CloneRetriever: An Automated Algorithm to Identify Clonal B and T Cell Gene Rearrangements by Next-Generation Sequencing for the Diagnosis of Lymphoid Malignancies.
BACKGROUND: Clonal immunoglobulin and T-cell receptor rearrangements serve as tumor-specific markers that have become mainstays of the diagnosis and monitoring of lymphoid malignancy. Next-generation sequencing (NGS) techniques targeting these loci have been successfully applied to lymphoblastic leu...
| Publicado en: | Clinical Chemistry Vol. 67; no. 11; pp. 1524 - 1534 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153642907&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153642907 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00099147 10CS jtl: Clinical Chemistry issn: 00099147 maglogo: N pubinfo: dt: Nov2021 vid: 67 iid: 11 pid: 622 pub: Oxford University Press / USA artinfo: ui: 153642907 10.1093/clinchem/hvab141 153642907 ppf: 1524 ppct: 10 formats: fmt: @attributes: type: P tig: atl: CloneRetriever: An Automated Algorithm to Identify Clonal B and T Cell Gene Rearrangements by Next-Generation Sequencing for the Diagnosis of Lymphoid Malignancies. aug: au: Halper-Stromberg, Eitan McCall, Chad M. Haley, Lisa M. Ming-Tseh Lin Vogt, Samantha Gocke, Christopher D. Eshleman, James R. Stevens, Wendy Martinson, Neil A. Epeldegui, Marta Holdhoff, Matthias Bettegowda, Chetan Glantz, Michael J. Ambinder, Richard F. Xian, Rena R. affil: Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD sug: ab: BACKGROUND: Clonal immunoglobulin and T-cell receptor rearrangements serve as tumor-specific markers that have become mainstays of the diagnosis and monitoring of lymphoid malignancy. Next-generation sequencing (NGS) techniques targeting these loci have been successfully applied to lymphoblastic leukemia and multiple myeloma for minimal residual disease detection. However, adoption of NGS for primary diagnosis remains limited. METHODS: We addressed the bioinformatics challenges associated with immune cell sequencing and clone detection by designing a novel web tool, CloneRetriever (CR), which uses machine-learning principles to generate clone classification schemes that are customizable, and can be applied to large datasets. CR has 2 applications-- a "validation" mode to derive a clonality classi- fier, and a "live" mode to screen for clones by applying a validated and/or customized classifier. In this study, CR-generated multiple classifiers using 2 datasets comprising 106 annotated patient samples. A custom classi- fier was then applied to 36 unannotated samples. RESULTS: The optimal classifier for clonality required clonal dominance ≥4.5x above background, read representation ≥8% of all reads, and technical replicate agreement. Depending on the dataset and analysis step, the optimal algorithm yielded sensitivities of 81%-90%, specificities of 97%-100%, areas under the curve of 91%-94%, positive predictive values of 92-100%, and negative predictive values of 88%-98%. Customization of the algorithms yielded 95%-100% concordance with gold-standard clonality determination, including rescue of indeterminate samples. Application to a set of unknowns showed concordance rates of 83%-96%. CONCLUSIONS: CR is an out-of-the-box ready and userfriendly software designed to identify clonal rearrangements in large NGS datasets for the diagnosis of lymphoid malignancies. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|