RTHP-07. TRANSCRIPTION FACTOR NETWORKS OF OLIGODENDROGLIOMAS (IDH-MUTANT AND 1p/19q CODELETED) TREATED WITH ADJUVANT RADIOTHERAPY OR OBSERVATION INFORMS PROGNOSIS
Bibliographic record
Abstract
Multicentre sequencing efforts have allowed for the molecular characterization of low-grade gliomas (LGG)(The Cancer Genome Atlas (TCGA), 2015). We sought to analyze TCGA gene expression datasets on oligodendrogliomas patients treated with adjuvant radiation (RT) or those observed to discover prognostic markers and pathways. 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. From our cohort of 137 patients, 65 received adjuvant RT and 72 were observed. In the cohort that received adjuvant RT, a transcription factor activity signature was generated that was associated with shorter progression-free survival (PFS) (HR = 2.3, p < 0.001). This increased risk was not seen in patients who were observed (HR = 0.8, p = 0.3). Within the observation cohort, transcription factors associated with the circadian clock pathway (ARNTL, ARNTL2, CLOCK) predicted for poorer PFS (HR = 1.6, p < 0.01). A transcription factor activity signature was generated that was associated with poor PFS (HR = 1.8, p < 10–5) and OS (HR = 1.7, p < 0.002) only for those patients who were observed. Median OS in the observation cohort negative for the signature was not reached, but was 70 months for patients positive for the signature. CONCLUSIONS: We identified a transcription factor activity signature associated with poor prognosis in patients with IDH mutated and 1p19q codeleted 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".