Dataset Information

Gene-based comparative analysis of tools for estimating copy number alterations using whole-exome sequencing data.

ABSTRACT: Accurate detection of copy number alterations (CNAs) using next-generation sequencing technology is essential for the development and application of more precise medical treatments for human cancer. Here, we evaluated seven CNA estimation tools (ExomeCNV, CoNIFER, VarScan2, CODEX, ngCGH, saasCNV, and falcon) using whole-exome sequencing data from 419 breast cancer tumor-normal sample pairs from The Cancer Genome Atlas. Estimations generated using each tool were converted into gene-based copy numbers; concordance for gains and losses and the sensitivity and specificity of each tool were compared to validated copy numbers from a single nucleotide polymorphism reference array. The concordance and sensitivity of the tumor-normal pair methods for estimating CNAs (saasCNV, ExomeCNV, and VarScan2) were better than those of the tumor batch methods (CoNIFER and CODEX). SaasCNV had the highest gain and loss concordances (65.0%), sensitivity (69.4%), and specificity (89.1%) for estimating copy number gains or losses. These findings indicate that improved CNA detection algorithms are needed to more accurately interpret whole-exome sequencing results in human cancer.

SUBMITTER: Kim HY

PROVIDER: S-EPMC5432334 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Gene-based comparative analysis of tools for estimating copy number alterations using whole-exome sequencing data.

Kim Hyung-Yong HY Choi Jin-Woo JW Lee Jeong-Yeon JY Kong Gu G

Oncotarget 20170401 16

Accurate detection of copy number alterations (CNAs) using next-generation sequencing technology is essential for the development and application of more precise medical treatments for human cancer. Here, we evaluated seven CNA estimation tools (ExomeCNV, CoNIFER, VarScan2, CODEX, ngCGH, saasCNV, and falcon) using whole-exome sequencing data from 419 breast cancer tumor-normal sample pairs from The Cancer Genome Atlas. Estimations generated using each tool were converted into gene-based copy num ...[more]

PMID: 28460482

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Project description:BackgroundRecently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors' complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.ResultsIn this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly.ConclusionsThe limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

Dataset Information

Gene-based comparative analysis of tools for estimating copy number alterations using whole-exome sequencing data.

Publications

Gene-based comparative analysis of tools for estimating copy number alterations using whole-exome sequencing data.

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