Stephen Friend, Mar 12, 2012. Koo Foundation / Sun Yat-Sen Cancer Center, Taipei, Taiwan

1. Moving beyond linear investigations
Both of the science and of how we work
Integrating layers of omics data models and building
compute spaces capable of enabling models
to be evolved by teams of teams
Koo Foundation / Sun Yat-Sen Cancer Center
March12, 2012
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam

3. So
what
is
the
problem?
Most
approved
therapies
were
assumed
to
be
monotherapies
for
diseases
represen4ng
homogenous
popula4ons
Our
exis4ng
disease
models
o9en
assume
pathway
knowledge
sufficient
to
infer
correct
therapies

4. Familiar but Incomplete

5. Reality: Overlapping Pathways

6. Explosion
of
Biological
Genomic
&
Clinical
Informa<on
• Computa<onal
methods
for
integra<ng
massive
molecular
and
clinical
datasets
obtained
across
sizable
popula<ons
into
predic<ve
disease
models
can
recapitulate
complex
biological
systems
• Data
should
feed
and
refine
a
set
of
models
that
inform
our
understanding
of
disease
causality
as
well
as
generate
new
mechanisms,
targets,
diagnos<cs
and
knowledge.

7. The value of appropriate representations/ maps

9. “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”

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

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

13. 2002 Can one build a “causal” model?

14. 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

15. Building Realistic, Predictive Models of Disease: Can this lead us from
gene to drug?

16. Extensive Publications now Substantiating Scientific Approach
Probabilistic Causal Bionetwork Models
• >60 Publications from Rosetta Genetics Group (~30 scientists) over 5 years
including high profile papers in PLoS Nature and Nature 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.

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

18. (Eric Schadt)

19. “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

20. We still consider much clinical research as if w
hunter gathers - not sharing
.

21. TENURE
FEUDAL
STATES

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

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

24. Lack of standard forms for future rights and consents

26. 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

27. Sage Bionetworks 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 27

28. Model of Breast Cancer: Co-expression
A) Miller 159 samples B) Christos 189 samples
NKI: N Engl J Med. 2002 Dec 19;347(25):1999.
Wang: Lancet. 2005 Feb 19-25;365(9460):671.
Miller: Breast Cancer Res. 2005;7(6):R953.
Christos: J Natl Cancer Inst. 2006 15;98(4):262.
C) NKI 295 samples
E) Super modules
Cell
cycle
Pre-mRNA
ECM
D) Wang 286 samples Blood vessel
Immune
response
28
Zhang B et al., Towards a global picture of breast cancer (manuscript).

29. CHRIS
GAITERI-­‐ALZHEIMER’S
What
is
this?
Bayesian
networks
enriched
in
inflamma<on
genes
correlated
with
disease
severity
in
pre-­‐frontal
cortex
of
250
Alzheimer’s
pa<ents.
What
does
it
mean?
Inflamma<on
in
AD
is
an
interac<ve
mul<-­‐pathway
system.
More
broadly,
network
structure
organizes
complex
disease
effects
into
coherent
sub-­‐systems
and
can
priori<ze
key
genes.
Are
you
joking?
Gene
valida<on
shows
novel
key
drivers
increase
Abeta
uptake
and
decrease
neurite
length
through
an
ROS
burst.
(highly
relevant
to
AD
pathology)

30. PLATFORM
Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)
PILOTS= PROJECTS FOR COMMONS
Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....)
M
S
FOR
MAP
PLAT
NEW
RULES GOVERN

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

33. Leveraging Existing Technologies
Addama
Taverna
tranSMART

34. sage bionetworks synapse project
Watch What I Do, Not What I Say

35. sage bionetworks synapse project
Most of the People You Need to Work with Don’t Work with You

36. sage bionetworks synapse project
My Other Computer is Cloudera Amazon Google

37. Sage Metagenomics Project
Processed Data
(S3)
• > 10k genomic and expression standardized datasets indexed in SCR
• Error detection, normalization in mG
• Access raw or processed data via download or API in downstream analysis
• Building towards open, continuous community curation

38. Sage Metagenomics using Amazon Simple Workflow
Full case study at http://aws.amazon.com/swf/testimonials/swfsagebio/

39. Synapse Roadmap
• Data Repository
• Projects and security Synapse Platform Functionality
• R integration • Workflow templates
• Analysis provenance • Social networking
• Publishing figures • User-customized
• Search • Wiki & collaboration tools dashboards
• Controlled Vocabularies • Integrated management • R Studio integration
• Governance of restricted of cloud resources • Curation tool integration
data
Internal Alpha Public Beta Testing Synapse 1.0 Synapse 1.5 Future
Q1-2012 Q2-2012 Q3-2012 Q4-2012 Q1-2013 Q2-2013 Q3-2013 Q4-2013
• TCGA • Predictive modeling • TBD: Integrations with other
• METABRIC breast workflows visualization and analysis
cancer challenge • Automated processing of packages
common genomics platforms
• 40+ manually curated clinical studies
• 8000 + GEO / Array Express datasets
• Clinical, genomic, compound sensitivity
• Bioconductor and custom R analysis
Data / Analysis Capabilities

40. Four
Pilots
involving
Sage
Bionetworks
CTCAP
The
Federa<on
Portable
Legal
Consent
M
S
Sage
Congress
Project
FOR
MAP
PLAT
NEW
RULES GOVERN

41. 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.
Started Sept 2010

42. Shared clinical/genomic data sharing and analysis will
maximize clinical impact and enable discovery
• Graphic
of
curated
to
qced
to
models

43. The
Federa<on

44. (Nolan
and
Haussler)

45. 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)

46. Reproducible
science==shareable
science
Sweave: combines programmatic analysis with narrative
Dynamic generation of statistical reports
using literate data analysis
Sweave.Friedrich Leisch. Sweave: Dynamic generation of statistical reports
using literate data analysis. In Wolfgang Härdle and Bernd Rönz,editors, Compstat 2002 –
Proceedings in Computational Statistics,pages 575-580.
Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9

47. Federated
Aging
Project
:
Combining
analysis
+
narra<ve
=Sweave Vignette
Sage Lab
R code + PDF(plots + text + code snippets)
narrative
HTML
Data objects
Califano Lab Ideker Lab Submitted
Paper
Shared
Data
JIRA:
Source
code
repository
&
wiki
Repository

48. For 11/12 compounds, the #1 predictive feature in an unbiased
analysis corresponds to the known stratifier of sensitivity
#2
CML
lineage
CML lineage
#1
EGFR
mut
EGFR mut
#1
EGFR
mut
EGFR mut
#1
CML
lineage
#1
EGFR
mut
CML linage
EGFR mut
#1
ERBB2
expr
ERBB2 expr
Can
the
approach
make
new
mut
#1
BRAF
discoveries?
BRAF mut
#1
HGF
expr
HGF expr
#2
NRAS
mut
NRAS mut
BRAF mut
#1
BRAF
mut
#3
KRAS
mut
KRAS mut
#2
NRAS
mut
NRAS mut
BRAF mut
#1
BRAF
mut
#3
KRAS
mut
KRAS mut
#2
NRAS
mut
NRAS mut
BRAF mut
#1
BRAF
mut
#2
TP53
mut
TP53 mut
#3
CDKN2A
copy
CDKN2A copy
#1
MDM2
expr
MDM2 expr
48

49. Portable
Legal
Consent
Ac<va<ng
Pa<ents
Enabling
Sharing
Becoming
Partners
(John
Wilbanks)

51. weconsent.us

52. Sage
Congress
Project
April
20
2012
RealNames
Parkinson’s
Project
Fanconi’s
Anemia
Revisi<ng
Breast
Cancer
Prognosis
(Responders
Compe<<ons-­‐
IBM-­‐DREAM)

56. Networking
Disease
Model
Building