EPCO-01. LUNG ADENOCARCINOMA BRAIN METASTASIS PREDICTION, PREVENTION, AND NON-INVASIVE DIAGNOSIS USING METHYLATION SIGNATURES WITHIN TISSUE AND CIRCULATING TUMOUR DNA
Bibliographic record
Abstract
Abstract BACKGROUND One quarter of lung adenocarcinoma (LUAD) patients develop brain metastases (BM) and experience a poorer median survival of 12 months despite treatment. Clinical variables do not robustly predict who will develop BM and targeted preventative treatments are limited. Tumour DNA methylation signatures predict outcomes in other cancers and can be detected in circulating tumour DNA (ctDNA). This work predicts BM development from LUAD using methylation data, identifies novel potential treatment targets to prevent metastases, and detects LUAD-BM ctDNA non-invasively. METHODS DNA methylation profiling was undertaken on N=124 LUAD tumours. A gradient boosted regression model built on differentially methylated CpGs (DMCs) between tumours with and without BM in 70% of samples was validated in an independent 30% testing cohort. Nine paired BM samples were profiled and DMCs between their corresponding LUAD tissue were identified along with copy number (CN) alterations. A total of 47 LUAD-BM plasma samples underwent sequencing of immunoprecipitated methylated ctDNA and differentially methylated regions (DMRs) between LUAD-BM and intrinsic brain lesions were identified. RESULTS The methylation-based model significantly predicted time to brain metastasis development within the testing cohort independently from cancer stage in a multivariate analysis (HR=4.3, 95%CI 1.1–17, p=0.038). Genes/pathways involved in the process of brain metastasis were identified through assessment of 83K DMCs (FDR< 0.2, mean difference >|0.1|) between paired samples as well as the CN losses found in chromosome 12q/19 of BM samples. A total of 5.5K DMRs were identified that distinguish BM samples from gliomas or primary CNS lymphomas (FDR< 0.05, logFC >1). CONCLUSIONS DNA methylation signatures in lung adenocarcinomas predict brain metastasis development independently from prognostic clinical factors. Genes and pathways involved in metastasis were identified as novel potential therapeutic targets. Methylated circulating tumor DNA signatures differentiate lung brain metastases from other ring-enhancing brain lesions and may have potential for non-invasive diagnosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".