Research Infrastructures to boost R&D in the field of rare Diseases

1. Research Infrastructures to boost R&D in the field of rare Diseases SégolèneAymé INSERM, Paris, France Fundacion Ramon Areces 29 Oct 2014

2. International Rare Disease Research Consortium (IRDiRC) Cooperation at international level to stimulate, better coordinate and maximize output of rare disease research efforts around the world

3. Public healthcare and research system Genomics • Industry & Manufactures • DIAGNOSIS • Technology • devices, instruments, bioinformatics, systems Multiple Government Departments Clinical expertise/experts Interpretation and application • RARE DISEASE SECTOR • Clinical & Academic • Industry & Manufactures • Multiple Government Departments • Private Healthcare Transcriptomics Metabolomics Training Natural History Phenomics Policy Phenomics • Public healthcare system VOICE OF DATA (EVIDENCE) Genomics Proteomics Training Position statements • Public healthcare system Policy Clinical expertise/experts • Private Healthcare Education Education Proteomics Education Metabolomics Proteomics • Public healthcare system • Clinical and • disability services Clinical expertise/experts Multiple Government Departments Interpretation and application Training THE CHALLENGE

4. Rare DiseasesPeculiarities DISADVANTAGE • no or little evidence available • small populations , scattered • coding and classification poor • no jurisdiction , or country with sufficient data • require collective data and case finding for evidence • not all rare diseases are the same in terms of evidence: e.g. Cystic Fibrosis ≠ Progeria • orphan therapies fail the cost effectiveness threshold ADVANTAGE • Clarity in the extreme • Phenotype: • genotype atomise disease; • permit re-aggregation based on pathways perturbed, not clinical presentation • New knowledge translation and the portal to Individualised medicine

5. Facioscapulohumeral dystrophy Rett syndrome Congenital myopathy Malaria Mesothelioma Huntington disease Hemophilia A Noonan Isolated Spina Bifida Cutaneous lupus erythematosus Hereditary breast & ovarian cancer syndrome Systemic sclerosis Familial long QT syndrome Fetal cytomegalovirus syndrome Partial chromosome Y deletion Williams syndrome Cystic fibrosis Motor Neurone Disease Retinoblastoma Angelman Syndrome Niemann-Pick disease Nemalinemyopathy Mucopolysaccharidosis 1-3 Duchenne muscular dystrophy Hereditary spastic paraplegia Young adult-onset Parkinsonism Sickle cell anemia Friedreich ataxia Alport syndrome Diffuse large B-cell lymphoma Fragile X syndrome Marfan syndrome Myasthenia gravis Tuberculosis Turner Syndrome Neurofibromatosis type 1 Charcot-Marie-Tooth disease Phenylketonuria Familial adenomatouspolyposis 70% of people living with a rare disease 75% of people living with a rare disease 80% of People living with a rare disease

6. Currentstatus of researchin the field of rare diseasesbased on Orphanet data

7. European rare diseases research landscape (36 countries) 5707 ongoing research projects in Orphanet covering 2129 diseases, excluding clinical trials (February 2014)

8. International rare diseases clinical trial landscape • 2476 ongoing national or international clinical trials for 629 diseases in 29 countries Percentage of clinical trials by category (April 2014)

9. Number of genestested in each country in Europe by year 2010 2011 2012 2013

10. Possibility to diagnose Rare Diseases:over 2 362genestested to date Number of rare diseases tested by country Number of genes tested by country (April 2014)

11. Medicinalproducts on the Europeanmarket in 2013 • 68orphan medicinal products • 92 medicinal products without orphan designation with at least an indication for a rare disease or a group of rare disease • (January 2014)

12. Satisfaction for professionalsFrustration for patientsAnxiety for payors • Slow translation from bench to bedside • Limited access to innovations • Too few treatments compared to needs • Most patients feeling abandonned • High cost of diagnostic tests and drugs • Not affordable • Necessity to de-risk research • Cheaper R&D

13. How to speed-up research and de-risk it ? • Improve coordination and synergies of research at world level • To increase the research volume and the quantity of data • Support in-silico research • to make optimal use of available data • Find new business-model for R&D • To reduce the cost and et profide affordable treatments

14. To boost coordination at world level

15. IRDiRCpolicy and guidelinesPrinciples applying to Research activities Sharing and collaborative work in RD research • Sharing of data and resources • Rapid release of data • Interoperability and harmonization of data • Data in open access databases Scientific standards, requirements and regulations in RD research • Projects should adhere to IRDiRC standards • Develop ontologies, biomarkers and patient-centered outcome data • Cite use of databases and biobanks in publications

16. IRDiRCpolicy and guidelines Participation by patients and / or their representatives in research • Act in the best interest of patients • Involve patients in all aspects of research • Involve patients in design and governance of registries • Involve patients in the design, conduct and analysis of clinical trials • Acknowledge patients contribution in articles

17. IRDiRCpolicy and guidelinesPrinciples applying to Funding Bodies • Promote the discovery of genes • Promote the development of therapies • Fund pre-clinical studies for proof of concept • Promote harmonization, interoperability, sharing, open access data • Promote coordination between human and animal models • Promote active exchanges between stakeholders through information dissemination of ongoing projects and events

18. IRDiRC policy and guidelines Endorsement of standards and tools • Endorsement of standards and tools contributing to IRDiRCobjectives • Ontologies: HPO, ORDO… • Standards: BRIF… • Data sharing: PhenomeCentral, DECIPHER… • Ouctome measures: NINDS, PROMIS…

19. IRDiRC Recommended • Label to be used in highlighting tools, standards and guidelines, which contributes directly to IRDiRC objectives • Application for ‘IRDiRC Recommended’ label is open to all, including non-IRDiRC members • ‘IRDiRC Recommended’ may be awarded to similar tools, standards and guidelines • Submission of 1-2 pages application • Evaluation of the application by a review panel • Approval/rejection of the application by the Executive Committee

20. Initiatives to Speed up Data Sharing

21. Rational • Research produces an enormous amount of data • If shared, will facilitate the development of diagnostics and treatments while ensuring efficient utilization of scarce resources • Resources include patient and family material (extracted DNA, cell lines, pathological samples), technical protocols, informatics infrastructure, and analysis tools • Datasets include phenotypes, genomic variants, other ‘omic’ data, natural histories, and clinical trial data…

22. Barriers to Data Sharing • Technical and Financial issues • Storingterabytes…Securing data • Providing the logistics for sharing data • Statistical and algorithmic issues to combine datasets • Ethical and Legal issues • Data across public and private networks • Pricacy protection at national level • Cultural issues • Reluctance to share data fromresearchers/ Institutions/Regulatory bodies

23. A ClearingHouseof Data Standardsis in development at IRDiRC • Five main fields of application • Standards in Genomics and other OMICS • Standards in Phenotyping • Standards in Outcome Measures for clinical trials • Standards in Human Data Registration • Open-access Data Repositories to store data • Alignment with other efforts to ensure interconnection and shareabilitybetween data • RD-Connect • PCORI, Comete • ELIXIR, BD2K, Data FAIRport

24. Open Acess Data Repositories • PhenoTips and PhenomeCentral • Repository of data • Hub for data sharing • CareforRare, RDConnect • NIH undiagnosed patients • ClinVar and ICCG • Public archive of variants and assertions about significance • NCBI resource • Decipher Database of Chromosome imbalances and phenotypes • Using Ensembl resources • Sanger Institute • Wellcome Trust

25. Initiatives to Speed up Data Mining

26. Rational Make the most of remarkableadvances in the molecular basis of humandiseases • dissectthe physiologicalpathways • improvediagnosis • developtreatments Make rare diseases visible in health information systems • to gain insight intothem • to access real life data alreadycollected Improvecoding of RD whichevercoding system used Cross-referencecodingsystems: Orpha nomenclature, ICD10, MeSH, SnoMed-CT, MedDRA

27. Whatis the problem ? Computers are not smart enough…. • The following descriptions mean the same thing to you: • generalizedamyotrophy • generalizedmuscle atrophy • muscularatrophy, generalized • But your computer thinks they're completely unrelated

28. Phenomes: a continuum • Disease • Malformation syndrome • Morphologicalanomaly • Biologicalanomaly • Clinical syndrome • Particular clinical situation • No type: waiting to have a type attributed

30. Orphan Diseasome An Orphan Diseasomepermits investigators to explore the orphan disease (OD) or rare disease relationships based on shared genes and shared enriched features (e.g., Gene Ontology Biological Process, Cellular Component, Pathways, Mammalian Phenotype). The red nodes represent the orphan diseases and the green ones the related genes. A disease is connected to a gene if and only if a mutation which is responsible of the disease has been identified on this gene. http://research.cchmc.org/od/01/index.html

31. UMLS = Unified Medical Language System • ICD = International Classification of Diseases • Since 1863 by WHO • Used by most countries to code medical activity, mortality data • MeSH = Medical Subject Headings • controlled vocabulary thesaurus used for indexing articles for PubMed by National Library of Medicine (USA) • SnoMed CT = Systematized Nomenclature of Medicine--Clinical Terms • clinical terminology by the International Health Terminology Standards Development Organisation (IHTSDO) in Denmark • Used in the USA and a few other countries • MedDRA = Medical Dictionary for Regulatory Activities • medical terminology to classify adverse event information associated with the use of medical products • by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA)

32. Differentresources, different terminologies (e)HR: SNOMED CT Others? Free text Mutation/patient registries, databases: HPO LDDB PhenoDB Elements of morphology Others? Free text? Tools for diagnosis: HPO LDDB Orphanet

33. Eachterminology has a purpose–drivenapproach • Indexinghealthstatus of individual patients for health management (SnoMED) • Detailed, focus on manifestations and complaints • Adapted to clinical habits • Analyticalapproach • Indexinghealthstatus of individual patients for statisticalpurpose in public health (ICD) • More agregated, interpretedphenotypicfeatures • Agregated concepts • Unambiguous to avoidblanks

34. Purpose–drivenapproach (2) • Indexinghealthstatus of individual patients for clinicalresearchpurpose(HPO / PhenoDB / Elements of morphology) • Highlydetailed to fit with the research questions • Specific terminologies developed for disease-specific patient registries • Indexinghealthstatus of individual patients for retrievingpossible diagnoses (LDDB,POSSUM,Orphanet) • Agregated concepts • Requires a judgement of clinicians about phenomic expressions that are relevant • Unambiguous to avoidblanks

35. How to make all these terminologies inter-operable ?

36. Convince the terminologies to converge in someway…. • Sept 2012: start of mappings (Orphanet) • EUGT2 – EUCERD workshop (Paris, September 2012) • ICHPT workshop (ASHG, Boston, October 2013) • Selection of 2,300 coreterms LDDB Elements of Morphology POSSUM SNOMED CT (IHTSDO) ICD (WHO) DECIPHER PhenoDB Orphanet HPO

37. Phenotypeterminologyproject • Aims: • Mapcommonlyusedclinical terminologies (Orphanet, LDDB, HPO, Elements of morphology, PhenoDB, UMLS, SNOMED-CT, MESH, MedDRA): • automaticmap, expert validation, detection and correction of inconsistencies • Findcommontermsin the terminologies • Produce a coreterminology • Common denominatorallowing to share/exchange phenotypic data betweendatabases • Mapped to every single terminology

38. MappingTerminologies • Orphanet: 1357 terms (Orphanet database, version 2008) • LDDB: 1348 dysmorphologicalterms (Installation CD) • Elements of Morphology: 423 terms (retrievedmanuallyfrom publication AJMG, January 2009) • HPO: 9895 terms (downloadbioportal, obo format, 30/08/12) • PhenoDB: 2846 terms (given in obo format, 02/05/2012) • UMLS: (version 2012AA) (integratingMeSH, MedDra, SNOMED CT)

39. Tools • OnaGUI (INSERM U729): ontologyalignmenttool • Workwith file in owl format • I-Subalgorithm: detectsyntaxicsimilarity • Graphical interface to check automaticmappings and manuallyaddones • Metamap (National Library of Medicine): a tool to map biomedical text to the UMLS Metathesaurus • Perl scripts: format conversion, launching Metamap, comparison of results…

40. Comparison of mappings and deduction • Perl script to compare all the mappings and infermappings of non-Orphanet terminologies Eg: Orphanet ID XX mapped to YY in HPO and ZZ in LDDB -> deduction: YY and ZZ shouldprobablymap • Retrieve HPO mappings versus UMLS, MeSH • First figures:

41. Mapping of non-Orphanet terminologies • Automatic and inferedmappingswerechecked by experts • UsingOnaGUI for all, except UMLS • Automatic I-Sub: 7.0 + deduction • Metamap + deduction + HPO mappings • Figures:

42. First list of commonterms • Present in at least 2 terminologies • Definition of rules for nomenclature • Addition of termspresent in eachterminology as synonyms

43. Workshop on 21-22 October 2013 in BostonSuccess! • Reviewed 2736 terms appearing 2 or more times in the 6 terminologies in 17 hours • 2302 terms chosen, including preferred term • Definitions are from Elements of Morphology if available, and HPO/Stedman’s Medical Dictionary, if not • List of terms, mapping to HPO, PhenoDB, Elements of Morphology will be available at http://ichpt.org by January 2015. • All tools will map to this terminology to allow interoperability among resources

44. Adoption of a core set of >2,300 terms common to all terminologies Workshop of validation, Boston 21-22 October 2013 • Workshop supported by HVP and EuroGenTest • Organized by AdaHamosh • Expert review of the initial proposal • Selection of 2,370 terms • Decision to propose them for adoption by all terminologies • Establishment of the International Consortium for Human Phenotype Terminologies – ICHPT • Publication on the IRDiRC website with definitions from • HPO • Elements of morphology Workshop on Terminologies for RD – Paris, 12 September 2012 • Many terminologies in use to describe phenomes - No interoperability • Joint EuroGenTest and EUCERD workshop • Organized by SégolèneAymé • Agreement to define a core set of terms common to all terminologies and a methodology • Core set identified by cross referencing • HPO • PhenoDB • Orphanet • UMLS: MeSH, MedDRA, SnoMed CT • LDDB • Elements of morphology

45. COMPUTERS ARE NOT SMART From a terminology to an ontology

46. Why ontologies are needed ? • Ontologies are representations of the knowledge in a waywhichisdirectlyunderstandable by computers • Ontologies allowreasoning • Ontologies define the objects AND the relationshipbetween the objects • Duchenne musculardystrophy (disease) Is a neuromusculardisease (group of diseases) • Schistosomias (disease) Is a cause ofanemia (manifestation)

47. Standardization of Phenotype Ontologies Workshop Sympathy, 19 Apr 2013, Dublin Organized by IRDiRC, supported by the University of Dublin, Forge and EuroGenTest Conclusion: Adopt HPO & ORDO & cross-reference with OMIM

48. Standardisation of PhenotypeOntologies Rare Diseases PhenotypicFeatures bioportal.bioontology.org/ontologies/ORDO bioportal.bioontology.org/ontologies/HP • Based on Orphanetmulti-hierarchical classification of RD • Genes– diseasesrelationships • Cross-references: • For RD nomenclature : OMIM, SNOMED CT, ICD10, MeSH, MedDRA, UMLS • For genes : OMIM, HGNC, UniProtKB, IUPHAR, ensembl, Reactome ICHPT (International Consortium for HumanPhenotype Terminologies) 2,307 terms- coreterminology Mapped to: HPO Elements of Morphology Orphanet LDDB SNOMED CT Pheno-DB (OMIM) MeSH UMLS Availablesoon for downloadat ichpt.org

49. Pleaseadopt/disseminateHPO and ORDOto speed up R&Dto the benefit of the patients

50. COMPUTERS ARE VERY SMART Theycan help repurposedrugs