Pooled Analysis of the Prognostic and Predictive Effects of <i>TP53</i> Comutation Status Combined With <i>KRAS</i> or <i>EGFR</i> Mutation in Early-Stage Resected Non–Small-Cell Lung Cancer in Four Trials of Adjuvant Chemotherapy
Bibliographic record
Abstract
Purpose Our previous work evaluated individual prognostic and predictive roles of TP53, KRAS, and EGFR in non-small-cell lung cancer (NSCLC). In this analysis, we explore the prognostic and predictive roles of TP53/KRAS and TP53/EGFR comutations in randomized trials of adjuvant chemotherapy versus observation. Patients and Methods Mutation analyses (wild-type [WT] and mutant) for TP53, KRAS, and EGFR were determined in blinded fashion in multiple laboratories. Primary and secondary end points of pooled analysis were overall survival and disease-free survival. We evaluated the role of TP53/KRAS comutation in all patients and in the adenocarcinoma subgroup as well as the TP53/EGFR comutation in adenocarcinoma only through a multivariable Cox proportional hazards model stratified by trial. Results Of 3,533 patients with NSCLC, 1,181 (557 deaths) and 404 (170 deaths) were used for TP53/KRAS and TP53/EGFR analyses. For TP53/KRAS mutation status, no prognostic effect was observed ( P = .61), whereas a borderline predictive effect ( P = .04) was observed with a deleterious effect of chemotherapy with TP53/KRAS comutations versus WT/WT (hazard ratio, 2.49 [95% CI, 1.10 to 5.64]; P = .03). TP53/EGFR comutation in adenocarcinoma was neither prognostic ( P = .83), nor significantly predictive ( P = .86). Similar results were observed for both groups for disease-free survival. Conclusion We could identify no prognostic effect of the KRAS or EGFR driver and TP53 tumor suppressor comutation. Our observation of a potential negative predictive effect of TP53/KRAS comutation requires validation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".