Dual Antigen T Cell Engagers Targeting CA9 as an Effective Immunotherapeutic Modality for Targeting CA9 in Solid Tumors
Bibliographic record
Abstract
Glioblastomas (GBM), the most common malignant primary adult brain tumors, are uniformly lethal and are in need of improved therapeutic modalities. GBM contain extensive regions of hypoxia and are enriched in therapy resistant brain tumor-initiating cells (BTICs). Carbonic anhydrase 9 (CA9) is a hypoxia-induced cell surface enzyme that plays an important role in maintenance of stem cell survival and therapeutic resistance. Here we demonstrate that CA9 is highly expressed in patient-derived BTICs. CA9+ GBM BTICs showed increased self-renewal and proliferative capacity. To target CA9, we developed dual antigen T cell engagers (DATEs) that were exquisitely specific for CA9-positive patient-derived clear cell Renal Cell Carcinoma (ccRCC) and GBM cells. Combined treatment of either ccRCC or GBM cells with the CA9 DATE and T cells resulted in T cell activation, increased release of pro-inflammatory cytokines and enhanced cytotoxicity in a CA9-dependent manner. Treatment of ccRCC and GBM patient-derived xenografts markedly reduced tumor burden and extended survival. These data suggest that the CA9 DATE could provide a novel therapeutic strategy for patients with solid tumors expressing CA9 to overcome treatment resistance.
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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 | 0.001 |
| 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".