CMET-32. DNA METHYLATION ALTERATIONS IN LUNG ADENOCARCINOMAS THAT DEVELOP BRAIN METASTASES
Bibliographic record
Abstract
Abstract INTRODUCTION The development of brain metastases from primary cancer profoundly impacts patient prognosis. Metastases are the most common adult brain tumor with up to one quarter of lung cancers developing metastases and median overall survival after metastasis being one year. Clinical factors do not reliably predict brain metastasis development and over 90% are identified after symptoms develop. DNA methylation signatures predict outcomes in other cancers and so identifying signatures that predict metastasis development may allow for treatment strategies that prevent development in high risk patients. METHODS Whole genome DNA-methylation profiling was undertaken on N=124 lung adenocarcinoma patients after bisulfite conversion of DNA from formalin-fixed paraffin-embedded tissue. In a randomly selected 70% training cohort, the most differentially methylated CpG sites between patients developing and not developing brain metastases were identified with p< 0.05. A generalized boosted regression model built on these selected features output brain metastasis risk scores for patients in the independent 30% testing cohort. 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 in a univariate cox regression model (HR=3.2, 95% CI 1.1–9.4, p=0.03). A corresponding area under the receiver operating characteristic curve at 52 months was 0.64. A multivariate cox analysis including tumor size and nodal status, representing the non-metastatic components of cancer stage, identified methylation score as the only independent predictor of brain metastasis (HR=4.3, 95%CI 1.1–17, p=0.038). CONCLUSIONS DNA methylation signatures in lung adenocarcinoma predict brain metastasis development independent of stage components, which classically predict patient outcome in cancer. Future work developing a comprehensive nomogram utilizing methylation scores together with other clinical factors to determine patient specific risk values may aid in treatment decisions and patient prognosis counselling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".