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    Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform.

    Citation

    Wibowo Arindrarto, Daniel M Borràs, Ruben A L de Groen, Redmar R van den Berg, Irene J Locher, Saskia A M E van Diessen, Rosalie van der Holst, Edith D van der Meijden, M Willy Honders, Rick H de Leeuw, Wina Verlaat, Inge Jedema, Wilma G M Kroes, Jeroen Knijnenburg, Tom van Wezel, Joost S P Vermaat, Peter J M Valk, Bart Janssen, Peter de Knijff, Cornelis A M van Bergen, Erik B van den Akker, Peter A C 't Hoen, Szymon M Kiełbasa, Jeroen F J Laros, Marieke Griffioen, Hendrik Veelken. Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing. Leukemia. 2021 Jan;35(1):47-61

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    PMID: 32127641

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