CloneRetriever: An Automated Algorithm to Identify Clonal B and T Cell Gene Rearrangements by Next-Generation Sequencing for the Diagnosis of Lymphoid Malignancies
| العنوان: | CloneRetriever: An Automated Algorithm to Identify Clonal B and T Cell Gene Rearrangements by Next-Generation Sequencing for the Diagnosis of Lymphoid Malignancies |
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| المؤلفون: | Eitan Halper-Stromberg, James R. Eshleman, Michael Glantz, Neil A. Martinson, Ming-Tseh Lin, Wendy Stevens, Chetan Bettegowda, Marta Epeldegui, Richard F. Ambinder, Lisa Haley, Christopher D. Gocke, Chad M. McCall, Rena R. Xian, Matthias Holdhoff, Samantha L. Vogt |
| المصدر: | Clin Chem Clinical chemistry, vol 67, iss 11 |
| بيانات النشر: | Oxford University Press, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Neoplasm, Residual, Concordance, lymphoma diagnostics, Clinical Biochemistry, Medical Biotechnology, Clinical Sciences, Bioengineering, Computational biology, Biology, Medical Biochemistry and Metabolomics, Gene Rearrangement, T-Lymphocyte, DNA sequencing, Rare Diseases, Genetics, Humans, General Clinical Medicine, Cancer, Gene Rearrangement, screening and diagnosis, Biochemistry (medical), Human Genome, High-Throughput Nucleotide Sequencing, Replicate, bioinformatics, Hematology, Articles, Minimal residual disease, 4.1 Discovery and preclinical testing of markers and technologies, Detection, T-Lymphocyte, immunoglobulin sequencing, Networking and Information Technology R&D (NITRD), Automated algorithm, Residual, T-Cell Gene Rearrangements, Neoplasm, Clone (B-cell biology), Classifier (UML), Algorithms |
| الوصف: | 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 classifier, 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 classifier was then applied to 36 unannotated samples. Results The optimal classifier for clonality required clonal dominance ≥4.5× 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 user-friendly software designed to identify clonal rearrangements in large NGS datasets for the diagnosis of lymphoid malignancies. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0618fe5ce397db6bac1f0dbc79388b92Test https://europepmc.org/articles/PMC8965457Test/ |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....0618fe5ce397db6bac1f0dbc79388b92 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |