Stephen Friend, Jan 10, 2013. National Academy of Sciences, Washington DC

1. Scien&fic
Opportuni&es
from
Heterogeneous
Biological
Data
Analysis:
Overcoming
Complexity
Stephen
Friend
MD
PhD
President
Sage
Bionetworks
(Non-­‐Profit)
Integra&ng
Environmental
Health
Data
to
Advance
Discovery
Session
1
Using
Heterogeneous
Data
to
Advance
DIscovery

2. Navigating between states of wellness
Normal State
Disease State
Rui Chang et al. PLoS Computational Biology

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

4.
Network
Modeling
Approaches
for
Diseases
are
emerging

5. IT
Infrastructure
and
Cloud
compute
capacity
allows
a
genera&ve
open
approach
to
solving
problems

6. Nascent
Movement
for
pa&ents
to
Control
Sensi&ve
informa&on
allowing
sharing

7. Open
Social
Media
allows
ci&zens
and
experts
to
use
gaming
to
solve
problems

8. 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
genera&ve
open
approach
to
biomedical
problem
solving
4-­‐Nascent
Movement
for
pa&ents
to
Control
Sensi&ve
informa&on
allowing
sharing
5-­‐
Open
Social
Media
allows
ci&zens
and
experts
to
use
gaming
to
solve
problems
A
HUGE
OPPORTUNITY
-­‐-­‐
A
HUGE
RESPONSIBILITY

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

18. .

19. TENURE
FEUDAL
STATES

21. • alchemist

22. The value of appropriate representations/ maps

24.
BUILDING
PRECISION
MEDICINE
Extensions
of
Current
Ins&tu&ons
Proprietary
Short
term
Solu&ons
Open
Systems
of
Sharing
in
a
Commons

25. Why
Sage
Bionetworks?
(non-­‐profit)
We
believe
in
a
world
where
biomedical
research
is
about
to
fundamentally
change.
We
think
it
will
be
o^en
conducted
in
an
open,
collabora1ve
way
where
teams
of
teams
can
contribute
to
making
be_er,
faster,
relevant
discoveries
We
research
• Leading
biomedical
modeling
We
ac1vate/We
challenge
research
• Novel
training
doctoral
and
• Diverse
collabora&ons
with
internship
programs
individuals/researchers
and
ins&tu&ons
to
collec&vely
We
enable
others
encourage
sharing
• Developing
pla%orms
for
• Use
Crowdsourcing
collabora&on
and
engagement
–
approaches
to
engage
the
Synapse,
BRIDGE
communi&es
• Defining
governance
approaches–
Portable
Legal
Consent

26. Collaborators
(par&al)
§ Government
§ NIH, LSDF, NCI
§ Pharma
Partners
§ Merck,
Pfizer,
Takeda,
Astra
Zeneca,
Amgen,
Roche,
Johnson
&Johnson,
H3
§ Foundations
§ Kauffman CHDI, Gates Foundation
§ RWJF, Sloan, OneMind
§ Academic
§ Levy (Framingham)
§ Rosengren (Lund)
§ Krauss (CHORI)
§
§ Schadt (MSSM)
c
§ Federation
26
§ Ideker, Califano, Nolan, Schadt, Vidal

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

28. Two
recurring
problems
in
Alzheimer’s
disease
research
Ambiguous
pathology
Are
disease-­‐associated
molecular
systems
&
genes
destruc&ve,
adap&ve,
or
both?
Bo_om
line:
We
need
to
iden&fy
causal
factors
vs
correla&ve
or
adap&ve
features
of
disease.
Diverse
mechanisms
How
do
diverse
muta&ons
and
environmental
factors
combine
into
a
core
pathology?
Bo_om
line:
There
is
no
rigorous
/
consistent
global
framework
that
integrates
diverse
disease
factors.
28

29. Iden&fying
key
disease
systems
and
genes-­‐
Gaiteri
et
al.
1.)
Iden&fy
groups
of
genes
that
move
together
–
co-­‐expressed
“modules”
-­‐
correlated
expression
of
mul&ple
genes
across
many
pa&ents
-­‐
co-­‐expression
calculated
separately
for
Disease/healthy
groups
-­‐
these
gene
groups
are
o^en
coherent
cellular
subsystems,
enriched
in
one
or
more
GO
func&ons
Example
“modules”
of
coexpressed
genes,
color-­‐coded

30. Iden&fying
key
disease
systems
and
genes
1.)
Iden&fy
groups
of
genes
that
move
together
–
coexpressed
“modules”
2.)
Priori&ze
the
disease-­‐relevance
of
the
modules
by
clinical
and
network
measures
Priori&ze
modules
through
expression
synchrony
with
clinical
measures
or
tendency
to
reconfigure
themselves
in
disease
vs

31. Iden&fying
key
disease
systems
and
genes
1.)
Iden&fy
groups
of
genes
that
move
together
–
coexpressed
“modules”
2.)
Priori&ze
the
disease-­‐relevance
of
the
modules
by
clinical
and
network
measures
3.)
Incorporate
gene&c
informa&on
to
find
directed
rela&onships
between
genes
Infer
directed/causal
rela&onships
Priori&ze
modules
through
expression
and
clear
hierarchical
structure
by
synchrony
with
clinical
measures
or
tendency
too
reconfigure
themselves
in
disease
incorpora&ng
eSNP
informa&on
(no
hair-­‐balls
here)
vs

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

33. Transforming
networks
into
biological
hypotheses

34. Tes&ng
network-­‐based
hypotheses

35. Design-­‐stage
AD
projects
at
Sage
Fusing
our
exper&se
in…
Gene
regulatory
networks
Diffusion
Spectrum
Imaging
Feedback
Microcircuits
&
neuronal
diversity
Join
us
in
uni&ng
genes,
circuits
and
regions
to
build
mul&-­‐scale
biophysical
disease
models.
Contact
chris.gaiteri@sagebase.org

36. Tool:
PORTABLE
LEGAL
CONSENT
Control
of
Private
informa&on
by
Ci&zens
allows
sharing
weconsent.us
John
Wilbanks
John
Wilbanks
• Online
educa&onal
wizard
TED
Talk
• Tutorial
video
•
Legal
Informed
Consent
Document
“Let’s
pool
our
medical
data”
•
Profile
registra&on
weconsent.us
•
Data
upload

37. two approaches to building common scientific 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

38. 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

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

40. “Synapse
is
a
compute
plaiorm
for
transparent,
reproducible,
and
modular
collabora&ve
research.”

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

42. 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

43. Pancancer collaborative subtype discovery

44. Objective assessment of factors influencing model
performance (>1 million predictions evaluated)
Sanger
CCLE
Cross
valida1on
predic1on
accuracy
(R2)
Predic&on
accuracy
improved
by…
Not
discre&zing
data
Including
expression
data
Elas&c
net
regression
130
compounds
In
Sock
Jang
24
compounds

46. Erich
Huang,
Brian
Bot,
Dave
Burdick

48. Sage-­‐DREAM
Breast
Cancer
Prognosis
Challenge
Building
be_er
disease
models
together
Caldos/Aparicio
breast
cancer
data
154
par&cipants;
27
countries
334
par&cipants;
>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
Team:
A_ractor
Metagenes
Team
Members:
Wei-­‐Yi
Cheng,
Tai-­‐Hsien
Ou
Yang,
and
Dimitris
Anastassiou

49. How
to
accelerate
and
make
affordable
the
efforts
required
to
build
be_er
models
of
disease
?
Build
a
way
for
the
pa&ents
ac&vely
to
engage
with
exis&ng
researchers
to
share
their
insights
in
real-­‐&me
around
what
is
happening
to
them
(
their
state
of
wellness
or
disease)
where
their
narra&ves,
samples,
data,
insights,
and
funds
are
shown
to
enable
decision
making
in
what
they
should
do,
what
treatments
they
need

51. BRIDGE Seed Projects
Fanconi
Diabetes
Melanoma
Anemia
Ac1vated
Hunt
Community
Project
Chronic
Breast
Fa1gue
Cancer
Syndrome
51

52. MELANOMA
Screening
–
Could
it
be
be_er?
Educa&on
is
derived
Best
accuracy
of
from
top-­‐down
clinical
diagnosis
=
experien&al
64%
knowledge
(Grin,
1990)
160k
new
cases/year
48k
deaths
in
2012
in
US
HPI
ABCDE
Both
intra-­‐
and
“ugly
duckling”
inter-­‐
ins&tu&onal
MD
Dermoscopy
Pathology
data
are
siloed
Molecular
?Photos
There
is
no
standard
screening
program
for
skin
lesions;
seeing
an
MD
is
self
directed
52

54. Initial focus on building the data needed
Novel Data collection
4.
Give
back
risk-­‐
+ Usage assessment
&
educa1on
to
the
ci1zens
1.Ac1vated
ci1zens
take
skin
pictures
virtual
cycle:
con&nuous
2.
Store
aggrega&on
of
data
tons
of
data!
enriching
the
model
3.
Run
algorithmic
cChallenges
in
the
compute
space
54

55.
1-­‐Now
possible
to
generate
massive
amount
of
human
“omic’s”
data
2-­‐
Network
Modeling
for
Diseases
are
emerging
3-­‐
IT
Infrastructure
and
Cloud
compute
capacity
allows
a
genera&ve
open
approach
to
biomedical
problem
solving
4-­‐Nascent
Movement
for
pa&ents
to
Control
Private
informa&on
allowing
sharing
5-­‐Open
Social
Media
allowing
ci&zens
and
experts
to
use
gaming
to
solve
problems
THESE
FIVE
TRENDS
CAN
ENABLE
SUSTAINABLE
AFFORDABLE
WAYS
TO
DEVELOP
THE
REQUIRED
DATA
INTEGRATION
TO
OVERCOME
THE
PUZZLE
OF
THE
CURRENT
COMPLEXITY

57. Navigating between states of wellness
Normal State
Disease State
Rui Chang et al. PLoS Computational Biology

58. Fourth
Sage
Commons
Congress
–
San
Francisco
April
19-­‐20
Ten
Young
Inves&gator
Awards
Bob
Young
Top
Hat
Joep
Lange
AIDS
Organizer
Wadah
Khanfar
Ex-­‐
Al
Jazeera
Patrick
Meier
Ex-­‐
Ushhidi
Jennifer
Pahlka
Code
for
America