Ethnicity affects EGFR and KRAS gene alterations of lung adenocarcinoma
Why this work is in the frame
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Bibliographic record
Abstract
Mutations or copy number gains (CNGs) of the EGFR and KRAS genes are representative alterations in lung adenocarcinomas that are individually associated with patient characteristics such as ethnicity, smoking status and gender. However, the effects of combinations of these genetic alterations have not been statistically examined. The present study analyzed previously examined lung adenocarcinoma cases in Asian (n=166) and non‑Asian (n=136) individuals in whom all four EGFR and KRAS alterations had been studied. The polynomial logistic regression models were used following adjustment for gender and smoking status, and using patients without any type of EGFR/KRAS alterations as a reference. Between the two ethnic groups, EGFR CNGs (gEGFR) occurred more frequently than EGFR mutations (mEGFR) (46 vs. 38% in Asians; 21 vs. 10% in non‑Asians), whereas KRAS mutations (mKRAS) were more frequent than KRAS CNGs (gKRAS) (13 vs. 7% and 35 vs. 4%, respectively). Additionally, gEGFR and gKRAS occurred significantly more frequently in respective mutant cases, and all EGFR alterations were almost exclusive of all KRAS alterations. The polynomial logistic regression models confirmed that all types of EGFR alterations were significantly more frequent among Asian individuals than among non‑Asian individuals, independent of gender and smoking status (odds ratios, 2.36‑6.67). KRAS alterations occurred less frequently among Asian individuals than among non‑Asian individuals, although a significant difference was not detected. The present study results indicated that the EGFR and KRAS profiles, including mutations and CNGs, differ between Asian and non‑Asian individuals with lung adenocarcinoma, suggesting that ethnicity strongly affects the molecular characteristics of lung adenocarcinoma.
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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