STEM-17. THE UROKINASE RECEPTOR AS A NOVEL IMMUNOTHERAPEUTIC TARGET IN BRAIN CANCERS
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the common malignant brain tumor in adults, accounting for approximately 15% of all CNS tumors, and 48.6% of malignant brain tumors, with a median survival of approximately 15 months. GBM is characterized by extensive inter- and intra-tumoral heterogeneity and an extremely immunosuppressive tumor microenvironment. Following Standard-of-Care surgical resection and chemoradiotherapy, patients inevitably relapse, at which point few therapeutic avenues exist, owing in part due to a lack of clinically relevant targets. Data from our target identification pipeline shows the urokinase plasminogen activator receptor (uPAR) is significantly upregulated at recurrence on putative GBM brain tumor initiating cells (BTICs), which are believed to drive de novo tumor formation, recurrence, and therapeutic resistance. uPAR plays an important role in the plasminogen activation system, and in the context of cancer, has been implicated in numerous pro-tumorigenic processes such as invasion, proliferation, and therapy resistance. We used CRISPR-Cas9 to genetically delete uPAR from our in-house patient-derived GBM cell lines, and found that knockout of uPAR in recurrent GBM cells significantly reduces various functional characteristics of BTICs in vitro. In our patient-derived mouse model of GBM, we found that knockout of uPAR significantly increases survival, and decreases tumor burden. Further, we generated novel anti-uPAR single domain antibodies (sdAbs) which specifically and efficiently bind uPAR at low nanomolar concentrations in vitro. We developed anti-uPAR CAR Ts using the binder sequence of the sdAbs, which showed potent cytotoxicity in vitro, and drastically reduced tumor burden and extended survival in vivo. Further, we identified uPAR as being highly expressed on brain metastases from multiple origins (lung-to-brain, melanoma-to-brain, etc.), and demonstrate that anti-uPAR CAR Ts effectively kill brain metastases in vitro, and significantly extend survival using in vivo brain metastasis models. From this work we believe uPAR to be a clinically relevant target in both recurrent GBM and brain metastases, and investigation into therapeutic strategies targeting uPAR-positive brain cancers should be explored further.
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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) | 0.002 | 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".