Abstract 2816 CRISPR screens identify GSR as a vulnerability for KEAP1/NRF2 mutant NSCLC
Bibliographic record
Abstract
OBJECTIVE: The study aims to identify key antioxidant enzymes whose inhibition reverses the profound resistance of non-small-cell lung cancer (NSCLC) with KEAP1/NRF2 mutations to oxidative stress, a key mediator of chemo/radiation therapy.METHODS: We conducted negative selection CRISPR-based screens using an antioxidant enzyme-focused sgRNA library in KEAP1 mutant NSCLC to identify genes whose loss sensitizes cells to b-Lapachone (b-Lap), a ROS inducing reagent.RESULTS: Among the genes selectively required for resistance we found GSR, which encodes the antioxidant enzyme that reduces oxidized glutathione disulfide (GSSG) to glutathione (GSH).Furthermore, we found GSR deletion strongly sensitizes NRF2 active NSCLC to ROS treatment but not KEAP1/NRF2 wildtype cells.It has been previously reported that GSH synthesis does not play a role in b-Lap sensitivity, suggesting that GSH reduction may play a unique role.Consistently, we found that GSR deletion led to a significant reduction of the GSH/GSSG ratio by promoting GSSG accumulation following b-Lap treatment.Through nontargeted metabolomics profiling, we found that inhibition of GSR led to alterations in TCA cycle metabolite levels and significant defects in mitochondrial function upon b-Lap treatment.Further, we showed GSR plays a critical role in maintaining the redox state of the mitochondria of KEAP1/ NRF2 mutant NSCLC cells.Of note, the mitochondrial thioredoxin reductase 2 is neither NRF2 regulated nor a hit in the CRISPR screens, suggesting GSR is the major antioxidant enzyme protecting the mitochondria of NRF2/KEAP1 mutant cells.CONCLUSION: These data collectively provide strong evidence that GSR represents a vulnerability for NSCLC harboring KEAP1/NRF2 mutation in response to exogenous ROS.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".