MET Exon 14 Splice-Site Mutations Preferentially Activate KRAS Signaling to Drive Tumourigenesis
Bibliographic record
Abstract
Targeted therapies for MET exon 14-skipping (METΔex14)-driven lung cancers have generated some promising results but response rates remain below that seen for other kinase-driven cancers. One strategy for improving treatment outcomes is to employ rational combination therapies to enhance the suppression of tumour growth and delay or prevent the emergence of resistance. To this end, we profiled the transcriptomes of MET-addicted lung tumours and cell lines and identified the RAS-mitogen-activated protein kinase (MAPK) pathway as a critical effector required for METΔex14-dependent growth. Ectopic expression of MET in an isogenic cell line model showed that overexpression of the mutant MET receptor led to higher levels of MAPK phosphorylation and nuclear import, resulting in increased expression and phosphorylation of nuclear MAPK targets. In comparison, other known MET effectors were unaffected. Inhibition of this pathway by KRAS knockdown in MET-addicted cells in vitro led to decreased viability in only the METΔex14-mutant cells. Conversely, decoupling RAS-MAPK axis, but not other effector pathways, from MET activity via the introduction of constitutively active mutants conferred resistance to MET inhibitors in vitro. Our results suggest that aberrant hyperactivity of the MET receptor caused by the exon 14-skipping mutation does not uniformly upregulate all known downstream effectors, rather gaining a predilection for aberrantly activating and subsequently relying on the RAS-MAPK pathway. These findings provide a rationale for the co-targeting of the RAS-MAPK pathway alongside MET to prolong therapeutic response and circumvent resistance to improve patient survival.
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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".