An evaluation of copy number variation detection tools for cancer using whole exome sequencing data

Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1705-x) contains supplementary material, which is available to authorized users.

Whole‐exome sequencing identifies novel pathogenic variants across the ATP7B gene and some modifiers of Wilson's disease phenotype

“…Thus, while the clinical usefulness of WES is unquestionable, our WES results were negative and inconclusive in three and 27 of WD patients, respectively. These negative and inconclusive results likely reflect challenges in distinguishing structural variation of the DNA sequence, including multiplication and heterozygous deletions . In addition, information from flanking noncoding regions such as promoters, introns and splice sites may be ignored by current analysis pipelines .…”

Copy Number Variation Disorders

“…Several algorithms have been developed to identify CNVs from WES data, most of which use read-depth based approaches for the detection of changes in copy number [74]. However, several limitations both, of WES methodology as well as the algorithms for CNV detection from WES data, prevent the reliable application of WES-based CNV detection in the clinical laboratory [75,76]. Whole genome sequencing (WGS) and analysis pipelines developed for variation detection have become increasingly robust over the past several years enabling the detection of thousands of novel CNVs and other structural variations [10–12].…”

Liquid biopsy in breast cancer: A comprehensive review

“…On the other hand, qualitative studies use NGS to examine ctDNA for variations in cancer hotspots or the entire genome to examine early‐stage disease, response to treatment and prognosis . Detection of single‐nucleotide variations is generally performed through sensitive methods such as BEAMing, Safe‐SeqS, TamSeq and ddPCR assays, while WGS is used to detect copy number variations (CNVs) and chromosomal instabilities . Analysis of single‐locus hotspots is achieved through ddPCR assays, but this approach is limited when searching for a large number of variants or when looking for novel variants.…”

“…108,109 Detection of single-nucleotide variations is generally performed through sensitive methods such as BEAMing, Safe-SeqS, TamSeq and ddPCR assays, while WGS is used to detect copy number variations (CNVs) and chromosomal instabilities. 102,110,111 Analysis of single-locus hotspots is achieved through ddPCR assays, 112,113 but this approach is limited when searching for a large number of variants or when looking for novel variants. For these applications, NGS could potentially help, but the challenge is that the allele frequency of ctDNA mutations in the pool of cfDNA is the same as the range of the NGS technology error rate.…”