Stephen Friend, Jan 31, 2013. Collaborative Summit on Breast Cancer Research, Washington DC

1. ENABLING A PARADIGM SHIFTING PRECOMPETITIVE
OPEN ACCESS RESEARCH STRATEGY and ENGAGING
ADVOCATES IN RESEARCH/ BREAST CANCER CHALLENGE
COLLABORATIVE SUMMIT ON BREAST CANCER RESEARCH
January 31, 2013
Washington DC
Stephen H Friend MD PhD
Sage Bionetworks (non-profit)

5. Background: Information Commons for Biological Functions

6. .

7. Iterative Networked Approaches
To Generating Analyzing and Supporting New Models
Data
Biological
System Analysis
Uncouple the automatic linkage between the
data generators, analyzers, and validators

8. TENURE FEUDAL STATES

9. Lessons Learned: Realities of Building Disease Models-
Sharing , Rewards, Training, and Affordability
Stephen Friend MD PhD

11. We focus on a world where biomedical research is about
to fundamentally change. We think it will be often
conducted in an open, collaborative way where teams of
teams far beyond the current guilds of experts will
contribute to making better, faster, relevant discoveries

12. The Current R&D Ecosystem Is In Serious 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
• >30,000 pharma employees laid off from downsizing in each of last four
years
• 90% of 2013 prescriptions will be for generic drugs
12

13. 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)
1. The complete data from failed trials are rarely, if ever,
released to the public
13

14. Open access research tools
drive
Precompetitive science
14

15. Structural Genomics Consortium:
Open Access Chemical Biology
a great success
• PPP:
- GSK, Pfizer, Novartis, Lilly, Abbott, Takeda
- Genome Canada, Ontario, CIHR, Wellcome Trust
• Based in Universities of Toronto and Oxford
• 200 scientists
• Academic network of more than 250 labs
• Generate freely available reagents (proteins, assays, structures,
inhibitors, antibodies) for novel, human, therapeutically relevant proteins
• Give these to academic collaborators to dissect pathways and disease
networks, and thereby discover new targets for drug discovery 15

16. Schematic of project and current participants
GSK Chemistry
SGC
Lilly
Novartis
Biochemical assays
Chemical screening Pfizer
Protein structure
Computational chemistry
Abbott
U. North Carolina
Takeda

17. SGC EPIGENETICS PROBE PIPELINE (Mar.2012)
G9a/GLP
Pan 2-OG
BET
Probe/ Tool PHD2
BET 2nd
Compound
L3MBTL3
JMJD3
JMJD2 FBXL11 WDR5
Potent & CREBBP 1-3
G9a/GLP
BRD9 FBXL11 2nd
Selective SMARCA4
SETD7 DOT1L
BAZ2B
CECR2 SUV39H2 GCN5L2
JMJD2 2nd
PB1@5 EP300
Potent DNMT1
Screening / Chemistry
CREBBP 4th
BAZ2A 53BP1 JMJD3 2nd
JMJD1 L3MBTL1 PRMT3
PCAF ATAD2 TIF1α
PB1@2 PRMT5 UHRF1
Weak EZH2
FALZ SMYD2 HAT1
BRPF3 SETDB1 SETD8
JMJD2A MLL
None JMJD2C SMYD3 PHIP JARID1A
SPIN1 MYST3
In vitro assay Cell assay Cell activity
2OG Oxygenase BRD HAT (H)MT KDM Me Lys Binders TUD WD Domain

18. 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 publications from 2011)
Vedadi et al, Nature Chem Biol, in press (2011); Evans et al, Nature Genetics 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
18

19. Moving the pre-competitive barrier:
Open access to the clinic?
19

20. Drug Discovery Is a Lottery Because:
Knowledge about clinical disease is limiting
- patients are heterogeneous
- do not know how some drugs work (i.e., paracetamol)
- different doses effective in different patients
- efficacy is short lived
- poor biomarkers…..
Too many targets/preclinical assays do not prioritize
20

21. Other Problems With How We Do Drug Discovery
• Most targets are worked on in parallel and in
secret across pharma
• No one organization has all capabilities
• Early IP makes it even harder (slower, harder
and more expensive)
21

22. Most Novel Targets Fail at Clinical POC
Hit/
Target HTS Probe/ LO Clinical
Tox./ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery ID
ID
50% 10% 30% 30% 90+%
this is killing
our industry
…we can generate “safe” molecules, but they are not
developable in the chosen patient group 22

23. This Failure Is Repeated, Many Times
Hit/
Target HTS Probe/ LO Clinical
Toxicology/ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery Hit/ ID
Target ID Clinical
Probe/ Toxicology/ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery Hit/ ID 30% 30% 90+%
Target ID Clinical
Probe/ Toxicology/ Phase Phase
ID/ Hit/ candidate
Target Lead Clinical Pharmacy I IIa/ b
Discovery Probe/ ID Toxicology/ Phase Phase
ID/ ID candidate 30% 30% 90+%
Lead Pharmacy I IIa/ b
Discovery Hit/ ID
Target ID Clinical
Probe/ Toxicology/
30% Phase
30% Phase
90+%
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery Hit/ ID
Target ID Clinical 30% 30% 90+%
Probe/ Toxicology/ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery Hit/ ID 30% 30% 90+%
Target ID Clinical
Probe/ Toxicology/ Phase Phase
ID/ candidate
Lead Pharmacy I IIa/ b
Discovery ID
ID 30% 30% 90+%
50% 10% 30% 30% 90+%
…and neither data nor outcomes are shared 23

24. A Possible Soution:Arch2POCM
An Open Access Clinical Validation PPP
• A PPP to clinically validate (Ph IIa) pioneer targets
• Pharma, public, academia, regulators and patient groups are active participants
• Cultivate a common stream of knowledge
– Avoid patents
– Place all data into the public domain
– Crowdsource the PPP’s drug-like compounds
• Failed targets are identified before pharma makes a substantial proprietary investment
– Reduces the number of redundant trials on bad targets
– Reduces safety concerns
• Validated targets are de-risked for pharma investment
– Pharma can initiate proprietary effort when risks are balanced with returns
– PPP pharma members can acquire Arch2POCM IND for validated targets and benefit from
shorter development timeline and data exclusivity for sales
24

25. Arch2POCM Pilots
• Initiate 1-2 projects, (1-2 novel target mechanisms), as pilots to
assess Arch2POCM principles
• Epigenetic targets in Oncology and/or Neuroscience (new
innovative target class applied to high risk disease areas in need of
new approaches)
• Interested funders include disease foundations, pharma, public
research foundations and venture philanthropists
• Objectives
• Select two pre-clinical candidates: Leverage SGC PPP to identify two
chemotypes for medicinal chemistry optimization
• Develop a biomarker strategy for surrogate endpoints and/or patient
stratification
• Implement crowdsourced research on compounds
25

26. The First Arch2POCM Oncology Pilot Project
26

27. The Arch2POCM Project Team:
Premier Oncology Institutions and Researchers
• Institute of Cancer Research (ICR)
– Prof Paul Workman: Director of the Cancer Research UK Centre for Cancer
Therapeutics at The Institute of Cancer Research and one of the world's
leading experts in the discovery and development of new cancer drugs.
– Prof Julian Blagg is Deputy Director of the Cancer Therapeutics Unit at the
ICR and Head of Chemistry
• Newcastle University
– Prof Herbie Newell, world class expert in cancer pharmacology
• Structural Genomics Consortium (SGC)-Oxford University
– Dr Chas Bountra: Chief Science Officer for SGC and involved in progressing
over 30 candidates into clinical trials during his 20 years of pharmaceutical
industry experience
– Dr Paul Brennan: SGC Principal Investigator for Medicinal Chemistry and
leads SGC’s effort to generate chemical probes for novel epigenetic targets.
27

28. Epigenetics: Exciting Science and Also A New Area
For Innovative Drug Discovery
Lysine
DNA
Histone
Modification Write Read Erase
Acetyl HAT Bromo HDAC
Methyl HMT MBT DeMethyl
28

29. 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 have only recently been 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
29

30. KDM4B:
an epigenetic target implicated in ER-positive breast cancer
• Member of KDM4 family of Histone Demethylase
(HDM) “eraser” enzymes that site-specifically
demethylate target histone lysines
• Silencing of KDM4B in ER-positive breast cancer cell
lines attenuates ER gene expression and reduces
proliferation (in vitro and in vivo)
• Elevated levels of KDM4B correlate with a worse
patient outcome
• KDM4B depletion in the ER-negative cells fails to
reduce proliferation: suggests an exclusive benefit for
ER+-BC patients

31. Data/Findings That Are Already Being Shared on The
KDM4B Project
• Newcastle:
– KDM4B is unique within the KDM family for an impact on breast cancer
• siRNA screen shows that KDM4B but not other KDM4 family members, modulates ER activity
and cell proliferation
– Targeting KDM4B is likely to be tumor-specific
• Data mining of publicly available data sets shows that post-natal expression of KDM4B in
tissues is minimal
– Opportunity for KDM4B biomarkers
• Depletion of KDM4B in Bca effects global histone H3 methylation and acetylation
levels
• ICR: KDM4B is a druggable target
– Fragment screen against KDM4B resulted in 70 confirmed hits (peptides substrates and
20G mimics)
• SGC
– High throughput JDM4B biochemical screens available for the project
– KDM4B crystal structure pending

32. The Ask:
Why Breast Cancer Foundations Should Support this Arch2POCM
KDM4B Project
• Cancer Research UK (CRUK) has designated this KDM4B demethylase
project for its support and is enthusiastic to apply an Arch2POCM strategy
• Continued CRUK funding of this project requires matching funds
– This equates to funding 4-5 FTEs for the full POCM effort (i.e., 3-5 years).
– This funding would currently support the following 4 FTEs: 3 chemistry, 1
biology and 1 drug metabolism
– As project evolves, FTE coverage will shift to cover new activities
• Because open data-sharing and crowdsourcing do not align with the early
partnering business interests of a company that might otherwise bring
matching funds to this project, we need to identify a different source of
funds to cover the matching costs
• Therefore we are reaching out to all breast cancer disease foundations to
seek this important seed funding for 3FTEs/3 years

33. THE POWER OF DISTRIBUTED ANALYSIS
THE SECOND BREAST CANCER CHALLENGE

34. The Sage Bionetworks/DREAM Breast Cancer
Prognosis Challenge
Building Better Models of Disease Together
34

35. The Sage Bionetworks/DREAM Breast Cancer
Prognosis Challenge
Goal: use crowdsourcing to forge a computational model that accurately
predicts breast cancer survival
How it works:
• Training data set: genomic and clinical data from 2000
women diagnosed with breast cancer (the Metabric data
set)
• Data access and analysis tools: Synapse
• Compute resources: each participant provided with a
standardized virtual machine donated by Google
• Model scoring: models submitted to Synapse for scoring on
a real-time leaderboard 35

36. 1ST Sage-DREAM Breast Cancer Prognosis Challenge
Three months of building better disease models together
Caldos/Aparicio
breast cancer data
154 participants; 27 countries
354 participants; >35 countries
October 15 Status
Challenge Launch: July 17
>500 models submitted to Leaderboard
36

37. Unique Attributes
1. The First Challenge was designed to be open source and
encouraged code-sharing to forge innovative computational
models:
– The standardized and shared computational infrastructure
enables participants to use code submitted by others in their
own model building
– Winning code must be reproducible
2. The Challenge used a brand new dataset to select the
winning model:
– Derived from approx. 350 breast cancer samples
– Data generation funded by Avon
– Winning model: the one that, having been trained using Metabric data, is
most accurate for survival prediction when applied to a brand new dataset
3. This Challenge’s overall winner is submitting a pre-approved
article about his/her winning model to Science Translational 37
Medicine 37

38. Incentivizing Continuous
Participation
• Monthly leaderboard winners
– Winner is highlighted within the
Challenge community
– Winner posts a blog on winning
model to Synapse
• Communities that link to the
Leaderboard
– Stackoverflow: Q&A site with
1,000,000 users
– Science Translational Medicine
community
38

39. “A MODEL CHALLENGE”
39

40. Next Generation Sage Bionetworks Challenges:
what will they look like?
• Disease Communities/Groups that have contacted us to run a
Challenge: GBM-NBTS, Colon, CHDI, NCI (pan-cancer), BROAD,
NIEHS, Alzheimer’s- NIA
40

41. Next generation Sage Bionetworks Challenges:
Opportunities for running an open Breast Cancer Challenge
Focus of Initial Challenge- Proving a challenge can be done
with Clinical data and in an open way
Focus of Second Challenge- Proving a challenge can answer an
important clinical question rapidly and affordably
Strategy- Let the question not the convenience of data drive
the Challenge
Approach- Form an Advisory Group of breast cancer thought
leaders
41

42. The Second Sage/DREAM Breast Cancer Challenge
Co Leaders: Stephen Friend and Dan Hayes
Scientific Advisory Board:
Fabrice Andre- Inst. Gustave Roussy
Jose Baselga- MSKCC
John Bartlett- OICR
Mitch Dowsett- Royal Marsden
Daniel Hayes- University of Michigan
Larry Norton- MSKCC
Lisa McShane- NCI
Martine Piccart- Universite Libre de Bruxelles
1) Determine the best clinical question regarding the
treatment of breast cancer that can be developed using
existing datasets
2) Determine the best clinical question regarding the
treatment of breast cancer that can be developed not
constrained by using existing datasets 42

43. The Second Sage/DREAM Breast Cancer Challenge
One or more case control studies to determine patients with,
or without, residual risk to better guide enrollment into future
clinical trials. The Case Control studies could be broken into
categories based on ER, or HER2, or neither:
a. ER pos:
i. Those who got ET plus chemo: this is an important
group. If we can identify those who relapse anyway (vs. those
who don't) we could focus future trials on the former.
ii. those who got ET only (like in TailorRx, plus B20, B14,
8814) - can we build a better oncotypeDx?
43

44. The Second Sage/DREAM Breast Cancer Challenge
One or more case control studies to determine patients with, or
without, residual risk to better guide enrollment into future clinical
trials. The Case Control studies could be broken into categories
based on ER, or HER2, or neither:
b. HER2 Pos (amplified or 3+).
i. Those who got only chemo: Is there a group that does not
NEED herceptin?
ii. those who got Herceptin. This is the key group - who's cured,
who's not? Focus future anti-HER2 trials on the latter.
c. ER, PgR, HER2 neg.
i. those who got "standard" chemo. There is a large group that
are cured with standard chemo. Why enroll such patients in future
trials? Focus future trials only on those who are likely to recur. 44

45. The Second Sage/DREAM Breast Cancer Challenge
The Ask?
Funds to coordinate and run the actual Challenge~ $250,000
Funds to coordinate the generation of new datasets including
trials/ sample collections= TBD
45

46. "Harnessing the power of teams to build
better models of disease in real time"
If not