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 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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".