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.