Stephen Friend, Nov 2, 2011. Norwegian Academy of Science and Letters, Oslo, Norway

1. Future of Genetics in Medicine
Stephen Friend MD PhD
Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam
The Norwegian Academy of Science and Letters, Oslo
November 2, 2011

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

6. Alzheimers Diabetes
Treating Symptoms v.s. Modifying Diseases
Autism Cancer
Will it work for me?

7. The Current Pharma Model is Broken:
• In 2010, the pharmaceutical industry spent ~$100B for R&D
• Half of the 2010 R&D spend ($50B) covered pre-PH III activities
• Half of the pre-PH III costs ($25B) were for program targets that at
least one other pharmaceutical company was actively pursuing
• Only 8% of pharma company small molecule PCCs make it to PH III
• In 2010, only 21 new medical entities were approved by FDA
7 7

8. What is the problem?
• Regulatory hurdles too high?
• Low hanging fruit picked?
• 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

9. What is the problem?
We need a better understand disease biology
before testing proprietary compounds on sick
patients

10. What is the problem?
Most approved therapies assumed indications
represent homogenous populations
Our existing disease models often assume
pathway knowledge sufficient to infer correct
therapies

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

12. Reality: Overlapping Pathways

13. The value of appropriate representations/ maps

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

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

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

20. 2002 Can one build a “causal” model?

21. 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
21
Integrated Genetics Approaches

22. Causal Inference

23. Constructing Co-expression Networks
Start with expression measures for ~13K genes most variant genes across 100-150 samples
1 2 3 4 Note: NOT a gene
expression heatmap
1
1 0.8 0.2 -0.8
Establish a 2D correla9on 2
matrix for all gene pairs
expression
0.8 1 0.1 -0.6
3
0.2 0.1 1 -0.1
4
-0.8 -0.6 -0.1 1
Brain sample
Correla9on Matrix
Define Threshold
eg >0.6 for edge
1 2 4 3 1 2 3 4
1 1
1 4 1 1 1 0 1 1 0 1
2 2
1 1 1 0 1 1 0 1
Iden9fy 4 1 1 1 0 Hierarchically 3
0 0 1 0
2 3 modules cluster
3 0 0 0 1 4
1 1 0 1
Network Module Clustered Connec9on Matrix Connec9on Matrix
sets of genes for which many
pairs interact (relaPve to the
total number of pairs in that
set)

24. Constructing Bayesian Networks

25. Preliminary Probalistic Models- Rosetta- Eric 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

26. Map compound signatures to disease networks
Sub-­‐network contains genes Compound Gene expression
associated with diabetes traits signatures
Sub-­‐network
contains genes
associated
with toxici5es
1
2
3
Sub-­‐network contains genes
Tissue Disease Networks associated with obesity traits
Compound 1: Drug signature significantly enriched in subnetwork associated with diabetes traits
Compound 2: Drug signature significantly enriched in subnetwork associated with obesity traits
Compound 3: Drug signature significantly enriched in subnetwork associated with obesity traits
BUT also in subnetwork associated with toxicities

27. 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.

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

29. (Eric Schadt)

30. “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 computational models
in a “Compute Space F

31. hunter gathers - not sharing

32. TENURE FEUDAL STATES

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

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

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

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

37. 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

38. 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 38

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

40. Sage Datasets- 49+
40

41. Sage Neuro Collaborations
Neurodegenerative
• Huntington’s Disease : Marcy MacDonald/Jim Gusella (MGH)
• $2.5M Grant to CHDI to fund generation of RNA-seq data for brain regions and peripheral tissues
for well phenotyped cohorts (also methyl genome studies)
• HD/AD/PD : MacDonald/Gusella (MGH), Myers (BU), Paulsen (Iowa)
• $6.8M RC4 grant to NIH to fund generation of SNP and RNA-seq data for brain regions from HD,
AD, PD for well phenotyped cohorts
• Alzheimer’s Disease: Green (BU), Johnson (UWM)
• Collaboration opportunity around longitudinal neuroimaging & cognitive phenotyped cohorts
(ADNI, ADGC, etc). Intersect gene expression studies. Funding for further data generation
Psychiatric
• Autism/ Schizophrenia- Consortium
Sleep & Stress
• Genetics of Sleep: Turek (Northwestern)
• Collaboration with Turek lab & Merck focused on mouse. DARPA
• Enabling Stress Resistance
• DARPA-funded collaboration with Turek lab to look at genetic and brain molecular mechanisms
that regulate physical and emotional stress responses in mouse
• Currently looking to expand to human 41

42. Alzheimer’s
Disease
• Cross-­‐Pssue
coexpression
networks
for
both
normal
and
AD
brains
– prefrontal
cortex,
cerebellum,
visual
cortex
• DifferenPal
network
analysis
on
AD
and
normal
networks
• Integrate
coexpression
networks
and
Bayesian
networks
to
idenPfy
key
regulators
for
the
modules
associated
with
AD
42

43. IdenPficaPon of Disease (AD) Pathways via ComparaPve
Gene Network Analysis
40,000 genes from three Pssues
Glutathione transferase
Gain connecPvity by 91 fold
AD
(PFC, CB, VC)
nerve ensheathment
Control
Lose connecPvity by 40%
(PFC, CB, VC)
Module ConnecPvity Change (AD/Normal)
43
Bayesian Subnetworks

44. DifferenPally
Connected
Modules
in
AD
• Unfolded
protein
response
(UPR)
• AKT

HIF1

VEGF
• Olfactory
receptor
acPvity
• Sensory
percepPon
of
smell
chemical
sPmulus
• Inflammatory
Response
• Extra
cellular
matrix
(ECM)
• SynapPc
transmission
(suppressed)
• Nerve
ensheathment
44
44
44

45. Key Regulators
GlutathioneTransferase NerveEnsheathment ExtracellularMatrixPECAM1: Platelet-­‐endothelial cell
adhesion molecule, a tyrosine
phosphatase acPvator that plays a
role in the platelet acPvaPon,
increased expression correlates
with MS, Crohn disease, chronic B-­‐
cell leukemia, rheumatoid arthriPs,
and ulceraPve coliPs
ENPP2: Phosphodiesterase I alpha,
a lysophospholipase that acts in
chemotaxis, phosphaPdic acid
biosynthesis, regulates apoptosis
and PKB signaling; aberrant
expression is associated with
Alzheimer type demenPa, major
depressive disorder, and various
cancers
SLC22A25: solute carrier family 22,
member 25, Protein with high
similarity to mouse Slc22a19, which
is a renal steroid sulfate transporter
that plays a role in the uptake of
estrone sulfate, member of the
sugar (and other) transporter family
and the major facilitator
superfamily
Glutathione Transferase Module (Pink)
• 983 probes from all three brain regions (9% from CB, 15% from PFC and 76% from VC)
45
• Most predicPve of Braak severity score

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

48. Leveraging Existing Technologies
Addama
Taverna
tranSMART

49. INTEROPERABILITY
SYNAPSE
Genome Pattern
CYTOSCAPE
tranSMART
I2B2
INTEROPERABILITY

50. 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

51. Six Pilots at Sage Bionetworks
CTCAP
Arch2POCM
The FederaPon
M
S
FOR
MAP
Portable Legal Consent
PLAT
Sage Congress Project
EW N
Ashoka/Sage MedXChange RULES GOVERN

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

53. 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

54. Arch2POCM
Restructuring the PrecompePPve
Space for Drug Discovery
How to potenPally De-­‐Risk
High-­‐Risk TherapeuPc Areas

56. The FederaPon

57. 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

59. 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
! ! !!
!
! !
!! ! !
! !
! !
! ! ! !! !
! ! !
! ! !!!!
!! ! ! ! !! !
! !! !
! !!!
! ! !! !
80
Biological Age
Biological Age
!! !
!!!! !!
! !! !
! ! ! !!! !
80
! !!!
!! ! ! ! !
!! !
! !!!!
!!! !
!! ! ! ! !!!
!! ! !
! !!!
! !! !!!!
! ! ! ! ! !!
!! !! ! ! !
!!
! !!! !
! !!
! !!
! !!
! ! !!! !
!! !
! !!!! ! !
! ! !! !
!! !
! ! !! !!
!!! !!! !
! !
!! !
! ! !!! ! !!
!
!
!!! !!
!! !
!
! ! !! ! !
!
60
!!! ! ! !
! ! !
! ! !
! !! !
60
! ! ! !!
!! ! ! !
! !
!!
! !!!!
! ! !
! ! !! !
! !!
!! ! !!
! ! !
! !
! 40
40
! !
40 50 60 70 80 90 100 40 50 60 70 80 90
Chronological Age Chronological Age

60. 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)

61. 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

62. Federated
Aging
Project
:
Combining
analysis
+
narraPve
=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

63. Portable Legal Consent
(AcPvaPng PaPents)
John Wilbanks

67. Sage Congress Project
April 20 2012
RA
Parkinson’s
Asthma
(Responders CompePPons)

68. Ashoka/Sage
MedXChange

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

70. IMPACT ON PATIENTS

71. IMPACT ON PATIENTS

72. IMPACT ON PATIENTS