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.
Highlights
The accumulation of genetic aberrations, ranging from the single nucleotide to the chromosome level, leads to various human diseases, including cancer
To evaluate the accuracy of conventional copy number alterations (CNAs) detection tools at the gene level, we compared the accuracy of seven different whole-exome sequencing (WES)-based CNA estimation algorithms (ExomeCNV, CoNIFER, VarScan2, CODEX, ngCGH, saasCNV, and falcon) to the SNP6.0 copy numbers generated using the same DNA samples from the The Cancer Genome Atlas (TCGA) dataset (Supplementary Figure 1)
These data indicate that CNA estimations based on a single WES BAM file differ depending on which estimation tool is used
Summary
The accumulation of genetic aberrations, ranging from the single nucleotide to the chromosome level, leads to various human diseases, including cancer. Several types of genetic aberrations, such as single nucleotide polymorphisms (SNP), insertions, deletions, duplications, and inversions, are associated with cancer. Many CNAs have been identified in regions of the genome that contain multiple oncogenes and tumor suppressors [1–3], and these CNAs correlate with clinical outcomes and prognosis in various types of cancer, including colon, prostate, and breast cancers and leukemia [4–9]. These findings indicate that CNAs are important predictive and prognostic biomarkers in human cancer. Additional bioinformatics tools are needed to more precisely estimate CNAs from WES data
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