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. 2016 Dec 16;6(12):e510.
doi: 10.1038/bcj.2016.115.

The complexity of interpreting genomic data in patients with acute myeloid leukemia

Affiliations

The complexity of interpreting genomic data in patients with acute myeloid leukemia

A Nazha et al. Blood Cancer J. .

Abstract

Acute myeloid leukemia (AML) is a heterogeneous neoplasm characterized by the accumulation of complex genetic alterations responsible for the initiation and progression of the disease. Translating genomic information into clinical practice remained challenging with conflicting results regarding the impact of certain mutations on disease phenotype and overall survival (OS) especially when clinical variables are controlled for when interpreting the result. We sequenced the coding region for 62 genes in 468 patients with secondary AML (sAML) and primary AML (pAML). Overall, mutations in FLT3, DNMT3A, NPM1 and IDH2 were more specific for pAML whereas UTAF1, STAG2, BCORL1, BCOR, EZH2, JAK2, CBL, PRPF8, SF3B1, ASXL1 and DHX29 were more specific for sAML. However, in multivariate analysis that included clinical variables, only FLT3 and DNMT3A remained specific for pAML and EZH2, BCOR, SF3B1 and ASXL1 for sAML. When the impact of mutations on OS was evaluated in the entire cohort, mutations in DNMT3A, PRPF8, ASXL1, CBL EZH2 and TP53 had a negative impact on OS; no mutation impacted OS favorably; however, in a cox multivariate analysis that included clinical data, mutations in DNMT3A, ASXL1, CBL, EZH2 and TP53 became significant. Thus, controlling for clinical variables is important when interpreting genomic data in AML.

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Figures

Figure 1
Figure 1
(a) Mutation distribution between primary and secondary AML. (b) Association between individual mutated genes and clinically defined secondary and primary AML as described by odds ratio on a log10 scale. (c) Impact of individual genes on OS as described by hazard ratio.
Figure 2
Figure 2
Association of individual mutations with each clinical subtype of AML defined by age, cytogenetics and WBC as described by odds ratio on a log10 scale. Blue indicates mutations that are >95% specific for primary AML and red indicates mutations that are >95% specific for secondary AML.
Figure 3
Figure 3
Impact of each mutation on OS in each clinical subtype of AML defined by age, cytogenetics and WBC as described by HR on a log10 scale. Blue indicates mutations that have positive impact on OS and red indicates mutations that have negative impact on OS.

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