Identification of a Clinically Relevant Androgen-Dependent Gene Signature in Prostate Cancer
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
A Study of Chemotherapy Resistance in Acute Myeloid Leukemia
A Study of Chemotherapy Resistance in Acute Myeloid Leukemia A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Susan Kay Rathe IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY David A. Largaespada October, 2013 © Susan Kay Rathe 2013 Acknowledgements Funding: The leukemia research was funded by The Leukemia & Lymphoma Society (grant 7019-04). The MMuFLR workflow was funded by The Children's Cancer Research Fund. University of Minnesota Resources: The BioMedical Genomics Center provided services for gene expression microarray, RNA sequencing, oligo preparation, and Sanger sequencing. The Minnesota Supercomputing Institute maintains the Galaxy Software, as well as provides data management services and training. The Masonic Cancer Center Bioinformatics Core provided guidance in the analysis of the RNA-seq data. The FDA Drug Screen was performed at the Institute for Therapeutics Discovery and Development. Colleagues: First and foremost, I would like to recognize Dr. David Largaespada for his mentorship and the opportunity to continue research he started while a postdoc in Dr. Neal Copeland’s lab. I would also like to thank the many members of the Largaespada lab who provided direct or indirect assistance to my research. Dr. Bin Yin continued Dr. Largaespada’s work on the BXH-2 AML cell lines by making drug resistant derivatives and provided training in cell culture and drug assay techniques. Dr. Won-Il Kim developed the TNM mouse model and provided training in mouse related experimental techniques. Miechaleen Diers did all of the tail vein injections for the AML transplants and assisted with necropsies. -
Molecular Characterization of Acute Myeloid Leukemia by Next Generation Sequencing: Identification of Novel Biomarkers and Targets of Personalized Therapies
Alma Mater Studiorum – Università di Bologna Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale Dottorato di Ricerca in Oncologia, Ematologia e Patologia XXX Ciclo Settore Scientifico Disciplinare: MED/15 Settore Concorsuale:06/D3 Molecular characterization of acute myeloid leukemia by Next Generation Sequencing: identification of novel biomarkers and targets of personalized therapies Presentata da: Antonella Padella Coordinatore Prof. Pier-Luigi Lollini Supervisore: Prof. Giovanni Martinelli Esame finale anno 2018 Abstract Acute myeloid leukemia (AML) is a hematopoietic neoplasm that affects myeloid progenitor cells and it is one of the malignancies best studied by next generation sequencing (NGS), showing a highly heterogeneous genetic background. The aim of the study was to characterize the molecular landscape of 2 subgroups of AML patients carrying either chromosomal number alterations (i.e. aneuploidy) or rare fusion genes. We performed whole exome sequencing and we integrated the mutational data with transcriptomic and copy number analysis. We identified the cell cycle, the protein degradation, response to reactive oxygen species, energy metabolism and biosynthetic process as the pathways mostly targeted by alterations in aneuploid AML. Moreover, we identified a 3-gene expression signature including RAD50, PLK1 and CDC20 that characterize this subgroup. Taking advantage of RNA sequencing we aimed at the discovery of novel and rare gene fusions. We detected 9 rare chimeric transcripts, of which partner genes were transcription factors (ZEB2, BCL11B and MAFK) or tumor suppressors (SAV1 and PUF60) rarely translocated across cancer types. Moreover, we detected cryptic events hiding the loss of NF1 and WT1, two recurrently altered genes in AML. Finally, we explored the oncogenic potential of the ZEB2-BCL11B fusion, which revealed no transforming ability in vitro. -
Essential Genes and Their Role in Autism Spectrum Disorder
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Essential Genes And Their Role In Autism Spectrum Disorder Xiao Ji University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, and the Genetics Commons Recommended Citation Ji, Xiao, "Essential Genes And Their Role In Autism Spectrum Disorder" (2017). Publicly Accessible Penn Dissertations. 2369. https://repository.upenn.edu/edissertations/2369 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2369 For more information, please contact [email protected]. Essential Genes And Their Role In Autism Spectrum Disorder Abstract Essential genes (EGs) play central roles in fundamental cellular processes and are required for the survival of an organism. EGs are enriched for human disease genes and are under strong purifying selection. This intolerance to deleterious mutations, commonly observed haploinsufficiency and the importance of EGs in pre- and postnatal development suggests a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication and repetitive behavior. More and more genetic evidence points to a polygenic model of ASD and it is estimated that hundreds of genes contribute to ASD. The central question addressed in this dissertation is whether genes with a strong effect on survival and fitness (i.e. EGs) play a specific oler in ASD risk. I compiled a comprehensive catalog of 3,915 mammalian EGs by combining human orthologs of lethal genes in knockout mice and genes responsible for cell-based essentiality. -
Chromosomal Rearrangements Are Commonly Post-Transcriptionally Attenuated in Cancer
bioRxiv preprint doi: https://doi.org/10.1101/093369; this version posted February 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Chromosomal rearrangements are commonly post-transcriptionally attenuated in cancer 1 3 1 3, 4, 5 Emanuel Gonçalves , Athanassios Fragoulis , Luz Garcia-Alonso , Thorsten Cramer , 1,2# 1# Julio Saez-Rodriguez , Pedro Beltrao 1 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK 2 RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen 52057, Germany 3 Molecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, Pauwelsstraße 30, 52074 Aachen, Germany 4 NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands 5 ESCAM – European Surgery Center Aachen Maastricht, Germany and The Netherlands # co-last authors: [email protected]; [email protected] Running title: Chromosomal rearrangement attenuation in cancer Keywords: Cancer; Gene dosage; Proteomics; Copy-number variation; Protein complexes 1 bioRxiv preprint doi: https://doi.org/10.1101/093369; this version posted February 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Chromosomal rearrangements, despite being detrimental, are ubiquitous in cancer and often act as driver events. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Human Lectins, Their Carbohydrate Affinities and Where to Find Them
biomolecules Review Human Lectins, Their Carbohydrate Affinities and Where to Review HumanFind Them Lectins, Their Carbohydrate Affinities and Where to FindCláudia ThemD. Raposo 1,*, André B. Canelas 2 and M. Teresa Barros 1 1, 2 1 Cláudia D. Raposo * , Andr1 é LAQVB. Canelas‐Requimte,and Department M. Teresa of Chemistry, Barros NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829‐516 Caparica, Portugal; [email protected] 12 GlanbiaLAQV-Requimte,‐AgriChemWhey, Department Lisheen of Chemistry, Mine, Killoran, NOVA Moyne, School E41 of ScienceR622 Co. and Tipperary, Technology, Ireland; canelas‐ [email protected] NOVA de Lisboa, 2829-516 Caparica, Portugal; [email protected] 2* Correspondence:Glanbia-AgriChemWhey, [email protected]; Lisheen Mine, Tel.: Killoran, +351‐212948550 Moyne, E41 R622 Tipperary, Ireland; [email protected] * Correspondence: [email protected]; Tel.: +351-212948550 Abstract: Lectins are a class of proteins responsible for several biological roles such as cell‐cell in‐ Abstract:teractions,Lectins signaling are pathways, a class of and proteins several responsible innate immune for several responses biological against roles pathogens. such as Since cell-cell lec‐ interactions,tins are able signalingto bind to pathways, carbohydrates, and several they can innate be a immuneviable target responses for targeted against drug pathogens. delivery Since sys‐ lectinstems. In are fact, able several to bind lectins to carbohydrates, were approved they by canFood be and a viable Drug targetAdministration for targeted for drugthat purpose. delivery systems.Information In fact, about several specific lectins carbohydrate were approved recognition by Food by andlectin Drug receptors Administration was gathered for that herein, purpose. plus Informationthe specific organs about specific where those carbohydrate lectins can recognition be found by within lectin the receptors human was body. -
A Dissertation Entitled the Androgen Receptor
A Dissertation entitled The Androgen Receptor as a Transcriptional Co-activator: Implications in the Growth and Progression of Prostate Cancer By Mesfin Gonit Submitted to the Graduate Faculty as partial fulfillment of the requirements for the PhD Degree in Biomedical science Dr. Manohar Ratnam, Committee Chair Dr. Lirim Shemshedini, Committee Member Dr. Robert Trumbly, Committee Member Dr. Edwin Sanchez, Committee Member Dr. Beata Lecka -Czernik, Committee Member Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo August 2011 Copyright 2011, Mesfin Gonit This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of The Androgen Receptor as a Transcriptional Co-activator: Implications in the Growth and Progression of Prostate Cancer By Mesfin Gonit As partial fulfillment of the requirements for the PhD Degree in Biomedical science The University of Toledo August 2011 Prostate cancer depends on the androgen receptor (AR) for growth and survival even in the absence of androgen. In the classical models of gene activation by AR, ligand activated AR signals through binding to the androgen response elements (AREs) in the target gene promoter/enhancer. In the present study the role of AREs in the androgen- independent transcriptional signaling was investigated using LP50 cells, derived from parental LNCaP cells through extended passage in vitro. LP50 cells reflected the signature gene overexpression profile of advanced clinical prostate tumors. The growth of LP50 cells was profoundly dependent on nuclear localized AR but was independent of androgen. Nevertheless, in these cells AR was unable to bind to AREs in the absence of androgen. -
Whole-Genome Cancer Analysis As an Approach to Deeper Understanding of Tumour Biology
British Journal of Cancer (2010) 102, 243 – 248 & 2010 Cancer Research UK All rights reserved 0007 – 0920/10 $32.00 www.bjcancer.com Minireview Whole-genome cancer analysis as an approach to deeper understanding of tumour biology ,1 2 RL Strausberg* and AJG Simpson 1J Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA; 2Ludwig Institute for Cancer Research, 605 Third Avenue, New York, NY 10158, USA Recent advances in DNA sequencing technology are providing unprecedented opportunities for comprehensive analysis of cancer genomes, exomes, transcriptomes, as well as epigenomic components. The integration of these data sets with well-annotated phenotypic and clinical data will expedite improved interventions based on the individual genomics of the patient and the specific disease. British Journal of Cancer (2010) 102, 243–248. doi:10.1038/sj.bjc.6605497 www.bjcancer.com Published online 22 December 2009 & 2010 Cancer Research UK Keywords: genome; transcriptome; exome; chromosome; sequencing The family of diseases that we refer to as cancer represents a field of tumourigenesis, on the basis of knowledge of gene families and of application for genomics of truly special importance and signal transduction networks. More recently, technological changes opportunity. It is perhaps the first area in which not only will in DNA sequencing, including the recently introduced ‘NextGen’ genomics continue to make major contributions to the under- instruments and associated molecular technologies, have enabled standing of the disease through holistic discovery of causal both higher-throughput and more sensitive assays that provide genome-wide perturbations but will also be the first field in which important new opportunities for basic discovery and clinical whole-genome analysis is used in clinical applications such as application (Mardis, 2009). -
Identification of Novel Genes in Human Airway Epithelial Cells Associated with Chronic Obstructive Pulmonary Disease (COPD) Usin
www.nature.com/scientificreports OPEN Identifcation of Novel Genes in Human Airway Epithelial Cells associated with Chronic Obstructive Received: 6 July 2018 Accepted: 7 October 2018 Pulmonary Disease (COPD) using Published: xx xx xxxx Machine-Based Learning Algorithms Shayan Mostafaei1, Anoshirvan Kazemnejad1, Sadegh Azimzadeh Jamalkandi2, Soroush Amirhashchi 3, Seamas C. Donnelly4,5, Michelle E. Armstrong4 & Mohammad Doroudian4 The aim of this project was to identify candidate novel therapeutic targets to facilitate the treatment of COPD using machine-based learning (ML) algorithms and penalized regression models. In this study, 59 healthy smokers, 53 healthy non-smokers and 21 COPD smokers (9 GOLD stage I and 12 GOLD stage II) were included (n = 133). 20,097 probes were generated from a small airway epithelium (SAE) microarray dataset obtained from these subjects previously. Subsequently, the association between gene expression levels and smoking and COPD, respectively, was assessed using: AdaBoost Classifcation Trees, Decision Tree, Gradient Boosting Machines, Naive Bayes, Neural Network, Random Forest, Support Vector Machine and adaptive LASSO, Elastic-Net, and Ridge logistic regression analyses. Using this methodology, we identifed 44 candidate genes, 27 of these genes had been previously been reported as important factors in the pathogenesis of COPD or regulation of lung function. Here, we also identifed 17 genes, which have not been previously identifed to be associated with the pathogenesis of COPD or the regulation of lung function. The most signifcantly regulated of these genes included: PRKAR2B, GAD1, LINC00930 and SLITRK6. These novel genes may provide the basis for the future development of novel therapeutics in COPD and its associated morbidities. -
Detailed Characterization of Human Induced Pluripotent Stem Cells Manufactured for Therapeutic Applications
Stem Cell Rev and Rep DOI 10.1007/s12015-016-9662-8 Detailed Characterization of Human Induced Pluripotent Stem Cells Manufactured for Therapeutic Applications Behnam Ahmadian Baghbaderani 1 & Adhikarla Syama2 & Renuka Sivapatham3 & Ying Pei4 & Odity Mukherjee2 & Thomas Fellner1 & Xianmin Zeng3,4 & Mahendra S. Rao5,6 # The Author(s) 2016. This article is published with open access at Springerlink.com Abstract We have recently described manufacturing of hu- help determine which set of tests will be most useful in mon- man induced pluripotent stem cells (iPSC) master cell banks itoring the cells and establishing criteria for discarding a line. (MCB) generated by a clinically compliant process using cord blood as a starting material (Baghbaderani et al. in Stem Cell Keywords Induced pluripotent stem cells . Embryonic stem Reports, 5(4), 647–659, 2015). In this manuscript, we de- cells . Manufacturing . cGMP . Consent . Markers scribe the detailed characterization of the two iPSC clones generated using this process, including whole genome se- quencing (WGS), microarray, and comparative genomic hy- Introduction bridization (aCGH) single nucleotide polymorphism (SNP) analysis. We compare their profiles with a proposed calibra- Induced pluripotent stem cells (iPSCs) are akin to embryonic tion material and with a reporter subclone and lines made by a stem cells (ESC) [2] in their developmental potential, but dif- similar process from different donors. We believe that iPSCs fer from ESC in the starting cell used and the requirement of a are likely to be used to make multiple clinical products. We set of proteins to induce pluripotency [3]. Although function- further believe that the lines used as input material will be used ally identical, iPSCs may differ from ESC in subtle ways, at different sites and, given their immortal status, will be used including in their epigenetic profile, exposure to the environ- for many years or even decades. -
Supplementary Table 1
Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7