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411 The multi-omics analyses of “off-the-shelf” CD19-CAR-T cells identifies the subpopulation complexity and the deep characterization of the anti-tumor properties
  1. Asma Al Sulaiti1,
  2. Mohammed El-Anbari1,
  3. Shana Jacob1,
  4. Toufiq Mohamed1,
  5. Saroja Kotegar Balayya1,
  6. Suruchi Mohan1,
  7. Damilola Olagunju1,
  8. Chiara Cugno1,
  9. Sara Deola1,
  10. Jean-Charles Grivel1,
  11. Damien Chaussabel1,
  12. Chiara Bonini2,
  13. Monica Casucci2 and
  14. Cristina Maccalli1
  1. 1Sidra Medicine, Doha, Qatar
  2. 2San Raffaele Hospital, Milano, Italy


Background The goal of this study is to generate and optimize the manufacturing of "off-the-shelf" CD19-CAR-T cells utilizing umbilical cord blood (UCB) as starting material.

Methods T cells were isolated from UBCs (N=15) through negative magnetic selection and upon activation in vitro using CD3 and CD28 mAbs. The T lymphocytes were then transduced with lentiviral vectors encoding for CD19-CD28z-or CD19-4-1BBz-CARs. CD19-CAR-T cells were also generated from the peripheral blood lymphocytes (PBL; N=5) as reference. The multiparametric phenotype analysis was performed assessing the expression of markers associated with T cell differentiation and activation by CAR-T cells. Functional assays, through either Elispot, FluoroSpot or Luminex platforms, were carried out to assess cytokine, chemokine and cytotoxic profiles of the T cells. A machine learning technique called L0-regularized logistic regression,1 2 implemented in the R packageL0Learn. was used to select the optimal values of the tuning parameters. The metabolomic and transcriptomic profiles of CD19-CAR-T cells was determined upon the antigen-specific or not engagement of the CARs.

Results The enrichment of both CD4+ and CD8+ CD19-CAR-T cells with stem memory-like or early stage of differentiation (CD45RA+) phenotype and co-expressing either ICOS or BTLA was observed in UCB- vs. PBL-derived CD19-CAR-T cells (p<0.0002-<0.05). Moreover, differential phenotype of CAR-T cells was associated with the variable costimulatory signals comprised in the structure of the CARs (CD28z or 4-1BBz). The differential antigen-specific anti-tumor activity of these CAR-T cells was identified, with diversities depending on the type and source of engineered T cells. Distinct metabolomic profiles, including pathways related to amino acid, tryptophan and nucleotide sugar metabolism and protein biosynthesis were detected in relation to the antigen-specific or unrelated stimulation and the manufacturing procedures. Integration of multi-omics results, including the transcriptomic profile allowed to identify the complexities of CD19-CAR-T cells phenotypes and functions.

Conclusions The characterization of phenotype and functional properties of CAR-T cells through multi-omics platforms allowed to prove the suitability of UCB to generate "off-the-shelf" CAR-T cells and to identify sub-populations endowed with superior anti-tumor activity.


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

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

Ethics Approval The study obtained ethics approval from Sidra Medicine review board; approval #1500788. Participants to the study gave informed consent before taking part.

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