Glioblastoma tumor microtubes and brain fatty acid-binding protein: Path to directional infiltration
Bibliographic record
Abstract
BACKGROUND: Glioblastoma (GBM) is a deadly brain cancer with a dismal prognosis. There is evidence that infiltration and therapy resistance in GBM are driven by tumor microtubes (TMs), ultra-long membrane-enclosed protrusions that serve as intercellular communication channels. The aims of this study were to investigate the role of TMs and identify the molecular drivers involved in TM formation. METHODS: We used patient-derived GBM neurosphere cultures that produce TMs to investigate TM dynamics, the proteins and pathways involved in TM formation, and the effect of targeting brain fatty acid-binding protein (FABP7) on mouse survival using an orthotopic model of GBM. RESULTS: The radial glial cell marker, FABP7, is highly expressed in TMs. Like GAP43, FABP7 is critically important for the formation of TMs in GBM neurosphere cultures. We show that GBM cells use TMs as a fiber network for rapid and directional migration. Our results indicate that GAP43 phosphorylation is required for TM formation, with GAP43 phosphorylation facilitated by FABP7 expression. We also show that depletion or inhibition of protein kinase C (PKC), the kinase responsible for GAP43 phosphorylation, decreases TM formation. Targeting FABP7 in an orthotopic mouse model of TM-forming GBM cells increases survival but does not sensitize tumors to radiation. CONCLUSIONS: We found that the FABP7-PKC-pGAP43 axis is key to GBM TM formation, with TMs serving as networks for efficient long-distance cell migration. Our results indicate that TM formation can be mitigated by FABP7 inhibition with the potential of improving clinical outcomes in GBM patients.
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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) | 0.001 | 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".