1
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Schleimer E, Pearce J, Barnecut A, Rowles W, Lizee A, Klein A, Block VJ, Santaniello A, Renschen A, Gomez R, Keshavan A, Gelfand JM, Henry RG, Hauser SL, Bove R. A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development. J Med Internet Res 2020; 22:e15605. [PMID: 32628124 PMCID: PMC7381029 DOI: 10.2196/15605] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/16/2019] [Accepted: 02/04/2020] [Indexed: 01/11/2023] Open
Abstract
Background Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. Objective This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. Methods We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical—many times throughout the process, we went back to the drawing board. Results This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. Conclusions After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions.
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Affiliation(s)
- Erica Schleimer
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | | | - Andrew Barnecut
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - William Rowles
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Antoine Lizee
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Arno Klein
- Child Mind Institute, New York, NY, United States
| | - Valerie J Block
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Adam Santaniello
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Adam Renschen
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Refujia Gomez
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Anisha Keshavan
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Jeffrey M Gelfand
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Roland G Henry
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Stephen L Hauser
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
| | - Riley Bove
- Department of Neurology, UCSF Weill Institute for Neurosciences, San Francisco, CA, United States
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2
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Daya M, Rafaels N, Brunetti TM, Chavan S, Levin AM, Shetty A, Gignoux CR, Boorgula MP, Wojcik G, Campbell M, Vergara C, Torgerson DG, Ortega VE, Doumatey A, Johnston HR, Acevedo N, Araujo MI, Avila PC, Belbin G, Bleecker E, Bustamante C, Caraballo L, Cruz A, Dunston GM, Eng C, Faruque MU, Ferguson TS, Figueiredo C, Ford JG, Gan W, Gourraud PA, Hansel NN, Hernandez RD, Herrera-Paz EF, Jiménez S, Kenny EE, Knight-Madden J, Kumar R, Lange LA, Lange EM, Lizee A, Maul P, Maul T, Mayorga A, Meyers D, Nicolae DL, O'Connor TD, Oliveira RR, Olopade CO, Olopade O, Qin ZS, Rotimi C, Vince N, Watson H, Wilks RJ, Wilson JG, Salzberg S, Ober C, Burchard EG, Williams LK, Beaty TH, Taub MA, Ruczinski I, Mathias RA, Barnes KC. Author Correction: Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations. Nat Commun 2019; 10:4082. [PMID: 31484942 PMCID: PMC6726619 DOI: 10.1038/s41467-019-12158-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Michelle Daya
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Nicholas Rafaels
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Tonya M Brunetti
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Sameer Chavan
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, 48202, USA
| | - Aniket Shetty
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | | | | | - Genevieve Wojcik
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Monica Campbell
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Candelaria Vergara
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21224, USA
| | - Dara G Torgerson
- Department of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Victor E Ortega
- Center for Human Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, USA
| | - Ayo Doumatey
- Center for Research on Genomics & Global Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | | | - Nathalie Acevedo
- Institute for Immunological Research, Universidad de Cartagena, Cartagena, 130000, Colombia
| | - Maria Ilma Araujo
- Immunology Service, Universidade Federal da Bahia, Salvador, 401110170, Brazil
| | - Pedro C Avila
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Gillian Belbin
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eugene Bleecker
- Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, 85724, USA
| | - Carlos Bustamante
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Luis Caraballo
- Institute for Immunological Research, Universidad de Cartagena, Cartagena, 130000, Colombia
| | - Alvaro Cruz
- Universidade Federal da Bahia, Salvador, 401110170, Brazil
| | - Georgia M Dunston
- Department of Microbiology, Howard University College of Medicine, Washington, DC, 20059, USA
| | - Celeste Eng
- Department of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Mezbah U Faruque
- National Human Genome Center, Howard University College of Medicine, Washington, DC, 20059, USA
| | - Trevor S Ferguson
- Caribbean Institute for Health Research, The University of the West Indies, Kingston, 00007, Jamaica
| | - Camila Figueiredo
- Departamento de Biorregulacao, Universidade Federal da Bahia, Salvador, 401110170, Brazil
| | - Jean G Ford
- Department of Medicine, Einstein Medical Center, Philadelphia, PA, 19141, USA
| | - Weiniu Gan
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Pierre-Antoine Gourraud
- Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064, ATIP-Avenir, Equipe 5, Nantes, France
| | - Nadia N Hansel
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21224, USA
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Edwin Francisco Herrera-Paz
- Facultad de Medicina, Universidad Católica de Honduras, San Pedro Sula, 21102, Honduras.,Universidad Tecnológica Centroamericana (UNITEC), Facultad de Ciencias Médicas, Tegucigalpa, Honduras
| | - Silvia Jiménez
- Institute for Immunological Research, Universidad de Cartagena, Cartagena, 130000, Colombia
| | - Eimear E Kenny
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jennifer Knight-Madden
- Caribbean Institute for Health Research, The University of the West Indies, Kingston, 00007, Jamaica
| | - Rajesh Kumar
- Department of Pediatrics, Northwestern University, Chicago, IL, 60611, USA
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Ethan M Lange
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA
| | - Antoine Lizee
- Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064, ATIP-Avenir, Equipe 5, Nantes, France
| | - Pissamai Maul
- Genetics and Epidemiology of Asthma in Barbados, The University of the West Indies, Chronic Disease Research Centre, Jemmots Lane, St. Michael, BB11115, Barbados
| | - Trevor Maul
- Genetics and Epidemiology of Asthma in Barbados, The University of the West Indies, Chronic Disease Research Centre, Jemmots Lane, St. Michael, BB11115, Barbados
| | - Alvaro Mayorga
- Centro de Neumologia y Alergias, San Pedro Sula, 21102, Honduras
| | - Deborah Meyers
- Department of Medicine, University of Arizona College of Medicine, Tucson, AZ, 85724, USA
| | - Dan L Nicolae
- Department of Medicine, University of Chicago, Chicago, IL, 60637, USA
| | - Timothy D O'Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Ricardo Riccio Oliveira
- Laboratório de Patologia Experimental, Centro de Pesquisas Gonçalo Moniz, Salvador, 40296-710, Brazil
| | - Christopher O Olopade
- Department of Medicine and Center for Global Health, University of Chicago, Chicago, IL, 60637, USA
| | | | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Charles Rotimi
- Center for Research on Genomics & Global Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nicolas Vince
- Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064, ATIP-Avenir, Equipe 5, Nantes, France
| | - Harold Watson
- Faculty of Medical Sciences, The University of the West Indies, Queen Elizabeth Hospital, Bridgetown, St. Michael, BB11000, Barbados
| | - Rainford J Wilks
- Caribbean Institute for Health Research, The University of the West Indies, Kingston, 00007, Jamaica
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Steven Salzberg
- Departments of Biomedical Engineering and Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94143, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, 48202, USA
| | - Terri H Beaty
- Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, 21205, USA
| | - Margaret A Taub
- Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, MD, 21205, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, MD, 21205, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21224, USA
| | - Kathleen C Barnes
- Department of Medicine, University of Colorado Denver, Aurora, CO, 80045, USA.
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3
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Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, Green A, Khankhanian P, Baranzini SE. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife 2017; 6:26726. [PMID: 28936969 PMCID: PMC5640425 DOI: 10.7554/elife.26726] [Citation(s) in RCA: 214] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Accepted: 09/11/2017] [Indexed: 12/16/2022] Open
Abstract
The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.
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Affiliation(s)
- Daniel Scott Himmelstein
- Biological and Medical Informatics Program, University of California, San Francisco, San Francisco, United States.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, United States
| | - Antoine Lizee
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,ITUN-CRTI-UMR 1064 Inserm, University of Nantes, Nantes, France
| | - Christine Hessler
- Department of Neurology, University of California, San Francisco, San Francisco, United States
| | - Leo Brueggeman
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,University of Iowa, Iowa City, United States
| | - Sabrina L Chen
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,Johns Hopkins University, Baltimore, United States
| | - Dexter Hadley
- Department of Pediatrics, University of California, San Fransisco, San Fransisco, United States.,Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, United States
| | - Ari Green
- Department of Neurology, University of California, San Francisco, San Francisco, United States
| | - Pouya Khankhanian
- Department of Neurology, University of California, San Francisco, San Francisco, United States.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, United States
| | - Sergio E Baranzini
- Biological and Medical Informatics Program, University of California, San Francisco, San Francisco, United States.,Department of Neurology, University of California, San Francisco, San Francisco, United States
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4
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Vince N, Daya M, Hollenbach J, Lizee A, Barnes K, Torgerson D, Gourraud PA. P039 HLA component of the consortium on asthma among African-ancestry populations in the Americas (CAAPA). Hum Immunol 2017. [DOI: 10.1016/j.humimm.2017.06.099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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5
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Johnston HR, Hu YJ, Gao J, O’Connor TD, Abecasis GR, Wojcik GL, Gignoux CR, Gourraud PA, Lizee A, Hansen M, Genuario R, Bullis D, Lawley C, Kenny EE, Bustamante C, Beaty TH, Mathias RA, Barnes KC, Qin ZS. Identifying tagging SNPs for African specific genetic variation from the African Diaspora Genome. Sci Rep 2017; 7:46398. [PMID: 28429804 PMCID: PMC5399604 DOI: 10.1038/srep46398] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 03/17/2017] [Indexed: 12/15/2022] Open
Abstract
A primary goal of The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to develop an 'African Diaspora Power Chip' (ADPC), a genotyping array consisting of tagging SNPs, useful in comprehensively identifying African specific genetic variation. This array is designed based on the novel variation identified in 642 CAAPA samples of African ancestry with high coverage whole genome sequence data (~30× depth). This novel variation extends the pattern of variation catalogued in the 1000 Genomes and Exome Sequencing Projects to a spectrum of populations representing the wide range of West African genomic diversity. These individuals from CAAPA also comprise a large swath of the African Diaspora population and incorporate historical genetic diversity covering nearly the entire Atlantic coast of the Americas. Here we show the results of designing and producing such a microchip array. This novel array covers African specific variation far better than other commercially available arrays, and will enable better GWAS analyses for researchers with individuals of African descent in their study populations. A recent study cataloging variation in continental African populations suggests this type of African-specific genotyping array is both necessary and valuable for facilitating large-scale GWAS in populations of African ancestry.
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Affiliation(s)
| | - Yi-Juan Hu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Jingjing Gao
- Data and Statistical Sciences, AbbVie, North Chicago, IL, USA
| | - Timothy D. O’Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Gonçalo R. Abecasis
- Department of Biostatistics, SPH, University of Michigan, Ann Arbor, MI, USA
| | - Genevieve L Wojcik
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Antoine Lizee
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | | | | | | | | | - Eimear E. Kenny
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carlos Bustamante
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Terri H. Beaty
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Rasika A. Mathias
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kathleen C. Barnes
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Zhaohui S. Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
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6
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Cree BAC, Gourraud PA, Oksenberg JR, Bevan C, Crabtree-Hartman E, Gelfand JM, Goodin DS, Graves J, Green AJ, Mowry E, Okuda DT, Pelletier D, von Büdingen HC, Zamvil SS, Agrawal A, Caillier S, Ciocca C, Gomez R, Kanner R, Lincoln R, Lizee A, Qualley P, Santaniello A, Suleiman L, Bucci M, Panara V, Papinutto N, Stern WA, Zhu AH, Cutter GR, Baranzini S, Henry RG, Hauser SL. Long-term evolution of multiple sclerosis disability in the treatment era. Ann Neurol 2016; 80:499-510. [PMID: 27464262 PMCID: PMC5105678 DOI: 10.1002/ana.24747] [Citation(s) in RCA: 271] [Impact Index Per Article: 33.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 07/12/2016] [Accepted: 07/24/2016] [Indexed: 12/20/2022]
Abstract
Objective To characterize the accrual of long‐term disability in a cohort of actively treated multiple sclerosis (MS) patients and to assess whether clinical and magnetic resonance imaging (MRI) data used in clinical trials have long‐term prognostic value. Methods This is a prospective study of 517 actively managed MS patients enrolled at a single center. Results More than 91% of patients were retained, with data ascertained up to 10 years after the baseline visit. At this last assessment, neurologic disability as measured by the Expanded Disability Status Scale (EDSS) was stable or improved compared to baseline in 41% of patients. Subjects with no evidence of disease activity (NEDA) by clinical and MRI criteria during the first 2 years had long‐term outcomes that were no different from those of the cohort as a whole. 25‐OH vitamin D serum levels were inversely associated with short‐term MS disease activity; however, these levels had no association with long‐term disability. At a median time of 16.8 years after disease onset, 10.7% (95% confidence interval [CI] = 7.2–14%) of patients reached an EDSS ≥ 6, and 18.1% (95% CI = 13.5–22.5%) evolved from relapsing MS to secondary progressive MS (SPMS). Interpretation Rates of worsening and evolution to SPMS were substantially lower when compared to earlier natural history studies. Notably, the NEDA 2‐year endpoint was not a predictor of long‐term stability. Finally, the data call into question the utility of annual MRI assessments as a treat‐to‐target approach for MS care. Ann Neurol 2016;80:499–510
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Affiliation(s)
| | - Bruce A C Cree
- Department of Neurology, University of California, San Francisco, San Francisco, CA.
| | | | - Jorge R Oksenberg
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Carolyn Bevan
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | | | - Jeffrey M Gelfand
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Douglas S Goodin
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jennifer Graves
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Ari J Green
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Ellen Mowry
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Darin T Okuda
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Daniel Pelletier
- Department of Neurology, University of Southern California, Los Angeles, CA
| | | | - Scott S Zamvil
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Alisha Agrawal
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stacy Caillier
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Caroline Ciocca
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Refujia Gomez
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Rachel Kanner
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Robin Lincoln
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Antoine Lizee
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Pamela Qualley
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Adam Santaniello
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Leena Suleiman
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Monica Bucci
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Valentina Panara
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Nico Papinutto
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - William A Stern
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Alyssa H Zhu
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Gary R Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL
| | - Sergio Baranzini
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Roland G Henry
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Stephen L Hauser
- Department of Neurology, University of California, San Francisco, San Francisco, CA
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7
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Le Gall C, Laurent J, Vince N, Lizee A, Agrawal A, Walencik A, Rettman P, Gagne K, Retiere C, Hollenbach J, Cesbron A, Limou S, Gourraud PA. Multidimensional reduction of multicentric cohort heterogeneity: An alternative method to increase statistical power and robustness. Hum Immunol 2016; 77:1024-1029. [PMID: 27262455 DOI: 10.1016/j.humimm.2016.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 05/13/2016] [Accepted: 05/18/2016] [Indexed: 11/25/2022]
Abstract
Modern clinical research takes advantage of multicentric cohorts to increase sample size and gain in statistical power. However, combining individuals from different recruitment centers provides heterogeneity in the dataset that needs to be accounted for to obtain robust results. Sophisticated statistical multivariate models adjusting for center effect can be implemented, but they can become unstable and can be complex to interpret with the increasing number of covariates to consider. Here, we present a multidimensional reduction technique to identify heterogeneity in a French multicentric cohort of hematopoietic stem cell transplantations and characterize a homogeneous subgroup prior to performing simple statistical univariate analyses. The exclusion of outliers allowed the identification of two genetic factors associated with post-transplantation overall survival. We therefore provide proof-of-concept that a sample size reduction method can efficiently account for heterogeneity and center effect in multicentric cohorts while increasing statistical power and robustness for discovery of new association signals.
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Affiliation(s)
| | | | - Nicolas Vince
- Laboratory of Experimental Immunology, Cancer and Inflammation Program, Leidos Biomedical Research Inc., Frederick National Laboratory, Frederick, MD, USA; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Antoine Lizee
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Alisha Agrawal
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Alexandre Walencik
- Etablissement français du Sang, Nantes, France; Inserm Unit 1064, Hospital and University of Nantes, Nantes, France
| | | | - Katia Gagne
- Etablissement français du Sang, Nantes, France
| | | | - Jill Hollenbach
- Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Sophie Limou
- Molecular Genetic Epidemiology Section, Basic Research Laboratory, Basic Science Program, NCI/NIH, Leidos Biomedical Research Inc., Frederick National Laboratory, Frederick, MD, USA
| | - Pierre-Antoine Gourraud
- Methodomics, Toulouse, France; Department of Neurology, University of California, San Francisco, CA, USA; Etablissement français du Sang, Nantes, France.
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Khankhanian P, Gourraud PA, Lizee A, Goodin DS. Haplotype-based approach to known MS-associated regions increases the amount of explained risk. J Med Genet 2015; 52:587-94. [PMID: 26185143 PMCID: PMC4552900 DOI: 10.1136/jmedgenet-2015-103071] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 05/07/2015] [Indexed: 11/18/2022]
Abstract
Genome-wide association studies (GWAS), using single nucleotide polymorphisms (SNPs), have yielded 110 non-human leucocyte antigen genomic regions that are associated with multiple sclerosis (MS). Despite this large number of associations, however, only 28% of MS-heritability can currently be explained. Here we compare the use of multi-SNP-haplotypes to the use of single-SNPs as alternative methods to describe MS genetic risk. SNP-haplotypes (of various lengths from 1 up to 15 contiguous SNPs) were constructed at each of the 110 previously identified, MS-associated, genomic regions. Even after correcting for the larger number of statistical comparisons made when using the haplotype-method, in 32 of the regions, the SNP-haplotype based model was markedly more significant than the single-SNP based model. By contrast, in no region was the single-SNP based model similarly more significant than the SNP-haplotype based model. Moreover, when we included the 932 MS-associated SNP-haplotypes (that we identified from 102 regions) as independent variables into a logistic linear model, the amount of MS-heritability, as assessed by Nagelkerke's R-squared, was 38%, which was considerably better than 29%, which was obtained by using only single-SNPs. This study demonstrates that SNP-haplotypes can be used to fine-map the genetic associations within regions of interest previously identified by single-SNP GWAS. Moreover, the amount of the MS genetic risk explained by the SNP-haplotype associations in the 110 MS-associated genomic regions was considerably greater when using SNP-haplotypes than when using single-SNPs. Also, the use of SNP-haplotypes can lead to the discovery of new regions of interest, which have not been identified by a single-SNP GWAS.
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Affiliation(s)
- Pouya Khankhanian
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA UCSF MS Center, University of California, San Francisco, San Francisco, California, USA
| | - Pierre-Antoine Gourraud
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA UCSF MS Center, University of California, San Francisco, San Francisco, California, USA
| | - Antoine Lizee
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA UCSF MS Center, University of California, San Francisco, San Francisco, California, USA
| | - Douglas S Goodin
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA UCSF MS Center, University of California, San Francisco, San Francisco, California, USA
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Gourraud PA, Henry RG, Cree BAC, Crane JC, Lizee A, Olson MP, Santaniello AV, Datta E, Zhu AH, Bevan CJ, Gelfand JM, Graves JS, Goodin DS, Green AJ, von Büdingen HC, Waubant E, Zamvil SS, Crabtree-Hartman E, Nelson S, Baranzini SE, Hauser SL. Precision medicine in chronic disease management: The multiple sclerosis BioScreen. Ann Neurol 2014; 76:633-42. [PMID: 25263997 DOI: 10.1002/ana.24282] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Revised: 09/24/2014] [Accepted: 09/24/2014] [Indexed: 01/18/2023]
Abstract
We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine-that is, the application of information technology to medicine-has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real-time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence-based medicine, support shared decision making, and ultimately lead to improved outcomes.
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Affiliation(s)
- Pierre-Antoine Gourraud
- Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, CA
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