Pooled Analysis of the Prognostic and Predictive Effects of <i>KRAS</i> Mutation Status and <i>KRAS</i> Mutation Subtype in Early-Stage Resected Non–Small-Cell Lung Cancer in Four Trials of Adjuvant Chemotherapy
Bibliographic record
Abstract
PURPOSE: We undertook this analysis of KRAS mutation in four trials of adjuvant chemotherapy (ACT) versus observation (OBS) to clarify the prognostic/predictive roles of KRAS in non-small-cell lung cancer (NSCLC). METHODS: KRAS mutation was determined in blinded fashion. Exploratory analyses were performed to characterize relationships between mutation status and subtype and survival outcomes using a multivariable Cox model. RESULTS: Among 1,543 patients (763 OBS, 780 ACT), 300 had KRAS mutations (codon 12, n = 275; codon 13, n = 24; codon 14, n = 1). In OBS patients, there was no prognostic difference for overall survival for codon-12 (mutation v wild type [WT] hazard ratio [HR] = 1.04; 95% CI, 0.77 to 1.40) or codon-13 (HR = 1.01; 95% CI, 0.47 to 2.17) mutations. No significant benefit from ACT was observed for WT-KRAS (ACT v OBS HR = 0.89; 95% CI, 0.76 to 1.04; P = .15) or codon-12 mutations (HR = 0.95; 95% CI, 0.67 to 1.35; P = .77); with codon-13 mutations, ACT was deleterious (HR = 5.78; 95% CI, 2.06 to 16.2; P < .001; interaction P = .002). There was no prognostic effect for specific codon-12 amino acid substitution. The effect of ACT was variable among patients with codon-12 mutations: G12A or G12R (HR = 0.66; P = .48), G12C or G12V (HR = 0.94; P = .77) and G12D or G12S (HR = 1.39; P = .48; comparison of four HRs, including WT, interaction P = .76). OBS patients with KRAS-mutated tumors were more likely to develop second primary cancers (HR = 2.76, 95% CI, 1.34 to 5.70; P = .005) but not ACT patients (HR = 0.66; 95% CI, 0.25 to 1.75; P = .40; interaction, P = .02). CONCLUSION: KRAS mutation status is not significantly prognostic. The potential interaction in patients with codon-13 mutations requires validation. At this time, KRAS status cannot be recommended to select patients with NSCLC for ACT.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.000 | 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".