Acute myeloid leukemia (AML) is a heterogeneous disease with dismal response warranting the need for enhancing our understanding of AML biology. One prognostic feature associated with inferior response is the presence of activating mutations in FMS-like tyrosine kinase 3 (FLT3) especially occurrence of internal tandem duplication (FLT3-ITD). Although poorly understood, differential metabolic and signaling pathways associated with FLT3-ITD might contribute towards the observed poor prognosis. We performed a non-targeted global metabolic profiling of matched cell and plasma samples obtained at diagnosis to establish metabolic differences within FLT3-ITD and FLT3-WT pediatric AML. Metabolomic profiling by Ultra-High Performance-Liquid-Chromatography–Mass Spectrometry identified differential abundance of 21 known metabolites in plasma and 33 known metabolites in leukemic cells by FLT3 status. These metabolic features mapped to pathways of significant biological importance. Of interest were metabolites with roles in cancer, cell progression and involvement in purine metabolism and biosynthesis, cysteine/methionine metabolism, tryptophan metabolism, carnitine mediated fatty acid oxidation, and lysophospholipid metabolism. Although validation in a larger cohort is required, our results for the first time investigated global metabolic profile in FLT3-ITD AML.
BackgroundThiazide and thiazide‐like diuretics are first‐line medications for treating uncomplicated hypertension. However, their use has been associated with adverse metabolic events, including hyperglycemia and incident diabetes mellitus, with incompletely understood mechanisms. Our goal was to identify genomic variants associated with thiazide‐like diuretic/chlorthalidone‐induced glucose change.Methods and ResultsGenome‐wide analysis of glucose change after treatment with chlorthalidone was performed by race among the white (n=175) and black (n=135) participants from the PEAR‐2 (Pharmacogenomic Evaluation of Antihypertensive Responses‐2). Single‐nucleotide polymorphisms with P<5×10−8 were further prioritized using in silico analysis based on their expression quantitative trait loci function. Among blacks, an intronic single‐nucleotide polymorphism (rs9943291) in the HMGCS2 was associated with increase in glucose levels following chlorthalidone treatment (ß=12.5; P=4.17×10−8). G‐allele carriers of HMGCS2 had higher glucose levels (glucose change=+16.29 mg/dL) post chlorthalidone treatment compared with noncarriers of G allele (glucose change=+2.80 mg/dL). This association was successfully replicated in an independent replication cohort of hydrochlorothiazide‐treated participants from the PEAR study (ß=5.54; P=0.023). A meta‐analysis of the 2 studies was performed by race in Meta‐Analysis Helper, where this single‐nucleotide polymorphism, rs9943291, was genome‐wide significant with a meta‐analysis P value of 3.71×10−8. HMGCS2, a part of the HMG‐CoA synthase family, is important for ketogenesis and cholesterol synthesis pathways that are essential in glucose homeostasis.ConclusionsThese results suggest that HMGCS2 is a promising candidate gene involved in chlorthalidone and Hydrochlorothiazide (HCTZ)‐induced glucose change. This may provide insights into the mechanisms involved in thiazide‐induced hyperglycemia that may ultimately facilitate personalized approaches to antihypertensive selection for hypertension treatment.Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00246519 and NCT01203852.
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%. The heterogeneous nature of AML contributes significantly to the variability in treatment response and survival outcomes. Several known genetic lesions and cytogenetic features contribute to disease progression. However, our understanding of how molecular mechanisms contribute to variation in treatment outcomes is still limited. Previous metabolomics studies have successfully identified significant metabolic alterations in hematological malignancies, but very few metabolomics studies have been conducted for the pediatric AML patient population. In this study, we used global and targeted metabolomics to identify differential metabolite abundance associated with chemosensitivity and treatment outcomes in pediatric AML patients. Serum metabolomics profiles were generated with serum samples obtained at diagnosis from patients treated in the multicenter AML02 study (n=94, NCT00136084). Clinical outcomes tested for association included half-maximal inhibitory concentration (IC50) of cytarabine, minimal residual disease (MRD), relapse free survival (RFS), and overall survival (OS). Global metabolomics profiling was performed using liquid chromatography/mass spectrometry (LC/MS). 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 featured 20 varieties of acylcarnitines detectable in human serum. Statistical analyses were performed using MetaboAnalyst and various R packages. A total of 3205 features were detected in the global metabolome, with 124 known metabolites and 3081 unknown features. All metabolites were used for association analysis, while annotated metabolites were used in pathway analyses. Association analysis of clinical endpoints vs. metabolome identified 10 known metabolites significantly associated with IC50 values, 17 associated with MRD, 7 associated with RFS, and 7 associated with OS (p<0.05). Targeted metabolomics generated the absolute abundance profile of 8 organic acid metabolites and 20 acylcarnitine metabolites in patient samples. Spearman correlation analysis identified five acylcarnitines significantly correlated with IC50 values. Among the significant metabolites, the most interesting is pantothenic acid, showing higher serum abundance associated with poorer IC50, MRD, and RFS outcomes. Pantothenic acid is an essential component for Coenzyme A synthesis, leading into energy production through the tricarboxylic acid cycle. A previous study has shown a reduced capacity for pantothenic acid uptake in leukemia cells resistant to daunorubicin. Our results suggest a similar relationship for pantothenic acid uptake and cytarabine resistance. Pathway enrichment analysis identified 11 metabolic pathways showing significant association with IC50 values and 12 pathways associated with MRD (FDR<0.05). Some of the most significantly associated pathways included alanine, aspartate and glutamate metabolism, arginine and proline metabolism, and pantothenic acid based CoA biosynthesis. Overall, differences in chemosensitivity and clinical outcomes appear to be most closely related to amino acid synthesis and energy production. This study identifies several metabolites and metabolic pathways significantly associated with chemosensitivity and clinical endpoints in pediatric AML patients. These results help expand on previously conducted AML pilot studies, and metabolomics studies on other cancer types, to further clarify the metabolic differences associated with interpatient variability in chemotherapy response for AML patients. Continued metabolic profiling of AML patient populations can help establish targetable pathways that can be used to improve treatment efficiency for AML. In addition, in vitro functional modeling to validate results of the metabolomics study are currently underway. Disclosures No relevant conflicts of interest to declare.
BackgroundAcute myeloid leukemia (AML) is a hematological malignancy with a dismal prognosis. For over four decades, AML has primarily been treated by cytarabine combined with an anthracycline. Although a significant proportion of patients achieve remission with this regimen, roughly 40% of children and 70% of adults relapse. Over 90% of patients with resistant or relapsed AML die within 3 years. Thus, relapsed and resistant disease following treatment with standard therapy are the most common clinical failures that occur in treating this disease. In this study, we evaluated the relationship between AML cell line global metabolomes and variation in chemosensitivity.MethodsWe performed global metabolomics on seven AML cell lines with varying chemosensitivity to cytarabine and the anthracycline doxorubicin (MV4.11, KG-1, HL-60, Kasumi-1, AML-193, ME1, THP-1) using ultra-high performance liquid chromatography – mass spectrometry (UHPLC-MS). Univariate and multivariate analyses were performed on the metabolite peak intensity values from UHPLC-MS using MetaboAnalyst to identify cellular metabolites associated with drug chemosensitivity.ResultsA total of 1,624 metabolic features were detected across the leukemic cell lines. Of these, 187 were annotated to known metabolites. With respect to doxorubicin, we observed significantly greater abundance of a carboxylic acid (1-aminocyclopropane-1-carboxylate) and several amino acids in resistant cell lines. Pathway analysis found enrichment of several amino acid biosynthesis and metabolic pathways. For cytarabine resistance, nine annotated metabolites were significantly different in resistance vs. sensitive cell lines, including D-raffinose, guanosine, inosine, guanine, aldopentose, two xenobiotics (allopurinol and 4-hydroxy-L-phenylglycine) and glucosamine/mannosamine. Pathway analysis associated these metabolites with the purine metabolic pathway.ConclusionOverall, our results demonstrate that metabolomics differences contribute toward drug resistance. In addition, it could potentially identify predictive biomarkers for chemosensitivity to various anti-leukemic drugs. Our results provide opportunity to further explore these metabolites in patient samples for association with clinical response.
Bronchopulmonary dysplasia (BPD) is one of the most common health complications of premature birth. Corticosteroids are commonly used for treatment of BPD, but their use is challenging due to variability in treatment response. Previous pharmacometabolomics study has established patterns of metabolite levels with response to dexamethasone. We obtained additional patient samples for metabolomics analysis to find associations between the metabolome and dexamethasone response in a validation cohort. A total of 14 infants provided 15 plasma and 12 urine samples. The measure of treatment response was the calculated change in respiratory severity score (deltaRSS) from pre-to-post treatment. Each metabolite was assessed with paired analysis of pre and post-treatment samples using Wilcoxon signed rank test. Correlation analysis was conducted between deltaRSS and pre-to-post change in metabolite level. Paired association analysis identified 20 plasma and 26 urine metabolites with significant level difference comparing pre to post treatment samples (p < 0.05). 4 plasma and 4 urine metabolites were also significant in the original study. Pre-to-post treatment change in metabolite analysis identified 4 plasma and 8 urine metabolites significantly associated with deltaRSS (p < 0.05). Change in urine citrulline levels showed a similar correlation pattern with deltaRSS in the first study, with increasing level associated with improved drug response. These results help validate the first major findings from pharmacometabolomics of BPD including key metabolites within the urea cycle and trans-4-hydroxyproline as a potential marker for lung injury. Ultimately, this study furthers our understanding of the mechanisms of steroid response in BPD patients and helps to design future targeted metabolomics studies in this patient population.
Indomethacin is used commonly in preterm neonates for the prevention of intracranial hemorrhage and closure of an abnormally open cardiac vessel. Due to biomedical advances, the infants who receive this drug in the neonatal intensive care unit setting have become younger, smaller, and less mature (more preterm) at the time of treatment. To develop a pharmacokinetics (PK) model to aid future dosing, we designed a prospective cohort study to characterize indomethacin PK in a dynamically changing patient population. A population PK base model was created using NONMEM, and a covariate model was developed in a primary development cohort and subsequently was tested for accuracy in a validation cohort. Postnatal age was a significant covariate for hepatic clearance (CL H ) and renal clearance (CL R ). The typical value of the total clearance (CL, the sum of CL R and CL H ) was 3.09 ml/h and expressed as CL/WT median = 3.96 ml/h/kg, where WT median is the median body weight. The intersubject variability of CL R and CL H were 61% and 207%, respectively. The typical value of the volume of distribution V p = 366 ml (V p /WT median = 470 ml/kg), and its intersubject variability was 38.8%.Half-life was 82.1 h. Compared with more mature and older preterm populations studied previously, indomethacin CL is considerably lower in this contemporary population. Model-informed precision dosing incorporating important covariates other than weight alone offers an opportunity to individualize dosing in a susceptible patient undergoing rapid change. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?With current weight-based dosing, indomethacin exposure is variable, and clinical response is unpredictable in preterm infants.
Background: AML is a hematological disorder resulting from proliferation and expansion of malignant myeloid cells. Expansion of malignant cells within the bone marrow and bloodstream causes severe clinical consequences. AML is a clinically challenging disease as many chemotherapeutic agents recommended in current guidelines have narrow therapeutic ranges and significant interpatient variation in treatment response. This can lead to severe therapeutic complications that are potentially fatal if concentrations are too high or failure to achieve treatment response if concentrations are too low. There is an ongoing search for predictive biomarkers to improve appropriate selection and dosing of chemotherapeutic agents. The emerging field of metabolomics has yielded promising results in the pursuit of precision medicine. However, there is still a gap in our knowledge of how metabolomics relate to treatment response to anti-leukemic agents for AML treatment. In this study, we propose to perform global metabolomics profiling of AML cell lines representing different risk groups to identify metabolic biomarkers predictive of in vitro cytotoxicity of most commonly used anti-leukemic agents. Methods: We performed global metabolomics on eight AML cell lines (MV4.11, KG-1, HL-60, Kasumi-1, AML-193, ME1, MOLM16, THP-1) using liquid chromatography high resolution mass spectrometry (LC-HRMS). Univariate and multivariate analyses were performed on the metabolite peak intensity values from LC-HRMS using MetaboAnalyst. Each cell line was treated with varying concentrations of cytarabine. Cell viability was then determined using MTT cell survival assays. Area under the cell survival curve (AUC) and IC50 values as well as correlation between metabolites and in vitro cytotoxicity were conducted using GraphPad Prism software. Results: A total of 2344 metabolites were detected in the global metabolome for the 6 cell lines. The metabolomes were filtered by removing metabolites present in fewer than 80% of cells or a signal below the cutoff threshold. Further analysis was conducted on the filtered dataset of 1634 metabolites. Since not all the metabolites detected have been successfully annotated, we performed an additional analysis with the dataset filtered to only include 197 annotated metabolites We classified cell lines into sensitive vs. resistant based on in vitro cytotoxicity. As shown in Figure 1, we observed significant differences in metabolic profiles (23 differentially expressed metabolites) between cytarabine sensitive vs. resistant cell lines. Multivariate analysis showed clear separation between sensitive and resistant groups, also shown in Figure 1. Through Spearman correlation analysis, we identified 6 unique known metabolites with significant correlation with cytotoxic AUC values for cytarabine. Of these known metabolites, one also showed significant differential expression between the sensitivity groups in the filtered dataset (Guanosine). 39 currently unknown metabolites also showed significant correlation with cytotoxic AUC values for cytarabine, with 9 metabolites also showing significant differential expression between the sensitivity groups. We expanded our correlation analysis to include an additional three commonly used chemotherapeutic agents in AML treatment regimens (clofarabine, doxorubicin and etoposide). We identified 12 unique known metabolites with significant correlation with drug sensitivity (6 for clofarabine, 6 for doxorubicin, 0 for etoposide). For unknown metabolites, 237 also showed significant correlation with cytotoxic AUC values among the four chemotherapeutic agents. Conclusion: Overall, our results demonstrate that metabolomics differences contribute towards drug resistance. In addition, it could potentially identify predictive biomarkers for chemosensitivity to various anti-leukemic drugs. Our results provide opportunity to further explore these metabolites in patient samples for association with clinical response. Disclosures No relevant conflicts of interest to declare.
AML is a hematological disorder resulting from proliferation and expansion of malignant myeloid cells. Clinical outcome for AML remains dismal despite intensive therapy in part due to the disease heterogeneity with various cytogenetic and molecular lesions. Fms-Like Tyrosine Kinase-3 (FLT3) is a receptor tyrosine kinase expressed hematopoietic stem/progenitor cells. Activating mutations of FLT3 gene due to internal tandem duplication of the juxtamembrane domain coding sequence (FLT3/ITD) causes autonomous cellular proliferations leading to disease progression. Metabolomic profiling has been successfully utilized to identify metabolic alterations in hematological disorders. However, no studies on metabolic alterations associated with pediatric AML have been reported at this time. In this study we propose to establish the metabolomic landscape in pediatric AML patients and identify differential expression of metabolites based on FLT3/ITD status. Cellular and plasma metabolomics profile was generated from 32 matching diagnostic material from 16 patients with and without FLT3/ITD (N=8 for each cohort and each sample was run in duplicate) treated on COG-AAML0531 study. Global metabolomics profiling was performed on a Thermo Q-Exactive Oribtrap mass spectrometer with Dionex UHPLC and autosampler. All samples were analyzed in duplicate in positive and negative heated electrospray ionization with a mass resolution of 35,000 at m/z 200 as separate injections. Separation was achieved on an ACE 18-pfp 100 x 2.1 mm, 2 µm column with mobile phase A as 0.1% formic acid in water and mobile phase B as acetonitrile. This is a polar embedded stationary phase that provides comprehensive coverage, but does have some limitation is the coverage of very polar species. The flow rate was 350 µL/min with a column temperature of 25¡C. 4 µL was injected for negative ions and 2 µL for positive ions. Statistical analysis was performed using MetaboAnalyst software using all metabolites (known and unknown) as well as only the annotated metabolites. Univariate analysis was performed by volcano plot and Multivariate analysis was performed using PCA, PLS-DA and OPLS-DA. Total of 2966 plasma metabolome (779 negative and 2187 positive) and 1742 (227 negative and 1515 positive) cellular metabolome features were identified. All subsequent data analyses were normalized to the sum of metabolites for each sample. Comparison of the cellular metabolome in patients with and without FLT3/ITD identified 12 known and 135 unknown metabolites that were significantly different between two cohorts (p<0.05). Similar comparison of the plasma metabolome identified19 known and 300 unknown metabolites in the patient with and without FLT3/ITD (top results are shown in Fig.1). Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed clear separation between the 2 groups (Fig.2). Some of the top known plasma metabolites (p<0.01) differentiating patients by FLT3 status include guanine, pyrimidine-2-3dicarboxylate, acetylglycine, acetyl-L-alanine, aminobutonate gaba, L-carnitine, methyl-2 oxovaleric acid, asparagine, acetyl arginine, Hydroxydecanoic acid, cysteic acid and glycocholic acid. Within leukemic cells top metabolites differentiating between FLT3 status included actyly carnitine, adenosine monophosphate, hypoxanthine, diaminohepatanedioate, guanine and sphingosine. Metaboanalyst Pathway analysis module mapped the differentiating metabolites to aminoacyl-tRNA biosynthesis, Glycerophospholipid metabolism, Cysteine and methionine metabolism, Pantothenate and CoA biosynthesis, Purine metabolism. This pilot study defines distinct metabolomics signature associated with genomic subtype of AML (FLT3/ITD). As metabolomics provides an insight into the ultimate metabolic destination of normal and malignant hematopoiesis, it has a potential to provide a unique insight into the altered metabolic pathways in AML and identify pathways and networks that might be shared by varied genomic subtypes of AML. Such data can help merge rare genomic variants based on shared metabolic signatures and more appropriately guide directed therapies. Disclosures No relevant conflicts of interest to declare.
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