1. Sage Bionetworks:
BRIDGE
(are you making the right investments?)
Stephen H Friend
President Sage Bionetworks
(Non-Profit Foundation)

2. Options as a Citizen

3. Options as a Foundation

4. What will it take to understand disease?
Biobanks
RNA, DNA and proteins
Moving beyond altered
components lists

5. What will it take to understand disease?
Driver Mutations
Modifier Genes
Environmental factors
Context dependencies
Co-Medications
Pharmacogenomic factors
State of the Immune System

6. What will it cost to understand disease?

7. How can we afford to get there?
Institutional Extensions
Foundational Walled Gardens
Academic Consortia
New Proprietary Data
Aggregators

8. Five Powerful Convergence Breakthroughs
Enable some Alternative Paths
1- Now possible to generate massive amount of human “omic’s” data
2-“Top Down” Network Modeling for Diseases are emerging
3- IT Infrastructure and Cloud compute capacity allows a generative open approach to
biomedical problem solving
4- Nascent Movement for patients to Control Private information allowing sharing
5- Open Social Media allowing citizens and experts to use gaming to solve problems
THESE FIVE TRENDS TOGETHER CAN ENABLE AN OPEN COMMUNITY OF IMPATIENT CITIZENS
-- AS PATIENTS/RESEARCHERS/FUNDERS

9. The Biomedical Information Commons Alternative
Collecting
Storing Data
DataBiomedicine
Information
Commons
Processing
Sharing Data
Data
Commons are resources that are owned in common or shared among communities.
-David Bollier

10. Components of the Biomedical Commons
Data
Generators Patients/
Citizens
CURATED
DATA Data
TOOLS/ Analysts
METHODS
RAW
DATA
Clinicians
ANALYZES/
MODELS
SYNAPSE
Experimentalists

11. Why Sage Bionetworks?
We believe in a world where biomedical research has changed. It
will be conducted in an open, collaborative way where each of us
can contribute to making better, faster, relevant discoveries
We enable others We activate
• Develop platforms for We perform research • Diverse collaborations with
collaboration and • Leading computational individuals/researchers and
engagement – Synapse, biology research institutions to grow the
BRIDGE • Novel training and biomedical Commons together
• Defining governance internship programs • Crowdsourcing approaches to
approaches– PLC challenge the communities

12. So…What is BRIDGE?
A place where patients, researchers and
funders can collaborate to define and
contribute to research in their, and other
disease, communities
An online platform we are defining with five
disease communities and their launch
projects

13. What will BRIDGE give us?
Changing the research dialogue Sharing of data and
Rich data from a
research with a wider
wide participant base
audience
A networked team to
Crowdsourcing
collaborate and
method of research
Really involving Citizen-Patients learn

14. TO
CONSENT
RESEARCH
BRIDGE
Education
Surverys/Forums
Data Use Tracking
Games
The six domains
Learning From Adjacent
Diseases
BRIDGE’s main components and interactions
Crowdsourcing
14

15. Synapse is GitHub for Biomedical Data
“Synapse is a compute platform for transparent,
reproducible, and modular collaborative research.”
• Data and code versioned • Every code change versioned
• Analysis history captured in real time • Every issue tracked
• Work anywhere, and share the results with anyone • Every project the starting point for new work
• Social/Interactive Science • Social/Interactive Coding

16. Currently at 16K+ datasets and ~1M models

17. BRIDGE Seed Projects
Fanconi Diabetes
Melanoma
Anemia Activated
Hunt Community
Project
Breast Cancer Real Names
Genomic Parkinson’s
Research Project

18. CURRENT APPROACH –
BREAST CANCER GENOMIC RESEARCH

19. BREAST CANCER GENOMIC RESEARCH: CURRENT APPROACHES
1. Isloated
breast cancer
cohorts
2. Many funders,
many disparate
Funded researchers 3. Data objectives
4. Clinical/genomic is siloed
data are accessible
but minimally
useable
5. Little incentive to
annotate data and curate
for other scientists
6. Limited impact of 7. Many published
today’s fragmented breast cancer
data on standard-of- prognosis models
care improvements but little consensus
19
for breast cancer

20. BRIDGE APPROACH–
BREAST CANCER GENOMIC RESEARCH

21. BREAST CANCER PROGNOSIS “CO-OPETITIONS” TO BUILD BETTER
DISEASE MODELS TOGETHER
2. Core/surgical
biopsy
Path lab Novel Data usage
Clinical
informatics
1. Activated 8. Field-test best models
breast cancer in clinic and hospital
patients
3. Aggregate 7. Give back education
Com and risk assessment to
Findin
BC patient 5. Open community-
citizens
data via muni
Citizen based “co-opetitions”
gs
BRIDGE portal Portal forge new computational
ty models
Foru 6. “Co-opetitions”
leaderboard allows
4. BC data curated,
ms researchers to work
open and supported by together
analysis tools
21

22. Crowdsourced Research in Action
Sage Bionetworks- DREAM Breast Cancer Prognosis Challenge | The Dream Project 26/ 11/ 2012 11:39
Home Challenges Team Ranking Conferences Discussion Literature Reverse Engineering News Contact us Login / Register.
DREAM is a Dialogue for Reverse Engineering Assessments and Methods. The main
objective is to catalyze the interaction between experiment and theory in the area of cellular
network inference and quantitative model building in systems biology. A Model Challenge 26/ 11/ 2012 11:40
Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge
Click here to get started with the Sage Bionetworks - DREAM Breast Cancer Prognosis Challenge
NEW: Final phase of the challenge has started!
Science Translational Medicine Enter Search Term ADVANCED
AAAS.ORG FEEDBACK HELP LIBRARIANS
Announcement
1. To remind you, we have set a deadline of October 15 to receive all of your submitted models for scoring and for determining Challenge winners (using the METABRIC
data and then a little later this fall, using the Oslo-Val data). To make sure that none of you misses this crucial deadline, we will receive your models up to 11:59 pm
Pacific on October 15. Please don't miss this deadline!!
Sci TM Home Current Issue Rapid Publication Issue Archive Multimedia Sci TM Collections My Sci TM About Sci TM
2. To select the top model as assessed using METABRIC data, we will choose no more than 5 models from each individual or team.>Shortly Journals > Science Translational Medicine Hom e > 12 Septem ber 2012 >
Home Science
after the October 15 LaMarco, 4:(151): 151ec162
deadline, we will send out a message letting you know that unless we receive a note from you to the alternative, we will submit your 5 top-scoring models for the final
METABRIC model assessment (as listed on the October 15 leader board). Science Translational Medicine Prev | Table of Contents | Next
stm .sciencemag.org
Sci Transl Med 12 Septem ber 2012:
3. Please note that a key aspect of our judging procedure will be to confirm that your model code is readable and reusable (i.e., such that others could use it or combine it151ec162
Vol. 4, Issue 151, p.
Sci. Transl. Med. DOI: 10.1126/ scitranslmed.3004863
with their own code to build a new and potentially better model).
EDITORS' CHOICE
46 teams (or individuals)
Synopsis
COMPUTATIONAL BIOLOGY
A Model Challenge
The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical
>1700 models submitted
information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles. Kelly LaMarco
+ Author Affiliations
Background
Many outperformed clinical co-variance
What’ s first on the list in Robert Fulghum’ s book, All I Really Need to Know I Learned in Kindergarten?
Molecular diagnostics for cancer therapeutic decision-making are among the most promising applications of genomic technology. Several diagnostic tests have gained Second? “Play f air.” Designers of the open- science Sage/ DREAM Breast Cancer
“Share everything.”
regulatory approval in recent years. Molecular profiles have proved particularly powerful in adding prognosis information to standard clinical practice in breast cancer,
Prognosis Challenge learned these lessons well, and there is still tim e for other com putational
m odelers to join in the show- and- tell. This open com putational challenge to identify predictors of
predictions
using gene-expression-based diagnostic tests such as MammaPrint [1] and Oncotype Dx [2].
breast cancer progression is accepting subm issions of m odels until 15 October 2012.
Based on initial promising clinical results, computational approaches to infer molecular predictors of cancer clinical phenotypes are one of the most active areas of
Breast cancer is the second leading cause of cancer death am ong wom en in the United States. Despite
research in both industrial and academic institutions, leading to a flood of published reports of signatures predictive of cancer phenotypes. Several trends have that billions of dollars are spent each year on research and treatm ent, biom edical scientists
the fact emerged
through these numerous studies: 1) genes defining predictive signatures of the same phenotype often do not overlap across multiple studies; 2) predictive signaturesplete understanding of prognosis and survival rates, which vary greatly am ong patients.
have an incom
reported by one group may not prove robust in other studies; 3) there is no consensus regarding the most accurate signatures or computational methods for inferring Challenge is to use crowdsourcing to m old a com putational m odel that accurately
The goal of the
predicts breast cancer survival. Challenge participants are invited to use genom ic and clinical
predictive signatures; 4) there is no consensus regarding the added value of incorporating molecular data in addition to or instead of traditionally used clinical covariates.

23. CURRENT APPROACH–
MELANOMA SCREENING

24. MELANOMA Screening – Could it be better?
Education is derived Best accuracy of
from top-down clinical diagnosis =
experiential 64%
knowledge (Grin, 1990)
160k new cases/year
48k deaths in 2012
in US HPI
ABCDE Both intra- and
“ugly duckling” inter- institutional
MD Dermoscopy
Pathology
data are siloed
Molecular
?Photos
There is no standard
screening program for
skin lesions; seeing an
MD is self directed
24

25. BRIDGE APPROACH–
MELANOMA SCREENING

26. Initial focus on building the data needed
Novel Data collection 4. Give back risk-
+ Usage assessment & education
to the citizens
1.Activated citizens
take skin pictures
virtual cycle:
continuous
2. Store
tons of data!
aggregation of data
enriching the model
3. Run
algorithmic
cChallenges in
the compute
26
space

27. Data handling and governance
Data collection and storage Participant Consent
Genetic and other test
results
Electronic medical records
Journals – history and
progressions
Structured Surveys
Self-generated images

28. Next steps to Distributed Decoding of Diseases
Make the
benefit
Borrowing apparent
Finding
Adjacent
Next Gen
Reward
Foundations Shifting from
Structures
Finite to
Infinite
Finding Challenges
Activated
Communities BRIDGE

29. Sage Bionetworks:
BRIDGE
(are you making the right investments?)
How are you activating citizens?
How are you shifting rewards and
incentives?
Stephen H Friend
President Sage Bionetworks
(Non-Profit Foundation)