CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题：Deep Learning-based Modeling Enhances Efficacy of Natural Ligand CAR Binders Targeting CD70.
Deep Learning-based Modeling Enhances Efficacy of Natural Ligand CAR Binders Targeting CD70.
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CD70 被公认为一个有前景的“泛癌”嵌合抗原受体（CAR）T 细胞靶点。既往研究表明，基于“天然配体”（NL）的靶向 CD70 的 CAR，利用其生理性相互作用伙伴 CD27，可能比基于抗体的 CAR 具有治疗优势。然而，尽管基于抗体的 CAR 通常通过其 scFv 的亲和力成熟进行优化，但 NL CAR 的结合序列是否可以通过工程化改造来改善其功能仍未被探索。在此，我们将深度学习与基于物理的建模相结合，重新设计 CD27:CD70 界面的残基，鉴定出一个 CD27 变体“N88A”，该变体在急性髓系白血病、多发性骨髓瘤和肾细胞癌模型中增强了靶向 CD70 的 CAR-T 细胞的疗效。
CD70 is well-recognized as a promising "pan-cancer" chimeric antigen receptor (CAR) T-cell target. Prior work has shown that a "natural ligand" (NL)-based CAR targeting CD70, employing its physiological interaction partner CD27, may have therapeutic advantages over antibody- based CARs. Yet while antibody-based CARs are routinely optimized by affinity maturation of their scFv, whether the binding sequence of an NL CAR can be engineered to improve its function remains unexplored.
Here, we combined deep learning with physics-based modeling to redesign residues at the CD27:CD70 interface, identifying a CD27 variant, "N88A", which enhances the efficacy of CD70-targeting CAR T-cells across models of acute myeloid leukemia, multiple myeloma, and renal cell carcinoma. Biophysical approaches, including molecular dynamics simulations, support a mechanism of increased binder conformational freedom underlying potency enhancement.
Our work presents CD27 N88A CAR T-cells as a promising new therapeutic option and proposes that computational modeling could be applied to enhance efficacy of other NL-based immunotherapies.
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