Partial Monosomy 21 (Q11.2→Q21.3) Combined with 3P25.3→Pter Monosomy Due to an Unbalanced Translocation in a Patient Present
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Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma
Anatomy and Pathology Whole-Genome Microarray Detects Deletions and Loss of Heterozygosity of Chromosome 3 Occurring Exclusively in Metastasizing Uveal Melanoma Sarah L. Lake,1 Sarah E. Coupland,1 Azzam F. G. Taktak,2 and Bertil E. Damato3 PURPOSE. To detect deletions and loss of heterozygosity of disease is fatal in 92% of patients within 2 years of diagnosis. chromosome 3 in a rare subset of fatal, disomy 3 uveal mela- Clinical and histopathologic risk factors for UM metastasis noma (UM), undetectable by fluorescence in situ hybridization include large basal tumor diameter (LBD), ciliary body involve- (FISH). ment, epithelioid cytomorphology, extracellular matrix peri- ϩ ETHODS odic acid-Schiff-positive (PAS ) loops, and high mitotic M . Multiplex ligation-dependent probe amplification 3,4 5 (MLPA) with the P027 UM assay was performed on formalin- count. Prescher et al. showed that a nonrandom genetic fixed, paraffin-embedded (FFPE) whole tumor sections from 19 change, monosomy 3, correlates strongly with metastatic death, and the correlation has since been confirmed by several disomy 3 metastasizing UMs. Whole-genome microarray analy- 3,6–10 ses using a single-nucleotide polymorphism microarray (aSNP) groups. Consequently, fluorescence in situ hybridization were performed on frozen tissue samples from four fatal dis- (FISH) detection of chromosome 3 using a centromeric probe omy 3 metastasizing UMs and three disomy 3 tumors with Ͼ5 became routine practice for UM prognostication; however, 5% years’ metastasis-free survival. to 20% of disomy 3 UM patients unexpectedly develop metas- tases.11 Attempts have therefore been made to identify the RESULTS. Two metastasizing UMs that had been classified as minimal region(s) of deletion on chromosome 3.12–15 Despite disomy 3 by FISH analysis of a small tumor sample were found these studies, little progress has been made in defining the key on MLPA analysis to show monosomy 3. -
Advancing a Clinically Relevant Perspective of the Clonal Nature of Cancer
Advancing a clinically relevant perspective of the clonal nature of cancer Christian Ruiza,b, Elizabeth Lenkiewicza, Lisa Eversa, Tara Holleya, Alex Robesona, Jeffrey Kieferc, Michael J. Demeurea,d, Michael A. Hollingsworthe, Michael Shenf, Donna Prunkardf, Peter S. Rabinovitchf, Tobias Zellwegerg, Spyro Moussesc, Jeffrey M. Trenta,h, John D. Carpteni, Lukas Bubendorfb, Daniel Von Hoffa,d, and Michael T. Barretta,1 aClinical Translational Research Division, Translational Genomics Research Institute, Scottsdale, AZ 85259; bInstitute for Pathology, University Hospital Basel, University of Basel, 4031 Basel, Switzerland; cGenetic Basis of Human Disease, Translational Genomics Research Institute, Phoenix, AZ 85004; dVirginia G. Piper Cancer Center, Scottsdale Healthcare, Scottsdale, AZ 85258; eEppley Institute for Research in Cancer and Allied Diseases, Nebraska Medical Center, Omaha, NE 68198; fDepartment of Pathology, University of Washington, Seattle, WA 98105; gDivision of Urology, St. Claraspital and University of Basel, 4058 Basel, Switzerland; hVan Andel Research Institute, Grand Rapids, MI 49503; and iIntegrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, AZ 85004 Edited* by George F. Vande Woude, Van Andel Research Institute, Grand Rapids, MI, and approved June 10, 2011 (received for review March 11, 2011) Cancers frequently arise as a result of an acquired genomic insta- on the basis of morphology alone (8). Thus, the application of bility and the subsequent clonal evolution of neoplastic cells with purification methods such as laser capture microdissection does variable patterns of genetic aberrations. Thus, the presence and not resolve the complexities of many samples. A second approach behaviors of distinct clonal populations in each patient’s tumor is to passage tumor biopsies in tissue culture or in xenografts (4, 9– may underlie multiple clinical phenotypes in cancers. -
(P -Value<0.05, Fold Change≥1.4), 4 Vs. 0 Gy Irradiation
Table S1: Significant differentially expressed genes (P -Value<0.05, Fold Change≥1.4), 4 vs. 0 Gy irradiation Genbank Fold Change P -Value Gene Symbol Description Accession Q9F8M7_CARHY (Q9F8M7) DTDP-glucose 4,6-dehydratase (Fragment), partial (9%) 6.70 0.017399678 THC2699065 [THC2719287] 5.53 0.003379195 BC013657 BC013657 Homo sapiens cDNA clone IMAGE:4152983, partial cds. [BC013657] 5.10 0.024641735 THC2750781 Ciliary dynein heavy chain 5 (Axonemal beta dynein heavy chain 5) (HL1). 4.07 0.04353262 DNAH5 [Source:Uniprot/SWISSPROT;Acc:Q8TE73] [ENST00000382416] 3.81 0.002855909 NM_145263 SPATA18 Homo sapiens spermatogenesis associated 18 homolog (rat) (SPATA18), mRNA [NM_145263] AA418814 zw01a02.s1 Soares_NhHMPu_S1 Homo sapiens cDNA clone IMAGE:767978 3', 3.69 0.03203913 AA418814 AA418814 mRNA sequence [AA418814] AL356953 leucine-rich repeat-containing G protein-coupled receptor 6 {Homo sapiens} (exp=0; 3.63 0.0277936 THC2705989 wgp=1; cg=0), partial (4%) [THC2752981] AA484677 ne64a07.s1 NCI_CGAP_Alv1 Homo sapiens cDNA clone IMAGE:909012, mRNA 3.63 0.027098073 AA484677 AA484677 sequence [AA484677] oe06h09.s1 NCI_CGAP_Ov2 Homo sapiens cDNA clone IMAGE:1385153, mRNA sequence 3.48 0.04468495 AA837799 AA837799 [AA837799] Homo sapiens hypothetical protein LOC340109, mRNA (cDNA clone IMAGE:5578073), partial 3.27 0.031178378 BC039509 LOC643401 cds. [BC039509] Homo sapiens Fas (TNF receptor superfamily, member 6) (FAS), transcript variant 1, mRNA 3.24 0.022156298 NM_000043 FAS [NM_000043] 3.20 0.021043295 A_32_P125056 BF803942 CM2-CI0135-021100-477-g08 CI0135 Homo sapiens cDNA, mRNA sequence 3.04 0.043389246 BF803942 BF803942 [BF803942] 3.03 0.002430239 NM_015920 RPS27L Homo sapiens ribosomal protein S27-like (RPS27L), mRNA [NM_015920] Homo sapiens tumor necrosis factor receptor superfamily, member 10c, decoy without an 2.98 0.021202829 NM_003841 TNFRSF10C intracellular domain (TNFRSF10C), mRNA [NM_003841] 2.97 0.03243901 AB002384 C6orf32 Homo sapiens mRNA for KIAA0386 gene, partial cds. -
Comprehensive Analyses of DNA Repair Pathways, Smoking and Bladder Cancer Risk in Los Angeles and Shanghai
IJC International Journal of Cancer Comprehensive analyses of DNA repair pathways, smoking and bladder cancer risk in Los Angeles and Shanghai Roman Corral1,2, Juan Pablo Lewinger1,2, David Van Den Berg1,2, Amit D. Joshi3, Jian-Min Yuan4, Manuela Gago-Dominguez1,2,5, Victoria K. Cortessis1,2,6, Malcolm C. Pike1,2,7, David V. Conti1,2, Duncan C. Thomas1,2, Christopher K. Edlund2, Yu-Tang Gao8, Yong-Bing Xiang8, Wei Zhang8, Yu-Chen Su1,2 and Mariana C. Stern1,2 1 Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 2 Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 3 Department of Epidemiology, Harvard School of Public Health, Boston, MA 4 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh Cancer Institute, University of Pittsbugh, Pittsburgh, PA 5 Galician Foundation of Genomic Medicine, Complexo Hospitalario Universitario Santiago, Servicio Galego de Saude, IDIS, Santiago de Compostela, Spain 6 Department of Obstetrics and Gynecology, Keck School of Medicine of University of Southern California, Los Angeles, CA 7 Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 8 Department of Epidemiology, Shanghai Cancer Institute and Cancer Institute of Shanghai Jiaotong University, Shanghai, China Tobacco smoking is a bladder cancer risk factor and a source of carcinogens that induce DNA damage to urothelial cells. Using data and samples from 988 cases and 1,004 controls enrolled in the Los Angeles County Bladder Cancer Study and the Shanghai Bladder Cancer Study, we investigated associations between bladder cancer risk and 632 tagSNPs that comprehensively capture genetic variation in 28 DNA repair genes from four DNA repair pathways: base excision repair (BER), nucleotide excision repair (NER), non-homologous end-joining (NHEJ) and homologous recombination repair (HHR). -
SETMAR Antibody
Product Datasheet SETMAR Antibody Catalog No: #43086 Orders: [email protected] Description Support: [email protected] Product Name SETMAR Antibody Host Species Rabbit Clonality Polyclonal Purification Antigen affinity purification. Applications WB IHC Species Reactivity Hu Specificity The antibody detects endogenous levels of total SETMAR protein. Immunogen Type protein Immunogen Description Fusion protein of human SETMAR Target Name SETMAR Other Names Mar1; HsMar1; METNASE Accession No. Swiss-Prot#: Q53H47Gene ID: 6419 Calculated MW 78kd Concentration 1mg/ml Formulation Rabbit IgG in pH7.4 PBS, 0.05% NaN3, 40% Glycerol. Storage Store at -20°C Application Details Western blotting: 1:200-1:1000 Immunohistochemistry: 1:20-1:100 Images Gel: 6%SDS-PAGE Lysate: 40 µg Lane: Jurkat cell Primary antibody: 1/400 dilution Secondary antibody: Goat anti rabbit IgG at 1/8000 dilution Exposure time: 30 seconds Address: 8400 Baltimore Ave., Suite 302, College Park, MD 20740, USA http://www.sabbiotech.com 1 Immunohistochemical analysis of paraffin-embedded Human liver cancer tissue using #43086 at dilution 1/20. Immunohistochemical analysis of paraffin-embedded Human thyroid cancer tissue using #43086 at dilution 1/20. Background This gene encodes a fusion protein that contains an N-terminal histone-lysine N-methyltransferase domain and a C-terminal mariner transposase domain. The encoded protein binds DNA and functions in DNA repair activities including non-homologous end joining and double strand break repair. The SET domain portion of this protein specifically methylates histone H3 lysines 4 and 36. This gene exists as a fusion gene only in anthropoid primates, other organisms lack mariner transposase domain. Note: This product is for in vitro research use only and is not intended for use in humans or animals. -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
Mouse Setmar Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Setmar Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Setmar conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Setmar gene (NCBI Reference Sequence: NM_178391 ; Ensembl: ENSMUSG00000034639 ) is located on Mouse chromosome 6. 2 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 2 (Transcript: ENSMUST00000049246). Exon 2 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Setmar gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-93D20 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 2 covers 82.85% of the coding region. Start codon is in exon 1, and stop codon is in exon 2. The size of intron 1 for 5'-loxP site insertion: 10466 bp. The size of effective cKO region: ~2167 bp. The cKO region does not have any other known gene. Page 1 of 7 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Setmar Homology arm cKO region loxP site Page 2 of 7 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
Title Human Genetic Diversity Possibly Determines Human's Compliance to COVID-19
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 16 April 2020 Title Human genetic diversity possibly determines human's compliance to COVID-19 1* 2 1 1 1 1 2 2 Yiqiang Zhao , Yongji Liu , Sa Li , Yuzhan Wang , Lina Bu , Qi Liu , Hongyi Lv , Fang Wan , Lida Wu2, Zeming Ning3, Yuchun Gu2,4* 1. Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, China 2. Allife Medicine Ltd., Beijing, China 3. The Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK 4. Regenerative Medicine Center, Aston University, Birmingham, UK Corresponding Author Prof. Yiqiang Zhao Beijing Advanced Innovation Center for Food Nutrition and Human Health China Agricultural University Beijing, China Email: [email protected] And Prof. Yuchun Gu The regenerative Medicine Center Aston University Birmingham, UK & Allife Medicine Co.Ltd Beijing, China Email: [email protected] © 2020 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 16 April 2020 Abstract The rapid spread of the coronavirus disease 2019 (COVID-19) is a serious threat to public health systems globally and is subsequently, a cause of anxiety and panic within human society. Understanding the mechanisms and reducing the chances of having severe symptoms from COVID-19 will play an essential role in treating the disease, and become an urgent task to calm the panic. However, the COVID-19 test developed to identify virus carriers is unable to predict symptom development in individuals upon infection. Experiences from other plagues in human history and COVID-19 statistics suggest that genetic factors may determine the compliance with the virus, i.e., severe, mild, and asymptomatic. -
Supplementary Materials and Tables a and B
SUPPLEMENTARY MATERIAL 1 Table A. Main characteristics of the subset of 23 AML patients studied by high-density arrays (subset A) WBC BM blasts MYST3- MLL Age/Gender WHO / FAB subtype Karyotype FLT3-ITD NPM status (x109/L) (%) CREBBP status 1 51 / F M4 NA 21 78 + - G A 2 28 / M M4 t(8;16)(p11;p13) 8 92 + - G G 3 53 / F M4 t(8;16)(p11;p13) 27 96 + NA G NA 4 24 / M PML-RARα / M3 t(15;17) 5 90 - - G G 5 52 / M PML-RARα / M3 t(15;17) 1.5 75 - - G G 6 31 / F PML-RARα / M3 t(15;17) 3.2 89 - - G G 7 23 / M RUNX1-RUNX1T1 / M2 t(8;21) 38 34 - + ND G 8 52 / M RUNX1-RUNX1T1 / M2 t(8;21) 8 68 - - ND G 9 40 / M RUNX1-RUNX1T1 / M2 t(8;21) 5.1 54 - - ND G 10 63 / M CBFβ-MYH11 / M4 inv(16) 297 80 - - ND G 11 63 / M CBFβ-MYH11 / M4 inv(16) 7 74 - - ND G 12 59 / M CBFβ-MYH11 / M0 t(16;16) 108 94 - - ND G 13 41 / F MLLT3-MLL / M5 t(9;11) 51 90 - + G R 14 38 / F M5 46, XX 36 79 - + G G 15 76 / M M4 46 XY, der(10) 21 90 - - G NA 16 59 / M M4 NA 29 59 - - M G 17 26 / M M5 46, XY 295 92 - + G G 18 62 / F M5 NA 67 88 - + M A 19 47 / F M5 del(11q23) 17 78 - + M G 20 50 / F M5 46, XX 61 59 - + M G 21 28 / F M5 46, XX 132 90 - + G G 22 30 / F AML-MD / M5 46, XX 6 79 - + M G 23 64 / M AML-MD / M1 46, XY 17 83 - + M G WBC: white blood cell. -
Birth of a Chimeric Primate Gene by Capture of the Transposase Gene
Birth of a chimeric primate gene by capture of the SEE COMMENTARY transposase gene from a mobile element Richard Cordaux*, Swalpa Udit†, Mark A. Batzer*, and Ce´ dric Feschotte†‡ *Department of Biological Sciences, Biological Computation and Visualization Center, Center for BioModular Multi-Scale Systems, Louisiana State University, 202 Life Sciences Building, Baton Rouge, LA 70803; and †Department of Biology, University of Texas, Arlington, TX 76019 Edited by Susan R. Wessler, University of Georgia, Athens, GA, and approved March 27, 2006 (received for review February 10, 2006) The emergence of new genes and functions is of central impor- SETMAR transcript, which consists of these three exons, is tance to the evolution of species. The contribution of various types predicted to encode a protein of 671 amino acids and is of duplications to genetic innovation has been extensively inves- supported by 48 human cDNA clones from 18 different normal tigated. Less understood is the creation of new genes by recycling and͞or cancerous tissues (Table 1, which is published as sup- of coding material from selfish mobile genetic elements. To inves- porting information on the PNAS web site; refs. 14 and 15). tigate this process, we reconstructed the evolutionary history of These data suggest that the SETMAR protein is broadly ex- SETMAR, a new primate chimeric gene resulting from fusion of a pressed and has an important, yet unknown, function in human. SET histone methyltransferase gene to the transposase gene of a Recently, it was shown that the SET domain of the SETMAR mobile element. We show that the transposase gene was recruited protein exhibits histone methyltransferase activity (15), as do all as part of SETMAR 40–58 million years ago, after the insertion of known SET domains (16, 17). -
Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma
Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma James Chen Submitted in partial fulfillment of the requirements for the Doctor of Philosophy Degree in the Graduate School of Arts and Sciences Columbia University 2013 ! 2013 James Chen All rights reserved ABSTRACT Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma James Chen This dissertation reviews the development and implementation of integrative, systems biology methods designed to parse driver mutations from high- throughput array data derived from human patients. The analysis of vast amounts of genomic and genetic data in the context of complex human genetic diseases such as Glioblastoma is a daunting task. Mutations exist by the hundreds, if not thousands, and only an unknown handful will contribute to the disease in a significant way. The goal of this project was to develop novel computational methods to identify candidate mutations from these data that drive the molecular differentiation of glioblastoma into the mesenchymal subtype, the most aggressive, poorest-prognosis tumors associated with glioblastoma. TABLE OF CONTENTS CHAPTER 1… Introduction and Background 1 Glioblastoma and the Mesenchymal Subtype 3 Systems Biology and Master Regulators 9 Thesis Project: Genetics and Genomics 20 CHAPTER 2… TCGA Data Processing 23 CHAPTER 3… DIGGIn Part 1 – Selecting f-CNVs 33 Mutual Information 40 Application and Analysis 45 CHAPTER 4… DIGGIn Part 2 – Selecting drivers 52 CHAPTER 5… KLHL9 Manuscript 63 Methods 90 CHAPTER 5a… Revisions work-in-progress 105 CHAPTER 6… Discussion 109 APPENDICES… 132 APPEND01 – TCGA classifications 133 APPEND02 – GBM f-CNV list 136 APPEND03 – MES f-CNV candidate drivers 152 APPEND04 – Scripts 149 APPEND05 – Manuscript Figures and Legends 175 APPEND06 – Manuscript Supplemental Materials 185 i ACKNOWLEDGEMENTS I would like to thank the Califano Lab and my mentor, Andrea Califano, for their intellectual and motivational support during my stay in their lab. -
Egfr Activates a Taz-Driven Oncogenic Program in Glioblastoma
EGFR ACTIVATES A TAZ-DRIVEN ONCOGENIC PROGRAM IN GLIOBLASTOMA by Minling Gao A thesis submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland March 2020 ©2020 Minling Gao All rights reserved Abstract Hyperactivated EGFR signaling is associated with about 45% of Glioblastoma (GBM), the most aggressive and lethal primary brain tumor in humans. However, the oncogenic transcriptional events driven by EGFR are still incompletely understood. We studied the role of the transcription factor TAZ to better understand master transcriptional regulators in mediating the EGFR signaling pathway in GBM. The transcriptional coactivator with PDZ- binding motif (TAZ) and its paralog gene, the Yes-associated protein (YAP) are two transcriptional co-activators that play important roles in multiple cancer types and are regulated in a context-dependent manner by various upstream signaling pathways, e.g. the Hippo, WNT and GPCR signaling. In GBM cells, TAZ functions as an oncogene that drives mesenchymal transition and radioresistance. This thesis intends to broaden our understanding of EGFR signaling and TAZ regulation in GBM. In patient-derived GBM cell models, EGF induced TAZ and its known gene targets through EGFR and downstream tyrosine kinases (ERK1/2 and STAT3). In GBM cells with EGFRvIII, an EGF-independent and constitutively active mutation, TAZ showed EGF- independent hyperactivation when compared to EGFRvIII-negative cells. These results revealed a novel EGFR-TAZ signaling axis in GBM cells. The second contribution of this thesis is that we performed next-generation sequencing to establish the first genome-wide map of EGF-induced TAZ target genes.