The hepatocyte growth factor/mesenchymal epithelial transition factor axis in high-risk pediatric solid tumors and the anti-tumor activity of targeted therapeutic agents
Bibliographic record
Abstract
Clinical trials completed in the last two decades have contributed significantly to the improved overall survival of children with cancer. In spite of these advancements, disease relapse still remains a significant cause of death in this patient population. Often, increasing the intensity of current protocols is not feasible because of cumulative toxicity and development of drug resistance. Therefore, the identification and clinical validation of novel targets in high-risk and refractory childhood malignancies are essential to develop effective new generation treatment protocols. A number of recent studies have shown that the hepatocyte growth factor (HGF) and its receptor Mesenchymal epithelial transition factor (c-MET) influence the growth, survival, angiogenesis, and metastasis of cancer cells. Therefore, the c-MET receptor tyrosine kinase and HGF have been identified as potential targets for cancer therapeutics and recent years have seen a race to synthesize molecules to block their expression and function. In this review we aim to summarize the literature that explores the potential and biological rationale for targeting the HGF/c-MET pathway in common and high-risk pediatric solid tumors. We also discuss selected recent and ongoing clinical trials with these agents in relapsed pediatric tumors that may provide applicable future treatments for these 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".