OS05.2.A DECIPHERING THE LONGITUDINAL TRAJECTORIES OF GLIOBLASTOMA ECOSYSTEMS BY INTEGRATIVE SINGLE-CELL GENOMICS
Bibliographic record
Abstract
Abstract BACKGROUND The mechanisms governing the progression of IDH-wildtype glioblastoma (GBM) following standard-of-care therapy remain incompletely understood. While recurrence is nearly universal, the cellular and molecular trajectories that underpin this process - particularly the dynamics of tumor and microenvironmental compartments - have yet to be fully resolved. MATERIAL AND METHODS To dissect the longitudinal evolution of the GBM ecosystem, we analyzed paired primary and recurrent tumor specimens from 59 patients. We employed single-nucleus RNA sequencing to profile transcriptomic states at single-cell resolution and integrated these data with bulk DNA sequencing to assess genomic alterations. This approach enabled a comprehensive interrogation of cellular heterogeneity and malignant state transitions over time. RESULTS Across the cohort, the most consistent feature at recurrence was a decreased proportion of malignant cells, accompanied by a reciprocal expansion of non-malignant glial and neuronal cell populations within the tumor microenvironment (TME). While the dominant malignant cell state often differed between primary and recurrent samples, no state was uniquely associated with a specific disease stage. Moreover, no singular evolutionary trajectory characterized the cohort as a whole. Instead, subsets of patients exhibited enriched and partially convergent state transitions. Notably, shifts in malignant cell states were mirrored by concurrent remodeling of the TME, implicating a tightly interwoven pattern of tumor-microenvironment co-evolution. CONCLUSION These findings reveal diverse and patient-specific evolutionary trajectories in IDH-wildtype GBM, shaped by both therapeutic pressure and microenvironmental context. Our study provides a reference framework for understanding longitudinal GBM dynamics and highlights the importance of ecosystem-level interactions in driving recurrence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".