Patient-tailored analysis of minimal residual disease in acute myeloid leukemia using next generation sequencing.
Journal article, 2017

Next generation sequencing techniques have revealed that leukemic cells in acute myeloid leukemia often are characterized by a limited number of somatic mutations. These mutations can be the basis for detection of leukemic cells in follow-up samples. The aim of this study was to identify leukemia-specific mutations in cells from patients with acute myeloid leukemia and to use these mutations as markers for minimal residual disease. Leukemic cells and normal lymphocytes were simultaneously isolated at diagnosis from 17 patients with acute myeloid leukemia using fluorescence activated cell sorting. Exome sequencing of these cells identified 240 leukemia-specific single nucleotide variations and 22 small insertions and deletions. Based on estimated allele frequencies and their accuracies, 191 of these mutations qualified as candidates for minimal residual disease analysis. Targeted deep sequencing with a significance threshold of 0.027% for single nucleotide variations and 0.006% for NPM1 type A mutation was developed for quantification of minimal residual disease. When tested on follow-up samples from a patient with acute myeloid leukemia, targeted deep sequencing of single nucleotide variations as well as NPM1 was more sensitive than minimal residual disease quantification with multiparameter flow cytometry. In conclusion, we here describe how exome sequencing can be used for identification of leukemia-specific mutations in samples already at diagnosis of acute myeloid leukemia. We also show that targeted deep sequencing of such mutations, including single nucleotide variations, can be used for high-sensitivity quantification of minimal residual disease in a patient-tailored manner.


Erik Br Malmberg

University of Gothenburg

Sara Ståhlman

University of Gothenburg

Anna Rehammar

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

Tore Samuelsson

University of Gothenburg

Sofie J Alm

University of Gothenburg

Erik Kristiansson

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Jonas Abrahamsson

University of Gothenburg

Hege Garelius

Sahlgrenska University Hospital

Louise Pettersson

Hallands Hospital Halmstad

Mats Ehinger

Lund University

Lars Palmqvist

University of Gothenburg

Linda Fogelstrand

University of Gothenburg

European Journal of Haematology

0902-4441 (ISSN) 1600-0609 (eISSN)

Vol. 98 1 26-37

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Life Science Engineering (2010-2018)





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