Oncotarget

Research Papers:

Development and implementation of an automated and highly accurate reporting process for NGS-based clonality testing

Sean T. Glenn _, Phillip M. Galbo Jr., Jesse D. Luce, Kiersten Marie Miles, Prashant K. Singh, Manuel J. Glynias and Carl Morrison

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Oncotarget. 2023; 14:450-461. https://doi.org/10.18632/oncotarget.28429

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Abstract

Sean T. Glenn1, Phillip M. Galbo Jr.1, Jesse D. Luce2, Kiersten Marie Miles1, Prashant K. Singh2, Manuel J. Glynias1 and Carl Morrison1

1 Department of Pathology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA

2 Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA

Correspondence to:

Sean T. Glenn, email: [email protected]

Keywords: clonality; NGS; bioinformatics; molecular diagnostics; leukemia

Received: January 27, 2023     Accepted: May 05, 2023     Published: May 12, 2023

Copyright: © 2023 Glenn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ABSTRACT

B and T cells undergo random recombination of the VH/DH/JH portions of the immunoglobulin loci (B cell) and T-cell receptors before becoming functional cells. When one V-J rearrangement is over-represented in a population of B or T cells indicating an origin from a single cell, this indicates a clonal process. Clonality aids in the diagnosis and monitoring of lymphoproliferative disorders and evaluation of disease recurrence. This study aimed to develop objective criteria, which can be automated, to classify B and T cell clonality results as positive (clonal), No evidence of clonality, or invalid (failed). Using clinical samples with “gold standard” clonality data obtained using PCR/CE testing, we ran NGS-based amplicon clonality assays and developed our own model for clonality reporting. To assess the performance of our model, we analyzed the NGS results across other published models. Our model for clonality calling using NGS-based technology increases the assay’s sensitivity, more accurately detecting clonality. In addition, we have built a computational pipeline to use our model to objectively call clonality in an automated fashion. Collectively the results outlined below will have a direct clinical impact by expediting the review and sign-out process for concise clonality reporting.


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