Aberrant MET Receptor Tyrosine Kinase Signaling in Glioblastoma: Targeted Therapy and Future Directions
Bibliographic record
Abstract
Brain tumors represent a heterogeneous group of neoplasms characterized by a high degree of aggressiveness and a poor prognosis. Despite recent therapeutic advances, the treatment of brain tumors, including glioblastoma (GBM), an aggressive primary brain tumor associated with poor prognosis and resistance to therapy, remains a significant challenge. Receptor tyrosine kinases (RTKs) are critical during development and in adulthood. Dysregulation of RTKs through activating mutations and gene amplification contributes to many human cancers and provides attractive therapeutic targets for treatment. Under physiological conditions, the Met RTK, the hepatocyte growth factor/scatter factor (HGF/SF) receptor, promotes fundamental signaling cascades that modulate epithelial-to-mesenchymal transition (EMT) involved in tissue repair and embryogenesis. In cancer, increased Met activity promotes tumor growth and metastasis by providing signals for proliferation, survival, and migration/invasion. Recent clinical genomic studies have unveiled multiple mechanisms by which MET is genetically altered in GBM, including focal amplification, chromosomal rearrangements generating gene fusions, and a splicing variant mutation (exon 14 skipping, METex14del). Notably, MET overexpression contributes to chemotherapy resistance in GBM by promoting the survival of cancer stem-like cells. This is linked to distinctive Met-induced pathways, such as the upregulation of DNA repair mechanisms, which can protect tumor cells from the cytotoxic effects of chemotherapy. The development of MET-targeted therapies represents a major step forward in the treatment of brain tumours. Preclinical studies have shown that MET-targeted therapies (monoclonal antibodies or small molecule inhibitors) can suppress growth and invasion, enhancing the efficacy of conventional therapies. Early-phase clinical trials have demonstrated promising results with MET-targeted therapies in improving overall survival for patients with recurrent GBM. However, challenges remain, including the need for patient stratification, the optimization of treatment regimens, and the identification of mechanisms of resistance. This review aims to highlight the current understanding of mechanisms underlying MET dysregulation in GBM. In addition, it will focus on the ongoing preclinical and clinical assessment of therapies targeting MET dysregulation in GBM.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".