Life science eresearch platform Marienne E Hibbert

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Life science e-research platform Marienne
E Hibbert
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BioGrid Australia

• Victorian and Australian Government investment
• First cross-institution, cross-discipline
  clinical research data integration platform (IBM)
• Nightly upload of data on-site
• No identified health data leaves the site
• No health data stored centrally
• Individual records linked via linkage key

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BioGrid Platform

• Move from paper ..
• Data collected at source
• Clinical outcomes
• Linked Biospecimens, Genomics, Images, Death,
  Statewide data.
• Dynamic queries
• Privacy protected

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Publish
And
Present
Collect Data Clinical Scientific Tools
TITLE OF SLIDE
Body copy here. Lorum ipsum id sub quanto
cerberus.
Collaborative Research Analysis
Virtual Data
Store
Research Tools Discovery Access Tools
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Why ?

• Research power
• Increase the sample size
• Increase the potential for research
  collaborations
• Link specialist databases covering common
  diseases
• Screening activity - Genetic predisposition
• Environmental exposures
• Genomics, proteomics epigenetics
• Co morbidities
• Quality and audit
• Treatment strategies
• Outcomes

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Scope - Collaborations
Other Australian states NSW St Vincents
POW Tas RHH Act Canberra SA
Flinders Royal Adelaide
Queen Elizabeth Lyell McEwin
RACs
Victorian Sites Melbourne Austin Western Peter
Mac Alfred St Vincents Monash RCH RWH Box
Hill Peninsula Barwon-tbc Ballarat Latrobe Bendig
o Hume DHS Uni of Melbourne WEHI LICR
BioGrid
De-identified data
Sites International in progress USA
Michigan Moffitt Vanderbilt
Venter Brazil Sao Paulo New Zealand ?
Researcher
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Scope - Datasets
BioGrid

• Neuroscience
• Epilepsy
• Neuropsychiatry
• MS
• Stroke

• Cancer
• Colorectal
• Brain
• Breast
• Lung
• Sarcoma
• Gynaecology
• Prostate
• Head Neck
• Upper GI
• Melanoma
• Renal
• Prostate
• Rare

• Diabetes
• Type 1
• Type 2

De-identified data

• Other
• IBD - Crohns
• Cystic Fibrosis
• Well womens
• Population

Researcher
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Scope - Analysis
BioGrid

• Data
• Clinical outcome
• Treatment
• Genetic (Microarray, Biomarker, Proteomic)
• Images
• Biospecimen Banks

Medicare tbc
Deaths tbc
DHS
High Performance Computing

• Tools
• Bioinformatics
• Statistical
• Drug Discovery
• Image Analysis

De-identified data
Researcher
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Medical Research - the Challenges

• Large amount of data
• Lack of data standards
• Lack of interoperability between databases
• Need a cohesive approach between disciplines
• Ethics, Privacy and Regulation
• Who owns the Intellectual Property

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BioGrid processes

• Privacy, Ethics and authorisation
• BioGrid infrastructure processes must have
  Ethics approval
• Membership, IP and support
• BioGrid membership agreements signed
• Subscriptions
• Computer Systems
• Install computers
• Establish secure connectivity
• Research Collaboration

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How?
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Enabling Research
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Research Outcomes

• Cost benefit studies Colorectal Cancer
  screening
• Chemotherapy prescribing appropriate dosing
• Quality audits and feedback CSSANZ and other
• Referral practices
• Prediction of appropriate therapy epilepsy
• Extrapolation testing of clinical trials to
  clinical care

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Diabetes Research questions

• Diabetes and managing its effects
• Investigate the progression of symptoms such as
  neuropathy and eye disease in relation to blood
  glucose control using measurements taken over a
  20 year period (Prof. Peter Colman, Melbourne
  Health)

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Colorectal Cancer question - drug development

• BRAF mutation in 10 15 these tumours
• Developing BRAF inhibitor drug -
• How effective is a BRAF inhibitor drug in
  reducing the cancer growth?
• Target the patient group - only possible by
  linking tumour and clinical databases

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Brain Research questions

• Identifying brain tumours
• Linking PET images and MRI images to examine the
  uptake of radio-labelled amino acids and their
  possible use as markers to identify brain tumours
  (Eddie Lau, Peter MacCallum Cancer Centre)
•  Investigate brain changes in epilespy
• Use of MRI images to investigate longitudinal
  changes in the hippocampus of patients with
  Temporal Lobe Epilepsy (Dr. Sophie Adams,
  Melbourne Neuropsychiatry Centre)

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Epilepsy Research questions

• Targeting the best drug in epilepsy based on
  genetics
• Pharmaco-genetic study the effectiveness and side
  effects of epilepsy drugs based on a patients
  genetic profile, with the long-term goal of
  personalised prescribing for each patient (Dr.
  Terry OBrien and Slave Petrovski, Melbourne
  University and Melbourne Health)

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Diabetes Cancer Research question

• Diabetes and its impact on colorectal cancer
• Investigation of the effect of diabetes on
  survival and response to chemo-therapy in
  patients with colorectal cancer by linking
  BioGrids diabetes and colorectal cancer
  databases. (Dr. Suzanne Kosmider, BioGrid)

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RMH Neurosciences
Epilepsy Bioinformatics
Epilepsy Bioinformatics
BioGrid Australia
Slave Petrovski B. Sc (Honours) / B.
IS Department of Medicine (RMH/WH) University of
Melbourne

• Supervisors
• A/Prof. Terence J OBrien
• Prof. Richard Huggins
• Royal Melbourne Hospital
• Dr. Cassandra Szoeke
• Neurology Clinic

• Department of Medicine
• RMH/WH (UoM)
• BioGrid Australia
• Dr. Marienne Hibbert
• Henry Gasko
• Diana Salim

Menzies Research Institute Dr. Wendyl
DSouza Dept. Theoretical Physics (Lund
Uni.) Mattias Ohlsson Markus Ringner Jari Hakkinen
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Epilepsy Prevalence

• Affects 50 million people globally
• Non-Selective
• Anyone can be affected at any age
• Associated with increased risk of death
• Seizures in dangerous circumstances
• Prolonged Seizures
• Sudden Unexpected Death in Epilepsy (SUDEP)
• 70 achieve complete seizure control from
  anti-epileptic medication (AED)

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Epilepsy Treatment Outcomes

• Epilepsy treatment is limited by up to 40 of
• patients encountering
• Pharmacoresistance
• Attaining therapeutic levels
• Spontaneous seizure recurrence
• Adverse Drug Reactions (ADRs)
• Neurological
• Fatigue, Sedation, Depression, Neurocognitive,
  Memory/Concentration difficulties, Tremor
• Metabolic
• Weight Gain, Weight Loss
• Immunological
• Other

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SNPs

• Single nucleotide polymorphism.
• A DNA sequence variation in a single nucleotide
  (A/T/G/C) in the genome.
• Make up 90 of all human genetic variation (HGP).
• SNPs are a great value for biomedical research
  and for developing pharmaceutical
  products/medical diagnostics.

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Clinical Genotypic
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Pharmacogenomics of pharmacoreponsiveness

• Using 5 SNPs exclusively
• Detecting combination effects that provides a
  high predictive value.
• Combinatory model outperforms all of the single
  SNP analyses
• Novel approach to pharmacogenomic studies
• Petrovski et al., A multi-SNP pharmacogenomic
  classifier is superior to single SNP models for
  predicting drug outcome in complex diseases
  submitted

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Pharmacogenetics vs. Pharmacogenomics

• The study of the variation in drug metabolism
• and action amongst a population.
• Pharmacogenetics in epilepsy
• One gene theory
• Genetic limitations Polygenic phenotypes
• Pharmacogenomics in epilepsy
• Study of the entire spectrum of genes that may
  influence pharmacoresponsiveness
• 1st to look at combinatory gene effects

NEW
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Colorectal Cancer research Peter Gibbs Suzy
Kosmider Kathryn Field Julie Johns Ngio
Murigu Daniel Compston
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1. Colorectal Cancer Lymph node yield

• Large studies already carried out internationally

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Staging for colon cancer
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Lymph nodes harvested in patients at a number of
BioGrid sites
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Quick reponses

• Journal of the National Cancer Institute
• "Re Residual Treatment Disparities After
  Oncology Referral for Rectal Cancer"
• Field K, Kosmider S, Desai J, Lim L, Barnett F,
  McLaughlin S, Jones I, Gibbs P

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• 1992-1999 SEER data
• 2716 patients (1931 excluded)
• Patients 66 yo
• Resection of stage II or III rectal cancer
• Looked at disparities between black and white in
  receipt of adjuvant chemotherapy
• Younger, fitter black patients LESS likely to
  receive chemotherapy

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Physician recommendation and patient acceptance
of adjuvant therapy for stage II and III
colorectal cancer according to  tumor site,
patient age, and timing of therapy

• 4 hospitals
• Jan 2003 Feb 2008

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Clinical practise Issues with clinical trials

• Patients treated in trials are
• Younger (on average 10 years)
• Fitter (better performance status)
• Fewer co-morbidities
• And have normal organ function
• than patients in routine clinical practice

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Can we extrapolate trial results to the clinic?

• Are trial patients the same patient population as
  those treated in clinic?
• Can trial results and importantly the observed
  toxicities be inferred for those treated in
  clinic?

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Trial

• X-ACT Study

5FU

• Stage 3 colon cancer
• (node positive)
• 2000pts

XELODA
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Reality

• WH and RMH (200pts)
• Xeloda
• Median age
• X-ACT study 62yo (80 pts under 70)
• WH and MH 75 years
• Patients requiring a dose reduction
• 42 on trial
• 79 in reality

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Can we extrapolate trial results to the clinic?

• THE ANSWER IS NO

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• New prognostic markers for patients undergoing
  surgery for colorectal cancer are urgently sought
  
• Tumour stage
• Albumin level
• Survival data
• 378 patients with potentially curative resection
  of colon cancer between January 2003 and August
  2007.
• Median follow-up was 20.5 months.
• Annals of Surgery, Jan 2008

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Albumin and Overall Survival
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Diabetes Peter ColmanDiabetes Clinic Database
established 1998Gavin Becker and Doug Matthews

• Statistics/QA
• Tony Wohlers, Henry Gasko, Diana Salim

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Diabetes medical and economic cost of
complications

• Cardiovascularproblems
• Heart
• Stroke
• PVD
• Diabeticretinopathy

Diabetic neuropathy and foot problems
Renal disease
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Possible Intervention Studies
BETA CELL MASS
GENETIC PREDISPOSITION
INSULITIS BETA CELL INJURY
PRE-DIABETES
DIABETES
TIME
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Process of diabetes care reducing complications

• Much is process driven
• Well suited to automated data approach
• Assess performance regularly
• Follow trends in outcomes over time
• Identify subjects who may benefit from research
  into new treatments

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Further analyses underway

• Retinopathy
• Foot pathology
• Renal
• Medication usage
• Diabetes
• Other medications ACEi, A2RB, statins, aspirin
• Life expectancy

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Impact of Diabetes Mellitus on the development
and outcomes of Colorectal Cancer
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• Using the BioGrid Colorectal Cancer database over
  1000 patients with bowel cancer were analysed
• The impact of Diabetes Mellitus on tumour
  pathology and survival outcomes was examined

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Overall Survival Diabetes vs No-Diabetes
50 of diabetes patients died by 78 months Not
reached for non-diabetic patients
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• Diabetes is a strong risk factor for the
  development of Colorectal Cancer
• The expected incidence of diabetes would be lt20,
  however in our series 29
• Poorer survival for diabetes patients with
  Colorectal Cancer is likely multi-factorial

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Source Data Collection

• Data entered into source system
• Patient details
• Diagnosis
• Treatments Chemo, Radiotherapy etc.
• Treatment Outcomes

• Data used in Clinical care
• Review notes
• Plan treatment
• communication

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Data aggregation

• Data in de-identified form
• Research
• Quality and audit
• Linked records

Translate research into improving patient care
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www.biogrid.org.aumarienne.hibbert_at_biogrid.org.au
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BioGrid Linkage key Model Probabilistic matching
Authorised researchers query the Federated Data
Repository for analysis. They can only view the
USI and health data.
BioGrid
Data is loaded into institute-specificLocal
Research Repository nightly
Internet
FDI
De-identified Linked data
Query
LRR
Eg. LRH
Linkage Server
Patients are allocated a USI using 6 identifiers
to match with existing patients
The identifiers are sent to the Linkage server
where a key is allocated and sent back to the
LRR. The key in the LRR is encrypted for extra
security
Hospital
IT Services (MH WH NH)

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Technology robust options
Institution based Local Research Repository
ETL Tools
Institution based source data
ETL
Data Source
LRR
BioGrid Architecture

• Databases
• SQL Server
• IBM DB2
• Access
• Oracle
• Files
• MS Excel
• Other flat files

• IBM Ascential Datastage
• IBM DB2 Warehouse Center
• Microsoft SQL Server SSIS

• IBM DB2 UDB
• Microsoft SQL Server
• Oracle

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Current BioGrid Toolset
USI Generation Propagation
Metadata Discovery Server
USI Server

• Sun Java CAPS Integration Suite eIndex
• Oracle 9i

Data Discovery

• IBM DB2
• IBM Websphere Business Glossary

USI
XMETA
Data Analytics Server
Data Discovery and Query Server
Participating Institution Options
USIDB
Query
Query
FDIDB

• IBM Websphere Business Glossary
• SAS Enterprise Guide

Federated Data Integration Server

• SAS Enterprise Business Intelligence Server
• SAS Web Report Studio

• IBM DB2
• IBM Websphere Information Integrator

MS Internet Explorer v6 sp1
Researcher desktop
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The BioGrid Team

• Business and admin
• Robert Merriel (Chair of Committee)
• Marienne Hibbert (Project Director)
• Richard Tate
• Vicki Vlekkert

• Cancer
• Peter Gibbs (CSO)
• Jayesh Desai
• Suzy Kosmider
• Kathryn Field
• Julie Johns
• Ngio Murigu
• Sandy Dupuis
• Daniel Compston

• Technical
• Naomi Rafael
• Kee Ming Kong
• Pranabh Jain
• Xiaobin Shen
• Nelson Wu
• Jana Graenz

• Life Sciences
• Henry Gasko
• Diana Salim

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Who ?

• Monash MC
• Oncology
• Biospecimens
• Cystic Fibrosis

Authorised researchers and applications query
the Federated Data Repository for analysis.

• South Aust
• R Adelaide H
• Q Elizabeth H
• Flinders MC
• LMMC
• Oncology
• Cystic Fibrosis

• Box Hill
• Oncology

• St Vincents
• Neuroscience
• Diabetes
• Oncology

Terms Glossary

• Bendigo
• Oncology

• Tasmania
• RHH
• Diabetes
• Oncology

• Hume
• Oncology

• PeterMac
• Oncology
• Biospecimens
• PET images

Internet

• Gippsland
• Oncology

• ACT
• Canberra
• Oncology

• Alfred
• Cystic Fibrosis
• Neuroscience
• Oncology

Federated Data Integrator

• Grampians
• Oncology

Queries Analysis Reports

• NSW
• St Vincents
• POW
• Oncology
• Tissue Bank

de-identified data

• Barwon tbc
• Oncology

• Austin
• Oncology
• Biospecimens
• Diabetes

VPN - each site

• Peninsula
• Oncology
• Diabetes

• Queensland
• Brisbane tbc
• Oncology
• Epilepsy

• Western
• Oncology
• Biospecimens

Cabrini Oncology

• Epworth tbc
• Oncology

• Melbourne Oncology
• Biospecimens
• Diabetes
• Neuroscience
• MRI Images

• AIHW _at_ MH
• Death cause

Linkage Keys
Data cached on computers at the sites - owned and
controlled by site.
BioGrid Australia Health through information

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How is BioGrid novel?

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Governance
JVA Management Committee
Company Board
Appointment inter-relationships as specified in
BG Constitution
BioGrid
Unincorporated Joint Venture
BioGrid Service Company (Not for Profit
Proprietary Company Limited by Guarantee)
Service Level Agreement including IP License and
specification of services to be provided by
Service Company to JVA Members

• Foundation members invited
• Ludwig Institute for Cancer Research
• WEHI
• Melbourne University
• Melbourne Health
• Austin Health
• Bayside Health
• Peter MacCallum Cancer Institute
• Western Health
• Eastern Health
• Southern Health
• St Vincents Health

Simplified Deed of Accession
New Joining Member
New Joint Venture Agreement based upon
Collaboration Agreement
Constitution
Constitution to be approved by Joint Venture
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BioGridService Company Key Relationship
Agreements

Project Agreement
Company Board
Services e.g. For profit company
As required for different projects
Collaborative Research and Development
Agreement
BioGrid Aust Service Company
New Research Collaborator
Services provided in accordance with JVA and
Service Level Agreement between JVA and BioGrid
Service Company
If additional services required
JVA Member
Client Services Agreement
New Client
Project Services Agreement
Data Transfer Agreement
Melbourne Health (Host Organisation)
New Data Provider
Constitution
For a New Client, only applies to additional
services not included in standard Collaborative
Research and Development Agreement.
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Approval Process

• Management Committee
• Must approve
• data requested
• the project
• the researcher

• Researcher request Specify
• data required
• Science of project
• Agree to terms and conditions

Custodian Authorisation Must approve data
requested for the project

• Database administrator
• Creates logon for
• Specific data

• Reporting and auditing
• of all queries on the system
• of all users
• to all databases

• Reports all queries on the system
• To Management Committee
• To Ethics committees as required.
• To data custodians as required.

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Project tasks

• Privacy, Ethics and authorisation
• BioGrid infrastructure processes must have
  Ethics approval
• Membership, IP and support
• BioGrid membership agreements signed
• Subscriptions
• Computer Systems
• Install computers
• Establish Virtual Private networks
• Research Collaboration

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Data at the click(s) of a mouse
ONLINE ACCESS  to integrated clinical data on
patients with cancer (all 12 tumours), diabetes,
cystic fibrosis, stroke, asthma and epilepsy from
more than 120,000 patients and 1,900,000 clinical
records with up to 25 years clinical
history. DATA QUALITY  BioGrid systematically
collects clinical data from over 30 major
hospitals in Victoria and nationally with
information such as dates of diagnosis, clinical
symptoms, pathology, radiology reports,
biospecimen samples, genomic data, MRI images,
treatments drug therapy, radiotherapy and
surgical status in a privacy protected way for
authorised users.  DATA LINKAGE  Once the
researcher receives proper authorization to
access the BioGrid system, the clinical data from
different databases could be analysed across
different institutions and disease types as per
researcher needs. Questions such as What is the
probability of a patient with colon cancer
developing TYPE II Diabetes? could be answered
in minutes. DATA FLEXIBILITY The data is
updated every night and can be accessed 24/7 via
an online portal. It is also available to be
easily exported to statistical software of your
choice such as Excel, SPSS, etc. For more
complicated queries, expert BioGrid staff is
available to provide data mining and analysis
support.