Prognostic and predictive effects of <i>TP53</i> mutation in patients with <i>EGFR</i>-mutated non-small cell lung cancer (NSCLC).
Bibliographic record
Abstract
11585 Background: TP53 mutations are common in NSCLC; they occur frequently with other driver mutations, and have been reported as prognostic of poor outcome. The impact of TP53 mutations in EGFR-mutated NSCLC is unclear. Methods: Tissue from 73 patients with EGFR-mutated NSCLC at Princess Margaret Cancer Centre were analyzed by next-generation sequencing. TP53status was correlated with baseline demographics (sex, ethnicity, age, stage, CNS mets, smoking history, treatment) and outcomes (response [ORR], progression-free [PFS] and overall survival [OS]). Results: Dual TP53/EGFR mutations were found in 31 patients (42%), and were associated with younger age (median 53 v 64 y, p < 0.001). There were more patients with multiple EGFR mutations in the TP53 WT group (19% vs 0%) and more exon 19 deletions in TP53 mut group (68% vs 52%, p = 0.047). OS for all 73 patients was influenced by stage at diagnosis (HR 2.91 stage III, 4.27 stage IV, v I-II) and TP53 mut (HR 2.05, CI 1.02-4.12, p = 0.04). Only 59 patients (26 TP53 mut; 33 TP53 WT) received EGFR TKIs. ORR was not significantly different for TP53 WT v mut (64% v 54%, p = 0.62). In univariable analysis, non-Caucasian ethnicity (HR 0.47, CI 0.25-0.88, p = 0.02) and dual TP53/EGFR mut (HR 1.91, CI 1.04-3.49, p = 0.04) were associated with significant differences in PFS on EGFR TKIs, but not in multivariable analysis. Among 38 patients (19 in each group) treated with chemotherapy, ORR was not influenced by TP53 status. Among 12 patients tested, 4 (80%) TP53 WT and 3 (43%) TP53 mut had T790M mutations. All 6 patients treated with T790M inhibitors (2 TP53 mut; 4 TP53 WT) achieved partial response. There was a non-significant trend for more brain metastases with dual TP53/EGFR mut (58% v 37% at 5 years, p = 0.10); when excluding patients with brain metastases at diagnosis, time to brain metastases was significantly shorter in patients with dual TP53/EGFRmut (HR 2.2, CI 1.07-4.54, p = 0.03). Conclusions: In patients with EGFR-mutated NSCLC, TP53 mutation negatively impacts OS and PFS on EGFR TKIs, and may be associated with brain metastases. Larger datasets are required to validate whether TP53 mutational status is an independent factor predictive of differential benefit from EGFR TKIs.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".