Molecular Determinants of Prognosis and Evolution in Diffuse-Lower Grade Astrocytomas
Bibliographic record
Abstract
ABSTRACT Low grade astrocytomas (LGAs) are classified based on the mutational status of the isocitrate dehydrogenase (IDH) gene. While IDH wild-type (WT) LGAs evolve rapidly to glioblastoma, mutant tumors generally have a more indolent course. To identify potential drivers of the differential progression of LGAs, we analyzed transcriptomes from The Cancer Genome Atlas. Compared to mutant LGAs, platelet-derived growth factor (PDGF) signaling is enriched in WT cases, and PDGFA is the top overexpressed gene in the pathway. Putative mechanisms for differential PDGFA expression included copy number gains of chromosome 7 in WT cases and methylation of the PDGFA promoter in mutant LGAs. Additionally, we found that high PDGFA expression is associated with aneuploidy, immunosuppressive features, and worse prognosis, and that WT LGAs use multiple means to inactivate the p53 pathway to progress to GBM. Our work highlights the contribution of PDGF gene family towards the unique behaviour of LGAs. STATEMENT OF SIGNIFICANCE This study of gene expression in LGAs suggests that differential regulation of the PDGF pathway may underlie the different natural histories of IDH WT and IDH mutant LGAs including divergent evolutionary trajectories to GBM. This insight may inspire new therapeutic strategies to suppress the transformation of LGAs to higher-grade cancers.
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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 | 0.001 | 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".