Abstract SY42-01: Targeting fluidic force sensing mechanism to treat brain tumor metastasis
Bibliographic record
Abstract
Abstract Biofluid flow generates fluid shear stress (FSS), a mechanical force widely present in tissue microenvironment. How brain tumor growth alters the conduit of biofluid, thereby impacting FSS-regulated cancer progression is unknown. Medulloblastoma (MB), the most common pediatric malignant brain tumor, frequently obstructs the 4th ventricle and infiltrates the cerebrospinal fluid (CSF) conduit. Dissemination of MB cells into the CSF initiates metastasis within the central nervous system. By simulating CSF flow based on magnetic resonance imaging of MB patients, we discovered that FSS is elevated at the cervicomedullary junction. FSS promotes MB metastasis in mice and zebrafish. Mechanistically, FSS promotes cell behaviors conducive to metastatic escape and survival within the nutrient-deprived CSF: FSS weakens cell-substrate adhesion, increases motility, cell clustering, and plasma membrane localization of glucose transporter 1 (GLUT1) to enhance glucose uptake. FSS is perceived by mechanosensitive ion channel PIEZO2, which drives actomyosin contractility-dependent GLUT1 recruitment at the plasma membrane. Genetic targeting of PIEZO2 or pharmacologic inhibition of GLUT1 mitigates metastasis. Collectively, these findings define a targetable FSS-activated mechano-metastatic cascade for the treatment of MB metastasis. Citation Format: Xi Huang. Targeting fluidic force sensing mechanism to treat brain tumor metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr SY42-01.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".