Pan-Cancer Analysis of Oncogenic MET Fusions Reveals Distinct Pathogenomic Subsets with Differential Sensitivity to MET-Targeted Therapy
Bibliographic record
Abstract
MET fusions (MET-F) are oncogenic drivers that remain poorly characterized. Analysis of 56 MET-F-positive tumors from an institutional cohort of 91,119 patients (79,864 DNA sequencing plus 11,255 RNA sequencing) uncovered two forms of MET-F pathobiology. The first group featured 5' partners with homodimerization domains fused in-frame with the MET tyrosine kinase domain, primarily originated from translocations, frequently excluded MET exon 14, mediated oncogenesis through cytoplasmic aggregation and constitutive activation, and were markedly sensitive to MET tyrosine kinase inhibitors (TKI) in preclinical models and patients with lung cancer. The second group lacked partner homodimerization motifs and retained MET transmembrane and extracellular domains. Their pathogenesis involved intrachromosomal rearrangements, resulting in partner selection for promoter hijacking and fusion allele amplification. Membrane-bound fusions were enriched in gliomas with receptor tyrosine kinase co-alterations. We provide a framework to comprehend the heterogeneous landscape of MET-Fs, supporting that fusion oncogenicity and MET TKI sensitivity are determined by structural topology and pathogenomic context. SIGNIFICANCE: MET fusions are primary drivers of tumor growth in multiple tumor types - lung cancer and gliomas - and can be effectively targeted with either type I (crizotinib, capmatinib, tepotinib, and savolitinib) or type II (cabozantinib) MET TKIs, with best responses in tumors harboring fusions with partner homodimerization.
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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 | 0.001 | 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".