Abstract 755: Identifying the molecular signature of infiltrating edge cells in glioblastoma as drivers of tumor invasion and recurrence
Bibliographic record
Abstract
Abstract Background: Glioblastoma (GBM) is the most common malignant brain tumor 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. Complete tumor resection in GBM patients is not possible. Residual therapy-resistant cells drive tumor recurrence and infiltrative expansion. Our knowledge on GBM heterogeneity is mostly restricted to the surgically resectable tumor core, while the functional characterization of tumor 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 tumor recurrence, making them action targets for novel treatment approaches. Methods: To resolve the transcriptional heterogeneity of GBM within the spatial context, we profiled gene expression of tumor regions selected based on histological features (“edge”, “core”, and “infiltrating zone”) obtained from 4 primary and 2 matched pairs of primary/recurrent IDH-WT GBM patients at single-cell resolution with Visium HD. Results: We show that infiltrative cells are spatially segregated and are characterized by regionally shared distinct transcriptomic signatures which define their cell state and identity. We complement non-spatial leiden clustering approach with BANKSY spatial clustering to augment the features of each cell with both an average of the features of its spatial neighbors along with neighborhood feature gradients. Using pathologically annotated H&E images integrated with spatial gene expression, we identify patterns related to tissue structure and identify transcriptional programs that promote invasiveness and underly disease recurrence in GBM. To further characterize invasive cells at the normal brain-tumor boundary, we identify top spatially variable genes in this cell population using unsupervised cell phenotyping. Upregulated DEGs of tumor edge cells are significantly associated with chemical synaptic transmission, and nervous system development. These modules represent tumor cell hijacking of neuronal programs as described in the context of glioma-neuron synaptic communication and formation of neurite-like tumor microtubes. Upregulation of ion regulation transport at the tumor periphery indicates enhanced neuronal activity and excitability driving infiltrating growth. Significance: 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. Citation Format: Alyona Ivanova, Shamini Ayyadhury, Megan Wu, David G. Munoz, Trevor J. Pugh, Sunit Das. Identifying the molecular signature of infiltrating edge cells in glioblastoma as drivers of tumor invasion and recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 755.
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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 | 0.000 |
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".