1. Research Infrastructures to boost R&D in the field of rare Diseases 1 Ségolène Aymé 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 2

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

4. Rare Diseases Peculiarities 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 4

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

6. CURRENT STATUS OF RESEARCH IN THE FIELD OF RARE DISEASES BASED ON ORPHANET DATA 6

7. 5707 ongoing research projects in Orphanet covering 2129 diseases, excluding clinical trials European rare diseases research landscape (36 countries) (February 2014) 513 Gene search 595 Mutations search 281 Gene expression profile 393 Genotype-phenotype correlation 1048 In vitro functional study 509 Animal model creation / study 748 Human physiopathology study 179 Pre-clinical gene therapy 90 Pre-clinical cell therapy 31Pre-clinical vaccine development 452Observational clinical study 224Epidemiological study 295 Diagnostic tool / protocol development 158Biomarker development 25 Medical device / instrumentation development 79Health sociology study 15Health economics study 72 Public health / health services study 7

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

9. Number of genes tested in each country in Europe by year 20102011 2012 2013

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

11. Medicinal products on the European market in 2013 68 orphan 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 professionals Frustration for patients Anxiety 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 12

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 13

14. TO BOOST COORDINATION AT WORLD LEVEL 14

15. IRDiRC policy and guidelines Principles applying to Research activities 15 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. IRDiRC policy and guidelines 16 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. IRDiRC policy and guidelines Principles applying to Funding Bodies 17  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 IRDiRC objectives  Ontologies: HPO, ORDO…  Standards: BRIF…  Data sharing: PhenomeCentral, DECIPHER…  Ouctome measures: NINDS, PROMIS… 18

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 19

20. INITIATIVES TO SPEED UP DATA SHARING 20

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… 21

22. Barriers to Data Sharing  Technical and Financial issues  Storing terabytes…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 from researchers/ Institutions/Regulatory bodies 22

23. A ClearingHouse of Data Standards is 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 shareability between data  RD-Connect  PCORI, Comete  ELIXIR, BD2K, Data FAIRport 23

24.  PhenoTips and PhenomeCentral  Repository of data  Hub for data sharing  CareforRare, RDConnect  NIH undiagnosed patients 24 Open Acess Data Repositories  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 25

26. Rational Make the most of remarkable advances in the molecular basis of human diseases  dissect the physiological pathways  improve diagnosis  develop treatments Make rare diseases visible in health information systems  to gain insight into them  to access real life data already collected Improve coding of RD whichever coding system used Cross-reference coding systems: Orpha nomenclature, ICD10, MeSH, SnoMed-CT, MedDRA

27. What is the problem ? Computers are not smart enough….  The following descriptions mean the same thing to you:  generalized amyotrophy  generalized muscle atrophy  muscular atrophy, generalized  But your computer thinks they're completely unrelated 27

28. 28 Phenomes: a continuum Group of phenomes « Disorder » level Subtypes Top of classification = System disorder Group Clinical criterion Disease, syndrome, condition,anomaly… Etiological Clinical Histopathological… No type: waiting to have a type attributed Disease Malformation syndrome Morphological anomaly Biological anomaly Clinical syndrome Particular clinical situation

30. Orphan Diseasome http://research.cchmc.org/od/01/index.html An Orphan Diseasome permits 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.

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

33. Each terminology has a purpose– driven approach  Indexing health status of individual patients for health management (SnoMED)  Detailed, focus on manifestations and complaints  Adapted to clinical habits  Analytical approach  Indexing health status of individual patients for statistical purpose in public health (ICD)  More agregated, interpreted phenotypic features  Agregated concepts  Unambiguous to avoid blanks

34. Purpose–driven approach (2)  Indexing health status of individual patients for clinical research purpose (HPO / PhenoDB / Elements of morphology)  Highly detailed to fit with the research questions  Specific terminologies developed for disease-specific patient registries  Indexing health status of individual patients for retrieving possible diagnoses (LDDB,POSSUM,Orphanet)  Agregated concepts  Requires a judgement of clinicians about phenomic expressions that are relevant  Unambiguous to avoid blanks

35. HOW TO MAKE ALL THESE TERMINOLOGIES INTER-OPERABLE ? 35

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

37. Phenotype terminology project  Aims:  Map commonly used clinical terminologies (Orphanet, LDDB, HPO, Elements of morphology, PhenoDB, UMLS, SNOMED-CT, MESH, MedDRA): – automatic map, expert validation, detection and correction of inconsistencies  Find common terms in the terminologies  Produce a core terminology – Common denominator allowing to share/exchange phenotypic data between databases – Mapped to every single terminology

38. Mapping Terminologies  Orphanet: 1357 terms (Orphanet database, version 2008)  LDDB: 1348 dysmorphological terms (Installation CD)  Elements of Morphology: 423 terms (retrieved manually from publication AJMG, January 2009)  HPO: 9895 terms (download bioportal, obo format, 30/08/12)  PhenoDB: 2846 terms (given in obo format, 02/05/2012)  UMLS: (version 2012AA) (integrating MeSH, MedDra, SNOMED CT)

39. Tools  OnaGUI (INSERM U729): ontology alignment tool – Work with file in owl format – I-Sub algorithm: detect syntaxic similarity – Graphical interface to check automatic mappings and manually add ones  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 infer mappings of non-Orphanet terminologies Eg: Orphanet ID XX mapped to YY in HPO and ZZ in LDDB -> deduction: YY and ZZ should probably map  Retrieve HPO mappings versus UMLS, MeSH  First figures: LDDBEl. MorphoPhenoDBHPOUMLS… OrphanetE: 1062E: 416E: 978E: 2228E: 6948 LDDBD: 275D: 533D: 1123D:2678 El. MorphoD: 177D: 716D: 409 PhenoDBD: 1045D:3268 HPOD: 6307+4800 UMLS…

41. Mapping of non-Orphanet terminologies  Automatic and infered mappings were checked by experts  Using OnaGUI for all, except UMLS Automatic I-Sub: 7.0 + deduction  Metamap + deduction + HPO mappings  Figures: El. MorphoPhenoDBHPOUMLS… LDDBD: 257 +23 added D:528, 92%E A:674, 38%E D: 1105, 87%E A: 2084, 23%E D: 2654, 83%E A: 11731 El. MorphoD:174, 50%E A:189, 74%E D:393, 93%E A: 436, 16%E D:405, 84%E A:1248 PhenoDBD:1018, 91%E A: 4168, 6%E D: 3222, 82%E A: 18776 HPOD: 7389 A: 65535 UMLS…

42. First list of common terms  Present in at least 2 terminologies  Definition of rules for nomenclature  Addition of terms present in each terminology as synonyms

43. Workshop on 21-22 October 2013 in Boston Success!  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.http://ichpt.org  All tools will map to this terminology to allow interoperability among resources

44. 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 Ségolène Aymé  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 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 Ada Hamosh  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

45. FROM A TERMINOLOGY TO AN ONTOLOGY COMPUTERS ARE NOT SMART 45

46. Why ontologies are needed ?  Ontologies are representations of the knowledge in a way which is directly understandable by computers  Ontologies allow reasoning  Ontologies define the objects AND the relationship between the objects  Duchenne muscular dystrophy (disease) Is a neuromuscular disease (group of diseases)  Schistosomias (disease) Is a cause of anemia (manifestation) 46

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 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 Phenotype Ontologies Rare Diseases bioportal.bioontology.org/ontologies/ORDO Phenotypic Features bioportal.bioontology.org/ontologies/HP Based on Orphanet multi-hierarchical classification of RD Genes– diseases relationships Cross-references: -For RD nomenclature : OMIM, SNOMED CT, ICD10, MeSH, MedDRA, UMLS -For genes : OMIM, HGNC, UniProtKB, IUPHAR, ensembl, Reactome Based on Orphanet multi-hierarchical classification of RD Genes– diseases relationships Cross-references: -For RD nomenclature : OMIM, SNOMED CT, ICD10, MeSH, MedDRA, UMLS -For genes : OMIM, HGNC, UniProtKB, IUPHAR, ensembl, Reactome ICHPT (International Consortium for Human Phenotype Terminologies) 2,307 terms- core terminology Mapped to: HPOElements of Morphology OrphanetLDDB SNOMED CT Pheno-DB (OMIM) MeSHUMLS Available soon for download at ichpt.org ICHPT (International Consortium for Human Phenotype Terminologies) 2,307 terms- core terminology Mapped to: HPOElements of Morphology OrphanetLDDB SNOMED CT Pheno-DB (OMIM) MeSHUMLS Available soon for download at ichpt.org

49. Please adopt/disseminate HPO and ORDO to speed up R&D to the benefit of the patients 49

50. THEY CAN HELP REPURPOSE DRUGS COMPUTERS ARE VERY SMART 50

51. Rational: Make optimal use of molecules already known  Drug Repositioning or Repurposing is a strategy used to generate new or additional value for a drug, by targeting diseases other than those for which it was originally intended  Address unmet medical needs  Reduce time to market due to provided information on  Unbiased clinical safety and efficacy data  Add value to exiting porfolio  Increase drug pipeline  Decrease R&D failure risks  Decrease development costs  Creates new revenue potential 51

52. Graph Theory Enables Drug Repurposing Gramatica et Al.: PLOS one, Vol 1 e84912, 2014  23 Million articles from PubMed  Possible to link the gathered information on drugs, physiological pathways and resulting biological activities with the pathophysiological signs & symptoms of diseases  Possible to rank the matches in order to identify the most promising leads 52

53. Graph Theory Enables Drug Repurposing Gramatica et Al.: PLOS one, Vol 1 e84912, 2014 53

54. Graph Theory Enables Drug Repurposing Gramatica et Al.: PLOS one, Vol 1 e84912, 2014 54

55. Conclusion  Open access to dataWe now leave in an open world  It is an opportunity in research  Evidence that open-access to data is beneficial, especially for the data producer !  Orphadata is accessed by 3000 researchers/ month  Agreed standards to make data interoperable  Responsibility of Institutions and of individual researchers 55

56. Thank you for your invitation 56