Predictive biomarkers validation of CD3 cell apheresis yield in CAR-T manufacturing for diffuse large B-cell lymphoma: a machine learning approach.

Chimeric antigen receptor (CAR) T-cell therapy has shown significant success in treating diffuse large B-cell lymphoma (DLBCL). The initial step involves collecting autologous CD3 lymphocytes through apheresis, in which obtaining an adequate CD3 cell yield is essential for therapeutic efficacy. Despite prior research, the factors influencing CD3 cell apheresis remain poorly understood. Traditional statistical analyses offer limited insights, but machine learning (ML) approaches enable precision modeling of clinical predictors owing to their advanced pattern-recognition capabilities. In this study, we employed three ML algorithms, random forest classifier (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) to analyze a homogeneous cohort of 98 DLBCL patients who underwent mononuclear cell (MNC) apheresis. The LR model, which achieved an area under the curve (AUC) of 0.824, identified four key predictive features: CD3 cell absolute count, NK cell percentage, total blood volume, and CD3 cell percentage. Among these, NK cell percentage and CD3 cell absolute count showed the most significant negative impact on CD3 cell apheresis yield. This study underscores the potential of ML approaches as a complementary analytical approach for identifying key factors that impact CD3 cell apheresis efficiency, offering valuable insights for optimizing CAR-T therapy outcomes in patients with DLBCL.