How Do We Study Network Perturbations in Clinical Specimens? How do we select which targets are effective for what diseases and which patients?- Stephen Friend July 18th FDA
1. Clinical Trial Comparator Arm Project “CTCAP” 2. “Arch2POCM”- Compounds to decode biology 3. Oncology Non-Responders Project 4. Freeing up Failed Compounds
What are the potential opportunities to participate in these projects? What actions might the FDA take? For actions needed beyond the FDA- executive or legislative?
Alzheimers Diabetes
Depression Cancer
Treating Symptoms v.s. Modifying Diseases
Will it work for me?
Personalized Medicine 101: Capturing Single bases pair mutations = Rresponders
Illusion that Altered Component Lists = Correct decisions about who will benefit
Use of Sub-populations to ID Responders Illusion that 1000 patients will provide Sub-populations
Reality: Overlapping Pathways generate Context Complexity
WHY NOT USE “DATA INTENSIVE” SCIENCE
TO BUILD BETTER DISEASE MAPS?
Equipment capable of generating massive amounts of data
“Data Intensive Science”- “Fourth Scientific Paradigm” For building: “Better Maps of Human Disease”
Standard Annotations
IT Interoperability
Evolving Models hosted in a Compute Space- Knowledge Expert
It is now possible to carry out comprehensive monitoring of many traits at the population level
Monitor disease and molecular traits in populations
Putative causal gene
Disease trait
trait trait trait trait trait trait trait trait trait trait trait trait trait
How can 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 T
umor
s
Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization Microarray hybirdization
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
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
50 network papers http://sagebase.org/research/resources.php
List of Influential Papers in Network Modeling
(Eric Schadt)
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
17
Foundations CHDI, Gates Foundation
Government NIH, LSDF
Academic Levy (Framingham)
Rosengren (Lund)
Krauss (CHORI)
Federation Ideker, Califarno, Butte, Schadt
Research Platform Research Platform Commons
Data Repository
Discovery Platform
Building Disease
Maps
Tools & Methods
Repository
Discovery
Maps
Tools &
Repository
Discovery Platform
Repository Repository
Discovery
Repository
Discovery
Commons Pilots
Outposts Federation
CCSB
LSDF-WPP Inspire2Live
POC
Cancer Neurological Disease
Metabolic Disease
Pfizer Merck Takeda
Astra Zeneca CHDI Gates NIH
Curation/Annotation
CTCAP Public Data Merck Data TCGA/ICGC
Hosting Data Hosting Tools
Hosting Models
LSDF
Bayesian Models Co-expression Models
KDA/GSVA
Clinical Trial Comparator Arm Partnership
Sharon Terry President & CEO, Genetic Alliance
Stephen Friend President, Sage Bionetworks
PROBLEM: Serious Need for Very Large Clinical and Genomic Datasets to Build Better Disease Maps
Sage Bionetworks: Platform
A data set containing genome-wide DNA variation and intermediate trait, as well as physiological phenotype data across a population of individuals large enough to power association or linkage studies, typically 50 or more individuals. To be coherent, the data needs to be matched with consistent identifiers. Intermediate traits are typically gene expression, but may also include proteomic, metabolomic, and other molecular data.
See http://www.sagebase.org/commons/repository.php
GLOBAL COHERENT DATASETS
MODELS
TOOLS Key Driver Analysis (KDA) Tool (R package/Cystoscape plug in)
http://sagebase.org/research/tools.php
Sage Bionetworks Repository
Key Objective
Provide public access to curated, QC ed and documented global coherent datasets (GCDs) and the network models derived from these datasets.
Curated GCD Data
Curated & QC d
GCD Data
Network Models
documented documented
Public Domain
How we share data- Build Models
Evolution of a Software Project
Evolution of a Biology Project
Clinical Trial Comparator Arm Partnership
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.
Clinical Trial Comparator Arm Partnership
Aim 1: Identify, collect, QC, curate and host 4-6 CTCAP coherent genomic datasets each year Aim 2: Develop and host network models built from these datasets to drive public target mining, biomarkers identification, and patient stratification efforts Aim 3: Establish a framework/process for ongoing release of clinical genomics data
Challenges: Landscape of available datasets; Process & scale of project Behavioral challenges; Incentives for individuals to give data to Sage Move model from pull to push Compliance, Privacy, Data use, etc Independent Board to look at broader societal implications of providing data
CTCAP Workstreams
Public Domain
GCDs
Collaborators GCDs
Uncurated GCD
Database (Sage)
• Public • Collaboration
• Internal
Uncurated GCD
Sage
Curated GCD
Curated & QC’d GCD
Network Models
Curated GCD • Single common identifier to link datatypes
• Gender mismatches removed
Curated & QC d GCD • Gene expression data corrected for batch
effects, etc
Public Databases dbGAP
Co-expression
Network Analysis
Bayesian Network Analysis
Integrated Network Analysis
Private Domain
GCDs
Benefits of working in CTCAP shared generative environment
Value: Represents a time and cost-efficient way to re-use and gain full value from existing, expensive trial data. Reduced costs for patients, payers and government when effective, tailored treatments become the standard of care. Better outcomes for patients when appropriate therapies are used first.
Product Development: Reduction in cost, time and failure rate for drug development; pharma, biotech companies and academic researchers will have full access to the resultant platform without jeopardizing proprietary molecules or therapies.
A generative resource: No one company or research group has the data or the tools to do this alone.
“… the world is becoming too fast, too complex, and too networked for any
company to have all the answers inside”
Y. Benkler, The Wealth of Networks
Is the Industry managing itself into irrelevance?
$130 billion of patented drug sales will face generics in the 2011-2016 decade (55% of 2009 US sales)
Sales exposed to generics will double in 2012 (to $33 billion)
98% of big pharma sales come from products 5 years and older (avg patent life = 11 years)
6 big pharmas were lost in the last 10 years
R&D spending is flattening, threatening future innovation
Largest Attrition For Pioneer Targets is at Clinical POC (Ph II)
Target ID/ Discovery
50% 10% 30% 30% 90%
This is killing drug discovery
We can generate effective and “safe” molecules in animals, but they do not have sufficient efficacy and/or safety in the chosen patient group.
Hit/Probe/Lead ID
Clinical Candidate ID
Toxicology/ Pharmacolo
gy
Phase I Phase IIa/IIb
Attrition
The current pharma model is redundant
50% 10% 30% 30% 90%
Negative POC information is not shared
Attrition
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate ID
Toxicology/ Pharmacolo
gy
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate ID
Toxicology/ Pharmacolo
gy
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate ID
Toxicology/ Pharmacolo
gy
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate ID
Toxicology/ Pharmacolo
gy
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate ID
Toxicology/ Pharmacolo
gy
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate ID
Toxicology/ Pharmacolo
gy
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate ID
Toxicology/ Pharmacolo
gy
Phase I Phase IIa/IIb
“Remember the two benefits of
failure. First if you do fail, you
learn what doesn’t work and
second the failure gives you
the opportunity to try a new
approach.”
Roger van Oech
Cost of Negative Ph II POC Estimated at $12.5 Billion Annually
• We want to improve health
• New medicines are part of this equation
• In this, we are failing, and we want to find a solution
Let’s imagine….
• A pool of dedicated, stable funding
• A process that attracts top scientists and clinicians
• A process in which regulators can fully collaborate to solve key scientific problems
• An engaged citizenry that promotes science and acknowledges risk
• Mechanisms to avoid bureaucratic and administrative barriers
• Sharing of knowledge to more rapidly achieve understanding of human biology
• A steady stream of targets whose links to disease have been validated in humans
A globally distributed public private partnership (PPP) committed to:
• Generate more clinically validated targets by sharing data
• Deliver more new drugs for patients by using compounds to understand disease biology
Arch2POCM
Arch2POCM: what’s in a name?
Arch: as in archipelago and referring to the distributed network of academic labs, pharma partners and clinical sites that will contribute to Arch2POCM programs
POCM: Proof Of Clinical Mechanism: demonstration in a Ph II setting that the mechanism of the selected disease target can be safely and usefully modulated.
Toronto Feb-2011 meeting: output on Arch2POCM Feasibility
Pharma
- 6 organisations supportive
Academic Labs - access to discovery biology and test compounds
Patient groups
- access to patients more quickly and cheaply
- access to “personal data”
Regulators
- access to historical data
- want to help with new clinical endpoints and study designs
Arch2POCM: April San Francisco Meeting
• Selected Disease Areas of Focus: Oncology,, Neuroscience and Opportunistic (O, CNS and X, respectively)
• Defined primary entry points of Arch2POCM test compounds into overall development pipeline
• Committed academic centers identified: UCSF, Toronto, Oxford
• CROs engaged
• Evaluated Arch2POCM business model
• Two Science Translational Medicine manuscripts published
Entry Points For Arch2POCM Programs
Lead identification Phase I Phase II Preclinical
Lead optimisation
Assay in vitro probe
Lead Clinical candidate
Phase I asset
Phase II asset
- genomic/ genetic Pioneer target sources - disease networks
- academic partners - private partners - Sage Bionetworks, SGC,
Early Discovery
Arch2POCM and the Power of Crowdsourcing
• “Crowdsourcing:” the act of outsourcing tasks traditionally performed by an employee to a large group of people or community
• By making Arch2POCM’s clinically characterized probes available to all, Arch2POCM will seed independently funded, crowdsourced experimental medicine
• Crowdsourced studies on Arch2POCM probes will provide clinical information about the pioneer targets in MANY indications
ArchPOCM Oncology Disease Area
Focus: Unprecedented targets and mechanisms
Novelty MOA and clinical findings
Arc2POCM Capacity: 5 targets/year for ~ 4 years
Gate 1: ~75% effort • New target with lead and Sage bionetworks insights on MOA (increase
likelihood of success), or • New target (enabled by Sage) with assay
Gate 2: ~25% effort • Pharma failed or deprioritized/parked compounds • Compound ID is followed by a Sage systems biology effort to define MOA and
clinical entry point
ArchPOCM Oncology: Epigenetics selected as the target area of choice
Top Targets:
• Discovery • Jard1 • Ezh1 • G9A
• Lead • Dyrk1
• Pre-Clin • ̀Brd4
Arch2POCM: Next Steps • Oncology and CNS Arch2POCM strategic design teams to generate project workflow plans and timelines (September)
• Seed Arch2POCM strategic design team around a disease area of high interest to private foundation(s) to generate project workflow and timelines (Q4, 2011)
• Define critical details of Arch2POCM leadership, organizational and decision-making structures (Q3-Q4, 2011)
• Develop business case to support Arch2POCM programs (Q3-Q4, 2011)
• Obtain financial backing in order to launch operations in early 2012 in at least one disease area
Non-Responder Cancer Project Mission
Section 1 – Project Overview
Sage Bionetworks • Non-Responder Project
To identify Non-Responders to approved 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 Project is an international initiative with funding for 6 initial cancers anticipated from both the public and private sectors
Section 1 – Project Overview
Sage Bionetworks • Non-Responder Project
Ovarian Renal Breast AML Colon Lung
United States China
Seeking private sector funding
Likely to be funded by the
Federal Government
Pilot Funded by the Chinese private sector partners
GEOGRAPHY
TARGET CANCER
FUNDING SOURCE
The study results will aid in the development of assays to identify non-responders to current treatments, creating clinical and financial benefits
Section 1 – Project Overview
Sage Bionetworks • Non-Responder Project
Assays developed based on the study’s results will allow for patient stratification by identifying those that will not respond to standard-of-care therapies in advance of treatment, therefore accelerating access to second tier and experimental compounds
Avoidance of Unnecessary Toxicity
Improved Clinical Outcomes
Reduced Medical Costs
Patient stratification results in:
e.g. Bisgrove Trials
• Patients identified as non-responders can skip standard-of-care treatments and avoid experiencing side effects from a round of ineffective therapy
• Selecting therapies based on molecular profiling improves treatment results
The Non-Responder Cancer Project Leadership Team
Section 1 – Project Overview
Sage Bionetworks • Non-Responder Project
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
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
“This study aims to provide both a material near term improvement in cancer patient outcomes and a long term blueprint for the future of oncology trails, prognosis and care. I believe the team of scientific, clinical and patient advocate partners we have assembled is unique in its ability to execute this study. With public and private sector support, I know we will be able to change the future of cancer care and research around the world.”
“Having focused on molecular medicine in my decades of conducting clinical trials, I am excited by the opportunity for the Non-responder project to change the way we select treatments for patients. My passion for this project and for improving our ability to better target therapies is immeasurable and I look forward to being an active part of this research.”
The Non-Responder Cancer Project Leadership Team
Section 1 – Project Overview
Sage Bionetworks • Non-Responder Project
Charles Sawyers, MD Chair, Human Oncology Memorial Sloan-Kettering Cancer Center, Investigator, Howard Hughes Medical Institute, Member, National Academy of Sciences, past President American Society of Clinical Investigation, 2009 Lasker-DeBakey Clinical Medical Research Award
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
“Stephen and I have worked together for many years on developing innovative network approaches to analyzing disease. Identifying signatures of non-response is the most exciting project I have been involved with in recent years and one which I believe can dramatically shift the way cancer patients receive treatment.”
“I have considered many opportunities to engage in personalized medicine, and believe the greatest value can be in developing assays to better target treatments for patients at the molecular level. I have worked with Stephen for 3 years and believe he is uniquely qualified to lead a project of this caliber to great success.”
For each tumor-type, the non-responder project will follow a common workflow, with patient identification and sample collection the most variable across studies
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
Iden%fica%on and Enrollment
Data and Sample
Collec%on
Sample Processing
Clinical Data
Repor%ng
Disease Modeling
Feedback and Results
Payment and Reimbursement
Project Management
Non-Responder Project Workflow
The remaining parts of the study will be largely similar, and potentially shared, across all projects
Identification and enrollment, and data and sample collection may differ by tumor-type
The non-responder project will require the coordination of a number of stakeholders to handle the various components of the research process
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
Physicians & AMCs
Patient Advocacy Groups
Patient Consent
Pathology
Genome Sequencing
Core Bioinformatics
Analysis and Disease Modeling
Potential Non-Responder Project US partners include:
Identification and Enrollment
The number of patients and enrollment procedures will vary for each study based on the biology and stage of the disease and the size of the advocate community
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
How many patients are required?
Who will be responsible for
enrolling patients?
What data will need to be collected at
enrollment?
What will be the cost of
identification and enrollment?
Ovarian Cancer
Since most ovarian cancer patients see a Gynecologic Oncologist who manages the entirety of their treatment, this tumor-type is well structured to use a select group of physicians/AMCs to target patients for enrollment 70% Physician-
driven
Ovarian Cancer Patients
Surgical removal and initial chemo
No initial response* 20%
+ Initial Response* 80%
No recurrence <24mo
Recurrence 6-24 months
Second series of Doublet Chemo
Responders 30-50%
Non-Responders 50-70%
In Ovarian Cancer, the target patient population will be those who experience recurrence within 6-24 months of stopping initial treatment. This population will require enrollment of 150 patients to identify groups with distinct response/non-response biology
• The number of patients differs according to the biology of each tumor-type being investigated
• The sample will require enough patients to identify 100-150 patients for each arm (responders and non-responders) that have distinct biology
• Enrollment sources will vary based on the makeup of the physician and patient communities
• Each study will entail a mix of physician-driven and patient-initiated enrollment , with those with strong advocate communities trending towards patient-initiated, and those with leverageable physician relationships involving more physician targeting
• Data will include a questionnaire to determine eligibility and to collect additional information that may inform analysis (e.g. age, race, etc.)
• Additionally, patient consent will need to be obtained • Genetic Alliance will own and standardize the consenting process
• Costs to identify and enroll patients will vary by channel • Patient-driven will be predominantly marketing and shipping costs
(e.g. marketing through the Love/Army of women costs $1500 until study is filled)
• Physician-driven enrollment may require educating physicians and a grant of approximately $20,000 per patient plus some administrative expenses
Patient-initiated
30%
The renal cancer study will aim to identify the biomarkers related to patients whose disease progresses during treatment with VEGF receptor inhibitors Currently 10-20% of all patients diagnosed are considered to have no response to this therapy
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
Patient presents with metastatic renal cancer
(25-30K annually)
Non-Responders Progress through treatment
(10-20%)
Treatment with VEGF receptor
inhibitors
Responders Stable Disease 30-40%
Partial Response 30-40%
Clinical Flow of Renal Cancer Patients The non-responder population will be those patients who experience disease progression throughout initial treatment with VEGFR TKIs(Tyrosine Kinase Inhibitors)
Definition of Non-
Response
Based on the number of renal cancer diagnoses and the proportion of non-response, the study will require enrollment of roughly 1,500 patients (500 patients/year) to identify a total of 100-150 patients for each arm (response, non-response)
Size of Sample
Population
Timeline for Enrollment
Enrollment Strategy
Nephrectomy Procedure (30-40%) It is estimated to take approximately 3 years to enroll and
collect viable samples from the required 1,500 patients
Patient-driven enrollment is expected to fulfill 25% of enrollees, as AMCs are seeing fewer first line patients
Enrollment and sample collection will require a network of approximately 25 targeted community hospitals and AMCs to ensure samples can be gathered and stored appropriately
With only 30-40% of patients having a nephrectomy procedure, the study will need to cover the cost of sample collection for at least 60% of patients
Sample Collection
Target Population
Nephrectomy is not a required treatment for the study but will aid in sample collection
Clinical Leads: Bob Motzer, James Hseih
The specific targets of the breast cancer study are still being defined, however there is a committed clinical leader and support from a leading patient advocate
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
Patient diagnosed with metastatic breast cancer
Non-Responders Progress through treatment
Treatment with Avastin
Responders
Clinical Flow of Breast Cancer Patients The clinical team is still selecting the ideal patient population to study
The leading population being considered is patients with metastatic breast cancer who are being treated with Avastin or a similar therapy
Definition of Non-
Response
Enrollment Strategy
With a highly active patient advocate community, the breast cancer study is likely to be filled largely by patient-initiated enrollment
Leveraging the relationship with the AVON/Love Army of Women will provide access to a network of over 350,000 women interested in participating in studies related to breast cancer
This network can help to virally spread the word about the study and generate national interest in participation
Target Population
Clinical Lead: Craig Henderson Patient Advocate Group: AVON/Love Army of Women
Preliminary
Ovarian Cancer
Sample Collection
In most cases, samples will be collected during required diagnostic procedures conducted by the patient’s treating surgeon and shipped to a central location
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
What type of sample is required?
Where will the samples be
collected and by whom?
What materials will be required for
collection?
What is the cost of sample collection?
• Both tumor and normal tissue samples will be required in all cases, where possible an adjacent or recurrence sample should be obtained
• Sample collection will be conducted during a patient’s required biopsy procedure
• The location of collection will vary based on the specific projects; projects being completed through physician enrollment at targeted AMCs will require collection at these sites, while patient-driven studies will allow for collection at any community location
• Procedures for collection will require standard medical materials available to participating physicians
• Physicians will be provided a copy of instructions for storing shipping and handling of the samples
• All samples will need to be shipped FedEx overnight to the sequencing location
• Sample collection will leverage procedures that are already being conducted and (in many cases) reimbursed by insurers or paid out of pocket by patients
• Cost for additional samples in cases where biopsies were not medically required will cost approximately $5,400 per patient, including sample prep
Since the treatment plan for Ovarian Cancer lends itself to a physician-driven enrollment plan, ten to twelve AMC partners will be selected to be primary enrollment and treatment sites for the study
These sites will be expected to enroll roughly one patient per month to reach the 150 patient target
An estimated two-thirds of ovarian cancer patients will not have a medically necessary surgical procedure after their first recurrence, requiring the study to fund biopsy procedures to collect samples from these patients
Of the 150 patients enrolled, approximately 99 patients will require biopsies to collect samples specifically for the study
Labs & Pathology
• Each cancer type will have designated sites for conduc%ng rou%ne labs and pathological review to ensure consistency of analysis
Gene%c Analysis
• Analysis will include: Whole Genome Sequencing, transcriptome gene expression and copy number varia%on
• Each study will have a primary processing site, which will be selected from among leaders in gene%c sequencing that have par%cipated in similar projects, such as The Cancer Genome Atlas
Core Bioinforma%cs
• Bioinforma%cs will be conducted by the most cost-‐effec%ve, trusted provider to ensure the quality and consistency of data for analysis
• The core bioinforma%cs processing will turn the raw data into usable altera%on component lists of muta%ons and dele%ons
Sample Processing
Sample processing will involve whole genome sequencing, conducted at leading TCGA participating sequencing centers, as well as bioinformatics and pathological review
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
Clinical Data Reporting
While the patient is undergoing treatment, the physician will be required to submit data regarding the patient’s therapies and outcomes
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
• The treating physician will submit data on the patient’s treatment and outcomes to the CRO on a regular basis
• This information will include: – Type and dosage of treatment the patient is receiving (i.e. a specific platinum-doublet
chemotherapy) – Details on the progress of the patient’s cancer
Clinical Reporting
Data Collating and Disease Modeling
The genetic and clinical information will be combined and analyzed by Sage Bionetworks to design a disease model identifying the causes of non-response
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
Combines genomic and clinical data
Applies sophisticated mathematical modeling
Generates a map of drivers of non-response
All scientific output will be publicly available and no members of the research group will own any
resulting IP
1 2 3
Feedback and Results
Material findings related to a patient’s potential treatment will be communicated when discovered; The resulting disease maps will be publicly available to be revised and validated by the scientific community
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
Patients/Physicians Scientific Community
Near-term
Long-term
Within one year after project
Longer than one year after project completion
Occasionally, specific insights will be shared with the patient through their physician – mainly related to the potential benefits of specific treatments
Over time, as the study results facilitate guidance on therapy selection, patients may be notified of a specific signature of response/non-response that can be used to make treatment decisions if relapse occurs
The first versions of disease maps will be available publicly identifying hypotheses of non-response signatures for use by physicians and scientists to validate
Once the initial maps are published they data and maps will be dynamically updated as new patients and tests are added to the results, with scientists globally able to refine the disease maps
Project Management
Each study will have an independent team to manage the tumor-specific study, which will roll up to a central project coordinator
Section 2 – Research Plan
Sage Bionetworks • Non-Responder Project
Project Coordinator Clinical Coordinator
Non-Responder Project Coordinator Overall PMO
Study-specific PMO
(Ovarian example)
Entity/Person --- Role and responsibilities
Administrative support, coordination and marketing
Oversees overall operations
Genetic Alliance
Manage and standardize the enrollment and consenting process
CRO
Data management, clinical operations and monitoring activities , safety management
These Data May Teach Valuable Lessons About:
• mechanism of action/target heterogeneity
• off target effects
• experimental design flaws
• duration of effect/compensatory pathways
• genotype/phenotype relationships
• predictive power of disease models
With These Insights Researchers Could:
• build better maps/more predictive models of disease
• identify patient subsets that benefit
• identify repurposing opportunities
• reduce off target toxicity/side effects
• form new hypothesis about pathophysiology
• avoid replicating others failures
• design more informed future trials
Sponsors Perceive A Negative Risk to Reward
• reputational/legal concerns
• competitor intelligence
• potential damage to existing product franchises
• potential damage to IP portfolios
• collaborator restrictions must be negotiated
• nobody wants to be first
Today, disclosing negative data is all risk and no reward for a sponsor company
We need creative solutions to balance the risk/reward ratio
Without incentives or a mandated change to corporate behavior assets will be wasted, mistakes repeated and
opportunities for innovation missed
The Carrot Approach • Priority Review Vouchers
- in exchange for committing to disclose failed studies over a 5 year period a sponsor will receive a priority review voucher that can be used for any submission or transferred for economic value
- similar to FDAAA Section 524 establishing a priority review voucher for sponsors that pursue therapeutics for tropical disease
- legislative framework is already established and can be appropriated
- NPV of voucher calculated at US$300 million
The Stick Approach
• Shareholder Activism
- a new take on demanding corporate social responsibility
- educate shareholders on benefits of disclosing data sets
- dialogue with management and seek compliance
- file shareholder resolution for vote at annual meeting
- coordinate media campaign to raise public awareness
The Stick Approach • Enlist Payors To Call Out Pricing Dichotomy
- the cost of new drug development is estimated to range from $800 million - $1.3 billion
- new drug launches must price at levels to recoup those costs in order to drive further innovation
- DiMasi et al shows that 40% of drug development costs are due to clinical failures
- payors and patients are subsidizing failed clinical trials but never benefit from the data
- insurers and Medicare should press for fairness either hold drug prices constant and disclose the failed data or cut prices by 40% so that payments accurately reflect the goods delivered
How Do We Study Network Perturbations in Clinical Specimens? How do we select which targets are effective for what diseases and which patients?- Stephen Friend July 18th FDA
1. Clinical Trial Comparator Arm Project “CTCAP” 2. “Arch2POCM”- Compounds to decode biology 3. Oncology Non-Responders Project 4. Freeing up Failed Compounds
What are the potential opportunities to participate in these projects? What actions might the FDA take? For actions needed beyond the FDA- executive or legislative?
Top Related