PIDD interaction with KEAP1 as a new mutation-independent mechanism to promote NRF2 stabilization and chemoresistance in NSCLC
Bibliographic record
Abstract
Chemotherapy resistance is a major problem in non-small cell lung cancer (NSCLC) treatment. A major mechanism of chemoresistance involves stabilization of the NRF2 transcription factor. NRF2 levels are normally tightly regulated through interaction with KEAP1, an adaptor that targets NRF2 to the CUL3 E3 ubiquitin ligase for proteolysis. In NSCLC, aberrant NRF2 stabilization is best understood through mutations in NRF2, KEAP1, or CUL3 that disrupt their interaction. Biochemical studies, however, have revealed that NRF2 can also be stabilized through expression of KEAP1-interacting proteins that competitively sequester KEAP1 away from NRF2. Here, we have identified PIDD, as a novel KEAP1-interactor in NSCLC that regulates NRF2. We show that this interaction allows PIDD to reduce NRF2 ubiquitination and increase its stability. We also demonstrate that PIDD promotes chemoresistance in NSCLC cells both in vitro and in vivo, and that this effect is dependent on NRF2. Finally, we report that NRF2 protein expression in a NSCLC cohort exceeds the typical incidence of combined NRF2, KEAP1, and CUL3 mutations, and that NRF2 expression in this cohort is correlated with PIDD levels. Our data identify PIDD as a new NRF2 regulator, and suggest that variations in PIDD levels contribute to differential chemosensitivities among NSCLC patients.
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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".