Bioinformatic avenues to improve the specificity of CAR immune therapies

Abstract

Chimeric Antigen Receptor (CAR) T-cell therapy has proven to be a successful alternative to more traditional treatment methods for patients with B-cell malignancies. This success has not yet been repeated in other cancer types (especially solid tumors) which requires further development of CAR technology. Several aspects of CAR development can be aided with bioinformatics methods, and potentially new in silico methods need to be developed alongside the improvements of CAR-based immune therapies. We examine various proposed adapta,ons of CAR-T cell therapy, and the role bioinforma,cs plays in this. The use of nanobody-based targeting domains, using Natural Killer (NK) cells instead of T-cells, improved downstream signaling and a combinatoric approach to targeting domains seem to be useful improvements to CAR T-cell therapy that will help to improve the next generation of immune therapy. For these to succeed, we need to adapt existing bioinformatics methods to include the combined molecular modeling for several antigen-antibody pairs and a good estimate of the downstream response of CAR cells. Overall, the improvements to CAR-based immune therapy give reason to be optimistic about future treatment options.