Back

Anti-PD-1-iRGD Peptide Conjugate Boosts Antitumor Efficacy via Engagement Augmentation and Penetration Enhancement of T cells

Pan, Y.; Xue, Q.; Yang, Y.; Shi, T.; Wang, H.; Song, X.; Luo, Y.; Liu, B.; Song, Z.; Li, J. P.; Wei, J.

2023-08-04 immunology
10.1101/2023.08.04.551949 bioRxiv
Show abstract

Despite the important breakthroughs of immune-checkpoint inhibitors (ICIs) in recent years, the overall objective response rate (ORR) remains limited in various cancers. Here, we synthesized programmed cell death protein-1 (PD-1) antibody iRGD conjugate (PD-1-(iRGD)2) through glycoengineering and bio-orthogonal reaction. PD-1-(iRGD)2 exhibited extra iRGD receptor dependent affinity to several cancer cell lines rather than normal cell lines. Via dual targeting, PD-1-(iRGD)2 engageed tumor cells and T cells thus mediating T cell activation and facilitating tumor elimination. Besides, the attachment of iRGD impressively improved the penetrability of both PD-1 antibody and PD-1+ T cells. In multiple syngeneic mouse models, PD-1-(iRGD)2 effectively reduced tumor growth with satisfactory biosafety. Moreover, results of flow cytometry and single-cell RNA-seq revealed that PD-1-(iRGD)2 remodeled the tumor microenvironment (TME) and expanded a unique population of "better effector" CD8+ tumor infiltrating T cells (TILs) expressing stem and memory associated genes including Tcf7, Il7r, Lef1 and Bach2. Conclusively, PD-1-(iRGD)2 could be a novel and promising therapeutic approach for cancer immunotherapy. Statement of significanceDesigned against the clinical dilemma of unsatisfied response rate after contemporary cancer immunotherapy, PD-1-(iRGD)2 engages T cells and tumor cells, promotes T cell infiltration and expands a unique population of "better effectors" with enhanced therapeutic potential for the treatment of cancer.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.