High Chromosome Number in hematological cancer cell lines is a Negative Predictor of Response to the inhibition of Aurora B and C by GSK1070916

<p>Abstract</p> <p>Background</p> <p>Aurora kinases play critical roles in mitosis and are being evaluated as therapeutic targets in cancer. GSK1070916 is a potent, selective, ATP competitive inhibitor of Aurora kinase B and C. Translation of predictive biomarkers to the clinic can benefit patients by identifying the tumors that are more likely to respond to therapies, especially novel inhibitors such as GSK1070916.</p> <p>Methods</p> <p>59 Hematological cancer-derived cell lines were used as models for response where <it>in vitro </it>sensitivity to GSK1070916 was based on both time and degree of cell death. The response data was analyzed along with karyotype, transcriptomics and somatic mutation profiles to determine predictors of response.</p> <p>Results</p> <p>20 cell lines were sensitive and 39 were resistant to treatment with GSK1070916. High chromosome number was more prevalent in resistant cell lines (p-value = 0.0098, Fisher Exact Test). Greater resistance was also found in cell lines harboring polyploid subpopulations (p-value = 0.00014, Unpaired t-test). A review of NOTCH1 mutations in T-ALL cell lines showed an association between NOTCH1 mutation status and chromosome number (p-value = 0.0066, Fisher Exact Test).</p> <p>Conclusions</p> <p>High chromosome number associated with resistance to the inhibition of Aurora B and C suggests cells with a mechanism to bypass the high ploidy checkpoint are resistant to GSK1070916. High chromosome number, a hallmark trait of many late stage hematological malignancies, varies in prevalence among hematological malignancy subtypes. The high frequency and relative ease of measurement make high chromosome number a viable negative predictive marker for GSK1070916.</p

Optimizing Combination Therapies with Existing and Future CML Drugs

Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2]

Crystal Structures of ABL-Related Gene (ABL2) in Complex with Imatinib, Tozasertib (VX-680), and a Type I Inhibitor of the Triazole Carbothioamide Class†

ABL2 (also known as ARG (ABL related gene)) is closely related to the well-studied Abelson kinase cABL. ABL2 is involved in human neoplastic diseases and is deregulated in solid tumors. Oncogenic gene translocations occur in acute leukemia. So far no structural information for ABL2 has been reported. To elucidate structural determinants for inhibitor interaction, we determined the cocrystal structure of ABL2 with the oncology drug imatinib. Interestingly, imatinib not only interacted with the ATP binding site of the inactive kinase but was also bound to the regulatory myristate binding site. This structure may therefore serve as a tool for the development of allosteric ABL inhibitors. In addition, we determined the structures of ABL2 in complex with VX-680 and with an ATP-mimetic type I inhibitor, which revealed an interesting position of the DFG motif intermediate between active and inactive conformations, that may also serve as a template for future inhibitor design

Sharpin Contributes to TNFα Dependent NFκB Activation and Anti-Apoptotic Signalling in Hepatocytes

TNFα stimulates both pro- and anti-apoptotic signalling in hepatocytes. Anti-apoptotic signalling depends on a cascade of ubiquitylation steps leading to NFκB activation. Using Sharpin-deficient mice, we show that the ubiquitin binding protein Sharpin interacts with Hoip, an E3 ligase which generates linear ubiquitin chains. Sharpin-deficiency sensitized hepatocytes to induction of apoptosis by TNFα even in the absence of transcriptional inhibition. TNFα induced activation of NFκB was strongly reduced in hepatocytes from Sharpin-deficient mice, due to reduced and delayed phosphorylation and degradation of IκBα. Injection of TNFα-inducing lipopolysaccharides led to strongly exacerbated liver damage and premature death in Sharpin-deficient mice. Our findings point to an essential role of Sharpin in linear ubiquitin chain formation, NFκB activation, and protection of the liver against inflammatory damaging signals

Successful treatment of a chronic-phase T-315I-mutated chronic myelogenous leukemia patient with a combination of imatinib and interferon-alfa

The T315I BCR-ABL mutation in chronic myelogenous leukemia (CML) patients is responsible for up to 20% of all clinically observed resistance. This mutation confers resistance not only to imatinib, but also to second-generation BCR-ABL tyrosine kinases, such as nilotinib and dasatinib. A number of strategies have been implemented to overcome this resistance, but allogeneic stem cell transplantation remains the only established therapeutic option for a cure. A 61-year-old male was diagnosed with Philadelphia chromosome-positive chronic-phase CML in 2002. He was initially treated with imatinib and complete cytogenetic response (CCyR) was achieved 12 months later. However, after 18 months, a loss of CCyR was observed and a molecular study at 24 months revealed a T315I mutation of the BCR-ABL gene. At 30 months, imatinib/interferon-alfa (IFNα) combination therapy was initiated in an effort to overcome the resistance. Thirty months later, he re-achieved CCyR, and the T315I BCR-ABL mutation disappeared at 51 months. To our knowledge, this is the first case report showing the effectiveness of imatinib/IFNα combination therapy for CML patients bearing the T315I BCR-ABL mutation

Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors

In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs