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<p>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<sup>+</sup> lymphocytes through apheresis, in which obtaining an adequate CD3<sup>+</sup> cell yield is essential for therapeutic efficacy. Despite prior research, the factors influencing CD3<sup>+</sup> 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<sup>+</sup> cell absolute count, NK cell percentage, total blood volume, and CD3<sup>+</sup> cell percentage. Among these, NK cell percentage and CD3<sup>+</sup> cell absolute count showed the most significant negative impact on CD3<sup>+</sup> cell apheresis yield. This study underscores the potential of ML approaches as a complementary analytical approach for identifying key factors that impact CD3<sup>+</sup> cell apheresis efficiency, offering valuable insights for optimizing CAR-T therapy outcomes in patients with DLBCL.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Diego Carbonell,
  • Alejandra Rodríguez-Sosa,
  • Leticia Gómez-Serrano,
  • Ana Pérez-Corral,
  • Gloria Ruano,
  • Marian Galayo,
  • Nuria Panadero,
  • Laura Rufián,
  • Natalia Lorente,
  • Pedro Rodríguez-Barquero,
  • Diego Domingo-Merino,
  • Cristina Muñoz-Martínez,
  • Mi Kwon,
  • Sudipto Das,
  • Ramón García-Sanz,
  • Javier Anguita

摘要

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.