IMMU-33. MULTI-ANTIGEN RECOGNITION CIRCUITS OVERCOME CHALLENGES OF SPECIFICITY, HETEROGENEITY, AND DURABILITY IN T-CELL THERAPY FOR GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Treatment of solid cancers with chimeric antigen receptor (CAR) T-cells is challenging because of a lack of target antigens that are both tumor-specific and homogenously expressed. While epidermal growth factor receptor (EGFR)vIII represents a glioblastoma (GBM)-specific antigen, its expression is heterogeneous within the tumor resulting in tumor escape. In contrast, more homogenously expressed GBM-associated antigens (GAA), such as EphA2, are non-ideal because of expression in other normal organs, yielding potential cross-reactive toxicity. As a way to safely target GAAs in the tumor without attacking normal cells expressing the same GAAs outside of the brain, we adapted a novel synthetic Notch (synNotch) receptor system and established a “prime and kill” sequential two-receptor CAR circuit. A synNotch receptor recognizes a specific priming antigen; the heterogeneous GBM neoantigen EGFRvIII or a brain tissue-specific antigen to prime the local expression of a CAR that mediates cytotoxicity against a GAA (e.g. EphA2). In orthotopic GBM6 glioma model, a patient-derived xenograft (PDX) with heterogeneous expression of EGFRvIII, intravenous infusion of T-cells transduced with EGFRvIII synNotch→anti-IL-13Rα2/EphA2 tandem CAR circuit resulted in long-term (over 100 days) survival and eradication of the heterogeneous tumor in all of 12 mice in two independent experiments. In contrast, constitutive CARs targeting EGFRvIII or IL-13Rα2/EphA2 (as a tandem CAR) failed to exhibit long-term anti-tumor response. Moreover, T-cells transduced with synNotch-regulated CAR maintain a less differentiated state which is associated with higher durability compared with ones with constitutive CAR in vivo. T-cells transduced with a synNotch→CAR circuit primed by a brain-specific antigen, myelin oligodendrocyte glycoprotein (MOG), exhibited a precise and potent local control of intracranial PDX without evidence of priming in extracranial organs. These data support the utility of synNotch→CAR circuits in EGFRvIII-negative GBM cases. By integrating multiple imperfect but complementary antigens, we improve both the specificity and persistence of T-cells directed against GBM.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".