Abstract 5238: 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 shows 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. We used CRISPR-Cas9 to genetically delete uPAR from our in-house patient-derived GBM cell lines, and found that 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, we found that uPAR knockout significantly reduced the frequency of BTICs in a given population. Additionally, uPAR knockout cells display increased sensitivity to standard-of-care chemoradiotherapy, implicating uPAR expression in therapy resistance, as seen in recurrent disease. We orthotopically injected immunocompromised mice to generate patient-derived xenograft models of GBM, and found that knockout of uPAR significantly increases survival, and decreases tumor burden. Further, we generated novel anti-uPAR single domain antibodies (sdAbs) and found that they specifically bind uPAR at low nanomolar concentrations in vitro. Using these sdAbs, we created an anti-uPAR CAR T which showed potent cytotoxicity in vitro, and drastically reduced tumor burden and extended survival in vivo. From this work 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. Citation Format: William T. Maich, Muhammad Vaseem Shaikh, Anish Puri, Alisha Anand, Chirayu Chokshi, Sabra Salim, Neil Savage, Chitra Venugopal, Martin Rossotti, Thomas Kislinger, Kevin Henry, Sheila Singh. uPAR as a novel immunotherapeutic target in recurrent glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5238.
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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) | 0.001 | 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) | 0.003 | 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".