ArticleJCO precision oncology2022
Acute Leukemia Classification Using Transcriptional Profiles From Low-Cost Nanopore mRNA Sequencing.
Article in JCO precision oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups.HemaSphere · 2026Article
- Pitfalls and Challenges in the Detection of ZNF384 Rearrangements by Fluorescence in Situ Hybridization.Genes, chromosomes & cancer · 2026Article
- Long-read cDNA sequencing reveals novel isoforms and spliceosome-mutant-enriched transcripts in AML and MDS.bioRxiv : the preprint server for biology · 2026Article
- Long-Read Whole-Transcriptome Sequencing and Selective Gene Panel Profiling Enable Sensitive Detection of Fusion Oncogenes in Pediatric B-Cell Acute Lymphoblastic Leukemia.The Journal of molecular diagnostics : JMD · 2026Article
- The transformative impact of ultra-rapid nanopore sequencing in precision medicine.Frontiers in immunology · 2026Review
- Advancements in genetic analysis: Insights from a case study and review of next-generation sequencing techniques for veterinary oncology applications.Veterinary clinical pathology · 2025Review
- Genomic Evaluation of AML-Main Techniques and Novel Approaches.Journal of clinical medicine · 2025Review
- Article
- Global, regional, and national burden of children and adolescents with acute lymphoblastic leukemia from 1990 to 2021: a systematic analysis for the global burden of disease study 2021.Frontiers in public health · 2025Article
- Global view of haematolymphoid tumor classifications and their application in low- and middle-income countries.Histopathology · 2025Review
- Transcriptome profiling of pediatric extracranial solid tumors and lymphomas enables rapid low-cost diagnostic classification.Scientific reports · 2024Article
- Diagnostic and treatment strategies for pediatric acute lymphoblastic leukemia in low- and middle-income countries.Leukemia · 2024Review
- [The progress in classification and prognosis evaluation of BCR::ABL1 positive acute lymphoblastic leukemia].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2024Review
- Enhancing Molecular Testing for Effective Delivery of Actionable Gene Diagnostics.Bioengineering (Basel, Switzerland) · 2022Review
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12 authors.
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Abstract
purposeMost cases of pediatric acute leukemia occur in low- and middle-income countries, where health centers lack the tools required for accurate diagnosis and disease classification. Recent research shows the robustness of using unbiased short-read RNA sequencing to classify genomic subtypes of acute leukemia. Compared with short-read sequencing, nanopore sequencing has low capital and consumable costs, making it suitable for use in locations with limited health infrastructure. MATERIALS AND
methodsWe show the feasibility of nanopore mRNA sequencing on 134 cryopreserved acute leukemia specimens (26 acute myeloid leukemia [AML], 73 B-lineage acute lymphoblastic leukemia [B-ALL], 34 T-lineage acute lymphoblastic leukemia, and one acute undifferentiated leukemia). Using multiple library preparation approaches, we generated long-read transcripts for each sample. We developed a novel composite classification approach to predict acute leukemia lineage and major B-ALL and AML molecular subtypes directly from gene expression profiles.
resultsWe demonstrate accurate classification of acute leukemia samples into AML, B-ALL, or T-lineage acute lymphoblastic leukemia (96.2% of cases are classifiable with a probability of > 0.8, with 100% accuracy) and further classification into clinically actionable genomic subtypes using shallow RNA nanopore sequencing, with 96.2% accuracy for major AML subtypes and 94.1% accuracy for major B-lineage acute lymphoblastic leukemia subtypes.
conclusionTranscriptional profiling of acute leukemia samples using nanopore technology for diagnostic classification is feasible and accurate, which has the potential to improve the accuracy of cancer diagnosis in low-resource settings.
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