Abstract 2421: Targeted and low-pass whole genome sequencing of cerebrospinal fluid circulating tumor DNA for the diagnosis and monitoring of pediatric, adolescent and young adult CNS tumors
Bibliographic record
Abstract
Abstract Molecular analysis of CNS tumors is essential to their diagnosis and guides individualized treatment for patients, but tissue biopsy of primary and/or relapsed lesions to allow for this characterization often carries intolerable risk. The analysis of genetic material in cerebrospinal fluid (CSF), known as liquid biopsy, can allow for minimally invasive tumor identification and diagnosis. Furthermore, liquid biopsy can serve as a more sensitive test for residual disease than imaging or cytologic analyses. We aimed to implement CSF liquid biopsy at Canada’s largest pediatric hospital. >150 CSF samples were collected from CNS tumor patients with a median age of 9. The most common tumor types were low-grade gliomas (40%), high-grade gliomas (21%) and medulloblastomas (MB) (17%). The majority were collected via lumbar puncture (46%), external ventricular drain (24%) and intraoperatively (22%). The most frequent collection times were diagnosis (53%) and progression/relapse (28%). Cell-free DNA was extracted, and circulating tumor DNA was analyzed using low-pass whole genome sequencing and a targeted hybridization capture glioma gene panel for select cases. Pathogenic mutations and copy number alterations (CNA) were called using an in-house bioinformatics pipeline. Somatic alterations were detected in 57% of samples from patients with pathologically confirmed CNS tumors. CNAs were identified in 5/7 MB patients at the time of pre-treatment staging lumbar puncture. We also detected CNAs in several MB patients during treatment and at progression/relapse, including a patient with disseminated MB who was mid-induction chemotherapy with 1 suspicious cell in their CSF, highlighting our ability to detect minimal residual disease. Among samples with no previous biopsy, we identified tumor DNA in 29%, often providing diagnostic clarity and guiding management. For example, we identified a targetable FGFR1 N546D missense mutation in a patient with an unbiopsied suprasellar lesion. We also diagnosed recurrent MB in a patient 6 years off treatment with a new ambiguous nodule in their tumor bed by detecting an isochromosome 17q CNA. Overall, our liquid biopsy platform is feasible and can yield clinically impactful results. Next steps include refining our assays to increase their sensitivity and systematically evaluating our platform’s efficacy for monitoring response to treatment and tumor surveillance. Citation Format: Ian Burns, Liana Nobre, Yoshiko Nakano, Michelle Ku, Monique Johnson, Javal Sheth, Logine Negm, Chantel Cacciotti, Mansuba Rana, Richard Yuditskiy, Andrew Bondoc, Robert Siddaway, Uri Tabori, Cynthia Hawkins. Targeted and low-pass whole genome sequencing of cerebrospinal fluid circulating tumor DNA for the diagnosis and monitoring of pediatric, adolescent and young adult CNS tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2421.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".