Abstract
Acute myeloid leukemia (AML) is a clinically challenging disease with high interpatient variability in response to chemotherapy. Despite continuing advances in treatment options, current 5-year survival rates for pediatric AML are suboptimal at ~60%. Variability in treatment response and survival outcomes are due in part to the heterogeneous nature of AML, with many genetic lesions and cytogenetic features contributing to disease progression. One of the most well known genetic lesions associated with AML involves Fms-Like Tyrosine Kinase-3 (FLT3), a receptor tyrosine kinase expressed in hematopoietic stem cells. Internal tandem duplication of the juxtamembrane domain coding sequence of FLT3 (FLT3/ITD) causes autonomous cellular proliferations leading to disease progression. Previous metabolomics studies have successfully identified significant metabolic alterations in hematological malignancies. However, no metabolomics studies on pediatric AML have been reported at this time. In this study, we propose to use global and targeted metabolomics to identify differential metabolite abundance associated with FLT3-ITD status in pediatric AML patients treated in the St Jude AML02 clinical trial.
Serum metabolomics profiles were generated with samples obtained at diagnosis from patients treated in the St. Jude AML02 study. Patients were assigned to FLT3 Wild Type (n=59) and FLT3-ITD (n=13) groups. Global metabolomics profiling was performed on a Thermo Q-Exactive Orbitrap mass spectrometer with Dionex UHPLC and autosampler. Targeted metabolomics profiling was generated for a select group of organic acids and acylcarnitines. The organic acid panel included eight metabolites related to the tricarboxylic acid cycle and glycolysis. The acylcarnitine panel included 57 varieties of acylcarnitine. Metabolomics profiling was performed on an Agilent 6490 triple quadrupole with an Agilent 1290 HPLC. Absolute quantification was achieved through external comparison on standard curves generated for each targeted metabolite on the organic acid and acylcarnitine panels. Univariate and multivariate analyses were performed using MetaboAnalyst web based software.
A total of 3218 features were detected in the global metabolome, with 137 known metabolites and 3081 unknown features. Only the known metabolites were used for association analysis. All subsequent data analyses were log transformed, auto-scaled, and normalized to the sum of metabolites for each sample. Statistical analysis on metabolomics data identified 17 known metabolites significantly associated with FLT3 status (p<0.05). Some of the top significant metabolites include nicotinamide, 5,6-dihydrouracil, L-proline, 4-imidazoleacetic acid, taurine, L-asparagine, L-kynurenine, L-serine, D-ribose, and L-histidine. Pathway enrichment analysis identified 22 metabolic pathways significantly impacted by difference in FLT3 status, which included nicotinate and nicotinamide metabolism, aminoacyl-tRNA biosynthesis, arginine and proline metabolism, and histidine metabolism.
Organic acid targeted metabolomics generated the concentrations of eight metabolites in patient samples. Analysis identified four organic acids with significantly different abundance associated with FLT3 status (p<0.05), including succinate, pyruvate, lactate, and α-ketoglutarate. Pathway enrichment analysis identified 24 metabolic pathways significantly impacted by difference in FLT3 status (p<0.05), including pyruvate metabolism, TCA cycle, butanoate metabolism, and glycolysis/gluconeogenesis. Acylcarnitine targeted metabolomics generated the concentrations of 57 acylcarnitine metabolites. Analysis identified two acylcarnitines (octadecanoylcarnitine and hexanoylcarnitine) significantly associated with difference in FLT3 status.
Overall, this study identifies several metabolites and metabolic pathways significantly associated with the FLT3-ITD status in pediatric AML patients. These results help expand on previously conducted pilot studies and further clarify the metabolic differences associated with the FLT3-ITD form of AML. Ideally, continued metabolic profiling of additional AML subtypes can reveal pathways and networks that can be used to improve the efficiency of AML diagnosis and risk evaluation.
No relevant conflicts of interest to declare.
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