Single-workflow Nanopore whole genome sequencing with adaptive sampling for accelerated and comprehensive pediatric cancer profiling
Bibliographic record
Abstract
ABSTRACT Timely and comprehensive molecular classification is critical for therapeutic decisions in pediatric oncology. However, current diagnostic workflows rely on multi-step testing and are resource- and time-intensive. We present whole-genome sequencing with adaptive sampling (AS-WGS) protocol using Oxford Nanopore Technologies optimized for pediatric oncology, enabling unified detection of genomic, structural, and epigenomic alterations in a single assay. Applied to 31 pediatric cancer patient samples, AS-WGS achieved high on-target coverage across hundreds of loci of interest for identifying somatic anomalies, while maintaining pan-genomic coverage for copy number assessment and methylome data. We demonstrate that AS-WGS, as a single approach, captures all categories of clinically relevant alterations, including most copy number changes, fusions, and mutations, even subclonal ones. Time stamp analyses revealed that clonal alterations are confidently supported within the first sequencing day, sometimes within the first hours. We developed and reported an open-source bioinformatic pipeline (nf-core-oncoseq) that facilitates streamlined and fully integrated analysis. This approach consolidates complex testing into a single, rapid assay, enabling near real-time cancer characterization. Our findings support AS-WGS as a transformative diagnostic platform for pediatric oncology.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".