Molecular insight into thiopurine resistance: transcriptomic signature in lymphoblastoid cell lines

Abstract

Background: There has been considerable progress in the management of acute lymphoblastic leukemia (ALL) but further improvement is needed to increase long-term survival. The thiopurine agent 6-mercaptopurine (6-MP) used for ALL maintenance therapy has a key influence on clinical outcomes and relapse prevention. Genetic inheritance in thiopurine metabolism plays a major role in interindividual clinical response variability to thiopurines; however, most cases of thiopurine resistance remain unexplained.

Methods: We used lymphoblastoid cell lines (LCLs) from healthy donors, selected for their extreme thiopurine susceptibility. Thiopurine metabolism was characterized by the determination of TPMT and HPRT activity. We performed genome-wide expression profiling in resistant and sensitive cell lines with the goal of elucidating the mechanisms of thiopurine resistance.

Results: We determined a higher TPMT activity (+44%; P = 0.024) in resistant compared to sensitive cell lines, although there was no difference in HPRT activity. We identified a 32-gene transcriptomic signature that predicts thiopurine resistance. This signature includes the GTPBP4 gene coding for a GTP-binding protein that interacts with p53. A comprehensive pathway analysis of the genes differentially expressed between resistant and sensitive cell lines indicated a role for cell cycle and DNA mismatch repair system in thiopurine resistance. It also revealed overexpression of the ATM/p53/p21 pathway, which is activated in response to DNA damage and induces cell cycle arrest in thiopurine resistant LCLs. Furthermore, overexpression of the p53 target gene TNFRSF10D or the negative cell cycle regulator CCNG2 induces cell cycle arrest and may also contribute to thiopurine resistance. ARHGDIA under-expression in resistant cell lines may constitute a novel molecular mechanism contributing to thiopurine resistance based on Rac1 inhibition induced apoptosis and in relation with thiopurine pharmacodynamics.

Conclusion: Our study provides new insights into the molecular mechanisms underlying thiopurine resistance and suggests a potential research focus for developing tailored medicine.

Figure 1

Growth inhibition by 6-MP (2 μM) in sensitive, resistant, and HPRT-deficient cell lines. **Mann Whitney test, P <0.01.

Figure 2

Transcriptomic signature characterizing cell lines resistant to thiopurines. (A) Heatmap of the transcriptomic signature validated by qPCR for resistant (orange) compared to sensitive (blue) cell lines. Overexpressed genes are in red and underexpressed genes in green. Fold-changes in the relative expression of each gene are reported in Additional file 1: Table S5. (B) Validation by qPCR of the transcriptomic signature including 32 genes. Fold-changes in the relative expression of each of the 32 genes as determined using qPCR (X axis) and micro-array (Y axis), with GUSB as the reference gene (r _s = 0.87; P <0.0001).

Figure 3

Top Ingenuity® canonical pathways enriched by genes that were significantly differentially expressed in resistant cell lines. The Ingenuity® canonical pathway analysis associates the 943 gene dataset with the canonical pathways in Ingenuity’s Knowledge Base and returns two measures of association: (1) a ratio of the number of genes from the list that maps to the pathway divided by the total number of genes that map to the same pathway, and (2) a P value of the Fisher’s exact test for each pathway. Ingenuity® canonical pathways associated with a P value <0.01 are presented.

Figure 4

Molecular insight into thiopurine resistance. Proposal mechanisms and candidate biomarkers contributing to thiopurine cellular resistance phenotype in lymphoblastoid cell lines.