Stephen Friend Inspire2Live Discovery Network 2011-10-29

Actionable Cancer Network Models And Open Medical Information Systems

Stephen Friend MD PhD

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

Discovery Networks October 29th, 2011

Why not use data intensive science to build models of disease

Current Reward Structures

Organizational Structures and Tools

Pilots

Opportunities

What  is  the  problem?  

     We  need  to  rebuild  the  drug  discovery  process  so  that  we  be6er  understand  disease  biology  before  tes8ng  proprietary  compounds  on  sick  pa8ents  

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 Models in a Compute Space- Knowledge Expert

WHY  NOT  USE    “DATA  INTENSIVE”  SCIENCE  

TO  BUILD  BETTER  DISEASE  MAPS?  

what will it take to understand disease?

                   DNA    RNA  PROTEIN  (dark  maGer)    

MOVING  BEYOND  ALTERED  COMPONENT  LISTS  

2002 Can one build a “causal” model?

db/db mouse (p~10E(-30))

AVANDIA in db/db mouse

= up regulated = down regulated

Our ability to integrate compound data into our network analyses

db/db mouse (p~10E(-20) p~10E(-100))

  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 Models in a Compute Space- Knowledge Expert 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

Lack of standard forms for sharing data and lack of forms for future rights and consentss

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

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

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 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…)

NEW MAPS Disease Map and Tool Users-

( Scientists, Industry, Foundations, Regulators...)

RULES AND GOVERNANCE Data Sharing Barrier Breakers-

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

Developing predictive models of genotype-specific sensitivity to compound treatment

Pred

ic8ve  Features  

(biomarkers)  

Gene8c  Feature  Matrix  Expression,  copy  number,  somaQc  mutaQons,  etc.  

Sensi8ve   Refractory  

(e.g.  EC50)  

Cancer  samples  with  varying  degrees  of  response  to  therapy  

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Elastic net regression 500  

Features  

100  

Features  

20  

Features  

1  Feature  

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Bootstrapping retains robust predictive features

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Our approach identifies mutations in genes upstream of MEK as top predictors of sensitivity to MEK inhibition

#1  Mut  BRAF  

#3  Mut  NRAS  

PD-­‐0325901  

PD-­‐0325901  

#9  Mut  BRAF  

#312  Mut  NRAS  

!"#$% &"#$%

'"#(%

)*!+,-% #./0-11%2/345-674+%

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Other top predictive features include expression levels of genes regulated by MEK

PraQlas  et  al.,  (2009),  PNAS  

#19  ETV5  expr  

#8  DUSP6  expr  

#5  ETV4  expr  #3  NRAS  mut  #2  SPRY2  expr  #1  BRAF  mut  

PD-­‐0325901  

!"#$% &"#$%

'"#(%

)*!+,-% #./0-11%2/345-674+%

31  

Model built excluding expression data identifies BRAF, NRAS, and KRAS top predictive features for both MEK inhibitors

!"#$% &"#$%

'"#(%

)*!+,-% #./0-11%2/345-674+%

BRAF mut

NRAS mut

KRAS mut

PD-­‐0325901  

BRAF mut

NRAS mut

KRAS mut

AZD6244  

#3  KRAS  mut  #2  NRAS  mut  #1  BRAF  mut  

#3  KRAS  mut  #2  NRAS  mut  #1  BRAF  mut  

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TP53 mut

CDKN2A copy

MDM2 expr

HGF expr

CML linage EGFR mut

EGFR mut

EGFR mut

CML lineage

ERBB2 expr

BRAF mut

BRAF mut

NRAS mut

BRAF mut

NRAS mut

KRAS mut

BRAF mut

NRAS mut

KRAS mut

#1  BRAF  mut  

#2  NRAS  mut  #1  BRAF  mut  

#3  KRAS  mut  #2  NRAS  mut  #1  BRAF  mut  

#3  KRAS  mut  #2  NRAS  mut  #1  BRAF  mut  

#1  EGFR  mut  

#1  ERBB2  expr  

#1  EGFR  mut  

#2  CML  lineage  #1  EGFR  mut  

#1  CML  lineage  

#1  HGF  expr  

#2  TP53  mut  #3  CDKN2A  copy  #1  MDM2  expr  

How  accurate  would  predic8ve  models  perform  for  diagnos8cs?  

For 11/12 compounds, the #1 predictive feature in an unbiased analysis corresponds to the known stratifier of sensitivity

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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

INTEROPERABILITY  

INTEROPERABILITY

Genome Pattern CYTOSCAPE tranSMART I2B2

SYNAPSE  

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

CTCAP  Arch2POCM  The  FederaQon  Portable  Legal  Consent  Sage  Congress  Project  

Select  Six  Pilots  at  Sage  Bionetworks  

RULES GOVERN

PLAT

FORM

NEW

MAP

S

Clinical Trial Comparator Arm Partnership “CTCAP” Strategic Opportunities For Regulatory Science

Leadership and Action

FDA September 27, 2011

CTCAP  

Clinical Trial Comparator Arm Partnership (CTCAP)

  Description: Collate, Annotate, Curate and Host Clinical Trial Data with Genomic Information from the Comparator Arms of Industry and Foundation Sponsored Clinical Trials: Building a Site for Sharing Data and Models to evolve better Disease Maps.

  Public-Private Partnership of leading pharmaceutical companies, clinical trial groups and researchers.

  Neutral Conveners: Sage Bionetworks and Genetic Alliance [nonprofits].

  Initiative to share existing trial data (molecular and clinical) from non-proprietary comparator and placebo arms to create powerful new tool for drug development.

Started Sept 2010

Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery

•  Graphic  of  curated  to  qced  to  models  

Arch2POCM  

Restructuring  the  PrecompeQQve  Space  for  Drug  Discovery  

How  to  potenQally  De-­‐Risk      High-­‐Risk  TherapeuQc  Areas  

What  is  the  problem?  

     We  need  to  rebuild  the  drug  discovery  process  so  that  we  be6er  understand  disease  biology  before  tes8ng  proprietary  compounds  on  sick  pa8ents  

Jan 09

Well. Trust (£4.1M) NCGC (20HTSs)

GSK (8FTEs)

Ontario ($5.0M)

OICR (2FTEs)

UNC (3FTEs)

April 09 June 09 June 10

Pfizer (8FTEs)

Novartis (8FTEs)

A PPP to generate novel chemical probes

Sweden ($3.0M)

15 acad. labs

….more than £30M of resource….now Lilly (8FTEs)

Academic, scientific, drug discovery & economic impact

  Published Dec 23 2010 - already cited 30 times

  Distributed to >100 labs/companies - profile in several therapeutic areas

  Pharmas - started proprietary efforts

  Harvard spin off - $15 M seed funding

  Opened new area: Zuber et al : BRD4/ JQ1 in acute leukaemia Nature, 2011 Aug 3 Delmore et al: BRD4/ JQ1 in multiple myeloma Cell, 2011 Volume 146, 904-917, 16 Dawson et al: BRD4/ JQ1 in MLL Nature 2011, in press.

Floyed et al: BRD4 in DNA damage response Cell, revised Filippakopoulos et al: Bromodomains structure and function Cell, revised Natoli et al: BRD4 in T-cell differentiation manuscript in preparation Bradner et al: BRDT in spermatogenesis submitted

collaborations with SGC

The  FederaQon  

2008   2009   2010   2011  

How can we accelerate the pace of scientific discovery?

Ways to move beyond “traditional” collaborations?

Intra-lab vs Inter-lab Communication

Colrain/ Industrial PPPs Academic Unions

(Nolan  and  Haussler)  

human aging: predicting bioage using whole blood methylation

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Training Cohort: San Diego (n=170)

Chronological Age

Bio

logic

al A

ge

RMSE=3.35

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Validation Cohort: Utah (n=123)

Chronological Age

Bio

logic

al A

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RMSE=5.44

•  Independent training (n=170) and validation (n=123) Caucasian cohorts •  450k Illumina methylation array •  Exom sequencing •  Clinical phenotypes: Type II diabetes, BMI, gender…

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

Reproducible  science==shareable  science  

Sweave: combines programmatic analysis with narrative

Sweave.Friedrich Leisch. Sweave: Dynamic generation of statistical reports using literate data analysis. In Wolfgang Härdle and Bernd Rönz,editors, Compstat 2002 –

Proceedings in Computational Statistics,pages 575-580. Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9

Dynamic generation of statistical reports using literate data analysis

Federated  Aging  Project  :    Combining  analysis  +  narraQve    

=Sweave Vignette Sage Lab

Califano Lab Ideker Lab

Shared  Data  Repository  

JIRA:  Source  code  repository  &  wiki  

R code + narrative

PDF(plots + text + code snippets)

Data objects

HTML

Submitted Paper

Portable  Legal  Consent  

(AcQvaQng  PaQents)  

John  Wilbanks  

Sage  Congress  Project  April  20  2012  

RA  Parkinson’s  Asthma  

(Responders  CompeQQons)  

Why not use data intensive science to build models of disease

Current Reward Structures

Organizational Structures and Tools

Six Pilots

Opportunities