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

Area of Science:

• Immunology
• Molecular Biology
• Computational Biology

Background:

• B and T cells undergo V-J recombination to generate antigen receptors.
• Over-representation of a specific V-J rearrangement indicates clonality, crucial for diagnosing lymphoproliferative disorders.

Purpose of the Study:

• To develop objective, automated criteria for classifying B and T cell clonality results.
• To enhance the sensitivity and accuracy of clonality detection using next-generation sequencing (NGS).

Main Methods:

• Developed an NGS-based amplicon clonality assay using clinical samples with established clonality data.
• Created a computational pipeline for automated clonality calling based on a novel reporting model.
• Compared the performance of the developed model against published NGS clonality models.

Main Results:

• The developed NGS model demonstrated increased sensitivity and accuracy in detecting clonality compared to existing methods.
• The automated computational pipeline enables objective and efficient clonality classification.
• The findings suggest improved diagnostic capabilities for lymphoproliferative disorders.

Conclusions:

• The novel NGS-based model and automated pipeline offer a more sensitive and accurate approach to B and T cell clonality assessment.
• This approach has the potential to expedite clinical review and reporting of clonality, aiding in disease management.
• The study provides a robust tool for objective clonality classification in clinical settings.