Transcription factor networks of oligodendrogliomas treated with adjuvant radiotherapy or observation inform prognosis
Bibliographic record
Abstract
BACKGROUND: Recent international sequencing efforts have allowed for the molecular taxonomy of lower-grade gliomas (LGG). We sought to analyze The Cancer Genome Atlas (TCGA, 2015) gene expression datasets on molecularly defined oligodendrogliomas (IDH-mutated and 1p/19q-codeleted) patients treated with adjuvant radiation or those observed to discover prognostic markers and pathways. METHODS: mRNA expression and clinical information of patients with oligodendroglioma were taken from the TCGA "Brain Lower Grade Glioma" provisional dataset. Transcription factor network reconstruction and analysis were performed using the R packages "RTN" and "RTNsurvival." Elastic net regularization and survival modeling were performed using the "biospear," "plsRCox," "survival" packages. RESULTS: From our cohort of 137 patients, 65 received adjuvant radiation and 72 were observed. In the cohort that received adjuvant radiotherapy, a transcription factor activity signature, that correlated with hypoxia, was associated with shorter disease-free survival (DFS) (median = 45 months vs 108 months, P < .001). This increased risk was not seen in patients who were observed (P = .2). Within the observation cohort, a transcription factor activity signature was generated that was associated with poor DFS (median = 72 months. vs 143 months., P < .01). CONCLUSIONS: We identified a transcription factor activity signature associated with poor prognosis in patients with molecular oligodendroglioma treated with adjuvant radiotherapy. These patients would be potential candidates for treatment intensification. A second signature was generated for patients who were more likely to progress on observation. This potentially identifies a cohort who would benefit from upfront adjuvant radiotherapy.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".