EPCO-28. UNVEILING INVASIVE MECHANISMS OF GLIOBLASTOMA CELLS THROUGH MULTIMODAL SINGLE-CELL SEQUENCING
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common malignancy of the central nervous system, characterized by a dismal prognosis and inevitable recurrence despite aggressive standard-of-care therapy. Extensive colonization of the surrounding brain parenchyma precludes complete surgical resection, posing a significant therapeutic challenge. While well-studied in model systems, the molecular features and epigenetic regulators of invasive GBM cells remain under-explored directly in clinical samples. To address this gap, we performed a multimodal single-cell sequencing characterization of GBM tumor samples from anatomically distinct regions collected using MRI guidance. Our results demonstrate an enrichment of progenitor-like (neural- and oligodendrocyte-like) malignant states and neurons at the tumor margins. In contrast, differentiated-like states and myeloid cells were more abundant in the tumor core. Peri-tumoral progenitor-like malignant states expressed a unique neuronal signature associated with synaptic signaling, neurogenesis, and Notch signaling. Further, the expression of this neuronal signature was correlated with increased invasiveness. Analysis of matched primary-recurrent GBM patient cohorts revealed an expansion of this neuronal activity program, which was associated with worse overall survival. Motifs of proneural transcription factors implicated in neuronal lineage differentiation were also found to be differentially accessible in progenitor-like malignant states marked by the neuronal invasive signature, potentially contributing to the remodeling of cell states at the invasive margin. Further, cell-cell interaction analysis predicted greater communication between neurons and peri-tumoral progenitor-like malignant states, mediated predominantly through neurexin-neuroligin trans-synaptic signaling, facilitating synaptogenesis and the integration of tumor cells into neural circuits. Altogether, these findings suggest a model in which GBM invasive cells communicate with normal brain neurons in peri-tumoral regions, exploiting neurodevelopmental pathways to facilitate invasion and potentially seed recurrence. Our characterization of invasive GBM cells provides insight into the mechanisms of brain invasion and highlights potential therapeutic vulnerabilities of malignant invasive cells. Understanding these mechanisms opens new avenues for targeted therapies aimed at curtailing the invasive and adaptive capabilities 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 | 0.000 |
| 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 | 0.001 |
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".