BIOM-12. LIQUID BIOPSIES IN CNS TUMORS: A COMBINED APPROACH TO PROFILING CSF
Bibliographic record
Abstract
Abstract In precision medicine, molecular profiling of pediatric tumors is crucial for diagnosis, prognosis, and therapeutic decision-making. Traditional biopsy methods, often invasive, have inherent risks and may not capture a tumor’s heterogeneity. Liquid biopsy, however, offers a minimally invasive and comprehensive alternative. We demonstrate its utility in diagnosing and monitoring brain tumors using cell-free DNA (cfDNA) from cerebrospinal fluid (CSF), where conventional cfDNA plasma analysis has shown low sensitivity. Through digital droplet PCR, we analyzed 23 plasma samples from patients with BRAFV600E or H3K27M gliomas, detecting H3K27M in a single case. Conversely, CSF samples revealed 8 positives out of 9 for these mutations, underscoring CSF’s diagnostic value. We advanced our methodology by developing a 21-gene hybrid-capture panel paired with low-pass whole-genome sequencing to pinpoint copy number variations. In our reference sample set, the panel achieved a detection limit with variant allele frequencies as low as 0.5%. With 10ng of cfDNA, we reached 83% sensitivity and 100% specificity, which increased to 100% sensitivity with a 30ng input. Using this assay, we profiled 141 CSF samples from 119 patients, achieving 70% positivity rate in active disease cases in a validation cohort—ranging from 50% in low-grade gliomas to 84% in high-grade gliomas and 75% in medulloblastomas. Intriguingly, of 14 queried tumor samples lacking biopsy confirmation, half showed positive ctDNA results using the panel and/or low-pass whole-genome sequencing. This approach allowed for differential diagnoses in suspected medulloblastoma relapses, tailored therapeutic interventions based on CSF profiles, and effective treatment response monitoring. In summary, our integrated liquid biopsy strategy showcases a powerful, clinically applicable tool that significantly enhances patient care in pediatric neuro-oncology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".