NRF2 Pathway Activation and Adjuvant Chemotherapy Benefit in Lung Squamous Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: Genomic profiling of lung squamous cell carcinomas (SCC) has identified NRF2 pathway alterations, which activate oxidative response pathways, in one third of tumors. Preclinical data suggest these tumors may be resistant to platinum-based chemotherapy. We evaluated the clinical relevance of these findings and assessed whether NRF2 activation predicts benefit from adjuvant chemotherapy in SCC. EXPERIMENTAL DESIGN: Logistic regression (LR) and significance analysis of microarrays (SAM) were applied to all 104 TCGA (The Cancer Genome Atlas) SCC cases that had microarray gene expression and mutation data to identify genes associated with somatic NRF2 pathway alterations. The resulting signature (NRF2(ACT)) was tested in 3 independent SCC datasets to evaluate its prognostic and predictive effects. IHC and sequencing for NRF2 and KEAP1 were evaluated in one cohort (n = 43) to assess the relationship between gene expression, mutational status, and protein expression. RESULTS: Twenty-eight genes were identified by overlap between LR (291 genes) and SAM (30 genes), and these consistently separated SCC into 2 groups in all datasets, corresponding to putatively NRF pathway-activated and wild-type (WT) tumors. NRF2(ACT) was not prognostic. However, improved survival with adjuvant chemotherapy in the JBR.10-randomized trial appears limited to patients with the WT signature (HR 0.32, P = 0.16; NRF2(ACT) HR 2.28, P = 0.48; interaction P = 0.15). NRF2(ACT) was highly correlated with mutations in NRF2 and KEAP1, and with high NRF2 protein expression. CONCLUSIONS: A gene expression signature of NRF2 pathway activation is associated with benefit from adjuvant cisplatin/vinorelbine in SCC. Patients with NRF2 pathway-activating somatic alterations may have reduced benefit from this therapy.
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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 | 0.001 | 0.001 |
| 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".