Stephen Friend, Oct 29, 2011. Inspire2Live Discovery Network, Cambridge, UK

1. Actionable Cancer Network Models
And Open Medical Information Systems
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam
Discovery Networks
October 29th, 2011

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

3. What
is
the
problem?
We
need
to
rebuild
the
drug
discovery
process
so
that
we
be6er
understand
disease
biology
before
tes8ng
proprietary
compounds
on
sick
pa8ents

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

5. Reality: Overlapping Pathways

6. The value of appropriate representations/ maps

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

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

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

12. 2002 Can one build a “causal” model?

13. 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

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

15. (Eric Schadt)

16. “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 Models in a
Compute Space- Knowledge Expert F

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

18. TENURE
FEUDAL
STATES

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

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

21. Lack of standard forms for sharing data
and lack of forms for future rights and consentss

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

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

24. 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

25. 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 25

26. 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
Data Tool and Disease Map Generators-
(Global coherent data sets, Cytoscape,
Clinical Trialists, Industrial Trialists, CROs…)
ORM
APS
NEW MAPS
M
F
PLAT
Disease Map and Tool Users-
NEW
( Scientists, Industry, Foundations, Regulators...)
RULES GOVERN RULES AND GOVERNANCE
Data Sharing Barrier Breakers-
(Patients Advocates, Governance
and Policy Makers, Funders...)

27. Developing predictive models of genotype-specific
sensitivity to compound treatment
Gene8c
Feature
Matrix
Expression,
copy
number,
somaQc
mutaQons,
etc.
Predic8ve
Features
(biomarkers)
Cancer
samples
with
varying
degrees
of
response
to
therapy
Sensi8ve
Refractory
(e.g.
EC50)
27

28. 1
20
100
500
Feature
Features
Features
Features
Elastic net regression
28

29. Bootstrapping retains robust predictive features
29

30. Our approach identifies mutations in genes upstream of MEK
as top predictors of sensitivity to MEK inhibition
#3
Mut
NRAS
!"#$% &"#$% #1
Mut
BRAF
PD-­‐0325901
'"#(%
#312
Mut
NRAS
)*!+,-% #./0-11%
2/345-674+%
#9
Mut
BRAF
30
PD-­‐0325901

31. Other top predictive features include expression
levels of genes regulated by MEK
#19
ETV5
expr
#8
DUSP6
expr
#5
ETV4
expr
#3
NRAS
mut
#2
SPRY2
expr
!"#$% &"#$% #1
BRAF
mut
PD-­‐0325901
'"#(%
)*!+,-% #./0-11%
2/345-674+%
31
PraQlas
et
al.,
(2009),
PNAS

32. Model built excluding expression data identifies BRAF, NRAS, and KRAS top
predictive features for both MEK inhibitors
KRAS RAS
mut
#3
K mut
NRAS RAS
mut
#2
N mut
BRAF RAF
mut
#1
B mut
PD-­‐0325901
!"#$% &"#$%
'"#(% #3
KRAS
mut
KRAS mut
NRAS mut mut
#2
NRAS
BRAF mut mut
#1
BRAF
)*!+,-% #./0-11%
AZD6244
2/345-674+%
32

33. 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
How
accurate
would
predic8ve
#1
BRAF
mut
models
perform
for
diagnos8cs?
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
33

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

36. Leveraging Existing Technologies
Addama
Taverna
tranSMART

37. INTEROPERABILITY
SYNAPSE
Genome Pattern
CYTOSCAPE
tranSMART
I2B2
INTEROPERABILITY

38. 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

39. Select
Six
Pilots
at
Sage
Bionetworks
CTCAP
Arch2POCM
The
FederaQon
ORM
S
MAP
Portable
Legal
Consent
F
PLAT
Sage
Congress
Project
NEW
RULES GOVERN

40. CTCAP
Clinical Trial Comparator Arm Partnership “CTCAP”
Strategic Opportunities For Regulatory Science
Leadership and Action
FDA
September 27, 2011

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. Arch2POCM
Restructuring
the
PrecompeQQve
Space
for
Drug
Discovery
How
to
potenQally
De-­‐Risk
High-­‐Risk
TherapeuQc
Areas

44. What
is
the
problem?
We
need
to
rebuild
the
drug
discovery
process
so
that
we
be6er
understand
disease
biology
before
tes8ng
proprietary
compounds
on
sick
pa8ents

45. A PPP to generate novel chemical probes
June 10
June 09
April 09
Jan 09
Pfizer OICR UNC Novartis
(8FTEs) (2FTEs) Well. Trust (£4.1M) (3FTEs) (8FTEs)
NCGC (20HTSs)
GSK (8FTEs)
Ontario ($5.0M)
15 acad. labs
Sweden ($3.0M)
….more than £30M of resource….now Lilly (8FTEs)

46. Academic, scientific, drug
discovery & economic impact
 Published Dec 23 2010 - already cited 30 times
 Distributed to >100 labs/companies - profile in several therapeutic areas
 Pharmas - started proprietary efforts
 Harvard spin off - $15 M seed funding
 Opened new area:
Zuber et al : BRD4/ JQ1 in acute leukaemia Nature, 2011 Aug 3
Delmore et al: BRD4/ JQ1 in multiple myeloma Cell, 2011 Volume 146, 904-917, 16
Dawson et al: BRD4/ JQ1 in MLL Nature 2011, in press.
Floyed et al: BRD4 in DNA damage response Cell, revised
Filippakopoulos et al:Bromodomains structure and function Cell, revised
Natoli et al: BRD4 in T-cell differentiation manuscript in preparation
Bradner et al: BRDT in spermatogenesis submitted
collaborations with SGC

48. The
FederaQon

49. How can we accelerate the pace of scientific discovery?
2008
2009
2010
2011
Ways to move beyond
“traditional” collaborations?
Intra-lab vs Inter-lab
Communication
Colrain/ Industrial PPPs Academic
Unions

50. (Nolan
and
Haussler)

51. human aging:
predicting bioage using whole blood methylation
• Independent training (n=170) and validation (n=123) Caucasian cohorts
• 450k Illumina methylation array
• Exom sequencing
• Clinical phenotypes: Type II diabetes, BMI, gender…
Training Cohort: San Diego (n=170) Validation Cohort: Utah (n=123)
RMSE=3.35 RMSE=5.44
100
100
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!! ! !
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! !! !
! !!
! ! !! !
80
Biological Age
Biological Age
!! !
!!!! !!
! !! !
! ! ! !!! !
80
! !!!
!! ! ! ! !
!!
! !
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! !! !
! ! ! !!!!
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!!
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60
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60
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! ! ! 40
40
! !
40 50 60 70 80 90 100 40 50 60 70 80 90
Chronological Age Chronological Age

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. 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

54. Federated
Aging
Project
:
Combining
analysis
+
narraQve
=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

55. Portable
Legal
Consent
(AcQvaQng
PaQents)
John
Wilbanks

59. Sage
Congress
Project
April
20
2012
RA
Parkinson’s
Asthma
(Responders
CompeQQons)

60. Why not use data intensive science
to build models of disease
Current Reward Structures
Organizational Structures and Tools
Six Pilots
Opportunities