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102 The deep phenotype characterization of ‘Off-the-Shelf’ CD19-chimeric antigen receptor (CAR) T cells allows to identify their subset complexity and to optimize their manufacturing
  1. Cristina Maccalli1,
  2. Asma Al-Sulaiti1,
  3. Mohammed El-Anbari1,
  4. Moza Al Khulaifi1,
  5. Mohammed Toufiq1,
  6. Rebecca Mathew1,
  7. Chiara Cugno1,
  8. Sara Deola1,
  9. Suruchi Mohan1,
  10. Damilola Olagunju1,
  11. Chiara Bonini3,
  12. Monica Casucci2,
  13. Sara Tomei1 and
  14. Damien Chaussabel1
  1. 1Sidra Medicine, Doha, Qatar
  2. 2San Raffaele Hospital, Milano, Italy


Background Umbilical cord blood (UCB) represents a promising source of T cells for the generation of ‘off-the-shelf’ T cells engineered to express a chimeric antigen receptor (CAR). This study is aimed at understanding the composition of T cell subsets within UCB-CAR-T cells.

Methods T cells, either from UCB or peripheral mononuclear cells (PBMCs) of healthy donors, were activated in vitro with CD3/CD28 mAbs either conjugated to magnetic beads (Dynabeads) or to a colloidal polymeric nanomatrix (TransAct; Miltenyi Biotec). T cells were then transduced with lentiviral vectors encoding for CD19-CD28z or CD19-4-1BBz CARs. The deep phenotype analyses of the CD19-CAR-T cells (N=32) was performed through a multidimensional flow cytometry to assess the expression/co-expression of T cell-associated markers (N=29). The NGFR was utilized as probe for the expression of CD19-CAR. To select the pertinent markers characterising the different groups, we applied a machine learning technique called L0-regularized logistic regression,1 2 and implemented in the R packageL0Learn. 5-fold cross-validation (CV) was used to select the optimal values of the tuning parameters. CD19-CAR-T cells have been also characterized for the transcriptomic profile by parallel quantitative PCR using the high throughput BioMark HD platform and for cytokines, perforin and granzyme B release upon the co-culture with CD19 expressing or not target cells.

Results T lymphocytes UCB showed efficient expression of the CARs (40–70% of positive cells). Different T cell subsets could discriminate the composition of T cells activated with either Beads or TranAct. CD4+NGFR+CD45RA+ or CD8+NGFR+CD45RA+ T cells associated with different combinations of CCR7, CD62L, LAG3, CD57, CD56 could discriminate between cells activated with Beads vs. TranAct (figures 2–3). CD8+NGFR+CD45RO+CD279−CD152+ T cells were also differentially expressed in TranAct vs. Beads. The PCA analyses also highlighted differences in terms of CD19-CAR-T cell subsets (such as CD8+NGFR+CD45RO+CD62L+, CD8+NGFR+CD45RO+CCR7+, CD8+NGFR+CD45RO+CD272+TIM−3+, CD8+NGFR+CD45RO+CD272+TIM−3+, CD8+NGFR+CD45RA+CD272+TIM−3− and CD4+NGFR+CD45RA+CD272−TIM−3+) in PBMCs vs. UCBs (figure 1). In addition, bystander T cells with different phenotype not expressing the CARs were also detected within the populations of T cells with different origins. Similarly, different T subsets were found in relationship with the sources of T cells. These CD19-CAR-T cells were also characterized for the anti-tumor activity and transcriptomic profiling.

Abstract 102 Figure 1

PCA of CAR-T cells from UCB vs. PBMCs

Abstract 102 Figure 2

PCA of CAR-T cells from UCB to compare TransAct vs. beads

Abstract 102 Figure 3

PCA of CD19-CAR-T cells to compare TransAct vs. Beads irrespective of the source of the T cells

Conclusions The combination of deep phenotype characterization with novel statistical tools allowed to identify the complexity of subsets in the engineered T cells in relationship with the starting material and the methods for the activation of the lymphocytes. These findings have important implications for the optimization of the manufacturing of CD19-CAR-T cells.


  1. Antoine Dedieu, Hussein Hazimeh, and Rahul Mazumder. Learningsparse classifiers: Continuous and mixed integer optimization perspectives. Journal of Machine Learning Research 2021.

  2. Hussein Hazimeh and Rahul Mazumder. Fast best subset selection: Coordinatedescent and local combinatorial optimization algorithms. Operations Research 2020;68(5):1517–1537.

Ethics Approval Sidra Medicine’s Ethics Board approval, #1812044429

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