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Background: Recently, we have shown that the ATP-binding cassette (ABC) transporter ABCB1 interferes with the anti-cancer activity of the pan-aurora kinase inhibitor tozasertib (VX680, MK-0457) but not of the aurora kinase A and B inhibitor alisertib (MLN8237). Preliminary data had suggested tozasertib also to be a substrate of the ABC transporter ABCG2, another ABC transporter potentially involved in cancer cell drug resistance. Here, we studied the effect of ABCG2 on the activity of tozasertib and alisertib.
Results: The tozasertib concentration that reduces cell viability by 50 % (IC50) was dramatically increased in ABCG2-transduced UKF-NB-3ABCG2 cells (48.8-fold) compared to UKF-NB-3 cells and vector-transduced control cells. The ABCG2 inhibitor WK-X-34 reduced tozasertib IC50 to the level of non-ABCG2-expressing UKF-NB-3 cells. Furthermore, ABCG2 depletion from UKF-NB-3ABCG2 cells using another lentiviral vector expressing an shRNA against the bicistronic mRNA of ABCG2 and eGFP largely re-sensitised these cells to tozasertib. In contrast, alisertib activity was not affected by ABCG2 expression.
Conclusions: Tozasertib but not alisertib activity is affected by ABCG2 expression. This should be considered within the design and analysis of experiments and clinical trials investigating these compounds.
The development of resistance to chemotherapeutic agents, such as Doxorubicin (DOX) and cytarabine (AraC), is one of the greatest challenges to the successful treatment of Acute Myeloid Leukemia (AML). Such acquisition is often underlined by a metabolic reprogramming that can provide a therapeutic opportunity, as it can lead to the emergence of vulnerabilities and dependencies to be exploited as targets against the resistant cells. In this regard, genome-scale metabolic models (GSMMs) have emerged as powerful tools to integrate multiple layers of data to build cancer-specific models and identify putative metabolic vulnerabilities. Here, we use genome-scale metabolic modelling to reconstruct a GSMM of the THP1 AML cell line and two derivative cell lines, one with acquired resistance to AraC and the second with acquired resistance to DOX. We also explore how, adding to the transcriptomic layer, the metabolomic layer enhances the selectivity of the resulting condition specific reconstructions. The resulting models enabled us to identify and experimentally validate that drug-resistant THP1 cells are sensitive to the FDA-approved antifolate methotrexate. Moreover, we discovered and validated that the resistant cell lines could be selectively targeted by inhibiting squalene synthase, providing a new and promising strategy to directly inhibit cholesterol synthesis in AML drug resistant cells.