Cellular Metabolomics Profiles Associated With Drug Chemosensitivity in AML

Bradley Stockard; Neha Bhise; Miyoung Shin; Joy Guingab-Cagmat; Timothy J Garrett; Stanley Pounds; Jatinder K Lamba

doi:10.3389/fonc.2021.678008

Cellular Metabolomics Profiles Associated With Drug Chemosensitivity in AML

Front Oncol. 2021 Jun 10:11:678008. doi: 10.3389/fonc.2021.678008. eCollection 2021.

Authors

Bradley Stockard¹, Neha Bhise¹, Miyoung Shin¹, Joy Guingab-Cagmat², Timothy J Garrett², Stanley Pounds³, Jatinder K Lamba^{1

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Affiliations

¹ Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States.
² Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, United States.
³ Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN, United States.
⁴ University of Florida Health Cancer Center, Gainesville, FL, United States.
⁵ Center for Pharmacogenetics, University of Florida, Gainesville, FL, United States.

Abstract

Background: Acute 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.

Methods: We 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.

Results: A 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.

Conclusion: Overall, 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.

Keywords: AML; cytarabine; doxorubicin; drug resistance; leukemia; metabolomics.