Post-genomic Clinical Trials: Needs and Requirements

1. Post-genomic Clinical Trials:Needs and Requirements Norbert Graf, Alexander Hoppe, Christine Desmedt, Georgios Stamatakos, Mathias Brochhausen, Gabriele Weiler, Nikolaus Fórgo, Yuzuru Tanaka, Manolis Tsiknakis ACGT Workshop, EGGE conference Budapest, 4th October 2007

2. Clinical Trials: Challenges • Heavily manual, paper-based processing • “Islands of Information” • Syntactic interfaces vs. semantic interoperability • Variable/conflicting vocabulary, coding and nomenclature standards • Every trial is “de novo” • Time/money spent on building an infrastructure, rather than scientific and research endeavors

3. Research Development CT Enrollment & Patient care ResearchDevelopment Trial Setup PatientEnrollment New IdeaGeneration PatientTreatment DataAnalysis Data mining & Analysis Financial& Billing Reporting & Administration CT Reporting & Administration Clinical trials life cycle management • Administrative and Regulatory Management • Clinical Research Management • Financial Management • Study Parameters Development • Data mining and De-Identification • Patient Enrollment

4. DNA (genes) RNA (transcripts) Proteins Metabolites Genomics Transcriptomics Proteomics Metabolomics The ‘-omics Revolution’

5. Issues and challenges for post-genomic approaches • Poor and/or heterogeneous medical records. • Bio-banks for molecular classification and/or genotyping. • Inability to share technologies, bioinformatics tools and data. • Even within a single laboratory, researchers have difficulty integrating data from different technologies because of a lack of common standards. • Internationalization (multi-site, multinational-race) of clinical trials, especially for rare diseases.

6. Gene Database V.O. Grid Services Infrastructure (Metadata, Registry, Publishing, Query, Invocation, Security, etc.) Image Microarray Tool 2 Tool 3 Grid-Enabled Client Analytical Services Tool 1 Clinical data Research Center Tool 2 Research Center Protein Database Tool 3 Grid Data Service Grid Data Services Tool 4 Analytical Services Grid Data Service Research Center Grid Data Service Grid Portal Analytical Services

7. The ACGT Vision and Objective Personalized Treatment

8. Uni Lund Uni Oxford Uni Hamburg , Uni Hosp Saarland, IFOMIS Fraunhofer (IBMT, AiS ), Uni Hannover PSNC Poznan Uni Amsterdam, Philips J. Bordet Institute , Custodix , Uni Namur INRIA, HealthGrid , ERCIM SIB Lausanne SIVECO Uni Madrid, Uni Malaga FORTH, Uni Hosp Crete , ICCS - NTU Athens , Biovista Uni Hokkaido The ACGT Consortium

9. ACGT is fighting Cancer • ACGT infrastructure is to facilitate clinical research and trials • ACGT is Scenario based • Scenario descriptions integrate what and howthe user carries out activities • There is a step by step approach • starting with a minimal set of databases and services and incorporating new collections as they are needed step by step • The priority of tasks conducted in clinical trials is driven by clinical relevance • ACGT is open for Clinicogenomic trials

10. UoO Nephroblastoma Multi-centric Oncogenomic Study JBI 2 UoS In Silico Oncology Study 3 IEO Prolipsis FORTH UoC Breast Ca Multicentric Oncogenomic Study 1 The ACGT clinical trials • Multicentric TOP trial – Breast Cancer • SIOP 2001 – paediatric nephroblastoma • In Silico modeling and simulation of tumor growth & response to treatment

11. Scenarios • SC1: A Complex Query Scenario for the TOP Trial • SC2: Identification of nephroblastoma antigens • SC3: Correlating phenotypical and genotypical profiles • SC4: Reporting of Adverse Events and Severe Adverse Reactions • SC5: In-silico modelling of tumor response to therapy • SC6: Molecular apocrine breast cancer • SC7: van ‘t Veer study • SC8: Antigen Characterisation Scenario

12. In silico modeling and simulation • Seeks to extract information related to tumor growth & response to treatment • Requires • access to multi-level clinico-genomic data. • implementation of a variety of processing tools and • specification of complex analytical workflows

13. The Post Genomic ‘tower of Babel’ • Each research community speaks its own scientific “dialect” • Even within a research community it is difficult to share information and exchange data • Overwhelming volume of data from a multitude of sources • Integration critical to achieve promise of genomic medicine

14. Regulatory guidelines • EU Directive on Clinical Trials (2001/20/EC) • To protect the rights, safety and well being of trial participants • To simplify and harmonise the administrative provisions governing clinical trials • To establish a transparent procedure that will harmonise trial conduct in the EU and ensure the credibility of results • EU Directive 2005/28/EC on GCP • Specific Provisions for Non-commercial Trials

15. Dataprocessing within ACGT - Value conflicts Individual interest Collectiveinterests conflictbetween Privacy Access Balance?

16. Ethical considerations In addition to existing legal regulations, ethical principles may be used to address these value conflicts, namely: • Autonomy • Human dignity • Beneficence and non-maleficence • Justice • Solidarity • ….. Basic principle: Autonomy Autonomy needs Consent

17. What is ACGT responsible for?

18. ACGT data protection safety net

19. ACGT data protection safety net

20. Step 1 - De facto anonymous data

21. Inquiry [Commercial Software]

22. Summary of the questionnaire • Contra • Not all available standards used • No Ontology • No Data Mining • Clinicians need technical support • Too expensive • Pro • GCP conform and approved • (high) experience

23. ObTiMA Repository Trial Outline Builder general view CRF Creator graphical schema Trial Builder patient specific view Patient Data Management System

24. Overview of the Trial Builder • TrialBuilder • Will support the design phase of a clinical trial • Allows a clinician to capture data definition and further design specifications for a clinical trial in a standardized way • Allows to create all CRFs for a trial, integrating the ACGT master ontology in a way that the data collected with the CRFs can be later queried in terms of the ontology • Integrates a CRF repository for reuse of the created CRFs • Clinical Data Management System • will be set up by the definitions done in the Trial Builder • Web based application that allows to collect the patient data for multicentric clinical trials

25. Functionality of the Clinical Data Management System • Patient Management • Providing data in terms of the ontology • Study Management • User Management • Roles &Rights Management • Security solution

26. Ontology integration • User friendly GUI  Clinician‘s View of the Ontology • by selecting semantic descriptions the items for the ontology will be created automatically on CRF • Tools are fully functioning without Ontology • databases are independent from ontology • descriptions from ontology are stored in mapping-files • Queries with SPARQL in terms of the ACGT master ontology are possible • Mapping of all relevant data will be done once

27. This is the Ontology build in Protegé

28. This is the way a clinician thinks

29. Show demonstrator

31. Disease S R Personal Data S R Tumor S R Patient Therapeutic Procedure S R Measurements S R Pharmacotherapy S R Symptom S R Surgical Proc. S R Radiotherapy S R Begin A End A Duration A SingleDose A Radiotherapy of Lung S R TotalDose A S R Device Field A

33. Advantages • Enables trial chairmen to create reusable CRFs that allow collection of data that are standardized based on an underlying ontology • Enables trial chairmen to create trial databases with comprehensive metadata (automatically generated from designed CRFs) • data collected in the generated systems can be directly integrated over the mediator with the data in the ACGT environment (no additional mapping is needed) • Systems created in this way have built-in semantic interoperability • Inferring new knowledge from data entered in the ontology based clinical data management system may be possible according to relationships, axioms and rules specified in the ACGT master ontology

34. Trial Outline Builder

35. Administration area for timeline dependent events Trial related Diagnostics Treatment Medical Scratchboard Surgery Trial protocol Radiatio timeline Follow-up area for timeline independent events Support. Biobank Research Patient Communication all selection zoom PDF General Event handling

36. C S 6 15 4 9 Definitions of Events Chemotherapy Randomisation Stratification Supportive therapy Shortening Radiotherapy Delay Communication Surgery SAE /SUSAR Diagnostics Reporting S

37. ICE 50 Gy 50 Gy I I I I I C C C C C Scratchboard Trials Queryboard Tools Trial 1 Query Query I II Descriptive Statistics Correlations Cox-Regression Descriptive Statistics Correlations Cox-Regression Multivariate Analysis Life-Table Analysis …… C C Trial 2 II I C C Trial 3 Visualisation C Trial 4 Report CE C Trial 5 I Trial 6 II II S C C C II II I S I C C C C Trial 7 Trial 8 0 Trial 9 Trial 8

38. ICE 1,0 ,9 ,8 50 Gy 50 Gy 50 Gy 50 Gy ,7 relapse free survival ,6 ,5 ,4 I C ,3 ,2 ,1 0,0 0 1 2 3 4 5 6 7 8 9 10 time [years] Scratchboard Trials Queryboard Results Trial 1 I I I S C C C Trial 2 Histogramm II II II S C C C Trial 3 Life table C Trial 4 Cox- Regression CE C Trial 5 … I Trial 6 II S C C II II I S C C C Trial 7 Visualisation Board Trial 8 0 Trial 9 Trial 8

39. Summary Individualized medicine: Which therapy will work best? Who can be spared therapy? Prognostic factors needed Predictive factors needed