Immunotherapy is gaining traction as a viable therapeutic methodology for infectious disease, chronic malignancies, and autoimmune disorders. Although immunotherapeutics come in myriad forms, cellular immunotherapy is, and will be at the core of future disease treatment. Cellular immunotherapies rely on the selection, expansion, and growth of effector leukocytes, the particulars of which, are highly variable. Currently, numerous protocols and techniques are being evaluated for their ability to successfully manufacture large numbers of efficacious cells. However, there is currently no standard or validated method for evaluating a cell therapy product’s potential in vivo efficacy. Using a systems biology approach and access to clinical samples from adoptive T cell transfer trials, we are attempting to evaluate and predict the potential efficacy of a manufactured product. This will inform the optimisation and improvement of manufacturing methodologies. To achieve this, we assessed the expression
of key immune transcripts using a custom array chip analysed on the NanoString system, and assayed the expression of immune checkpoint inhibitors and functional markers by flow cytometry. Furthermore, we have employed multidimensional data analytics such as K-means clustering and t-distributed stochastic neighbour embedding (tSNE) for both flow and transcript data analysis. Additionally, the T cell receptor (TCR) repertoire was evaluated using next generation sequencing. This systems biology and bioinformatics approach allows for a deeper interrogation of cell therapy products population structure. In combination with clinical data, this methodology will allow the in vivo therapeutic potential of cell based immunotherapy products to be predicted.