BIOM-25. MOLECULAR PROFILING PREDICTS EARLY AND LATE PROGRESSION IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Patients with glioblastoma experience a wide variation in response to standard treatment, with nearly 30% experiencing tumour progression during treatment, and nearly 6% surviving more than 5 years. To date, there are few non-invasive clinical biomarkers to predict response to first – line treatment. Chemical exchange saturation transfer (CEST) MRI may have the potential to fill this gap. CEST MRI is sensitive to treatment-induced changes and changes in tumor metabolism. Our team has obtained CEST data for patients before, during and after the end of standard chemoradiation treatment, and found that CEST provides markers of early response and can identify early, standard and late progressors before treatment initiation. In this study, we aimed to establish molecular profiles of early, standard and late progressors with IDH wild-type glioblastoma. Patients (n=180) with primary, IDH wild-type glioblastoma were imaged with CEST-MRI at multiple time points throughout standard chemoradiation treatment. DNA and RNA were co-extracted from matched normal and tumour pairs and processed for whole genome sequencing, enzymatic methyl-seq and gene expression analysis using Nanostring. Clinical variables such as age, extent of resection, sex, ECOG status and MGMT promoter methylated were also collected. A survival analysis was conducted using the Kaplan-Meier method with log-rank tests. Univariate and multi-variate hazard ratios for clinical variables were calculated by fitting Cox Proportional Hazards Models. Early progressors reported a median progression-free survival (PFS) of 142 days compared to 832 days in late progressors (p<0.0001). Early progressors also harbored distinct and statistically significant differences in gene expression and genomic alterations, namely in DNA damage repair, glucose transport and arginine metabolism pathways. A gene signature was prognostic of PFS and overall survival. Collectively, this data has the potential to serve as a radiogenomic biomarker to assess treatment response before or within early phases of treatment and allow for individual tailoring of the treatment plan.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".