Stephen Friend, Dec 9, 2012. NRNB Symposium on Network Biology, San Francisco, CA

1. Corruption of Denial
Stephen Friend
Sage Bionetworks

2. Now possible to generate massive amount of human “omic’s” data

3. Network Modeling Approaches for Diseases are emerging

4. IT Infrastructure and Cloud compute capacity allows
a generative open approach to solving problems

5. Nascent Movement for patients to Control Sensitive information allowing sharing

6. Open Social Media allows citizens and experts to use gaming to solve problems

7. 1- Now possible to generate massive amount of human “omic’s” data
2-Network Modeling Approaches 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 Sensitive information
allowing sharing
5- Open Social Media allows citizens and experts to use gaming to
solve problems
A HUGE OPPORTUNITY -- A HUGE RESPONSIBILITY

13. ENVIRONMENT
Non-coding RNA network
BRAIN
HEART
ENVIRONMENT
GI TRACT
protein network
KIDNEY
ENVIRONMENT
metabolite network
IMMUNE SYSTEM
VASCULATURE
transcriptional network
ENVIRONMENT

17. .

18. TENURE FEUDAL STATES

20. • alchemist

21. The value of appropriate representations/ maps

22. OR

24. BUILDING PRECISION MEDICINE
Extensions of Current Institutions
Proprietary Short term Solutions
Open Systems of Sharing in a Commons

25. An Alternative
Biomedicine
Information
Commons
Commons are resources that are owned in common or shared among
communities.
-David Bollier

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

27. Collaborators
 Pharma Partners
 Merck, Pfizer, Takeda, Astra Zeneca,
Amgen,Roche, Johnson &Johnson
 Foundations
 Kauffman CHDI, Gates Foundation
 Government
 NIH, LSDF, NCI
 Academic
 Levy (Framingham)
 Rosengren (Lund)
 Krauss (CHORI)
 Federation
 Ideker, Califano, Nolan, Schadt, Vidal 27

28. Governance
Technology Platform
Impactful Models
Better Models of
Disease:
INFORMATION
COMMONS
Challenges

29. Two recurring problems in Alzheimer’s disease research
Ambiguous pathology
Are disease-associated molecular systems &
genes destructive, adaptive, or both?
Bottom line: We need to identify causal factors
vs correlative or adaptive features of disease.
Diverse mechanisms
How do diverse mutations and environmental
factors combine into a core pathology?
Bottom line: There is no rigorous / consistent global
framework that integrates diverse disease factors.
29

30. Identifying key disease systems and genes- Gaiteri et al.
1.) Identify groups of genes that move together – coexpressed “modules”
- correlated expression of multiple genes across many patients
- coexpression calculated separately for Disease/healthy groups
- these gene groups are often coherent cellular subsystems, enriched in one or
more GO functions
Example “modules” of coexpressed genes, color-coded

31. Identifying key disease systems and genes
1.) Identify groups of genes that move together – coexpressed “modules”
2.) Prioritize the disease-relevance of the modules by clinical and network measures
Prioritize modules through expression
synchrony with clinical measures or
tendency too reconfigure themselves in
disease
vs

32. Identifying key disease systems and genes
1.) Identify groups of genes that move together – coexpressed “modules”
2.) Prioritize the disease-relevance of the modules by clinical and network measures
3.) Incorporate genetic information to find directed relationships between genes
Infer directed/causal relationships
Prioritize modules through expression
and clear hierarchical structure by
synchrony with clinical measures or tendency
too reconfigure themselves in disease incorporating eSNP information
(no hair-balls here)
vs

33. Example network finding: microglia activation in AD
Module selection – what identifies these modules as relevant to Alzheimer’s disease?
The eigengene of a module of ~400 probes correlates with Braak score, age, cognitive disease severity
and cortical atrophy. Members of this module are on average differentially expressed (both up- and
down-regulated).
Evidence these modules are related to microglia function
The members of this module are enriched with GO categories (p<.001) such as “response to biotic
stimulus” that are indicative of immunologic function for this module.
The microglia markers CD68 and CD11b/ITGAM are contained in the module (this is rare – even when a
module appears to represent a specific cell-type, the histological markers may be lacking).
Numerous key drivers (SYK, TREM2, DAP12, FC1R, TLR2) are important elements of microglia signaling .
Alzgene hits found in co-regulated microglia module:

34. Figure key:
Five main immunologic families
found in Alzheimer’s-associated
module
Square nodes in surrounding network
denote literature-supported nodes.
Node size is proportional to
connectivity in the full module.
Core family members are shaded.
(Interior circle) Width of
connections between 5
immune families are
linearly scaled to the
number of inter-family
connections.
Labeled nodes are either highly
connected in the original network,
implicated by at least 2 papers as
associated with Alzheimer’s disease,
or core members of one of the 5
immune families.

35. Transforming networks into biological hypotheses

36. Testing network-based hypotheses

37. Design-stage AD projects at Sage
Fusing our expertise in… Gene regulatory networks
Diffusion Spectrum Imaging
Feedback
Microcircuits &
neuronal diversity
Join us in uniting genes, circuits and regions
to build multi-scale biophysical disease models.
Contact chris.gaiteri@sagebase.org

38. Tool: PORTABLE LEGAL CONSENT
Control of Private information by Citizens allows sharing
weconsent.us
John Wilbanks
John Wilbanks • Online educational wizard
TED Talk • Tutorial video
• Legal Informed Consent Document
“Let’s pool our medical data” • Profile registration
weconsent.us • Data upload

39. two approaches to building common
scientific and technical knowledge
Every code change versioned
Every issue tracked
Text summary of the completed project Every project the starting point for new work
Assembled after the fact All evolving and accessible in real time
Social Coding

40. Synapse is GitHub for Biomedical Data
• Every code change versioned
• Every issue tracked
• Every project the starting point for new work
• Data and code versioned • Social/Interactive Coding
• Analysis history captured in real time
• Work anywhere, and share the results with anyone
• Social/Interactive Science

41. Data Analysis with Synapse
Run Any Tool
On Any Platform
Record in Synapse
Share with Anyone

42. “Synapse is a compute platform
for transparent, reproducible, and
modular collaborative research.”

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

44. Download analysis and meta-analysis
Download another Cluster Result Download Evaluation and view more stats
• Perform Model averaging
• Compare/contrast models
• Find consensus clusters
• Visualize in Cytoscape

45. Pancancer collaborative subtype discovery

46. Objective assessment of factors influencing model
performance (>1 million predictions evaluated)
Sanger CCLE
Cross validation prediction accuracy (R2)
Prediction accuracy
improved by…
Not discretizing
data
Including
expression data
Elastic net
regression
130 compounds In Sock Jang 24 compounds

48. Erich Huang, Brian Bot, Dave Burdick

50. Sage-DREAM Breast Cancer Prognosis Challenge
one month of building better disease models together
Caldos/Aparicio
breast cancer data
154 participants; 27 countries
334 participants; >35 countries
Sep 26 Status
Challenge Launch: July 17
>500 models posted to Leaderboard
Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge Phase 2 Best Performing Te

51. Sage Bionetworks-DREAM Breast Cancer Prognosis
Challenge
Phase 2 Best Performing Team: Attractor Metagenes
Team Members: Wei-Yi Cheng, Tai-Hsien Ou Yang, and
Dimitris Anastassiou
Affiliation: Center for Computational Biology and
Bioinformatics and Department of Electrical Engineering,
Columbia University

52. How to disrupt the System?
Build a way for the patients actively to engage their
insights in real-time around what is happening to
them ( their state of wellness or disease) where their
narratives, samples, data, insights, and funds are
shown to enable decision making in what they should
do, what treatments they need

54. BRIDGE Seed Projects
Fanconi Diabetes
Melanoma
Anemia Activated
Hunt Community
Project
Breast
Real Names
Cancer
Parkinson’s
Genomic
Project
Research
54

55. BREAST CANCER GENOMIC RESEARCH: CURRENT APPROACHES
1. Isloated
breast cancer
cohorts
2. Many funders,
many disparate
objectives
Funded researchers 3. Data
is siloed
4. Clinical/genomic
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
for breast cancer
patients 55

56. BRIDGE APPROACH: BREAST CANCER PROGNOSIS “CO-OPETITIONS”
TO BUILD BETTER DISEASE MODELS TOGETHER
2. Core/surgical
biopsy
Path lab
Clinical
informatics
1. Activated
8. Field-test best models
breast cancer
in clinic and hospital
patients
3. Aggregate
BC patient Com 7. Give back education
Findings
data via and risk assessment to
muni
Citizen citizens
BRIDGE portal
ty
Portal
5. Open community-
Foru based “co-opetitions”
ms forge new computational
models
4. BC data 6. “Cco-opetitions”
curated, open leaderboard allows
and supported researchers to work
by analysis tools together
56

57. 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
57

59. 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 aggregation of data
tons of data!
enriching the model
3. Run
algorithmic
cChallenges in
the compute
space 59

60. Now possible to generate massive amount of human “omic’s”
data
Network Modeling for Diseases are emerging
IT Infrastructure and Cloud compute capacity allows
a generative open approach to biomedical problem solving
Nascent Movement for patients to Control Private information
allowing sharing
Open Social Media allowing citizens and experts to use gaming
to solve problems
THESE FIVE TRENDS CAN ENABLE AN OPEN COMMUNITY OF
IMPATIENT CITIZENS-- AS PATIENTS/RESEARCHERS/FUNDERS

61. DYNAMIC MULTI-SCALE PATIENT COMMUNITIES
ENABLING REAL TIME LEARNING
USING OPEN APPROACHES
DRIVEN BY “INFINITE CHALLENGES”

62. Navigating between states
Normal State
Disease State
Rui Chang et al. PLoS Computational Biology

63. CORRUPTION OF DENIAL

64. CORRUPTION OF DENIAL
Complexity of systems
Proximity of Solutions
Sufficiency of current phenotypic data and
appropriateness of role of patients
Effectiveness of how we work with big Data

65. " #$$%&'! Bob Young
Top Hat
my Jane McConigal
gene IoF
my Wadah Khanfar
Al Jazeera
dat a
Patrick Meier
m y
paper
Ushhidi
Jennifer Pahlka
Code for America
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Keyn ot e Sp eak er s: Law r en ce Lessi g – author “The future of ideas” &“Remix”
Jam i e Heyw ood – patients like me Lan ce Ar m st r on g – LiveStrong Davi d Hau ssl er - UCSC
Genome Browser Jam es Boyl e – Duke Law School Ad r i en Tr eu ille –Foldit
Sage Commons Congress – San Francisco April 19-20
! " #$%'$( ) *+% -" .*/&
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TenCong r ess i n SF!
Ear n one of t en t r i p s t o Com m ons
Young Investigator Awards
– t o ap p l y vi si t h t t p :/ / b i t .l y/ 2012YIA!