ERBB3 and IGF1R Signaling Are Required for Nrf2-Dependent Growth in KEAP1-Mutant Lung Cancer
Bibliographic record
Abstract
(encoding the protein Nrf2) are prevalent in both adeno and squamous subtypes of non-small cell lung cancer, as well as additional tumor indications. The consequence of these mutations is stabilized Nrf2 and chronic induction of a battery of Nrf2 target genes. We show that knockdown of Nrf2 caused modest growth inhibition of cells growing in two-dimension, which was more pronounced in cell lines expressing mutant KEAP1. In contrast, Nrf2 knockdown caused almost complete regression of established KEAP1-mutant tumors in mice, with little effect on wild-type (WT) KEAP1 tumors. The strong dependency on Nrf2 could be recapitulated in certain anchorage-independent growth environments and was not prevented by excess extracellular glutathione. A CRISPR screen was used to investigate the mechanism(s) underlying this dependence. We identified alternative pathways critical for Nrf2-dependent growth in KEAP1-mutant cell lines, including the redox proteins thioredoxin and peroxiredoxin, as well as the growth factor receptors IGF1R and ERBB3. IGF1R inhibition was effective in KEAP1-mutant cells compared with WT, especially under conditions of anchorage-independent growth. These results point to addiction of KEAP1-mutant tumor cells to Nrf2 and suggest that inhibition of Nrf2 or discrete druggable Nrf2 target genes such as IGF1R could be an effective therapeutic strategy for disabling these tumors. SIGNIFICANCE: This study identifies pathways activated by Nrf2 that are important for the proliferation and tumorigenicity of KEAP1-mutant non-small cell lung cancer.
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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".