PATH-25. Identifying the molecular signature of infiltrating edge cells in glioblastoma as drivers of tumour invasion and recurrence
Bibliographic record
Abstract
Abstract Glioblastoma is the most common malignant brain tumour in adults. Despite extensive research, there haven’t been remarkable gains in resolving the seeds of glioblastoma recurrence, and the outcomes for many patients suffering from this devastating disease remain poor. Our knowledge on GBM heterogeneity is mostly restricted to the surgically resectable tumour core, while the functional characterization of tumour cells at the infiltrating edge remains largely elusive due to the presence of normal functional brain tissue in the peritumoural lesion. Edge-derived cells exhibit larger capacity for infiltrative expansion and are the main drivers of treatment failure and tumour recurrence, making them action targets for novel treatment approaches. To resolve the transcriptional heterogeneity of GBM within the spatial context, we profiled gene expression of tumour regions selected based on histological features (“core” and “infiltrating edge”) obtained from 4 primary and 2 matched pairs of primary/recurrent IDH-WT GBM patients with Visium HD. Using pathologically annotated H&E images integrated with spatial gene expression, we resolved patterns related to tissue structure and architecture in each sample to capture continuous patterns of gene expression variation. We used NicheCompass to deepen our understanding of tissue hierarchies, and spatially localized cellular processes in infiltrative GBM. We identified top spatially variable genes and active gene programs in this cell population using unsupervised cell phenotyping and examined their combined activity. Identified modules represent tumour cell hijacking of neuronal programs as described in the context of glioma-neuron synaptic communication and formation of neurite-like tumour microtubes and is consistent with OPC-like & NPC-like cell state enrichment at the tumour periphery. Identifying biomarkers that are specific to malignant edge-derived cells may serve as new diagnostic feature that would help assess treatment response before or within early phases of therapy and allow for individual tailoring of the treatment plan to slow disease progression.
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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 | 0.001 | 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 | 0.000 |
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".