Abstract 2373: Functional and specific T-cell engagers against a peptide-MHC tumor target
Bibliographic record
Abstract
Abstract Accessing peptides from intracellular tumor antigens displayed on MHC class I (pMHC) can expand the T-cell engager (TCE) target pool for solid tumor applications. A challenge limiting TCE development for pMHC targets is the identification of rare, high-affinity, and high-specificity pMHC-binders. In this work, we describe TCEs in a 1 × 1 format with potent tumor-cell killing activity and high specificity for MAGE-A4, a pMHC tumor antigen expressed in multiple cancer types. We identified functional MAGE-A4 x CD3 TCEs with high specificity and affinity to a human MAGE-A4 peptide sequence of 10 amino acids presented on MHC-I (HLA-A:02*01). We strategically selected MAGE-A4-binders with high binding specificity for pMAGE-A4230-239, but not MHC-I or many closely related MHC-restricted peptides. We then paired them with diverse and developable CD3-binders with a range of binding affinities, subunit specificities, and binding kinetic profiles. High-throughput in vitro functional characterization of hundreds of these TCEs enabled identification of molecules with optimal T-cell dependent cellular cytotoxicity in MAGE-A4-expressing tumor cell lines, with comparable tumor-killing activity and cytokine release to a clinical-stage TCE in a 2 × 1 format. Previous studies have highlighted unique challenges associated with pMHC targets, including their potential for cross-reactive binding to pMHCs on healthy tissues, and the absence of pharmacologically relevant species for preclinical toxicity testing. To address these challenges, we have designed and implemented an in vitro and in silico workflow to identify antibody binding to potential off-target peptides. Using this approach, we selected TCEs with several pMHC binding orientations and high specificity and affinity. These TCEs killed tumor cell lines endogenously expressing MAGE-A4 pMHCs and not isogenic cells with MAGE-A4 knocked out. Results demonstrate that our high-resolution, multiparametric antibody discovery capabilities can identify pMHC-binding antibodies with desired functional and specificity attributes. This approach unlocks the discovery and development of optimal TCEs against complex pMHC antigens. Citation Format: Davide Tortora, Peter Bergqvist, Allison Goodman, Ryan Blackler, Nathalie Blamey, Stefania Carrara, Lauren Chong, Gabrielle Conaghan, Cindy-Lee Crichlow, Valentine de Puyraimond, Harveer Dhupar, Patrick Farber, Jessica Fernandes Scortecci, Kate Gibson, Rodrigo Goya, Ahn Lee, Franco Li, Tova Pinsky, Craig Robb, Patrick Rowe, Antonios Samiotakis, Eduardo Solano Salgado, Ping Xiang, Irene Yu, Kelly Bullock, Tara Fernandez, Stephanie K. Masterman, Kush Dalal, Tim Jacobs, Bryan C. Barnhart. Functional and specific T-cell engagers against a peptide-MHC tumor target [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2373.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".