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      Molecular docking studies shows tivozanib and lapatinib as potential inhibitors of EML4-ALK translocation mediated fusion protein in non small cell lung cancer

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          Abstract

          Identification of activating mutations in non-small cell lung cancers (NSCLC) has been a focus in recent years. This led to successful evidence of using tyrosine kinase inhibitors (TKIs) over the standard platinum doublet based chemotherapy as the first line treatment in the metastatic setting.The rearrangements of fusion protein EML4-ALK in NSCLC lead to the use of crizotinib for this class of tumors. Preclinical and Phase 1 clinical studies show that ceritinib is more effective against both crizotinib sensitive and resistant tumors. Although robust responses to crizotinib are observed in NSCLC harboring ALK mutations, majority of tumors eventually become resistant, posing a major challenge in treatment course. Thus, there is a need for the identification and development of second-generation of ALK inhibitors. Computer aided molecular docking data show Tivozanib and Lapatinib bind EML4-ALK with high score. Tivozanib is in clinical trials for renal cell cancer and Lapatinib is a known dual tyrosine kinase inhibitor effective in breast cancer patients with HER2 over-expression. Additional data on these compounds for use in EML4-ALK positive NSCLC will provide evidence for use in patients treated with crizotinib. Data shows the importance of computer aided molecular docking in developing candidates with improved activity for further consideration in vitro and in vivo validation.

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          Most cited references19

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          Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer.

          Despite the success of tyrosine kinase-based cancer therapeutics, for most solid tumors the tyrosine kinases that drive disease remain unknown, limiting our ability to identify drug targets and predict response. Here we present the first large-scale survey of tyrosine kinase activity in lung cancer. Using a phosphoproteomic approach, we characterize tyrosine kinase signaling across 41 non-small cell lung cancer (NSCLC) cell lines and over 150 NSCLC tumors. Profiles of phosphotyrosine signaling are generated and analyzed to identify known oncogenic kinases such as EGFR and c-Met as well as novel ALK and ROS fusion proteins. Other activated tyrosine kinases such as PDGFRalpha and DDR1 not previously implicated in the genesis of NSCLC are also identified. By focusing on activated cell circuitry, the approach outlined here provides insight into cancer biology not available at the chromosomal and transcriptional levels and can be applied broadly across all human cancers.
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            Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.

            We explore the ability of a simple simulated annealing procedure to assemble native-like structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear as the first two terms in a series expansion for the residue probability distributions in the protein database; the decoupling of the distance and environment dependencies of the distributions resolves the major problems with current database-derived scoring functions noted by Thomas and Dill. The simulated annealing procedure rapidly and frequently generates native-like structures for small helical proteins and better than random structures for small beta sheet containing proteins. Most of the simulated structures have native-like solvent accessibility and secondary structure patterns, and thus ensembles of these structures provide a particularly challenging set of decoys for evaluating scoring functions. We investigate the effects of multiple sequence information and different types of conformational constraints on the overall performance of the method, and the ability of a variety of recently developed scoring functions to recognize the native-like conformations in the ensembles of simulated structures.
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              EML4-ALK fusion gene and efficacy of an ALK kinase inhibitor in lung cancer.

              The EML4-ALK fusion gene has been detected in approximately 7% of Japanese non-small cell lung cancers (NSCLC). We determined the frequency of EML4-ALK in Caucasian NSCLC and in NSCLC cell lines. We also determined whether TAE684, a specific ALK kinase inhibitor, would inhibit the growth of EML4-ALK-containing cell lines in vitro and in vivo. We screened 305 primary NSCLC [both U.S. (n = 138) and Korean (n = 167) patients] and 83 NSCLC cell lines using reverse transcription-PCR and by exon array analyses. We evaluated the efficacy of TAE684 against NSCLC cell lines in vitro and in vivo. We detected four different variants, including two novel variants, of EML4-ALK using reverse transcription-PCR in 8 of 305 tumors (3%) and 3 of 83 (3.6%) NSCLC cell lines. All EML4-ALK-containing tumors and cell lines were adenocarcinomas. EML4-ALK was detected more frequently in NSCLC patients who were never or light (<10 pack-years) cigarette smokers compared with current/former smokers (6% versus 1%; P = 0.049). TAE684 inhibited the growth of one of three (H3122) EML4-ALK-containing cell lines in vitro and in vivo, inhibited Akt phosphorylation, and caused apoptosis. In another EML4-ALK cell line, DFCI032, TAE684 was ineffective due to coactivation of epidermal growth factor receptor and ERBB2. The combination of TAE684 and CL-387,785 (epidermal growth factor receptor/ERBB2 kinase inhibitor) inhibited growth and Akt phosphorylation and led to apoptosis in the DFCI032 cell line. EML4-ALK is found in the minority of NSCLC. ALK kinase inhibitors alone or in combination may nevertheless be clinically effective treatments for NSCLC patients whose tumors contain EML4-ALK.
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                Author and article information

                Journal
                Bioinformation
                Bioinformation
                Bioinformation
                Bioinformation
                Biomedical Informatics
                0973-8894
                0973-2063
                2014
                30 October 2014
                : 10
                : 10
                : 658-663
                Affiliations
                [1 ]Department of Preventive Oncology, Cancer Institute (WIA), Adyar, Chennai
                [2 ]Department of Bioinformatics, Madras Veterinary College, Vepery, Chennai
                [3 ]Department of Surgical Oncology, Cancer Institute (WIA), Adyar, Chennai
                Author notes
                [* ]Vijayalakshmi Ramshankar: vijiciwia@ 123456gmail.com Phone: 044-24910754; extn 243; 9940015796
                Article
                97320630010658
                10.6026/97320630010658
                4248349
                25489176
                6747a911-0a1c-4514-a5d9-afd4368f8fa1
                © 2014 Biomedical Informatics

                This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.

                History
                : 03 October 2014
                : 22 October 2014
                Categories
                Hypothesis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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