ECOA-8. Lung adenocarcinoma brain metastasis prediction using tumor DNA methylation profiling
Bibliographic record
Abstract
Abstract Introduction The development of brain metastases from primary cancer profoundly impacts patient prognosis. Up to one quarter of lung cancers develop brain metastases and subsequent median overall survival is one year. Although clinical factors do not reliably predict brain metastasis development, DNA methylation signatures have been recently shown to predict outcomes in other cancers. It is hypothesized that DNA methylation signatures predicting brain metastasis development from lung cancer will be identified. This work may allow for treatment strategies that prevent brain metastasis development in high risk lung cancer patients. Methods DNA methylation profiling was undertaken on N=124 lung adenocarcinoma patients. In a randomly selected 70% training cohort, differentially methylated CpG sites between patients developing and not developing brain metastases were identified and used to build a generalized boosted regression model. Patients in the independent 30% testing cohort were assigned brain metastasis risk scores by the model. Results Brain metastases developed in 49/124 (39.5%) of patients and 2.3K CpG sites were significantly differentially methylated between patients developing and not developing metastases. Methylation-based brain metastasis risk scores predicted time to brain metastasis development in the testing cohort (Univariate cox: HR=3.2, 95% CI 1.1–9.4, p=0.03). A multivariate cox analysis assessing tumor size and nodal positivity together with methylation scores as covariates identified methylation scores as the only independent predictor of brain metastasis development in the testing cohort (HR=4.3, 95%CI 1.1–17, p=0.038). Conclusions DNA methylation signatures in lung adenocarcinomas predict brain metastasis development independently from the non-metastatic components of cancer stage. Future work developing a comprehensive nomogram utilizing methylation scores together with clinical factors to determine patient specific risk values may aid in treatment decisions and patient prognosis counselling.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".