TMIC-34. Targeting mechanically regulated cell state in glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a highly invasive brain tumour. During invasion, tumour cells encounter mechanical microenvironments with varying tissue stiffness. How tissue mechanics regulate GBM cell state to influence tumour progression is poorly understood. We found that GBM cells display two predominant morphological states: mesenchymal and amoeboid states. GBM-brain interface and axon-rich regions harbor a greater proportion of tumour cells with the mesenchymal state compared to tumour core and axon-poor regions. We identified that TACAN, a putative fatty acid elongase, is a regulator of mesenchymal versus amoeboid cell state in GBM. Stiff substrate promotes the mesenchymal cell state in a TACAN-dependent manner. Mechanistically, TACAN regulates cholesterol and sphingolipids, the structural constituents of lipid raft. TACAN knockdown disrupts lipid raft-organized focal adhesion kinase signaling, neuron-GBM contact, and neuron-induced GBM cell proliferation. In orthotopic xenograft mouse models, TACAN knockdown mitigates GBM growth and prolongs mouse survival. Together, our results establish TACAN as a substrate stiffness reader, which promotes mesenchymal cell state-enriched lipid raft to govern tumour cell-intrinsic focal adhesion kinase signaling and tumour cell-neuron communication, which collectively enhance GBM growth. Targeting TACAN is a strategy to disrupt tissue mechanics-regulated cell state to treat 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 | 0.001 |
| 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".