CloneRetriever: An Automated Algorithm to Identify Clonal B and T Cell Gene Rearrangements by Next-Generation Sequencing for the Diagnosis of Lymphoid Malignancies

Author:

Halper-Stromberg Eitan1ORCID,McCall Chad M2,Haley Lisa M1ORCID,Lin Ming-Tseh1,Vogt Samantha3,Gocke Christopher D14,Eshleman James R14,Stevens Wendy5,Martinson Neil A36,Epeldegui Marta7,Holdhoff Matthias4,Bettegowda Chetan89,Glantz Michael J10,Ambinder Richard F4,Xian Rena R14ORCID

Affiliation:

1. Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD

2. Department of Pathology, Duke University School of Medicine, Durham, NC

3. Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD

4. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD

5. Department of Molecular Medicine and Haematology, University of the Witwatersrand, Johannesburg, South Africa

6. Perinatal HIV Research Unit (PHRU), University of the Witwatersrand, Johannesburg, South Africa

7. Department of Obstetrics and Gynecology, David Geffen School of Medicine at UCLA, Los Angeles, CA

8. Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD

9. Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, Baltimore, MD

10. Department of Neurosurgery, Medicine, and Neurology, Penn State Milton S. Hershey Medical Center, Hershey, PA

Abstract

Abstract 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.

Funder

U.S. Department of Health & Human Services | NIH | National Cancer Institute

Pfizer Inc

Publisher

Oxford University Press (OUP)

Subject

Biochemistry (medical),Clinical Biochemistry

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