Inflammation-Related Genetic Variations and Survival in Patients With Advanced Non–Small Cell Lung Cancer Receiving First-Line Chemotherapy
Bibliographic record
Abstract
Accurate prognostic prediction is challenging for patients with advanced-stage non-small cell lung cancer (NSCLC). We systematically investigated genetic variants within inflammation pathways as potential prognostic markers for advanced-stage NSCLC patients treated with first-line chemotherapy. A discovery phase in 502 patients and an internal validation phase in 335 patients were completed at the MD Anderson Cancer Center. External validation was performed in 371 patients at Harvard University. A missense single-nucleotide polymorphism (SNP) in the gene encoding the major histocompatibility complex class II, DO-β chain (HLA-DOB:rs2071554), predicted to influence protein function, was significantly associated with poor survival in the discovery (hazard ratio (HR): 1.46; 95% confidence interval (CI): 1.02-2.09), internal validation (HR: 1.51; 95% CI: 1.02-2.25), and external validation (HR: 1.52; 95% CI: 1.01-2.29) populations. KLRK1:rs2900420 was associated with reduced risk in the discovery (HR: 0.76; 95% CI: 0.60-0.96), internal validation (HR: 0.77; 95% CI: 0.61-0.99), and external validation (HR: 0.80; 95% CI: 0.63-1.02) populations. A strong cumulative effect on overall survival was observed for these SNPs. Genetic variations in inflammation-related genes could have potential to complement prediction of prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".