TMOD-04. A COMPREHENSIVE GENOMIC LANDSCAPE OF GLIOMA SPHEROID CULTURES RECAPITULATES THE HETEROGENEITY OF GLIOBLASTOMA AND IDENTIFIES DNA METHYLATION PREDICTORS OF RADIOTHERAPY RESPONSE
Bibliographic record
Abstract
Glioma sphere-forming cells (GSCs) are important in glioblastoma (GBM) initiation, maintenance, and treatment resistance. We performed whole exome and transcriptome sequencing, DNA methylation profiling, DNA copy number determination, and functional characterization of 43 GSCs and matching tumors. Comparative analyses revealed that GSCs recapitulate the molecular landscape of GBM and provide a unique means for discovering the inter- and intra-tumor heterogeneity of GBM. We performed clonogenic assays to explore the relationship between methylation status and radiation response in twelve IDH wild type GSCs irradiated with 2-, 4-, and 6-Gy ionizing radiation. The survival fraction at 4Gy and 6Gy (SF4 and SF6, respectively) were used to dichotomize GSCs as either radiation-sensitive or resistant. DNA CpG methylomes of the GSCs were profiled using Infinium 450K methylation beadchip arrays. We observed that 304,458 out of 465,844 methylation probes (65.4%) showed increased methylation in radiation-resistant relative to radiation-sensitive GSCs (Fisher’s Exact Test, p < 1e-15). Using GSEA, we observed that fifteen of sixteen oxidative stress genes were methylated in radiation-resistant GSCs (p-value=0.019), suggesting an association between radiation-resistance and reactive oxygen species metabolism (ROS). To validate our finding, we derived a methylation signature differentiating the two GSC radiation response groups and used this to classify TCGA cases that received radiotherapy into a responder and non-responder group. We found that survival was significantly different between the two groups (median survival 84 vs. 61 weeks; HR 1.64 adjusting for patient age, p-value<0.008), suggesting that the methylation signature predicts clinical response to radiation treatment. This study identified a novel predictor of radiation response and confirms that the genomic landscape of GSCs can be used to determine clinical and functional properties of GBM.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".