Dataset Information

Whole genome amplification with SurePlex results in better copy number alteration detection using sequencing data compared to the MALBAC method.

ABSTRACT: Current whole genome amplification (WGA) methods lead to amplification bias resulting in over- and under-represented regions in the genome. Nevertheless, certain WGA methods, such as SurePlex and subsequent arrayCGH analysis, make it possible to detect copy number alterations (CNAs) at a 10?Mb resolution. A more uniform WGA combined with massive parallel sequencing (MPS), however, could allow detection at higher resolution and lower cost. Recently, MALBAC, a new WGA method, claims unparalleled performance. Here, we compared the well-established SurePlex and MALBAC WGA for their ability to detect CNAs in MPS generated data and, in addition, compared PCR-free MPS library preparation with the standard enrichment PCR library preparation. Results showed that SurePlex amplification led to more uniformity across the genome, allowing for a better CNA detection with less false positives compared to MALBAC amplified samples. An even more uniform coverage was observed in samples following a PCR-free library preparation. In general, the combination of SurePlex and MPS led to the same chromosomal profile compared to a reference arrayCGH from unamplified genomic DNA, underlining the large potential of MPS techniques in CNA detection from a limited number of DNA material.

SUBMITTER: Deleye L

PROVIDER: S-EPMC4485032 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Whole genome amplification with SurePlex results in better copy number alteration detection using sequencing data compared to the MALBAC method.

Deleye Lieselot L De Coninck Dieter D Christodoulou Christodoulos C Sante Tom T Dheedene Annelies A Heindryckx Björn B Van den Abbeel Etienne E De Sutter Petra P Menten Björn B Deforce Dieter D Van Nieuwerburgh Filip F

Scientific reports 20150630

Current whole genome amplification (WGA) methods lead to amplification bias resulting in over- and under-represented regions in the genome. Nevertheless, certain WGA methods, such as SurePlex and subsequent arrayCGH analysis, make it possible to detect copy number alterations (CNAs) at a 10 Mb resolution. A more uniform WGA combined with massive parallel sequencing (MPS), however, could allow detection at higher resolution and lower cost. Recently, MALBAC, a new WGA method, claims unparalleled p ...[more]

PMID: 26122179

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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

Whole genome amplification with SurePlex results in better copy number alteration detection using sequencing data compared to the MALBAC method.

Publications

Whole genome amplification with SurePlex results in better copy number alteration detection using sequencing data compared to the MALBAC method.

Similar Datasets

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