transmart community meeting 5-7 nov 13 - session 3: the trait user stories for transmart

TraIT user stories for tranSMART

tranSMART User Meeting; Paris

Jan-Willem Boiten; Jelle ten Hoeve

7 Nov 2013

Contents

• Introduction TraIT project

• A taster of the existing tranSMART demonstrators

– DeCoDe: colorectal cancer

– PCMM; prostate cancer

• Current user stories on TraIT roadmap

• Implementation within Netherlands Cancer Institute

(Jelle ten Hoeve)

Global positioning of TraIT

Facts & figures:

• Netherlands

(AKA Holland)

• 40.000 km2

• 17 million

people

• 8 UMCs ( )

300

Km

150

Km

CTMM, TIPharma and BMM

offer an integrated approach for innovations in

the Dutch health care sector

CTMM: diagnosis

• Early detection of disease by in-

vitro and in-vivo diagnostics

• Stratification of patients for

personalized treatment

• Assessing efficiency and efficacy

of medicines by imaging

• Image guided delivery of

medication

• Focus on cancer, cardiovascular,

neurodegenerative and infectious

/autoimmune disease.

TIPharma: drugs

• Translational research on novel

pharmaceutical therapies

• Target finding, animal models and

lead selection

• Drug formulation, delivery and

targeting

• Special Theme focusing on the

efficiency of the process of drug

development

BMM: devices

• Smart drug delivery systems

• Innovations in contemporary organ replacement

therapies

• Passive and active scaffolds, including cell

signalling functions

Image guided

drug delivery

Biomarkers

Drug

delivery

Imaging for

regenerative

medicine

CTMM projects

Breast

Prostate Colon

Lung

Leukemia

Heart

Failure

Stroke

Diabetes

Kidney Failure

Arrhythmia

Peripheral Vascular

Disease

Thrombosis

Alzheimer Rheumatoid Arthritis

Sepsis

Growth of active participation in TraIT:

2011 2013: increase from 11 26 partners

Growing TraIT project team

EUR 16 million / 4 years

TraIT aims to support the translational

research process by means of IT

Epi/Genetics

Transcriptome

Peripheral Markers

Organ Systems

DNA Variants,

Copy Number

modifications

mRNA, ncRNA

miRNA

Proteins, Metabolites

Cells, Microbes

Scientific Output

Patient enters medical center

Intellectual Property

Improved Healthcare

Experimental data

Downstream analysis

Clinical Procedures

Imaging Samples Experiments Electronic

Health Record

Data Integration

External data

Image database Biobank database

Clinical database

13 November 2013

9

z

Connecting initiatives

the 21st century

the middle ages

TraIT incentives

• Increase efficiency of translational research

– End to end workflow

– Multicenter studies

– Connect initiatives (ESFRI, IMI, national programs, etc)

• Cope with data challenges

– Volume

– Silo’s

– Interoperability

– Stewardship

– (open) access

• QA/QC

– Improve validity of proof of concepts

– Diminish scientific misconduct

TraIT tools & applications: the landscape

Hospital (IT) Translational Research (IT)

data domains

clinical data

imaging data

experimental data

biobanking

integrated

data

translational

analytics

workbench

HIS

PACS

LIS

Galaxy

tranSMART/

cohort explorer

R tranSMART/i2b2

dataware house CBM-NL

OpenClinica

NBIA + XNAT

e.g.

PhenotypeDB,

coLIMS

e.g.

Galaxy,

Chipster

Samples (IT)

P

s

e

u

d

o

n

y

m

i

z

a

t

i

o

n

Public Data

BIMS

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

nu

mb

er

of stu

die

s

calendar years

OpenClinica Use

2008

July

2008

Oct

2011

Oct

2012

Start

DeCoDe

OpenClinica

Start

TraIT

OpenClinica

Sept

2013

Pre TraIT effect: all multicenter VUmc studies

Also multicenter studies UMCU, UMCN, EMC, Meander MC

31 studies

30 sites 185 users

55 studies

84 sites 300 users

Today

Uptake of OpenClinica

TraIT Data Integration Roadmap

2012 2013 2014/

2015

2012:

Data integration platform

evaluation and selection

tranSMART

2013:

Study driven enhancement

of data integration platform

using “ready to use” data:

=> enhanced functionality

and robustness

(tranSMART++)

2014/2015:

Study-driven system

integration with TraIT data

capturing systems

=> enhanced interoperability

and usability

(TraIT platform)

2012 2013 2014/2015

TraIT foundation team

Foundation team:

• TraIT core development

team

• Adapt & adopt existing

solutions like tranSMART

• Distributed Scrum Team

• Four core centers and

several associated ( )

ones

2 FTE

4 FTE

2 FTE

2 FTE

NKI

Foundation team user stories & epics

• User stories are collected for every potential TraIT customer

project (large research consortia)

• User stories are collected on the TraIT Wiki and broken down

in epics that can be taken up by the foundation team

• Transformed into an actively maintained TraIT roadmap

CAIRO studies

The Dutch Colorectal Cancer Group (DCCG)

provides an excellent infrastructure for the performance of

multicentre clinical studies in patients with colorectal cancer

CAIRO studies: principal investigator Prof.dr. C.J.A. Punt

Collaborative translational research: Prof.dr. G.A. Meijer

combine clinical trial information with molecular profiling data

CAIRO studies

Clinical data, e.g.: - TNM staging - gender

- age - treatment arm of study

Non-omics data, e.g.:

- MSI/MSS - MLH1

- KRAS - BRAF

Genomics:

- Comparative genomic hybridisation microarray (arrayCGH)

Examine study data Overall survival summary statistics in ‘Results’

Comparison of different groups Overall survival in subjects with MSI vs MSS

Comparison of different groups Overall survival in subjects with MSI vs MSS

Survival analysis Overall survival of subjects < and >60 years of age

Survival analysis Overall survival of subjects < and >60 years of age

Comparison of chromosomal alterations

between different groups Are there significant differences between two groups, e.g. MSS vs MSI?

Chromosomal alterations and overall survival

• Most common cancer in men (>900 K ww cases p.a.)

• Every 2.5 minutes a man is newly diagnosed

• Every 19 minutes a man dies from prostate cancer

• Ageing population

The Prostate Cancer Crisis: Statistics

Rudolph Guiliani

diagnosed at age 56

Andrew Lloyd

Webber

diagnosed at age 61

Ryan O’Neal

diagnosed at age

70

Warren Buffet

diagnosed at age

81

26

Data collection

MRI

UltraSound

Clinical

Urine Blood

Tissue

27

Examine study data: summary statistics

Comparison of readcounts (RNA-Seq) between

different groups

Conclusions demo sessions June-Sep 2013

Praise:

• "Oh, wow, you just dragged that in!", "I've never been able to

do this“

• "This is already great for exploring data.“

Conclusions demo sessions June-Sep 2013

But also many new wishes & issues identified:

• Improve user interface

– Standard navigation for all studies

– Zoom in/select (group of) subjects from any plot

• Basic functionality for facilitating data exploration to be

extended

– Better handling of units

– Stratification

– Combinations

• Improve genome/chromosome viewing

– Implement standard genome browser

• Important data sets are still missing

Projects are still not actively using tranSMART

Further roadmap

Current portfolio of projects for a tranSMART implementation:

• DeCoDe: Colorectal cancer (demonstrator available)

• PCMM: Prostate cancer consortium (demonstrator available)

• Maastricht Study: A longitudinal diabetes study

• POSEIDON: A national registry for outcome data in lung cancer

• NKI: Internal data warehouse Netherlands Cancer Institute

• And many more in the queue…….

Each project has specific user stories requiring new features

Currently app. 200 resulting epics on the roadmap

Improvement theme: data security

Data security is number one concern for principal investigators

Study

1

Study

2

Study

…

Inter-study security

Intrusion protection

Intra-study

security

Improvement theme: molecular viewing

wet-lab-person

tech-operator

(bio)informatician

PI / (end)user

PI / (end)user

Recent work: Include Dalliance Genome browser

cBioportal example for molecular viewing

molecular data integration

Processed data Import to TranSMART Suitable for molecular data integration Suitable for viewer Suitable for data querying

Improvement themes: Longitudinal data

Timeline of disease progression

Diagnosis

Surgery

Chemo

Observational studies tend to demand flexible identification of patient events

New use cases: sample data

CBM-NL Summary data

about samples

tranSMART Integration &

study workspace

Biobank

Information

System

Biobank

Information

System

Collect sample summary data

Sample order process

System integration and referenced data

Referencing clinical

images based on meta

data in tranSMART

Referencing pathology

scans based on meta

data in tranSMART

Upload and drill-down into

molecular pipelines using

tools like R and Galaxy

Automated upload of

clinical data from OpenClinica

TraIT/tranSMART at the Netherlands Cancer

Institute

Jelle ten Hoeve

The Netherlands Cancer Institute

• 650 employees • Budget: € 80 million/year • 34 professors • 50 PIs (group leaders) in basic research • 33 PIs in clinical research • distribution among positions in basic research

other; 3% group leader; 7%

postdoc; 31%

PhD student; 29%

technician; 31%

November 2012

+ AvL hospital = Comprehensive Cancer Center

High Performance Computing at NKI-AvL

- 10 High Performance Computers (HPCs) and the Life Science Grid - Each HPC: 32-64 cores, 128-512 GB RAM, 20-40 TB storage - 50 research end users - Linux / Ubuntu, R, Matlab and specialized bioinformatics tools - Support together with IT department

Support

Infrastructure

A Research Datawarehouse stores and integrates research data from many data sources across data domains and makes these accessible to researchers. The main challenges for implementing a research datawarehousing are: • Storage: secure central storage of research data • Search and access: govern search of, and data access to, research data • Data integration: integrate research data across projects and domains • System integration: integrate data from clinical and laboratory software • Sustainability: embed into existing IT architecture and into the organization at

large To clarify the concept ‘research data’, we define ‘data domains’ and ‘data sources’. Data sources can be categorized into three categories: ‘project’ data sources, ‘registry’ data sources, and ‘workflow’ data sources.

Translational Research Datawarehouse

Project Ready Domain # patients

Kinome Yes Clinical, Biobank, Pathology, Molecular

2,500

NKI295 Yes Clinical, Biobank, Pathology, Molecular

295

BOSOM Yes Clinical, Molecular 8,000

MindAct Yes Clinical, Molecular 6,000

Many more …

Data source Domain Department # patients (per year)

EZIS (Electronic Hospital Records)

Clinical Hospital 8,000

Tumor registry All Dept. of Biometrics

PALGA, LMS, MolPA Pathology, Biobanking

Dept. of Pathology 80,000

ART Pathology,Biobanking

Biobanking Core Facility

5,000

Array and BAM repositories

Molecular (Clinical) Genomic Core facility

3,000

Many more ….

IT systems and Curated databases

Clinical and research studies

TransMartEndusers

Groupleaders(clinical)researchers

Pa entSelec on

Researchers

Browse/Extract

ResearchersDatamanagers

Templates

Upload

DATAGOVERNANCE

- QualityControl- Development- Support

Translational Research Datawarehouse

Project Ready Domain # patients

Kinome Yes Clinical, Biobank, Pathology, Molecular

2,500

NKI295 Yes Clinical, Biobank, Pathology, Molecular

295

BOSOM Yes Clinical, Molecular 8,000

MindAct Yes Clinical, Molecular 6,000

Many more …

Data source Domain Department # patients (per year)

EZIS (Electronic Hospital Records)

Clinical Hospital 8,000

Tumor registry All Dept. of Biometrics

PALGA, LMS, MolPA Pathology, Biobanking

Dept. of Pathology 80,000

ART Pathology,Biobanking

Biobanking Core Facility

5,000

Array and BAM repositories

Molecular (Clinical) Genomic Core facility

3,000

Many more ….

ETLs, ETLs, ETLs

IT systems and Curated databases

Clinical and research studies

What do we expect from our community?

Jelle ten Hoeve Project leader NKI

Robbert Hardenberg Integration specialist NKI

Jan Hudecek Scientific programmer NKI

Marco Janssen QQ TraIT WP5 Philips

• A comprehensive Datawarehouse (Clinical + Research data) • Active directory and user roles • ETL tooling • “State of the art” exploration of data and basic analysis • Bioinformatician API (TranSMART R/BioC package) • Upload support for end users - stepwise data upload

And many more…

Acknowledgements