friend nigm 2012-05-23

Building Be*er Models of Disease Together

Stephen H Friend MD PhD Sage Bionetworks

NIGM 4th Annual Retreat May 23 2012

Sea*le

What is the problem?

Most approved therapies assume indica2ons would represent homogenous popula2ons

Our exis2ng disease models o8en 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

Disease PrevenLon and Treatment

•  To Prevent need to: – Have clinical & molecular definiLon of disease – Be able to predict progression – Have drugs that target mechanisms that drive progression

•  To Treat need to: – Have clinical & molecular definiLon of disease – Disease modifying therapies

For Alzheimer’s we need work to develop all of these!

Data-‐driven Target Iden2fica2on

Disease progression

Disease Modifying Therapy

Healthy State

Disease State

If we accept that disease is driven by the complex interplay of geneLcs and environment mediated through molecular networks…….

GeneLcs

Environment

GeneLcs

Environment

………………………….then it follows we must study these networks and how they respond to perturbagens, how they differ in disease, etc

what will it take to understand disease?

DNA RNA PROTEIN (dark ma*er)

MOVING BEYOND ALTERED COMPONENT LISTS

WHY NOT USE “DATA INTENSIVE” SCIENCE

TO BUILD BETTER DISEASE MAPS?

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

14

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

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

18

  Foundations   Kauffman CHDI, Gates Foundation

  Government   NIH, LSDF

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

  Federation   Ideker, Califarno, Butte, Schadt

1.) IdenLfy groups of genes that move together – coexpressed “modules” -‐ correlated expression of mulLple genes across many paLents -‐ coexpression calculated separate for Disease/healthy groups -‐ these gene groups are ogen coherent cellular subsystems, enriched in one or more GO funcLons

Alzheimer’s Disease: IdenLfying key disease systems and genes

Data source: Harvard Brain Tissue Resource Center

SNPs, Gene Expression, Clinical Traits

Pre Frontal Cortex AD n = 284

Control 153

Visual Cortex AD 168

Control 116

Cerebellum AD 220

Control 122


IdenLfying key disease systems and genes

Transcription factor

Gene A Gene B

Alzheimer’s-‐specific regulatory relaLonship Microarray result

#2/TF

Where does coexpression come from? What does a “link” in these networks mean?

#1 #4

#3

Gene A Gene B Gene C Promoter x Promoter y

Chromosome segment

21

•  What is the evidence that coexpression is produced by regulatory rela6onships?

•  Gene coexpression has mulLple biophysical sources: 1: TranscripLonal overrun / chromosome locaLon (Ebisuya 2008) 2: Common transcripLon factor binding sites (Marco 2009) 3: EpigeneLc regulaLon (Chen 2005) 4: 3D Chromosome configuraLon (Deng 2010) –  VariaLon in cell-‐type density (Oldham 2008)


Example “modules” of coexpressed genes, color-‐coded


1.) IdenLfy groups of genes that move together – coexpressed “modules”

2.) PrioriLze the disease-‐relevance of the modules by clinical and network measures

PrioriLze modules through expression synchrony with clinical measures or tendency too reconfigure themselves in disease

vs


vs

CombinaLon of cogniLve funcLon, Braak score, corLcal atrophy with differenLal expression and differenLal coexpression rank modules.





Infer directed/causal relaLonships and clear hierarchical structure by incorporaLng eSNP informaLon (no hair-‐balls here)

vs





3.) Incorporate geneLc informaLon to find directed relaLonships between genes

Example network finding: microglia acLvaLon in AD

Module selec2on – what iden2fies these modules as relevant to Alzheimer’s disease? The eigengene of a module of ~400 probes correlates with Braak score, age, cogniLve disease severity and corLcal atrophy. Members of this module are on average differenLally expressed (both up-‐ and down-‐regulated).

Evidence these modules are related to microglia func2on The members of this module are enriched with GO categories (p<.001) such as “response to bioLc sLmulus” that are indicaLve of immunologic funcLon for this module.

The microglia markers CD68 and CD11b/ITGAM are contained in the module (this is rare – even when a module appears to represent a specific cell-‐type, the histological markers may be lacking).

Numerous key drivers (SYK, TREM2, DAP12, FC1R, TLR2) are important elements of microglia signaling.

Alzgene hits found in co-‐regulated microglia module:

Figure key:

Five main immunologic families found in Alzheimer’s-‐associated module

Square nodes in surrounding network denote literature-‐supported nodes.

Node size is propor6onal to connec6vity in the full module.

(Interior circle) Width of connec6ons between 5 immune families are linearly scaled to the number of inter-‐family connec6ons.

Labeled nodes are either highly connected in the original network, implicated by at least 2 papers as associated with Alzheimer’s disease, or core members of one of the 5 immune families.

Core family members are shaded.

Transforming networks into biological hypotheses

TesLng network-‐based hypotheses

Current AD projects with Sage in collaboraLon

Follow-‐up microglia experiments Confirming TYROBP relevance in human-‐derived microglia-‐neuron co-‐culture

Similar microglia experiments with Fc receptor (Neumann, Gaiteri)

Novel genes validated with in vitro and in vivo model systems Cell culture & transgenic FAD crosses with novel gene KO’s

(Wang, Kitazawa, Gaiteri)

Addi2onal microarrays from model systems Check network predic6ons to refine both algorithm & biology

(Schadt/Neumann)

Larger cohorts, proteomics Building networks in 3x larger dataset, newer plaZorm, w/ detailed clinical info

(Myers, Gaiteri)

Design-‐stage AD projects at Sage

Fusing our experLse in…

To build mulL-‐scale biophysical disease models. Join us in uniLng genes, circuits and regions! Contact [email protected]

Diffusion Spectrum Imaging

Microcircuits & neuronal diversity

Gene regulatory networks

Feedback

  http://sagebase.org/research/resources.php

List of 50 Influential Papers in Network Modeling

Biological System

Data Analysis

Fundamentally Biological Science hasn’t changed because of the ‘Omics RevoluLon……

…..it is about the process of linking a system to a hypothesis to some data to some analyses

But the way we do it has changed…………………………………………

Biological System

Data

Analysis

Biological System

Analysis

Data

Single Lab Model

Multiple Lab Model

•  R01 Funding •  Hypothesis->data->analysis->paper •  Small-scale data / analysis •  Reproducible?

•  P01 Funding •  Hypothesis->data->analysis->paper •  Medium-scale data / analysis •  Data Generators/Analysts/Validators maybe

different groups •  Reproducible?

Driven by molecular technologies we have become more data intensive leading to more specializaLon: data generators (centralized cores), data analyzers (bioinformaLcians), validators (experimentalists: lab & clinical) This is reflected in the tendency for more mulL lab consorLum style grants in which the data generators, analyzers, validators may be different labs.

Biological System

Data

Analysis

“Open Market” Model

•  Democratization of Biology •  Large scale data, compute,

analysis open to all

•  Dissociation of Data Generators from Analysts from Validators – if scientists want to work on other people’s data they can, or validate someone else’s findings?

•  New ways to fund and incentivize research

•  BRIDGE •  Collaborative Competitions

What does this New Model Enable

SYNAPSE

CURATED DATA

TOOLS/ METHODS

ANALYZES/ MODELS

RAW DATA

BioMedicine Information Commons

Data Generators

Data Analysts

Experimentalists

Clinicians

Patients/ Citizens

Open and Networked Approaches and the “Democratization” of Science

•  “Open” access to data, tools, models

•  Wide constituency of users and contributors

•  Break the “link” between data and ownership

UlLmately these efforts will fail without more ambiLous thinking

– AcLvate PaLents •  PaLents want to be involved, to fund research, to direct the research quesLons, to hold the scienLfic community to account

•  Portable Legal Consent –  Collect Large Scale Longitudinal Data

•  We need to collect the right kind of data. Molecular and Phenotypic in a longitudinal fashion on 10s-‐100,000s of individuals

•  Real Names Discovery Project –  Build an InformaLon Commons

•  Synapse –  Engage in CollaboraLve Challenges

•  Breast Cancer Challenge-‐ IBM/Google/ Science Transl Med

SYNAPSE

CURATED DATA

TOOLS/ METHODS

ANALYZES/ MODELS

RAW DATA

BioMedical Information Commons

Data Generators

Data Analysts

Experimentalists

Clinicians

Patients/ Citizens

Networked Approaches and the “Democratization” of Science

•  “Open” access to data, tools, models

•  Wide constituency of users and contributors

•  Break the “link” between data and ownership

1 PRIVACY BARRIERS

3 REWARDS

RECOGNITION

4 GOVERNANCE

2 USABLE DATA

6 ROLES FOR

CITIZENS

5 HOW TO

DISTRIBUTE TASKS

Open and Networked Approaches:Democratization of Science

1 PRIVACY BARRIERS

2 USABLE DATA

4 RULES

GOVERNANCE

3 REWARDS

RECOGNITION

6 ROLES FOR

CITIZENS

5 HOW TO

DISTRIBUTE TASKS

PORTABLE LEGAL CONSENT

SYNAPSE

SYNAPSE

THE FEDERATION

COLLABORATIVE CHALLENGES

BRIDGE


1 PRIVACY BARRIERS

PORTABLE LEGAL CONSENT: weconsent.us John Wilbanks


2 USABLE DATA

3 REWARDS

RECOGNITION

SYNAPSE

SYNAPSE

Why not share clinical /genomic data and model building in the ways currently used by the software industry

(power of tracking workflows and versioning

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

sage bionetworks synapse project


4 RULES

GOVERNANCE

THE FEDERATION

A B C

Pipeline Strategy

A B C

D

Divide and Conquer Strategy

A B C

Parallel/IteraLve Strategy

sage federation: model of biological age

Faster Aging

Slower Aging

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

edicted Age (liver expression)

Chronological Age (years)

Age Differential

sage federation: Google- SageBionetworks / The Federation


5 HOW TO

DISTRIBUTE TASKS

COLLABORATIVE CHALLENGES

LOG ON AND SIGN-‐UP FOR THE BREAST CANCER CHALLENGE SAGE/DREAM/GOOGLE


6 ROLES FOR

CITIZENS

BRIDGE

We pursue Medical Care is if it were an “Infinite Game”

and

We pursue Medical Research as if it were a “Finite Game”

We pursue Medical Care is if it were an “Infinite Game”

and

We pursue Medical Research as if it were a “Finite Game”

YET

We should pursue Medical Care is if it were a “Finite Game”

and

We should pursue Medical Research as if it were an “Infinite Game”

Who will build the datasets/ models capable of providing powerful insights enabling disease modifying therapies?

Scientists Physicians Citizens “Knowledge Expert”

NETWORK PLATFORM

InsLtutes

Industry

FoundaLons

PPP

Or

??????

Power of CollaboraLve Challenges Evolving Models from Deep Data Driven Longitudinal Cohorts

in Worldwide Open InformaLon Commons

friend nigm 2012-05-23

Health & Medicine

models of disease

alzheimers disease

correlates of disease

disease variations

disease prevenlon

better disease maps

expression data

exis2ng disease models