Searching for opportunities to use data-intensive science to build better disease maps through open innovation and collaboration across silos. The key advantages discussed are generating more clinically validated targets through data sharing, helping deliver more new drugs for patients, and improving health outcomes. Issues around current drug discovery redundancy and high failure rates are also addressed.

1. Searching for opportunities for WIN
it is more about how we do science than what
advantages of an open innovation compute space
for building better models of disease
beyond siloed drug discovery- Arch2POCM

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

5. Reality: Overlapping Pathways: 90% Phase I Cpds do not make it

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

10. “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

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

12. 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
12
Integrated Genetics Approaches

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

14. (Eric Schadt)

15. Sage Mission
Sage Bionetworks is a non-profit organization founded in 2009 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

16. Sage Bionetworks Collaborators
 Pharma Partners
 Merck, Pfizer, Takeda, Astra Zeneca, Amgen
 Foundations
 CHDI, Gates Foundation
 Government
 NIH, LSDF
 Academic
 Levy (Framingham)
 Rosengren (Lund)
 Krauss (CHORI)
 Federation
 Ideker, Califarno, Butte, Schadt 16

17. Platform Commons Research
Cancer
Neurological Disease
Metabolic Disease
Curation/Annotation
Building
Data Disease
Repository Maps
CTCAP
Public Data Pfizer
Merck Data Outposts Merck
TCGA/ICGC Federation Takeda
CCSB Astra Zeneca
CHDI
Commons Gates
NIH
Pilots
LSDF-WPP
Inspire2Live
Hosting Data POC
Hosting Tools Bayesian Models
Co-expression Models
Hosting Models
Discovery Tools &
Platform Methods
KDA/GSVA
LSDF
17

18. Example 1: Breast Cancer- 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.
18

19. Coexpression Networks
Module combination
Partition BN
Bayesian Network
Survival Analysis
19
Zhang B et al., manuscript

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

21. Key
Driver
Analysis
• IdenFfy
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
21

23. A) Cell Cycle (blue) B) Chromatin modification (black)
C) Pre-mRNA Processing (brown) D) mRNA Processing (red)
Global driver
Global driver & RNAi
validation
23

24. Signaling between Super Modules

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

26. Example 2. The Sage Non-Responder Project in Cancer
• To identify Non-Responders to approved drug regimens so
Purpose: we can improve outcomes, spare patients unnecessary
toxicities from treatments that have no benefit to them, and
reduce healthcare costs
Leadership: • Co-Chairs Stephen Friend, Todd Golub, Charles Sawyers &
Rich Schilsky
Initial • AML (at first relapse)-funded NIH
Studies: • Non-Small Cell Lung Cancer- Started Guangdong General
Hospital Prof Yi-long WU
• Colon
Cancer
Sun
Yat
Sen
Univ-­‐Prof
WANG
• Ovarian Cancer (at first relapse)
• Breast Cancer
• Renal Cell
Sage Bionetworks • Non-Responder Project

27. 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.

28. Example 4: THE FEDERATION
Butte Califano Friend Ideker Schadt
vs

29. Federated
Aging
Project
:
Combining
analysis
+
narraFve
=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

30. Synapse
as
a
Github
for
building
models
of
disease

31. Platform for Modeling
SYNAPSE

36. IMPACT
ON
PATIENTS

37. TENURE
FEUDAL
STATES

38. … the world is becoming too
fast, too complex, and too
networked for any company to have
all the
answers inside
Y. Benkler, The Wealth of Networks

39. Largest Attrition For Pioneer Targets is at
Clinical POC (Ph II)
Target ID/ Hit/Probe/ Clinical Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
logy
Attrition 50% 10% 30% 30% 90%
This is killing drug discovery
We can generate effective and safe molecules in animals, but
they do not have sufficient efficacy and/or safety in the chosen
patient group.

40. The current pharma model is redundant
Target ID/ Hit/Probe/ Clinical Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
Phase
Target ID/ Hit/Probe/ Clinical
ID Pharmaco
Toxicolog Phase I
Discovery Lead ID Candidate logy
y/ IIa/IIb
ID Pharmaco
Target ID/ Hit/Probe/ Clinical
logy
Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
Target ID/ Hit/Probe/ Clinical Toxicolog
logy Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
logy
Target ID/ Hit/Probe/ Clinical Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
Target ID/ Hit/Probe/ Clinical
logy
Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
Target ID/ Hit/Probe/ Clinical Toxicolog
logy Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
ID Pharmaco
logy
Attrition 50% 10% 30% 30% 90%
Negative POC information is not shared

41. Cost of Negative Ph II POC Estimated at $12.5 Billion Annually
Remember the two
benefits of failure. First if
you do fail, you learn
what doesn t work and
second the failure gives
you the opportunity to try
a new approach.
Roger van Oech

42. • We want to improve health
• New medicines are part of this equation
• In this, we are failing, and we want to find a
solution

43. Innovation is the ability to see change as an opportunity
– not a threat

44. Let s imagine….
• A pool of dedicated, stable funding
• A process that attracts top scientists and clinicians
• A process in which regulators can fully collaborate to solve key
scientific problems
• An engaged citizenry that promotes science and acknowledges
risk
• Mechanisms to avoid bureaucratic and administrative barriers
• Sharing of knowledge to more rapidly achieve understanding of
human biology
• A steady stream of targets whose links to disease have been
validated in humans

45. Arch2POCM
A globally distributed public private partnership (PPP) committed to:
• Generate more clinically validated targets by sharing data
• Help deliver more new drugs for patients

46. Arch2POCM: what s in a name?
Arch: as in archipelago and referring to the
distributed network of academic labs, pharma
partners and clinical sites that will contribute to
Arch2POCM programs
POCM: Proof Of Clinical Mechanism:
demonstration in a
Ph II setting that the
mechanism of the
selected disease
target can be safely
and usefully
modulated.

47. Arch2POCM: a new drug development model?
• Pool public and private sector funding into an independent
organization
• Public sector provides stability and new ideas
• Private sector brings focus and experience
• Funding can focus explicitly on high-risk targets
• Pre-competitive model to test hypotheses from financial gain
• Will attract top scientists and clinicians
• Will allow regulators to participate as scientists
• Will reduce perceived conflicts of interests – engages citizens/
patients
• Will reduce bureaucratic and administrative overhead
• Will allow rapid dissemination of information without restriction
- informs public and private sectors and reduces duplication

48. Toronto Feb-2011 meeting:
output on Arch2POCM Feasibility
Pharma
- 6 organisations supportive
Academic Labs
- access to discovery biology and test compounds
Patient groups
- access to patients more quickly and cheaply
- access to “personal data”
Regulators
- access to historical data
- want to help with new clinical endpoints and study designs

49. Arch2POCM: April San Francisco Meeting
• Selected Disease Areas of Focus: Oncology,, Neuroscience and
Opportunistic (O, CNS and X, respectively)
• Defined primary entry points of Arch2POCM test compounds into
overall development pipeline
• Committed academic centers identified: UCSF, Toronto, Oxford
• CROs engaged
• Evaluated Arch2POCM business model
• Two Science Translational Medicine manuscripts published

51. Entry Points For Arch2POCM Programs
- genomic/ genetic
Pioneer target sources - disease networks
- academic partners
- private partners
- Sage Bionetworks, SGC,
Lead Lead
Preclinical Phase I Phase II
identification optimisation
Assay
in vitro
probe
Lead Clinical Phase I Phase II
candidate asset asset
Early Discovery

52. Arch2POCM and the Power of
Crowdsourcing
• Crowdsourcing: the act of outsourcing tasks
traditionally performed by an employee to a large group
of people or community- such as WIN
• By making Arch2POCM s clinically characterized
probes available to all, Arch2POCM will seed
independently funded, crowdsourced experimental
medicine- advantage WIN
• Crowdsourced studies on Arch2POCM probes will
provide clinical information about the pioneer targets in
MANY indications- opportunity for WIN

53. ROI for Pharma Partner
• Option to in-licence asset after positive POCM
• Early data for new clinically validated (and
invalidated) targets
• Easier access to the crowd of “proven” experts/
centers: leverage the crowd’s learnings to ID the
most promising unmet medical need
• Collaborate in more open way with regulatory
agencies and patient groups
• Jointly invalidate a larger number of pioneer targets

54. ArchPOCM Oncology Disease Area
Focus:
Unprecedented targets and mechanisms
Novelty  MOA and clinical findings
Arc2POCM Capacity:
5 targets/year for ~ 4 years
Gate 1: ~75% effort
• New target with lead and Sage bionetworks insights on MOA
(increase likelihood of success), or
• New target (enabled by Sage) with assay
Gate 2: ~25% effort
• Pharma failed or deprioritized/parked compounds
• Compound ID is followed by a Sage systems biology effort to define
MOA and clinical entry point

55. ArchPOCM Oncology: Epigenetics
selected as the target area of choice
Top Targets:
• Discovery
• Jard1
• Ezh1
• G9A
• Lead
• Dyrk1
• Pre-Clin
• `Brd4

56. ArchPOCM Oncology: Epigenetics
selected as the target area of choice

57. ArchPOCM Oncology: Epigenetics
selected as the target area of choice

58. Arch2POCM: Next Steps
• Oncology and CNS Arch2POCM strategic design teams
to generate project workflow plans and timelines
(September)
• Define critical details of Arch2POCM leadership,
organizational and decision-making structures
• (Q3-Q4, 2011)
• Develop business case to support Arch2POCM
programs (Q3-Q4, 2011)
• Obtain financial backing and launch operations in early
2012

59. Arch2POCM:
an idea whose time has come
In a world of abundant knowledge, hoarding technology is a self-limiting
strategy. Nor can any organization, even the largest, afford any longer to
ignore the tremendous external pools of knowledge that exist. Henry
Chesbrough
Ideas are only as good as your ability to make
them happen.

60. it is more about how we do science than what
advantages of an open innovation compute space
for building better models of disease
beyond siloed drug discovery- Arch2POCM
Each of these are opportunities
For the WIN Consortium