Precision Medicine Informatics: Principles, Prospects, and Challenges
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Integrative Analysis of Exogenous, Endogenous, Tumour and Immune
Gut Online First, published on February 6, 2018 as 10.1136/gutjnl-2017-315537 Recent advances in basic science Integrative analysis of exogenous, endogenous, Gut: first published as 10.1136/gutjnl-2017-315537 on 6 February 2018. Downloaded from tumour and immune factors for precision medicine Shuji Ogino,1,2,3,4 Jonathan A Nowak,1 Tsuyoshi Hamada,2 Amanda I Phipps,5,6 Ulrike Peters,5,6 Danny A Milner Jr,7 Edward L Giovannucci,3,8,9 Reiko Nishihara,1,3,4,8,10 Marios Giannakis,4,11,12 Wendy S Garrett,4,11,13 Mingyang Song8,14,15 For numbered affiliations see ABSTRACT including inflammatory and immune cells. A fraction end of article. Immunotherapy strategies targeting immune checkpoints of somatic mutations may result in the generation of such as the CTLA4 and CD274 (programmed cell death new antigens (neoantigens) that can be recognised as Correspondence to 1 ligand 1, PD-L1)/PDCD1 (programmed cell death 1, non-self by the immune system. During an individu- Dr Shuji Ogino, Program in MPE Molecular Pathological PD-1) T-cell coreceptor pathways are revolutionising al's life-course, cells may acquire somatic molecular Epidemiology, Brigham and oncology. The approval of pembrolizumab use for alterations, and some of these cells undergo clonal Women’s Hospital, Boston, MA solid tumours with high-level microsatellite instability expansion, displaying hallmarks of early neoplasia. 02215, USA; shuji_ ogino@ dfci. or mismatch repair deficiency by the US Food and Many of these cells are likely kept in check or killed harvard. edu Drug Administration highlights promise of precision by the host immune system before they can develop Received 24 October 2017 immuno-oncology. -
The Genomic Organization and Expression Pattern of the Low-Affinity Fc Gamma Receptors (Fcγr) in the Göttingen Minipig
Immunogenetics (2019) 71:123–136 https://doi.org/10.1007/s00251-018-01099-1 ORIGINAL ARTICLE The genomic organization and expression pattern of the low-affinity Fc gamma receptors (FcγR) in the Göttingen minipig Jerome Egli1 & Roland Schmucki1 & Benjamin Loos1 & Stephan Reichl1 & Nils Grabole1 & Andreas Roller1 & Martin Ebeling1 & Alex Odermatt2 & Antonio Iglesias1 Received: 9 August 2018 /Accepted: 24 November 2018 /Published online: 18 December 2018 # The Author(s) 2018 Abstract Safety and efficacy of therapeutic antibodies are often dependent on their interaction with Fc receptors for IgG (FcγRs). The Göttingen minipig represents a valuable species for biomedical research but its use in preclinical studies with therapeutic antibodies is hampered by the lack of knowledge about the porcine FcγRs. Genome analysis and sequencing now enabled the localization of the previously described FcγRIIIa in the orthologous location to human FCGR3A. In addition, we identified nearby the gene coding for the hitherto undescribed putative porcine FcγRIIa. The 1′241 bp long FCGR2A cDNA translates to a 274aa transmembrane protein containing an extracellular region with high similarity to human and cattle FcγRIIa. Like in cattle, the intracellular part does not contain an immunoreceptor tyrosine-based activation motif (ITAM) as in human FcγRIIa. Flow cytometry of the whole blood and single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) of Göttingen minipigs revealed the expression profile of all porcine FcγRs which is compared to human and mouse. The new FcγRIIa is mainly expressed on platelets making the minipig a good model to study IgG-mediated platelet activation and aggregation. In contrast to humans, minipig blood monocytes were found to express inhibitory FcγRIIb that could lead to the underestimation of FcγR-mediated effects of monocytes observed in minipig studies with therapeutic antibodies. -
The Future of Precision Medicine : Potential Impacts for Health Technology Assessment
This is a repository copy of The Future of Precision Medicine : Potential Impacts for Health Technology Assessment. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/133069/ Version: Accepted Version Article: Love-Koh, James orcid.org/0000-0001-9009-5346, Peel, Alison, Rejon-Parilla, Juan Carlos et al. (6 more authors) (2018) The Future of Precision Medicine : Potential Impacts for Health Technology Assessment. Pharmacoeconomics. pp. 1439-1451. ISSN 1179-2027 https://doi.org/10.1007/s40273-018-0686-6 Reuse This article is distributed under the terms of the Creative Commons Attribution-NonCommercial (CC BY-NC) licence. This licence allows you to remix, tweak, and build upon this work non-commercially, and any new works must also acknowledge the authors and be non-commercial. You don’t have to license any derivative works on the same terms. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ The Future of Precision Medicine: Potential Impacts for Health Technology Assessment Title: The Future of Precision Medicine: Potential Impacts for Health Technology Assessment Authors: James Love-Koh1,2, Alison Peel1, Juan Carlos Rejon-Parilla3, Kate Ennis1,4, Rosemary Lovett3, Andrea Manca2,5, Anastasia Chalkidou6, Hannah Wood1, Matthew Taylor1 1 YorK Health Economics Consortium 2 Centre for Health Economics, University of YorK 3 National Institute for Health and Care Excellence 4 Institute of Infection and Global Health, University of Liverpool 5 Luxembourg Institute of Health 6 Kings Technology Evaluation Centre Corresponding Author Details: Name: James Love-Koh Address: Centre for Health Economics, Alcuin A Block, University of York, Heslington, York, YO10 5DD, UK. -
Chain Locus Heavy Genomic Organization of the Variable
Antibody Repertoire Development in Fetal and Neonatal Piglets. XI. The Relationship of Variable Heavy Chain Gene Usage and the Genomic Organization of the Variable Heavy This information is current as Chain Locus of September 27, 2021. Tomoko Eguchi-Ogawa, Nancy Wertz, Xiu-Zhu Sun, Francois Puimi, Hirohide Uenishi, Kevin Wells, Patrick Chardon, Gregory J. Tobin and John E. Butler J Immunol published online 5 March 2010 Downloaded from http://www.jimmunol.org/content/early/2010/03/05/jimmun ol.0903616 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2010/03/05/jimmunol.090361 Material 6.DC1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 27, 2021 • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts Errata An erratum has been published regarding this article. Please see next page or: /content/190/3/1383.full.pdf The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published March 5, 2010, doi:10.4049/jimmunol.0903616 The Journal of Immunology Antibody Repertoire Development in Fetal and Neonatal Piglets. -
What Are Precision Medicine and Personalized Medicine?
What Are Precision Medicine and Personalized Medicine? Precision medicine, also known as personalized medicine, is a new frontier for healthcare combining genomics, big data analytics, and population health. Source: Thinkstock Since the beginning of recorded history, healthcare practitioners have striven to make their actions more effective for their patients by experimenting with different treatments, observing and sharing their results, and improving upon the efforts of previous generations. Becoming more accurate, precise, proactive, and impactful for each individual that comes under their care has always been the goal of all clinicians, no matter how basic the tools at their disposal. But now, modern physicians and scientists are now able to take this mission far, far beyond the reach of their ancestors with the help of electronic health records, genetic testing, big data analytics, and supercomputing – all the ingredients required to engage in what is quickly becoming truly precise and personalized medicine. Precision medicine, also commonly referred to as personalized medicine, is one of the most promising approaches to tackling diseases that have thus far eluded effective treatments or cures. Cancer, neurodegenerative diseases, and rare genetic conditions take an enormous toll on individuals, families and societies as a whole. Approximately 1.7 million new cancer cases were diagnosed in the United States in 2017. Around 600,000 deaths were expected during that year, according to the American Cancer Society. The Agency for Healthcare Research and Quality adds that the direct economic impact of cancer is around $80 billion per year – loss of productivity, wages, and caregiver needs sap billions more from the economy. -
Advancing Standards for Precision Medicine
Advancing Standards for Precision Medicine FINAL REPORT Prepared by: Audacious Inquiry on behalf of the Office of the National Coordinator for Health Information Technology under Contract No. HHSM-500-2017-000101 Task Order No. HHSP23320100013U January 2021 ONC Advancing Standards for Precision Medicine Table of Contents Executive Summary ...................................................................................................................................... 5 Standards Development and Demonstration Projects ............................................................................ 5 Mobile Health, Sensors, and Wearables ........................................................................................... 5 Social Determinants of Health (SDOH) ............................................................................................. 5 Findings and Lessons Learned .......................................................................................................... 6 Recommendations ........................................................................................................................................ 6 Introduction ................................................................................................................................................... 7 Background ................................................................................................................................................... 7 Project Purpose, Goals, and Objectives .................................................................................................. -
Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings
Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Nishihara, Reiko, Tyler J. VanderWeele, Kenji Shibuya, Murray A. Mittleman, Molin Wang, Alison E. Field, Edward Giovannucci, Paul Lochhead, and Shuji Ogino. 2015. “Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings.” European Journal of Epidemiology 30 (10): 1129–35. https://doi.org/10.1007/ s10654-015-0088-4. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41392032 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP HHS Public Access Author manuscript Author Manuscript Author ManuscriptEur J Epidemiol Author Manuscript. Author Author Manuscript manuscript; available in PMC 2016 October 07. Published in final edited form as: Eur J Epidemiol. 2015 October ; 30(10): 1129–1135. doi:10.1007/s10654-015-0088-4. Molecular Pathological Epidemiology Gives Clues to Paradoxical Findings Reiko Nishiharaa,b,c, Tyler J. VanderWeeled,e, Kenji Shibuyac, Murray A. Mittlemand,f, Molin Wangd,e,g, Alison E. Fieldd,g,h,i, Edward Giovannuccia,d,g, Paul Lochheadi,j, and Shuji Oginob,d,k aDepartment of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Ave., Boston, Massachusetts 02115 USA bDepartment of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450 Brookline Ave., Boston, Massachusetts 02215 USA cDepartment of Global Health Policy, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan dDepartment of Epidemiology, Harvard T.H. -
Molecular Epidemiology and Precision Medicine
Pharmacogenetics: Past, Present, and Future Brooke L. Fridley, PhD Associate Professor of Biostatistics University of Kansas Medical Center Site Director, K-INBRE Bioinformatics Core Director, Biostatistics and Informatics Shared Resource, University of Kansas Cancer Center What is Pharmacogenomics? Pharmacogenomics & Precision Medicine Aims to deliver the correct drug or treatment: • At the right time • To the right patient • At the right dose Can only be achieved when we have accurate clinical tests (BIOMARKER) and companion drugs at our disposal What is a Biomarker? • Biomarker: “A characteristic that is objectively measured and evaluated as an indicator of – normal biological processes, – pathogenic processes, or – pharmacologic responses to a therapeutic intervention.” Biomarkers Definitions Working Group. Biomarkers and surrogate end points: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69:89–95 Candidate Gene Era 1950 – 1990 – Structure of 1980 – 1985 – HGP DNA TPMT PCR begins 1997 – 1977 – 1983 – First 1987 – NHGRI Sanger human P450 formed Sequencing disease supergene gene family mapped Candidate • Genotyping Variant & • RT-PCR Gene • Re-sequencing genes (exons) with Sanger Studies Sequencing PK and PD and * Alleles (haplotypes) PGx Effect = Gene*Drug Interaction Drug 1 Drug 2 Genome-wide Array Era 2003 – FDA 2002 – issues draft 1998 -Illumina HapMap guidelines for founded begins PGx 2001 – 1st 2003 – 2004 – 1st NGS version of Completion of platform human HGP genome completed Genome- • SNP arrays (~mid 2000s) wide • mRNA arrays (~mid 1990s) Association Studies • Methylation arrays (~2008-2010) Study of Genetic Association with Drug Response Phenotypes Cases: Non-responders Cases: Responders Genetic association studies look at the frequency of genetic changes to try to determine whether specific changes are associated with a phenotype. -
The Personalized Medicine Report
THE PERSONALIZED MEDICINE REPORT 2017 · Opportunity, Challenges, and the Future The Personalized Medicine Coalition gratefully acknowledges graduate students at Manchester University in North Manchester, Indiana, and at the University of Florida, who updated the appendix of this report under the guidance of David Kisor, Pharm.D., Director, Pharmacogenomics Education, Manchester University, and Stephan Schmidt, Ph.D., Associate Director, Pharmaceutics, University of Florida. The Coalition also acknowledges the contributions of its many members who offered insights and suggestions for the content in the report. CONTENTS INTRODUCTION 5 THE OPPORTUNITY 7 Benefits 9 Scientific Advancement 17 THE CHALLENGES 27 Regulatory Policy 29 Coverage and Payment Policy 35 Clinical Adoption 39 Health Information Technology 45 THE FUTURE 49 Conclusion 51 REFERENCES 53 APPENDIX 57 Selected Personalized Medicine Drugs and Relevant Biomarkers 57 HISTORICAL PRECEDENT For more than two millennia, medicine has maintained its aspiration of being personalized. In ancient times, Hippocrates combined an assessment of the four humors — blood, phlegm, yellow bile, and black bile — to determine the best course of treatment for each patient. Today, the sequence of the four chemical building blocks that comprise DNA, coupled with telltale proteins in the blood, enable more accurate medical predictions. The Personalized Medicine Report 5 INTRODUCTION When it comes to medicine, one size does not fit all. Treatments that help some patients are ineffective for others (Figure 1),1 and the same medicine may cause side effects in only certain patients. Yet, bound by the constructs of traditional disease, and, at the same time, increase the care delivery models, many of today’s doctors still efficiency of the health care system by improving prescribe therapies based on population averages. -
Genomic Insights Into Drug Resistance Determinants in Cedecea Neteri, a Rare Opportunistic Pathogen
microorganisms Article Genomic Insights into Drug Resistance Determinants in Cedecea neteri, A Rare Opportunistic Pathogen Dorothea K. Thompson * and Stephen M. Sharkady Department of Pharmaceutical Sciences, College of Pharmacy & Health Sciences, Campbell University, Buies Creek, NC 27506, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-910-893-7463 Abstract: Cedecea, a genus in the Enterobacteriaceae family, includes several opportunistic pathogens reported to cause an array of sporadic acute infections, most notably of the lung and bloodstream. One species, Cedecea neteri, is associated with cases of bacteremia in immunocompromised hosts and has documented resistance to different antibiotics, including β-lactams and colistin. Despite the potential to inflict serious infections, knowledge about drug resistance determinants in Cedecea is limited. In this study, we utilized whole-genome sequence data available for three environmental strains (SSMD04, M006, ND14a) of C. neteri and various bioinformatics tools to analyze drug resistance genes in this bacterium. All three genomes harbor multiple chromosome-encoded β-lactamase genes. A deeper analysis of β-lactamase genes in SSMD04 revealed four metallo-β-lactamases, a novel variant, and a CMY/ACT-type AmpC putatively regulated by a divergently transcribed AmpR. Homologs of known resistance-nodulation-cell division (RND)-type multidrug efflux pumps such as OqxB, AcrB, AcrD, and MdtBC were also identified. Genomic island prediction for SSMD04 indicated that tolC, involved in drug and toxin export across the outer membrane of Gram-negative bacteria, Citation: Thompson, D.K.; Sharkady, was acquired by a transposase-mediated genetic transfer mechanism. Our study provides new S.M. Genomic Insights into Drug insights into drug resistance mechanisms of an environmental microorganism capable of behaving as Resistance Determinants in Cedecea a clinically relevant opportunistic pathogen. -
A Bioinformatics Approach for Precision Medicine Off-Label Drug
View metadata, citation and similar papers at core.ac.uk brought to you by CORE Cheng L, et al. J Am Med Inform Assoc 2016;23:741–749. doi:10.1093/jamia/ocw004, Research and Applications provided by IUPUIScholarWorks RECEIVED 5 September 2015 A bioinformatics approach for precision REVISED 10 December 2015 ACCEPTED 10 January 2016 medicine off-label drug selection among PUBLISHED ONLINE FIRST 23 April 2016 triple negative breast cancer patients Lijun Cheng,1,2 Bryan P Schneider,3,4 and Lang Li1,2,4 ABSTRACT .................................................................................................................................................... Background Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. Objective The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer RESEARCH AND APPLICATIONS mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. Methods In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowl- edge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selec- tion in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. Results By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. -
Human J3 Satellite
Proc. Nati. Acad. Sci. USA Vol. 86, pp. 6250-6254, August 1989 Genetics Human j3 satellite DNA: Genomic organization and sequence definition of a class of highly repetitive tandem DNA (human genome/concerted evolution/acrocentric chromosomes) JOHN S. WAYE AND HUNTINGTON F. WILLARD* Department of Medical Genetics, University of Toronto, Toronto, ON M5S 1A8 Canada Communicated by Charles R. Cantor, May 15, 1989 (receivedfor review August 23, 1988) ABSTRACT We describe a class ofhuman repetitive DNA, In this report, we describe the isolation and characteriza- called (3 satellite, that, at a most fundamental level, exists as tion of a human satellite DNA family, f satellitet which is tandem arrays of diverged -68-base-pair monomer repeat unrelated in structure or sequence to any previously de- units. The monomer units are organized as distinct subsets, scribed human satellite DNAs. These sequences comprise a each characterized by a multimeric higher-order repeat unit minimum of several million base pairs of DNA in the human that is tandemly reiterated and represents a recent unit of genome and are organized, at a fundamental level, as tandem amplification. We have cloned, characterized, and determined arrays of diverged -68-bp monomer repeat units. The P the sequence of two (3 satellite higher-order repeat units: one satellite DNA family is subdivided into two or more distinct located on chromosome 9, the other on the acrocentric chro- subsets, at least one of which is shared by the five pairs of mosomes (13, 14, 15, 21, and 22) and perhaps other sites in the acrocentric chromosomes. genome. Analysis by pulsed-field gel electrophoresis reveals that these tandem arrays are localized in large domains (50-300 MATERIALS AND METHODS kilobase pairs) that are marked by restriction fragment length polymorphisms.