Authors
Alexei Yumashev, Zumrad Ergasheva, Ozimbay Jabbarov, Abdulatif Sanakulov, Dilovar Karshiyeva, Dilorom Namozova, Zubaydillo Narziyev, Ranohon Gulomova, Zarina Khalbayeva
Published in
Transplantation and cellular therapy. Nov 12, 2026. Epub Nov 12, 2026.
Abstract
Engineering immune cells, particularly T lymphocytes, with chimeric antigen receptors (CARs) has become one of the most successful modalities of cancer immunotherapy, as evidenced by the approval and clinical use of multiple CAR-T cell products. Current CAR platforms predominantly rely on monoclonal antibody-derived single-chain variable fragments (scFvs) for target recognition. While scFv-based CARs have achieved remarkable clinical successes, they also exhibit notable limitations, including the potential to elicit anti-CAR immune responses, tonic signaling that contributes to CAR-T cell exhaustion, tumor escape via antigen loss or downregulation, and on-target, off-tumor toxicities. In response, researchers have explored alternative binding moieties for CAR architectures. Natural ligands and receptors represent one such class of alternatives. These approaches may offer advantages over conventional scFv-based CARs, such as potentially lower immunogenicity with fully human-derived domains, the ability to engage multiple targets or ligands, access to antigens that are challenging for scFvs, and the use of protein-protein interfaces shaped by evolution within human physiology. However, these benefits are not absolute. CAR junctions, non-native orientations, expression levels, modifications, and the cellular context can still generate immunogenic epitopes or dysfunctional products. Additionally, natural ligand/receptor pairs may transmit unwanted agonistic or regulatory signals that require careful engineering to mitigate. This review discusses recent advances in ligand/receptor-based CARs from a translational perspective. It also provides a critical overview of their inherent limitations, alongside potential solutions to support the rational design of next-generation constructs.
PMID:
43020332
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.
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