Analytical Validation and Clinical Sensitivity of the Belay Summit Assay for the Detection of DNA Variants in Cerebrospinal Fluid of Primary and Metastatic Central Nervous System Cancer
Bibliographic record
Abstract
In contrast to most solid tumors, cancers of the central nervous system (CNS) pose a unique challenge for effective detection and tracking via plasma because of the blood-brain barrier. Informed diagnosis of primary and metastatic CNS tumors can be facilitated using a liquid biopsy assay that evaluates tumor-derived DNA from the cerebrospinal fluid (CSF), potentially increasing the efficacy of diagnosis and reducing the uncertainty and morbidities associated with the current standard of care that involves neurosurgical procedures. The Belay Summit assay involves tumor-derived DNA-based genomic profiling of CSF to inform diagnosis of CNS tumors. The analytical sensitivity of Summit for single-nucleotide/multinucleotide variants and insertions/deletions is 96% at a 95% limit of detection of 0.30% variant allele fraction. Analytical sensitivity for chromosomal arm-level aneuploidy is 91% at abs(log2r) of 0.09 limit of detection. Clinical sensitivity across a cohort of 124 specimens, including primary and metastatic CNS tumors, was demonstrated to be 90% with a specificity of 95%, supporting the potential for positive clinical utility. These results demonstrate that the Belay Summit assay can accurately and reproducibly be used to inform the diagnosis of primary and metastatic CNS tumors using CSF.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".