STEM-26. BLOOD-TUMOR BARRIER IS COMPOSED OF MECHANOSENSING TUMOR CELLS THAT MASK THERAPEUTIC VULNERABILITY
Bibliographic record
Abstract
Abstract Two major obstacles in brain cancer treatment are the blood-tumor barrier (BTB), which restricts delivery of most therapeutic agents, and the quiescent brain tumor-initiating cells (BTICs), which evade cell cycle-targeting chemotherapy. Mechanosensation, the transduction of mechanical cues into cellular signaling, underlies physiological processes such as touch, pain, proprioception, hearing, respiration, epithelial homeostasis, and vascular and lymphatic development. We report that medulloblastoma (MB) BTICs are mechanosensing, a property conferred by force-activated ion channel Piezo2. In contrast to the prevailing view that astrocytes function as a physical barrier in BTB, BTICs project endfeet to ensheathe capillaries. MB develops a tissue stiffness gradient as a function of distance to capillaries. Piezo2 senses substrate stiffness to sustain local intracellular calcium, actomyosin tension, and adhesion at BTIC growth cones, which allow BTICs to mechanically interact with their substrate and sequester β-Catenin to prevent WNT/β-Catenin signaling in BTICs. Our single cell analysis uncovers a two-branched BTIC trajectory that progresses from a deep quiescent state to two cycling states. Tumor cell-specific Piezo2 knockout reverses the off-on WNT/β-Catenin signaling states in BTICs and endothelial cells, collapses the BTB, reduces quiescence depth of BTICs, and markedly enhances MB response to chemotherapy. Our study reveals that BTICs co-opt astrocytic mechanism to contribute to the BTB and provides the first evidence that BTB depends on mechanochemical signaling to mask tumor chemosensitivity. Targeting Piezo2 addresses BTB and BTIC properties that underlie therapy failures in brain cancer.
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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 | 0.001 |
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".