Abstract 5264: Harnessing glioma-inhibitory macrophages for sustained control of glioblastoma progression
Bibliographic record
Abstract
Abstract Despite intensive basic and clinical research over the past 30 years, there has been minimal improvement in outcomes for patients with glioblastoma, the deadliest form of adult brain cancer. Several factors contribute to this therapeutic challenge, including a complex interaction between the tumor and the innate immune compartment of the brain microenvironment, that is thought to support glioma proliferation and treatment resistance. Previously, we established that glioma cells can communicate with microglia/macrophage by producing the dual-function (secreted and nuclear) cytokine interleukin 33 (IL33). We found that IL33 supports the recruitment and reprogramming of pro-tumorigenic macrophage to fuel rapid and fatal tumor growth. However, in contrast, when IL33 is prevented from entering the nucleus, by loss of its nuclear localization signal (ND-IL33), tumor growth is dramatically suppressed. Using multiplex immunohistochemistry across different stages of tumor progression, we uncovered a population of macrophages unique to this growth restrictive environment, which we term glioma-inhibitory macrophages (GIMs). To resolve the molecular features of GIMs, we performed spatial transcriptomics and a computational workflow based on unsupervised deconvolution to identify cell types and activities enriched within the ND-IL33 environment. This strategy revealed that GIMs upregulate phagocytosis and antigen processing/presentation pathways and exhibit features of granulocytic cells. When examining xenografts established from patient-derived brain tumor-initiating cells, we observed a notable increase in the presence of GIMs in xenografts that exhibited long-term survival (>300 days) compared to those with short-term survival (<100 days). The tumor-suppressive nature of GIMs was further supported by experiments in which tumors formed by a combination of ND-IL33-expressing cancer cells and highly tumorigenic cells led to a growth-restrictive environment that significantly extended survival. Further functional characterization of this macrophage phenotype and development of strategies to deliver ND-IL33 to brain tumors is necessary to determine whether the recruitment and activation of GIMs could be an effective therapeutic approach for patients with glioblastoma. Citation Format: Shyam V. Menon, Xueqing Lun, Tala-Maria Mouannes, Peipei Zeng, Varsha T. Manoharan, Jianbo Zhang, Isabelle Carrier, Eduardo Diez, Alisha Poole, Ngoc Ha Dang, Bo Young Ahn, Katalin Osz, Sorana A. Morrissy, Jennifer A. Chan, Daniela F. Quail, Stephen M. Robbins, Donna L. Senger. Harnessing glioma-inhibitory macrophages for sustained control of glioblastoma progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5264.
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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".