1196 TriTCE Co-stim: a differentiated T cell engager platform with conditional <i>cis</i> CD28 co-stimulation is transferable to diverse targeting strategies
Bibliographic record
Abstract
Background While bispecific T cell engagers (TCE) have demonstrated impressive clinical efficacy in B cell malignancies garnering multiple regulatory approvals, clinical progress in solid tumors and other cancers has proven more challenging due in part to the immunosuppressive environment and low T cell numbers in these settings. To overcome these challenges, we engineered a trispecific T cell engager platform with integrated CD28 co-stimulation (TriTCE Co-Stim) through exquisite optimization and function-forward screening. We previously showed that the TriTCE Co-stim platform provides a differentiated target-dependent antitumor activity and safety profile facilitated by conditional CD28 co-stimulation, requiring CD3 engagement and obligate cis T cell binding resulting in no target-independent T cell activation or T cell-T cell bridging.1 2 Here, we explore transferability of the TriTCE Co-Stim platform to improve anti-tumor activity and specificity via avidity-driven multivalent, logic-gated, and TCR mimetic (TCRm) targeting approaches.Methods Based on the TriTCE Co-stim platform scaffold, construct libraries comprising different tumor targeting paratopes from heme and solid tumor targets and format geometries were manufactured and screened using high-throughput methods. Cytotoxic activity was evaluated in co-culture assays at low effector cell:tumor cell (E:T) ratios and in repeat challenge assays using high-content imaging analysis. Safety was evaluated in pan T cell monocultures via cytokine release by MSD or T cell viability with CellTox TM Green, in T cell bridging assays by flow cytometry, and in T cell fratricide assays by high content imaging.Results TriTCE Co-Stim molecules were constructed and screened in multiple functional modalities and in all cases showed strict target-dependent cytotoxicity and no T cell-only activity in both heme and solid-tumors. We show that our TriTCE Co-Stim platform is compatible with avidity-driven selectivity against tumor targets with normal tissue expression liabilities and is extensible to alternative targets such as pMHC receptors.Importantly, all TriTCE Co-Stim molecules demonstrated superior antitumor activity compared to CD3-only bispecific TCEs in low E:T settings and in repeat-challenge assays, and maintained an enhanced safety profile including no T cell-T cell bridging, no T cell fratricide, and no reduction in T cell viability.Conclusions Our TriTCE Co-Stim technology, optimized via high-throughput screening, enables fine-tuning target-dependent activity across diverse modalities. We show it is highly transferable across indications and tumor targets and can be extended to novel avidity and tumor targeting solutions, like 2+1+1, logic-gated, and TCRm designs. This versatility broadens the utility of TriTCE Co-Stim to varied tumor settings and patient populations, while minimizing on-target off-tumor toxicities.References Lau Desmond, et al. ZW209, a DLL3 targeted trispecific T cell engager with integrated CD28 co-stimulation, demonstrates safety and potent preclinical efficacy in models of small cell lung cancer. Cancer Res. 2025;85(8_Supplement_1):7318.Newhook, Lisa, et al. TriTCE Co-Stim: a next generation trispecific T cell engager platform with integrated CD28 costimulation, engineered to improve responses in the treatment of solid tumors. Cancer Res. 2024;84(6_Supplement):6719.
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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) | 0.007 | 0.003 |
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".