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 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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".