Association between certain non–small cell lung cancer driver mutations and predictive markers for chemotherapy or programmed death‐ligand 1 inhibition
Why this work is in the frame
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Bibliographic record
Abstract
This study aimed to analyze the association between driver mutations and predictive markers for some anti-tumor agents in non-small cell lung cancer (NSCLC). A cohort of 785 Chinese patients with NSCLC who underwent resection from March 2016 to November 2017 in the First Affiliated Hospital of Guangzhou Medical University was investigated. The specimens were subjected to hybridization capture and sequence of 8 important NSCLC-related driver genes. In addition, the slides were tested for PD-L1, excision repair cross-complementation group 1 (ERCC1), ribonucleotide reductase subunit M1 (RRM1), thymidylate synthase (TS) and β-tubulin III by immunohistochemical staining. A total of 498 (63.4%) patients had at least 1 driver gene alteration. Wild-type, EGFR rare mutation (mut), ALK fusion (fus), RAS mut, RET fus and MET mut had relatively higher proportions of lower ERCC1 expression. EGFR 19del, EGFR L858R, EGFR rare mut, ALK fus, HER2 mut, ROS1 fus and MET mut were more likely to have TS low expression. Wild-type, EGFR L858R, EGFR rare mut and BRAF mut were associated with lower β-tubulin III expression. In addition, wild-type, RAS mut, ROS1 fus, BRAF and MET mut had higher proportion of PD-L1 high expression. As a pilot validation, 21 wild-type patients with advanced NSCLC showed better depth of response and response rate to taxanes compared with pemetrexed/gemcitabine (31.2%/60.0% vs 26.6%/45.5%). Our study may aid in selecting the optimal salvage regimen after targeted therapy failure, or the chemo-regimen where targeted therapy has not been a routine option. Further validation is warranted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it