Neurotrophin Signaling via TrkB and TrkC Receptors Promotes the Growth of Brain Tumor-initiating Cells
Bibliographic record
Abstract
Neurotrophins and their receptors are frequently expressed in malignant gliomas, yet their functions are largely unknown. Previously, we have shown that p75 neurotrophin receptor is required for glioma invasion and proliferation. However, the role of Trk receptors has not been examined. In this study, we investigated the importance of TrkB and TrkC in survival of brain tumor-initiating cells (BTICs). Here, we show that human malignant glioma tissues and also tumor-initiating cells isolated from fresh human malignant gliomas express the neurotrophin receptors TrkB and TrkC, not TrkA, and they also express neurotrophins NGF, BDNF, and neurotrophin 3 (NT3). Specific activation of TrkB and TrkC receptors by ligands BDNF and NT3 enhances tumor-initiating cell viability through activation of ERK and Akt pathways. Conversely, TrkB and TrkC knockdown or pharmacologic inhibition of Trk signaling decreases neurotrophin-dependent ERK activation and BTIC growth. Further, pharmacological inhibition of both ERK and Akt pathways blocked BDNF, and NT3 stimulated BTIC survival. Importantly, attenuation of BTIC growth by EGFR inhibitors could be overcome by activation of neurotrophin signaling, and neurotrophin signaling is sufficient for long term BTIC growth as spheres in the absence of EGF and FGF. Our results highlight a novel role for neurotrophin signaling in brain tumor and suggest that Trks could be a target for combinatorial treatment of malignant glioma. Background: The role of Trk neurotrophin receptors in glioma is unknown. Results: TrkB and TrkC are required for survival of brain tumor-initiating cells in the absence of EGF and FGF. Conclusion: Trk receptors can control the survival of BTICs in the absence of EGF and FGF. Significance: Trks may be important targets for treatment of malignant gliomas.
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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".