Abstract 2123: KJ-103: First-in-class naked anti-TROP2 HCAb with immune modulatory mechanisms and tumor regression potential in clinical trials
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".