Nrf2 Hyperactivation as a Driver of Radiotherapy Resistance and Suppressed Antitumor Immunity in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: Alterations in the KEAP1/NFE2L2 (NRF2)/CUL3 pathway occur in ∼20% of human head and neck squamous cell carcinomas (HNSCC) and are associated with resistance to standard-of-care therapy. However, this pathway's role in radiotherapy resistance in HNSCC has not been well studied. EXPERIMENTAL DESIGN: We generated genetically engineered mouse models and developed primary murine cancer cell lines harboring mutations commonly observed in human HNSCC, including inducible activation of PIK3CA and deletion of Trp53, with or without Keap1 loss. Primary tumors were initiated via 4-hydroxytamoxifen injection ± the tobacco carcinogen benzo[a]pyrene (BAP) into the oral buccal mucosa. Tumors were analyzed by Western blotting, IHC, and RNA sequencing and subjected to fractionated radiotherapy to investigate the role of the KEAP1/NRF2 pathway in radioresistance and modulation of the tumor-immune microenvironment. RESULTS: BAP exposure accelerated primary tumor formation within 1 month, with histologic analysis confirming invasive squamous cell carcinoma, validated by cytokeratin and differentiation marker expression. Primary cell lines derived from Keap1-haploinsufficient tumors exhibited upregulation of NRF2 target genes and a radioresistant phenotype, which was reversed after Nrf2 knockdown in vitro. Bulk RNA sequencing revealed that Keap1 haploinsufficiency correlated with NRF2 pathway activation, increased myeloid infiltration, and enhanced angiogenic signatures. In vivo, Keap1 haploinsufficiency promoted accelerated tumor growth and decreased survival. Finally, using fractionated radiotherapy, we showed that Keap1-haploinsufficient primary tumors were significantly more radioresistant than Keap1-proficient tumors, regardless of BAP exposure. CONCLUSIONS: These data demonstrate that Keap1 haploinsufficiency in HNSCC is linked to unfavorable tumor-immune microenvironment, aggressive growth, and a radioresistant phenotype.
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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".