Mechanisms of Pinometostat (EPZ-5676) Treatment-Emergent Resistance in MLL-Rearranged Leukemia.

DOT1L is a protein methyltransferase involved in the development and maintenance of MLL-rearranged (MLL-r) leukemia through its ectopic methylation of histones associated with well-characterized leukemic genes. Pinometostat (EPZ-5676), a selective inhibitor of DOT1L, is in clinical development in relapsed/refractory acute leukemia patients harboring rearrangements of the MLL gene. The observation of responses and subsequent relapses in the adult trial treating MLL-r patients motivated preclinical investigations into potential mechanisms of pinometostat treatment-emergent resistance (TER) in cell lines confirmed to have MLL-r. TER was achieved in five MLL-r cell lines, KOPN-8, MOLM-13, MV4-11, NOMO-1, and SEM. Two of the cell lines, KOPN-8 and NOMO-1, were thoroughly characterized to understand the mechanisms involved in pinometostat resistance. Unlike many other targeted therapies, resistance does not appear to be achieved through drug-induced selection of mutations of the target itself. Instead, we identified both drug efflux transporter dependent and independent mechanisms of resistance to pinometostat. In KOPN-8 TER cells, increased expression of the drug efflux transporter ABCB1 (P-glycoprotein, MDR1) was the primary mechanism of drug resistance. In contrast, resistance in NOMO-1 cells occurs through a mechanism other than upregulation of a specific efflux pump. RNA-seq analysis performed on both parental and resistant KOPN-8 and NOMO-1 cell lines supported two unique candidate pathway mechanisms that may explain the pinometostat resistance observed in these cell lines. These results are the first demonstration of TER models of the DOT1L inhibitor pinometostat and may provide useful tools for investigating clinical resistance. Mol Cancer Ther; 16(8); 1669-79. ©2017 AACR.

Construction of a computable cell proliferation network focused on non-diseased lung cells.

BACKGROUND: Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD), and fibrosis. Unfortunately, no such network has been available prior to this work. RESULTS: To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics), and contains a total of 848 nodes (biological entities) and 1597 edges (relationships between biological entities). The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN) are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data. CONCLUSIONS: To the best of our knowledge, this lung-focused Cell Proliferation Network provides the most comprehensive connectivity map in existence of the molecular mechanisms regulating cell proliferation in the lung. The network is based on fully referenced causal relationships obtained from extensive evaluation of the literature. The computable structure of the network enables its application to the qualitative and quantitative evaluation of cell proliferation using systems biology data sets. The network is available for public use.