The <scp>NRF2</scp> antagonist <scp>ML385</scp> inhibits <scp>PI3K‐mTOR</scp> signaling and growth of lung squamous cell carcinoma cells
Bibliographic record
Abstract
BACKGROUND: Lung squamous cell carcinoma (LUSC) currently has limited therapeutic options because of the relatively few validated targets and the lack of clinical drugs for some of these targets. Although NRF2/NFE2L2 pathway activation commonly occurs in LUSC, NRF2 has predominantly been studied in other cancer models. Here, we investigated the function of NRF2 in LUSC, including in organoid models, and we explored the activity of a small molecule NRF2 inhibitor ML385, which has not previously been investigated in LUSC. METHODS: We first explored the role of NRF2 signaling in LUSC cancer cell line and organoid proliferation through NRF2 knockdown or ML385 treatment, both in vivo and in vitro. Next, we performed Western blot and immunofluorescence assays to determine the effect of NRF2 inhibition on PI3K-mTOR signaling. Finally, we used cell viability and clonogenic assays to explore whether ML385 could sensitize LUSC cancer cells to PI3K inhibitors. RESULTS: We find that downregulation of NRF2 signaling inhibited proliferation of LUSC cancer cell lines and organoids, both in vivo and in vitro. We also demonstrate that inhibition of NRF2 reduces PI3K-mTOR signaling, with two potential mechanisms being involved. Although NRF2 promotes AKT phosphorylation, it also acts downstream of AKT to increase RagD protein expression and recruitment of mTOR to lysosomes after amino acid stimulation. We also find that ML385 potentiates LUSC growth inhibition by a pan-PI3K inhibitor, which correlates with stronger inhibition of PI3K-mTOR signaling. CONCLUSIONS: Our data provide additional support for NRF2 promoting LUSC growth through PI3K-mTOR activation and support development of NRF2 inhibitors for the treatment of LUSC.
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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 | 0.001 |
| 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".