Stephen Friend, Oct 24, 2011. Dana Farber Cancer Institute, Boston, MA

1. Actionable Cancer Network Models
And Open Medical Information Systems
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam
DFCI
October 24th, 2011

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

3. What is the problem?
• Regulatory hurdles too high?
• Low hanging fruit picked?
• Payers unwilling to pay?
• Genome has not delivered?
• Valley of death?
• Companies not large enough to execute on strategy?
• Internal research costs too high?
• Clinical trials in developed countries too expensive?
In fact, all are true but none is the real problem

4. 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

5. What is the problem?
Most approved cancer therapies assumed tumor
indica8ons would represent homogenous popula8ons
Most new cancer therapies are in search of single altered
components
Our exis8ng tumor models o>en assume pathway
knowledge sufficinet to infer correct therapies

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

7. Reality: Overlapping Pathways

8. The value of appropriate representations/ maps

10. “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

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

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

14. 2002 Can one build a “causal” model?

15. 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

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)
..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 Models in a
Compute Space- Knowledge Expert F

20. 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. Assumption that genetic alterations
in human conditions should be owned

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

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

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

28. 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

29. 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 29

30. 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,
M Clinical Trialists, Industrial Trialists, CROs…)
S
FOR
MAP
NEW MAPS
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...)

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. INTEROPERABILITY
SYNAPSE
Genome Pattern
CYTOSCAPE
tranSMART
I2B2
INTEROPERABILITY

35. 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

36. Six Pilots at Sage Bionetworks
CTCAP
Non-­‐Responders
Arch2POCM
M
S
FOR
MAP
The FederaOon
PLAT
Portable Legal Consent
EW N
Sage Congress Project RULES GOVERN

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

38. 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

39. Shared clinical/genomic data sharing and analysis will
maximize clinical impact and enable discovery

40. Non-­‐Responders Project
To identify Non-Responders to approved
Oncology drug regimens in order to improve
outcomes, spare patients unnecessary toxicities
from treatments that have no benefit to them, and
reduce healthcare costs

41. The Non-­‐Responder Cancer Project Leadership Team
Stephen Friend, MD, PhD
Todd Golub, MD
President and Co-Founder of Founding Director Cancer Biology
Program Broad Institute, Charles Dana
Sage Bionetworks, Head of
Merck Oncology 01-08, Investigator Dana-Farber Cancer
Founder of Rosetta Institute, Professor of Pediatrics Harvard
Inpharmatics 97-01, co- Medical School, Investigator, Howard
Founder of the Seattle Project Hughes Medical Institute
Richard Schilsky, MD
Garry Nolan, PhD Chief, Hematology- Oncology, Deputy
Professor, Baxter Laboratory of Stem
Director, Comprehensive Cancer
Cell Biology, Department of Microbiology
Center, University of Chicago; Chair,
and Immunology, Stanford University
National Cancer Institute Board of
Director, Proteomics Center at Stanford
Scientific Advisors; past-President
University
ASCO, past Chairman CALGB clinical
trials group
11

42. The
Non-­‐Responder
Project
is
an
internaOonal
iniOaOve
with
funding
for
6
iniOal
cancers
anOcipated
from
both
the
public
and
private
sectors
GEOGRAPHY
United
States
China
TARGET
CANCER
Ovarian
Renal
Breast
AML
Colon
Lung
RetrospecOve
FUNDING
SOURCE
Seeking
private
sector
and
study;
likely
to
Funded
by
the
Chinese
government
philanthropic
funding
for
be
funded
by
and
private
sector
partners
the
Federal
prospec8ve
studies
Government
5

43. Arch2POCM
Restructuring the PrecompeOOve
Space for Drug Discovery
How to potenOally De-­‐Risk
High-­‐Risk TherapeuOc 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 FederaOon

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

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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Biological Age
Biological Age
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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
+
narraOve
=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
(AcOvaOng PaOents)
John Wilbanks

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

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

61. And Open Medical Information Systems