stephen friend institute of development, aging and cancer 2011-11-28

Use of Bionetworks to build maps of disease: Moving beyond the linear

Integrating layers of omics data models and use of compute spaces

Stephen Friend MD PhD

Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam

International Symposium for 70th Anniversary IDAC November 29, 2011

Alzheimer’s Diabetes

Cancer Obesity Treating Symptoms v.s. Modifying Diseases

Will it work for me? Biomarkers?

Why not use data intensive science to build models of disease?

Current Reward Structures

Organizational Structures and Tools

Pilots

What is the problem?

Most approved therapies assume indications would represent homogenous populations

Our existing disease models often assume pathway knowledge sufficient to infer correct therapies

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

Reality: Overlapping Pathways

The value of appropriate representations/ maps

Equipment capable of generating massive amounts of data

“Data Intensive” Science- Fourth Scientific Paradigm

Open Information System

IT Interoperability

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 matter)

MOVING BEYOND ALTERED COMPONENT LISTS

2002 Can one build a “causal” model?

trait

How is genomic data used to understand biology?

“Standard” GWAS Approaches Profiling Approaches

“Integrated” Genetics Approaches

Genome scale profiling provide correlates of disease   Many examples BUT what is cause and effect?

Identifies Causative DNA Variation but provides NO mechanism

  Provide unbiased view of molecular physiology as it

relates to disease phenotypes

  Insights on mechanism

  Provide causal relationships and allows predictions

RNA amplification Microarray hybirdization

Gene Index

Tum

ors

Tum

ors

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Define a Gene Co-expression Similarity

Define a Family of Adjacency Functions

Determine the AF Parameters

Define a Measure of Node Distance

Identify Network Modules (Clustering)

Relate the Network Concepts to External Gene or Sample Information

Gene Co-Expression Network Analysis

Zhang B, Horvath S. Stat Appl Genet Mol Biol 2005

Constructing Co-expression Networks

Start with expression measures for genes most variant genes across 100s ++ samples

Note: NOT a gene expression heatmap

1 -0.1 -0.6 -0.8

-0.1 1 0.1 0.2

-0.6 0.1 1 0.8

-0.8 0.2 0.8 1 1

2

3

4

1 2 3 4

Correlation Matrix Brain sample

expr

essi

on

1 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 1

2

3

4

1 2 3 4

Connection Matrix

1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1

2

4

3

1 2 4 3

4 1

3 2

Establish a 2D correlation matrix for all gene pairs

Define Threshold eg >0.6 for edge

Clustered Connection Matrix

Hierarchically cluster

sets of genes for which many pairs interact (relative to the total number of pairs in that

set)

Network Module

Identify modules

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

"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

(Eric Schadt)

Equipment capable of generating massive amounts of data A-

“Data Intensive” Science- Fourth Scientific Paradigm Score Card for Medical Sciences

Open Information System D-

IT Interoperability 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

Assumption that genetic alterations in human conditions should be owned

Lack of standard forms for future rights and consents

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

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

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, Amgen, Johnson &Johnson

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  Foundations   Kauffman CHDI, Gates Foundation

  Government   NIH, LSDF

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

  Federation   Ideker, Califarno, Butte, Schadt

RULES GOVERN

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...)

PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots-

(Federation, CCSB, Inspire2Live....)

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

Clinical Trialists, Industrial Trialists, CROs…)

RULES AND GOVERNANCE Data Sharing Barrier Breakers-

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

Alzheimer’s Disease

•  Cross-tissue coexpression networks for both normal and AD brains

–  prefrontal cortex, cerebellum, visual cortex

•  Differential network analysis on AD and normal networks

•  Integrate coexpression networks and Bayesian networks to identify key regulators for the modules associated with AD

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nerve ensheathment

Glutathione transferase Gain connectivity by 91 fold

Lose connectivity by 40%

Module Connectivity Change (AD/Normal)

Identification of Disease (AD) Pathways via Comparative Gene Network Analysis

40,000 genes from three tissues

Bayesian Subnetworks

Control (PFC, CB, VC)

AD (PFC, CB, VC)

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Key Regulators PECAM1: Platelet-endothelial cell adhesion molecule, a tyrosine phosphatase activator that plays a role in the platelet activation, increased expression correlates with MS, Crohn disease, chronic B-cell leukemia, rheumatoid arthritis, and ulcerative colitis

ENPP2: Phosphodiesterase I alpha, a lysophospholipase that acts in chemotaxis, phosphatidic acid biosynthesis, regulates apoptosis and PKB signaling; aberrant expression is associated with Alzheimer type dementia, 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) •  Most predictive of Braak severity score

GlutathioneTransferase NerveEnsheathment ExtracellularMatrix

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The Federation

(Nolan and Haussler)

sage federation: model of biological age

Faster Aging

Slower Aging

Clinical Association -  Gender -  BMI -  Disease Genotype Association Gene Pathway Expression Pr

edic

ted

Age

(live

r exp

ress

ion)

Chronological Age (years)

Age Differential

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

The Non-Responder Cancer Project Leadership Team

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Garry Nolan, PhD Professor, Baxter Laboratory of Stem Cell Biology, Department of Microbiology and Immunology, Stanford University Director, Proteomics Center at Stanford University

Richard Schilsky, MD Chief, Hematology- Oncology, Deputy Director, Comprehensive Cancer Center, University of Chicago; Chair, National Cancer Institute Board of Scientific Advisors; past-President ASCO, past Chairman CALGB clinical trials group

Todd Golub, MD Founding Director Cancer Biology Program Broad Institute, Charles Dana Investigator Dana-Farber Cancer Institute, Professor of Pediatrics Harvard Medical School, Investigator, Howard Hughes Medical Institute

Stephen Friend, MD, PhD President and Co-Founder of Sage Bionetworks, Head of Merck Oncology 01-08, Founder of Rosetta Inpharmatics 97-01, co-Founder of the Seattle Project

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

Leveraging Existing 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

Networking Disease Model Building

IMPACT ON PATIENTS

Why not use data intensive science to build models of disease

Current Reward Structures

Organizational Structures and Tools

Pilots

Opportunities