NUCLEAR DEFECTIVE IL-33 DRIVES AN ANTI-TUMORIGENIC MICROENVIRONMENT IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Despite a sophisticated treatment regimen, including surgery, chemotherapy, and radiotherapy, survival outcomes for glioblastoma remain at a dismal 14.6 months. Multi-omics profiling have established that myeloid phagocytes drive glioma progression and contribute to therapeutic resistance. However, effective targeting of this axis remains an unmet clinical opportunity. Previously, we found that the dual-function (secreted and nuclear) cytokine IL-33 is a key regulator of the inflammatory microenvironment that aids glioma tumorigenesis through phenotypic and functional changes in the innate immune cell repertoire. Strikingly, when IL-33 is prevented from entering the nucleus, by deletion of its nuclear localization signal (ΔNLS IL-33), but is still secreted, in vivo tumor growth is dramatically suppressed resulting in extended long-term survival. Using spatial transcriptomics and multiplex immunohistochemistry with temporal resolution at different stages of tumor progression, we identified a population of glioma-inhibitory macrophages (GIMs) unique to this suppressive environment. Assessment of xenografts generated from patient brain tumor initiating cells found an enrichment of GIMs in xenografts with long-term survival (>300 days) compared to short-term survivors (<100 days). The ability of GIMs to inhibit glioma progression was further highlighted when tumors established using a combination of ΔNLS IL-33 expressing cancer cells together with highly tumorigenic cells resulted in a growth inhibitory environment that significantly prolonged survival through the polarization and activation of GIMs. Additional characterization of this phenotype and development of clinical strategies to deliver ΔNLS IL-33 to brain tumors is warranted to determine if recruitment and activation of GIMs is a translatable therapeutic strategy for glioma patients.
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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 | 0.000 |
| 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".