Core pathway mutations induce de-differentiation of murine astrocytes into glioblastoma stem cells that are sensitive to radiation but resistant to temozolomide
Bibliographic record
Abstract
BACKGROUND: Glioma stem cells (GSCs) from human glioblastomas (GBMs) are resistant to radiation and chemotherapy and may drive recurrence. Treatment efficacy may depend on GSCs, expression of DNA repair enzymes such as methylguanine methyltransferase (MGMT), or transcriptome subtype. METHODS: To model genetic alterations in human GBM core signaling pathways, we induced Rb knockout, Kras activation, and Pten deletion mutations in cortical murine astrocytes. Neurosphere culture, differentiation, and orthotopic transplantation assays were used to assess whether these mutations induced de-differentiation into GSCs. Genome-wide chromatin landscape alterations and expression profiles were examined by formaldehyde-assisted isolation of regulatory elements (FAIRE) seq and RNA-seq. Radiation and temozolomide efficacy were examined in vitro and in an allograft model in vivo. Effects of radiation on transcriptome subtype were examined by microarray expression profiling. RESULTS: Cultured triple mutant astrocytes gained unlimited self-renewal and multilineage differentiation capacity. These cells harbored significantly altered chromatin landscapes that were associated with downregulation of astrocyte- and upregulation of stem cell-associated genes, particularly the Hoxa locus of embryonic transcription factors. Triple-mutant astrocytes formed serially transplantable glioblastoma allografts that were sensitive to radiation but expressed MGMT and were resistant to temozolomide. Radiation induced a shift in transcriptome subtype of GBM allografts from proneural to mesenchymal. CONCLUSION: A defined set of core signaling pathway mutations induces de-differentiation of cortical murine astrocytes into GSCs with altered chromatin landscapes and transcriptomes. This non-germline genetically engineered mouse model mimics human proneural GBM on histopathological, molecular, and treatment response levels. It may be useful for dissecting the mechanisms of treatment resistance and developing more effective therapies.
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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".