DOCSLIB.ORG

Large-Scale Identification of Functional Genes Regulating Cancer Cell Migration and Metastasis Using the Self-Assembled Cell Microarray

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Large-Scale Identification of Functional Genes Regulating Cancer Cell Migration and Metastasis Using the Self-Assembled Cell Microarray Large-scale Identification of Functional Genes Regulating Cancer Cell Migration and Metastasis Using the Self-assembled Cell Microarray A Dissertation Presented to The Academic Faculty By Hanshuo Zhang A dissertation submitted to the Faculty of the Graduate School of Emory University and Georgia Institute of Technology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Engineering 2013 Georgia Institute of Technology and Emory University August 2013 Copyright © 2013 by Hanshuo Zhang Large-scale Identification of Functional Genes Regulating Cancer Cell Migration and Metastasis Using the Self-assembled Cell Microarray Approved by: Dr. Jianzhong Xi, Advisor Dr. Chunyang Xiong, Committee Member Department of Biomedical Engineering Department of Biomedical Engineering Collage of Engineering Collage of Engineering Peking University Peking University Dr. May Dongmei Wang, Co-advisor Dr. Xiaoyu Li, Committee Member Department of Biomedical Engineering Collage of Chemistry and Molecular Georgia Institute of Technology and Emory Engineering University Peking University Dr. Brani Vidakovic, Committee Member Department of Biomedical Engineering Georgia Institute of Technology and Emory University Date Approved: June 03, 2013 ACKNOWLEDGEMENTS First of all, I wish to thank my advisor Dr. Jianzhong Xi and co-advisor Dr. May Wang, for educating and supporting me to accomplish the research work of my PhD career. I wish to thank other thesis committee members, including Dr. Brani Vidakovic, Dr. Chunyang Xiong, and Dr. Xiaoyu Li, for directing and suggesting me with my thesis. Secondly, I wish to thank lab members of both Dr. Xi’s lab and Dr. Wang’s lab, including Yang Hao, Junyu Yang, Shenyi Yin, Ming Ma, Yanzhen Ye, Qi Wang, Dr. Kuaichang Zhu, Dr. Changhong Sun, Dr. Huang Huang, Dr. Zhao Wang, Dr. Yu Fan, Dr. Huiping Yuan and Dr. Juan Li of Xi lab and Leo Wu, Raghu Chandramohan, James Cheng, Chanchala Kaddi, Koon-Yin Kong, Sonal Kothari, Janani Venugopalan, Dr. John H. Phan and Dr. Todd H. Stokes of Wang lab, for helping me with my research work. Thirdly, I wish to thank the collaborator labs, including Dr. Yanyi Huang’s lab, Dr. Jin Gu’s lab, Dr. Xiaoyu Li’ lab, Dr. Li Yu’s lab and Dr. Peace Chen’ lab, for collaborating with me to accomplish many research projects. Finally, I wish to thank my family for always supporting and encouraging me to accomplish my PhD career. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS .................................................................................................................... iii LIST OF TABLES ................................................................................................................................. vi LIST OF FIGURES .............................................................................................................................. vii LIST OF SYMBOLS AND ABBREVIATIONS ........................................................................................ viii SUMMARY ........................................................................................................................................ ix CHAPTER 1 INTRODUCTION ........................................................................................................... 1 1.2 RNA interference technology ...................................................................................................... 2 1.2.1 Biogenesis and mechanism of miRNA ...................................................................................... 3 1.2.2 RNAi high-throughput screening .............................................................................................. 5 1.3 miRNAs governing cancer metastasis ......................................................................................... 6 1.3.1 miRNA as metastasis activator ................................................................................................. 7 1.3.2 miRNA as metastasis suppressor .............................................................................................. 8 1.3.3 miRNA involved in EMT ............................................................................................................ 9 1.4 Protein kinase modulating cancer metastasis ........................................................................... 11 1.4.1 Protein kinase as biomarker for diagnosis ............................................................................. 11 1.4.2 Protein kinase as drug target for therapy............................................................................... 12 1.5 Research approaches for migratory genes ................................................................................ 12 1.5.1 Conventional migration assays ............................................................................................... 12 1.5.2 HTS assays for identification of migratory genes ................................................................... 13 1.6 Problem statement and content of this thesis .......................................................................... 14 CHAPTER 2 DEVELOPMENT OF SELF-ASSEMBLED CELL MICROARRAY ......................................... 16 2.1 Fabrication of SAMcell .............................................................................................................. 16 2.2 SAMcell studying cell migration ................................................................................................ 18 2.3 Statistical preparation for HTS ................................................................................................... 19 2.4 Summary of SAMcell method ................................................................................................... 21 CHAPTER 3 SCREENING OF HUMAN GENOME MIRNAS REGULATING CANCER METASTASIS ...... 23 3.1 Screening of miRNAs regulating cell migration ......................................................................... 23 3.2 General regulation of miRNA on cell migration ........................................................................ 33 iv 3.3 Results of the miRNA functional screens .................................................................................. 35 3.4 miR-23b inhibiting metastasis-related traits ............................................................................. 40 3.5 miR-23b repressing EMT ........................................................................................................... 41 3.6 Expression patterns of miR-23b ................................................................................................ 43 3.7 miR-23b regulating a cohort of prometastatic genes ................................................................ 44 3.8 Summary of miRNA screening................................................................................................... 47 CHAPTER 4 SCREENING OF HUMAN GENOME KINASE GENES REGULATING CANCER METASTASIS ........................................................................................................................................................ 49 4.1 Strategy of systematic investigation .......................................................................................... 49 4.2 Loss-of-function study of kinase genes regulating cell migration ............................................. 50 4.3 Functional screening for cell migration and invasion ................................................................ 51 4.4 Bioinformatics analysis to identify important genes ................................................................. 61 4.5 Experimental validation of prospective metastasis-related genes ............................................ 74 4.6 Summary of kinase screening ................................................................................................... 75 CHAPTER 5 CONCLUSION ........................................................................................................... 77 APPENDIX A SEQUENCE OF PRIMERS AND BINDING SITES .............................................. 78 APPENDIX B METHODS AND MATERIAL ................................................................................... 81 REFERENCES .................................................................................................................................... 84 PUBLICATIONS ................................................................................................................................. 95 VITA ................................................................................................................................................. 97 v LIST OF TABLES Table 3.1A List of human miRNAs examined in this work .................................. 24 Table 3.1B List of miRNAs capable of regulating cell migration ........................ 28 Table 3.1C List of miRNAs demonstrating non-concordant behaviors ................ 32 Table 3.3A List of miRNAs capable of inhibiting cell invasion ........................... 36 Table 3.3B List of miRNAs capable of inhibiting cell migration, invasion and promoting apoptosis ....................................................................................... 38 Table 4.3A List of entire 710 kinase genes screened ............................................ 54 Table 4.3B List of PR genes validated by Transwell............................................ 56 Table 4.3C List of discordant genes interfered by proliferation or apoptosis ....... 58 Table 4.3D List of invasion screening results
Load more

Recommended publications
  • Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
    Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent
    [Show full text]
  • PERK Antibody / EIF2AK3 (RQ4206)
    PERK Antibody / EIF2AK3 (RQ4206) Catalog No. Formulation Size RQ4206 0.5mg/ml if reconstituted with 0.2ml sterile DI water 100 ug Bulk quote request Availability 1-3 business days Species Reactivity Human, Mouse, Rat Format Antigen affinity purified Clonality Polyclonal (rabbit origin) Isotype Rabbit IgG Purity Antigen affinity purified Buffer Lyophilized from 1X PBS with 2% Trehalose and 0.025% sodium azide UniProt Q9NZJ5 Applications Western Blot : 0.5-1ug/ml Flow cytometry : 1-3ug/10^6 cells Direct ELISA : 0.1-0.5ug/ml Limitations This PERK antibody is available for research use only. Western blot testing of human 1) HeLa, 2) COLO320, 3) A549, 4) SK-OV-3, 5) A431, 6) rat brain and 7) mouse brain lysate with PERK antibody at 0.5ug/ml. Predicted molecular weight ~125 kDa, observed here at ~140 kDa. Flow cytometry testing of human HepG2 cells with PERK antibody at 1ug/10^6 cells (blocked with goat sera); Red=cells alone, Green=isotype control, Blue= PERK antibody. Description Eukaryotic translation initiation factor 2-alpha kinase 3, also known as protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK), is an enzyme that in humans is encoded by the EIF2AK3 gene. The protein encoded by this gene phosphorylates the alpha subunit of eukaryotic translation-initiation factor 2, leading to its inactivation, and thus to a rapid reduction of translational initiation and repression of global protein synthesis. This protein is thought to modulate mitochondrial function. It is a type I membrane protein located in the endoplasmic reticulum (ER), where it is induced by ER stress caused by malfolded proteins.
    [Show full text]
  • Gene Essentiality Landscape and Druggable Oncogenic Dependencies in Herpesviral Primary Effusion Lymphoma
    ARTICLE DOI: 10.1038/s41467-018-05506-9 OPEN Gene essentiality landscape and druggable oncogenic dependencies in herpesviral primary effusion lymphoma Mark Manzano1, Ajinkya Patil1, Alexander Waldrop2, Sandeep S. Dave2, Amir Behdad3 & Eva Gottwein1 Primary effusion lymphoma (PEL) is caused by Kaposi’s sarcoma-associated herpesvirus. Our understanding of PEL is poor and therefore treatment strategies are lacking. To address this 1234567890():,; need, we conducted genome-wide CRISPR/Cas9 knockout screens in eight PEL cell lines. Integration with data from unrelated cancers identifies 210 genes as PEL-specific oncogenic dependencies. Genetic requirements of PEL cell lines are largely independent of Epstein-Barr virus co-infection. Genes of the NF-κB pathway are individually non-essential. Instead, we demonstrate requirements for IRF4 and MDM2. PEL cell lines depend on cellular cyclin D2 and c-FLIP despite expression of viral homologs. Moreover, PEL cell lines are addicted to high levels of MCL1 expression, which are also evident in PEL tumors. Strong dependencies on cyclin D2 and MCL1 render PEL cell lines highly sensitive to palbociclib and S63845. In summary, this work comprehensively identifies genetic dependencies in PEL cell lines and identifies novel strategies for therapeutic intervention. 1 Department of Microbiology-Immunology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. 2 Duke Cancer Institute and Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA. 3 Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. Correspondence and requests for materials should be addressed to E.G. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:3263 | DOI: 10.1038/s41467-018-05506-9 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-05506-9 he human oncogenic γ-herpesvirus Kaposi’s sarcoma- (IRF4), a critical oncogene in multiple myeloma33.
    [Show full text]
  • Price List for Out-Of-State Patients (Jul 2017 – Dec 2017)
    Department of Diagnostic Genomics QEII Medical Centre PRICE LIST FOR OUT-OF-STATE PATIENTS (JUL 2017 – DEC 2017) What methods of testing do we employ? Available Methods PCR and/or Sanger DNA Sequencing for predictive testing and familial cascade screening. Targeted Massive Parallel Sequencing (MPS) panels and Sanger sequencing to analyse large genes. MLPA to detect larger deletions and duplications. MS-MLPA to detect methylation changes in addition to deletions and duplications. If you are unsure which method is appropriate for your patient, please contact us by phone on 08 6383 4223 or email on [email protected]. Who do we accept testing requests from? Requesting Clinicians Diagnostic testing can only be requested by a suitably qualified clinician – we do not provide a service direct to the public. For some tests, we will only accept requests once the patient has undergone genetic counselling from a recognised genetic counsellor, due to the clinical sensitivity of these tests. What types of sample(s) are required for testing? Sample requirements for each test are listed below. EDTA Samples Most tests will require a single 2-4mls sample of blood collected with an EDTA preservative. EDTA samples must arrive at our lab within 5 days of phlebotomy, and must be sent at room temperature. Tissue 10-50mg of tissue is required for DNA extraction DNA 1-5µg of extracted DNA (depending on test request) in place of EDTA blood Predictive Testing We recommend testing two separate EDTA blood samples collected from the patient at least 10 minutes apart. Familial Cancer and We recommend testing a second EDTA blood sample in cases where a pathogenic variant is found.
    [Show full text]
  • Defective Lymphocyte Chemotaxis in Я-Arrestin2- and GRK6-Deficient Mice
    Defective lymphocyte chemotaxis in ␤-arrestin2- and GRK6-deficient mice Alan M. Fong*, Richard T. Premont*, Ricardo M. Richardson*, Yen-Rei A. Yu†, Robert J. Lefkowitz*‡§, and Dhavalkumar D. Patel*†¶ Departments of *Medicine, ‡Biochemistry, and †Immunology, and §Howard Hughes Medical Institute, Duke University Medical Center, Durham, NC 27710 Contributed by Robert J. Lefkowitz, April 4, 2002 Lymphocyte chemotaxis is a complex process by which cells move kinase, extracellular receptor kinase, and c-jun terminal kinase within tissues and across barriers such as vascular endothelium and activation (9–12), they might also act as positive regulators of is usually stimulated by chemokines such as stromal cell-derived chemotaxis. To evaluate the role of the GRK-arrestin pathway factor-1 (CXCL12) acting via G protein-coupled receptors. Because in chemotaxis, we studied the chemotactic responses of lym- members of this receptor family are regulated (‘‘desensitized’’) by phocytes from ␤-arrestin- and GRK-deficient mice toward G protein-coupled receptor kinase (GRK)-mediated receptor phos- gradients of stromal cell-derived factor 1 (CXCL12), a well ␤ phorylation and -arrestin binding, we examined signaling and characterized chemokine whose receptor is CXCR4, a core- chemotactic responses in splenocytes derived from knockout mice ceptor for HIV. deficient in various ␤-arrestins and GRKs, with the expectation that these responses might be enhanced. Knockouts of ␤-arrestin2, Materials and Methods GRK5, and GRK6 were examined because all three proteins are :expressed at high levels in purified mouse CD3؉ T and B220؉ B Mice. The following mouse strains were used in this study splenocytes. CXCL12 stimulation of membrane GTPase activity was ␤-arrestin2-deficient (back-crossed for six generations onto the unaffected in splenocytes derived from GRK5-deficient mice but C57͞BL6 background; ref.
    [Show full text]
  • Combined Integrin Phosphoproteomic Analyses and Small Interfering RNA–Based Functional Screening Identify Key Regulators for Cancer Cell Adhesion and Migration
    Published OnlineFirst April 7, 2009; DOI: 10.1158/0008-5472.CAN-08-2515 Published Online First on April 7, 2009 as 10.1158/0008-5472.CAN-08-2515 Research Article Combined Integrin Phosphoproteomic Analyses and Small Interfering RNA–Based Functional Screening Identify Key Regulators for Cancer Cell Adhesion and Migration Yanling Chen,1 Bingwen Lu,2 Qingkai Yang,1 Colleen Fearns,3 John R. Yates III,2 and Jiing-Dwan Lee1 Departments of 1Immunology, 2Chemical Physiology, and 3Chemistry, The Scripps Research Institute, La Jolla, California Abstract cytoskeletal components (2, 7) and regulation of cell survival, Integrins interact with extracellular matrix (ECM) and deliver proliferation, differentiation, cell cycle, and migration (8, 9). Understanding the mechanism by which integrins modulate these intracellular signaling for cell proliferation, survival, and motility. During tumor metastasis, integrin-mediated cell cellular activities is of significant biological importance. adhesion to and migration on the ECM proteins are required The most common cancers in human include breast cancer, for cancer cell survival and adaptation to the new microen- prostate cancer, lung cancer, colon cancer, and ovarian cancer (10, 11), and their metastasis is the leading cause of mortality in vironment. Using stable isotope labeling by amino acids in cell cancer patients, causing 90% of deaths from solid tumors (11, 12). culture–mass spectrometry, we profiled the phosphoproteo- During the process of metastasis, tumor cells leave the primary mic changes induced by the interactions of cell integrins with site, travel via blood and/or lymphatic circulatory systems, attach type I collagen, the most common ECM substratum. Integrin- to the substratum of ECM at a distant site, and establish a ECM interactions modulate phosphorylation of 517 serine, secondary tumor, accompanied by angiogenesis of the newly threonine, or tyrosine residues in 513 peptides, corresponding formed neoplasm (12).
    [Show full text]
  • Profiling Data
    Compound Name DiscoveRx Gene Symbol Entrez Gene Percent Compound Symbol Control Concentration (nM) JNK-IN-8 AAK1 AAK1 69 1000 JNK-IN-8 ABL1(E255K)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317I)-nonphosphorylated ABL1 87 1000 JNK-IN-8 ABL1(F317I)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317L)-nonphosphorylated ABL1 65 1000 JNK-IN-8 ABL1(F317L)-phosphorylated ABL1 61 1000 JNK-IN-8 ABL1(H396P)-nonphosphorylated ABL1 42 1000 JNK-IN-8 ABL1(H396P)-phosphorylated ABL1 60 1000 JNK-IN-8 ABL1(M351T)-phosphorylated ABL1 81 1000 JNK-IN-8 ABL1(Q252H)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(Q252H)-phosphorylated ABL1 56 1000 JNK-IN-8 ABL1(T315I)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(T315I)-phosphorylated ABL1 92 1000 JNK-IN-8 ABL1(Y253F)-phosphorylated ABL1 71 1000 JNK-IN-8 ABL1-nonphosphorylated ABL1 97 1000 JNK-IN-8 ABL1-phosphorylated ABL1 100 1000 JNK-IN-8 ABL2 ABL2 97 1000 JNK-IN-8 ACVR1 ACVR1 100 1000 JNK-IN-8 ACVR1B ACVR1B 88 1000 JNK-IN-8 ACVR2A ACVR2A 100 1000 JNK-IN-8 ACVR2B ACVR2B 100 1000 JNK-IN-8 ACVRL1 ACVRL1 96 1000 JNK-IN-8 ADCK3 CABC1 100 1000 JNK-IN-8 ADCK4 ADCK4 93 1000 JNK-IN-8 AKT1 AKT1 100 1000 JNK-IN-8 AKT2 AKT2 100 1000 JNK-IN-8 AKT3 AKT3 100 1000 JNK-IN-8 ALK ALK 85 1000 JNK-IN-8 AMPK-alpha1 PRKAA1 100 1000 JNK-IN-8 AMPK-alpha2 PRKAA2 84 1000 JNK-IN-8 ANKK1 ANKK1 75 1000 JNK-IN-8 ARK5 NUAK1 100 1000 JNK-IN-8 ASK1 MAP3K5 100 1000 JNK-IN-8 ASK2 MAP3K6 93 1000 JNK-IN-8 AURKA AURKA 100 1000 JNK-IN-8 AURKA AURKA 84 1000 JNK-IN-8 AURKB AURKB 83 1000 JNK-IN-8 AURKB AURKB 96 1000 JNK-IN-8 AURKC AURKC 95 1000 JNK-IN-8
    [Show full text]
  • De Novo EIF2AK1 and EIF2AK2 Variants Are Associated with Developmental Delay, Leukoencephalopathy, and Neurologic Decompensation
    bioRxiv preprint doi: https://doi.org/10.1101/757039; this version posted September 16, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. De novo EIF2AK1 and EIF2AK2 variants are associated with developmental delay, leukoencephalopathy, and neurologic decompensation Dongxue Mao1,2, Chloe M. Reuter3,4, Maura R.Z. Ruzhnikov5,6, Anita E. Beck7, Emily G. Farrow8,9,10, Lisa T. Emrick1,11,12,13, Jill A. Rosenfeld12, Katherine M. Mackenzie5, Laurie Robak2,12,13, Matthew T. Wheeler3,14, Lindsay C. Burrage12,13, Mahim Jain15, Pengfei Liu12, Daniel Calame11,13, Sebastien Küry17,18, Martin Sillesen19, Klaus Schmitz-Abe20, Davide Tonduti21, Luigina Spaccini22, Maria Iascone23, Casie A. Genetti20, Madeline Graf16, Alyssa Tran12, Mercedes Alejandro12, Undiagnosed Diseases Network, Brendan H. Lee12,13, Isabelle Thiffault8,9,24, Pankaj B. Agrawal#,20, Jonathan A. Bernstein#,3,25, Hugo J. Bellen#,2,12,26,27,28, Hsiao- Tuan Chao#,1,2,11,12,13,28,27,29 #Correspondence should be addressed: [email protected] (P.A.), [email protected] (J.A.B.), [email protected] (H.J.B.), [email protected] (H.T.C.) 1Department of Pediatrics, Baylor College of Medicine (BCM), Houston, TX 2Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 3Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA 4Stanford Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine,
    [Show full text]
  • Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
    www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1.
    [Show full text]
  • Lemur Tyrosine Kinase 2 Acts As a Positive Regulator of NF-Κb Activation and Colon Cancer Cell Proliferation T
    Cancer Letters 454 (2019) 70–77 Contents lists available at ScienceDirect Cancer Letters journal homepage: www.elsevier.com/locate/canlet Original Articles Lemur tyrosine kinase 2 acts as a positive regulator of NF-κB activation and colon cancer cell proliferation T ∗ Rongjing Zhanga,1, Xiuxiu Lia,1, Lumin Weib, Yanqing Qina, Jing Fangc,d, a CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China b Ruijin Hospital, Shanghai Jiaotong University, Shanghai, 200025, China c Cancer Institute, The Affiliated Hospital of Qingdao University, Qingdao, 266061, China d Cancer Institute, Qingdao University, 26601, Qingdao, 266061, China ARTICLE INFO ABSTRACT Keywords: Lemur tyrosine kinase 2 (LMTK2) belongs to both protein kinase and tyrosine kinase families. LMTK2 is less LMTK2 studied and little is known about its function. Here we demonstrate that LMTK2 modulates NF-κB activity and NF-κB functions to promote colonic tumorigenesis. We found that LMTK2 protein was abundant in colon cancer cells Colon cancer and LMTK2 knockdown (LMTK2-KD) inhibited proliferation of colon cancer cells through inactivating NF-κB. In unstimulated condition, LMTK2 modulated NF-κB through inhibiting phosphorylation of p65 at Ser468. Mechanistically, LMTK2 phosphorylated protein phosphatase 1A (PP1A) to prevent PP1A from depho- sphorylating p-GSK3β(Ser9). The p-GSK3β(Ser9) could not phosphorylate p65 at Ser468, which maintained the basal NF-κB activity. LMTK2 also modulated TNFα-activated NF-κB. LMTK2-KD repressed TNFα-induced IKKβ phosphorylation, IκBα degradation and NF-κB activation, implying that LMTK2 modulates TNFα-activated NF- κB via IKK.
    [Show full text]
  • Targeting Myddosome Signaling in Waldenström’S
    Author Manuscript Published OnlineFirst on August 20, 2018; DOI: 10.1158/1078-0432.CCR-17-3265 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. IRAK 1/4 Inhibition in Waldenström’s Ni, H. et al. Targeting Myddosome Signaling in Waldenström’s Macroglobulinemia with the Interleukin-1 Receptor- associated Kinase 1/4 Inhibitor R191 Haiwen Ni1,2,#, Fazal Shirazi2,#, Veerabhadran Baladandayuthapani3, Heather Lin3, Isere Kuiatse2, Hua Wang2, Richard J. Jones2, Zuzana Berkova2, Yasumichi Hitoshi4, Stephen M. Ansell5, Steven P. Treon6, Sheeba K. Thomas2, Hans C. Lee2, Zhiqiang Wang2, R. Eric Davis2, and Robert Z. Orlowski2,7,* 1Department of Hematology, The Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, JangSu, China; 2Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX; 3Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX; 4Rigel, South San Francisco, CA; The 5Division of Hematology, Mayo Clinic, Rochester, MN; The 6Dana Farber Cancer Institute, Harvard Medical School, Boston, MA. 7Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX #Indicates that these authors contributed equally. Address correspondence to: Dr. Robert Z. Orlowski, The University of Texas MD Anderson Cancer Center, Department of Lymphoma and Myeloma, 1515 Holcombe Blvd., Unit 429, Houston, TX 77030-4009, E-mail: [email protected], Telephone 713-794-3234, Fax 713- 563-5067 Page 1 Downloaded from clincancerres.aacrjournals.org on September 24, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 20, 2018; DOI: 10.1158/1078-0432.CCR-17-3265 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
    [Show full text]
  • Ponatinib Shows Potent Antitumor Activity in Small Cell Carcinoma of the Ovary Hypercalcemic Type (SCCOHT) Through Multikinase Inhibition Jessica D
    Published OnlineFirst February 9, 2018; DOI: 10.1158/1078-0432.CCR-17-1928 Cancer Therapy: Preclinical Clinical Cancer Research Ponatinib Shows Potent Antitumor Activity in Small Cell Carcinoma of the Ovary Hypercalcemic Type (SCCOHT) through Multikinase Inhibition Jessica D. Lang1,William P.D. Hendricks1, Krystal A. Orlando2, Hongwei Yin1, Jeffrey Kiefer1, Pilar Ramos1, Ritin Sharma3, Patrick Pirrotte3, Elizabeth A. Raupach1,3, Chris Sereduk1, Nanyun Tang1, Winnie S. Liang1, Megan Washington1, Salvatore J. Facista1, Victoria L. Zismann1, Emily M. Cousins4, Michael B. Major4, Yemin Wang5, Anthony N. Karnezis5, Aleksandar Sekulic1,6, Ralf Hass7, Barbara C. Vanderhyden8, Praveen Nair9, Bernard E. Weissman2, David G. Huntsman5,10, and Jeffrey M. Trent1 Abstract Purpose: Small cell carcinoma of the ovary, hypercalcemic type three SWI/SNF wild-type ovarian cancer cell lines. We further (SCCOHT) is a rare, aggressive ovarian cancer in young women identified ponatinib as the most effective clinically approved that is universally driven by loss of the SWI/SNF ATPase subunits RTK inhibitor. Reexpression of SMARCA4 was shown to confer SMARCA4 and SMARCA2. A great need exists for effective targeted a 1.7-fold increase in resistance to ponatinib. Subsequent therapies for SCCOHT. proteomic assessment of ponatinib target modulation in Experimental Design: To identify underlying therapeutic vul- SCCOHT cell models confirmed inhibition of nine known nerabilities in SCCOHT, we conducted high-throughput siRNA ponatinib target kinases alongside 77 noncanonical ponatinib and drug screens. Complementary proteomics approaches pro- targets in SCCOHT. Finally, ponatinib delayed tumor dou- filed kinases inhibited by ponatinib. Ponatinib was tested for bling time 4-fold in SCCOHT-1 xenografts while reducing efficacy in two patient-derived xenograft (PDX) models and one final tumor volumes in SCCOHT PDX models by 58.6% and cell-line xenograft model of SCCOHT.
    [Show full text]