Development of an induced pluripotent stem cell-specific microRNA assay for detection of residual undifferentiated cells in natural killer cell therapy products

Most clinically evaluated chimeric antigen receptor (CAR)-based cell therapies are generated from autologous immune cells. However, there are several limitations to autologous cell therapy, including low availability, poor quality of starting cellular material and limited expansion capability. Recently, induced pluripotent stem cell (iPSC)-derived allogeneic cell therapy platforms have gained popularity, as they seek to overcome many of the challenges inherent to current autologous cell therapies. However, teratoma risk associated with residual undifferentiated cells (i.e., iPSCs) in the drug product may restrict potential clinical applications if left unaddressed. To ensure the safety of the final cell therapy product, there is a need to develop quality control assays to detect residual iPSCs. Combining microRNA (miRNA) sequencing data with publicly archived miRNA microarray datasets, we demonstrated that miRNAs belonging to the 300 family (miR-302a5p, miR-302c-3p and miR-302d-5p) and 500 family (miR-518f-5p and miR-519-3p) were highly expressed in iPSCs (both periperal blood mononuclear cell- and T cell-derived iPSCs) compared with a number of differentiated cell types. We developed and validated a sensitive digital droplet polymerase chain reaction (ddPCR) assay targeting these miRNAs to detect low levels of residual iPSCs in differentiated cell samples. The miRNA ddPCR-based method with primers for miR-302a-5p, miR-302c-3p and miR-302d-5p detected as few as 5, 3 and 10 undifferentiated iPSCs, respectively, in the background of 106 iPSC-derived natural killer (iNK) cells. These results suggest that our method targeting identified iPSC-specific miRNA transcripts is specific and sensitive for the quality assessment of NK cell therapy products derived from iPSCs. (c) 2022 Published by Elsevier Inc. on behalf of International Society for Cell & Gene Therapy.