MBRS-20. CSF-DERIVED CIRCULATING TUMOR DNA AS A BIOMARKER FOR DISEASE PROGRESSION AND TUMOR EVOLUTION IN MEDULLOBLASTOMA
Bibliographic record
Abstract
Abstract BACKGROUND Cell-free DNA (cfDNA) profiling has been shown to carry utility as a clinically relevant biomarker in a variety of cancers, but studies in pediatric brain tumors, including medulloblastoma, are scarce. We hereby evaluated the actionability of profiling cfDNA from cerebrospinal fluid (CSF) based on a multi-institutional cohort of children with medulloblastoma. METHODS 103 children aged ≥ 3 years with medulloblastoma harboring chromosomal aneuploidy enrolled on two prospective therapeutic trials were included. cfDNA was extracted from CSF obtained longitudinally, and profiled by low-coverage whole-genome sequencing (lcWGS) for annotating copy-number variants (CNVs). cfDNA-derived CNVs were compared against patient-matched primary tumor-derived CNVs and correlated with outcome. cfDNA profiles at diagnosis and relapse were compared to evaluate tumor evolution. RESULTS Tumor-derived somatic CNVs were detected in 72% of baseline cfDNA samples, with higher detection rate in samples from patients with metastatic disease than those without (90% versus 50%, chi-square p=0.001). Longitudinal profiling of cfDNA revealed correlation between CNV detectability and clinical course, with detection of tumor-derived CNVs in cfDNA samples predating radiographic progression for ≥ 3 months in 62% of relapsing patients. Presence of cfDNA-derived CNVs in CSF collected during chemotherapy and at the end of therapy was significantly associated with inferior PFS (log-rank p<0.0001 for both time-points). Comparison of CNV profiles from cfDNA at baseline and relapse revealed molecular divergence in a subset of patients. CONCLUSION These results carry major implications and supports the incorporation of cfDNA profiling in upcoming medulloblastoma protocols for more sensitive and accurate disease monitoring and personalization of treatment.
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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 | 0.002 |
| 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) | 0.001 | 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".