Abstract IA010: Opportunities and challenges for liquid biopsies in pediatric oncology
Bibliographic record
Abstract
Abstract Understanding the molecular mechanisms underlying tumour progression and resistance to treatment is crucial to the development of new treatment approaches. In high risk pediatric cancers, somatic DNA alterations such as genomic amplifications, copy number alterations, translocations or mutations play an important role as molecular diagnostic, prognostic and predictive biomarkers. However it is now known that in many high risk pediatric cancers, clonal evolution most likely plays an important role in tumor progression and treatment resistance. Circulating tumour DNA, a fraction of cell free DNA can be readily isolated from plasma and now provides an important tool and surrogate for tumor molecular analyses at diagnosis, during treatment and follow-up. At diagnosis, the prospective clinical trials MICCHADO (NCT03496402) enrolled 599 patients, including high-risk pediatric cancer (Neuroblastoma, Rhabdomyosarcoma, Ewing sarcoma, other high-risk cancers). Whole -Exome-Sequencing (WES) was performed on tumor, germline DNA and cfDNA extracted from plasma at diagnosis, during treatment and follow up. Whereas all cfDNA samples obtained at follow-up in patients without evidence of disease revealed no or few tumor cell-specific SNVs, cfDNA samples obtained at relapse harbored additional, new relapse-specific SNVs in all cases, targeting genes of interest. Deep sequencing capture techniques enable to develop models of clonal evolution. In pediatric embryonal brain tumours, ctDNA isolated from CSF enables detection of tumor cell specific genetic alterations with a high sensitivity. At relapse, ctDNA studies can provide complementary information to molecular analyses of tumour samples performed within programs such as MAPPYACTS (NCT02613962), with 76% of actionable alterations detected in tumor also identified in ctDNA, while also highlighting the importance of spatial heterogeneity. MONALISA, a SIOPEN pragmatic clinical trial to MOnitor NeuroblastomA relapse with LIquid biopsy Sensitive Analysis aims to establish liquid biopsies as standard-of-care to monitor relapsed/refractory neuroblastoma. Reliable, early assessment of molecular progression or relapse determined by mRNA and/or ctDNA analysis is the main aim of this randomized clinical trial. ctDNA also enables to infer expression profiles. Gene expression levels are reflected by nucleosome positioning, and differences in nucleosome organization at transcription start sites (TSS) lead to differential clipping of fragments upon ctDNA release and distinct nucleosome footprints depending on the expression of a given gene in the originating cells. Altogether the presence of tumor genetic and epigenetic abnormalities in ctDNA can be documented in most patients with high risk pediatric cancer and frequently suggest spatial and temporal heterogeneity. Sequential studies will further elucidate mechanisms of clonal evolution, tumor progression and therapy resistance. Thus, sequential studies based on liquid biopsies are now integrated into the development and optimization of targeted treatment strategies. Citation Format: Gudrun Schleiermacher. Opportunities and challenges for liquid biopsies in pediatric oncology [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 IA010.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".