The Diverse Analysis Identifies Mutated <i>KRAS</i> Associated With Radioresistance in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Background: To analyze the relationship between V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog ( KRAS ) status and radioresistance in non-small cell lung cancer (NSCLC), we identified potential genotypic differences and pathways involved. Methods: We retrospectively analyzed epidermal growth factor receptor ( EGFR ) and KRAS status in patients undergoing definitive radiotherapy for NSCLC between 2004 and 2018. Cox proportional hazard models were used to evaluate local progression-free survival (LPFS). Using clonogenic survival and measurement of yH2AX foci, we analyzed the difference in radiosensitivity between NSCLC cell lines with different KRAS status. The Cancer Genome Atlas (TCGA) analysis was used to explore the potential pathways involved. Results: The results showed that of the 286 patients identified, 68 (24%) had local tumor progression (mean ± standard deviation (SD), 27 ± 17.4 months); of these patients, KRAS mutations were found in 14 (23%), and KRAS status was associated with LPFS. After adjusting for concurrent chemotherapy, gross tumor volume, and mutation status in multivariate analysis, KRAS mutation was associated with shorter LPFS (hazard ratio: 1.961; 95% confidence interval: 1.03 - 2.17; P = 0.032). KRAS mutation showed higher radioresistance in vitro . TCGA data showed that the ERK1/2 pathway, phosphatidylinositol I3 kinase (PI3K)/mTOR, p38 MAPK pathway, cell cycle checkpoint signaling, DNA damage, repair pathways, and EGFR/PKC/AKT pathway were differentially expressed in patients with KRAS mutations or cell lines compared with their expression in the wild-type group. Conclusions: Diverse analyses identified that KRAS mutation was associated with radioresistance in NSCLC. KRAS mutation status may be helpful as a biomarker of radioresistance and a potential target to increase radiosensitivity. World J Oncol. 2022;13(2):84-95 doi: https://doi.org/10.14740/wjon1465
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".