PATH-42. UNCOVERING AN ASSOCIATION BETWEEN OLIGODENDROGLIOMA AND THE HUMAN PAPILLOMAVIRUS (HPV) THROUGH MOLECULAR SUBPOPULATION ANALYSIS USING NETRAAI
Bibliographic record
Abstract
Abstract Oligodendrogliomas (OGs) are a rare subset of primary brain tumors, accounting for approximately 5% of all brain tumors. While the etiology of OG has yet to be fully characterized, genomic heterogeneity is linked to clinical heterogeneity with variability in progression and survival. Currently, OGs are stratified into three molecular subtypes based on IDH mutation and 1p/19q co-deletion with additional molecular heterogeneity related to EGFR, PTEN, 10q deletion, and several other markers observed among 1p/19q co-deleted OGs. Using a public OG dataset, we assembled a set consisting of 156 primary OG tumors, 14 primary glioma samples, and 9 normal samples profiled using mRNA expression arrays. Using NetraAI, a novel machine learning platform, we identified three OG patient subpopulations based on survival and differentially expressed genes. Examining low- versus high-grade OG, we identified two subpopulations: (1) 26 low-grade and 21 high-grade OGs characterized by higher RPS6KV1 expression, and (2) 6 low-grade and 61 high-grade OGs characterized by higher LFNG expression. A third subpopulation emerged examining 1p/19p co-deletion, consisting of 60 no co-deletion and 3 co-deletion OGs characterized by higher HDAC1 expression. These driving genes have a common link to human papillomavirus (HPV) infection based on functional genomic analysis. HDAC1 is a histone deacetylase involved in gene expression regulation, LFNG is implicated in the Notch signaling pathway, and RPS6KB1 is a kinase regulating protein synthesis and cell growth. We hypothesize that concomitant HPV infection may negatively impact OG prognosis, warranting further investigation into the precise molecular interactions linking HPV infection to clinical outcomes. Furthermore, exploring the link between HPV infection and vaccination to OG outcomes on a population basis may provide valuable insights. The identification of these subpopulations, not previously described by other groups, demonstrates the utility of the NetraAI approach in uncovering subpopulations with potential implications in developing targeted therapies for OG.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".