SynNotch-CAR T cells overcome challenges of specificity, heterogeneity, and persistence in treating glioblastoma
Bibliographic record
Abstract
Treatment of solid cancers with chimeric antigen receptor (CAR) T cells is plagued by the lack of ideal target antigens that are both absolutely tumor specific and homogeneously expressed. We show that multi-antigen prime-and-kill recognition circuits provide flexibility and precision to overcome these challenges in the context of glioblastoma. A synNotch receptor that recognizes a specific priming antigen, such as the heterogeneous but tumor-specific glioblastoma neoantigen epidermal growth factor receptor splice variant III (EGFRvIII) or the central nervous system (CNS) tissue-specific antigen myelin oligodendrocyte glycoprotein (MOG), can be used to locally induce expression of a CAR. This enables thorough but controlled tumor cell killing by targeting antigens that are homogeneous but not absolutely tumor specific. Moreover, synNotch-regulated CAR expression averts tonic signaling and exhaustion, maintaining a higher fraction of the T cells in a naïve/stem cell memory state. In immunodeficient mice bearing intracerebral patient-derived xenografts (PDXs) with heterogeneous expression of EGFRvIII, a single intravenous infusion of EGFRvIII synNotch-CAR T cells demonstrated higher antitumor efficacy and T cell durability than conventional constitutively expressed CAR T cells, without off-tumor killing. T cells transduced with a synNotch-CAR circuit primed by the CNS-specific antigen MOG also exhibited precise and potent control of intracerebral PDX without evidence of priming outside of the brain. In summary, by using circuits that integrate recognition of multiple imperfect but complementary antigens, we improve the specificity, completeness, and persistence of T cells directed against glioblastoma, providing a general recognition strategy applicable to other solid tumors.
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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) | 0.000 | 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".