CPTR RDST Data Platform Concept › wp-content › uploads › 2014 › 11 › ...Nov 22, 2014 · s2 y1 y2 y3 y4 y5 y6 y7 s.. z1 z2 z3 z4 z5 z6 z7 Strain 1 sequence data Strain 2

CPTR RDST Data Platform Concept September 22, 2014

Outline

• C-Path overview and examples of data projects

• Knowledge sharing concept

• RDST approach

• Examples of RDST data types

• Database architecture

• Next steps – timeline

CPTR-RDST Data Platform 2014 Workshop Slides 2

C-Path Consortia

Coalition Against Major Diseases UNDERSTANDING DISEASES OF THE BRAIN

Critical Path to TB Drug Regimens TESTING DRUG COMBINATIONS

Multiple Sclerosis Outcome Assessments Consortium

DRUG EFFECTIVENESS IN MS

Polycystic Kidney Disease Consortium NEW IMAGING BIOMARKERS

Patient-Reported Outcome Consortium DRUG EFFECTIVENESS

Electronic Patient-Reported Outcome Consortium DRUG EFFECTIVENESS

Predictive Safety Testing Consortium DRUG SAFETY

Seven global consortia developing novel drug development tools

Biomarkers

Clinical Outcome Assessment Instruments

Clinical Trial Simulation Tools

In vitro tools

Data Standards

3

C-Path Online Data Repository

Current C-Path examples CAMD – AD Clinical Trial Simulation Tool PKD - Biomarker Qualification Project MSOAC – New Outcome Assessment Instrument for MS

MSOAC

4

CDC TB study data now available

Datasets contributed to C-Path for consortia projects

Consortium Therapeutic Area # of

Studies

Total Number

of Subjects

Number of

Data

Contributors

Coalition Against

Major Diseases

Alzheimer's disease 27 7340 11

Parkinson's disease 7 2597 2

Critical Path to

TB drug Regimens Tuberculosis 10 2495 5

MS Outcome

Assessments Consortium Multiple sclerosis 6 4700 4

Polycystic Kidney Disease Polycystic kidney

disease 5 2941 4

Predictive

Safety

Testing

Consortium

Normal healthy

volunteer-kidney 1 172 1

Skeletal-muscular

(non- clinical) 38 1766 6

Hepato-toxicity

(non-clinical) 43 2340 7

Nephro-toxicity

(non-clinical) 14 941 8

5

Value of data sharing, data standards & data pooling

Nine member companies agreed to share data from 24 Alzheimer’s disease (AD) trials

The data were not in a common format The data were remapped to the CDISC AD

standard and pooled

A new clinical trial simulation tool was created and has

been the first model endorsed by the FDA and EMA Researchers utilizing database to advance research

Start Point

Result

24 studies, >6500 patients

6

7

Model endorsed by FDA and EMA

Access to AD data available to

qualified researchers

Future TB model

8

Rapid DST TB Data Sharing Platform Architecture Concept

9

CPTR TB Drug Resistance DB

Data Platform to Inform Assay Development

10

How do we build this system?

Linking Global TB Sequence Researchers

• TB sequence community inputs

• Expert review to advance investigational biomarkers to validated status

• Can use separate or consolidated DBs

• FDA compliant CDISC architecture for regulatory submission DB

Approved members • Academic labs • Reference labs • Commercial companies • Others…

Validated DR biomarkers

Expert Panel

Review

Approved biomarkers

Sequence repository Anonymized sequence data Clinical annotation Phenotypic methods User friendly cloud interface

Analysis files generated

Investigational DB






CPTR-RDST Data Platform 2014 Workshop Slides

How do we accomplish this?

• Clear objective

– Improved research resource to enable development of new rapid diagnostics for TB

• Future objective

– With sustainability funding: resource for clinicians

• Build on previous efforts for TB and for data sharing

– Apply technology product development discipline

– Design to handle wide range of data types

– Quality criteria and defined process for incoming data

– Lean, efficient and well managed implementation

– Expandable / adaptable / flexible

– Great usability

• Strong alignment with anticipated analysis use cases

11 CPTR-RDST Data Platform 2014 Workshop Slides

Related efforts: TBDReamDB

12 CPTR-RDST Data Platform 2014 Workshop Slides http://www.tbdreamdb.com/index.html

Example of future objective: Stanford HIV database

13 CPTR-RDST Data Platform 2014 Workshop Slides http://hivdb.stanford.edu/

Product development discipline

• Detailed, documented requirements

• Early prototyping

• Design to requirements

• Staged development with clear milestones

• Extensive testing

– Verification of all features and function

– Usability

– Performance

– Scalability

• Phased rollout

– RDST members

– Qualified external researchers

• Ongoing support and enhancements based on user feedback

Requirements

Prototype

Design

Build

Test

Deploy

Support and enhance


RDST Data: multiple data types

15

Need to incorporate multiple types of data

• sequence data

• SNP reports

• resistance test data

• clinical trial/study/registry data

• external information resources

• any other data that may be necessary

Which need to be analyzed to find and validate correlations


RDST Data: genotypic data example

@M00347:61:000000000-A9B8J:1:1101:15324:1677 1:N:0:1

TCTTGATCGCGAGTTCGCGGCCCGGGGTGAGCACCCAGGTGAGCGGGAAATGCGTGGTGTCGTGGTAGCTGACGTCGACGATGCCGTGGCGGTATTCGAGGTCTGTGAACGTGTCGTCGTCGAGGAAGTTCTGCAGCACCAGCAGCGGATC

+

11>>1@BF1>>11AEF00000AA////A//A1AB/?/GAEC1GBE??///FFG/?E?EFHF/F?A?EG1BDBC/FCGGCCCC-.1011/0//?/333333/030333044300///B1111/00//>22222211111111101111100

RDST Data: SNP report example

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SNP reports


RDST Data: phenotypic data example

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resistance test data

CPTR-RDST Data Platform 2014 Workshop Slides https://tbdr.org/cgi/tbdr

STUDYID DOMAIN USUBJID AGE SEX RACE ARM

19 DM 10001 27 F WHITE Ethambutol 5 Times Per Week

19 DM 10002 63 M WHITE Moxifloxacin 3 Times Per Week

19 DM 10003 42 M BLACK OR AFRICAN AMERICAN Moxifloxacin 5 Times Per Week

19 DM 10004 30 F ASIAN Moxifloxacin 5 Times Per Week


19 DM 10006 35 M BLACK OR AFRICAN AMERICAN Ethambutol 3 Times Per Week

19 DM 10007 46 F UNKNOWN Ethambutol 3 Times Per Week

19 DM 10008 34 F BLACK OR AFRICAN AMERICAN Moxifloxacin 5 Times Per Week


19 DM 10010 42 M ASIAN Moxifloxacin 5 Times Per Week

19 DM 10011 23 F BLACK OR AFRICAN AMERICAN Ethambutol 3 Times Per Week

19 DM 10012 47 F WHITE Ethambutol 3 Times Per Week

19 DM 10013 25 F BLACK OR AFRICAN AMERICAN Moxifloxacin 5 Times Per Week

19 DM 10014 21 M WHITE Ethambutol 3 Times Per Week

19 DM 10015 79 M WHITE Moxifloxacin 3 Times Per Week

19 DM 10016 27 F ASIAN Moxifloxacin 3 Times Per Week



RDST Data: clinical data example (hypothetical data)

19

STUDYID DOMAIN USUBJID MBTESTCD MBTEST MBORRES MBSPEC VISIT

13 MB 10001 AFB Acid Fast Bacilli NEGATIVE SPONT SPUTUM WEEK 8

13 MB 10001 ORGANISM Organism Present NEGATIVE FOR TUBERCULOSIS SPONT SPUTUM WEEK 4

13 MB 10001 MTBINH M.tuberculosis INH Resistant POSITIVE NON-OVERNIGHT SPUTUMSCREENING


13 MB 10002 AFB Acid Fast Bacilli NEGATIVE SPONT SPUTUM WEEK 8

13 MB 10002 ORGANISM Organism Present POSITIVE FOR M. TUBERCULOSIS COMPLEX SPONT SPUTUM SCREENING

15 MB 10002 ORGANISM Organism Present POSITIVE FOR M. TUBERCULOSIS COMPLEX SPONT SPUTUM SCREENING

13 MB 10003 ORGANISM Organism Present NEGATIVE FOR TUBERCULOSIS INDUCED SPUTUM WEEK 4


STUDYID DOMAIN USUBJID MOTESTCDMOTEST MOORRES MOSTRESC MOLOC VISIT MODY

17 MO 10001 CAVIT Cavitation Y Y LUNG, LEFT SCREENING -4

17 MO 10002 CAVIT Cavitation Y Y LUNG, LEFT SCREENING -5

17 MO 10002 PLEURALD Pleural Disease N N LUNG, LEFT SCREENING 1

17 MO 10004 PLEURALD Pleural Disease N N LUNG, LEFT SCREENING -8

17 MO 10005 CAVIT Cavitation N N LUNG, LEFT SCREENING -9

17 MO 10005 CAVIT Cavitation N N LUNG, LEFT SCREENING -15

17 MO 10006 CAVIT Cavitation Y Y LUNG, LEFT SCREENING 1

17 MO 10006 PLEURALD Pleural Disease N N LUNG, LEFT SCREENING -7

clinical trial data


RDST Data: TB strain summary table

http://www.ncbi.nlm.nih.gov/genome/genomes/166 20

external resources


RDST data platform: design to handle multiple data types

•

Aggregated Research Database

Subject – Level Clinical Trial Data

VAR1 1 2 3 4 5 6 7

s1 x1 x2 x3 x4 x5 x6 x7

s2 y1 y2 y3 y4 y5 y6 y7

s.. z1 z2 z3 z4 z5 z6 z7

Time

VAR2 1 2 3 4 5 6 7

s1 x1 x2 x3 x4 x5 x6 x7

s2 y1 y2 y3 y4 y5 y6 y7

s.. z1 z2 z3 z4 z5 z6 z7

VAR3 1 2 3 4 5 6 7

s1 x1 x2 x3 x4 x5 x6 x7

s2 y1 y2 y3 y4 y5 y6 y7

s.. z1 z2 z3 z4 z5 z6 z7

Strain 1 sequence data



ACAAGATGCCATTGTCCCGCT…

CCTGGAGGGTGGGAGACA…

CTTTCCTCGCTTGGGTGG…..

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Clinical Trial Data Data

Analysis

Data Analysis

OBS1 1 2 3 4 5 6 7

s1 x1 x2 x3 x4 x5

s2 y1 y2 y3 y4 y5

s.. z1 z2 z3 z4

VAR1 1 2 3 4 5 6 7

s1 x1 x6 x7

s2 y1 y4 y5 y6 y7

s.. z1 z2 z3 z4 z5 z6 z7

TEST1 1 2 3 4 5 6 7

s1 x_base x_chk1 x7

s2 y_base y_chk1 y7

s.. z_base z2 z3 z7

Surveillance Data Time

Apply CDISC Data Standards

Surveillance data

Genotypic data

Phenotypic data data analysis


Key success factors for incoming data

• Buy in for data contributions

– Survey and prioritize

– Proactive engagement

– Recognition and incentives for contributions

• Clearly defined quality criteria

– Develop and vet during initial data survey & prioritization

• Consistent process for incoming data processing

– Unified pipeline for incoming sequence data

– Ability to apply CDISC standards to create efficient database (vs large number of small data buckets)

• Ongoing curation and quality control


Quality criteria and defined process for incoming sequence data

23

Incoming FASTQ plus associated SNP report

New SNP report generated with RDST unified pipeline

RDST unified pipeline for sequence data


Data Element: Phase of TB treatment

Data Element: TB Symptoms

24

TB clinical data mapping to CDISC

We do this today for CPTR

USUBJID EXTRT EXDOS EXDOSU

USUBJID CETERM CEPRESP CEOCCUR

Clin

ical E

vents

(CE

)

Exposu

re

(E

X)

Skin

Response

(SR

)

USUBJID SRTESTCD SRTEST SRORRES SRORRESU

12345 INDURDIA Induration

Diameter

16 mm

Controlled Terminology

Map to CDISC domains

CDISC Variables

Data Element: Tuberculin Skin Test Result Definition: The number of millimeters in diameter of the induration, or raised hardening, at the tuberculin skin test site. Permissible value set: mm of induration.

Preserve, do not change the data content A place for everything, everything in its place Capture the smallest usable elements of data


Rapid DST Data Platform

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• Can we apply CDISC standards to TB genotypic and phenotypic data?

• What is the benefit of doing this?


Interventions Special

Purpose

Demographics

Subject Elements

Subject Visits

Findings

ECG

Incl/Excl Exceptions

Events

Con Meds

Disposition Comments

Trial Design

Trial Elements

Trial Arms

Trial Visits

Trial Incl/Excl

Exposure

Substance Use

Adverse Events

Medical History

Deviations

Clinical Events

PK Concentrations

Vital Signs

Microbiology Spec.

Questionnaire

Drug Accountability

Subject Characteristics

Labs

Microbiology Suscept. PK Parameters

Physical Exam

Trial Summary Findings About


CDISC Study Data Tabulation Model (SDTM) domains for classification of data elements

Data Mapping: SNP report example

27

PFORREF – reference result (can apply to nucleotides or amino acids, depends on value in PFTEST)

PFORRES – experimental result (can apply to nucleotides or amino acids, depends on value in PFTEST)

PFRESCAT – category of result (is this a nonsense or missense mutation? frameshift? etc.)

PFGENTYP – type of feature we’re looking at (gene, sector, protein, etc.)

PFGENRI – region of interest (it is defined as the specific gene or locus being looked at)

PFSTRESC – standard result of the analysis. Usually uses HGVS nomenclature



28

Three primary categories of data

• Data as received from contributors

• Quality checked, processed, standardized data

–Master copy

–Full complement of data for RDST consortium use

–Authorized subset for external researchers (as broad as possible within sharing terms and conditions imposed by each data contributor)

• Analysis data extracts and reports to support research



+ Lean, efficient and well managed implementation + Expandable / adaptable / flexible + Great usability

Strong alignment with anticipated analysis use

cases

29


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Next Steps


2014 2015 2016 2017

S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O

2014 2015 2016 2017

RDST Data Sharing Platform Timeline v4

1.1 Governance Model

1.5 Dev Ph 1 Dev Ph 2

Data Platform Available for Consortium Members

2.4 Perform phase 1 program assessment

2.6 Enable for external researchers

Sustainability Funding Secured

C-Path Milestone

1.3 Req’s, Arch and Design

1.2 Value proposition, DUA updates and Communication Plan

1.5 Test Ph 1 1.7 Test Ph 2

Dev Ph 3

1.8 Test Ph 3

3.5 Perform Phase 2 program assessment

Data Platform Available for external researchers

3.6/3.8 Release 2 Dev/Test

FIND Milestone

2.5 Expand Capacity 3.7 Expand Capacity

1.6/2.2/3.3 Prepare and load contributed data in Data Platform as it becomes available

2.3/3.4 Review and approve access requests as they are submitted

2.1/3.2 Monitor performance and usage

1.1.1 Inventory of available DBs

1.2.1 Form Expert Panel and support Data Platform development and use

1.2.2 Develop criteria for determination of resistance mutations

1.2.3 Develop algorithms for interpretation of genotypic data

1.2.3 Published algorithm for interpretation of genotypic data

1.3.1 Develop guidelines/criteria for clinical validation of assays to detect/interpret resistance mutation

1.4.1 Support for development of access models and tools for broad access

PHASE 2 PHASE 3

C-PATH Milestones

3.9 Pursue funding to support clinical use

2.8/3.1 Pursue sustainability funding

1.6 Load early data

2.7 Beta Test

1.4 Request early data

1.9 Prep for production

Request early data

1.5.1/1.5.2 Support for sustainable business model and review process

1.1.2 Early Data Packages available for inclusion in Data Platform

1.2.2 Defined Criteria for determination of resistance mutations

1.3.1 WHO report on guidelines/criteria for validation of assays to detect and intrerpret resistance mutations

1.1.2 Prepare data packages for inclusion in Data Platform

1.1.3 Input to C-Path on design of Data Platform

PHASE 1

FIND Milestones

Assist with development of Value

Proposition and Communications Plan

Next steps: the art of the start

Build and deploy Expanded access Sustainability


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• Big job in front of us

• We are not starting from scratch

• We have lots of help

We can do this!


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www.c-path.org



34

Backup



35

+ Lean, efficient and well managed implementation + Expandable / adaptable / flexible + Great usability



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Investigational DB with user access levels

(data team, RDST, external)

use

r

frie

nd

ly

cl

ou

d

in

terf

ace

FASTQ data files

internal

Incoming data

storage

external



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Strong alignment with anticipated analysis use cases


Documents

CPTR RDST Data Platform Concept › wp-content › uploads › 2014 › 11 › ...Nov 22, 2014 · s2 y1 y2 y3 y4 y5 y6 y7 s.. z1 z2 z3 z4 z5 z6 z7 Strain 1 sequence data Strain 2