A new community based vision of open access innovation in

personalized medicine

Stephen H Friend MD PhD President Sage Bionetworks

Non-Profit Organization Seattle/ Amsterdam/ Beijing

Gastein Oct 4, 2012

MISSION IMPOSSIBLE

NOT

MISSION IMPOSSIBLE

NOT

1. It’s going to be harder than you think but inevitable

2. Without deep citizen activation it will be unaffordable

3. Sharing data and models between researchers especially between and within Universities will need to fundamentally change

x

Seattle

Gastein

The value of appropriate representations/ maps

DIVERSE POWERFUL USE OF MODELS AND NETWORKS

"Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003)

"Variations in DNA elucidate molecular networks that cause disease." Nature. (2008)

"Genetics of gene expression and its effect on disease." Nature. (2008)

"Validation of candidate causal genes for obesity that affect..." Nat Genet. (2009)

….. Plus 10 additional papers in Genome Research, PLoS Genetics, PLoS Comp.Biology, etc

"Identification of pathways for atherosclerosis." Circ Res. (2007)

"Mapping the genetic architecture of gene expression in human liver." PLoS Biol. (2008)

…… Plus 5 additional papers in Genome Res., Genomics, Mamm.Genome

"Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005)

“..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009)

"An integrative genomics approach to infer causal associations ...” Nat Genet. (2005)

"Increasing the power to detect causal associations… “PLoS Comput Biol. (2007)

"Integrating large-scale functional genomic data ..." Nat Genet. (2008)

…… Plus 3 additional papers in PLoS Genet., BMC Genet.

d

Metabolic

Disease

CVD

Bone

Methods

Extensive Publications now Substantiating Scientific Approach

Probabilistic Causal Bionetwork Models

•>80 Publications from Reseach

50 network papers

http://sagebase.org/research/resources.php

List of Influential Papers in Network Modeling

Background: Information Commons for Biological Functions

INFORMATION COMMONS

SYNAPSE

CURATED

DATA

TOOLS/

METHODS

ANALYSES/

MODELS

RAW

DATA

BioMedicine Information Commons

Data

Generators

Data

Analysts

Experimentalists

Clinicians

Patients/

Citizens

Networked Approaches

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MISSION IMPOSSIBLE

NOT

1. It’s going to be harder than you think but inevitable

2. Without deep citizen activation it will be unaffordable

3. Sharing data and models between researchers especially between and within Universities will need to fundamentally change

.

We still consider much clinical research as if we were

“hunter gathers”- not sharing

TENURE FEUDAL STATES

Sage Mission

Sage Bionetworks is a non-profit organization with a vision to

create a “commons” where integrative bionetworks are evolved by

contributor scientists and citizens

SYNAPSE

CURATED

DATA

TOOLS/

METHODS

ANALYSES/

MODELS

RAW

DATA

BioMedical Information Commons

Data

Generators

Data

Analysts

Experimentalists

Clinicians

Patients/

Citizens

Networked Team Approaches 2

PRIVACY BARRIERS

4 REWARDS

FOR SHARING

1 USABLE

DATA

3 HOW TO

DISTRIBUTE TASKS

5 EDUCATION

BIOINFORMATICS

22

SYNAPSE

GEEKS AND SCIENTISTS SANDBOX PLACE TO BUILD MODELS OF DISEASE

COMPONENTS NEEDED FOR NETWORKED APPROCHES TO BUILDING EVOLVING MODELS OF DISEASE: RESEARCH 2.0

Synapse Platform: a compute space for collaborative research

• Development of Robust, Reproducible, and Reusable analytical methods

• Integration of Data, Tools and Methods from across community

• Development of a Disease Model Repository

• Forum for New Collaborations between technically and geographically distinct scientific groups

• Access to Cloud-Compute resources co-located with large-scale data

synapse.sagebase.org 24

SYNAPSE

GEEKS AND SCIENTISTS SANDBOX PLACE TO BUILD MODELS OF DISEASE

PORTABLE

LEGAL CONSENT

ALLOWS PATIENT TO REQUEST DATA BACK GIVES CONTROL OF DATA TO PATIENT WHO CAN THEN SAY I WANT TO SHARE IT

COMPONENTS NEEDED FOR NETWORKED APPROCHES TO BUILDING EVOLVING MODELS OF DISEASE: RESEARCH 2.0

Tool: PORTABLE LEGAL CONSENT weconsent.us John Wilbanks

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• Online educational wizard • Tutorial video • Legal Informed Consent Document • Profile registration • Data upload

US- approved

Open and Networked Approaches- Regulatory issues and bottlenecks PRIVACY

BARRIERS

Yes- proceed No- Is data pseudonymized?

Is data anonymized?

Yes- Is it “sensitive” data

(health, genomic,..)

No

Yes

No- Will key to person’s ID

be shared with 3rd party?

No- Proceed with

appropriate safeguards

for data access and

safekeeping

Consent is required

27

SYNAPSE

GEEKS AND SCIENTISTS SANDBOX PLACE TO BUILD MODELS OF DISEASE

ALLOWS PATIENT TO REQUEST DATA BACK GIVES CONTROL OF DATA TO PATIENT WHO CAN THEN SAY I WANT TO SHARE IT

COMPONENTS NEEDED FOR NETWORKED APPROCHES TO BUILDING EVOLVING MODELS OF DISEASE: RESEARCH 2.0 INCLUDING CITIZENS: DEMOCRATIZATION OF MEDICINE

BRIDGE

ENGAGES CITIZENS AS PARTNERS PATIENTS, RESEARCHERS, FUNDERS

PORTABLE

LEGAL CONSENT

The Sage/Bionetworks/DREAM Breast Cancer Prognosis Challenge

Building Better Models of Diseases Together

Goal: Assess the accuracy of computational models designed to predict breast cancer survival based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles.

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USE OF CO-OPETITIONS

Sage-DREAM Breast Cancer Prognosis Challenge one month of building better disease models together

154 participants; 27 countries

334 participants; >35 countries

>500 models posted to Leaderboard

breast cancer data

Challenge Launch: July 17

Sep 26 Status

Caldos/Aparicio

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Targeted treatment and drug repositioning in type 2 diabetes using molecular disease signatures

Goal: identify pathophysiological subgroups of type 2 diabetes (T2D) to enable specific treatment targeted to the cellular disease mechanisms.

Physician

Patient

Researcher

Community based vision of open access innovation in personalized medicine 31

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32

ABCDE

“ugly duckling”

Dermoscopy

Pathology

Molecular

MD

There is no standard screening program for skin lesions; seeing an MD is self directed

Education is derived from top-down experiential knowledge

?Photos

HPI

Best accuracy of clinical diagnosis = 64% (Grin, 1990)

160k new cases/year 48k deaths in 2012 in US

Both intra- and inter- institutional data are siloed

MELANOMA

1. activated citizens take skin pictures

2. store tons of data!

3. run algorithmic challenges in the compute space

4. give back risk-assessment & education to the citizens

virtual cycle: continuous aggregation of data enriching the model

MELANOMA

The challenge of Open science Regulatory issues and bottlenecks

• Cultural barriers

• Lack of leadership

• Privacy barriers

• Complex, country-specific regulations try to codify ethical principals

35

Common areas of Concern with Genomic Data •Privacy •Research Oversight •Informed Consent •Data Stewardship

Enabling Cooperative Discovery Common Concerns with use of genomic data • Privacy • Research Oversight • Informed Consent • Data Stewardship

Common Concerns with sharing scientific data

• Being scooped • Loss of funding • Tenure denied • Publication record • Loss of potential profit • Lack of recognition • Loss of control

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Consent must be a freely given, unambiguous and specific. Consent may involve clicking an icon, sending an email or subscribing to a service. Consent can be withdrawn at anytime (research exemption).

Potential Issues:

• Single study focus: Use of existing data is often difficult due to consent language either too vague or obsolete.

• Re-consenting isn’t always feasible: Use of archival data and/or specimen collected from deceased individuals prior to genomics era.

• Consent conditional on guarantee of anonymity, privacy and confidentiality

Questions:

• Is the DNA data of a deceased 50 years old male, smoker, codename XY12ZS, identifiable data subject to consent requirement?

• How can we ensure optimal use of data expected by participants?

• How will standard information notice and consent keep up with new technologies?

Potential Opportunities:

• Promote continuous interaction between subject and researchers- educate

• Roll-out Portable Legal Consent within Europe

CONSENT

37

Appropriate technical and organizational measures shall be taken against unauthorized or unlawful processing of personal data and against accidental loss or destruction of, or damage to, personal data. Privacy by default and by design. Data controller is liable and accountable for data processor

Synapse safeguards: Multiple solutions to address compliance Potential Issues: • Guaranteed anonymity and privacy is a myth: Unintentional misuse of the data, accidental data

breach or intentional violation of terms may still occur whether the data is handled electronically or not.

• Enforcement challenges: Cannot police each activity from all users or assess the adequacy of data protection by each user in a open collaborative space.

• Obtaining written contracts with each users is a bottleneck- Questions: • Shouldn’t we focus on education rather than on unrealistic guarantees of privacy? • Will we introduce legislations that prevent discrimination based on personal data: Anti-

discrimination by default?

Anticipated actions: • Engage fines, exclusion, public shame as possible

responses to breach or violations

SAFEGUARDS

Personal data shall not be transferred to a country or territory outside the European Economic Area unless that country or territory ensures an adequate level of protection for the rights and freedoms of data subjects in relation to the processing of personal data.

Issue:

• Web technology doesn’t tie to geographical boundaries

• US Safe-harbor stamp from US department of commerce for e-commerce, not research.

• Restrict feasibility of international Challenge/modeling competitions

• Incompatible with Cloud computing for BIG DATA analysis

TRANSFER

Questions: • Will we need to restrict EU data to EU servers and • have them used by EU scientists only? • Will we need to split international datasets?

Anticipated actions: • Discuss possibility to certify non-EU data repositories for inclusion

and transfer of EU data. • Let subjects determine where their data can be used

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Cloud providers must provide information on How, Where and by Whom the data is being processed at all time. Cloud customers should perform a risk assessment related to cloud provider’s data protection practices. Rules on data transfer remain.

Potential Issues: • Based on single data user for single dataset • Cloud providers will not accept to host sensitive data if they are liable for misuse of

the data by their customers or sub-processors • Same resource for both data storage and data analysis • Data location: EU data on EU-CLOUD. What about non-EU data? • Roles and responsibilities for Synapse developer vs. Cloud provider and synapse users

Opportunities: EU could develop certification for CLOUD providers with respect to data protection Cloud use and data transfer limitations Should not be incompatible

CLOUD

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Users must be informed of use of cookies or similar devices and be allowed to opt-out

Potential issues:

• The need for transparency and accountability of Synapse users implies renouncing to privacy by design

– Synapse users must register

– Actions are tracked

– Access and Compliance Team can audit data use in Synapse

– Opting out is not allowed.

E-privacy

41

Anticipated actions: Explain full transparency existing in Synapse and other Information Commons and refuse access to users who opt-out

MISSION IMPOSSIBLE

NOT IF WE WORK TOGETHER

1. It’s going to be much harder than you think - but inevitable

2. Without deep citizen activation it will be unaffordable

3. Sharing data and models between researchers especially between and within Universities will need to fundamentally change