Stephen Friend Institute for Cancer Research 2011-11-01

Actionable Cancer Network Models And Open Medical Information Systems

Integrating layers of omics data models and compute spaces

Stephen Friend MD PhD

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

ICR Oslo November 1, 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? •  Regulatory hurdles too high? •  Low hanging fruit picked? •  Payers unwilling to pay? •  Genome has not delivered? •  Valley of death? •  Companies not large enough to execute on strategy? •  Internal research costs too high? •  Clinical trials in developed countries too expensive?

In fact, all are true but none is the real problem

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  

What  is  the  problem?  

     Most  approved  cancer  therapies  assumed  tumor  indica8ons  would  represent  homogenous  popula8ons  

 Most  new  cancer  therapies  are  in  search  of  single  altered  components    

 Our  exis8ng  tumor  models  o>en  assume  pathway  knowledge  sufficinet  to  infer  correct  therapies  

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 computational models in a “Compute Space”

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?

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

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Integration of Genotypic, Gene Expression & Trait Data

Causal Inference

Schadt et al. Nature Genetics 37: 710 (2005) Millstein et al. BMC Genetics 10: 23 (2009)

Chen et al. Nature 452:429 (2008) Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005)

Zhu et al. Cytogenet Genome Res. 105:363 (2004) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)

“Global Coherent Datasets” •  population based

•  100s-1000s individuals

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Define a Gene Co-expression Similarity

Define a Family of Adjacency Functions

Determine the AF Parameters

Define a Measure of Node Distance

Identify Network Modules (Clustering)

Relate the Network Concepts to External Gene or Sample Information

Gene Co-Expression Network Analysis

Zhang B, Horvath S. Stat Appl Genet Mol Biol 2005

Constructing Co-expression Networks

Start with expression measures for genes most variant genes across 100s ++ samples

Note: NOT a gene expression heatmap

1 -0.1 -0.6 -0.8

-0.1 1 0.1 0.2

-0.6 0.1 1 0.8

-0.8 0.2 0.8 1 1

2

3

4

1 2 3 4

Correlation Matrix Brain sample

expr

essi

on

1 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 1

2

3

4

1 2 3 4

Connection Matrix

1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1

2

4

3

1 2 4 3

4 1

3 2

Establish a 2D correlation matrix for all gene pairs

Define Threshold eg >0.6 for edge

Clustered Connection Matrix

Hierarchically cluster

sets of genes for which many pairs interact (relative to the total number of pairs in that

set)

Network Module

Identify modules

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

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))

TH

E EV

OLU

TIO

N O

F SY

STEM

S B

IOLO

GY

Disease  Models  

Physiologic  /  Pathologic  

Phenotype  Regulation    

Literature  

Structure  Mol.  Profiles  

Model  Evolution  

Model  Topology  

Model  Dynamics  

Genomic  

Signaling  

Transcriptional  

Protein-­‐Protein  Complexes  

"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

  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 computational models in a “Compute Space 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

Assumption that genetic alterations in human conditions should be owned

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 NEW MAPS

Disease Map and Tool Users- ( Scientists, Industry, Foundations, Regulators...)

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…)

RULES AND GOVERNANCE Data Sharing Barrier Breakers-

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

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CNV Data Gene

Expression

Clinical Traits

Bayesian Network Co-Expression Network

Integration of Coexp. & Bayesian Networks

Integration of Multiple Networks for Pathway and Target Identification

Key Driver Analysis

42

Bin Zhang Jun Zhu

Key Driver Analysis

43 http://sagebase.org/research/tools.php

Bin Zhang Jun Zhu Justin Guinney

A) Miller 159 samples B) Christos 189 samples

C) NKI 295 samples

D) Wang 286 samples

Cell cycle

Pre-mRNA

ECM

Immune response

Blood vessel

E) Super modules

Zhang B et al., Towards a global picture of breast cancer (manuscript).

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NKI: N Engl J Med. 2002 Dec 19;347(25):1999.

Wang: Lancet. 2005 Feb 19-25;365(9460):671.

Miller: Breast Cancer Res. 2005;7(6):R953.

Christos: J Natl Cancer Inst. 2006 15;98(4):262.

Model of Breast Cancer: Co-expression Bin Zhang Xudong Dai Jun Zhu

Breast Cancer Bayesian Network Conserved Super-modules

mR

NA

proc

.

Chr

omat

in

Pathways & Regulators (Key drivers=yellow; key drivers validated in siRNA screen=green)

Cell Cycle (Blue) Chromatin Modification (Black) Pre-mRNA proc. (Brown) mRNA proc. (red)

Extract gene:gene relationships for selected super-modules from BN and define Key Drivers

Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)

45

Bin Zhang Xudong Dai Jun Zhu

Model of Breast Cancer: Integration

= predictive of survival

Predictive model

Developing predictive models of genotype specific sensitivity to Perturbations- Margolin

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  

47  

Elastic net regression 500  

Features  

100  

Features  

20  

Features  

1  Feature  

48  

Bootstrapping retains robust predictive features

49  

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+%

50  

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  Non-­‐Responders  Arch2POCM  The  FederaQon  Portable  Legal  Consent  Sage  Congress  Project  

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  

Non-­‐Responders  Project  

To identify Non-Responders to approved Oncology drug regimens in order to improve

outcomes, spare patients unnecessary toxicities from treatments that have no benefit to them, and

reduce healthcare costs

The  Non-­‐Responder  Cancer  Project  Leadership  Team  

11  

Garry Nolan, PhD Professor, Baxter Laboratory of Stem Cell Biology, Department of Microbiology and Immunology, Stanford University Director, Proteomics Center at Stanford University

Richard Schilsky, MD Chief, Hematology- Oncology, Deputy Director, Comprehensive Cancer Center, University of Chicago; Chair, National Cancer Institute Board of Scientific Advisors; past-President ASCO, past Chairman CALGB clinical trials group

Todd Golub, MD Founding Director Cancer Biology Program Broad Institute, Charles Dana Investigator Dana-Farber Cancer Institute, Professor of Pediatrics Harvard Medical School, Investigator, Howard Hughes Medical Institute

Stephen Friend, MD, PhD President and Co-Founder of Sage Bionetworks, Head of Merck Oncology 01-08, Founder of Rosetta Inpharmatics 97-01, co-Founder of the Seattle Project

The  Non-­‐Responder  Project  is  an  internaQonal  iniQaQve  with  funding  for  6  iniQal  cancers  anQcipated  from  both  the  public  and  private  sectors  

5  

Ovarian     Renal   Breast   AML   Colon   Lung  

United  States   China  

Seeking  private  sector  and  philanthropic  funding  for  

prospec8ve  studies  

RetrospecQve  study;  likely  to  be  funded  by  the  Federal  Government  

Funded  by  the  Chinese  government  and  private  sector  partners  

GEOGRAPHY  

TARGET  CANCER  

FUNDING  SOURCE  

Arch2POCM  

Restructuring  the  PrecompeQQve  Space  for  Drug  Discovery  

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

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

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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

IMPACT  ON  PATIENTS  

Actionable Cancer Network Models And Open Medical Information Systems