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 plagued by the lack of target antigens that are both tumor-specific and homogeneously expressed. We show that multiantigen prime-and-kill recognition circuits have the flexibility and precision to overcome these challenges in attacking glioblastoma. A synNotch receptor that recognizes a specific priming antigen – the heterogeneous glioblastoma neoantigen EGFRvIII or a brain tissue-specific antigen – is used to locally induce expression of a CAR, enabling thorough but controlled tumor killing by targeting of homogeneous antigens that are not absolutely tumor specific. Moreover, regulated CAR expression maintains a higher fraction of the T cells in the naïve-like state which is associated with higher durability in vivo . In summary, using circuits that integrate recognition of multiple imperfect but complementary antigens, we improve the specificity 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) | 0.001 | 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".