Single-workflow Nanopore whole genome sequencing with adaptive sampling for accelerated and comprehensive pediatric cancer profiling
Geoffrion, N.; Lawruk-Desjardins, C.; Langlois, S.; Aleman Alvarado, M.; Dreyer, N.; Carrier, A.; Lisi, V.; Richer, C.; St-Hilaire, A. R.; Tremblay-Dauphinais, P.; Bataille, A. R.; Sontag, T.; Landais, S.; Rouette, A.; Jouan, L.; Boumela, I.; Khakipoor, B.; Fong, S.; Vairy, S.; Goudie, C.; Jabado, N.; Santiago, R.; Shlien, A.; Smith, M. A.; Sinnett, D.; Cellot, S.; Tran, T. H.; Lavallee, V.-P.
Show 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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia 96%
- Multimodal Spatial Proteomic Profiling in Acute Myeloid Leukemia 95%
- Mutated clones driving leukemic transformation are already detectable at the single cell level in CD34-positive cells in the chronic phase of primary myelofibrosis 93%
Similar papers in this journal
- Partner-independent fusion gene detection by multiplexed CRISPR/Cas9 enrichment and long-read Nanopore sequencing 96%
- Joint profiling of DNA and proteins in single cells to dissect genotype-phenotype associations in leukemia 96%
- Copy number signatures predict chromothripsis and associate with poor clinical outcomes in patients with newly diagnosed multiple myeloma 95%
Similar papers in this journal
- Non-invasive multi-cancer detection using DNA hypomethylation of LINE-1 retrotransposons 93%
- cfTrack : Exome-wide mutation analysis of cell-free DNA to simultaneously monitor the full spectrum of cancer treatment outcomes: MRD, recurrence, and evolution 92%
- RNA splicing alterations induce a cellular stress response associated with poor prognosis in AML 92%
Similar papers in this journal
- Mapping AML heterogeneity – multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses 94%
- Resistance to decitabine and 5-azacytidine emerges from adaptive responses of the pyrimidine metabolism network 92%
- Multiple Myeloma DREAM Challenge Reveals Epigenetic Regulator PHF19 As Marker of Aggressive Disease 92%
Similar papers in this journal
- Dissecting the cell of origin of aberrant SALL4 expression in myelodysplastic syndrome 91%
- Pan-cancer proteogenomic landscape of whole-genome doubling reveals putative therapeutic targets in various cancer types 91%
- High-throughput single-cell DNA methylation and chromatin accessibility co-profiling with SpliCOOL-seq 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.