Methylation profiling of EGFR mutant primary and metastatic lung cancer with brain metastasis.
Bibliographic record
Abstract
e20574 Background: EGFR-mutant lung cancer is a key molecular subtype of lung cancer. In recent years there is clear recognition in the value of using methylation signature of cancer for improving diagnosis and predicting outcome as well as understanding the biology of cancer progression. Methods: In this study we chose to characterize the methylome signature of early stage surgically resected EGFR-mutant lung adenocarcinomas in the primary lung tumor. 90 NSCLC cases and 7 matched metastatic brain samples were profiled using Illumina Infinium MethylationEPIC Beadchip. We compared methylation profiles of 1) smokers versus lifetime non-smokers and 2) matched primary lung versus brain metastasis to identify methylation biomarkers. We performed supervised analysis and unsupervised clustering of the methylation data. Results: Unsupervised clustering of all lung and brain samples based on 10K most variable probes showed a similar methylation signature between metastatic brain samples and lung samples. The 7-matched brain and lung samples formed close cluster groups based on matching pairs for the most variable probes from 2.5K to 10K, reflecting the same cell of origin. Supervised analysis of smokers versus lifetime non-smokers did not show any significant methylation differences between the two groups, while unsupervised analysis did not create clusters of smokers and non-smokers based on various number of probe sets we analyzed. Conclusions: Lung tumors that metastasized to the brain share similar methylation features with primary lung tumors. Comprehensive methylation profiling demonstrated no difference between EGFR mutant tumors in smokers versus non-smokers, suggesting that the EGFR mutation is a stronger determinant of outcome independent of smoking.
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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 | 0.000 |
| 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) | 0.001 | 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".