| Literature DB >> 32127641 |
Wibowo Arindrarto1,2, Daniel M Borràs3,4, Ruben A L de Groen5, Redmar R van den Berg2, Irene J Locher5, Saskia A M E van Diessen5, Rosalie van der Holst5, Edith D van der Meijden5, M Willy Honders5, Rick H de Leeuw6, Wina Verlaat5, Inge Jedema5, Wilma G M Kroes7, Jeroen Knijnenburg7, Tom van Wezel8, Joost S P Vermaat5, Peter J M Valk9, Bart Janssen3, Peter de Knijff6, Cornelis A M van Bergen5, Erik B van den Akker1,10,11, Peter A C 't Hoen2,12, Szymon M Kiełbasa1, Jeroen F J Laros2, Marieke Griffioen13, Hendrik Veelken5.
Abstract
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.Entities:
Year: 2020 PMID: 32127641 DOI: 10.1038/s41375-020-0762-8
Source DB: PubMed Journal: Leukemia ISSN: 0887-6924 Impact factor: 11.528