一种用于个体化自体 T 细胞采集以制备 CAR-T 细胞的新型预测算法

This retrospective review of 262 T-cell harvests, with a control cohort of healthy donors, analyzed the parameters impacting CD3+ T-cell yield in adults with B-cell malignancies. The overall aim was to design a novel predictive algorithm to guide the required processed blood volume (PBV) (L) on the apheresis machine to achieve a specific CD3+ target yield.

Factors associated with CD3+ T-cell yield on multivariate analysis included peripheral blood CD3+ count (natural log, 10 9 /L), hematocrit (HCT) and PBV with coefficients of 0.86 (95% confidence interval [CI], 0.80-0.92, P < 0.001), 1.30 (95% CI, 0.51-2.08, P = 0.001) and 0.09 (95% CI, 0.07-0.11, P < 0.001), respectively. The authors' model, incorporating CD3+ cell count, HCT and PBV (L), with an adjusted R 2 of 0.87 and root-mean-square error of 0.26 in the training dataset, was highly predictive of CD3+ cell yield in the testing dataset. An online application to estimate PBV using this algorithm can be accessed at https://cd3yield.shinyapps.io/cd3yield/.

The authors propose a transferrable model that incorporates clinical and laboratory variables accessible pre-harvest for use across the field of T-cell therapy. Pending further validation, such a model may be used to generate an individual leukapheresis plan and streamline the process of cell harvest, a well-recognized bottleneck in the industry.