EPCO-05. SPATIOTEMPORAL MODELLING OF GLIOBLASTOMA FOR DRUG PREDICTION
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a lethal primary brain malignancy characterized by inevitable relapse. Understanding intra-tumoral heterogeneity and the microenvironmental influence on tumor progression is key to developing effective therapies. We employ 10X Genomics’ Visium Spatial Transcriptomics platform to study murine xenograft models of multi-regional and longitudinally derived cell lines from 4 GBM patients. This resource enables the study of tumor growth at spatial resolution and allows robust species-specific distinction of tumor and its microenvironment (TME). Using a matrix factorization approach, we delineate spatial gene expression programs (GEP) specific to the tumor and TME. We observe unique niches including progenitor cell-states along the leading edge (LE) of the tumor, oligodendrocyte-like (OC) and astrocytic-like (AC) states at the tumor core along with tumor-associated endothelial states. At early time-points, the homing of brain-resident microglia (MG), macrophages (BMDM), reactive astrocytes to the tumor site are observed. The invasiveness of GBM is a major obstacle, as these cells are therapeutically resistant and seed recurrence. Targeting the LE of GBM could therefore improve patient outcomes. Interestingly in our models, the LE state exhibits higher patient diversity; upregulation of ligand/receptor genes in secreted phosphoprotein, fibronectin, macrophage migration pathways highlighting prospective targets. Further, we analyzed protein-protein interaction networks of the LE programs, which revealed that upregulation of “hub genes” within the network significantly reduces patient survival. Further investigation of such program-specific vulnerabilities will help inform on combinatorial therapeutic strategies to control disease progression and paves the way for future testing of treatment response in both xenografts and syngeneic models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".