Bibliographic record
Abstract
High grade gliomas (HGG) are incurable, aggressive brain malignances that occur in patients of all ages. These cancers carry universally poor prognoses. Significant scientific advances made over the last decade have uncovered many of the features of these diseases, culminating in the latest World Health Organization (WHO) classification of Central Nervous System (CNS) tumors integrating histopathologic and molecular information for diagnosis. However, it is unclear how genetic and epigenetic features change as the CNS tumor grows. To elucidate mutations and transcriptional changes driving glioma growth and progression, we used a Nestin-Cre mouse model in combination with an extrinsic chemical mutagen (N-ethyl-N¬-nitrosurea, ENU), to sample discrete lesions during premalignant, early stage tumor, and end stage tumor phases. We show that the somatic mutations, copy number changes, and transcriptional profiles of tumors vary depending on the stage of growth. Importantly, we show that the Raf/Ras pathway is key for tumor growth with a recurring Braf mutation occurring in early stage lesions. Additionally, gene set enrichment analysis (GSEA) shows that end stage tumors have increased immunogenic/inflammatory activity, and increased signaling through Raf/Ras. Overall, this work sheds light on important differences between early and late stage tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".