IMMU-37. TARGETING NON-CATALYTIC ACTIVATORS OF THE PROTEASOME DECREASES TUMOR GROWTH AND ENHANCES ANTIGEN PRESENTATION IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common adult primary brain tumor plagued by inevitable recurrence and poor survival. We recently performed a genome wide CRISPR/Cas9 essentiality screen in GBM stem cells, revealing a plethora of potential targets for further exploration. The proteasome is a multimeric protein complex that degrades cellular proteins contributing to homeostatic proteostasis, stress response, and antigen presentation. Most proteasomal subunits are essential for GBM stem cell growth in vitro, however, they are also essential for non-malignant neural cells, suggesting that inhibition of those subunits may lead to toxicity. Indeed, adverse neurological symptoms were prevalent in phase III clinical trials for the brain penetrant proteasome inhibitor, Marizomib, which may be linked to the vital role of proteasome subunits in non-malignant neural counterparts. Proteasome inhibitors target the catalytic subunits of the proteasome, however the role of individual proteasome activators, most of which are non-essential for growth in vitro, have not been fully elucidated in GBM. Here, we examined the functionality of non-essential proteasome activator subunits in GBM stem cells in vitro and in vivo. Surprisingly, despite lack of growth changes in vitro, we observed abrogated stem-cell self-renewal in vitro and improved survival in vivo in orthotopic xenograft models following targeting of specific activator subunits. Molecular profiling of targeted cells revealed an upregulation of interferon-γ signaling and upregulation of antigen presentation machinery. Thus, targeting specific activator subunits may inhibit malignant growth in vivo while sparing normal neural counterparts from proteotoxic stress. We are further investigating enhanced antigen presentation by targeting these proteasome activator subunits and examining changes in the tumor microenvironment and survival in syngeneic immunocompetent models of GBM. Further understanding of this mechanism may provide novel targets for GBM treatment or improve immunotherapies in GBM.
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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 | 0.000 |
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".