TMIC-55. TURNING ENEMY INTO ALLY: HIJACK BRAIN TUMOR CELLS TO BREAK DOWN THE BLOOD-TUMOR BARRIER
Bibliographic record
Abstract
Abstract A major obstacle in brain cancer treatment is the blood-tumor barrier (BTB), which limits the delivery of a vast majority of therapeutic agents. Medulloblastoma (MB), the most common pediatric malignant brain tumor, consists of four subgroups (WNT, SHH, Group 3, Group 4). Increasing BTB permeability of non-WNT MB is essential for effectively treating patients by overcoming this drug delivery impediment. Since the BTB is exposed to biofluids such as blood and brain interstitial fluid, which present dynamic osmotic challenges, we hypothesized that cell volume regulation plays a crucial role in BTB integrity. Using a genetically engineered mouse model of SHH MB, we show that tumor-specific knockout of volume-regulated ion channel, Lrrc8a, significantly increases BTB permeability and enhances the therapeutic response of MB-bearing mice to vismodegib, an FDA-approved antagonist of the SHH pathway. Lrrc8a-deficient tumor cells develop ectopic contact with blood vessels, permitting tumor cell-derived Tgfβ2 to activate Tgfβ signaling in endothelial cells. Consequently, endothelial cells in Lrrc8a knockout MB lose endothelial characteristics and gain mesenchymal features, indicative of Tgfβ signaling-induced endothelial-mesenchymal transition (EndMT). Furthermore, tumor-specific LRRC8A knockdown enhances BTB permeability in a xenograft human Group 3 MB model. Collectively, our findings establish Lrrc8a/LRRC8A as a therapeutic target to increase the therapy response of MB and provide evidence that inducing EndMT could be a strategy for enhancing BTB permeability to treat 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".