Friend NIH Alzheimers Summit 2012-05-14

New Approaches for iden1fica1on and selec1on of therapeu1c targets for Complex Disease

Stephen H Friend MD PhD Sage Bionetworks

Alzheimer’s Disease Research Summit May 14-‐15 2012

NIH

Disease Preven1on and Treatment

•  To Prevent need to: – Have clinical & molecular defini1on of disease – Be able to predict progression – Have drugs that target mechanisms that drive progression

•  To Treat need to: – Have clinical & molecular defini1on of disease – Disease modifying therapies

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

Data-‐driven Target Iden0fica0on

Disease progression

Disease Modifying Therapy

Healthy State

Disease State

If we accept that disease is driven by the complex interplay of gene1cs and environment mediated through molecular networks…….

Gene1cs

Environment

Gene1cs

Environment

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

Data-‐driven Target Iden0fica0on

Disease progression

Disease Modifying Therapy

Healthy State

Disease State

If we accept that disease is driven by the complex interplay of gene1cs and environment mediated through molecular networks…….

Gene1cs

Environment

Gene1cs

Environment

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

Problem is Complex and will not be solved by any one group

– New Capabili1es •  Informa1on Commons •  Portable Legal Consent

– New Ways to Work Together •  Public-‐Private Partnerships eg ADNI

–  Recognize new Roles for: •  Pa1ents •  Ci1zens •  Funders •  Scien1sts

Ambiguous pathology

Are disease-‐associated molecular systems & genes destruc1ve, adap1ve, or both?

Bo\om line: We need to iden1fy causal factors vs correla1ve or adap1ve features of disease.

Diverse mechanisms

How do diverse muta1ons and environmental factors combine into a core pathology?

Bo\om line: There is no rigorous / consistent global framework that integrates diverse disease factors.

Two recurring problems in AD research

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Two recurring problems in AD research

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"There are very few new molecular en22es, very few novel ideas, and almost nothing that gives any hope for a transforma2on in the treatment of mental illness.”

-‐ Thomas Insel, Science 2010

One consequence…

Ambiguous pathology

Are disease-‐associated molecular systems & genes destruc1ve, adap1ve, or both?

Bo\om line: We need to iden1fy causal factors vs correla1ve or adap1ve features of disease.

Diverse mechanisms

How do diverse muta1ons and environmental factors combine into a core pathology?

Bo\om line: There is no rigorous / consistent global framework that integrates diverse disease factors.

1.) Iden1fy groups of genes that move together – coexpressed “modules” -‐ correlated expression of mul1ple genes across many pa1ents -‐ coexpression calculated separate for Disease/healthy groups -‐ these gene groups are ofen coherent cellular subsystems, enriched in one or more GO func1ons

Iden1fying 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



Transcription factor

Gene A Gene B

Alzheimer’s-‐specific regulatory rela1onship 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

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•  What is the evidence that coexpression is produced by regulatory rela2onships?

•  Gene coexpression has mul1ple biophysical sources: 1: Transcrip1onal overrun / chromosome loca1on (Ebisuya 2008) 2: Common transcrip1on factor binding sites (Marco 2009) 3: Epigene1c regula1on (Chen 2005) 4: 3D Chromosome configura1on (Deng 2010) –  Varia1on in cell-‐type density (Oldham 2008)


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


1.) Iden1fy groups of genes that move together – coexpressed “modules”

2.) Priori1ze the disease-‐relevance of the modules by clinical and network measures

Priori1ze modules through expression synchrony with clinical measures or tendency too reconfigure themselves in disease

vs


vs

Combina1on of cogni1ve func1on, Braak score, cor1cal atrophy with differen1al expression and differen1al coexpression rank modules.





Infer directed/causal rela1onships and clear hierarchical structure by incorpora1ng eSNP informa1on (no hair-‐balls here)

vs





3.) Incorporate gene1c informa1on to find directed rela1onships between genes

Example network finding: microglia ac1va1on in AD

Module selec0on – what iden0fies these modules as relevant to Alzheimer’s disease? The eigengene of a module of ~400 probes correlates with Braak score, age, cogni1ve disease severity and cor1cal atrophy. Members of this module are on average differen1ally expressed (both up-‐ and down-‐regulated).

Evidence these modules are related to microglia func0on The members of this module are enriched with GO categories (p<.001) such as “response to bio1c s1mulus” that are indica1ve of immunologic func1on 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 propor2onal to connec2vity in the full module.

(Interior circle) Width of connec2ons between 5 immune families are linearly scaled to the number of inter-‐family connec2ons.

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

Tes1ng network-‐based hypotheses



Current AD projects with Sage in collabora1on

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)

Addi0onal microarrays from model systems Check network predic2ons to refine both algorithm & biology

(Schadt/Neumann)

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

(Myers, Gaiteri)

Design-‐stage AD projects at Sage

Fusing our exper1se in…

To build mul1-‐scale biophysical disease models. Join us in uni1ng 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

Now add Dimensions of Circuits, Brain Regions, Individual Dynamic Heterogeneity, And Longitudinal Variations

Ul1mately these efforts will fail without more ambi1ous thinking

– Ac1vate Pa1ents •  Pa1ents want to be involved, to fund research, to direct the research ques1ons, to hold the scien1fic 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 Informa1on Commons

•  Synapse –  Engage in Collabora1ve Challenges

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

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

We pursue Alzheimer’s Care is if it were an “Infinite Game”

and

We pursue Alzheimer’s Research as if it were a “Finite Game”

We pursue Alzheimer’s Care is if it were an “Infinite Game”

and

We pursue Alzheimer’s Research as if it were a “Finite Game”

YET

We should pursue Alzheimer’s Care is if it were a “Finite Game”

and

We should pursue Alzheimer’s 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

Ins1tutes

Industry

Founda1ons

PPP

Or

??????

Power of Collabora1ve Challenges Evolving Models from Deep Data Driven Longitudinal Cohorts

in Worldwide Open Informa1on Commons

Friend NIH Alzheimers Summit 2012-05-14

Health & Medicine