Abstract A045 Implementing ultra-sensitive, ctDNA-based liquid biopsy for disease monitoring in paediatric tumours
Bibliographic record
Abstract
Abstract Introduction: Longitudinal application of liquid biopsy is commonly used in childhood acute lymphoblastic leukemia to measure minimal residual disease and has emerged as an attractive method for monitoring many adult solid tumours. However, liquid biopsy is not yet widely available across solid paediatric cancers. Here we set out to assess the value of high-sensitivity ctDNA based liquid biopsy in children with high-risk cancer (<30% chance of survival). Cancer types included brain, hematological, sarcoma, neuroblastoma and extra cranial tumours. We also tested the hypothesis that personalised assays, based on compressive genomic profiling of the tumour could improve insight into diagnosis and disease progression. Method: A cohort of 169 patients (661 CSF or blood samples) collected through the ZERO Childhood Cancer Precision Medicine program were retrospectively analyzed. By leveraging whole genome sequencing of the tumour (90x depth) and germline sequencing (30x) to identify patient specific mutations, we were able to design personalised ctDNA capture panels (TWIST Bioscience). These panels covered approximately 300 primary tumour-informed, patient specific mutations and 100 fixed recurrent mutations. We applied these across multiple samples (including cfDNA from plasma and CSF) taken at different timepoints for the same patient and sequenced at very high depth (>10,000x). Novel, error corrected informatic pipelines were developed to reduce noise, improve sensitivity, ensure accuracy of disease detection even at very low tumour burdens, and provide tumour fraction estimations that can used to track disease over time. Results: We achieved an average detection limit between 10^-4 and 10^-5 ctDNA fraction, overcoming limitations of low DNA input (average 10 ng) and limited number of recurrent mutations which are often limiting factors in childhood cancer. We observed a wide range of ctDNA burden, from 100% to 0.001%. For all cancer types except CNS, our method identifies ctDNA in most samples collected at time of clear disease manifestation from medical imaging. For CNS, ctDNA detection in blood is lower but still significantly higher compared to similar studies on CNS. We identified residual disease in some patients who appear to have completely responded to treatment according to medical imaging and could identify disease relapse 1-6 month before clinical manifestation. Through analysis of the ∼100 fixed recurrent mutations, we were also able to identify novel variants emerging during disease progression. Conclusion: This pioneering study has developed and applied an ultra-sensitive, patient informed, ctDNA-based approach to a range of paediatric cancers in high-risk childhood cancers. The improved sensitivity provides enhanced longitudinal tracking of disease and allows earlier detection of disease. Furthermore, the ability to detect common and actionable variants not found in the original tumour opens the possibility that this approach could be used to identify additional treatment options that arise with disease evolution. Citation Format: Robert Salomon, Wenhan Chen, Charles Shale, Mojgan Toumari, Aileen Lowe, Wenyan Li, Jingwei Chen, Sajad Razavi Bazaz, Paulette Barahona, Louise Cui, Chelsea Mayoh, Sam El-Kamand, Vanessa Tryell, Marie Wong-Erasmus, Loretta M.S. Lau, Noemi Fuentes Bolanos, Edwin Cuppen, Dong Anh Khuong Quang, Jordan Staunton, Marion K. Mateos, Toby Trahair, Michelle Haber, David S Ziegler, Paul G. Ekert, Peter Priestley, Mark J. Cowley. Implementing ultra-sensitive, ctDNA-based liquid biopsy for disease monitoring in paediatric tumours [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A045.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".