Foretinib Is Effective Therapy for Metastatic Sonic Hedgehog Medulloblastoma
Bibliographic record
Abstract
Medulloblastoma is the most common malignant pediatric brain tumor, with metastases present at diagnosis conferring a poor prognosis. Mechanisms of dissemination are poorly understood and metastatic lesions are genetically divergent from the matched primary tumor. Effective and less toxic therapies that target both compartments have yet to be identified. Here, we report that the analysis of several large nonoverlapping cohorts of patients with medulloblastoma reveals MET kinase as a marker of sonic hedgehog (SHH)-driven medulloblastoma. Immunohistochemical analysis of phosphorylated, active MET kinase in an independent patient cohort confirmed its correlation with increased tumor relapse and poor survival, suggesting that patients with SHH medulloblastoma may benefit from MET-targeted therapy. In support of this hypothesis, we found that the approved MET inhibitor foretinib could suppress MET activation, decrease tumor cell proliferation, and induce apoptosis in SHH medulloblastomas in vitro and in vivo. Foretinib penetrated the blood-brain barrier and was effective in both the primary and metastatic tumor compartments. In established mouse xenograft or transgenic models of metastatic SHH medulloblastoma, foretinib administration reduced the growth of the primary tumor, decreased the incidence of metastases, and increased host survival. Taken together, our results provide a strong rationale to clinically evaluate foretinib as an effective therapy for patients with SHH-driven medulloblastoma.
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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".