Stephen Friend May 23, 2012 Northwest Institute of Genetic Medicine 2012 Retreat Seattle, WA

1. Building
Be*er
Models
of
Disease
Together
Stephen
H
Friend
MD
PhD
Sage
Bionetworks
NIGM
4th
Annual
Retreat
May
23
2012
Sea*le

2. What
is
the
problem?
Most
approved
therapies
assume
indica2ons
would
represent
homogenous
popula2ons
Our
exis2ng
disease
models
o8en
assume
pathway
knowledge
sufficient
to
infer
correct
therapies

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

4. Reality: Overlapping Pathways

5. The value of appropriate representations/ maps

6. Disease
PrevenLon
and
Treatment
• To
Prevent
need
to:
– Have
clinical
&
molecular
definiLon
of
disease
– Be
able
to
predict
progression
– Have
drugs
that
target
mechanisms
that
drive
progression
• To
Treat
need
to:
– Have
clinical
&
molecular
definiLon
of
disease
– Disease
modifying
therapies
For
Alzheimer’s
we
need
work
to
develop
all
of
these!

7. Data-­‐driven
Target
Iden2fica2on
If
we
accept
that
disease
is
driven
by
the
complex
interplay
of
geneLcs
and
environment
mediated
through
molecular
networks…….
GeneLcs
GeneLcs
Disease
progression
Disease
Modifying
Therapy
Healthy
Disease
Environment
Environment
State
State
………………………….then
it
follows
we
must
study
these
networks
and
how
they
respond
to
perturbagens,
how
they
differ
in
disease,
etc

11. what will it take to understand disease?
DNA
RNA
PROTEIN
(dark
ma*er)
MOVING
BEYOND
ALTERED
COMPONENT
LISTS

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

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

14. 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
14
Integrated Genetics Approaches

15. 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

16. 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.

17. 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

18. 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 18

19. Alzheimer’s
Disease:
IdenLfying
key
disease
systems
and
genes
1.)
IdenLfy
groups
of
genes
that
move
together
–
coexpressed
“modules”
-­‐
correlated
expression
of
mulLple
genes
across
many
paLents
-­‐
coexpression
calculated
separate
for
Disease/healthy
groups
-­‐
these
gene
groups
are
ogen
coherent
cellular
subsystems,
enriched
in
one
or
more
GO
funcLons
Data
source:
Harvard
Brain
Tissue
Resource
Center
SNPs,
Gene
Expression,
Clinical
Traits
AD
n
=
284
Pre
Frontal
Cortex
Control
153
AD
168
Visual
Cortex
Control
116
AD
220
Cerebellum
Control
122

20. IdenLfying
key
disease
systems
and
genes
1.)
IdenLfy
groups
of
genes
that
move
together
–
coexpressed
“modules”
-­‐
correlated
expression
of
mulLple
genes
across
many
paLents
-­‐
coexpression
calculated
separate
for
Disease/healthy
groups
-­‐
these
gene
groups
are
ogen
coherent
cellular
subsystems,
enriched
in
one
or
more
GO
funcLons
Alzheimer’s-­‐specific
regulatory
relaLonship
Microarray
result
Transcription
factor
Gene A Gene B

21. Where
does
coexpression
come
from?
What
does
a
“link”
in
these
networks
mean?
• What
is
the
evidence
that
coexpression
is
produced
by
regulatory
rela6onships?
• Gene
coexpression
has
mulLple
biophysical
sources:
1:
TranscripLonal
overrun
/
chromosome
locaLon
(Ebisuya
2008)
2:
Common
transcripLon
factor
binding
sites
(Marco
2009)
3:
EpigeneLc
regulaLon
(Chen
2005)
4:
3D
Chromosome
configuraLon
(Deng
2010)
Chromosome
segment
– VariaLon
in
cell-­‐type
density
(Oldham
2008)
#1
#4
#2/TF
Gene
A
Gene
B
Gene
C
Promoter
x
Promoter
y
#3
21

22. IdenLfying
key
disease
systems
and
genes
1.)
IdenLfy
groups
of
genes
that
move
together
–
coexpressed
“modules”
-­‐
correlated
expression
of
mulLple
genes
across
many
paLents
-­‐
coexpression
calculated
separate
for
Disease/healthy
groups
-­‐
these
gene
groups
are
ogen
coherent
cellular
subsystems,
enriched
in
one
or
more
GO
funcLons
Example
“modules”
of
coexpressed
genes,
color-­‐coded

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

24. IdenLfying
key
disease
systems
and
genes
1.)
IdenLfy
groups
of
genes
that
move
together
–
coexpressed
“modules”
2.)
PrioriLze
the
disease-­‐relevance
of
the
modules
by
clinical
and
network
measures
PrioriLze
modules
through
expression
CombinaLon
of
cogniLve
funcLon,
Braak
score,
synchrony
with
clinical
measures
or
tendency
corLcal
atrophy
with
differenLal
expression
too
reconfigure
themselves
in
disease
and
differenLal
coexpression
rank
modules.
vs

25. IdenLfying
key
disease
systems
and
genes
1.)
IdenLfy
groups
of
genes
that
move
together
–
coexpressed
“modules”
2.)
PrioriLze
the
disease-­‐relevance
of
the
modules
by
clinical
and
network
measures
3.)
Incorporate
geneLc
informaLon
to
find
directed
relaLonships
between
genes
PrioriLze
modules
through
expression
Infer
directed/causal
relaLonships
synchrony
with
clinical
measures
or
tendency
and
clear
hierarchical
structure
by
too
reconfigure
themselves
in
disease
incorporaLng
eSNP
informaLon
(no
hair-­‐balls
here)
vs

26. Example
network
finding:
microglia
acLvaLon
in
AD
Module
selec2on
–
what
iden2fies
these
modules
as
relevant
to
Alzheimer’s
disease?
The
eigengene
of
a
module
of
~400
probes
correlates
with
Braak
score,
age,
cogniLve
disease
severity
and
corLcal
atrophy.
Members
of
this
module
are
on
average
differenLally
expressed
(both
up-­‐
and
down-­‐regulated).
Evidence
these
modules
are
related
to
microglia
func2on
The
members
of
this
module
are
enriched
with
GO
categories
(p<.001)
such
as
“response
to
bioLc
sLmulus”
that
are
indicaLve
of
immunologic
funcLon
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:

27. Figure
key:
Five
main
immunologic
families
found
in
Alzheimer’s-­‐associated
module
Square
nodes
in
surrounding
network
denote
literature-­‐supported
nodes.
Node
size
is
propor6onal
to
connec6vity
in
the
full
module.
Core
family
members
are
shaded.
(Interior
circle)
Width
of
connec6ons
between
5
immune
families
are
linearly
scaled
to
the
number
of
inter-­‐family
connec6ons.
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.

28. Transforming
networks
into
biological
hypotheses

29. TesLng
network-­‐based
hypotheses

30. TesLng
network-­‐based
hypotheses

31. TesLng
network-­‐based
hypotheses

32. Current
AD
projects
with
Sage
in
collaboraLon
Follow-­‐up
microglia
experiments
Confirming
TYROBP
relevance
in
human-­‐derived
microglia-­‐neuron
co-­‐culture
Similar
microglia
experiments
with
Fc
receptor
(Neumann,
Gaiteri)
Novel
genes
validated
with
in
vitro
and
in
vivo
model
systems
Cell
culture
&
transgenic
FAD
crosses
with
novel
gene
KO’s
(Wang,
Kitazawa,
Gaiteri)
Addi2onal
microarrays
from
model
systems
Check
network
predic6ons
to
refine
both
algorithm
&
biology
(Schadt/Neumann)
Larger
cohorts,
proteomics
Building
networks
in
3x
larger
dataset,
newer
plaZorm,
w/
detailed
clinical
info
(Myers,
Gaiteri)

33. Design-­‐stage
AD
projects
at
Sage
Fusing
our
experLse
in…
Gene
regulatory
networks
Diffusion
Spectrum
Imaging
Feedback
Microcircuits
&
neuronal
diversity
To
build
mulL-­‐scale
biophysical
disease
models.
Join
us
in
uniLng
genes,
circuits
and
regions!
Contact
chris.gaiteri@sagebase.org

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

36. Fundamentally
Biological
Science
hasn’t
changed
because
of
the
‘Omics
RevoluLon……
…..it
is
about
the
process
of
linking
a
system
to
a
hypothesis
to
some
data
to
some
analyses
Biological Data Analysis
System
But
the
way
we
do
it
has
changed…………………………………………

37. Driven
by
molecular
technologies
we
have
become
more
data
intensive
leading
to
more
specializaLon:
data
generators
(centralized
cores),
data
analyzers
(bioinformaLcians),
validators
(experimentalists:
lab
&
clinical)
This
is
reflected
in
the
tendency
for
more
mulL
lab
consorLum
style
grants
in
which
the
data
generators,
analyzers,
validators
may
be
different
labs.
Single Lab Model Data
• R01 Funding
• Hypothesis->data->analysis->paper
• Small-scale data / analysis
• Reproducible? Biological Analysis
System
Multiple Lab Model
Data
• P01 Funding
• Hypothesis->data->analysis->paper
• Medium-scale data / analysis
• Data Generators/Analysts/Validators maybe
different groups Biological Analysis
• Reproducible? System

38. What
does
this
New
Model
Enable
“Open Market” Model
Data
• Democratization of Biology
• Large scale data, compute,
analysis open to all
• Dissociation of Data Generators
from Analysts from Validators – if
scientists want to work on other
people’s data they can, or validate
someone else’s findings? Biological
Analysis
System
• New ways to fund and incentivize
research
• BRIDGE
• Collaborative Competitions

39. Open and Networked Approaches
and the “Democratization” of Science
BioMedicine Information Commons
Patients/
Citizens
Data
• “Open” access to Generators
CURATED
data, tools, DATA
models Data
TOOLS/ Analysts
METHODS
• Wide
constituency of RAW
DATA
users and
contributors
ANALYZES/
• Break the “link” MODELS
between data and
ownership Clinicians
SYNAPSE
Experimentalists

40. UlLmately
these
efforts
will
fail
without
more
ambiLous
thinking
– AcLvate
PaLents
• PaLents
want
to
be
involved,
to
fund
research,
to
direct
the
research
quesLons,
to
hold
the
scienLfic
community
to
account
• Portable
Legal
Consent
– Collect
Large
Scale
Longitudinal
Data
• We
need
to
collect
the
right
kind
of
data.
Molecular
and
Phenotypic
in
a
longitudinal
fashion
on
10s-­‐100,000s
of
individuals
• Real
Names
Discovery
Project
– Build
an
InformaLon
Commons
• Synapse
– Engage
in
CollaboraLve
Challenges
• Breast
Cancer
Challenge-­‐
IBM/Google/
Science
Transl
Med

42. Networked Approaches and the 3
2
REWARDS
“Democratization” of Science USABLE
RECOGNITION
DATA
1
PRIVACY
BARRIERS
BioMedical Information Commons
Patients/
Citizens
Data
• “Open” access to Generators
CURATED
data, tools, DATA
models Data
TOOLS/ Analysts
METHODS
• Wide
constituency of RAW
DATA
users and
contributors 6
ROLES
ANALYZES/
• Break the “link” MODELS 4
FOR
between data and CITIZENS
GOVERNANCE
ownership Clinicians
5
HOW
TO
SYNAPSE
DISTRIBUTE
Experimentalists
TASKS

43. Open and Networked Approaches:Democratization of Science
4
1
RULES
PRIVACY
GOVERNANCE
PORTABLE
LEGAL
CONSENT
THE
FEDERATION
BARRIERS
2
5
USABLE
HOW
TO
DATA
DISTRIBUTE
TASKS
SYNAPSE
COLLABORATIVE
CHALLENGES
3
6
REWARDS
ROLES
RECOGNITION
FOR
CITIZENS
SYNAPSE
BRIDGE

44. Open and Networked Approaches:Democratization of Science
1
PRIVACY
PORTABLE
LEGAL
CONSENT:
weconsent.us
BARRIERS
John
Wilbanks

45. Open and Networked Approaches:Democratization of Science
2
USABLE
DATA
SYNAPSE
3
REWARDS
RECOGNITION
SYNAPSE

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

47. 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

48. sage bionetworks synapse project

49. sage bionetworks synapse project

50. Open and Networked Approaches:Democratization of Science
THE
FEDERATION
4
RULES
GOVERNANCE

51. Pipeline
Strategy
A
B
C
Divide
and
Conquer
Strategy
D
A
B
C
Parallel/IteraLve
Strategy
A
B
C

52. 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)

53. sage federation: Google- SageBionetworks / The Federation

54. Open and Networked Approaches:Democratization of Science
5
HOW
TO
COLLABORATIVE
DISTRIBUTE
CHALLENGES
TASKS

56. LOG
ON
AND
SIGN-­‐UP
FOR
THE
BREAST
CANCER
CHALLENGE
SAGE/DREAM/GOOGLE

57. Open and Networked Approaches:Democratization of Science
6
ROLES
BRIDGE
FOR
CITIZENS

60. We
pursue
Medical
Care
is
if
it
were
an
“Infinite
Game”
and
We
pursue
Medical
Research
as
if
it
were
a
“Finite
Game”

61. We
pursue
Medical
Care
is
if
it
were
an
“Infinite
Game”
and
We
pursue
Medical
Research
as
if
it
were
a
“Finite
Game”
YET
We
should
pursue
Medical
Care
is
if
it
were
a
“Finite
Game”
and
We
should
pursue
Medical
Research
as
if
it
were
an
“Infinite
Game”

62. Who will build the datasets/ models capable of providing powerful
insights enabling disease modifying therapies?
Power
of
CollaboraLve
Challenges
Evolving
Models
from
Deep
Data
Driven
Longitudinal
Cohorts
in
Worldwide
Open
InformaLon
Commons
InsLtutes
Industry
FoundaLons
NETWORK
PLATFORM
PPP
Or
??????
Scientists Physicians Citizens “Knowledge Expert”