Monoallelic Expression Determines Oncogenic Progression and Outcome in Benign and Malignant Brain Tumors
Bibliographic record
Abstract
Although monoallelic expression (MAE) is a frequent genomic event in normal tissues, its role in tumorigenesis remains unclear. Here we carried out single-nucleotide polymorphism arrays on DNA and RNA from a large cohort of pediatric and adult brain tumor tissues to determine the genome-wide rate of MAE, its role in specific cancer-related genes, and the clinical consequences of MAE in brain tumors. We also used targeted genotyping to examine the role of tumor-related genes in brain tumor development and specifically examined the clinical consequences of MAE at TP53 and IDH1. The genome-wide rate of tumor MAE was higher than in previously described normal tissue and increased with specific tumor grade. Oncogenes, but not tumor suppressors, exhibited significantly higher MAE in high-grade compared with low-grade tumors. This method identified nine novel genes highly associated with MAE. Within cancer-related genes, MAE was gene specific; hTERT was most significantly affected, with a higher frequency of MAE in adult and advanced tumors. Clinically, MAE at TP53 exists only in mutated tumors and increases with tumor aggressiveness. MAE toward the normal allele at IDH1 conferred worse survival even in IDH1 mutated tumors. Taken together, our findings suggest that MAE is tumor and gene specific, frequent in brain tumor subtypes, and may be associated with tumor progression/aggressiveness. Further exploration of MAE at relevant genes may contribute to better understanding of tumor development and determine survival in brain tumor 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 | 0.001 | 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".