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Updated: Jun 5, 2026

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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
Published on: December 9, 2016
Long-read cDNA sequencing reveals novel isoforms and spliceosome-mutant-enriched transcripts in AML and MDS
Biorxiv : the Preprint Server for Biology
|June 4, 2026
Summary
Researchers mapped cancer transcriptomes using long-read sequencing, discovering over 174,000 new RNA isoforms. This breakthrough improves understanding of alternative splicing in diseases like acute myeloid leukemia.
Area of Science:
- Genomics
- Molecular Biology
- Cancer Research
Background:
- Alternative splicing in cancer transcriptomes is poorly understood due to limitations of short-read sequencing.
- Resolving complete transcript structures is crucial for characterizing cancer's molecular landscape.
Purpose of the Study:
- To characterize the alternative splicing landscape in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) using long-read sequencing.
- To identify novel RNA isoforms and their functional relevance in spliceosome-mutant cancers.
Main Methods:
- Utilized Oxford Nanopore cDNA sequencing to generate nearly 2 billion long reads from 71 human samples (48 AML/MDS, 23 healthy controls).
- Analyzed samples, including those with splicing-factor gene mutations (SRSF2, U2AF1, SF3B1).
- Performed proteomic validation to confirm translation of novel isoforms and investigate nonsense-mediated decay.
Main Results:
- Identified 174,162 novel RNA isoforms not present in the reference transcriptome.
- Confirmed translation of many novel isoforms through proteomic analysis.
- Discovered isoforms enriched in spliceosome-mutant samples and evidence of nonsense-mediated decay regulation.
Conclusions:
- Long-read sequencing provides unprecedented resolution for alternative splicing analysis in cancer.
- The identified novel isoforms and their regulation offer new insights into AML and MDS pathogenesis.
- This dataset serves as a valuable resource for detecting new transcripts in existing short-read data.
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