EPCO-02. DNA METHYLATION PROFILING OF BRAIN METASTASIS REVEALS UNDERLING TUMOR RECURRENCE SIGNAL AND ASSOCIATED PATHWAYS
Bibliographic record
Abstract
Abstract INTRODUCTION Brain metastases, the most common form of intracranial neoplasms, carry a poor survival rate, mostly attributed to their high recurrence rates. Currently there are no reliable methods to determine which patients will progress. DNA methylation profiling (DNAmp) has become a useful tool in the diagnosis and stratification of intracranial neoplasms, and has the potential to provide clues on the epigenetic mechanisms that govern tumor behavior. METHODS A cohort of 58 BM tumor samples were selected for analysis. The cohort was composed of three groups: N=21 primary metastatic tumors (PMT), N=21 matched paired recurrent tumors (RMT), and a cohort of N= 16 primary metastatic tumors without any evidence of recurrence (NRMT). All tumor samples underwent DNAmp on the Illumina Infinium EPIC array to determine their methylation status at 850,000 CpG sites. Tumors were profiled via unsupervised hierarchical clustering, pathway enrichment analysis, and cell deconvolution analysis. RESULTS Differential methylation analysis revealed over 32617 differentially methylated CpG sites between grouped PMT and RMT samples, and 835424 sites between PMT and NRMT samples. When comparing PMT with NRMT samples, we revealed a distinct DNAmp that was upheld upon hierarchical clustering analysis. NRMT samples show a significant downregulation of pathways involved in DNA-binding and transcriptional regulation. Paired analysis of PMT and RMT revealed a relative paucity of differentially methylated CpG sites shared across paired samples: a total of 257 probes were differentially methylated across 14/19 samples. These probes however showed enrichment primarily for DNA-binding and transcription factor regulation, in reverse from the non-recurrent samples. CONCLUSION Our data suggests that DNAmp may be capable of differentiating tumors destined to progress from those with less aggressive features, and that DNAmp appear to remain stable through to recurrence. This work demonstrates the potential for DNA methylation to be utilized to uncover pathways associated with BM recurrence.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".