Abstract 1859: Diverse CD28-binding IgG and heavy chain-only antibodies for T-cell engager development
Bibliographic record
Abstract
Abstract Engagement of the CD28 co-receptor by T-cell engager (TCE) molecules can enhance activation, proliferation, and anti-tumor activities, particularly in immunosuppressive tumor microenvironments. In this study, we present data on a diverse panel of CD28-binding IgG and heavy chain-only (HCAb) antibodies for T-cell co-stimulation. Our diverse panel of CD28-binding antibodies add co-stimulatory building blocks to our TCE repertoire. Using single B cell-screening, we identified fully human IgG and HCAb binders to human and non-human primate CD28 with a high degree of sequence diversity. We selected a subset of antibodies for expression and assessed binding to both human and cynomolgus CD28-expressing cells. These antibodies displayed a wide range of binding avidities in the sub-nanomolar to micromolar range, and epitope binning analysis identified seven epitope communities. Co-stimulatory activity for a subset of molecules was assessed in vitro. Antibodies displayed a wide range of crosslinked T-cell activation with the majority showing no activation independent of crosslinking. Data presented here describe a diverse set of CD28-binding antibodies ready for engineering into co-stimulatory TCEs. Combining these molecules with our TCE platform or other T-cell activating strategies will enable development of novel TCE modalities with co-stimulatory function for diverse tumor targets. Citation Format: Katherine Lam, Shirley Zhi, Lucas Kraft, Elena Vigano, Wei, Cristina Faralla, Esther Odekunle, Kelly Bullock, Erin Marshall, Melissa Cid, Grace Leung, Lindsay DeVorkin, Sherie Duncan. Diverse CD28-binding IgG and heavy chain-only antibodies for T-cell engager development [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 1859.
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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) | 0.007 | 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".