Abstract LBA018: Discovery of novel functional TROP2 antibodies for treatment of epithelial cancers
Bibliographic record
Abstract
Abstract Trophoblast cell surface antigen-2 (TROP2) is a membrane-bound protein with an extracellular domain highly expressed in a wide range of late-stage epithelial cancers. TROP2 upregulation in solid cancers is associated with increased tumor aggressiveness, metastasis, and an overall decreased survival in large groups of difficult-to-treat cancers, making it an attractive target for cancer therapy. TROP2 positive cancers have been successfully targeted by antibody-drug conjugates (ADC) but not naked functional antibodies. ADC’s have associated drug toxicity in healthy tissues, off-target effects, and payload delivery issues. Given ADC dose-limiting side effects, narrow therapeutic windows, and efficacy limitations, there is a pressing need to improve anti-TROP2 therapeutics for patients who are poorly served by current therapies, if at all. Understanding these needs, we applied our proprietary heavy chain-only antibody (HCAb) transgenic mouse platform to the creation of therapeutic anti-TROP2 antibodies. After immunization, anti-TROP2 antibodies were selected using VHH (variable region of HCAbs)-phage-display. Due to their size and shape, VHH antibodies target a broader spectrum of epitopes compared to full size VH/VL antibodies. Further, VHH antibodies are associated with high affinity, stability, and robustness. In this abstract, we describe five novel anti-TROP2 HCAbs without ADC payloads that induce in vivo tumor regression in murine xenograft models of human cancers. When tested in an in vitro viability assay with the MDA-MB-453 triple negative breast cancer (TNBC) cell line, a novel anti-TROP2 HCAb significantly reduced cancer cell numbers compared to other anti-TROP2 antibodies in the absence of complement or effector cells. In the in vivo MDA-MB-453 TNBC established xenograft model, tumor regression was achieved with five different TROP2 HCAbs at 8mg/kg administered once per week, and in the in vivo established model of MDA-MB-231 strong anti-tumor efficacy was also achieved. In binding cross-reactivity experiments, anti-TROP2 HCAbs showed similar binding to human and cynomolgus TROP2 and low binding to the mouse or rat orthologs. Anti-TROP2 HCAbs bind to a panel of TROP2-expressing human cancer cell lines, including a wide range of human epithelial tumours, such as breast, colon, lung and pancreatic cancers. Epitope binding by competitive ELISA showed that binding to TROP2 by these HCAbs could not be competed by hRS7 or other anti-TROP2 antibodies demonstrating that they target epitopes different from known TROP2 antibodies. Our preclinical data demonstrates the potent anti-tumor activity on TROP2-positive cancers by novel anti-TROP2 HCAbs without the need for cytotoxic payloads. These results and additional data highlight the feasibility of developing functional anti-TROP2 antibodies for clinical use which avoid the challenges of ADCs. Citation Format: Israel Matos, Hiba Zahreddine, Yahya Ashraf, Aditya Pandey, Emily Chen, Liying Gong, Alex Zhou, Claire Bonfils, Aniel Moya, Yun Cui, Xiaowei Wang, Elijus Undzyz, Shugang Yao, Jacynthe Toulouse, Dominic Hou, Gordon Ngan, Luis da Cruz, David Young. Discovery of novel functional TROP2 antibodies for treatment of epithelial cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr LBA018.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".