Prognostic and Predictive Importance of p53 and RAS for Adjuvant Chemotherapy in Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: p53 and RAS are multifunctional proteins that are critical to cell cycle regulation, apoptosis, cell survival, gene transcription, response to stress, and DNA repair. We have evaluated the prognostic and predictive value of p53 gene/protein aberrations using tumor samples from JBR.10, a North American phase III intergroup trial that randomly assigned 482 patients with completely resected stage IB and II non-small-cell lung cancer (NSCLC) to receive four cycles of adjuvant cisplatin plus vinorelbine or observation alone. METHODS: p53 protein expression was evaluated by immunohistochemistry. Mutations in exons 5 to 9 of the p53 gene were determined by denaturing high-performance liquid chromatography and confirmed by sequencing. RAS mutations were identified by allelic specific oligonucleotide hybridization. RESULTS: Of 253 patients, 132 (52%) were positive for p53 protein overexpression. Untreated p53-positive patients had significantly shorter overall survival than did patients with p53-negative tumors (hazard ratio [HR] = 1.89; 95% CI, 1.07 to 3.34; P = .03). However, these p53-positive patients also had a significantly greater survival benefit from adjuvant chemotherapy (HR = 0.54; P = .02) compared with patients with p53-negative tumors (HR = 1.40; P = .26; interaction P = .02). Mutations of p53 and RAS genes were found in 124 (31%) of 397 and 117 (26%) of 450 patients, respectively. Mutations in these genes were neither prognostic for survival nor predictive of a differential benefit from adjuvant chemotherapy. CONCLUSION: p53 protein overexpression is a significant prognostic marker of shortened survival, and also a significant predictive marker for a differentially greater benefit from adjuvant chemotherapy in completely resected NSCLC patients.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".