Avidity-Based Capture of PD-L1-Expressing Exosomes via Dendrimer-Peptide Conjugates: A Nanoengineered Platform for Enhanced Prediction of Immunotherapy Response

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초록

A major challenge in immunotherapy is the inability to reliably predict patient responses due to the lack of robust biomarkers. Programmed cell death-ligand 1 (PD-L1)-expressing exosomes represent a promising biomarker candidate; however, existing detection platforms lack the sensitivity and specificity required for clinical translation. It is hypothesized that an avidity-based capture strategy utilizing dendrimer-mediated multivalent binding will effectively enhance molecular avidity and improve the selective capture of PD-L1-expressing exosomes. Supporting this hypothesis, atomic force microscopy (AFM) revealed that dendrimer-peptide conjugates synthesized using generation 7 poly(amidoamine) dendrimers (G7-pPDL1) exhibited approximate to 2.48-fold higher binding avidity than conventional anti-PD-L1 antibodies (aPD-L1), attributed to multivalent interactions. This increased avidity led to enhanced in vitro specificity and enabled 1.55-fold greater sensitivity in capturing PD-L1-expressing exosomes, compared to aPD-L1. Clinical validation using serum samples from patients undergoing immune checkpoint inhibitor therapy demonstrated that PD-L1-expressing exosomes captured using the G7-pPD-L1 surface more accurately predicted treatment response and outperformed tissue-based PD-L1 scoring in prognostic value. Additionally, this platform is compatible with existing biosensing technologies and enables real-time exosome detection with a limit of detection as low as 9.6 x 101 vesicles mL-1. Taken together, these findings highlight the versatility and clinical promise of this avidity-based capture strategy for advancing precision immunotherapy.

키워드

avidity-based capture; dendrimer-peptide conjugates; exosomes; immunotherapy; multivalent binding; GENERATION
제목
Avidity-Based Capture of PD-L1-Expressing Exosomes via Dendrimer-Peptide Conjugates: A Nanoengineered Platform for Enhanced Prediction of Immunotherapy Response
저자
Lee, Jiah; Shin, Dongjun; Son, Chae Yeon; Kang, Hanbit; Choi, Jung Hyun; Park, Hyun Sung; Bang, Seha; Kim, Lucia; Lee, Tae Hee; Eun, Hyuk Soo; Poellmann, Michael J.; Jeong, Woo-jin; Kim, Dong Hyung; Lim, Jun Hyeok; Hong, Seungpyo; Bu, Jiyoon
DOI
10.1002/advs.202509270
발행일
2025-11
유형
Article
저널명
Advanced Science
권
12
호
42