Preclinical Development of T Cells Engineered to Express a T-Cell Antigen Coupler Targeting Claudin 18.2–Positive Solid Tumors
Bibliographic record
Abstract
The T-cell antigen coupler (TAC) is a chimeric receptor that facilitates tumor antigen-specific activation of T cells by co-opting the endogenous T-cell receptor complex in the absence of tonic signaling. Previous data demonstrate that the TAC affords T cells with the ability to induce durable and safe antitumor responses in preclinical models of hematologic and solid tumors. In this study, we describe the preclinical pharmacology and safety of an autologous Claudin 18.2 (CLDN18.2)-directed TAC T-cell therapy, TAC01-CLDN18.2, in preparation for a phase I/II clinical study in subjects with CLDN18.2-positive solid tumors. Following a screen of putative TAC constructs, the specificity, activity, and cytotoxicity of TAC T cells expressing the final CLDN18.2-TAC receptor were evaluated in vitro and in vivo using gastric, gastroesophageal, and pancreatic tumor models as well as human cells derived from normal tissues. CLDN18.2-specific activity and cytotoxicity of CLDN18.2-TAC T cells were observed in coculture with various 2D tumor cultures naturally expressing CLDN18.2 as well as tumor spheroids. These effects occurred in models with low antigen levels and were positively associated with increasing CLDN18.2 expression. CLDN18.2-TAC T cells effectively eradicated established tumor xenografts in mice in the absence of observed off-target or on-target/off-tumor effects, elicited durable efficacy in recursive killing and tumor rechallenge experiments, and remained unreactive in coculture with human cells representing vital organs. Thus, the data demonstrate that CLDN18.2-TAC T cells can induce a specific and long-lasting antitumor response in various CLDN18.2-positive solid tumor models without notable TAC-dependent toxicities, supporting the clinical development of TAC01-CLDN18.2.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".