AUTHOR=Stockard Bradley , Bhise Neha , Shin Miyoung , Guingab-Cagmat Joy , Garrett Timothy J. , Pounds Stanley , Lamba Jatinder K. 

TITLE=Cellular Metabolomics Profiles Associated With Drug Chemosensitivity in AML

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.678008

DOI=10.3389/fonc.2021.678008

ISSN=2234-943X

ABSTRACT=Background: Acute Myeloid Leukemia (AML) is a hematological disorder with dismal outcome. Cytarabine in combination with anthracycline has been a mainstay of AML chemotherapy for over 40 years. Though 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. Relapsed and resistant disease following treatment with standard therapy are thus 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 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 1624 metabolic features were detected across the leukemic cell lines of which 187 were annotated to known metabolites. With respect to doxorubicin, we observed significantly higher abundance of a carboxylic acid (1-aminocyclopropane-1-carboxylate) and several amino acids in resistant cell lines. Pathway analysis enriched several amino acid biosynthesis and metabolic pathways. For cytarabine resistance, 9 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 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.