Microenvironmental Activation of Nrf2 Restricts the Progression of Nrf2-Activated Malignant Tumors
Bibliographic record
Abstract
The transcription factor Nrf2 activates transcription of cytoprotective genes during oxidative and electrophilic insults. Nrf2 activity is regulated by Keap1 in a stress-dependent manner in normal cells, and somatic loss-of-function mutations of Keap1 are known to induce constitutive Nrf2 activation, especially in lung adenocarcinomas, conferring survival and proliferative benefits to tumors. Therefore, several therapeutic strategies that aim to inhibit Nrf2 in tumors have been developed for the treatment of Nrf2-activated cancers. Here we addressed whether targeting Nrf2 activation in the microenvironment can suppress the progression of Nrf2-activated tumors. We combined two types of Keap1-flox mice expressing variable levels of Keap1 with a Kras-driven adenocarcinoma model to generate Keap1-deficient lung tumors surrounded by normal or Keap1-knockdown host cells. In this model system, activation of Nrf2 in the microenvironment prolonged the survival of Nrf2-activated tumor-bearing mice. The Nrf2-activated microenvironment suppressed tumor burden; in particular, preinvasive lesion formation was significantly suppressed. Notably, loss of Nrf2 in bone marrow-derived cells in Nrf2-activated host cells appeared to counteract the suppression of Nrf2-activated cancer progression. Thus, these results demonstrate that microenvironmental Nrf2 activation suppresses the progression of malignant Nrf2-activated tumors and that Nrf2 activation in immune cells at least partially contributes to these suppressive effects. SIGNIFICANCE: This study clarifies the importance of Nrf2 activation in the tumor microenvironment and in the host for the suppression of malignant Nrf2-activated cancers and proposes new cancer therapies utilizing inducers of Nrf2.
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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) | 0.001 | 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".