Download - 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
7
Two recurring problems in AD research
8
"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
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
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
11
• 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)
Iden1fying key disease systems and genes
Example “modules” of coexpressed genes, color-‐coded
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
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
Iden1fying key disease systems and genes
vs
Combina1on of cogni1ve func1on, Braak score, cor1cal atrophy with differen1al expression and differen1al coexpression rank modules.
Priori1ze modules through expression synchrony with clinical measures or tendency too reconfigure themselves in disease
Iden1fying key disease systems and genes
1.) Iden1fy groups of genes that move together – coexpressed “modules”
2.) Priori1ze the disease-‐relevance of the modules by clinical and network measures
Infer directed/causal rela1onships and clear hierarchical structure by incorpora1ng eSNP informa1on (no hair-‐balls here)
vs
Priori1ze modules through expression synchrony with clinical measures or tendency too reconfigure themselves in disease
Iden1fying key disease systems and genes
1.) Iden1fy groups of genes that move together – coexpressed “modules”
2.) Priori1ze the disease-‐relevance of the modules by clinical and network measures
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
Tes1ng network-‐based 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