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A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma.

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cam.issuedOnline2016-09-02
dc.contributor.authorBolli, N
dc.contributor.authorLi, Y
dc.contributor.authorSathiaseelan, V
dc.contributor.authorRaine, K
dc.contributor.authorJones, D
dc.contributor.authorGanly, P
dc.contributor.authorCocito, F
dc.contributor.authorBignell, G
dc.contributor.authorChapman, MA
dc.contributor.authorSperling, AS
dc.contributor.authorAnderson, KC
dc.contributor.authorAvet-Loiseau, H
dc.contributor.authorMinvielle, S
dc.contributor.authorCampbell, PJ
dc.contributor.authorMunshi, NC
dc.contributor.orcidBolli, N [0000-0002-1018-5139]
dc.date.accessioned2018-11-23T00:31:28Z
dc.date.available2018-11-23T00:31:28Z
dc.date.issued2016-09-02
dc.description.abstractGenomic lesions are not investigated during routine diagnostic workup for multiple myeloma (MM). Cytogenetic studies are performed to assess prognosis but with limited impact on therapeutic decisions. Recently, several recurrently mutated genes have been described, but their clinical value remains to be defined. Therefore, clinical-grade strategies to investigate the genomic landscape of myeloma samples are needed to integrate new and old prognostic markers. We developed a target-enrichment strategy followed by next-generation sequencing (NGS) to streamline simultaneous analysis of gene mutations, copy number changes and immunoglobulin heavy chain (IGH) translocations in MM in a high-throughput manner, and validated it in a panel of cell lines. We identified 548 likely oncogenic mutations in 182 genes. By integrating published data sets of NGS in MM, we retrieved a list of genes with significant relevance to myeloma and found that the mutational spectrum of primary samples and MM cell lines is partially overlapping. Gains and losses of chromosomes, chromosomal segments and gene loci were identified with accuracy comparable to conventional arrays, allowing identification of lesions with known prognostic significance. Furthermore, we identified IGH translocations with high positive and negative predictive value. Our approach could allow the identification of novel biomarkers with clinical relevance in myeloma.
dc.format.mediumElectronic
dc.identifier.doi10.17863/CAM.33125
dc.identifier.eissn2044-5385
dc.identifier.issn2044-5385
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285781
dc.languageeng
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.publisher.urlhttp://dx.doi.org/10.1038/bcj.2016.72
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAlleles
dc.subjectCell Line, Tumor
dc.subjectDNA Copy Number Variations
dc.subjectGene Frequency
dc.subjectGene Rearrangement, B-Lymphocyte, Heavy Chain
dc.subjectHigh-Throughput Nucleotide Sequencing
dc.subjectHumans
dc.subjectImmunoglobulin Heavy Chains
dc.subjectLoss of Heterozygosity
dc.subjectMultiple Myeloma
dc.subjectMutation
dc.subjectReproducibility of Results
dc.subjectTranslocation, Genetic
dc.titleA DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma.
dc.typeArticle
dcterms.dateAccepted2016-06-16
prism.issueIdentifier9
prism.publicationDate2016
prism.publicationNameBlood Cancer J
prism.startingPagee467
prism.volume6
rioxxterms.licenseref.startdate2016-09-02
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1038/bcj.2016.72

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