A computational model of CAR T-cell immunotherapy predicts leukemia patient responses at remission, resistance, and relapse
Year: 2021
Type: preprint
Abstract: Adaptive CD19-targeted CAR (Chimeric Antigen Receptor) T-cell transfer has become a promising treatment for leukemia. Though patient responses vary across different clinical trials, there currently lacks reliable early diagnostic methods to predict patient responses to those novel therapies. Recently, computational models achieve to in silico depict patient responses, with prediction application being limited. We herein established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment based on a set of clinical data from different CAR T-cell trials, and revealed critical determinants related to patient responses at remission, resistance, and relapse. Furthermore, we performed a clinical trial simulation using virtual patient cohorts generated based on real clinical patient dataset. With input of early-stage CAR T-cell dynamics, our model successfully predicted late responses of various virtual patients compared to clinical observance. In conclusion, our patient-based computational immuno-oncology model may inform clinical treatment and management.
Source: medRxiv
Authors: Lunan Liu, Chao Ma, Zhuoyu Zhang, Weqiang Chen
Language: English
Topic: CAR-T cell therapy research
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3201281223