friend win symposium 2012-06-28

The Marriage of Transla/onal Medicine to “Big Data”: Key Opportuni/es to change how we do our Science

Stephen H Friend June 28, 2012

WIN

Background: Informa/on Commons for Biological Func/ons

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

what will it take to understand disease?

DNA RNA PROTEIN

MOVING BEYOND ALTERED COMPONENT LISTS

Familiar but Incomplete

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

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

List of Influential Papers in Network Modeling

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

12

  Foundations   Kauffman CHDI, Gates Foundation

  Government   NIH, LSDF, NCI

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

  Federation   Ideker, Califano, Nolan, Schadt

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

Biological System

Analysis

Data

Single Lab Model

Multiple Lab Model

•  R01 Funding •  Hypothesis->data->analysis->paper •  Small-scale data / analysis •  Reproducible?

•  P01 Funding •  Hypothesis->data->analysis->paper •  Medium-scale data / analysis •  Data Generators/Analysts/Validators maybe

different groups •  Reproducible?

Driven by molecular technologies we have become more data intensive leading to more specializa/on: data generators (centralized cores), data analyzers (bioinforma/cians), validators (experimentalists: lab & clinical) This is reflected in the tendency for more mul/ lab consor/um style grants in which the data generators, analyzers, validators may be different labs.

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

ANALYZES/ MODELS

RAW DATA

BioMedicine Information Commons

Data Generators

Data Analysts

Experimentalists

Clinicians

Patients/ Citizens

Networked Approaches

SYNAPSE

CURATED DATA

TOOLS/ METHODS

ANALYZES/ MODELS

RAW DATA

BioMedical Information Commons

Data Generators

Data Analysts

Experimentalists

Clinicians

Patients/ Citizens


4 PRIVACY BARRIERS

2 REWARDS

RECOGNITION

5 REWARDS

FOR SHARING

1 USABLE DATA

3 HOW TO

DISTRIBUTE TASKS

Open and Networked Approaches:Democratization of Science

1 USABLE DATA

2 REWARDS

RECOGNITION

SYNAPSE

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

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

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

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

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

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

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•  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.

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

3 HOW TO

DISTRIBUTE TASKS

COLLABORATIVE CHALLENGES

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 30

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

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

  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)

  Generic competition platforms •  Kaggle, Innocentive, MLComp

31

The Sage/DREAM breast cancer prognosis challenge

Goal: Challenge to assess the accuracy of computational models designed to predict breast cancer survival using patient clinical and genomic data

Why this is unique:   This Sage/DREAM Challenge is a pre-collated cohort: 2000 breast cancer samples

from the Metabric cohort   Accessible to all: A cloud-based common compute architecture is being made

available by Google to support the computational models needed to develop and test challenge models

  New Rigor: •  Contestants will evaluate their models on a validation data set composed of newly generated

data (provided by Dr. Anne-Lise Borreson Dale) •  Contestants must demonstrate their models can be reproduced by others

  New incentives: leaderboard to energize participants, Science Translational Medicine publication for winning team

  Breast cancer patients, funders and researchers can track this Challenge on BRIDGE, an open source online community being built by Sage and Ashoka Changemakers and affiliated with this Challenge

32

Sage/DREAM Challenge: Details and Timing

Phase 1: Apr thru end-Sep 2012

  Training data: 2,000 breast cancer samples from METABRIC cohort

•  Gene expression •  Copy number •  Clinical covariates •  10 year survival

  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

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

  Will evaluate accuracy of models built on METABRIC data to predict survival in:

•  Held out samples from METABRIC

•  Other datasets

Phase 2: Oct 1 thru Nov 12, 2012

  Evaluation of models in novel dataset.

  Validation data: ~500 fresh frozen tumors from Norway group with:

•  Clinical covariates •  10 year survival

  Gene expression and copy number data to be generated for model evaluation

•  Sent to Cancer Research UK to generate data at same facility as METABRIC

•  Models built on training data evaluated on newly generated data

  Winners announced at November 12 DREAM conference

33

Summary

Transparency, reproducibility

Valida;on in novel dataset

Publica;on in Science Transla;onal Medicine

Dona;on of Google-‐scale compute space.

For the goal of promo;ng democra;za;on of medicine… Registra;on star;ng NOW…

sign up at: synapse.sagebase.org

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34

Open and Networked Approaches

4 PRIVACY BARRIERS

PORTABLE LEGAL CONSENT: weconsent.us John Wilbanks

Arch2POCM

An approach to speed our basic understanding of the consequences of

targe/ng novel high risk drivers of disease states

Clinical valida/on (Ph IIa) of pioneer targets

5 REWARDS

FOR SHARING

The Current R&D Ecosystem Is In Need of a New Approach to Drug Development

•  $200B per year in biomedical and drug discovery R&D

•  Only a handful of new medicines are approved each year

•  Productivity in steady decline since 1950

•  >90% of novel drugs entering clinical trials fail, and negative POC information is not shared

•  Significant pharma revenues going off patent in next 5 years

•  >30,000 pharma employees laid off from downsizing in each of last four years

•  90% of 2013 prescriptions will be for generic drugs

37

Issues With Drug Discovery

1.  The greatest attrition is at clinical proof-of-concept – once a “target” is linked to a disease in the clinic, the risk of failure is far lower

2.  Most novel targets are pursued by multiple companies in parallel (and most fail at clinical POC)

3.  The complete data from failed trials are rarely, if ever, released to the public

38

Open access research tools drive science

39

SGC: Open Access Chemical Biology a great success

•  PPP: -‐ GSK, Pfizer, Novar/s, Lilly, Abbon, Takeda -‐ Genome Canada, Ontario, CIHR, Wellcome Trust

•  Based in Universi/es of Toronto and Oxford

•  200 scien/sts

•  Academic network of more than 250 labs

•  Generate freely available reagents (proteins, assays, structures, inhibitors, an/bodies) for novel, human, therapeu/cally relevant proteins

•  Give these to academic collaborators to dissect pathways and disease networks, and thereby discover new targets for drug discovery

40

Some SGC Achievements

•  Structural impact –  SGC contributed ~25% of global output of human structures annually

–  SGC contributes >40% of global output of human parasite structures annually

•  High quality science (some publica/ons from 2011) Vedadi et al, Nature Chem Biol, in press (2011); Evans et al, Nature Gene;cs in

press (2011); Norman et al Science Transl Med. 3(88):88mr1 (2011); Kochan G et al PNAS 108:7745 (2011); Clasquin MF et al Cell 145:969 (2011); Colwill et al, Nature Methods 8:551 (2011); Ceccarelli et al, Cell 145:1075 (2011; Strushkevich et al, PNAS 108:10139 (2011); Bian et al EMBO J in press (2011) Norman et al Science Trans. Med. 3:76cm10 (2011); Xu et al Nature Comm. 2: art. no. 227 (2011); Edwards et al Nature 470:163 (2011); Fairman et al Nature Struct, and Mol. Biol. 18:316 (2011); Adams-‐Cioaba et al, Nature Comm. 2 (1) (2011); Carr et al EMBO J 30:317 (2011); Deutsch et al Cell 144:566 (2011); Filippakopoulos et al Cell, in press; Nature Chem. Biol. in press, Nature in press

41

Open access to the clinic?

42

Most Novel Targets Fail at Clinical POC

Target ID/

Discovery

Hit/ Probe/ Lead

ID

Clinical candidate

ID

Tox./ Pharmacy

Phase I

Phase IIa/ b

HTS LO

10% 30% 30% 90+% 50%

this is killing our industry

…we can generate “safe” molecules, but they are not developable in chosen patient group 43

This Failure Is Repeated, Many Times

Target ID/

Discovery

Hit/ Probe/ Lead

ID

Clinical candidate

ID

Toxicology/ Pharmacy

Phase I

Phase IIa/ b

HTS

30% 30% 90+%

Target ID/

Discovery

Hit/ Probe/ Lead

ID

Clinical candidate

ID


Phase I

Phase IIa/ b

30% 30% 90+%

Target ID/

Discovery

Hit/ Probe/ Lead

ID

Clinical candidate

ID


Phase I

Phase IIa/ b

30% 30% 90+%

Target ID/

Discovery

Hit/ Probe/ Lead

ID

Clinical candidate

ID


Phase I

Phase IIa/ b

30% 30% 90+%

Target ID/

Discovery

Hit/ Probe/ Lead

ID

Clinical candidate

ID


Phase I

Phase IIa/ b

30% 30% 90+%

Target ID/

Discovery

Hit/ Probe/ Lead

ID

Clinical candidate

ID


Phase I

Phase IIa/ b

30% 30% 90+%

Target ID/

Discovery

Hit/ Probe/ Lead

ID

Clinical candidate

ID


Phase I

Phase IIa/ b

10% 30% 30% 90+% 50%

LO

…and outcomes are not shared 44

A Possible Soution:Arch2POCM An Open Access Clinical Validation PPP

•  PPP to clinically validate (Ph IIa) pioneer targets

•  Pharma, public, academia, regulators and pa/ent groups are ac/ve par/cipants

•  Cul/vate a common stream of knowledge –  Avoid patents

–  Place all data into the public domain

–  Crowdsource the PPP’s druglike compounds

•  In –validated targets are iden/fied before pharma makes a substan/al proprietary investment

–  Reduces the number of redundant trials on bad targets

–  Reduces safety concerns

•  Validated targets are de-‐risked for pharma investment –  Pharma can ini/ate proprietary effort when risks are balanced with returns

–  PPP pharma members can acquire Arch2POCM IND for validated targets and benefit from shorter development /meline and data exclusivity for sales

45

Arch2POCM: Scale and Scope •  Original Goal:

–  Initiate 2 programs. One for Oncology/Epigenetics/Immunology. One for Neuroscience/Schizophrenia/Autism.

–  Both programs will have 8 drug discovery projects (targets) –  By Year 5, 30% of projects will have started Ph 1 and 20% will have completed

Ph Iia –  $200-250M over five years is projected as necessary to advance up to 8 drug

discovery projects within each of the two therapeutic programs –  By investing $1.6 M annually into one or both of Arch2POCM’s selected disease

areas, partnered pharmaceutical companies: 1.  obtain a vote on Arch2POCM target selection 2.  gain real time data access to Arch2POCM’s 16 drug discovery projects 3.  have the strategic opportunity to expand their overall portfolio

•  Revised Goal: –  Initiate 1-2 projects, (1-2 novel target mechanisms), as pilots to assess

Arch2POCM principle of sharing data and reagents till clinical validation –  In either Oncology or Neuroscience –  Specific target mechanisms to be determined by funders’ interest –  Interested funders include pharma, public research foundations and

venture philanthropists 46

Histone

DNA

Lysine

Epigenetics: Exciting Science and Also A New Area For Drug Discovery

Modification Write Read Erase

Acetyl HAT Bromo HDAC Methyl HMT MBT DeMethyl

47

The Case For Epigenetics/Chromatin Biology

1.  There are epigenetic oncology drugs on the market (HDACs)

2.  A growing number of links to oncology, notably many genetic links (i.e. fusion proteins, somatic mutations)

3.  A pioneer area: More than 400 targets amenable to small molecule intervention - most of which only recently shown to be “druggable”, and only a few of which are under active investigation

4.  Open access, early-stage science is developing quickly – significant collaborative efforts (e.g. SGC, NIH) to generate proteins, structures, assays and chemical starting points

48

Evidence that this target plays an important role in tumors (in vitro, in vivo, animal

model data)

Maturity of the program

Posi;ve evidence of

the compound

playing a role in the given disease

Data showing a failed result

of the compound for

the given disease

Mouse knockout model (MGI)

SMARCA4 Expression correlates with development of prostate cancer BUT SMARCA4 in general acts as tumor suppressor and is necessary for genome stability; targeted knockdown of SMARCA4 poten/ates lung cancer development;

potent, selec/ve, cell ac/ve compound iden/fied

NA NA Homozygotes for a null allele die in utero before implanta/on. Embryos heterozygous for this null allele and an ENU-‐induced allele show impaired defini/ve erythropoiesis, anemia and lethality during organogenesis. Heterozygotes show cyanosis and cardiovascular defects and are pre-‐disposed to breast tumors

SMARCA2A Gastric cancer; mutated in CLL; deple/on of BRM causes accelerated progression to the differen/a/on phenotype BUT targeted dele/on is causa/ve for the development of prosta/c hyperplasia in mice


NA NA Mice homozygous for a targeted muta/on in this gene may exhibit infer/lity and a slightly increased body weight in some gene/c backgrounds.

CBP Transloca/on of CBP with MOZ, monocy/c leukemia zinc finger protein cause acute myeloid leukemia ; other transloca/ons involve MLL (HRX); Mutated in ALL BUT CBP has also been proposed as a classical tumor suppressor


NA NA Homozygotes for null or altered alleles die around midgesta/on with defects in hemopoiesis, blood vessel forma/on, and neural tube closure. Heterozygotes may exhibit skeletal, cardiac, and hematopoie/c defects, retarded growth, and hematologic tumors.

ATAD2 Correlated with survival of high-‐grade osteosarcoma pa/ents ayer chemo-‐therapy; required for breast cancer cell prolifera/on ; differen/ally expressed in NSCLC

Weak hits NA NA NA

BRD4 Transloca/ons produce BRD4-‐NUT fusion oncogene causing midline carcinoma

JQ1 JQ1 in BRD-‐NUT fusion and MLL

NA Homozygotes for a gene-‐trap null muta/on die soon ayer implanta/on. Heterozygotes exhibit impaired pre-‐ and postnatal growth, head malforma/ons, lack of subcutaneous fat, cataracts, and abnormal liver cells.

BRD2 In transgenic mice, cons/tu/ve lymphoid expression of Brd2 causes a malignancy most similar to human diffuse large B cell lymphoma

JQ1 JQ1 in BRD-‐NUT fusion and MLL

NA Mice homozygous for a null muta/on display embryonic lethality during organogenesis with decreased embryo size, decreased cell prolifera/on, a delay in the cell cycle, and increased cell death. Heterozygous mice also display decreased cell prolifera/on.

Poten;al Targets-‐ Bromodomain Family

Evidence that this target plays an important role in tumors (in vitro, in vivo, animal model data)


Posi;ve evidence of the compound

playing a role in the given disease

Data showing a failed result of the compound for the given

disease

Mouse model (MGI)

JMJD3 Upregulated in prostate cancer; expression is higher in metasta/c prostate cancer BUT JMJD3 contributes to the ac/va/on of the INK4A-‐ARF tumor suppressor locus in response to oncogene -‐ and stress-‐induced senescence.


NA; inhibits TNF-‐alpha

produc/on in macrophages of RA pa/ents

NA Mice homozygous for a knock-‐out allele exhibit perinatal lethality associated with thick alveolar septum and absences of air space in the lungs. Bone marrow chimera mice derived from fetal liver cells exhibit impaired eosinophil recruitment and abnormal response to helminth infec/on.

JARID1B High levels in breast cancer cell lines, strong expression in the invasive but not in the benign components of primary breast carcinomas. BUT tumor suppressor in melanoma cells

No progress NA NA NA

Poten;al Targets-‐ Demethylases

Evidence that this target plays an important role in tumors (in vitro, in vivo, animal model data)


Posi;ve evidence of the compound playing a role in the given disease

Data showing a failed result of the compound for the given disease

SETD8 Recent data indicates that SETD8 deregulates PCNA expression by degrada/on accelerated by methyla/on at K248. Expression levels of SETD8 and PCNA upregulated in cancer cells. Cancer Research May 2012 Takawa et al.

Weak inhibitors iden/fied (8 microM) in chemistry op/miza/on.

NA NA

EZH2 EZH2 upregulated in cancer cells. Studies on mutants indicates an interes/ng profile where both wild-‐type and mutant (Y641F) are required for malignant phenotype. Sneeringer et al. PNAS 2012. Compounds iden/fied in GSK patents WO 2011/140324 and 140315 and WO 2012/005805 and 075080.

potent, selec/ve, cell ac/ve compound iden/fied.

NA NA

MMSET MMSET, WHSC1, NSD2 is overexpressed in cancer cells. Hudlebusch et al. Clinical Cancer Res 2011

No hits—currently screening

NA NA

DOT1L Daigle et al. Cancer Cell 2011 elegantly show that potent DOT1L inhibitors kill cells containing MLL transloca/ons and do not kill cell not containing the transloca/ons

potent, selec/ve, cell ac/ve compound iden/fied.

Transgenic mouse model tumors shrunk by SC

dosing of inhibitor

Poten;al Targets-‐ Histone Methyltransferases

Program Activities Grid For Arch2POCM

Ac;vity Arch2POCM Loca;on/Inves;gator (TBD)

Target Structure

Compound libraries

Assay development for epigene/c screens and biomarkers

HTP screens for epigene/c hits

Med Chem SAR To ID Two Suitable Binding Arch2POCM Test Compounds

Non-‐GLP scaleup of Arch2POCM Test Compounds and associated analy/cs

Distribu/on of Arch2POCM Test Compounds

PK, PD, ADME, Tox Tes/ng

GMP Manufacturing of Arch2POCM Test Compounds

GMP Formula/on

GMP Drug Storage and Distribu/on

IND Prepara/on Support

Clinical Assay Development and Qualifica/on

Ph I-‐II Clinical Trials

Ph I-‐II Database Management and CSR Produc/on 52

SYNAPSE

CURATED DATA

TOOLS/ METHODS

ANALYZES/ MODELS

RAW DATA

BioMedical Information Commons

Data Generators

Data Analysts

Experimentalists

Clinicians

Patients/ Citizens


4 PRIVACY BARRIERS

2 REWARDS

RECOGNITION

5 REWARDS

FOR SHARING

1 USABLE DATA

3 HOW TO

DISTRIBUTE TASKS

friend win symposium 2012-06-28

Health & Medicine