Stephen Friend, June 28, 2012. WIN Symposium, Paris, FR

1. The
Marriage
of
Transla/onal
Medicine
to
“Big
Data”:
Key
Opportuni/es
to
change
how
we
do
our
Science
Stephen
H
Friend
June
28,
2012
WIN

2. Background:
Informa/on
Commons
for
Biological
Func/ons

3. Oncogenes only make good targets in particular molecular
contexts : EGFR story
ERBB2
• EGFR
Pathway
commonly
mutated/ac/vated
in
Cancer
EGFRi EGFR • 30%
of
all
epithelial
cancers
BCR/ABL
• Blocking
Abs
approved
for
treatment
of
metasta/c
colon
cancer
KRAS NRAS
• Subsequently
found
that
RASMUT
tumors
don’t
respond
–
“Nega/ve
Predic/ve
Biomarker”
BRAF
• However
s/ll
EGFR+
/
RASWT
pa/ents
who
don’t
MEK1/2 respond?
–
need
“Posi/ve
Predic/ve
Biomarker”
• And
in
Lung
Cancer
not
clear
that
RASMUT
status
is
Proliferation,
Survival useful
biomarker
Predic/ng
treatment
response
to
known
oncogenes
is
complex
and
requires
detailed
understanding
of
how
different
gene/c
backgrounds
func/on

4. what will it take to understand disease?
DNA
RNA
PROTEIN
MOVING
BEYOND
ALTERED
COMPONENT
LISTS

5. Familiar but Incomplete

6. Reality: Overlapping Pathways

8. Preliminary Probabalistic Models- Rosetta
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

9. 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)
d
“..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.

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

11. Sage Bionetworks
A non-profit organization with a vision to enable networked team
approaches to building better models of disease
BIOMEDICINE INFORMATION COMMONS INCUBATOR
Building Disease Maps Data Repository
Commons Pilots Discovery Platform
Sagebase.org

12. Sage Bionetworks Collaborators
 Pharma Partners
 Merck, Pfizer, Takeda, Astra Zeneca,
Amgen,Roche, Johnson &Johnson
 Foundations
 Kauffman CHDI, Gates Foundation
 Government
 NIH, LSDF, NCI
 Academic
 Levy (Framingham)
 Rosengren (Lund)
 Krauss (CHORI)
 Federation
 Ideker, Califano, Nolan, Schadt 12

13. Fundamentally
Biological
Science
hasn’t
changed
yet
because
of
the
‘Omics
Revolu/on……
…..it
is
s/ll
about
the
process
of
linking
a
system
to
a
hypothesis
to
some
data
to
some
analyses
Biological Data Analysis
System

14. Driven
by
molecular
technologies
we
have
become
more
data
intensive
leading
to
more
specializa/on:
data
generators
(centralized
cores),
data
analyzers
(bioinforma/cians),
validators
(experimentalists:
lab
&
clinical)
This
is
reflected
in
the
tendency
for
more
mul/
lab
consor/um
style
grants
in
which
the
data
generators,
analyzers,
validators
may
be
different
labs.
Single Lab Model Data
• R01 Funding
• Hypothesis->data->analysis->paper
• Small-scale data / analysis
• Reproducible? Biological Analysis
System
Multiple Lab Model
Data
• P01 Funding
• Hypothesis->data->analysis->paper
• Medium-scale data / analysis
• Data Generators/Analysts/Validators maybe
different groups Biological Analysis
• Reproducible? System

15. Iterative Networked Approaches
To Generating Analyzing and Supporting New Models
Data
Biological
System Analysis
Uncouple the automatic linkage between the
data generators, analyzers, and validators

16. Networked Approaches
BioMedicine Information Commons
Patients/
Citizens
Data
Generators
CURATED
DATA
Data
TOOLS/ Analysts
METHODS
RAW
DATA
ANALYZES/
MODELS
Clinicians
SYNAPSE
Experimentalists

17. Networked Approaches 2
1
REWARDS
USABLE
RECOGNITION
DATA
BioMedical Information Commons
Patients/
Citizens
Data
Generators
CURATED
DATA
Data
5
TOOLS/ 3
Analysts
REWARDS
METHODS HOW
TO
FOR
RAW DISTRIBUTE
SHARING
DATA TASKS
ANALYZES/
MODELS
Clinicians
4
PRIVACY
SYNAPSE
Experimentalists
BARRIERS

18. Open and Networked Approaches:Democratization of Science
1
USABLE
DATA
SYNAPSE
2
REWARDS
RECOGNITION
SYNAPSE

19. Two approaches to building common
scientific and technical 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

20. Synapse is GitHub for Biomedical Data
Every code change versioned
Every issue tracked
Data and code versioned Every project the starting point for new work
Analysis history captured in real time All evolving and accessible in real time
Work anywhere, and share the results with anyone Social Coding
Social Science

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

22. Leveraging Existing Technologies
Addama
Taverna
tranSMART

23. sage bionetworks synapse project
Watch What I Do, Not What I Say

24. sage bionetworks synapse project
Most of the People You Need to Work with Don’t Work with You

25. sage bionetworks synapse project
My Other Computer is “The Cloud”

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

27. • Automated
workflows
for
cura/on,
QC,
and
sharing
of
1%/2* 53,'6%(* !7"(%,2/"* large-­‐scale
datasets.
-./#"++0%(* (3&4"#*
• All
of
TCGA,
GEO,
and
user-­‐submined
data
processed
with
standard
normaliza/on
methods.
1%/2* 53,'6%(* !7"(%,2/"* • Searchable
TCGA
data:
-./#"++0%(* (3&4"#* • 23
cancers
• 11
data
plaoorms
• Standardized
meta-­‐data
ontologies
-./#"++0%(* -./#"++0%(*
!7"(%,2/"* !7"(%,2/"*
1%/2* 1%/2*
(3&4"#* (3&4"#*
53,'6%(* 53,'6%(*
!#"80)69"*&%8":*
;"("#'6%(*
!"#$%#&'()"*
'++"++&"(,*

28. 1%/2* 53,'6%(* !7"(%,2/"* • Comparison
of
many
modeling
approaches
applied
-./#"++0%(* (3&4"#*
to
the
same
data.
• Models
transparently
shared
and
reusable
through
-./#"++0%(*
1%/2* 53,'6%(* !7"(%,2/"* Synapse.
(3&4"#*
• Displayed
is
comparison
of
6
modeling
approaches
to
predict
sensi/vity
to
130
drugs.
• Extending
pipeline
to
evaluate
predic/on
of
-./#"++0%(* -./#"++0%(*
!7"(%,2/"* !7"(%,2/"* TCGA
phenotypes.
1%/2* 1%/2*
(3&4"#* (3&4"#* • Hos/ng
of
collabora/ve
compe//ons
to
compare
53,'6%(* 53,'6%(* models
from
many
groups.
1--'&2-3$4567$
!#"80)69"*&%8":*
*&+%,-./0$
;"("#'6%(*
!"#$%#&'()"*
'++"++&"(,*
!"#$%&'()$

29. REDEFINING HOW WE WORK TOGETHER:
Sage/DREAM Breast Cancer Prognosis Challenge
3
HOW
TO
COLLABORATIVE
DISTRIBUTE
CHALLENGES
TASKS

30. What
is the problem?
Our current models of disease biology are primitive and limit
doctor’s understanding and ability to treat patients
Current incentives reward those who
silo information and work in closed
systems 30

31. The Solution: Competitions to crowd-source research
in biology and other fields
 Why competitions?
• Objective assessments
• Acceleration of progress
• Transparency
• Reproducibility
• Extensible, reusable models
 Competitions in biomedical research
• CASP (protein structure)
• Fold it / EteRNA (protein / RNA structure)
• CAGI (genome annotation)
• Assemblethon / alignathon (genome assembly / alignment)
• SBV Improver (industrial methodology benchmarking)
• DREAM (co-organizer of Sage/DREAM competition)
 Generic competition platforms
• Kaggle, Innocentive, MLComp
31

32. The Sage/DREAM breast cancer prognosis
challenge
Goal: Challenge to assess the accuracy of computational models designed to
predict breast cancer survival using patient clinical and genomic data
Why this is unique:
 This Sage/DREAM Challenge is a pre-collated cohort: 2000 breast cancer samples
from the Metabric cohort
 Accessible to all: A cloud-based common compute architecture is being made
available by Google to support the computational models needed to develop and test
challenge models
 New Rigor:
• Contestants will evaluate their models on a validation data set composed of newly generated
data (provided by Dr. Anne-Lise Borreson Dale)
• Contestants must demonstrate their models can be reproduced by others
 New incentives: leaderboard to energize participants, Science Translational Medicine
publication for winning team
 Breast cancer patients, funders and researchers can track this Challenge on BRIDGE,
an open source online community being built by Sage and Ashoka Changemakers and
affiliated with this Challenge
32

33. Sage/DREAM Challenge: Details and Timing
Phase
1: Apr thru end-Sep 2012 Phase
2:
Oct 1 thru Nov 12, 2012
 Training data: 2,000 breast cancer  Evaluation of models in novel
samples from METABRIC cohort dataset.
• Gene expression
• Copy number
 Validation data: ~500 fresh frozen
• Clinical covariates tumors from Norway group with:
• 10 year survival • Clinical covariates
• 10 year survival
 Supporting data: Other Sage-
curated breast cancer datasets
 Gene expression and copy number
• >1,000 samples from GEO data to be generated for model
• ~800 samples from TCGA evaluation
• ~500 additional samples from • Sent to Cancer Research UK to
Norway group generate data at same facility as
• Curated and available on METABRIC
Synapse, Sage’s compute • Models built on training data
platform evaluated on newly generated
data
 Data released in phases on
Synapse from now through end-  Winners announced at November
September 12 DREAM conference
 Will evaluate accuracy of models
built on METABRIC data to predict
survival in:
• Held out samples from
METABRIC 33
• Other datasets

34. Summary
Transparency,
Valida;on
in
novel
reproducibility
-./#"++0%(*
1%/2*
(3&4"#* 53,'6%(* !7"(%,2/"*
dataset
1%/2* 53,'6%(* !7"(%,2/"*
-./#"++0%(* (3&4"#*
-./#"++0%(* -./#"++0%(*
!7"(%,2/"* !7"(%,2/"*
1%/2* 1%/2*
(3&4"#* (3&4"#*
53,'6%(* 53,'6%(*
!#"80)69"*&%8":*
;"("#'6%(*
!"#$%#&'()"*
'++"++&"(,*
Publica;on
in
Science
Dona;on
of
Google-­‐
Transla;onal
Medicine
scale
compute
space.
For
the
goal
of
promo;ng
democra;za;on
of
medicine…
Registra;on
star;ng
NOW…
sign
up
at:
synapse.sagebase.org
34

35. Open and Networked Approaches
4
PRIVACY
PORTABLE
LEGAL
CONSENT:
weconsent.us
BARRIERS
John
Wilbanks

36. 5
REWARDS
FOR
SHARING
Arch2POCM
An
approach
to
speed
our
basic
understanding
of
the
consequences
of
targe/ng
novel
high
risk
drivers
of
disease
states
Clinical
valida/on
(Ph
IIa)
of
pioneer
targets

37. The Current R&D Ecosystem Is In Need of a New
Approach to Drug Development
• $200B per year in biomedical and drug discovery R&D
• Only a handful of new medicines are approved each year
• Productivity in steady decline since 1950
• >90% of novel drugs entering clinical trials fail, and negative POC
information is not shared
• Significant pharma revenues going off patent in next 5 years
• >30,000 pharma employees laid off from downsizing in each of last four
years
• 90% of 2013 prescriptions will be for generic drugs
37

38. Issues With Drug Discovery
1. The greatest attrition is at clinical proof-of-concept – once
a “target” is linked to a disease in the clinic, the risk of
failure is far lower
2. Most novel targets are pursued by multiple companies in
parallel (and most fail at clinical POC)
3. The complete data from failed trials are rarely, if ever,
released to the public
38

39. Open access research tools drive science
39

40. SGC: Open Access Chemical Biology
a great success
• PPP:
-­‐
GSK,
Pfizer,
Novar/s,
Lilly,
Abbon,
Takeda
-­‐
Genome
Canada,
Ontario,
CIHR,
Wellcome
Trust
• Based
in
Universi/es
of
Toronto
and
Oxford
• 200
scien/sts
• Academic
network
of
more
than
250
labs
• Generate
freely
available
reagents
(proteins,
assays,
structures,
inhibitors,
an/bodies)
for
novel,
human,
therapeu/cally
relevant
proteins
• Give
these
to
academic
collaborators
to
dissect
pathways
and
disease
networks,
and
thereby
discover
new
targets
for
drug
discovery
40

41. Some SGC Achievements
• Structural
impact
– SGC
contributed
~25%
of
global
output
of
human
structures
annually
– SGC
contributes
>40%
of
global
output
of
human
parasite
structures
annually
• High
quality
science
(some
publica/ons
from
2011)
Vedadi
et
al,
Nature
Chem
Biol,
in
press
(2011);
Evans
et
al,
Nature
Gene;cs
in
press
(2011);
Norman
et
al
Science
Transl
Med.
3(88):88mr1
(2011);
Kochan
G
et
al
PNAS
108:7745
(2011);
Clasquin
MF
et
al
Cell
145:969
(2011);
Colwill
et
al,
Nature
Methods
8:551
(2011);
Ceccarelli
et
al,
Cell
145:1075
(2011;
Strushkevich
et
al,
PNAS
108:10139
(2011);
Bian
et
al
EMBO
J
in
press
(2011)
Norman
et
al
Science
Trans.
Med.
3:76cm10
(2011);
Xu
et
al
Nature
Comm.
2:
art.
no.
227
(2011);
Edwards
et
al
Nature
470:163
(2011);
Fairman
et
al
Nature
Struct,
and
Mol.
Biol.
18:316
(2011);
Adams-­‐Cioaba
et
al,
Nature
Comm.
2
(1)
(2011);
Carr
et
al
EMBO
J
30:317
(2011);
Deutsch
et
al
Cell
144:566
(2011);
Filippakopoulos
et
al
Cell,
in
press;
Nature
Chem.
Biol.
in
press,
Nature
in
press
41

42. Open access to the clinic?
42

43. Most Novel Targets Fail at Clinical POC
Hit/
Target HTS Probe/ LO Clinical
Tox./ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery ID
ID
50% 10% 30% 30% 90+%
this is killing
our industry
…we can generate “safe” molecules, but they
are not developable in chosen patient group 43

44. This Failure Is Repeated, Many Times
Hit/
Target HTS Probe/ LO Clinical
Toxicology/ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery Hit/ ID
Target ID Clinical
Probe/ Toxicology/ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery Hit/ ID 30% 30% 90+%
Target ID Clinical
Probe/ Toxicology/ Phase Phase
ID/ Hit/ candidate
Target Lead Clinical Pharmacy I IIa/ b
Discovery Probe/ ID Toxicology/ Phase Phase
ID/ ID candidate 30% 30% 90+%
Lead Pharmacy I IIa/ b
Discovery Hit/ ID
Target ID Clinical
Probe/ Toxicology/
30% Phase
30% Phase
90+%
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery Hit/ ID
Target ID Clinical 30% 30% 90+%
Probe/ Toxicology/ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery Hit/ ID 30% 30% 90+%
Target ID Clinical
Probe/ Toxicology/ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery ID
ID 30% 30% 90+%
50% 10% 30% 30% 90+%
…and outcomes are not shared 44

45. A Possible Soution:Arch2POCM
An Open Access Clinical Validation PPP
• PPP
to
clinically
validate
(Ph
IIa)
pioneer
targets
• Pharma,
public,
academia,
regulators
and
pa/ent
groups
are
ac/ve
par/cipants
• Cul/vate
a
common
stream
of
knowledge
– Avoid
patents
– Place
all
data
into
the
public
domain
– Crowdsource
the
PPP’s
druglike
compounds
• In
–validated
targets
are
iden/fied
before
pharma
makes
a
substan/al
proprietary
investment
– Reduces
the
number
of
redundant
trials
on
bad
targets
– Reduces
safety
concerns
• Validated
targets
are
de-­‐risked
for
pharma
investment
– Pharma
can
ini/ate
proprietary
effort
when
risks
are
balanced
with
returns
– PPP
pharma
members
can
acquire
Arch2POCM
IND
for
validated
targets
and
benefit
from
shorter
development
/meline
and
data
exclusivity
for
sales
45

46. Arch2POCM: Scale and Scope
• Original Goal:
– Initiate 2 programs. One for Oncology/Epigenetics/Immunology. One for
Neuroscience/Schizophrenia/Autism.
– Both programs will have 8 drug discovery projects (targets)
– By Year 5, 30% of projects will have started Ph 1 and 20% will have completed
Ph Iia
– $200-250M over five years is projected as necessary to advance up to 8 drug
discovery projects within each of the two therapeutic programs
– By investing $1.6 M annually into one or both of Arch2POCM’s selected disease
areas, partnered pharmaceutical companies:
1. obtain a vote on Arch2POCM target selection
2. gain real time data access to Arch2POCM’s 16 drug discovery projects
3. have the strategic opportunity to expand their overall portfolio
• Revised Goal:
– Initiate 1-2 projects, (1-2 novel target mechanisms), as pilots to assess
Arch2POCM principle of sharing data and reagents till clinical validation
– In either Oncology or Neuroscience
– Specific target mechanisms to be determined by funders’ interest
– Interested funders include pharma, public research foundations and
venture philanthropists
46

47. Epigenetics: Exciting Science and Also A New Area
For Drug Discovery
Lysine
DNA
Histone
Modification Write Read Erase
Acetyl HAT Bromo HDAC
Methyl HMT MBT DeMethyl
47

48. The Case For Epigenetics/Chromatin Biology
1. There are epigenetic oncology drugs on the market (HDACs)
2. A growing number of links to oncology, notably many genetic links (i.e.
fusion proteins, somatic mutations)
3. A pioneer area: More than 400 targets amenable to small molecule
intervention - most of which only recently shown to be “druggable”, and
only a few of which are under active investigation
4. Open access, early-stage science is developing quickly – significant
collaborative efforts (e.g. SGC, NIH) to generate proteins, structures,
assays and chemical starting points
48

49. Poten;al
Targets-­‐
Bromodomain
Family
Evidence
that
this
target
plays
an
important
Maturity
of
the
Posi;ve
Data
showing
Mouse
knockout
model
(MGI)
role
in
tumors
(in
vitro,
in
vivo,
animal
program
evidence
of
a
failed
result
model
data)
the
of
the
compound
compound
for
playing
a
role
the
given
in
the
given
disease
disease
Expression
correlates
with
development
of
potent,
NA
NA
Homozygotes
for
a
null
allele
die
in
utero
before
SMARCA4
prostate
cancer
selec/ve,
cell
implanta/on.
Embryos
heterozygous
for
this
null
BUT
SMARCA4
in
general
acts
as
tumor
ac/ve
allele
and
an
ENU-­‐induced
allele
show
impaired
suppressor
and
is
necessary
for
genome
compound
defini/ve
erythropoiesis,
anemia
and
lethality
stability;
targeted
knockdown
of
SMARCA4
iden/fied
during
organogenesis.
Heterozygotes
show
poten/ates
lung
cancer
development;
cyanosis
and
cardiovascular
defects
and
are
pre-­‐
disposed
to
breast
tumors
Gastric
cancer;
mutated
in
CLL;
deple/on
of
potent,
NA
NA
Mice
homozygous
for
a
targeted
muta/on
in
this
SMARCA2A
BRM
causes
accelerated
progression
to
the
selec/ve,
cell
gene
may
exhibit
infer/lity
and
a
slightly
increased
differen/a/on
phenotype
ac/ve
body
weight
in
some
gene/c
backgrounds.
BUT
targeted
dele/on
is
causa/ve
for
the
compound
development
of
prosta/c
hyperplasia
in
mice
iden/fied
Transloca/on
of
CBP
with
MOZ,
monocy/c
potent,
NA
NA
Homozygotes
for
null
or
altered
alleles
die
around
CBP
leukemia
zinc
finger
protein
cause
acute
selec/ve,
cell
midgesta/on
with
defects
in
hemopoiesis,
blood
myeloid
leukemia
;
other
transloca/ons
ac/ve
vessel
forma/on,
and
neural
tube
closure.
involve
MLL
(HRX);
Mutated
in
ALL
BUT
CBP
compound
Heterozygotes
may
exhibit
skeletal,
cardiac,
and
has
also
been
proposed
as
a
classical
tumor
iden/fied
hematopoie/c
defects,
retarded
growth,
and
suppressor
hematologic
tumors.
Correlated
with
survival
of
high-­‐grade
Weak
hits
NA
NA
NA
ATAD2
osteosarcoma
pa/ents
ayer
chemo-­‐therapy;
required
for
breast
cancer
cell
prolifera/on
;
differen/ally
expressed
in
NSCLC
Transloca/ons
produce
BRD4-­‐NUT
fusion
JQ1
JQ1
in
BRD-­‐ NA
Homozygotes
for
a
gene-­‐trap
null
muta/on
die
BRD4
oncogene
causing
midline
carcinoma
NUT
fusion
soon
ayer
implanta/on.
Heterozygotes
exhibit
and
MLL
impaired
pre-­‐
and
postnatal
growth,
head
malforma/ons,
lack
of
subcutaneous
fat,
cataracts,
and
abnormal
liver
cells.
In
transgenic
mice,
cons/tu/ve
lymphoid
JQ1
JQ1
in
BRD-­‐ NA
Mice
homozygous
for
a
null
muta/on
display
BRD2
expression
of
Brd2
causes
a
malignancy
most
NUT
fusion
embryonic
lethality
during
organogenesis
with
similar
to
human
diffuse
large
B
cell
and
MLL
decreased
embryo
size,
decreased
cell
lymphoma
prolifera/on,
a
delay
in
the
cell
cycle,
and
increased
cell
death.
Heterozygous
mice
also
display
decreased
cell
prolifera/on.

50. Poten;al
Targets-­‐
Demethylases
Evidence
that
this
target
plays
an
important
role
in
Maturity
of
Posi;ve
Data
showing
a
Mouse
model
(MGI)
tumors
(in
vitro,
in
vivo,
animal
model
data)
the
program
evidence
of
the
failed
result
of
compound
the
compound
playing
a
role
in
for
the
given
the
given
disease
disease
Upregulated
in
prostate
cancer;
expression
is
higher
potent,
NA;
inhibits
NA
Mice
homozygous
for
a
knock-­‐out
allele
JMJD3
in
metasta/c
prostate
cancer
selec/ve,
TNF-­‐alpha
exhibit
perinatal
lethality
associated
with
BUT
JMJD3
contributes
to
the
ac/va/on
of
the
cell
ac/ve
produc/on
in
thick
alveolar
septum
and
absences
of
air
INK4A-­‐ARF
tumor
suppressor
locus
in
response
to
compound
macrophages
of
space
in
the
lungs.
Bone
marrow
chimera
oncogene
-­‐
and
stress-­‐induced
senescence.
iden/fied
RA
pa/ents
mice
derived
from
fetal
liver
cells
exhibit
impaired
eosinophil
recruitment
and
abnormal
response
to
helminth
infec/on.
High
levels
in
breast
cancer
cell
lines,
strong
No
progress
NA
NA
NA
JARID1B
expression
in
the
invasive
but
not
in
the
benign
components
of
primary
breast
carcinomas.
BUT
tumor
suppressor
in
melanoma
cells

51. Poten;al
Targets-­‐
Histone
Methyltransferases
Evidence
that
this
target
plays
an
important
role
in
Maturity
of
the
Posi;ve
evidence
Data
showing
a
tumors
(in
vitro,
in
vivo,
animal
model
data)
program
of
the
compound
failed
result
of
the
playing
a
role
in
compound
for
the
the
given
disease
given
disease
Recent
data
indicates
that
SETD8
deregulates
PCNA
Weak
inhibitors
NA
NA
SETD8
expression
by
degrada/on
accelerated
by
methyla/on
at
iden/fied
(8
microM)
K248.
Expression
levels
of
SETD8
and
PCNA
upregulated
in
in
chemistry
cancer
cells.
Cancer
Research
May
2012
Takawa
et
al.
op/miza/on.
EZH2
upregulated
in
cancer
cells.
Studies
on
mutants
potent,
selec/ve,
cell
NA
NA
EZH2
indicates
an
interes/ng
profile
where
both
wild-­‐type
and
ac/ve
compound
mutant
(Y641F)
are
required
for
malignant
phenotype.
iden/fied.
Sneeringer
et
al.
PNAS
2012.
Compounds
iden/fied
in
GSK
patents
WO
2011/140324
and
140315
and
WO
2012/005805
and
075080.
MMSET,
WHSC1,
NSD2
is
overexpressed
in
cancer
cells.
No
hits—currently
NA
NA
MMSET
Hudlebusch
et
al.
Clinical
Cancer
Res
2011
screening
Daigle
et
al.
Cancer
Cell
2011
elegantly
show
that
potent
potent,
selec/ve,
cell
Transgenic
mouse
DOT1L
DOT1L
inhibitors
kill
cells
containing
MLL
transloca/ons
ac/ve
compound
model
tumors
and
do
not
kill
cell
not
containing
the
transloca/ons
iden/fied.
shrunk
by
SC
dosing
of
inhibitor

52. Program Activities Grid For Arch2POCM
Ac;vity
Arch2POCM
Loca;on/Inves;gator
(TBD)
Target
Structure
Compound
libraries
Assay
development
for
epigene/c
screens
and
biomarkers
HTP
screens
for
epigene/c
hits
Med
Chem
SAR
To
ID
Two
Suitable
Binding
Arch2POCM
Test
Compounds
Non-­‐GLP
scaleup
of
Arch2POCM
Test
Compounds
and
associated
analy/cs
Distribu/on
of
Arch2POCM
Test
Compounds
PK,
PD,
ADME,
Tox
Tes/ng
GMP
Manufacturing
of
Arch2POCM
Test
Compounds
GMP
Formula/on
GMP
Drug
Storage
and
Distribu/on
IND
Prepara/on
Support
Clinical
Assay
Development
and
Qualifica/on
Ph
I-­‐II
Clinical
Trials
Ph
I-­‐II
Database
Management
and
CSR
Produc/on
52

53. Networked Approaches 2
1
REWARDS
USABLE
RECOGNITION
DATA
BioMedical Information Commons
Patients/
Citizens
Data
Generators
CURATED
DATA
Data
5
TOOLS/ 3
Analysts
REWARDS
METHODS HOW
TO
FOR
RAW DISTRIBUTE
SHARING
DATA TASKS
ANALYZES/
MODELS
Clinicians
4
PRIVACY
SYNAPSE
Experimentalists
BARRIERS