Abstract B071: Identifying the clinical utility of CSF as a source of liquid biopsy in pediatric brain tumors
Bibliographic record
Abstract
Abstract Pediatric tumors of the central nervous system (CNS) are a leading cause of cancer-related childhood mortality. Along with imaging, it is essential to molecularly characterize tumor tissue for the diagnosis, treatment, and prognosis of these cancers. Biopsies of deeply-seeded lesions may not be possible or may carry significant risks. On the other hand, liquid biopsy, using cerebral spinal fluid (CSF), provides a minimally invasive alternative to identify and monitor tumor DNA at crucial time points, from diagnosis to follow-up. This allows for molecular diagnosis, identification of therapeutic targets, monitoring response to therapy, and early detection of recurrence or progression. The aim of our study was to assess the practicality and usefulness of liquid biopsies in a diverse group of CNS tumor patients. We used a combination of methods, including a targeted hybridization capture panel and low-pass whole genome sequencing (LP-WGS) based on the sample characteristics. We analyzed a total of 243 CSF samples from patients with CNS tumors including: low-grade glioma (LGG; N=57), high-grade glioma (HGG; N=53), medulloblastoma (MB; N=40), germinoma (CNS-GCT; N=29), and no prior biopsy (N=42). We found somatic mutations in 88% of CSF samples from HGG and 67% from LGG with known tumor DNA variants and sufficient sequencing quality. It's important to note that in LGG patients, samples collected from lumbar puncture were positive only in disseminated cases, while a higher positivity rate was found in cases where CSF was collected from ventricular spaces. Moreover, we observed positive copy number alterations (CNA) in CSF samples collected from HGG, embryonal tumors, and CNS-GCT patients at the time of diagnosis and follow-up with a sensitivity of 64%. Within our sample cohort that had an unknown or query tumor due to no prior biopsy, we were able to detect diagnostic, targetable mutations and/or a positive CNA profile in 30% of cases. This supports the utility of liquid biopsy as a diagnostic tool in in CNS lesions. This tool may also assist in monitoring for relapse in high-grade tumors. For example, in a case study, we were able to detect recurrent MB by indication of a positive CNA profile in keeping with the primary tumor CNV, six years post-surgical resection. In CNS- GCT patients, we found that LP-WGS was highly sensitive at diagnosis as we have observed similar CNV in both CSF and tumor DNA. In summary, the utility of our liquid biopsy platform in the context of CNS cancers is an impactful tool that can aid clinicians in diagnosis, treatment and monitoring disease in a minimally invasive manner. Citation Format: Liana Nobre, Mansuba Rana, Yoshiko Nakano, Ian Burns, Robert Siddaway, Richard Yuditskiy, Cyril Li, Andrew Bondoc, Anthony Liu, Uri Taboori, Cynthia Hawkins. Identifying the clinical utility of CSF as a source of liquid biopsy in pediatric brain tumors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B071.
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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 | 0.002 | 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".