EPCO-51. A MULTI-OMIC ANALYSIS OF REGIONAL HETEROGENEITY IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common malignant brain tumor in adults. A hallmark of GBM is its intratumoral heterogeneity as well as infiltration into the surrounding brain. The growing understanding of the cellular diversity and cellular state diversity within GBM necessitates a need for a more granular evaluation of the molecular landscape of this disease. This study aimed to investigate this using single cell RNA sequencing combined with single-cell ATAC sequencing and spatial transcriptomics in order to delineate cellular states and enriched pathways between malignant cells in different regions of GBM. The study cohort consisted of 15 patients with primary GBM. Tumor samples were taken at the time of surgical resection using intraoperative stereotactic navigation from three anatomically distinct locations: periphery – the cortex or white matter in the peri-tumoral region that is beyond contrast-enhancing tumor, border – the contrast-enhancing border of the tumor on T1-weighted MRI, and core - the hypointense core region on T1-weighted MRI. Samples underwent combined snRNA and ATAC sequencing (10x Multiome) as well as spatial transcriptomics (10x Visium). Single-nucleus RNA + ATAC sequencing captured a total of 37,547 neoplastic cells and 18,498 non-neoplastic cells within the tumor microenvironment. The majority of non-neoplastic cells were identified within the periphery alongside a distinct group of neoplastic cells that exhibited unique a unique distribution of malignant cellular state and differentially regulated genetic pathways compared to malignant cells within the tumor border and core. Gene regulatory analysis of the malignant cells in the periphery identified a previously undefined set of enriched transcription factors that are periphery-specific regulon drivers. Overall, this analysis sheds light on the genetic and epigenetic differences between malignant GBM cells based on their location and warrant further investigation into the biology and targetable aspects of the malignant cells that exist beyond the contrast-enhancing border of the tumor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".