IGF2 supports glioblastoma growth and immune evasion through a combination of tumor cell-intrinsic and -extrinsic mechanisms
Bibliographic record
Abstract
Abstract Abstract BackgroundGIntratumoral and intertumoral heterogeneity combined with immunosuppressive tumor microenvironments (TME) contribute to the poor outcomes associated with glioblastoma (GBM). Well-characterized immunocompetent models that recapitulate human GBM features are urgently needed to identify targets in the TME and develop novel therapeutics. Here, we used multiomic approaches to characterize syngeneic mouse brain tumor stem cell lines in vitro and in orthotopically engrafted tumors. Methods Whole-genome sequencing, transcriptomics, ATAC-sequencing, and imaging mass cytometry were used to characterize syngeneic brain tumor stem cell lines derived from Trp53+/−/Nf1+/− C57Bl6 mice. Mouse and human bulk, single-cell, and spatial sequencing datasets were analyzed for validation. CRISPR/Cas9 and shRNA were used for gene knockdowns. Tumor growth was investigated using orthotopic engraftment in syngeneic C57Bl6 mice. Results One of the syngeneic lines, mBT0309, generated tumors with histopathological characteristics of GBM. mBT0309 displayed amplification and high expression of Igf2. Copy number gains at the IGF2 locus were observed in human GBM tumors and stem cell lines. Furthermore, we determined that high IGF2 RNA expression is associated with poor survival in GBM patients. Imaging mass cytometry on mBT0309 tumors showed early infiltration of monocyte-derived macrophages, vascularization, and cell states characteristic of human GBM. Genetic targeting of Igf2 decreased in vitro cell growth, improved survival of engrafted mice, and decreased the percentage of Arginase-1+ macrophages in mBT0309 tumors. Conclusions mBT0309 is a valuable syngeneic model for studying immunosuppression and therapeutic resistance in GBM. IGF2 offers promise as a valuable therapeutic target to combat tumor growth and immunosuppression in GBM patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".