RNA expression of genes involved in cytarabine metabolism and transport predicts cytarabine response in acute myeloid leukemia

Abstract

Background: Variation in terms of outcome and toxic side effects of treatment exists among acute myeloid leukemia (AML) patients on chemotherapy with cytarabine (Ara-C) and daunorubicin (Dnr). Candidate Ara-C metabolizing gene expression in primary AML cells is proposed to account for this variation.

Methods: Ex vivo Ara-C sensitivity was determined in primary AML samples using MTT assay. mRNA expression of candidate Ara-C metabolizing genes were evaluated by RQPCR analysis. Global gene expression profiling was carried out for identifying differentially expressed genes between exvivo Ara-C sensitive and resistant samples.

Results: Wide interindividual variations in ex vivo Ara-C cytotoxicity were observed among samples from patients with AML and were stratified into sensitive, intermediately sensitive and resistant, based on IC50 values obtained by MTT assay. RNA expression of deoxycytidine kinase (DCK), human equilibrative nucleoside transporter-1 (ENT1) and ribonucleotide reductase M1 (RRM1) were significantly higher and cytidine deaminase (CDA) was significantly lower in ex vivo Ara-C sensitive samples. Higher DCK and RRM1 expression in AML patient's blast correlated with better DFS. Ara-C resistance index (RI), a mathematically derived quotient was proposed based on candidate gene expression pattern. Ara-C ex vivo sensitive samples were found to have significantly lower RI compared with resistant as well as samples from patients presenting with relapse. Patients with low RI supposedly highly sensitive to Ara-C were found to have higher incidence of induction death (p = 0.002; RR: 4.35 [95% CI: 1.69-11.22]). Global gene expression profiling undertaken to find out additional contributors of Ara-C resistance identified many apoptosis as well as metabolic pathway genes to be differentially expressed between Ara-C resistant and sensitive samples.

Conclusion: This study highlights the importance of evaluating expression of candidate Ara-C metabolizing genes in predicting ex vivo drug response as well as treatment outcome. RI could be a predictor of ex vivo Ara-C response irrespective of cytogenetic and molecular risk groups and a potential biomarker for AML treatment outcome and toxicity. Original submitted 22 December 2014; Revision submitted 9 April 2015.

Keywords: acute myeloid leukemia; cytarabine; drug resistance.

Figure 1. Metabolic pathway of Ara-C: key candidate genes involved in the metabolic activation of Ara-C are shown – ENT1 aids the transport of Ara-C into the cell.

Deoxycytidine kinase and the other activating kinases catalyze the conversion of inactive Ara-C to the active Ara-CTP. NT5C2, CDA and DCTD are the major deactivating enzymes. Ribonucleotide reductase increases the dNTP pool, which competitively inhibits the binding of Ara-CTP to DNA and thereby inhibits the cytotoxic action. MRP8 is an efflux transporter which effluxs out monophosphorylated nucleoside analogs.

Figure 2. Ara-C metabolizing genes expression across different ex vivo cytotoxicity groups.

RNA expression of Ara-C metabolizing genes DCK, ENT1, CDA and RRM1 in different ex vivo cytotoxicity groups based on IC50: (A) DCK, (B) ENT1, (C) CDA and (D) RRM1. RNA expression for each gene was normalized to GAPDH and the relative expression was calculated in comparison to that of AML001. Lines represent median relative expression for each *p-value obtained by comparing the groups by ANOVA. Statistical significance was measured using the nonparametric Kruskal–Wallis test.

Figure 3. Ara-C resistance index across different ex vivo cytotoxicity groups and in relapsed acute myeloid leukemia.

Ara-C resistance index across different ex vivo cytotoxicity groups and in relapsed acute myeloid leukemia: (A) RI across different ex vivo cytotoxicity groups based on IC50 obtained from MTT assay. (B) RI in low AUC and high AUC groups. (C) RI in relapse patients compared with ex vivo resistant, intermediate and sensitive patients at diagnosis. (D) RI in relapse patients compared with low AUC and high AUC groups at diagnosis. Lines represent median values for RI in each group. Statistical significance was measured using nonparametric Kruskal–Wallis or Mann–Whitney test. AUC: Area under the survival curve; RI: Resistance index.

Figure 4. Kaplan–Meier survival estimates for RI, DCK and RRM1 expression.

(A) EFS comparison for patients with lowest quartile RI (RI < 3.21) versus the rest (RI > 3.21). (B) Bar graph showing the number of patients with low RI (RI < 3.21) and high RI (RI > 3.21) with or without induction death. (C & D) DFS comparison with respect to RRM1 (lowest quartile vs the rest) and DCK (based on median).DFS: Disease-free survival.

Figure 5. Heat maps showing differentially expressed genes using hierarchical clustering based on Pearson coefficient correlation algorithm to identify significant gene expression patterns.

Genes were classified based on functional category and pathways using GeneSpring GX. Here, differentially expressed genes under the functional categories metabolism (A) and apoptosis (B) are shown.