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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Reliability of algorithmic somatic copy number alteration detection from targeted capture data.

Nora Rieber¹, Regina Bohnert¹, Ulrike Ziehm¹

¹Molecular Health GmbH, Kurfürsten-Anlage 21, 69115 Heidelberg, Germany.

Bioinformatics (Oxford, England)

|May 5, 2017

Summary

Evaluating SCNA callers on whole exome and gene panel sequencing data revealed significant false positive rates. Cross-validation with orthogonal methods is crucial for reliable oncological diagnostics.

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Area of Science:

Genomics
Bioinformatics
Oncology

Background:

Whole exome and gene panel sequencing are increasingly utilized in oncological diagnostics.
Accurate detection of somatic copy number alterations (SCNAs) is critical for cancer research and clinical applications.

Purpose of the Study:

To evaluate the accuracy, precision, and sensitivity of different SCNA detection algorithms.
To compare algorithm performance on simulated and clinical tumor samples using whole exome and targeted gene panel sequencing data.

Main Methods:

Assessed four SCNA callers using simulated whole exome and gene panel datasets (n=50 each).
Validated performance on 119 clinical TCGA tumor samples with available SNP array data.
Investigated the impact of tumor purity on SCNA detection accuracy.

Main Results:

VarScan2 produced numerous false positives on synthetic data. Control-FREEC showed high precision but low sensitivity. ONCOCNV demonstrated similar limitations, particularly for amplifications.
Tumor purity above 60% significantly decreased precision and sensitivity for all callers.
On clinical samples, Control-FREEC and CNVkit detected 71.8% and 94% of SNP array-identified SCNAs, respectively, but with substantial false positive rates.

Conclusions:

Current whole exome and gene panel sequencing methods inherently limit SCNA caller precision, leading to false positives.
Integrating SCNA calls from targeted capture-based sequencing into clinical pipelines is challenging.
Orthogonal method cross-validation is essential for reliable SCNA calling in clinical settings.