Dataset Information

Consistent copy number changes and recurrent PRKAR1A mutations distinguish Melanotic Schwannomas from Melanomas: SNP-array and next generation sequencing analysis.

ABSTRACT: Melanotic Schwannomas (MS) are rare tumors that share histological features with melanocytic tumors and schwannomas. However, their genetics are poorly understood. To elucidate the genetic characteristics of MS, we performed genome-wide studies in a series of cases. Twelve MS cases were available for the study. Genomic DNAs extracted from formalin-fixed paraffin embedded tumor tissues were subjected to copy number (CN) and allelic imbalance (AI) analysis by Single Nucleotide Polymorphism (SNP)-array and screened for mutations in coding exons of 341 key cancer-associated genes using a hybrid capture-based next-generation sequencing (NGS) assay. Sanger sequencing was used to further verify recurrent mutations detected by NGS study. SNP-array analysis revealed remarkably stereotypic chromosomal abnormalities in MS. Hypodiploidy was common, typically involving monosomies of chromosomes 1, 2, and 17. All 12 samples showed mutations in PRKAR1A gene, including 2 cases with 2 mutations each. The 14 mutations were scattered across PRKAR1A, and most were inactivating mutations. AI on 17q, presenting as loss of heterozygosity with or without CN losses, combined with a PRKAR1A mutation was observed in 9/12 MS cases. The remaining 3 cases included the two samples harboring two mutations in PRKAR1A. MS exhibits a stereotypic pattern of chromosomal losses. In contrast, melanomas are typically characterized by the presence of multiple CN aberrations, without demonstrable differences in the frequency of losses and gains. Inactivation of both alleles of PRKAR1A by "two hits" observed in almost all cases underscores the central role of PRKAR1A in the pathogenesis of this neoplasm. © 2015 Wiley Periodicals, Inc.

SUBMITTER: Wang L

PROVIDER: S-EPMC6446921 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Consistent copy number changes and recurrent PRKAR1A mutations distinguish Melanotic Schwannomas from Melanomas: SNP-array and next generation sequencing analysis.

Wang Lu L Zehir Ahmet A Sadowska Justyna J Zhou Nengyi N Rosenblum Marc M Busam Klaus K Agaram Narasimhan N Travis William W Arcila Maria M Dogan Snjezana S Berger Michael F MF Cheng Donavan T DT Ladanyi Marc M Nafa Khedoudja K Hameed Meera M

Genes, chromosomes & cancer 20150529 8

Melanotic Schwannomas (MS) are rare tumors that share histological features with melanocytic tumors and schwannomas. However, their genetics are poorly understood. To elucidate the genetic characteristics of MS, we performed genome-wide studies in a series of cases. Twelve MS cases were available for the study. Genomic DNAs extracted from formalin-fixed paraffin embedded tumor tissues were subjected to copy number (CN) and allelic imbalance (AI) analysis by Single Nucleotide Polymorphism (SNP)-a ...[more]

PMID: 26031761

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Project description:BackgroundCopy Number Variations (CNVs) are usually inferred from Single Nucleotide Polymorphism (SNP) arrays by use of some software packages based on given algorithms. However, there is no clear understanding of the performance of these software packages; it is therefore difficult to select one or several software packages for CNV detection based on the SNP array platform.We selected four publicly available software packages designed for CNV calling from an Affymetrix SNP array, including Birdsuite, dChip, Genotyping Console (GTC) and PennCNV. The publicly available dataset generated by Array-based Comparative Genomic Hybridization (CGH), with a resolution of 24 million probes per sample, was considered to be the "gold standard". Compared with the CGH-based dataset, the success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the "gold standard". Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package.ResultsSimply from the quantity of the detected CNVs, Birdsuite detected the most while GTC detected the least. We found that Birdsuite and dChip had obvious detecting bias. And GTC seemed to be inferior because of the least amount of CNVs it detected. Thereafter we investigated the detection consistency produced by one certain software package and the rest three software suits. We found that the consistency of dChip was the lowest while GTC was the highest. Compared with the CNVs detecting result of CGH, in the matching group, GTC called the most matching CNVs, PennCNV-Affy ranked second. In the non-overlapping group, GTC called the least CNVs. With regards to the reproducibility of CNV calling, larger CNVs were usually replicated better. PennCNV-Affy shows the best consistency while Birdsuite shows the poorest.ConclusionWe found that PennCNV outperformed the other three packages in the sensitivity and specificity of CNV calling. Obviously, each calling method had its own limitations and advantages for different data analysis. Therefore, the optimized calling methods might be identified using multiple algorithms to evaluate the concordance and discordance of SNP array-based CNV calling.

Project description:BACKGROUND: In recent years, genome-wide association studies have successfully uncovered single-nucleotide polymorphisms (SNPs) associated with complex traits such as diseases and quantitative phenotypes. These variations account for a small proportion of heritability. With the development of high throughput techniques, abundant submicroscopic structural variations have been found in organisms, of which the main variations are copy number variations (CNVs). Therefore, CNVs are increasingly recognized as an important and abundant source of genetic variation and phenotypic diversity. RESULTS: Analyses of CNVs in the genomes of three sheep breeds were performed using the Ovine SNP50 BeadChip array. A total of 238 CNV regions (CNVRs) were identified, including 219 losses, 13 gains, and six with both events (losses and gains), which cover 60.35 Mb of the sheep genomic sequence and correspond to 2.27% of the autosomal genome sequence. The length of the CNVRs on autosomes range from 13.66 kb to 1.30 Mb with a mean size of 253.57 kb, and 75 CNVRs events had a frequency > 3%. Among these CNVRs, 47 CNVRs identified by the PennCNV overlapped with the CNVpartition. Functional analysis indicated that most genes in the CNVRs were significantly enriched for involvement in the environmental response. Furthermore, 10 CNVRs were selected for validation and 6 CNVRs were further experimentally confirmed by qPCR. In addition, there were 57 CNVRs overlapped in our new dataset and other published ruminant CNV studies. CONCLUSIONS: In this study, we firstly constructed a sheep CNV map based on the Ovine SNP50 array. Our results demonstrated the differences of two detection tools and integration of multiple algorithms can enhance the detection of sheep genomic structure variations. Furthermore, our findings would be of help for understanding the sheep genome and provide preliminary foundation for carrying out the CNVs association studies with economically important phenotypes of sheep in the future.

Project description:BackgroundCopy number data are routinely being extracted from genome-wide association study chips using a variety of software. We empirically evaluated and compared four freely-available software packages designed for Affymetrix SNP chips to estimate copy number: Affymetrix Power Tools (APT), Aroma.Affymetrix, PennCNV and CRLMM. Our evaluation used 1,418 GENOA samples that were genotyped on the Affymetrix Genome-Wide Human SNP Array 6.0. We compared bias and variance in the locus-level copy number data, the concordance amongst regions of copy number gains/deletions and the false-positive rate amongst deleted segments.ResultsAPT had median locus-level copy numbers closest to a value of two, whereas PennCNV and Aroma.Affymetrix had the smallest variability associated with the median copy number. Of those evaluated, only PennCNV provides copy number specific quality-control metrics and identified 136 poor CNV samples. Regions of copy number variation (CNV) were detected using the hidden Markov models provided within PennCNV and CRLMM/VanillaIce. PennCNV detected more CNVs than CRLMM/VanillaIce; the median number of CNVs detected per sample was 39 and 30, respectively. PennCNV detected most of the regions that CRLMM/VanillaIce did as well as additional CNV regions. The median concordance between PennCNV and CRLMM/VanillaIce was 47.9% for duplications and 51.5% for deletions. The estimated false-positive rate associated with deletions was similar for PennCNV and CRLMM/VanillaIce.ConclusionsIf the objective is to perform statistical tests on the locus-level copy number data, our empirical results suggest that PennCNV or Aroma.Affymetrix is optimal. If the objective is to perform statistical tests on the summarized segmented data then PennCNV would be preferred over CRLMM/VanillaIce. Specifically, PennCNV allows the analyst to estimate locus-level copy number, perform segmentation and evaluate CNV-specific quality-control metrics within a single software package. PennCNV has relatively small bias, small variability and detects more regions while maintaining a similar estimated false-positive rate as CRLMM/VanillaIce. More generally, we advocate that software developers need to provide guidance with respect to evaluating and choosing optimal settings in order to obtain optimal results for an individual dataset. Until such guidance exists, we recommend trying multiple algorithms, evaluating concordance/discordance and subsequently consider the union of regions for downstream association tests.

Dataset Information

Consistent copy number changes and recurrent PRKAR1A mutations distinguish Melanotic Schwannomas from Melanomas: SNP-array and next generation sequencing analysis.

Publications

Consistent copy number changes and recurrent PRKAR1A mutations distinguish Melanotic Schwannomas from Melanomas: SNP-array and next generation sequencing analysis.

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