Inhibition of the MET Receptor Tyrosine Kinase as a Novel Therapeutic Strategy in Medulloblastoma
Bibliographic record
Abstract
Medulloblastoma is the most common pediatric posterior fossa malignancy, with a 5-year overall survival of only 60% and many survivors experiencing treatment-related morbidity secondary to current therapeutic regimens. With an improved understanding of the molecular basis for this disease, the opportunity to develop novel treatments with more tolerable toxicity profiles that target key molecular pathways, now exists. Recently, the hepatocyte growth factor (HGF)/MET signaling pathway has been implicated in medulloblastoma pathogenesis. Several therapeutic strategies targeting this pathway exist, including small molecule inhibitor therapy against the MET receptor tyrosine kinase. We examined the in vitro efficacy of targeting the MET receptor using the highly specific small molecule inhibitor PHA665752 as a novel treatment strategy in medulloblastoma. MET inhibition using PHA665752 was effective at reducing the proliferative capacity of the D283, ONS76, and MED8A medulloblastoma cell lines as assessed by MTS assay. Furthermore, PHA665752 treatment reduced D283 and ONS76 cell motility and impaired the growth of D283 cells in soft agar. Pretreatment of D283, ONS76, and MED8A cells with PHA665752 blocked exogenous recombinant human HGF-induced up-regulation of the downstream RAS/mitogen-activated protein kinase signaling pathway in D283, ONS76 and MED8A cell lines. Similarly, PHA665752 prevented HGF-induced phosphatidylinositol 3-kinase/AKT signaling in ONS76 and MED8A cells. These results highlight the efficacy of targeting the MET receptor tyrosine kinase therapeutically in medulloblastoma and provide support for further preclinical testing of small molecule inhibitors targeting the MET receptor in 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".