Abstract 2123: KJ-103: First-in-class naked anti-TROP2 HCAb with immune modulatory mechanisms and tumor regression potential in clinical trials
Notice bibliographique
Résumé
Trophoblast cell surface antigen-2 (TROP2) is a membrane protein highly expressed in a wide range of advanced epithelial cancers and is an attractive target for cancer therapies. TROP2-positive cancers have been targeted by antibody-drug conjugates (ADCs). Here we employed our proprietary heavy chain-only antibody (HCAb) transgenic mouse platform to discover and develop KJ-103, a novel naked anti-TROP2 HCAb.KJ-103 induced regression and eradication of large breast and colon cancer xenografts. The anti-tumor efficacy of KJ-103 was completely abolished when using an IgG1-LALAPG variant which lacks Fc gamma receptor (FcγR) interaction. Furthermore, the in vivo efficacy of KJ-103 was also abolished in a mouse model that lacks all FcγRs. These results underscored the importance of FcγR engagement in the therapeutic action of KJ-103. Although the precise immune cell responsible for this efficacy has yet to be determined, these results showed that, in immunodeficient mice, the anti-tumor efficacy of KJ-103 is mediated by effector function cells present in the tumor microenvironment. Further analysis of the tumor microenvironment via bulk RNA sequencing revealed that KJ-103 treatment reduced the content of immunosuppressive macrophages and activated genes associated with phagocytosis and cytotoxicity which may contribute to its potent antitumor effects. Additionally, KJ-103 treatment activated antigen-presenting genes, suggesting its potential to stimulate T-cell responses, which may amplify the immune-mediated anti-tumor response of KJ-103. These findings highlight the multifaceted immune response induced by KJ-103, making it a promising candidate for enhancing immune system-mediated tumor control. Surface plasmon resonance (SPR) analysis confirmed that KJ-103 strongly binds to human and cynomolgus TROP2 but has no interaction with murine and rat orthologs. KJ-103 was well tolerated in a five-week, weekly-dosing dose range finding (DRF) study in non-human primates, at levels up to 100mg/kg, with no clinical or lab-changes, and had a favorable PK profile. KJ-103 binds to the human and cynomolgus normal tissues where TROP2 is present, both by FFPE and frozen IHC highlighting its applicability for biomarker testing. Epitope mapping and co-crystal analysis revealed that KJ-103 targets a unique epitope on TROP2, which could not be competed by other known anti-TROP2 antibodies. This work highlights the feasibility of developing functional, payload-free anti-TROP2 antibodies for clinical use, addressing current challenges associated with ADCs and expanding therapeutic options for patients with TROP2-positive cancers. KJ-103, as the first naked anti-TROP2 HCAb is under development for clinical trials and represents a groundbreaking advancement in cancer immunotherapy. Citation Format: Amit Subedi, Hiba A. Zahreddine, Sophie E. Cousineau, Richard Wargachuk, Xiaowei Wang, Lucy Lai, Dominic Hou, Elijus Undzys, Gordon Ngan, Luis da Cruz, David Young. KJ-103: First-in-class naked anti-TROP2 HCAb with immune modulatory mechanisms and tumor regression potential in clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2123.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».