Genetic Modifiers of Progression-Free Survival in Never-Smoking Lung Adenocarcinoma Patients Treated with First-Line Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
RATIONALE: Patients with non-small cell lung cancer (NSCLC) with mutated epidermal growth factor receptor (EGFR) are relatively sensitive to EGFR-tyrosine kinase inhibitor (TKI) treatment and have longer progression-free survival (PFS) when treated with EGFR-TKI compared with platinum-based chemotherapy. However, many patients with advanced NSCLC who have mutated EGFR do not respond to first-line EGFR-TKI treatment and still have shorter PFS. OBJECTIVES: The aim of this study was to identify genetic variants associated with PFS among patients with lung adenocarcinoma who were treated with first-line EGFR-TKIs. METHODS: A genome-wide association study on PFS was performed in never-smoking women diagnosed with lung adenocarcinoma and who were treated with first-line EGFR-TKIs (n = 128). Significant single-nucleotide polymorphisms (SNPs) were selected for follow-up association analysis (n = 198) and for replication assay in another independent cohort (n = 153). MEASUREMENTS AND MAIN RESULTS: ) and with an estimated hazard ratio of more than 4. This association was also replicated in a larger but similar cohort and in an independent NSCLC cohort. Follow-up functional analyses showed that these SNPs were associated with the expression of EGFR, which encodes the TKI target, and with a nearby gene neuromedin-U, which encodes a G protein-coupled receptor ligand known to be involved in the progression of NSCLC. Considering these as possible prognostic biomarkers for the treatment of patients with late-stage lung cancer, we found that these SNPs were not associated with EGFR mutation status or with polymorphism of the Bcl2-interacting mediator of cell death gene. CONCLUSIONS: Genetic variants in 4q12 merit further investigation to assess their potential as pharmacogenomic predictors for and to understand the biology underlying its influence on PFS in patients treated with TKI 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".