P.083 Liquid biopsies reveal brain cell death in central nervous system tumors
Bibliographic record
Abstract
Background: Circulating cell-free DNA (cfDNA) is a novel type of biomarker with a broad utility in diagnostic medicine, based on the release of DNA fragments from dying cells to the circulation. We developed an approach for identifying the tissue origins of cfDNA, using cell-type-specific DNA methylation patterns, based on a massive reference atlas of the genome-wide methylomes of multiple human tissues and cell types. Cancer inflicts damage to surrounding normal tissues, which can culminate in fatal organ failure. We demonstrated that brain cell death in CNS cancer can be detected by tissue-specific methylation patterns of circulating cfDNA. Methods: We developed a cocktail of brain-specific DNA methylation markers, and used it to assess the presence of brain-derived-cfDNA in the plasma of patients with brain metastasis. Results: We identified significantly elevated neuron-, oligodendrocyte-, and astrocyte-derived cfDNA (p<0.0001) in patients with brain metastases (n=29) compared with cancer patients without brain metastasis (n=113). Conclusions: We show a new set of biomarkers to identify brain damage with high specificity and resolution. We detected brain (neurons, oligodendrocytes, astrocytes) cfDNA in the plasma of patients with brain metastasis. Cell-type-specific cfDNA methylation markers allow the identification of collateral tissue damage, reveals the presence of metastases, and potentially assist in early cancer detection.
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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 | 0.000 |
| 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) | 0.004 | 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".