CSIG-27. FLUID SHEAR STRESS ACTIVATES A MECHANO-METASTATIC CASCADE TO PROMOTE MEDULLOBLASTOMA METASTASIS
Bibliographic record
Abstract
Abstract Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Dissemination of MB cells into cerebrospinal fluid (CSF) and blood initiates metastasis in the central nervous system, which significantly worsens patient prognosis. Disseminated MB cells survive the nutrient-deprived CSF environment and are subjected to fluid shear stress (FSS) by CSF flow. Whether and how FSS contributes to MB metastasis is completely unknown. We computationally simulated FSS dynamics in MB patients using MRI-informed CSF flow models. Based on CSF flow rates and force magnitudes in patients, we engineered two FSS application systems: (1) an orbital shaker system that applies FSS to large populations of cells, and (2) a microfluidic system compatible with live-cell and high-resolution imaging. By applying physiologically relevant FSS to MB cells, we discovered that FSS-treated MB cells metastasize more frequently and form larger metastatic tumors along the spinal cords of mice. Mechanistically, FSS induces actomyosin contraction, which promotes cell clustering, glucose transporter 1 (GLUT1) localization at the cell surface, and GLUT1-dependent glucose uptake. FSS elevates intracellular calcium through mechanosensitive ion channel PIEZO2, which is required for FSS-induced actomyosin contraction, cell clustering, and GLUT1 activity. Genetically perturbing PIEZO2 or pharmacologically inhibiting GLUT1 robustly suppresses MB metastasis in mice. Collectively, we discover that MB cells perceive FSS, define an FSS-activated mechano-metastatic cascade, and demonstrate that this cascade is clinically targetable to mitigate MB metastasis.
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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) | 0.003 | 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".