MOLECULAR PROFILING TO UNDERSTAND TREATMENT RESISTANCE AND RESPONSE 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 7% surviving more than 5 years. CEST-MRI is sensitive to treatment-induced changes in tumour metabolism and can be used to evaluate treatment response, allowing for the differentiation between early and late progressors before treatment initiation. OBJECTIVE: The purpose of this study is to establish genomic and transcriptomic profiles of early and late progressors in patients with glioblastoma who have undergone CEST-MRI. METHODS: Patients (n=25) were imaged with CEST-MRI at multiple time points throughout standard chemoradiation treatment. DNA and RNA were co-extracted from 25 fresh-frozen matched normal and tumour pairs and processed for whole genome sequencing and gene expression analysis using Nanostring. RESULTS: Early progressors (n = 12) and late progressors (n =13) had significant differences in OS and PFS (log rank <0.0001) as well as in gene expression and pathway deregulation. Genes significantly expressed in early progressors compared to late progressors include CCNA1 (p =0.000557), IGFBP3 (p = 0.000602), ASS1 (p = 0.000698), and ID1 (p= 0.000993), which were also prognostic of PFS and OS in a Cox regression model. CONCLUSION: Upon validation in a larger cohort, this data may serve as a radiogenomic biomarker to assess treatment response within early phases of treatment and adapt the treatment plan to individual biology.
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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 | 0.000 | 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".