TP53 Mutation Mapping in Advanced Non-Small Cell Lung Cancer: A Real-World Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: TP53 is frequently mutated in solid tumors, but its basic mutation mapping is mixed, particularly in aggressive-stage lung cancer. EXPERIMENTAL DESIGN: We curated a total of 139 advanced non-small cell lung cancer (NSCLC) patients who harbored wild-type TP53 (TP53wt) or mutated TP53 (TP53mut) based on next-generation sequencing (NGS) to analyze multiple-dimensional data types, including tumor mutation burden (TMB), programmed death receptor ligand 1 (PD-L1) expression, co-mutant alterations, hotspot mutations distribution, and therapy response. RESULTS: TP53 was evident in 125 mutations and significantly associated with male sex, adenocarcinoma differentiation, smoking history, PD-L1 tumor proportion score, and TMB level. The most frequent mutations were distributed on exon 8, but there were no distinct hotspot mutations. After outlining the co-mutation genes, it is interesting to note that DNA damage repair (DDR) genes were frequent alterations in the mutated TP53 cohort. Even though there was no significant difference between the TP53wt and TP53mut cohorts on therapy response, patients with nucleotide variation in G>T achieved a relatively higher durable clinical benefit (DCB) rate. CONCLUSIONS: This real-world retrospective study suggests that molecular stratification on the basis of TP53 mutations should be deeply explored for NSCLC to optimize and modify clinical therapy choices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".