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 the 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.

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Mutations in NPM1 can be used for minimal residual disease (MRD) analysis in acute myeloid leukemia (AML). We here applied a newly introduced method, deep sequencing, allowing for simultaneous analysis of all recurrent NPM1 insertions and thus constituting an attractive alternative to multiple PCRs for the clinical laboratory. We retrospectively used deep sequencing for measurement of MRD pre- and post-allogeneic hematopoietic stem cell transplantation (alloHCT). For 29 patients in morphological remission at the time of alloHCT, the effect of deep sequencing MRD on outcome was assessed. MRD positivity was defined as variant allele frequency ≥0.02%. Post-transplant MRD status was significantly and independently associated with clinical outcome; 3-year relapse-free survival 20% vs 85% (p < .001), HR 45 (95% CI 2-1260), and overall survival 20% vs 89% (p < .001), HR 49 (95% CI 2-1253). Thus, the new methodology deep sequencing is an applicable and predictive tool for MRD assessment in AML.

Introduction Reverse transcriptase quantitative PCR (RT‐qPCR) is considered the method of choice for measurable residual disease (MRD) assessment in NPM1‐mutated acute myeloid leukemia (AML). MRD can also be determined with DNA‐based methods offering certain advantages. We here compared the DNA‐based methods quantitative PCR (qPCR), droplet digital PCR (ddPCR), and targeted deep sequencing (deep seq) with RT‐qPCR. Methods Of 110 follow‐up samples from 30 patients with NPM1‐mutated AML were analyzed by qPCR, ddPCR, deep seq, and RT‐qPCR. To select DNA MRD cutoffs for bone marrow, we performed receiver operating characteristic analyses for each DNA method using prognostically relevant RT‐qPCR cutoffs. Results The DNA‐based methods showed strong intermethod correlation, but were less sensitive than RT‐qPCR. A bone marrow cutoff at 0.1% leukemic DNA for qPCR or 0.05% variant allele frequency for ddPCR and deep seq offered optimal sensitivity and specificity with respect to 3 log10 reduction of NPM1 transcripts and/or 2% mutant NPM1/ABL. With these cutoffs, MRD results agreed in 95% (191/201) of the analyses. Although more sensitive, RT‐qPCR failed to detect leukemic signals in 10% of samples with detectable leukemic DNA. Conclusion DNA‐based MRD techniques may complement RT‐qPCR for assessment of residual leukemia. DNA‐based methods offer high positive and negative predictive values with respect to residual leukemic NPM1 transcripts at levels of importance for response to treatment. However, moving to DNA‐based MRD methods will miss a proportion of patients with residual leukemic RNA, but on the other hand some MRD samples with detectable leukemic DNA can be devoid of measurable leukemic RNA.

RT-qPCR is a valuable addition and could also be an alternative to FACS in cases where FACS is not achievable for MRD analysis.