Prognostic effects of <i>TP53</i> variants co-occuring in patients with <i>HER2</i>-mutated non-small cell lung cancer (NSCLC).
Bibliographic record
Abstract
e21065 Background: TP53 variants are common in NSCLC and have been reported as prognostic of poor outcome. The impact of TP53 variants in ERBB2/HER2-mutated NSCLC is unclear. Methods: A total of 40 patients with HER2-mutated NSCLC at Princess Margaret Cancer Centre were identified retrospectively; next generation sequencing results were reviewed for TP53 variant status. Associations between TP53 status with patient and tumor characteristics, and outcomes (overall survival [OS] and progression-free survival [PFS]), were investigated. Median survival was computed by Kaplan-Meier estimator; differences between groups were assessed via Cox proportional hazards regression. Results: Co-occurring TP53 variants and HER2 mutations were found in 19/40 patients (47.5%). Median age was 64.5 years and most patients were female (65%), never-smokers (74%), and had adenocarcinoma (90%; Table 1). Median OS was 39.3 months (95% CI:31 months – not reached [NR]) in the TP53 wildtype (WT) cohort compared to 20.5 months (95% CI:3.7 months - NR) in the TP53 variant cohort (HR 2.65 (95% CI: 1.09-6.43; p = 0.03). Median PFS was 30.9 months [16.7-59.8] vs 15.9 months [10.2-NR], although not statistically significant (HR 1.86, 95% CI 0.83-4.18; p = 0.13). There was a tendency for presence of brain metastases at any point for those with TP53 variants (38% versus 63%, p = 0.11). Of the 29 de novo stage IV patients, 10 (36%) had first line targeted treatment. The rest had chemoimmunotherapy (29%), chemotherapy (25%) or immunotherapy (11%). Overall response rates were similar to first line palliative treatment (33% vs 38%, p = 1.00). Conclusions: Co-occuring TP53 variants with HER2 mutations are prognostic of shorter OS and PFS in NSCLC, and may be associated with a higher incidence of brain metastases. Our larger validation dataset with ~75 patients will be presented.[Table: see text]
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".