68P The role of systemic reprogramming of GMPs in improving outcomes in glioblastoma
Bibliographic record
Abstract
Glioblastomas are grade IV gliomas of the central nervous system associated with a median survival rate of less than 15 months. Therefore, research has been focused on better understanding the role of the tumor immune microenvironment, specifically macrophages (tissue-resident and monocyte-derived), which make up to 30% of the tumour. Using imaging mass cytometry, we have demonstrated that long-term survival in glioblastoma is associated with an accumulation of a rare subset of MPO+ monocyte-derived macrophages (MDM) within tumors, which appeared to originate from a shift in monocytosis. Transcriptomics analyses revealed that these macrophages exhibited heightened effector functions, potentially explaining their association with prolonged survival. This raises the question of whether monocyte developmental trajectories can be targeted as a therapeutic approach to promote the accumulation of MDM with elevated MPO, by reprogramming progenitor cells systemically. To elucidate the effects of progenitor reprogramming on changes in the macrophage compartment, stimulants of myelopoiesis were administered. Bone marrow, blood, and spleen were characterized using flow cytometry. Furthermore, utilizing genetic and transplantable mouse models of glioblastoma (RCAS PDGFB-ink4a model and the GL261 model), durable reprogramming was induced, and tumor progression was assessed using MRI. In tumor-bearing and non-tumor-bearing mice, myelopoiesis was successfully reprogrammed as depicted in blood by increased proportions of Ly6C+ monocytes expressing high levels of TNF-α, IL-10, MPO, and CCR2. Similar changes were observed in the spleens of non-tumor-bearing mice. Studies using mouse models of glioblastoma indicated changes in survival and tumor volumes following progenitor reprogramming. Systemic reprogramming of the myeloid compartment could have beneficial effects in preclinical models of glioblastoma.
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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) | 0.002 | 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".