LGG-61. Cerebrospinal fluid as a source for liquid biopsy in pediatric gliomas
Bibliographic record
Abstract
Abstract Central nervous system neoplasms are currently the leading cause of morbidity and mortality among childhood cancers, gliomas account for 50% of these cases. The last decade has seen a massive growth in our understanding of the genetic underpinnings of these cancers, including the discovery of multiple diagnostic, prognostic and therapeutic markers. However, molecular characterization of these tumours requires a biopsy, with no added therapeutic benefit particularly in unresectable tumors. Liquid biopsy is a minimally-invasive alternative to biopsies which enables molecular characterization to diagnose, monitor response to therapy, and potentially predict progression/recurrence. We here present the results of a customized capture based NGS panel including 21 commonly altered genes present in pediatric and AYA gliomas coupled with low pass whole genome as a diagnostic and monitoring liquid biopsy tool. To assess for common fusions, exonic and intronic regions of specific genes are covered to capture different breakpoints. To establish the sensitivity and specificity of this assay we have used a commercially available control (SeraseqR) with 18 known mutated genes of interest and a in house control sample with two additional mutations. Samples with low ctDNA concentration (10 ng) and a limit of detection as low as 0.5% variant allele frequency, had a sensitivity of 83% and specificity of 100%. At higher concentrations (30 ng of ctDNA) we achieved a sensitivity and specificity of 100 %. We are currently finalizing the validation steps ctDNA samples extracted from CSF collected intra-surgically, through ventricular shunt or lumbar puncture. Twenty-two samples have been tested with additional 40 samples in processing. Driver alterations were identified in 16/22 samples, with additional 3/4 samples having concordant CNV alterations between tumor and CSF. This work supports further implementation of CSF use as a minimal invasive source of diagnostic and monitoring sample in children and adolescent patients with gliomas.
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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 | 0.000 |
| 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 | 0.001 |
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".