EPCO-34. DECIPHERING THE LONGITUDINAL TRAJECTORIES OF GLIOBLASTOMA BY INTEGRATIVE SINGLE-CELL GENOMICS
Bibliographic record
Abstract
Abstract The evolution of cellular heterogeneity in IDH-wildtype glioblastoma (GBM) after standard-of-care therapy remains poorly understood. To address it, we assembled a longitudinal cohort of 121 primary and recurrent GBM specimens from 59 patients, with extensive clinical annotations, and profiled it by single-nucleus RNA-sequencing and bulk tumor DNA sequencing. In most cases, longitudinal samples diverged in their composition of cell types and cell states. However, almost all theoretical trajectories were observed in our cohort such that the overall distribution of cell types and cell states was comparable between primary and recurrent samples. The most consistent longitudinal effect (66% of patients) was a lower malignant cell fraction at recurrence and a reciprocal increase in proportions of glio-neuronal TME cell types; in some cases, this was further accompanied by a coordinated shift of malignant cells towards neuronal-like states. MGMT methylation and radiation-related small deletion phenotypes were linked to particular trajectories, with depletion of mesenchymal-like cells and enrichment of hypoxia-related malignant cells, respectively. Importantly, changes in malignant states were also associated with specific changes in TME composition. In summary, our analysis highlights diverse longitudinal GBM trajectories that are shaped by treatment response and TME interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".