Biospecimen Collections:Biospecimen Collections:Economic IssuesBiobank Business Planning and
E i I tIWGSCMarch 12, 2009
Economic Impact
Jim Vaught, Ph.D.
November 5, 2007 Lisa B Miranda 1Jim Vaught, Ph.D.
Deputy Director, NCI OBBR
Deputy Director
Office of Biorepositories & Biospecimen Research
U.S. National Cancer Institute, NIH, DHHS
ESBBMarseilleNovember 2011
Topics
• National biospecimen network• NCI best practices & business planning• Some current biobanking networksg• Overview of economic issues
– Value proposition– Value proposition– Total cost of ownership
Cost recovery– Cost recovery– Business model
Economic impact examples– Economic impact examples
National Biospecimen Network Blueprint
Key principles :
•Standardized biospecimen collection and distribution procedures
•Standardized data sets and data vocabulary•Standardized data sets and data vocabulary
•Harmonized approached to ethical and legal issues
•Standardized consent, MTAs
•Transparent governance and business•Transparent governance and business models
•Transparent access policies
•Large well-designed specimen sets for a variety of research questions
Web version of NCI Best Practiceshttp://biospecimens.cancer.gov/bestpractices/
From the 2011 NCI Best Practices
B.1.3.3. Business Planning
•Business planning can provide justification for financial and institutional commitment and quantification of startup and sustainability costs.
B i l i h ld b i t t d i t ll t f ti bi i•Business planning should be integrated into all aspects of operations, biospecimen resource management, and evaluation.
•Resources should aim to establish a documented annual business plan developed with d t t t ff i t d li d ith th i i d i i f th B idepartment staff input and aligned with the vision and mission of the resource. Business plan items should be specific, measurable, actionable, relevant, and time bound.
•The resource business plan should also include a formal continuity plan that addresses ll ibl ti l di ti i l di di t l iall possible operational disruptions, including disaster planning.
•If the resource functions as a service center, the business plan should address issues related to service and revenue generation.
Qualities of well-designed biobanking networks
The biobanks outlined in the review although they operate under a variety ofThe biobanks outlined in the review, although they operate under a variety of models, share many of the following characteristics, which in most cases are detailed in their web sites:
• Governance models with clearly stated technical standards, ethical guidelines, access policies and procedures, scientific rationale, and long-term custodianship plans, i.e. assuring that the program is sustainable from a technical and economic perspective.assuring that the program is sustainable from a technical and economic perspective.
• A strong quality assurance/quality control program with clearly defined standard operating procedures, and regular audits to assure compliance.p g p , g p
• A comprehensive business model that, unless it is entirely supported by public funds, has a sustainable cost-recovery plan, or other means to assure consistent long-term y p gfinancial support.
• In general, adherence to a set of best practices governing both technical and ethical/legal issues, such as those published by the International Society for Biological and Environmental Repositories (ISBER http://www.isber.org) and NCI (http://biospecimens.cancer.gov).
B i & i iBusiness & economic issuesbeing addressed by OBBR
• Costs of establishing & maintaining biorepositories• Recovering costs is full cost recovery possible?• Recovering costs – is full cost recovery possible?• What is the “value” of specimens and data?• Costs of implementing best practices?p g p• Importance of quality management & evidence-based standard
operating proceduresE i b fit th b tifi d?• Economic benefits: can they be quantified?– Efficiencies of scale– Benefits of implementing best practicesBenefits of implementing best practices– Impact of more efficient informatics systems– Economic & scientific value of networks
Journal of the National Cancer Institute MonographJune 2011
Global market demand for biospecimens
Figure 1: Global Market Value of the Demand For Human Biospecimens and Related
Services
$1,750 $2,000 $2,250 $2,500
Market Value Growing at 20%‐30% Annually ($in millions)
Market Value Growing at 20%‐30% Annually ($in millions)
$750 $1,000 $1,250 $1,500
$-$250 $500
2009 2010 2011 2012 2013 2014 2015
From Business Insights March 2009
Biobanking Cost Modeling
Biobank Total Cost of Ownership
Initial Start-up
I t t
Steady State Phase of Operation
osts
($)
Investment
Periodic Technology & Equipment “Refresh”
Ann
ual C
Periodic Technology & Equipment Refresh Costs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Year of Operation
Capital Investment Operating Costs
Commodities and ServiceCommodities and Service‐‐Based ModelBased Model
GTEx
Specimen Catalog
Pathology Review
Quality Assurance PCC
Genotyping Protein Microarrays
CTEP (clinical trials)
Laboratory Best Practices
SOP Training
Proteomics Analysis
DNA Expression Profiling
Sequencing
yp g
Molecular Derivative Isolation
TCGASO a g
Research Services
Advocacy
Sample Orders C f ll
Advocacy
NIH NCI caHUB Economic Study
Sample OrdersCustomized Processing Services
Managed Collections“Front Door” Concept
Center of Excellence Training Data Orders
Cost Recovery ModelingBased on early NCI caHUB planning
Cost Recovery Example 1 with Highly Conservative assumptions shows recovering 70% of costs by Year 5:11
The More Realistic Example 2 uses going-market price data for samples, and demonstrates the potential to achieve Full Cost Recovery by Year 3 just from the sale of commodities alone:
22
Fundamental Factors That Drive Value
Fit for Purpose
The research application and scientific question being addressedSpecifics of the collection protocolS ifi j t d ( l di d ti i i i tSpecific project needs (e.g. normal, diseased, tissue origin, specimen type,
etc.)
Sample Quality and SpecificityFit for
Purpose
Quality and specificity price drivers: Specimen rarity and size requirements Extent of customized processing requested Clinical parameters (e g treatments etc ) and pathology
Quality and Specificity Clinical parameters (e.g. treatments, etc.), and pathology
parameters (e.g. tumor subtype, positive tissue markers) requested
and Specificity
Data Richness
Outcomes data are in high demandComprehensive data sets may double sample price
Data Richness
NIH NCI caHUB Economic Study
Customized data increases the sample price
Benefits framework for centralized biobank
Best Practices
• Decrease in time spent processing samples in order to meet research requirements• Avoided cost of having to replenish samples because of higher sample quality
• Improves “Lean” abilities to avoid redundant processing costsPractices
Stronger Clinical Correlation
InfrastructureLeverage
• Improves “Lean” abilities to avoid redundant processing costs
• Higher annotated data promotes improved data sets and more accurate modeling• Avoids re-collection of data, saving time and cost
• Smaller research organizations can leverage storage and bioinformatics system infrastructure, reducing the need to purchase their own
Job Creation Impact on Economy
LeverageMore Efficient
Research• Reduction in re-experimentation due to higher quality samples
• Avoided cost of having to replenish samples because of higher sample quality
• New jobs created with the potential to spur an increase in certified biobanking professional opportunities, and the resultant economic impact
Improved Patient Diagnosis and Therapeutic
Care
Clinical Trials Cost Savings
• Higher quality specimens reduce clinical trials timeframes and cost• Higher quality samples advance biomarker research
• Improved specimen handling standards reduce the risk of misdiagnosis
• Reduction in adverse impacts and loss of human lifeCare • Savings to patients and healthcare providers
Biobank Economic Benefits Impact
Economic Benefits Impact Category Annual V l
10-Year Value(Di t d)co o c e e s p c C ego y Value (Discounted)
1. Reductions in the Cost of Clinical Trials $116.8 $454.6
2. Patient Diagnosis and Therapeutic Care $48.5 $169.5
3. Efficiencies from Leveraging Infrastructure $14.8 $51.5
4. Avoidance of Repeat Experimentation $4.2 $14.6
5. Benefits Due to Implementation of Best Practices $2.4 $4.1
6. Industry Job Creation Impact on Economy $0.1 $5.9
7. Improved Modeling of Clinical Data $0.5 $1.5
Total Estimated Economic Benefits $187.3 $701.7
Savings are rough estimates – case studies with actual data needed
HER2 assay issues: Quantifiable Economic Benefits of Standardization
• HER2 (ERBB2) gene is amplified in ~ 20% of breast cancers
• HER2 “positive” status is an important f li i l dmeasure of clinical outcome and
recommended therapy• Positive result triggers therapy:
~$55K/year~$55K/year• False-positive: risk of cardiotoxicity, no
clinical benefit – stressful for patient• False negative: missing potentially• False-negative: missing potentially
beneficial treatment• Up to 5,000 false positives and 7,000
false negatives occur per year resulting inASCO/CAP recommendations to revise specimen handling to improve assay reliability published in 2007:false negatives occur per year resulting in
millions of dollars wasted • Problems with testing start with
variability in the way specimens are
handling to improve assay reliability published in 2007:J. Clinical Oncology, Archives Pathol Lab Med
collected and processed
TCGA: The Cancer Genome AtlasTissue Sample
Pathology QC
GDAC
Pathology QC
DNA & RNA
Sequencing
Data and Results Integrative DNA & RNA
Isolation, QC
Expression,CNA & LOH,E i ti
Results Storage
& QCAnalysis
Epigenetics
Comprehensive Characterization
of a Cancer Genome
= Process
= Data
of a Cancer Genome
= Results= BCR = GSCs = CGCCs = DCC = GDACs
TCGA:Example of benefit of implementing best practices
• Tissue quality parameters set by the technical demands of the molecular analysis platforms
• All 10 analysis centers would analyze exactly the same molecules from the same samples from the same patient - all data directly comparable
– Sufficient quantity to satisfy all platforms q y y p
– Sufficient quality to yield interpretable data on all platforms
• The target number of 500 cases per tumor type (lung, glioblastoma, ovary) in the pilot study: defined depth of
l i d b bilit f fi di i h th t analysis and probability of finding genomic changes that occur infrequently (3% level)
Early failures in sample qualityfrom existing collections
Early quality failure rate was over 70% at cost of over $2000 per case (tumor, normal tissue or blood, data, personnel costs) and failed cases had to be replaced to reach target 500 cases/tumor.g /
Repository 1(Major Academic Site)
Repository 2(Major Academic Site)Academic Site) Academic Site)
# Frozen samples logged in collection
5000+ 1200+Before full pathology
# Samples meeting spec upon detailed review of inventory
1392 120
p gyreview
of inventory
# Samples meeting 174 18p gphysical/pathological specs
The Cancer Genome Atlas (TCGA):Where Samples Fail Most
TCGA slides courtesy of Dr. Kenna Shaw, TCGA Program Director
Pass rate improves as prospective collectionusing best practices/SOPs implemented
9000
10000 80% Retrospective
7000
8000 50% pass rate 60% pass rate ~80% pass rate
5000
6000
80% Prospective
3000
400080% Prospective
1000
2000
0
3/16
/2007
5/16
/2007
7/16
/2007
9/16
/2007
11/16/2007
1/16
/2008
3/16
/2008
5/16
/2008
7/16
/2008
9/16
/2008
11/16/2008
1/16
/2009
3/16
/2009
5/16
/2009
7/16
/2009
9/16
/2009
11/16/2009
1/16
/2010
3/16
/2010
5/16
/2010
7/16
/2010
9/16
/2010
11/16/2010
1/16
/2011
3/16
/2011
5/16
/2011
Current TCGA Qualification Rates by Tumor Type
90%100%
60%70%80%90%
Pass Rate of the
30%40%50%60%
Future?60%: $4,800,000
0%10%20%30% 60%: $4,800,000
70%: $3,400,00080%: $2.570,000
0%
LUSC
LUA
DRE
AD
BLCA
COA
DG
BM KIRC OV
COA
DRE
AD
UCE
CST
AD
HN
SCPR
AD
CESC
SALD
BRCA
DLB
CPA
AD
LGG
THCA
KIRP
LIH
C
Savings achieved with higher future pass rates for ALL cases in the project
Economic AnalysesNext steps for OBBR
• Refine the caHUB program cost and pricing models through fine-tuning of:tuning of:– Annual accrual of cases– Types of cases to be collected for various partners– Specimen processing protocols and downstream analyses– Outsourcing plans for caHUB Pilot period and beyond
• Adjust cost recovery targets as more detailed information is received and• Adjust cost recovery targets as more detailed information is received and further guidance provided on NIH funding mechanisms, timing, etc.
• Develop use cases/studies to detail and document the economic benefits that th HUB ill t f h i th f bi i litthe caHUB will create for research in the areas of biospecimen quality; processing protocols; infrastructure leveraging
• Develop a cost analysis and tracking tool and resulting price schedule for the diti d i f HUBcommodities and services of caHUB
Special thanks to
Joyce Rogers OBBRJoyce Rogers, OBBR
T dd C li & B i A l ti T B All H iltTodd Carolin & Business Analytics Team, Booz-Allen-Hamilton
J ff F i t B t U i itJeffrey Furman, economist, Boston University
All ibAll contributors to:2008 NCI BioEconomics workshop 2011 JNCI Monograph
htt //bi ihttp://biospecimens.cancer.gov
