Improving the sensitivity of therapeutic receptors for cancer therapy
Improving the sensitivity of therapeutic receptors for cancer therapy
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Published in PNAS, a new paper by the Dushek GROUP SHOWS how the effectiveness of THERAPEUTIC chimeric antigen receptors might be improved for cancer treatment
T-cells are a type of white blood cell that play an essential role in the adaptive immune response. They can recognise ‘foreign’ pathogen-derived antigens via their T-cell receptors (TCRs). T-cells can also be genetically engineered to recognise antigens on cancer cells using chimeric antigen receptors (CARs). This approach is now clinically approved to treat B cell cancers. However, patients relapse when cancer cells emerge with lower levels of the antigen. Unlike the highly sensitive TCR, CARs require 100-fold more antigen to activate T cells. There is now an urgent need to improve this promising therapeutic.
In addition to the TCR, other ‘accessory’ receptors (such as CD2 and LFA-1) on the T cell surface are known to control antigen sensitivity by strengthening the adhesion between the T cell and the cancer cell. Burton et al systematically investigated the ability of the TCR and various CARs to exploit accessory receptors. In the absence of accessory receptors, CARs and TCRs exhibit similar antigen sensitivities, suggesting that the presence of accessory receptors could explain the observed differences in antigen sensitivity. By systematically testing different accessory receptors, Burton et al. identify that CARs fail to efficiently exploit the accessory receptors CD2 and LFA-1 relative to the TCR. Together, these data highlight a promising new strategy that could be used to improve the antigen sensitivity of CAR-T cells for a wide range of cancers.
Jake Burton, Jesús A. Siller-Farfán, Johannes Pettmann, Benjamin Salzer, Mikhail Kutuzov, P. Anton van der Merwe, and Omer Dushek (2023) Inefficient exploitation of accessory receptors reduces the sensitivity of chimeric antigen receptors, PNAS
Written by Isabella Maudlin (Murphy lab) @BellaMaudlin