Detection of minimal residual disease in NPM1-mutated acute myeloid leukemia by next-generation sequencing.

Publication Type:

Journal Article


Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc (2014)


2014, Human Biology Division, May 2014


Detection of minimal residual disease predicts adverse outcome in patients with acute myeloid leukemia. Currently, minimal residual disease may be detected by RQ-PCR or flow cytometry, both of which have practical and diagnostic limitations. Here, we describe a next-generation sequencing assay for minimal residual disease detection in NPM1-mutated acute myeloid leukemia, which encompasses ∼60% of patients with normal karyotype acute myeloid leukemia. Exon 12 of NPM1 was PCR amplified using sequencing adaptor-linked primers and deep sequenced to enable detection of low-prevalence, acute myeloid leukemia-specific activating mutations. We benchmarked our results against flow cytometry, the standard of care for acute myeloid leukemia minimal residual disease diagnosis at our institution. The performance of both approaches was evaluated using defined dilutions of an NPM1 mutation-positive cell line and longitudinal clinical samples from acute myeloid leukemia patients. Using defined control material, we found this assay sensitive to approximately 0.001% mutant cells, outperforming flow cytometry by an order of magnitude. Next-generation sequencing was precise and semiquantitative over four orders of magnitude. In 22 longitudinal samples from six acute myeloid leukemia patients, next-generation sequencing detected minimal residual disease in all samples deemed negative by flow cytometry. Further, in one-third of patients, sequencing detected alternate NPM1 mutations in addition to the patient's index mutation, consistent with tumor heterogeneity. Next-generation sequencing provides information without prior knowledge of NPM1 mutation subtype or validation of allele-specific probes as required for RQ-PCR assays, and without generation and interpretation of complex multidimensional flow cytometry data. This approach may complement current technologies to enhance patient-specific clinical decision-making.Modern Pathology advance online publication, 18 April 2014; doi:10.1038/modpathol.2014.57.