons from melanoma RNA-Seq.ConclusionsThese studies demonstrate how subclonal heterogeneity, prevalent in tumor samples, is reflected in aggregate signals measured by high-throughput techniques. Our proposed approach yields high robustness in detecting copy number alterations using high-throughput technologies and has the potential to identify specific subclonal markers from next-generation sequencing data.

Dataset Information

VCF2CNA: A tool for efficiently detecting copy-number alterations in VCF genotype data and tumor purity.

Publications

VCF2CNA: A tool for efficiently detecting copy-number alterations in VCF genotype data and tumor purity.

Similar Datasets

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