SYK-mediated epithelial cell state is associated with response to c-Met inhibitors in c-Met-overexpressing lung cancer
Bibliographic record
Abstract
Genomic MET amplification and exon 14 skipping are currently clinically recognized biomarkers for stratifying subsets of non-small cell lung cancer (NSCLC) patients according to the predicted response to c-Met inhibitors (c-Metis), yet the overall clinical benefit of this strategy is quite limited. Notably, c-Met protein overexpression, which occurs in approximately 20-25% of NSCLC patients, has not yet been clearly defined as a clinically useful biomarker. An optimized strategy for accurately classifying patients with c-Met overexpression for decision-making regarding c-Meti treatment is lacking. Herein, we found that SYK regulates the plasticity of cells in an epithelial state and is associated with their sensitivity to c-Metis both in vitro and in vivo in PDX models with c-Met overexpression regardless of MET gene status. Furthermore, TGF-β1 treatment resulted in SYK transcriptional downregulation, increased Sp1-mediated transcription of FRA1, and restored the mesenchymal state, which conferred resistance to c-Metis. Clinically, a subpopulation of NSCLC patients with c-Met overexpression coupled with SYK overexpression exhibited a high response rate of 73.3% and longer progression-free survival with c-Meti treatment than other patients. SYK negativity coupled with TGF-β1 positivity conferred de novo and acquired resistance. In summary, SYK regulates cell plasticity toward a therapy-sensitive epithelial cell state. Furthermore, our findings showed that SYK overexpression can aid in precisely stratifying NSCLC patients with c-Met overexpression regardless of MET alterations and expand the population predicted to benefit from c-Met-targeted therapy.
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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".