Genetic Predictors of MEK Dependence in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Hyperactivated extracellular signal-regulated kinase (ERK) signaling is common in human cancer and is often the result of activating mutations in BRAF, RAS, and upstream receptor tyrosine kinases. To characterize the mitogen-activated protein kinase/ERK kinase (MEK)/ERK dependence of lung cancers harboring BRAF kinase domain mutations, we screened a large panel of human lung cancer cell lines (n = 87) and tumors (n = 916) for BRAF mutations. We found that non-small cell lung cancers (NSCLC) cells with both V600E and non-V600E BRAF mutations were selectively sensitive to MEK inhibition compared with those harboring mutations in epidermal growth factor receptor (EGFR), KRAS, or ALK and ROS kinase fusions. Supporting its classification as a "driver" mutation in the cells in which it is expressed, MEK inhibition in (V600E)BRAF NSCLC cells led to substantial induction of apoptosis, comparable with that seen with EGFR kinase inhibition in EGFR mutant NSCLC models. Despite high basal ERK phosphorylation, EGFR mutant cells were uniformly resistant to MEK inhibition. Conversely, BRAF mutant cell lines were resistant to EGFR inhibition. These data, together with the nonoverlapping pattern of EGFR and BRAF mutations in human lung cancer, suggest that these lesions define distinct clinical entities whose treatment should be guided by prospective real-time genotyping. To facilitate such an effort, we developed a mass spectrometry-based genotyping method for the detection of hotspot mutations in BRAF, KRAS, and EGFR. Using this assay, we confirmed that BRAF mutations can be identified in a minority of NSCLC tumors and that patients whose tumors harbor BRAF mutations have a distinct clinical profile compared with those whose tumors harbor kinase domain mutations in EGFR.
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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) | 0.000 | 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".