Harnessing Gene Expression Profiling In Search Of New Candidate Genes For Ara-C Resistance In Acute Myeloid Leukemia
Authors: Abraham Ajay , Varatharajan Savitha , Karathedath Sreeja , Velayudhan Shaji R , Srivastava Alok , Mathews Vikram , Balasubramanian Poonkuzhali 
Published: October 2019

Wide inter-individual variation in terms of treatment outcome and toxic side effects of treatment exist among patients with AML receiving chemotherapy with cytarabine (Ara-C) and daunorubicin. We have previously evaluated the expression of the major genes involved in cytarabine transport and metabolism on ex-vivo Ara-C response and compared it with cytogenetic and molecular markers in AML (Blood (ASH Annual Meeting Abstracts) 2011 118: Abstract 3481). Our candidate gene expression data led us to propose Ara-C resistance index (Ara-C RI) (RI = ΔCT (DCK X ENT1)/ ΔCT CDA), which incorporates candidate Ara-C metabolizing genes whose RNA expression are significantly associated with ex-vivo Ara-C cytotoxicity. Ara-C RI values were significantly higher in resistant (IC50 >80 uM) and intermediate (IC50 6.25-80uM) samples when compared to sensitive samples (IC50 <6.25uM) (median 5.459 (1.759- 11.82) and 5.396 (1.89- 11.62) vs. 3.840 (1.89- 9.8); p <0.0001 (Fig 1a). This was then validated in the relapsed AML samples, which showed a significantly higher RI values (median RI 6.312 (2.01- 19.85)) when compared to sensitive ((3.840 (1.895- 9.8)) and resistant samples at diagnosis (5.412 (1.759- 11.82)); p <0.0001 (Fig 1b).

Though, the Ara-C RI correlated well with ex-vivo cytotoxicity as well as treatment response in vivo (data not shown), this did not completely explain the variation, suggestive of alternative resistance mechanisms. We undertook a genome-wide gene expression profiling to address possible mechanisms of Ara-C resistance other than the candidate gene approach. Based on ex vivo Ara-C cytotoxicity at diagnosis, Ara-C sensitive (IC50 <3uM AraC) and Ara-C resistant samples (IC50 >80uM) (each 5) were included for microarray analysis. Total RNA was extracted from bone marrow mononuclear cells using tri reagent, cDNA was synthesized and then subjected to one color 8 X 60K Agilent microarray analysis. Data was normalized, filtered and analyzed using Gene Spring GX (V 12.0) software. Normalization was done using the 75th percentile shift (Percentile shift normalization is a global normalization, where the locations of all the spot intensities in an array are adjusted). Using unpaired t-Test, 4436 genes were identified to be differentially expressed (Fold change expression values were provided as log-base 2) between Ara-C sensitive samples and Ara-C resistant samples. Differentially regulated genes were clustered 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 and Genotypic Biointerpreter-Biological Analysis Software. The differentially expressed genes fell into the following biological processes; transcription (375 genes), transport (364 genes), metabolism (267 genes; Fig 1d), immune (155 genes), cell cycle (129 genes), apoptosis (123 gene; Fig 1c) and so on. Upregulated gene list in Ara-C sensitive group included apoptotic related genes like PMAIP1, NDUFA, BAX, BCL2, TRAF2, transcriptional regulators including SMAD5, SMAD1, ZNF family proteins- ZNF644, ZNF469, ZNF195, ZNF22, ZNF3, ZNF713, ZNF777, ZNF234, CEBPa, PARP, ETV6, E2F3 and so on. Down-regulated genes in Ara-C sensitive group are of special interest as they could be potential candidates for targeting Ara-C non-responsive group. They included transcriptional regulators like HDAC4, KLF4, CREB5, CEBPb, RARA, E2F4, MNDA, MTA3 and PPARd also apoptotic genes like MCL1, PSEN1, ELMO2, PAK1, APAF1, MAPK1, CD40, FAS, CASP1 and CASP8. Interestingly, many of the genes involved in cellular metabolism were found to be down regulated in AraC sensitive group. Major down-regulated metabolic genes include CDA, SLC2A3, SLC2A8, GK, NADK, ACACB, ACSL1, PFKFB4, PFKFB3, PDK4, ME1 and PC. This study has come up with previously unrecognized aspects in Ara-C resistance in AML, and is suggestive of Ara-C sensitive samples having increased expression of anti-Warburg set of genes. Our data as well as growing evidences from various malignancies and altered cellular metabolism propose the possibility of using metabolic inhibitors alone or in combination with Ara-C to overcome drug resistance.

Disclosures:

No relevant conflicts of interest to declare.