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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".