UPAR AS A NOVEL IMMUNOTHERAPEUTIC TARGET IN RECURRENT GLIOBLASTOMA
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, and minimal clinical progress having been made in the past two decades. GBM is characterized by extensive inter- and intra-tumoral heterogeneity as well as an extremely immunosuppressive tumor microenvironment. Following Standard-of-Care (SoC) surgical resection and chemoradiotherapy at primary diagnosis, few therapeutic avenues exist at recurrence, owing in part due to a lack of clinically relevant targets. Data from our multi-institutional target pipeline suggests that the extracellular 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, tumor 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, epithelial-to-mesenchymal transition, and therapy resistance. In vitro, knockout of uPAR expression in recurrent GBM cells significantly reduces proliferation and sphere formation capacity, two stem-like traits associated with BTICs. Additionally, uPAR knockout cells display increased sensitivity to standard-of-care chemoradiotherapy, implicating uPAR expression in therapy resistance, as seen in recurrent disease. From these initial studies, we believe uPAR to be a clinically relevant target in recurrent GBM, and further investigation into therapeutic strategies to target uPAR-positive GBMs should be investigated further.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".