Abstract 1891: Breaking barriers to access intracellular targets with T-cell engagers: Discovery of diverse, developable, and ultra-specific antibodies against a MAGE-A4 pMHC
Bibliographic record
Abstract
Abstract In this study, we describe the discovery of antibodies against a MAGE-A4 peptide-major histocompatibility complex (pMHC). These antibodies will form the basis for the tumor-binding arm of T-cell engagers (TCEs) against this target. Bispecific CD3 TCEs have the potential to transform cancer treatment by redirecting T cells to tumor targets, but technological barriers have limited their development for solid tumors. Targets for TCEs have generally been limited to surface-expressed proteins, however, access to intracellular proteins that are mutated and/or differentially expressed in cancer cells would expand the target pool. Peptides of these intracellular proteins presented on MHC class I (MHC-I) provide opportunities for TCE development. Technologies powering discovery of rare antibodies that are ultra-specific, high-affinity pMHC binders are needed to expand this promising class of tumor targets. We have developed a technology platform for the discovery of optimal TCEs, including a diverse panel of CD3-binding antibodies and an antibody discovery and development engine that includes multispecific engineering capabilities, powered by OrthoMabTM. We are applying this platform to develop TCEs against MAGE-A4, an intracellular tumor target expressed by many solid tumors, but not by healthy tissues. Using proprietary immunization technologies, we triggered robust, diverse antibody responses against a complex of a human MAGE-A4 peptide presented on MHC-I. We used high-throughput microfluidic technology to screen single B cells using a multiplexed bead-binding assay to identify antibodies specific to the target, but not closely-related pMHCs. We then expressed and purified antibodies for downstream validation and characterization. Antibody specificity was initially validated using a panel of related pMHC complexes, and developability properties were assessed, including hydrophobicity, self-association, polyspecificity, stability, and aggregation. With complex data integration and analysis, we identified a panel of diverse and developable antibodies that bind with high affinity to a human MAGE-A4 peptide sequence of 10 amino acids presented on MHC-I (HLA:02*01). Strategic selection and pairing of these target-binding antibodies with our large and diverse panel of fully human CD3-binders will power the discovery of ultra-specific MAGE-A4 TCEs with optimal potency and cytokine release. Citation Format: Davide Tortora, Peter Bergqvist, Grace P. Leung, Elena Vigano, Antonios Samiotakis, Harveer Dhupar, Wei Wei, Shirley R. Zhi, Yukiko Sato, Allison Goodman, Cindy-Lee Crichlow, Melissa Cid, Jessica Fernandes Scortecci, Ping Xiang, Ahn Lee, Vivian Li, Stephanie Masterman, Sherie Duncan, Aaron Yamniuk, Kush Dalal, Tim Jacobs, Raffi Tonikian, Bryan C. Barnhart. Breaking barriers to access intracellular targets with T-cell engagers: Discovery of diverse, developable, and ultra-specific antibodies against a MAGE-A4 pMHC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1891.
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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) | 0.004 | 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".