Machine learning reveals distinct T-cell receptor clusters in plasma cell dyscrasias compared to healthy controls - David G. Coffey; Yong Zhang; Elizabeth Hill; Frank Cross; Reena Philip; Marc R. Theoret; Ola Landgren; Andrea C. Baines; Dickran Kazandjian (PLoS ONE, 2025)
T-cell receptor (TCR) repertoire diversity has been implicated in the progression and prognosis of multiple myeloma (MM). This study aimed to evaluate the association between T-cell clonality, immune response, and clinical outcomes in patients with plasma cell dyscrasias using next-generation sequencing of the TCR β chain (TCRB). TCRB sequencing was performed on peripheral blood samples from patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and newly diagnosed multiple myeloma (MM). Healthy individuals served as controls. No significant differences in TCR repertoire diversity were observed between healthy individuals and those with MGUS, SMM or MM after adjusting for age. Furthermore, TCR diversity did not correlate with treatment response in newly diagnosed MM or SMM patients. However, machine learning analysis revealed distinct TCR clusters differentially abundant between healthy individuals and those with plasma cell dyscrasias, exhibiting different amino acid properties. These findings suggest that shared T-cell receptor specificities among patients with plasma cell dyscrasias reflect underlying differences in antigen recognition and underscore the need for further studies to unravel the functional and clinical significance of these distinct immune signatures.