Leveraging of CDISC Standards to Support Dissemination, Integration and Analysis of Raw Clinical Trials Data to Advance Open Science

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1 Leveraging of CDISC Standards to Support Dissemination, Integration and Analysis of Raw Clinical Trials Data to Advance Open Science Ravi Shankar Research Scientist, Division of Systems Medicine, Stanford University Member, NIAID ImmPort 1

2 Open access to clinical trials data advances open science Broad open access to entire clinical trial data is on the rise Tremendous opportunity to evaluate new hypotheses that were not originally formulated in the studies by performing cross analysis of clinical trials by combining with other open biomedical datasets We are developing standards-based methodologies for uniformly representing clinical trial data to support dissemination, integration and analysis.

3 Industry and Government Opening the Clinical Trial Data Vault

4 immport.niaid.nih.gov

5 ImmPort redistributes data from major NIAID-funded programs Data from 60+ trials already released, involving: Collaborative Network for Clinical Research on Immune Tolerance Network Atopic Dermatitis Research Network (ADRN) Clinical Trials in Organ Transplantation (CTOT) Clinical Trials in Organ Transplantation in Children (CTOT-C) Population Genetics Analysis Program Protective Immunity for Special Populations HLA Region Genomics in Immune-mediated Diseases Maintenance of Macaque Specific Pathogen-Free Breeding Colonies Modeling Immunity for Biodefense Reagent Development for Toll-like and other Innate Immune Receptors Adjuvant Development Program Innate Immune Receptors and Adjuvant Discovery Program Human Immunology Project Consortium Non-human Primate Transplantation Tolerance Cooperative Study Group

6 Open Biomedical Datasets 5,178 compounds 1,300 off-patent FDA-approved drugs 700 bioactive tool compounds 2,000+ screening hits (MLPCN and others) 3,712 genes (shrna + cdna) targets/pathways of FDA-approved drugs (n=900) candidate disease genes (n=600) community nominations (n=500+) 15 cell types Banked primary cell types Cancer cell lines Primary htert immortalized Patient derived ips cells 5 community nominated

7 New Open Science by Integrating Open Clinical Trial Data with Biomedical Datasets Looking at data is good; reanalyzing it is great Clinical trial results can be reproduced and expanded Discover new markers for personalized medicine While individual studies are great, pooling studies may be more powerful Pooling experimental results enables new questions Let s learn why trials work and fail Linking open biomedical and clinical trial data will enable even more virtual studies and findings

8 Clinical Trial Data To support the integration and analysis of clinical trials data We are creating uniform representations of clinical trial data. We are enabling easy access to uniformly represented clinical trial data in analytical environments. represent Uniformly Represented Clinical Trial Data export bridge Open Clinical Trial Repositories Dissemination Analytical Environments (R, SAS) Analysis

9 The 5 Major Steps from Raw Data to to Publish to Ready-for-analysis 1. Scope the clinical trial data types that we want to include for dissemination purposes. 2. Define a set of clinical trial domain entities (an ontology) required to uniformly represent the clinical trial data. 3. Using the ontology, encode specific clinical trial data to create uniformly represented data. 4. Publish the encoded clinical trial data. 5. Create bridges between the encoded data and analytical environments to provide easy access to data for analysis.

10 Towards Minimum Information About Clinical Trial for Dissemination Study Summary Study Objectives and Outcome Measures Study Design Schedule of Activities Study Subjects Clinical Data Experimental Data Adverse Events Analysis Data

11 Building a Standards-Based Clinical Trial Ontology a Formal Description of the Clinical Trial Domain Leverage of data elements defined in CDISC Standards e.g. Study, Arm, Study Subject Link with appropriate reference biomedical ontologies Clinical Trial Ontology (CTO) PRM TDM CDASH SDTM Data Dissemination Elements ODM ADaM SEND BRIDG NCI Thesaurus SNOMED CT Use Upper-level, Structural and Domain Ontologies BFO RDF Data Cube GO NDF-RT BAO PRO CL IAO OGMS 11

12 Using Semantic web standards to represent and disseminate clinical trial data Semantic web standards allow data to be shared and reused over the web OWL Web Ontology Language SPARQL RDF Query Language RDF Resource Description Framework URI Uniform Resource Identifier

13 Describing the Clinical Trial Domain as a Set of OWL Axioms Descriptions of a clinical trial entity such as its parent entity, its relationships with other entities and value restrictions are formally expressed as logical axioms to create the Clinical Trial Ontology InterventionalStudy is a subtype of Study Study has title that is of type string arm relates Arm to a Study Study Subject is a subtype of Person with role as Subject Role

14 Exploring RDF Data Cube for representing and analyzing multi-dimensional longitudinal data CUBE - a set of observations indexed by dimensions (e.g., arms, months after transplant ) describing measures (e.g., standard height) interpreted according to attributes (e.g., z-score) OBSERVATION measured values (with attributes) at dimensions SLICE optional grouping of observations by fixing dimensions

15 Exploring RDF Data Cube for representing and analyzing multi-dimensional longitudinal data CUBE - a set of observations indexed by dimensions (e.g., arms, months after transplant ) describing measures (e.g., standard height) interpreted according to attributes (e.g., z-score) OBSERVATION measured values (with attributes) at dimensions SLICE optional grouping of observations by fixing dimensions

16 Identifying Data Elements using URI Each clinical trial data element is assigned a unique identifier, a URI Study: CCTPT_SW01 URI - Planned Activity: Medical History URI - Study Subject: Subject 10 URI -

17 Representing Clinical Trial Data as a Collection of RDF Triples In RDF, data is represented as triples predicate Subject Object arm1 Using the entities in the CTO ontology (Study, title, arm, Planned Activity, etc.), the entire CCTPT_SW01 data can be represented as a collection of RDF triples CCTPT_SW01 type of type of Arm Study CCTPT_SW01 CCTPT_SW01 arm title arm1 A double-blind randomized trial of steroid withdrawal label... arm1 Steroid withdrawal arm

18 Linked Clinical Trial Data The collection of RDF triples can be viewed as linked data And, can be linked to external data sets Study type of arm title CCTPT_SW01 A double-blind randomized trial of steroid withdrawal type of Arm arm1 label Steroid withdrawal arm NCI Thesaurus

19 Biomedical Research Data Moving Towards RDF

20 Open Clinical Trial Data can be part of the Open Linked Data Cloud CCTPT-SW01 Clinical Trial Data in RDF

21 RDF Encoding of Clinical Trials Data using a Standards-based Clinical Trial Ontology Clinical Trial Data Clinical Trial Ontology (OWL) represent OWL / RDF Editor Encoded Clinical Trials (RDF) CDISC Standards, Other Ontologies & Linked Data export bridge Open Clinical Trial Repositories Dissemination Analytical Environments (R, SAS) Analysis

22 Accessing RDF Encoded Clinical Trials Data as R Data Objects a protoype Clinical Trial Data Clinical Trial Ontology (OWL) represent OWL / RDF Editor Encoded Clinical Trials (RDF) CDISC Standards, Other Ontologies & Linked Data export Jena RDF API bridge rrdf Open Clinical Trial Repositories Dissemination Clinical Trial Data Objects in R Analysis

23 Loading the RDF Encoded Clinical Trial Data in R a prototype bridge Create R data classes that correspond to the Clinical Trial Ontology (CTO) entities. CTO Entities Study title (string) R Classes Study <- setrefclass("study", fields = list(title = "character )) Query the RDF triples and populate the R classes with clinical trial data. Using R RDF libraries for SPARQL queries

24 > cctpt_sw01 = loadstudy(<clinical Trial URI>) A single line of R code can load the entire clinical trial data into R for analysis > cctpt_sw01 = loadstudy(" > cctpt_sw01$title title "A DOUBLE-BLIND RANDOMIZED TRIAL OF STEROID WITHDRAWAL IN SIROLIMUS- AND CYCLOSPORINE-TREATED PRIMARY TRANSPLANT RECIPIENTS" > cctpt_sw01$arms [[1]] Reference class object of class "Arm" Field "name": name "Control arm" [[2]] Reference class object of class "Arm" Field "name": name "Steroid withdrawal arm"

25 Summary We are using semantic web standards to create the clinical trial ontology (CTO) and to uniformly represent and disseminate clinical trial data. Our work in scoping the clinical trial ontology space can be a step towards a standard for minimum information about a clinical trial. We are developing bridges between encoded clinical trial data and analytical environments to enable easy access to clinical trial data. We are enabling pooling of clinical trials data and integration with other biomedical datasets to advance open science. 25

26 Acknowledgements NIAID ImmPort at Northrop Grumman (Jeff Wiser and team) Stanford University (Atul Butte and team) University of Buffalo (Barry Smith and team) Funding: NIAID Bioinformatics Integration Support Contract (BISC) HSN C Initiating discussions with the CDISC community 26