Speaker
Description
Introduction:
Multiple myeloma (MM) is managed with complex multi-agent immunotherapy, including
daratumumab (dara), a CD38 antibody, and BCMA-targeted CAR-T cell therapy for
relapse. As the treatment landscape for MM is broad, it is important to identify
prognostic biomarkers to help personalize treatment for individuals. To investigate
immune interactions and response to immunotherapy, we used sparse identification of
nonlinear dynamics (SINDy).
Methods:
Patient’s immune populations were measured longitudinally via cytometry in two City of
Hope trials: (1) dara maintenance post-stem cell transplant and (2) BCMA-targeted
CAR-T therapy. Cell type abundances were input into SINDy to identify patient-specific
interaction dynamics.
Results:
Under dara maintenance therapy, SINDy discovered multiple 1st order interaction terms
between immune populations which distinguished responders from non-responders,
including NK cell-mediated stimulation of CD4+ T cells (AUC=0.90), and CD8+ T-cell
mediated stimulation of NK cells (AUC=0.88). Further, the dynamic stability of the
identified system predicted response (AUC=0.89). In BCMA CAR-T treatment, CAR-T
cells contributed towards dynamic system stability. Interactions with CD8+ T-cells
(Spearman R=0.87, p<0.001) and B-cells (Spearman R=0.94, p<0.0001) were strongly
correlated with the individual’s immune system stability.
Conclusion:
SINDy reveals interpretable immune interaction structures that separate treatment
responders from non-responders and characterize system-level stability, offering a data-
driven framework for personalizing immunotherapy in multiple myeloma.
