Dataset Information

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

ABSTRACT: Whole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available.On synthetic exome and panel data, VarScan2 mostly called false positives, whereas Control-FREEC was precise (>90% correct calls) at the cost of low sensitivity (<40% detected). ONCOCNV was slightly less precise on gene panel data, with similarly low sensitivity. This could be explained by low sensitivity for amplifications and high precision for deletions. Surprisingly, these results were not strongly affected by moderate tumor impurities; only contaminations with more than 60% non-cancerous cells resulted in strongly declining precision and sensitivity. On the 119 clinical samples, both Control-FREEC and CNVkit called 71.8% and 94%, respectively, of the SCNAs found by the SNP arrays, but with a considerable amount of false positives (precision 29% and 4.9%).Whole exome and targeted gene panel methods by design limit the precision of SCNA callers, making them prone to false positives. SCNA calls cannot easily be integrated in clinical pipelines that use data from targeted capture-based sequencing. If used at all, they need to be cross-validated using orthogonal methods.Scripts are provided as supplementary information.gunther.jansen@molecularhealth.com.Supplementary data are available at Bioinformatics online.

SUBMITTER: Rieber N

PROVIDER: S-EPMC5870863 | biostudies-other | 2017 Sep

REPOSITORIES: biostudies-other

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

Rieber Nora N Bohnert Regina R Ziehm Ulrike U Jansen Gunther G

Bioinformatics (Oxford, England) 20170901 18

<h4>Motivation</h4>Whole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available.<h4>Results</h4>On synthetic exome and panel data, VarScan2 mostly called fals ...[more]

PMID: 28472276

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Project description:BACKGROUND:Cancer associated copy number variation (CNV) events provide important information for identifying patient subgroups and suggesting treatment strategies. Technical and logistical issues, however, make it challenging to accurately detect abnormal copy number events in a cost-effective manner in clinical studies. RESULTS:Here we present CNV Radar, a software tool that utilizes next-generation sequencing read depth information and variant allele frequency patterns, to infer the true copy number status of genes and genomic regions from whole exome sequencing data. Evaluation of CNV Radar in a public multiple myeloma dataset demonstrated that CNV Radar was able to detect a variety of CNVs associated with risk of progression, and we observed >?70% concordance with fluorescence in situ hybridization (FISH) results. Compared to other CNV callers, CNV Radar showed high sensitivity and specificity. Similar results were observed when comparing CNV Radar calls to single nucleotide polymorphism array results from acute myeloid leukemia and prostate cancer datasets available on TCGA. More importantly, CNV Radar demonstrated its utility in the clinical trial setting: in POLLUX and CASTOR, two phase 3 studies in patients with relapsed or refractory multiple myeloma, we observed a high concordance rate with FISH for del17p, a risk defining CNV event (88% in POLLUX and 90% in CASTOR), therefore allowing for efficacy assessments in clinically relevant disease subgroups. Our case studies also showed that CNV Radar is capable of detecting abnormalities such as copy-neutral loss of heterozygosity that elude other approaches. CONCLUSIONS:We demonstrated that CNV Radar is more sensitive than other CNV detection methods, accurately detects clinically important cytogenetic events, and allows for further interrogation of novel disease biology. Overall, CNV Radar exhibited high concordance with standard methods such as FISH, and its success in the POLLUX and CASTOR clinical trials demonstrated its potential utility for informing clinical and therapeutic decisions.

Dataset Information

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

Publications

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

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