Friend UCSF 2012-07-27

Why not use data intensive science to build
better models of diseases together?
– beyond current rewards

Stephen
H
Friend
MD
PhD

Sage
Bionetworks
(non-‐proﬁt)

July
27,
2012

UCSF

So
what
is
the
problem?

Most
approved
therapies
were
assumed
to
be

monotherapies
for
diseases
represen4ng
homogenous

popula4ons

Our
exis4ng
disease
models
o9en
assume
pathway

knowledge
suﬃcient
to
infer
correct
therapies

The value of appropriate representations/ maps

“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”

WHY
NOT
USE

“DATA
INTENSIVE”
SCIENCE

TO
BUILD
BETTER
DISEASE
MAPS?

what will it take to understand disease?

DNA

RNA
PROTEIN
(dark
maVer)

MOVING
BEYOND
ALTERED
COMPONENT
LISTS

2002 Can one build a “causal” model?

Preliminary Probabalistic Models- Rosetta /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

DIVERSE
POWERFUL
USE
OF
MODELS
AND
NETWORKS

List of Influential Papers in Network Modeling

  50 network papers
  http://sagebase.org/research/resources.php

“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

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

TENURE

FEUDAL
STATES

Clinical/genomic data
are accessible but minimally usable

Little incentive to annotate and curate
data for other scientists to use

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

Lack of standard forms for future rights and consents

Background:
Informaon
Commons
for
Biological
Funcons

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

Sage Bionetworks Collaborators

  Pharma Partners
  Merck, Pfizer, Takeda, Astra Zeneca,
Amgen, Johnson &Johnson
  Foundations
  Kauffman CHDI, Gates Foundation

  Government
  NIH, LSDF, NCI

  Academic
  Levy (Framingham)
  Rosengren (Lund)
  Krauss (CHORI)

  Federation
  Ideker, Califano, Nolan, Schadt 27

Biomedical Information Commons

Better Models of
Disease:
INFORMATION
COMMONS

Products/Approaches
Technology Platform

Governance
Impactful Models Better Models of
Disease:
INFORMATION
COMMONS

Challenges

IT

Cons4tuencies

Individual

Pa4ents

Technology
PlaHorm

Biotech

Governance

ImpacHul
Models
BeVer
Models
Pa4ent

of
Disease:
Founda4ons

INFORMATION

COMMONS

Pharma
Challenges

Joint
Pa4ent/
Scien4st

Academic
Communi4es

Consor4a

Ongoing
Sage
Bionetworks
Ini4a4ves

IT
Individual

Pharma
EU
Pa4ents

Roche PARTICIPATION

RNDP/FA/MEL

Takeda
Technology
PlaHorm

Communi2es
engaging

COMMONS
PLATFORM

Governance

WPP
ImpacHul
Models

Pa4ent

BeVer
Models
of

Founda4ons

Biotech
Disease:

INFORMATION

COMMONS

Common
Mind/
BrCA/Challenges

Mt.
Sinai
Neuro
Cell
Line

Challenges

Challenge

TCGA/Challenge
Joint
Pa4ent/
SB/Gates
Academic
Scien4st

Discovery

Consor4a
ClearScience
Communi4es

Sage CCSB Network

Impactful Models
Breast Cancer: Co-expression Networks
A) Miller 159 samples B) Christos 189 samples
NKI: N Engl J Med. 2002 Dec 19;347(25):1999.

Wang: Lancet. 2005 Feb 19-25;365(9460):671.

Miller: Breast Cancer Res. 2005;7(6):R953.

Christos: J Natl Cancer Inst. 2006 15;98(4):262.

C) NKI 295 samples

E) Super modules

Cell
cycle

Pre-mRNA

ECM
D) Wang 286 samples Blood vessel

Immune
response

33
Zhang B et al., Towards a global picture of breast cancer (manuscript).

CHRIS
GAITERI-‐ALZHEIMER’S

What
is
this?

Bayesian
networks
enriched

in
inflammaon
genes

correlated
with
disease

severity
in
pre-‐frontal

cortex
of
250
Alzheimer’s

paents.

What
does
it
mean?

Inflammaon

in
AD
is
an

interacve
mul-‐pathway

system.

More
broadly,

network
structure
organizes

complex
disease
effects
into

coherent
sub-‐systems
and

can
priorize
key
genes.

Are
you
joking?

Gene
validaon
shows

novel
key
drivers
increase

Abeta
uptake
and
decrease

neurite
length
through
an

ROS
burst.
(highly
relevant

to
AD
pathology)

IMPACTFUL
MODELS

A
mul-‐ssue
immune-‐driven
theory
of
weight
loss

Hypothalamus

Lep4n

signaling

FaNy
acids

Macrophage/

inﬂamma4on

Liver
Adipose

M1
macrophage

Phagocytosis-‐
Phagocytosis-‐

induced
lipolysis
induced
lipolysis

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

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

Leveraging Existing Technologies

Addama

Taverna
tranSMART

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

Most of the People You Need to Work with Don’t Work with You

My Other Computer is “The Cloud”

Data Analysis with Synapse

Run Any Tool

On Any Platform

Record in Synapse

Share with Anyone

•  Automated
workﬂows
for
curaon,
QC,
and
sharing
of

1%/2* 53,'6%(* !7"(%,2/"* large-‐scale
datasets.

-./#"++0%(* (3&4"#*
•  All
of
TCGA,
GEO,
and
user-‐submiVed
data

processed
with
standard
normalizaon
methods.

1%/2* 53,'6%(* !7"(%,2/"* •  Searchable
TCGA
data:

-./#"++0%(* (3&4"#* •  23
cancers

•  11
data
plahorms

•  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%(*

!"#$%#&'()"*
'++"++&"(,*

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
sensivity
to
130
drugs.

•  Extending
pipeline
to
evaluate
predicon
of

-./#"++0%(* -./#"++0%(*
!7"(%,2/"* !7"(%,2/"* TCGA
phenotypes.

1%/2* 1%/2*
(3&4"#* (3&4"#* •  Hosng
of
collaborave
compeons
to
compare

53,'6%(* 53,'6%(* models
from
many
groups.

1--'&2-3$4567$

!#"80)69"*&%8":*
*&+%,-./0$

;"("#'6%(*

!"#$%#&'()"*
'++"++&"(,*

!"#$%&'()$

INTEROPERABILITY
SYNAPSE

Genome Pattern
CYTOSCAPE
tranSMART
I2B2
INTEROPERABILITY

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

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

•  Graphic
of
curated
to
qced
to
models

Arch2POCM

Restructuring
the
Precompeve

Space
for
Drug
Discovery

How
to
potenally
De-‐Risk

High-‐Risk
Therapeuc
Areas

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

Pfizer CTI/ Industrial PPPs
Academic Unions

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)

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

Portable
Legal
Consent

(Acvang
Paents)

John
Wilbanks

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

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

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

Sage/DREAM Challenge: Details and Timing
Phase
1: July thru end-Sep 2012 Phase
2:
Oct 1 thru Nov 12, 2012
  Training data: 2,000 breast cancer
samples from METABRIC cohort   Evaluation of models in novel
dataset.
•  Gene expression
•  Copy number   Validation data: ~500 fresh frozen
•  Clinical covariates tumors from Norway group with:
•  10 year survival •  Clinical covariates
  Supporting data: Other Sage-curated •  10 year survival
breast cancer datasets
•  >1,000 samples from GEO   Gene expression and copy number
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-September
  Winners announced at November
12 DREAM conference
  Will evaluate accuracy of models built
on METABRIC data to predict survival
in:
•  Held out samples from
METABRIC
•  Other datasets

METABRIC
cohort:

1%/2* 53,'6%(* !7"(%,2/"* 997
breast
cancer
samples

-./#"++0%(* (3&4"#*
Clinical
covariates

1%/2* 53,'6%(* !7"(%,2/"*
-./#"++0%(* (3&4"#* Gene
expression

(Illumina
HT12v3)

Copy
number

(Aﬀy
SNP
6.0)

-./#"++0%(* -./#"++0%(*
!7"(%,2/"* !7"(%,2/"*
1%/2* 1%/2*
(3&4"#* (3&4"#*
53,'6%(* 53,'6%(*

10
year
survival

!#"80)69"*&%8":*
;"("#'6%(*

!"#$%#&'()"*
'++"++&"(,*

Loaded
through
Synapse
R
client
as

Bioconductor
objects.

1%/2* 53,'6%(* !7"(%,2/"*
-./#"++0%(* (3&4"#*

1%/2* 53,'6%(* !7"(%,2/"*
-./#"++0%(* (3&4"#*

-./#"++0%(* -./#"++0%(*
!7"(%,2/"* !7"(%,2/"*
1%/2* 1%/2* Custom
models
implement
train()
and

(3&4"#* (3&4"#* predict()
API.

53,'6%(* 53,'6%(*

!#"80)69"*&%8":*
;"("#'6%(*

!"#$%#&'()"*
'++"++&"(,*
Implementa)on
of
simple
clinical-‐only
survival

model
used
as
baseline
predictor.

Models
submiNed
and

Federa4on
modeling
evaluated
in
real-‐4me

compe44on
leaderboard

>200
models
tested
within
3
months

9-.1+:2%
712<2:4=($)
5"8+,%
&,%726(%*+,F) >#,%7+-#"34,#) >4<+<)
71#G8#,%H"4#,') D+"6%I4'+<)

?'+C%D+"F2<4,)

?,'"#+%
9+-"+:%
*-.14,%9-4,,#$) >#,%J2F.'2,) @+<4A+,2)
5"46%768+'1) ;+,'#$)
B4.8+4%
9+""$%E2<+,) 784C2,4)

!"#$%&'#(#")
D-(#.8%
*+,-./%0-1(23.(4) >+,.+<) D+"4+,2%
?<:+"#/)

Summary
hVps://synapse.sagebase.org/
-‐
BCCOverview:0

Transparency,
Valida4on
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%(*

!"#$%#&'()"*
'++"++&"(,*

Publica4on
in
Science
Dona4on
of
Google-‐
Transla4onal
Medicine
scale
compute
space.

For
the
goal
of
promo4ng
democra4za4on
of
medicine…

Registra4on
star4ng
NOW…

sign
up
at:

synapse.sagebase.org

SUMMARY

These
new
data
intensive
models
of
disease

will
be
strikingly
powerful

They
will
not
arise
within
the
current
academic/industrial
loop

They
will
be
harder
and
more
expensive
than
we
can
accept

Cizenss
as
donors
of
data
insights
and
funds
will
be
crical

For
these
beneﬁts
to
be
realized
-‐
must
become
aﬀordable

therefore
we
willl
need:

Compute
spaces

A
Commons

New
ways
of
being
rewarded

More
eyeballs
working
without
being
paid

More
willingness
to
share
ll
aoer
Clinical
Proof
of
Concept

Alignments between the UCSF Strategic Plan
and the matchstick pilots for the Information
Commons being pursued by Sage Bionetworks

•  Invest in infrastructure that enables UCSF to excel in basic,
clinical and population science- SYNAPSE as a compute space
-links with Michael Wiener/ OneMind

•  Build a Bioinformatics initiative across all school by
June
2014- Impactful Models / Challenges with Laura Esserman/
LauraVant’Veer

•  Enhance existing data repositories and mining tools by June
2012 - Sage collaborations with Alex Pico, Geoff Manley

•  Develop infrastructure to support new team-based,
interdisciplinary learning models- PLC (The Federation?)

•  Accelerate translation of groundbreaking science into
therapies Arch2POCM

Friend UCSF 2012-07-27

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Friend UCSF 2012-07-27