Corruption of Denial

Stephen Friend Sage Bionetworks

Now possible to generate massive amount of human “omic’s” data

Network Modeling Approaches for Diseases are emerging

IT Infrastructure and Cloud compute capacity allows a generative open approach to solving problems

Nascent Movement for patients to Control Sensitive information allowing sharing

Open Social Media allows citizens and experts to use gaming to solve problems

1- Now possible to generate massive amount of human “omic’s” data 2-Network Modeling Approaches for Diseases are emerging 3- IT Infrastructure and Cloud compute capacity allows a generative open approach to biomedical problem solving 4-Nascent Movement for patients to Control Sensitive information allowing sharing 5- Open Social Media allows citizens and experts to use gaming to solve problems

A HUGE OPPORTUNITY -- A HUGE RESPONSIBILITY

HEART

VASCULATURE

KIDNEY

IMMUNE SYSTEM

transcriptional network

protein network

metabolite network

Non-coding RNA network

GI TRACT

BRAIN

ENVIRONMENT E

NV

IRO

NM

EN

T

ENVIRONMENT

EN

VIR

ON

ME

NT

.

TENURE FEUDAL STATES

• alchemist

The value of appropriate representations/ maps

OR

BUILDING PRECISION MEDICINE

Extensions of Current Institutions

Proprietary Short term Solutions

Open Systems of Sharing in a Commons

An Alternative

Commons are resources that are owned in common or shared among

communities.

-David Bollier

Biomedicine

Information

Commons

Why Sage Bionetworks?

We believe in a world where biomedical research has changed. It will be conducted in an open, collaborative way where all parties can contribute to making better, faster, relevant discoveries

We enable others

• Leading biomedical modeling research

• Novel training doctoral and internship programs

We activate/We challenge

• Diverse collaborations with individuals/researchers and institutions to collectively grow the biomedical Commons

• Crowdsourcing approaches to challenge the communities

• Developing platforms for collaboration and engagement – Synapse, BRIDGE

• Defining governance approaches– PLC

We research

Collaborators

Pharma Partners Merck, Pfizer, Takeda, Astra Zeneca,

Amgen,Roche, Johnson &Johnson

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Foundations

Kauffman CHDI, Gates Foundation

Government

NIH, LSDF, NCI

Academic

Levy (Framingham)

Rosengren (Lund)

Krauss (CHORI)

Federation

Ideker, Califano, Nolan, Schadt, Vidal

Better Models of

Disease:

INFORMATION

COMMONS

Techn

olo

gy P

latform

Challenges

Imp

actf

ul M

od

els

Governance

Two recurring problems in Alzheimer’s disease research

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Ambiguous pathology Are disease-associated molecular systems & genes destructive, adaptive, or both? Bottom line: We need to identify causal factors vs correlative or adaptive features of disease.

Diverse mechanisms How do diverse mutations and environmental factors combine into a core pathology? Bottom line: There is no rigorous / consistent global framework that integrates diverse disease factors.

Identifying key disease systems and genes- Gaiteri et al.

Example “modules” of coexpressed genes, color-coded

1.) Identify groups of genes that move together – coexpressed “modules” - correlated expression of multiple genes across many patients - coexpression calculated separately for Disease/healthy groups - these gene groups are often coherent cellular subsystems, enriched in one or more GO functions

1.) Identify groups of genes that move together – coexpressed “modules” 2.) Prioritize the disease-relevance of the modules by clinical and network measures

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

vs

Identifying key disease systems and genes

Infer directed/causal relationships and clear hierarchical structure by incorporating eSNP information (no hair-balls here)

vs

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

Identifying key disease systems and genes

1.) Identify groups of genes that move together – coexpressed “modules” 2.) Prioritize the disease-relevance of the modules by clinical and network measures 3.) Incorporate genetic information to find directed relationships between genes

Example network finding: microglia activation in AD

Module selection – what identifies these modules as relevant to Alzheimer’s disease?

The eigengene of a module of ~400 probes correlates with Braak score, age, cognitive disease severity and cortical atrophy. Members of this module are on average differentially expressed (both up- and down-regulated).

Evidence these modules are related to microglia function

The members of this module are enriched with GO categories (p<.001) such as “response to biotic stimulus” that are indicative of immunologic function 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 proportional to connectivity in the full module.

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

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

Testing network-based hypotheses

Design-stage AD projects at Sage

Fusing our expertise in…

Join us in uniting genes, circuits and regions to build multi-scale biophysical disease models. Contact chris.gaiteri@sagebase.org

Diffusion Spectrum Imaging

Microcircuits & neuronal diversity

Gene regulatory networks

Feed

back

Tool: PORTABLE LEGAL CONSENT Control of Private information by Citizens allows sharing

weconsent.us

John Wilbanks

• Online educational wizard • Tutorial video • Legal Informed Consent Document • Profile registration • Data upload

John Wilbanks TED Talk “Let’s pool our medical data” weconsent.us

two approaches to building common

scientific and technical knowledge

Text summary of the completed project

Assembled after the fact

Every code change versioned

Every issue tracked

Every project the starting point for new work

All evolving and accessible in real time

Social Coding

Synapse is GitHub for Biomedical Data

• Data and code versioned

• Analysis history captured in real time

• Work anywhere, and share the results with anyone

• Social/Interactive Science

• Every code change versioned

• Every issue tracked

• Every project the starting point for new work

• Social/Interactive Coding

Data Analysis with Synapse

Run Any Tool

On Any Platform

Record in Synapse

Share with Anyone

“Synapse is a compute platform for transparent, reproducible, and

modular collaborative research.”

Currently at 16K+ datasets and ~1M models

Download analysis and meta-analysis

Download another Cluster Result Download Evaluation and view more stats

• Perform Model averaging

• Compare/contrast models

• Find consensus clusters

• Visualize in Cytoscape

Pancancer collaborative subtype discovery

Objective assessment of factors influencing model

performance (>1 million predictions evaluated)

Sanger CCLE Prediction accuracy

improved by…

Not discretizing data

Including expression data

Elastic net regression

130 compounds 24 compounds

Cro

ss v

alid

atio

n p

red

icti

on

acc

ura

cy (

R2)

In Sock Jang

Erich Huang, Brian Bot, Dave Burdick

Sage-DREAM Breast Cancer Prognosis Challenge one month of building better disease models together

154 participants; 27 countries

334 participants; >35 countries

>500 models posted to Leaderboard

breast cancer data

Challenge Launch: July 17

Sep 26 Status

Caldos/Aparicio

Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge Phase 2 Best Performing Team:

Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge Phase 2 Best Performing Team: Attractor Metagenes Team Members: Wei-Yi Cheng, Tai-Hsien Ou Yang, and Dimitris Anastassiou Affiliation: Center for Computational Biology and Bioinformatics and Department of Electrical Engineering, Columbia University

How to disrupt the System?

Build a way for the patients actively to engage their insights in real-time around what is happening to them ( their state of wellness or disease) where their narratives, samples, data, insights, and funds are shown to enable decision making in what they should do, what treatments they need

BRIDGE Seed Projects

Fanconi Anemia Project

Melanoma Hunt

Diabetes Activated

Community

Breast Cancer

Genomic Research

Real Names Parkinson’s

Project

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Funded researchers

BREAST CANCER GENOMIC RESEARCH: CURRENT APPROACHES

5. Little incentive to annotate data and curate for other scientists

1. Isloated breast cancer cohorts

3. Data is siloed

6. Limited impact of today’s fragmented data on standard-of-care improvements for breast cancer patients

2. Many funders, many disparate objectives

7. Many published breast cancer prognosis models but little consensus

4. Clinical/genomic data are accessible but minimally useable

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3. Aggregate BC patient data via BRIDGE portal

Communi

ty Forums

Citizen Portal

BRIDGE APPROACH: BREAST CANCER PROGNOSIS “CO-OPETITIONS” TO BUILD BETTER DISEASE MODELS TOGETHER

1. Activated breast cancer patients

2. Core/surgical biopsy

Path lab

Clinical informatics

4. BC data curated, open and supported by analysis tools

Fin

din

gs 7. Give back education

and risk assessment to citizens

6. “Cco-opetitions” leaderboard allows researchers to work together

5. Open community-based “co-opetitions” forge new computational models

8. Field-test best models in clinic and hospital

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ABCDE

“ugly duckling”

Dermoscopy

Pathology

Molecular

MD

There is no standard screening program for skin lesions; seeing an MD is self directed

Education is derived from top-down experiential knowledge

?Photos

HPI

Best accuracy of clinical diagnosis = 64% (Grin, 1990)

160k new cases/year 48k deaths in 2012 in US

Both intra- and inter- institutional data are siloed

MELANOMA Screening – Could it be better?

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1.Activated citizens take skin pictures

2. Store tons of data!

3. Run algorithmic cChallenges in the compute space

4. Give back risk-assessment & education to the citizens

virtual cycle: continuous aggregation of data enriching the model

Initial focus on building the data needed

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Novel Data collection

+ Usage

Now possible to generate massive amount of human “omic’s” data Network Modeling for Diseases are emerging IT Infrastructure and Cloud compute capacity allows a generative open approach to biomedical problem solving Nascent Movement for patients to Control Private information allowing sharing Open Social Media allowing citizens and experts to use gaming to solve problems

THESE FIVE TRENDS CAN ENABLE AN OPEN COMMUNITY OF IMPATIENT CITIZENS-- AS PATIENTS/RESEARCHERS/FUNDERS

DYNAMIC MULTI-SCALE PATIENT COMMUNITIES ENABLING REAL TIME LEARNING

USING OPEN APPROACHES DRIVEN BY “INFINITE CHALLENGES”

Navigating between states

Rui Chang et al. PLoS Computational Biology

Normal State

Disease State

CORRUPTION OF DENIAL

CORRUPTION OF DENIAL

Complexity of systems

Proximity of Solutions

Sufficiency of current phenotypic data and

appropriateness of role of patients

Effectiveness of how we work with big Data

my

Keynote Speakers: Lawrence Lessig – author “The future of ideas” &“Remix”

Jam ie Heyw ood – patients like me Lance Armst rong – LiveStrong David Haussler - UCSC

Genome Browser James Boyle – Duke Law School Adr ien Treui l le –Foldit

! "#$%&'$( ) *+%, -" .*/&&Earn one of ten t r ips to Commons Congress in SF!

– t o apply visi t ht tp :/ / b i t .ly/ 2012YIA!

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Sage Commons Congress – San Francisco April 19-20 Ten Young Investigator Awards

Bob Young Top Hat Jane McConigal IoF Wadah Khanfar Al Jazeera Patrick Meier Ushhidi Jennifer Pahlka Code for America