Engineered In Vitro Tumor Model Recapitulates Molecular Signatures of Invasion in Glioblastoma
Bibliographic record
Abstract
Glioblastoma stem cells (GSCs) play an important role in the invasive nature of glioblastoma (GBM); yet, the mechanisms driving this behavior are poorly understood. To recapitulate tumor invasion in vitro, we developed a GBM tumor-mimetic hydrogel using extracellular matrix components upregulated in patients. We show that our hydrogel facilitates the infiltration of a subset of patient-derived GSCs, differentiating samples based on phenotypic invasion. Invasive GSCs are enriched for injury-responsive pathways while noninvasive GSCs are enriched for developmental pathways, reflecting established GSC stratifications. Using small molecule inhibitors, we demonstrate that the suppression of matrix metalloprotease and rho-associated protein kinase processes results in a significant reduction of cell invasion into the hydrogel, reflecting mesenchymal- and amoeboid-dependent mechanisms. Similar reduction in cell invasion was observed by siRNA knockdown of ITGB1 and FAK focal adhesion pathways. We elucidate the transcriptomic profile of cells invading in the hydrogel by performing bulk RNA sequencing of cells cultured in the hydrogel and compare these to cells cultured in conventional tissue culture polystyrene (TCP). In our 3D hydrogel cultures, invasion-related molecular signatures along with proliferation and injury response pathways are upregulated while development processes are downregulated compared to culture on 2D TCP. With this validated in vitro model, we establish a valuable tool to find therapeutic intervention strategies against cellular invasion in glioblastoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".