Use of Bionetworks to Build Maps of Diseases Moving Beyond

Stephen Friend MD PhD

Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ San Francisco

Fanconi Anemia Research Fund Annual Planning Meeting

January 21, 2012

Academia

Biotech

Industry

.

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

Why consider the fourth paradigm- data intensive science?

thinking beyond the narrative, beyond pathways

advantages of an open innovation compute space

Where are the precompetitive goalposts?

it is more about how than what

Familiar but Incomplete

Reality: Overlapping Pathways

WHY NOT USE “DATA INTENSIVE” SCIENCE

TO BUILD BETTER DISEASE MAPS?

Equipment capable of generating massive amounts of data

“Data Intensive Science”- “Fourth Scientific Paradigm” For building: “Better Maps of Human Disease”

Open Information System

IT Interoperability

Evolving Models hosted in a Compute Space- Knowledge Expert

It is now possible to carry out comprehensive monitoring of many traits at the population level

Monitor disease and molecular traits in populations

Putative causal gene

Disease trait

what will it take to understand disease?

DNA RNA PROTEIN (dark matter)

MOVING BEYOND ALTERED COMPONENT LISTS

2002 Can one build a “causal” model?

******BYRM

Yeast segregants

Synthetic complete medium

Logorithm growth

Gene expression

Yeas

t seg

rega

nts

genotypes

Public databases

Protein-protein interations

Transcription factor binding

sites

Bayesian network

Protein Metabolite interations

Data integration via Bayesian Network

Courtesy of Dr. Jun Zhu

Preliminary Probabalistic Models- Rosetta /Schadt

Gene symbol Gene name Variance of OFPM explained by gene expression*

Mouse model

Source

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

3a receptor 1 46% ko Purchased from Deltagen, CA

Tgfbr2 Transforming growth factor beta receptor 2

39% ko Purchased from Deltagen, CA

Networks facilitate direct identification of genes that are causal for disease

Evolutionarily tolerated weak spots

Nat Genet (2005) 205:370

db/db mouse (p~10E(-30))

AVANDIA in db/db mouse

= up regulated = down regulated

Our ability to integrate compound data into our network analyses

db/db mouse (p~10E(-20) p~10E(-100))

"Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003)

"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

"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

"Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005)

“..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009)

"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.

d

Metabolic Disease

CVD

Bone

Methods

Extensive Publications now Substantiating Scientific Approach Probabilistic Causal Bionetwork Models

• >80 Publications from Rosetta Genetics

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

List of Influential Papers in Network Modeling

(INTEGRATED BIONETWORK)

Recognition that the benefits of bionetwork based molecular models of diseases are powerful but that they require significant resources

Appreciation that it will require decades of evolving representations as real complexity emerges and needs to be integrated with therapeutic interventions

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

Sagebase.org

Data Repository

Discovery Platform

Building Disease Maps

Commons Pilots

Sage Bionetworks Collaborators

  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca,

Roche, Amgen

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  Foundations   CHDI, Gates Foundation

  Government   NIH, LSDF

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

  Federation   Ideker, Califarno, Butte, Schadt

RULES GOVERN

Engaging Communities of Interest

PLAT

FORM

NEW

MAP

S NEW MAPS

Disease Map and Tool Users- ( Scientists, Industry, Foundations, Regulators...)

PLATFORM Sage Platform and Infrastructure Builders-

( Academic Biotech and Industry IT Partners...)

RULES AND GOVERNANCE Data Sharing Barrier Breakers-

(Patients Advocates, Governance and Policy Makers,  Funders...)

NEW TOOLS Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape,

Clinical Trialists, Industrial Trialists, CROs…)

PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots-

(Federation, CCSB, Inspire2Live....)

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Example 1: Breast Cancer

Zhang B et al., manuscript

Bayesian Network

Survival Analysis

Coexpression Networks Module combination

Partition BN

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(25):1999-2009.

Wang Y et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 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 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 histologic grade to improve prognosis. J Natl Cancer Inst. 2006 Feb 15;98(4):262-72.

295 samples

286 samples

159 samples

189 samples

Generation of Co-expression & Bayesian Networks from published Breast Cancer Studies

Recovery of EGFR and Her2 oncoproteins downstream pathways by super modules

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

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Key Driver Analysis •  Identify 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

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A) Cell Cycle (blue)

C) Pre-mRNA Processing (brown)

B) Chromatin modification (black)

D) mRNA Processing (red)

Global driver

Global driver & RNAi validation

Signaling between Super Modules

(View Poster presented by Bin Zhang)

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.

Example 3: The Sage Federation

•  Founding Lab Groups

–  Seattle- Sage Bionetworks –  New York- Columbia: Andrea Califano –  Palo Alto- Stanford: Atul Butte –  San Diego- UCSD: Trey Ideker –  San Francisco: UCSF/Sage: Eric Schadt

–  NEW LABS: Gary Nolan Stanford/ David Haussler UCSC

•  Initial Projects –  Aging –  Diabetes –  Warburg

•  Goals: Share all datasets, tools, models Develop interoperability for human data

Federation’s Genome-wide Network and Modeling Approach

Califano group at Columbia Sage Bionetworks Butte group at Stanford

Genes Associated with Poor Prognosis are disproportionally found among the networks regulating the “glycolysis” Genes

Size of the node proportional to -log10 P value for recurrence free survival.

>5 fold enrichment of recurrence free prognostic genes with the Glycolysis BN module than random selection (p<1e-100)

P-Value<0.005

Inferred regulatory module for GGMSE Inferred regulatory module for Oxidative Phosphorylation and Sphingolipid

Metabolism genes

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

Synapse as a Github for building models of disease

Evolution of a Software Project

Biology Tools Support Collaboration

Potential Supporting Technologies

Taverna

Addama

tranSMART

Watch What I Do, Not What I Say Reduce, Reuse, Recycle

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

My Other Computer is Amazon

sage bionetworks synapse project

Arch2POCM

Restructuring the “Competitive” Phase of Drug Discovery

What is the described 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

What is the real problem?

We need to rebuild the drug discovery process so that we better understand disease biology before testing proprietary compounds on sick patients

The solution – Arch2POCM 1.  Create an Archipelago of clinicians and scientists from public

and private sectors to take projects from ideas to Proof of Clinical Mechanism (POCM)

2.  Arch2POCM is a collaborative, data-sharing network of scientists, whose drug discovery objective is to use robust compounds against new targets to disentangle the complexity of human biology, not to create a medicine

3.  Success? •  A compound that provides proof of concept for a novel target-

allowing companies to use this common information to compete, with dramatic increased chances of success

•  Culling targets with doomed mechanisms before multiple companies waste money exploring them - at $50M a pop

Why data sharing through to Phase IIb?

•  Most rapidly reveals limitations and opportunities associated with the target

•  Increases probability of success for internal proprietary programs

•  Scientific decisions are not influenced by market considerations or biased internal thinking

•  Target mechanism is only properly tested at Phase IIb

Why no IP on “Common Stream” compounds?

•  Allows multiple groups to test diverse indications without funds from Arch2POCM- crowdsourcing drug discovery

•  Broader and faster data dissemination

•  Far fewer legal agreements to negotiate

•  Generates “freedom to operate” on target because there are no patent thickets to wade through

•  Efficient way to access world’s top scientists and doctors without hassle

Existing Team Ready to Execute

APRIL  20-­‐21  

SAGE  BIONETWORKS  

SAGE  RESEARCH            FEDERATION                      CTCAP                                CITIZEN  LED  PROJECTS                                          SCIENTIST  LED  PROJECTS  

         Arch2POCM    “MedXChange-­‐Bridge”  

WEBSITES/COMMONS                                                    CONGRESS                                

                 SYNAPSE  CONSENTS  

Jan  12-­‐  APR  13  

2012-13: Year of Learning from our Pilots

IMPACT  ON  PATIENTS  

Actionable Disease Bionetwork Models

Open Medical Information Systems

Democratization of Science

Networked Science Approaches

OPPORTUNITIES FOR FANCONI’S COMMUNITY

Evolve Sharing of Data sets, Tools and Models

Joining Synapse Communities

Buiding your own “Federation Projects”

Paticipate in Sage Commons Congress April 20-21

Joining Arch2POCM

Change reward structures for sharing data (patients and academics)