FORETINIB IS EFFECTIVE THERAPY FOR METASTATIC SONIC HEDGEHOG MEDULLOBLASTOMA
Bibliographic record
Abstract
BACKGROUND: (blind field). METHODS: Expression profiling, molecular subgrouping and analysis of somatic copy number alterations were conducted on multiple independent cohorts of patient tumour samples to examine intermediates of the MET signaling pathway in medulloblastoma. To examine the in vitro and in vivo effects of foretinib treatment; MET signalling biochemical analysis; migration and invasion assays; and foretinib pharmacokinetic studies were performed. Medulloblastoma xenografts and transgenic mouse models were used to evaluate foretinib treatment in vivo. RESULTS: We analyzed three large non-overlapping cohorts of medulloblastoma patients (discovery cohort, n = 199; validation cohort 1, n = 439; validation cohort 2, n = 285) and demonstrated that cMET, known to be involved in tumor progression and dissemination, is a marker of sonic hedgehog (SHH) medulloblastoma. Importantly, immunohistochemical analysis of activated cMET (phosphorylated cMET) in another independent patient cohort (n = 385) revealed that cMET activation correlates with increased tumor relapse and a poor survival in pediatric patients with SHH medulloblastomas, thus defining a subset of patients that may benefit from cMET targeted therapy. We show that foretinib, an FDA approved inhibitor of cMET, suppresses cMET activation, decreases proliferation and induces apoptosis, both in medulloblastoma cell lines and in SHH medulloblastoma xenografts. Furthermore foretinib penetrates the blood-brain barrier and is effective both in the primary and in the metastatic compartments. Treatment of mouse xenografts and of an aggressive transgenic model of metastatic SHH medulloblastoma with foretinib reduced primary medulloblastoma growth, decreased the incidence of metastases by 36% and increased survival by 45%. CONCLUSIONS: Our results provide strong rationale for advancing foretinib into clinical trials for SHH-driven medulloblastomas. SECONDARY CATEGORY: Tumor Biology.
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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 | 0.000 |
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".