Stephen Friend, Jan 21, 2012. Fanconi Anemia Research Fund, Houston, TX

1. Use of Bionetworks to Build Maps of Diseases
Moving Beyond
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ San Francisco
Fanconi Anemia Research Fund
Annual Planning Meeting
January 21, 2012

2. Academia

3. Biotech

4. Industry

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

6. Why consider the fourth paradigm- data intensive science?
thinking beyond the narrative, beyond pathways
advantages of an open innovation compute space
Where are the precompetitive goalposts?
it is more about how than what

7. Familiar but Incomplete

8. Reality: Overlapping Pathways

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

12. “Data Intensive Science”- “Fourth Scientific Paradigm”
For building: “Better Maps of Human Disease”
Equipment capable of generating
massive amounts of data
IT Interoperability
Open Information System
Evolving Models hosted in a
Compute Space- Knowledge Expert

13. It is now possible to carry out comprehensive
monitoring of many traits at the population level
Monitor disease and molecular traits in
populations
Putative causal gene
Disease trait

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

15. 2002 Can one build a “causal” model?

16. Data integration via Bayesian Network
Yeast segregants Public
databases
******BYRM
Synthetic complete Protein-protein
medium interations
Logorithm growth
Transcription
factor binding
Gene expression sites
genotypes
Yeast segregants
Protein
Metabolite
interations
Bayesian network
Courtesy of Dr. Jun Zhu

17. 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

18. Our ability to integrate compound data into our network analyses
db/db mouse
(p~10E(-30))
= up regulated
= down regulated
db/db mouse
(p~10E(-20)
p~10E(-100))
AVANDIA in db/db mouse

19. 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.

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

21. (INTEGRATED BIONETWORK)

22. Recognition that the benefits of bionetwork based molecular
models of diseases are powerful but that they require
significant resources
Appreciation that it will require decades of evolving
representations as real complexity emerges and needs to be
integrated with therapeutic interventions

23. 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

24. Sage Bionetworks Collaborators
 Pharma Partners
 Merck, Pfizer, Takeda, Astra Zeneca,
Roche, Amgen
 Foundations
 CHDI, Gates Foundation
 Government
 NIH, LSDF
 Academic
 Levy (Framingham)
 Rosengren (Lund)
 Krauss (CHORI)
 Federation
 Ideker, Califarno, Butte, Schadt 24

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

27. Example 1: Breast Cancer
Coexpression Networks
Module combination
Partition BN
Bayesian Network
Survival Analysis
27
Zhang B et al., manuscript

28. Generation of Co-expression & Bayesian Networks from
published Breast Cancer Studies
4 Public Breast Cancer Datasets
NKI: van de Vijver et al. A gene-expression
signature as a predictor of survival in breast
cancer. N Engl J Med. 2002 Dec 19;347
295 samples
(25):1999-2009.
Wang Y et al. Gene-expression profiles to
predict distant metastasis of lymph-node-
negative primary breast cancer. Lancet. 286 samples
2005 Feb 19-25;365(9460):671-9.
Miller: Pawitan Y et al. Gene expression
profiling spares early breast cancer patients
from adjuvant therapy: derived and 159 samples
validated in two population-based cohorts.
Breast Cancer Res. 2005;7(6):R953-64.
Christos: Sotiriou C et al.. Gene
expression profiling in breast cancer:
understanding the molecular basis of 189 samples
histologic grade to improve prognosis. J
Natl Cancer Inst. 2006 Feb 15;98(4):
262-72.

29. Recovery of EGFR and Her2 oncoproteins
downstream pathways by super modules

30. Comparison of Super-modules with EGFR and Her2
signaling and resistance pathways

31. Key Driver Analysis
• Identify key regulators for a list of genes h and a network N
• Check the enrichment of h in the downstream of each node in N
• The nodes significantly enriched for h are the candidate drivers
31

32. A) Cell Cycle (blue) B) Chromatin modification (black)
C) Pre-mRNA Processing (brown) D) mRNA Processing (red)
Global driver
Global driver & RNAi
validation
32

33. Signaling between Super Modules
(View Poster presented by Bin Zhang)

34. Clinical Trial Comparator Arm
Partnership (CTCAP)
 Description: Collate, Annotate, Curate and Host Clinical Trial Data
with Genomic Information from the Comparator Arms of Industry and
Foundation Sponsored Clinical Trials: Building a Site for Sharing
Data and Models to evolve better Disease Maps.
 Public-Private Partnership of leading pharmaceutical companies,
clinical trial groups and researchers.
 Neutral Conveners: Sage Bionetworks and Genetic Alliance
[nonprofits].
 Initiative to share existing trial data (molecular and clinical) from
non-proprietary comparator and placebo arms to create powerful
new tool for drug development.

35. Example 3: The Sage Federation
• Founding Lab Groups
– Seattle- Sage Bionetworks
– New York- Columbia: Andrea Califano
– Palo Alto- Stanford: Atul Butte
– San Diego- UCSD: Trey Ideker
– San Francisco: UCSF/Sage: Eric Schadt
– NEW LABS: Gary Nolan Stanford/ David Haussler UCSC
• Initial Projects
– Aging
– Diabetes
– Warburg
• Goals: Share all datasets, tools, models
Develop interoperability for human data

36. Federation s Genome-wide Network and
Modeling Approach
Califano group at Columbia Sage Bionetworks Butte group at Stanford

37. Genes Associated with Poor Prognosis are disproportionally
found among the networks regulating the glycolysis Genes
P-Value<0.005 Size of the node proportional to -log10 P value for recurrence free survival.
Inferred regulatory module for GGMSE Inferred regulatory module for Oxidative
Phosphorylation and Sphingolipid
>5 fold enrichment of recurrence free prognostic genes with
Metabolism genes
the Glycolysis BN module than random selection (p<1e-100)

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

39. Synapse as a Github for building models of disease

40. Evolution of a Software Project

41. Biology Tools Support Collaboration

42. Potential Supporting Technologies
Addama
Taverna
tranSMART

44. 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

45. Arch2POCM
Restructuring the “Competitive” Phase
of Drug Discovery

46. What is the described problem?
• Regulatory hurdles too high?
• Low hanging fruit picked?
• Payers unwilling to pay?
• Genome has not delivered?
• Valley of death?
• Companies not large enough to execute on strategy?
• Internal research costs too high?
• Clinical trials in developed countries too expensive?
In fact, all are true but none is the real problem

47. What is the real problem?
We need to rebuild the drug discovery process so that we
better understand disease biology before testing proprietary
compounds on sick patients

48. The solution – Arch2POCM
1. Create an Archipelago of clinicians and scientists from public
and private sectors to take projects from ideas to Proof of
Clinical Mechanism (POCM)
2. Arch2POCM is a collaborative, data-sharing network of
scientists, whose drug discovery objective is to use robust
compounds against new targets to disentangle the complexity
of human biology, not to create a medicine
3. Success?
• A compound that provides proof of concept for a novel target-
allowing companies to use this common information to compete,
with dramatic increased chances of success
• Culling targets with doomed mechanisms before multiple companies
waste money exploring them - at $50M a pop

49. Why data sharing through to Phase IIb?
• Most rapidly reveals limitations and opportunities associated with the
target
• Increases probability of success for internal proprietary programs
• Scientific decisions are not influenced by market considerations or
biased internal thinking
• Target mechanism is only properly tested at Phase IIb

50. Why no IP on “Common Stream” compounds?
• Allows multiple groups to test diverse indications without funds
from Arch2POCM- crowdsourcing drug discovery
• Broader and faster data dissemination
• Far fewer legal agreements to negotiate
• Generates “freedom to operate” on target because there are
no patent thickets to wade through
• Efficient way to access world’s top scientists and doctors
without hassle

51. Existing Team Ready to Execute

57. APRIL
20-­‐21

59. 2012-13: Year of Learning from our Pilots
Jan
12-­‐
APR
13
SAGE
RESEARCH
SYNAPSE
FEDERATION
CONSENTS
CTCAP
CITIZEN
LED
PROJECTS
SCIENTIST
LED
PROJECTS
SAGE
BIONETWORKS
WEBSITES/COMMONS
Arch2POCM
CONGRESS
“MedXChange-­‐Bridge”

60. Actionable Disease Bionetwork Models
Open Medical Information Systems
Democratization of Science
Networked Science Approaches
IMPACT
ON
PATIENTS

61. OPPORTUNITIES FOR FANCONI’S COMMUNITY
Evolve Sharing of Data sets, Tools and Models
Joining Synapse Communities
Buiding your own “Federation Projects”
Paticipate in Sage Commons Congress April 20-21
Joining Arch2POCM
Change reward structures for sharing data
(patients and academics)