Friend Oslo 2012-09-09

Lessons Learned: Reali.es of Building Cancer Models-‐

Sharing , Rewards and Affordability

Stephen Friend MD PhD

KRAS NRAS

BRAF

MEK1/2

EGFR

ERBB2

BCR/ABL

EGFRi

Proliferation, Survival

•  EGFR Pathway commonly mutated/ac.vated in Cancer •  30% of all epithelial cancers

•  Blocking Abs approved for treatment of metasta.c colon cancer

•  Subsequently found that RASMUT tumors don’t respond – “Nega.ve Predic.ve Biomarker”

•  However s.ll EGFR+ / RASWT pa.ents who don’t respond? – need “Posi.ve Predic.ve Biomarker”

•  And in Lung Cancer not clear that RASMUT status is useful biomarker

Predic.ng treatment response to known oncogenes is complex and requires detailed understanding of how different gene.c backgrounds func.on

Oncogenes only make good targets in particular molecular contexts : EGFR story

Reality: Overlapping Pathways

Preliminary Probabalistic Models- Rosetta

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

"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

Ø  50 network papers Ø  http://sagebase.org/research/resources.php

List of Influential Papers in Network Modeling

Background: Informa.on Commons for Biological Func.ons

Sage Bionetworks A non-profit organization with a vision to enable networked team

approaches to building better models of disease

BIOMEDICINE INFORMATION COMMONS INCUBATOR

Sagebase.org

Data Repository

Discovery Platform

Building Disease Maps

Commons Pilots

Sage Bionetworks Collaborators

§  Pharma Partners §  Merck, Pfizer, Takeda, Astra Zeneca, Amgen,Roche, Johnson &Johnson

13

§  Foundations §  Kauffman CHDI, Gates Foundation

§  Government §  NIH, LSDF, NCI

§  Academic §  Levy (Framingham) §  Rosengren (Lund) §  Krauss (CHORI)

§  Federation §  Ideker, Califano, Nolan, Schadt

Predictive models of cancer phenotypes

Ø  mRNA Ø  copy number Ø  somatic

mutations Ø  epigenetics Ø  proteomics

Molecular characterization

Cancer phenotypes Ø  Drug sensitivity

screens Ø  Clinical

prognosis

Panel of tumor samples

Predic2ve model

months'a)er'beginning'treatment'Brian&J&Druker,&Nature'Medicine'15,&114901152&(2009)&

overall'survival'(%)'

Rela:ng'a'gene:c'feature'of'a'cancer'to'the'efficacy'of'a'drug:'Gleevec'(Ima:nib)'improves'survival'in'CML'pa:ents'harboring'the'

BCREABL'transloca:on'

KRAS% NRAS%

BRAF%

MEK1/2%

EGFR% ERBB2%MET%

PIKC3A%

BCR/ABL%

ARF% MDM2% TP53%

Ima9nib%Nilo9nib%

AZD0530%

AZD6244%

Erlo9nib%

Lap9nib%

NutlinG3%

PDG0325901%

PF2341066%PHAG665752%%

PLX4720%RAF265%

ZDG6474%

Biological System

Data Analysis

Fundamentally Biological Science hasn’t changed yet because of the ‘Omics Revolu.on……

…..it is s.ll about the process of linking a system to a hypothesis to some data to some analyses

Biological

System

Data

Analysis

Iterative Networked Approaches To Generating Analyzing and Supporting New Models

Uncouple the automatic linkage between the data generators, analyzers, and validators

SYNAPSE

CURATED DATA

TOOLS/ METHODS

ANALYSES/ MODELS

RAW DATA

BioMedicine Information Commons

Data Generators

Data Analysts

Experimentalists

Clinicians

Patients/ Citizens

Networked Approaches

SYNAPSE

CURATED DATA

TOOLS/ METHODS

ANALYSES/ MODELS

RAW DATA

BioMedical Information Commons

Data Generators

Data Analysts

Experimentalists

Clinicians

Patients/ Citizens

Networked Team Approaches

3 PRIVACY BARRIERS

2 REWARDS

RECOGNITION

5 REWARDS

FOR SHARING

1 USABLE DATA

4 HOW TO

DISTRIBUTE TASKS

Open and Networked Team Approaches

1 USABLE DATA

2 REWARDS

RECOGNITION

SYNAPSE

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 Science

Every code change versioned Every issue tracked Every project the starting point for new work All evolving and accessible in real time Social Coding

Watch What I Do, Not What I Say

sage bionetworks synapse project

Most of the People You Need to Work with Don’t Work with You


My Other Computer is “The Cloud”


Data Analysis with Synapse

Run Any Tool

On Any Platform

Record in Synapse

Share with Anyone

Performance*assessment*

Expression* Copy*number* Muta6on* Phenotype*


Expression*Copy*

number*

Muta6on*

Phenotype*Expression*

Copy*number*

Muta6on*

Phenotype*

Predic6ve*model*genera6on*

Synapse infrastructure for sharing, searching, and analyzing TCGA data

•  Automated workflows for cura.on, QC, and sharing of large-‐scale datasets.

•  All of TCGA, GEO, and user-‐submihed data processed with standard normaliza.on methods.

•  Searchable TCGA data: •  23 cancers •  11 data plajorms •  Standardized meta-‐data ontologies

130$drugs$

Pred

ic.o

n$Ac

curacy$(R

2 )$




Expression*Copy*

number*

Muta6on*


Copy*number*

Muta6on*

Phenotype*


Synapse infrastructure for sharing, searching, and analyzing TCGA data

•  Comparison of many modeling approaches applied to the same data.

•  Models transparently shared and reusable through Synapse.

•  Displayed is comparison of 6 modeling approaches to predict sensi.vity to 130 drugs.

•  Extending pipeline to evaluate predic.on of TCGA phenotypes.

•  Hos.ng of collabora.ve compe..ons to compare models from many groups.

Open and Networked Approaches

3 PRIVACY BARRIERS

PORTABLE LEGAL CONSENT: weconsent.us John Wilbanks

REDEFINING HOW WE WORK TOGETHER: Sage/DREAM Breast Cancer Prognosis Challenge

4 HOW TO

DISTRIBUTE TASKS

COLLABORATIVE CHALLENGES

5 REWARDS

FOR SHARING

What is the problem? Our current models of disease biology are primitive and limit

doctor’s understanding and ability to treat patients Current incentives reward those who silo information and work in closed systems

The Solution: Competitions to crowd-source research in biology and other fields

Ø  Why competitions? •  Objective assessments •  Acceleration of progress •  Transparency •  Reproducibility •  Extensible, reusable models

Ø  Competitions in biomedical research •  CASP (protein structure) •  Fold it / EteRNA (protein / RNA structure) •  CAGI (genome annotation) •  Assemblethon / alignathon (genome assembly / alignment) •  SBV Improver (industrial methodology benchmarking) •  DREAM (co-organizer of Sage/DREAM competition)

Ø  Generic competition platforms •  Kaggle, Innocentive, MLComp

METABRIC

• Array-CGH"• Expression arrays"• Sequencing TP53 PIK3CA"• Amplified DNA and cDNA banks"• miRNA profiling"

Anglo-Canadian collaboration"

Gene sequencing (ICGC)

Sage/DREAM Challenge: Details and Timing

Phase 1: July thru end-Sep 2012 Ø  Training data: 2,000 breast cancer

samples from METABRIC cohort •  Gene expression •  Copy number •  Clinical covariates •  10 year survival

Ø  Supporting data: Other Sage-curated breast cancer datasets

•  >1,000 samples from GEO •  ~800 samples from TCGA •  ~500 additional samples from

Norway group •  Curated and available on

Synapse, Sage’s compute platform

Ø  Data released in phases on Synapse from now through end-September

Ø  Will evaluate accuracy of models built

on METABRIC data to predict survival in:

•  Held out samples from METABRIC

•  Other datasets

Phase 2: Oct 15 thru Nov 12, 2012

Ø  Evaluation of models in novel

dataset. Ø  Validation data: ~500 fresh

frozen tumors from Norway group with:

•  Clinical covariates •  10 year survival




Expression*Copy*

number*

Muta6on*


Copy*number*

Muta6on*

Phenotype*


Synapse transparent, reproducible, versioned machine learning infrastructure for method comparison

METABRIC cohort: 997 breast cancer samples

Clinical covariates Gene expression (Illumina HT12v3) Copy number (Affy SNP 6.0) 10 year survival

Loaded through Synapse R client as Bioconductor objects.




Expression*Copy*

number*

Muta6on*


Copy*number*

Muta6on*

Phenotype*


Synapse transparent, reproducible, versioned machine learning infrastructure for method comparison

Custom models implement train() and predict() API.

Implementa)on of simple clinical-‐only survival model used as baseline predictor.

Trey%Ideker)Janusz%Dutkowski)

Eric%Schadt)Gaurav%Pandey)

Gustavo%Stolovi=ky)Erhan%

Bilal)

Andrea%Califano)

Yishai%Shimoni)

Mukesh%Bansal) Mariano%

Alvarez)

Garry%Nolan)

In%Sock%Jang)Ben%Sauerwine)Stephen%Friend)

Justin%Guinney)

Marc%Vidal)

Adam%Margolin)

Ben%Logsdon)

Federa2on modeling compe22on

Models submiVed and evaluated in real-‐2me

leaderboard

>200 models tested within 3 months

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

154 par.cipants; 27 countries 268 par.cipants; 32 countries

290 models posted to Leaderboard

breast cancer data

Challenge Launch: July 17

August 17 Status

Examples of Par.cipants

Summary of Breast Cancer Challenge #1 hVps://synapse.sagebase.org/ -‐ BCCOverview:0 Transparency, reproducibility

Valida2on in novel dataset

Publica2on in Science Transla2onal Medicine

Dona2on of Google-‐scale compute space.

For the goal of promo2ng democra2za2on of medicine… Registra2on star2ng NOW…

sign up at: synapse.sagebase.org




Expression*Copy*

number*

Muta6on*


Copy*number*

Muta6on*

Phenotype*


Breast Cancer Collaborative Challenges and Beyond

Start With Pre-‐Collated Cohort

Collabora.ve Challenge Hosted on

Synapse

Obtain research ques.ons from breast cancer community for Challenge 2

Generate and fund research Challenge 2 research proposal

The challenge on molecular predictors of breast cancer will create a community-‐based effort to provide an unbiased assessment of the most accurate models and methodologies for predic:on of breast cancer survival.

Announce best performing model to predict breast cancer

survival

43

SYNAPSE

CURATED DATA

TOOLS/ METHODS

ANALYSES/ MODELS

RAW DATA

BioMedical Information Commons

Data Generators

Data Analysts

Experimentalists

Clinicians

Patients/ Citizens

Networked Team Approaches

3 PRIVACY BARRIERS

2 REWARDS

RECOGNITION

5 REWARDS

FOR SHARING

1 USABLE DATA

4 HOW TO

DISTRIBUTE TASKS

Lessons Learned: Reali.es of Building Cancer Models-‐

Sharing , Rewards and Affordability

Stephen Friend MD PhD

Friend Oslo 2012-09-09

Health & Medicine

Transcript of Friend Oslo 2012-09-09