Concordance of Abundance for Mutational EGFR and Co-Mutational TP53 with Efficacy of EGFR-TKI Treatment in Metastatic Patients with Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
The present study aimed to investigate the influence of the mutation abundance of the epidermal growth factor receptor (EGFR) and its co-mutation with TP53 on the therapeutic efficacy of tyrosine kinase inhibitor (TKI) treatment in patients with metastatic lung adenocarcinoma (LUAD). In total, 130 patients (January 2018-September 2022) with metastatic LUAD from the Second Affiliated Hospital of Zhejiang University were included. Kaplan–Meier analysis was performed to measure the duration of drug application (DDA) and the log-rank test was used to compare differences. Univariate and multivariate analyses of Cox proportional hazard regression models were used to evaluate the association between the relevant clinicopathological factors and DDA. Hazard ratios with 95% confidence intervals were also calculated. Among the 130 patients who were treated with first-generation EGFR-TKIs, 86 showed high-EGFR mutation abundance (>22.0%) and 44 showed low-EGFR mutation abundance (≤22.0%). Patients in the high-EGFR group had a greater DDA than those in the low-EGFR group (p < 0.05). The results of the subgroup analysis were consistent with those of the total mutation population (exon19: >18.5% vs. ≤18.5%, 14 months vs. 10 months, p = 0.049; exon21: >22.0% vs. ≤22.0%, 15 months vs. 9 months, p = 0.005). In addition, the mutation abundance of TP53 was negatively correlated with the DDA (p < 0.05). Patients in the combination group had a better DDA than those in the monotherapy group (p < 0.05). Subgroup analysis showed that, among the low mutation abundance of the EGFR exon 21 or 19 cohort, the combination group had a better DDA than the monotherapy group (p < 0.05). An EGFR mutation abundance greater than 22.0% was a positive predictor of DDA in patients with metastatic LUAD. However, a TP53 mutation abundance higher than 32.5% could reverse this situation. Finally, first-line treatment with EGFR-TKIs plus chemotherapy is a potential treatment strategy for patients with low-abundance EGFR mutations.
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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 | 0.001 |
| 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 | 0.000 | 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".