MEK inhibitor resistance in lung cancer cells associated with addiction to sustained ERK suppression
Bibliographic record
Abstract
Abstract MEK inhibitors have yielded limited efficacy in KRAS-mutant lung adenocarcinoma (LUAD) patients due to drug resistance. We established trametinib-resistant KRAS-mutant LUAD cells and describe a state of “drug addiction” in a subset of resistant cases where cells are dependent on trametinib for survival. Dependence on ERK2 suppression underlies this phenomenon whereby trametinib removal hyperactivates ERK and results in ER stress and apoptosis. Amplification of KRAS G12C occurs in drug-addicted cells and blocking mutant specific activity with AMG 510 rescues the lethality after trametinib withdrawal. Furthermore, increased KRAS G12C expression is lethal to other KRAS mutant LUAD cells, consequential to ERK hyperactivation. Our study represents the first instance of this phenotype associated with KRAS amplification and demonstrates that acquired genetic changes that develop in the background of MAPK suppression can have unique consequence. We suggest that the presence of mutant KRAS amplification in patients may identify those that may benefit from a “drug holiday” to circumvent drug resistance. These findings demonstrate the toxic potential of hyperactive ERK signaling and highlight potential therapeutic opportunities in patients bearing KRAS mutations.
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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) | 0.002 | 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".