Anthony Nolan Cell Therapy Centre, Nottingham, UK.
The Anthony Nolan Research Institute, Nottingham, UK.
Cytotherapy. 2021 Jul;23(7):641-646. doi: 10.1016/j.jcyt.2021.01.002. Epub 2021 Mar 15.
Total nucleated cell (TNC) and CD34+ cell doses are considered among the most important parameters when assessing the suitability of a human leukocyte antigen-matched cord blood unit (CBU) for allogeneic hematopoietic stem cell transplantation (HSCT). Cord blood banks therefore frequently select CBUs for cryopreservation based on pre-process TNC content. However, cell loss during processing can lead to a significant quantity of CBUs that do not meet desired post-process quality criteria, and such grafts are less likely to be selected by transplant centers for HSCT. Here the authors present a multi-parameter linear regression (MLR) model capable of identifying CBUs that would process poorly, despite meeting established pre-process TNC and CD34+ quality thresholds.
Historically processed CBUs were graded from A+ to D depending on post-process cell content, and the utilization rate of each grade category was examined. Eight pre-process predictors of post-process cell content were used to train the MLR model, including red blood cell (RBC) content; CBU volume; age of CBU when received; and TNC constituent cell subsets. The selection efficacy of this model was then compared to that of methods conventionally used to select CBUs for processing, with receiver operating characteristic (ROC) and mean inventory quality analysis forming the basis of assessment.
Within the Anthony Nolan Cell Therapy Centre, CBUs graded 'D' accounted for 37% of processing expenditures despite providing only 11% of grafts shipped for HSCT. The MLR model significantly improved pre-process identification of 'D' grade CBUs relative to thresholds based primarily on CD34+ cell content (P < 0.0001) and TNC content (P < 0.0001). At a comparable financial investment, this translated to a banked graft inventory of significantly higher quality than that produced by CD34+ (+8.8% mean increase, P = 0.007) and TNC (+9.9% mean increase, P = 0.010) selection methods.
A predictive modelling approach to pre-process CBU selection is a simple and effective means to increase graft inventory quality and potentially future graft utilization, at no additional financial investment.
在评估人类白细胞抗原匹配的脐血单位（CBU）是否适合异基因造血干细胞移植（HSCT）时，总核细胞（TNC）和 CD34+细胞剂量被认为是最重要的参数之一。因此，脐血库经常根据预处理 TNC 含量选择用于冷冻保存的 CBU。然而，处理过程中的细胞损失会导致大量不符合所需的后处理质量标准的 CBU，并且这些移植物不太可能被移植中心选择用于 HSCT。在这里，作者提出了一种多参数线性回归（MLR）模型，能够识别尽管符合既定的预处理 TNC 和 CD34+质量阈值，但处理效果不佳的 CBU。
根据后处理细胞含量，对历史上处理过的 CBU 进行 A+至 D 级评分，并检查每个等级类别的利用率。使用 8 个预处理后处理细胞含量预测因子来训练 MLR 模型，包括红细胞（RBC）含量；CBU 体积；收到时 CBU 的年龄；以及 TNC 组成细胞亚群。然后将该模型的选择效果与传统用于选择用于处理的 CBU 的方法进行比较，以接收者操作特征（ROC）和平均库存质量分析为评估基础。
在 Anthony Nolan 细胞治疗中心，尽管“D”级 CBU 仅提供了用于 HSCT 的移植物的 11%，但其处理支出却占了 37%。与主要基于 CD34+细胞含量（P < 0.0001）和 TNC 含量（P < 0.0001）的阈值相比，MLR 模型显著提高了对“D”级 CBU 的预处理识别能力。在可比的财务投资下，这转化为库存质量显著高于 CD34+（平均增加 8.8%，P = 0.007）和 TNC（平均增加 9.9%，P = 0.010）选择方法的移植物库存。
一种预处理 CBU 选择的预测建模方法是一种简单有效的方法，可以在不增加额外财务投资的情况下提高移植物库存质量并可能增加未来的移植物利用率。
