Stephen Friend Nov 28-29, 2011. Institute of Development, Aging and Cancer, Sendai, Japan

1. Use of Bionetworks to build maps of disease:
Moving beyond the linear
Integrating layers of omics data models
and use of compute spaces
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam
International Symposium for 70th Anniversary IDAC
November 29, 2011

3. Alzheimer’s Diabetes
Treating Symptoms v.s. Modifying Diseases
Cancer Obesity
Will it work for me?
Biomarkers?

7. Why not use data intensive science
to build models of disease?
Current Reward Structures
Organizational Structures and Tools
Pilots

8. What is the problem?
Most approved therapies assume indications would represent
homogenous populations
Our existing disease models often assume pathway
knowledge sufficient to infer correct therapies

9. Personalized Medicine 101:
Capturing Single bases pair mutations = ID of responders

10. Reality: Overlapping Pathways

11. The value of appropriate representations/ maps

13. “Data Intensive” Science- Fourth Scientific Paradigm
Equipment capable of generating
massive amounts of data
IT Interoperability
Open Information System
Host evolving computational models
in a “Compute Space”

16. WHY NOT USE
“DATA INTENSIVE” SCIENCE
TO BUILD BETTER DISEASE MAPS?

17. what will it take to understand disease?
DNA RNA PROTEIN (dark matter)
MOVING BEYOND ALTERED COMPONENT LISTS

18. 2002 Can one build a “causal” model?

19. How is genomic data used to understand biology?
RNA amplification
Tumors
Microarray hybirdization
Tumors
Gene Index
Standard GWAS Approaches Profiling Approaches
Identifies Causative DNA Variation but Genome scale profiling provide correlates of disease
provides NO mechanism  Many examples BUT what is cause and effect?
 Provide unbiased view of
molecular physiology as it
relates to disease phenotypes
trait
 Insights on mechanism
 Provide causal relationships
and allows predictions
19
Integrated Genetics Approaches

20. Gene Co-Expression Network Analysis
Define a Gene Co-expression Similarity
Define a Family of Adjacency Functions
Determine the AF Parameters
Define a Measure of Node Distance
Identify Network Modules (Clustering)
Relate the Network Concepts to
External Gene or Sample Information 20
Zhang B, Horvath S. Stat Appl Genet Mol Biol 2005

21. Constructing Co-expression Networks
Start with expression measures for genes most variant genes across 100s ++ samples
1 2 3 4 Note: NOT a gene
expression heatmap
1
1 0.8 0.2 -0.8
Establish a 2D correlation matrix 2
for all gene pairs
expression
0.8 1 0.1 -0.6
3
0.2 0.1 1 -0.1
4
-0.8 -0.6 -0.1 1
Brain sample
Correlation Matrix
Define Threshold
eg >0.6 for edge
1 2 4 3 1 2 3 4
1 1
1 4 1 1 1 0 1 1 0 1
2 2
1 1 1 0 1 1 0 1
1 1 1 0 Hierarchically 3
Identify modules 4 0 0 1 0
2 3 cluster
4
3 0 0 0 1 1 1 0 1
Network Module Clustered Connection Matrix Connection Matrix
sets of genes for which many
pairs interact (relative to the
total number of pairs in that
set)

22. Preliminary Probabalistic Models- Rosetta /Schadt
Networks facilitate direct
identification of genes that are
causal for disease
Evolutionarily tolerated weak spots
Gene symbol Gene name Variance of OFPM Mouse Source
explained by gene model
expression*
Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics
Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics
Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg
Mirochnitchenko (University of
Medicine and Dentistry at New
Jersey, NJ) [12]
Lactb Lactamase beta 52% tg Constructed using BAC transgenics
Me1 Malic enzyme 1 52% ko Naturally occurring KO
Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple
(UCLA) [13]
Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg
(Columbia University, NY) [11]
C3ar1 Complement component 46% ko Purchased from Deltagen, CA
3a receptor 1
Tgfbr2 Transforming growth 39% ko Purchased from Deltagen, CA
Nat Genet (2005) 205:370 factor beta receptor 2

23. Extensive Publications now Substantiating Scientific Approach
Probabilistic Causal Bionetwork Models
• >80 Publications from Rosetta Genetics
Metabolic "Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003)
Disease "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
CVD "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
Bone "Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005)
d
..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009)
Methods "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.

24. List of Influential Papers in Network Modeling
 50 network papers
 http://sagebase.org/research/resources.php

25. (Eric Schadt)

26. “Data Intensive” Science- Fourth Scientific Paradigm
Score Card for Medical Sciences
Equipment capable of generating
massive amounts of data A-
IT Interoperability D
Open Information System D-
Host evolving computational models
in a “Compute Space F

27. .
We still consider much clinical research as if
We were hunter gathers - not sharing

28. TENURE
FEUDAL
STATES

29. Clinical/genomic data
are accessible but minimally usable
Little incentive to annotate and curate
data for other scientists to use

30. Mathematical
models of disease
are not built to be
reproduced or
versioned by others

31. Assumption that genetic alterations
in human conditions should be owned

32. Lack of standard forms for future rights and consents

33. sharing as an adoption of common standards..
Clinical Genomics Privacy IP

34. Publication Bias- Where can we find the (negative) clinical data?

35. Sage Mission
Sage Bionetworks is a non-profit organization with a vision to
create a commons where integrative bionetworks are evolved by
contributor scientists with a shared vision to accelerate the
elimination of human disease
Building Disease Maps Data Repository
Commons Pilots Discovery Platform
Sagebase.org

36. Sage Bionetworks Collaborators
 Pharma Partners
 Merck, Pfizer, Takeda, Astra Zeneca,
Amgen, Johnson &Johnson
 Foundations
 Kauffman CHDI, Gates Foundation
 Government
 NIH, LSDF
 Academic
 Levy (Framingham)
 Rosengren (Lund)
 Krauss (CHORI)
 Federation
 Ideker, Califarno, Butte, Schadt 36

37. NEW MAPS
Disease Map and Tool Users-
( Scientists, Industry, Foundations, Regulators...)
PLATFORM
Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)
PILOTS= PROJECTS FOR COMMONS
Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....)
NEW TOOLS
ORM
APS
Data Tool and Disease Map Generators-
(Global coherent data sets, Cytoscape,
M
F
PLAT
Clinical Trialists, Industrial Trialists, CROs…)
NEW
RULES GOVERN RULES AND GOVERNANCE
Data Sharing Barrier Breakers-
(Patients Advocates, Governance
and Policy Makers, Funders...)

38. Alzheimer’s Disease
• Cross-tissue coexpression networks for both
normal and AD brains
– prefrontal cortex, cerebellum, visual
cortex
• Differential network analysis on AD and
normal networks
• Integrate coexpression networks and
Bayesian networks to identify key regulators
for the modules associated with AD
38

39. Identification of Disease (AD) Pathways via Comparative
Gene Network Analysis
40,000 genes from three tissues
Glutathione transferase
Gain connectivity by 91 fold
AD
(PFC, CB, VC)
nerve ensheathment
Control
Lose connectivity by 40%
(PFC, CB, VC)
Module Connectivity Change (AD/Normal)
39
Bayesian Subnetworks

40. Key Regulators
GlutathioneTransferase NerveEnsheathment ExtracellularMatrix PECAM1: Platelet-endothelial cell
adhesion molecule, a tyrosine
phosphatase activator that plays a role
in the platelet activation, increased
expression correlates with MS, Crohn
disease, chronic B-cell leukemia,
rheumatoid arthritis, and ulcerative
colitis
ENPP2: Phosphodiesterase I alpha, a
lysophospholipase that acts in
chemotaxis, phosphatidic acid
biosynthesis, regulates apoptosis and
PKB signaling; aberrant expression is
associated with Alzheimer type
dementia, major depressive disorder,
and various cancers
SLC22A25: solute carrier family 22,
member 25, Protein with high similarity
to mouse Slc22a19, which is a renal
steroid sulfate transporter that plays a
role in the uptake of estrone sulfate,
member of the sugar (and other)
transporter family and the major
facilitator superfamily
Glutathione Transferase Module (Pink)
• 983 probes from all three brain regions (9% from CB, 15% from PFC and 76% from VC)
40
• Most predictive of Braak severity score

42. The Federation

43. (Nolan and Haussler)

44. sage federation:
model of biological age
Faster Aging
Predicted Age (liver expression)
Slower Aging
Clinical Association
- Gender
- BMI
- Disease
Age Differential Genotype Association
Gene Pathway Expression
Chronological Age (years)

45. Non-Responders Project
To identify Non-Responders to approved
Oncology drug regimens in order to improve
outcomes, spare patients unnecessary toxicities
from treatments that have no benefit to them, and
reduce healthcare costs

46. The Non-Responder Cancer Project Leadership Team
Stephen Friend, MD, PhD
Todd Golub, MD
Founding Director Cancer Biology
President and Co-Founder of
Program Broad Institute, Charles Dana
Sage Bionetworks, Head of
Investigator Dana-Farber Cancer
Merck Oncology 01-08,
Institute, Professor of Pediatrics Harvard
Founder of Rosetta
Medical School, Investigator, Howard
Inpharmatics 97-01, co-
Hughes Medical Institute
Founder of the Seattle Project
Richard Schilsky, MD
Garry Nolan, PhD Chief, Hematology- Oncology, Deputy
Professor, Baxter Laboratory of Stem Director, Comprehensive Cancer
Cell Biology, Department of Microbiology Center, University of Chicago; Chair,
and Immunology, Stanford University National Cancer Institute Board of
Director, Proteomics Center at Stanford Scientific Advisors; past-President
University
ASCO, past Chairman CALGB clinical
trials group
11

48. Why not share clinical /genomic data and model building in the
ways currently used by the software industry
(power of tracking workflows and versioning

49. Leveraging Existing Technologies
Addama
Taverna
tranSMART

50. sage bionetworks synapse project
Watch What I Do, Not What I Say Reduce, Reuse, Recycle
My Other Computer is Amazon
Most of the People You Need to Work with
Don’t Work with You

51. Networking Disease Model Building

52. Why not use data intensive science
to build models of disease
Current Reward Structures
Organizational Structures and Tools
Pilots
Opportunities
IMPACT ON PATIENTS