Abstract 3506: Clinic enabling development of a TCR-Ab half life extended T cell engager against a novel solid tumor target
Bibliographic record
Abstract
Abstract Introduction: T cell engagers (TCEs) are a promising class of targeted therapies with recent clinical successes in solid tumors. A unique advantage of HLA-presented targets is that they can be derived from any part of the human genome providing an opportunity to build next generation TCEs against novel targets. We here present our discovery and development of a half-life extended TCE against a novel tumor-target towards clinical candidate selection. Our proprietary TCE drug format is based on a solubilized TCR, engineered as an Fc-based bispecific, showing high potency and safety in pre-clinical evaluation. Experimental Procedures: We have performed T cell repertoire analysis on billions of T cells to identify the most potent TCRs from CD8 T cells against 100s of targets. This has resulted in isolation of specific TCRs against multiple novel targets. We have then taken the most potent TCRs (low micromolar affinity) through affinity engineering using a proprietary approach that combines machine learning-guided mutagenesis with high-throughput functional screening. A library of proprietary TCE formats with engineered TCRs and an anti-CD3 engager arm were optimized for yield, stability and function. The novel target in focus was selected based on high copy number on cancer tissue determined using epitope staining approaches. Finally, lead TCEs were assessed for efficacy, safety, longevity & biodistribution in ex vivo primary human cell assays and humanized xenograft models. Results: We have developed a half-life extended TCE in a unique format with specific picomolar binding affinity against a novel tumor target. Our machine learning guided affinity engineering process progressed a set of uM wild-type TCR molecules to pM affinity, enhancing TCR:target binding >100,000 fold. High yielding TCRs were formatted into TCR-Ab TCE engagers and tested on primary tumor cells taken from patients. The TCEs showed activation of primary T cells and subsequent killing of these novel target positive cancer patient cells. We observed no T cell activation or killing of primary normal cell panels. Selected TCEs tested in humanized mouse models showed long circulating properties and efficacy. We have identified a novel TCE lead candidate which is potent, specific and safe in ex vivo human samples with a large therapeutic window. Conclusions: We have developed a half-life extended T cell engager against a novel solid tumor target utilizing machine learning-guided TCR engineering that allows for the formation of an Fc-based anti-CD3 TCE. Currently, we are finalizing the preclinical evaluation and will enter IND enabling development mid 2025. Citation Format: Sarah Leonard, Hemza Ghadbane, Dominic Hine, Samantha Drennan, Peter Cain, Louise Holland, Eleanor Bagg, Jana Rundle, Helena Horvatic, Christos Gavriel, Sophie Wells, Samhita Rao, Mathew Veal, Michela Marongiu, Juan Bolivar, Xiaoyan Pan, Maria Busz, David Cook, Wilawan Bunjobpol, Alexander Stephens, Sophie Johnson, Oleksandr Dudchenko, Jong Fu Wong, Sophie Richard, Samuel Ward, Simon Wright, On Kan, Amalia Martinez, Dzmitry Batrakou, Mitchell P. Levesque, Rebecca Ashfield, Mark Lees, Graham Ogg, Nathaniel Davies, Thomas L. Andresen. Clinic enabling development of a TCR-Ab half life extended T cell engager against a novel solid tumor target. [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 3506.
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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 | 0.001 |
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".