CELL INTELLIGENCE · 肿瘤细胞治疗研究

Despite promising outcomes in CAR T-cell therapy for relapsed/refractory multiple myeloma (RRMM), nearly all patients eventually relapse. Resistance and relapse may be driven by CAR T-cell and tumor-intrinsic factors. Here, we developed a mechanistic quantitative systems pharmacology (QSP) model of multiple myeloma growth and CAR T-cell therapy using measurable biomarkers to predict and identify factors associated with response and relapse. The model incorporates key components to explore disease dynamics and CAR T-cell expansion. Our model reproduced published pharmacokinetics and biomarker response data from anti-BCMA and anti-GPRC5D CAR T-cell therapies. We then validated the model using clinical biomarker data from a total of 29 real-world RRMM patients treated with commercial anti-BCMA CAR T. Virtual trial simulations, exploring the impact of variable baseline disease and CAR T characteristics on response, predicted that factors associated with worse outcomes are intrinsic to tumor cells (disease burden, low-antigen expression) and CAR T cells (low CAR T-induced killing rate). Interestingly, simulations suggested that a lower baseline percentage of normal plasma cells is associated with higher overall response. The developed model was also used to predict the outcome of BCMA-targeted and GPRC5D-targeted combination CAR T-cell treatment. Sequential combination therapy simulations predicted a better response in scenarios starting with anti-GPRC5D CAR T infusion, followed by anti-BCMA CAR T infusion. Our model can serve as a framework to investigate response mechanisms as well as multi-antigen targeting, and to optimize clinical trial design and dosing regimens.