BIOM-101. STRATIFICATION OF GLIOBLASTOMA PROGRESSORS USING MULTI-OMIC PROFILING AND GRAPH NEURAL NETWORKS
Bibliographic record
Abstract
Abstract Patients with glioblastoma exhibit highly variable responses to standard chemoradiation, with approximately 30% progressing during treatment and a small subset surviving beyond 5 years. Despite this heterogeneity, non-invasive biomarkers to predict early treatment response remain limited. Chemical exchange saturation transfer (CEST) MRI, sensitive to metabolic and treatment-induced changes, has shown promise in identifying early, standard, and late progressors prior to treatment initiation. In this study, we aimed to characterize the molecular profiles of CEST-defined subgroups and develop predictive models for treatment stratification. A cohort of 180 patients with primary, IDH wild-type glioblastoma underwent CEST imaging at multiple timepoints. Tumor and matched normal tissue were profiled using whole genome sequencing, enzymatic methyl-seq, and RNA-seq. Clinical variables (age, sex, MGMT promoter status, extent of resection, ECOG score) were collected. Kaplan-Meier and Cox proportional hazards analyses were used to evaluate clinical and molecular correlates of progression-free survival (PFS).To integrate imaging, clinical, and molecular data, we developed a graph neural network (GNN)-based model for survival prediction and risk stratification. Our architecture incorporates pathway-level pooling and cross-modal attention, capturing synergistic patterns between RNA expression and DNA methylation. The model demonstrated superior concordance index and risk group separation compared to published ML models. SHAP-based interpretability analyses revealed that genes involved in DNA repair, glucose transport, and arginine metabolism were among the top contributors to risk predictions. Early progressors had a median PFS of 142 days versus 832 days for late progressors (p<0.0001), with distinct gene expression and epigenetic signatures. A multigene signature derived from the GNN was prognostic of both PFS and overall survival. Collectively, our findings suggest that integrating multi-omic profiles with deep learning on radiogenomic features may enable early, individualized treatment adaptation in glioblastoma.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".