BIOPRESERVATION AND BIOBANKING Volume 12, Number 6, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/bio.2014.0051

Biobank Bootstrapping: Is Biobank Sustainability Possible Through Cost Recovery? Monique Albert,1 John Bartlett,1 Randal N. Johnston,2 Brent Schacter,3 and Peter Watson 4,5

Background: The pre-eminent goal of biobanks is to accelerate scientific discovery and support improvements in healthcare through the supply of high quality biospecimens to enable excellent science. Despite the need for retrospective future-proofed cancer repositories, they are presented with significant fiscal challenges. While it was once thought that biobanks could recover most, if not all, operational costs through distribution fees, biobanks have been consistently unable to fully realize this dream. Methods: Using data from three mature Canadian cancer biobanks, common attributes and assumptions related to cost recovery were evaluated. The values were entered into a simple financial model to determine the cost recovery potential for biobanks. Results: Over a 5-year period analyzed, aliquots from almost 40% (8990) of 23055 cases collected have been distributed in whole or in part to researchers. The financial modeling demonstrates that, based on values derived from the real life experiences of three major Canadian biobanks, full cost recovery through distribution is not feasible. A more realistic, experience based, expectation of cost recovery from distribution fees is in the range of 5%–25%, and this range is lower if only academic research is supported as opposed to also supporting industry researchers. Conclusions: Biobanks are expensive and, to mitigate costs, are frequently challenged to operate under ‘‘selfsustainable’’ financial models. However, the only possible route to self-sustainability through distribution fees in today’s market would require an almost exclusive targeting of commercial researchers and, even then, evidence suggests this is an impossible goal to attain. Support for biobanks should recognize that they exist to further development of personalized treatments and diagnostics essential for precision medicine. For biobanks to continue to achieve this goal, pro bono publicum, funders need to be aware of the full funding requirements of biobanks and create appropriate funding streams.

Introduction

A

ccess to relevant biospecimens makes meaningful research possible. Biospecimens are the key to the development and testing of next-generation diagnostics and treatments. The NCI caHUB online survey revealed that significant numbers of researchers have difficulty obtaining specimens in the numbers and quality needed to support their studies.1 The Cancer Genome Atlas (TCGA) project exemplifies both the value of biorepositories2–6 and the challenge behind sourcing biospecimens.7 The result has been an unprecedented and fundamental shift in our understanding of the molecular landscape of cancer2–6 spanning genomics, transcriptomics, and proteomics. One inescapable conclusion

is that this pivotal research program could not have been achieved without the support of multiple high-quality biorepositories. Retrospective biobanks collect and preserve biospecimens as a resource for future scientific projects and significantly reduce the delivery time of a project. A carefully crafted biobank will comprehensively annotate biospecimens with the donor’s medical history, collect outcome data years after collection, and, importantly, document and control for quality. Because of the processes and procedures designed into a ‘‘platinum level’’ biobank,8 such as those accredited under programs like CAP,9 certified under programs like Canadian Tumour Repository Network (CTRNet),10,11 or following defined biobanking best practices,12 researchers

1

Ontario Tumour Bank, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta. 3 CTRNet, CancerCare Manitoba/University of Manitoba, Winnipeg, Manitoba, Canada. 4 Tumour Tissue Repository, Trev and Joyce Deeley Research Centre, BC Cancer Agency, Victoria, British Columbia, Canada. 5 Department of Pathology and Laboratory Medicine, BC Cancer Agency and UBC, Vancouver, British Columbia, Canada. 2

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can have confidence that data generated from biospecimens are reliable. Despite the obvious need for biobanks in a healthy research community, most biobanks face financial realities jeopardizing their existence. Biobanks are frequently established with seed funding with the optimistic belief that further funding will be secured or with the expectation that ‘‘cost recovery’’ will sustain continued operations.13 Conclusions from the Biospecimen Economics NCI workshop suggest that a better understanding of the economics of biobanks is needed, along with funding requirements and cost recovery potentials.8 Tupasela suggests that the long-term sustainability of biobanks depends on continuous support from public funders and institutions.14 In this study, we have sought to inform this debate using evidence of the potential for ‘‘sustainability’’ of biobanks. Sustainability is defined here as a state where a biobank can exist in perpetuity within existing funding and revenue streams without reducing operational scope. ‘‘Self-sustainability’’ is defined as meeting 100% of program costs solely through access fees, without funding from external sources. The actual costs of biobanking remain poorly understood and frequently underestimated by both researchers and biobankers. New work by Vaught and others offer excellent advice on capturing costs and building a sustainability framework.15 CTRNet (www.biobanking.org) plans to release a fee calculator tool to help cost out processes and propose a fee structure.16 Others have suggested similar processes to account for both direct and indirect costs and set access fees.17 A rough method of calculation, used herein, is to divide the total program costs for a given year by the total number of new cases (i.e., donors) accrued in that year to arrive at a cost per case to the program, providing data for setting appropriate access fees. This approach recognizes the legal and ethical norm that human tissues are not owned by the biobank and cannot be sold; only operational costs around the biobanking process are eligible for recovery. Current costing models fail to capture inventory carrying costs: the longer a biospecimen is retained, the more it costs the biobank. Also not captured are ancillary costs from unaffiliated staff who provide support ‘‘pro bono’’. A clear understanding of readily identifiable biobanking program costs is essential to set fees and develop a cost recovery model. Understanding the full costs of biobanking is only one dimension in planning long-term financial sustainability. Market demand and willingness/ability of recipient researchers to pay is a major driver of price. Academic researchers rarely understand the true cost of biospecimen acquisition that a multiuser biobank must cover. ‘‘Market forces’’ or ‘‘what the market will bear’’ impinge significantly on the fees a biobank can set. As will be seen, each case collected costs a cancer biobank close to $1000. The vast majority of academic researchers and funders are not able or willing to pay this amount and in reality biobanks tend to reduce fees considerably, indirectly subsidising academic users and funders, to fulfill their mandate of providing biospecimens to academic researchers. This fact alone precludes planning for ‘‘self-sustainability’’ based on cost recovery from academic researchers. This has been a long recognized reality amongst biobanks, however, evidence to support this has been lacking. The original business cases for three mature Canadian cancer biobanks are used to identify common assumptions

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underlying their business cases and financial forecasts. These assumptions are tested and adjusted using actual budgetary figures and provide the inputs for a simple financial model to test the viability of self-sustaining biobanks in today’s market. Each biobank included services the research community through provision of high-quality freshfrozen and paraffin-embedded tissue, matched blood specimens, derivatives, and comprehensive patient data (Alberta Cancer Research Biorepository, The BC Cancer Agency Tumor Tissue Repository, and the Ontario Tumour Bank). Each is a founding member of, and is certified by, CTRNet. All operate independently and have been in existence for over 10 years. In total, these biobanks have supported over 450 research requests resulting in over 100 documented (and many undocumented) publications.

Materials and Methods Information pertaining to direct biobanking activities was extracted from three Canadian cancer biorepositories. Revenues realized from non-biobanking activities (e.g., consulting, education, service provision) were omitted. Values are represented in Canadian dollars. These biobanks were selected for inclusion in this proof of concept study as they are all relatively successful in the Canadian landscape with respect to accrual and release, are generally recognized as lead cancer biobanks in their respective provinces, are leaders in the Canadian biobanking community as charter members of CTRNet, and have each been in existence for over a decade. For these reasons, they should represent close to the presentday ‘‘best case’’ reality for Canadian cancer biobanks, which is relevant for the assessment of financial sustainability potential that this article seeks to address. Each, however, is sufficiently different to represent the scale (e.g., accrual rates, number of collection nodes/centers) and types (e.g., provincial or institutional) of cancer biobanks typical in Canada and each resides in separate provinces spanning across Canada subject to local provincial legislation (British Columbia, Alberta, and Ontario). Identifying assumptions: The original business cases were used to determine the original expectations with respect to mandate, estimated accrual, utilization (distribution), and cost recovery.

Common criteria The following were extracted from historical budgets and operations as reported per year of operations: total program costs, cases (i.e., patients or donors) recruited, aliquots (i.e., samples) stored, researcher requests, cases or aliquots distributed, percent of cases and aliquots supplied to academic versus industry researchers, and total cost recovery. These values were used in subsequent calculations and analyses. Values were plotted over time to account for program rampup and select the years representing steady state operations (Fig. 1). The period 2008–2013 was selected as representing steady state with respect to performance indicators. For direct comparison between biobanks, costs were assessed per case rather than per aliquot, due to the differences in aliquot volumes, weights, etc. Results were then averaged for the three biobanks. The cost of collection was calculated in two ways. The ‘‘full per case cost’’ was calculated as the sum of all program costs/sum of all cases collected over the full duration

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FIG. 1. Average performance indices over time (year) among the three biobanks (case collected and distributed, total costs, and cost recovery). The most recent 5 years appear to most closely reflect ‘‘steady state.’’ of the program. ‘‘Ongoing per case cost’’ was calculated as yearly program costs/yearly cases collected, averaged over the last 5 years. The percent cost recovery was calculated as average yearly revenues/average yearly program costs. The percent distribution was calculated as average yearly cases or aliquots distributed/average yearly cases or aliquots collected (over a 5-year period). On a case basis, this was calculated using the number of cases accessed rather than the number depleted, as it was not possible to track depletion of cases. Processing strategy, aliquot size, and/or distribution approaches may allow for a case to be distributed multiple times.

these values to evaluate potential cost recovery, and thus self-sustainability. The basic formula is: %cost recovery = [(% distribution*academic price*academics %) + (% distribution*industry price*industry %)]/cost per case.

Results Biospecimen acquisition The three biobanks have collectively accrued biospecimens from over 36,000 voluntarily consented patients (i.e., cases) at a current rate of 4600 per annum. All primarily support academic researchers; support for industry research differs (Table 1).

Financial modeling and scenario analyses A simple model was created to test the cost recovery potential using the calculated values for cost per case, ratio of industry to academic researchers, and the subsidy applied to each demographic. Scenarios were devised manipulating

Costs and distribution Parameters relating to cost recovery, fee structures, and researcher demographics (Table 2) were used to calculate collection costs/case and percent distribution. The average

Table 1. General Parameters of the Three Canadian Cancer Biobanks in this Study Years of Collection Paid Total Yearly Biobank* collection nodes** FTEs^ accrual{ accrual{ ACRB

13

7

19

17809

2536

BC-TTR

11

2

4

4925

516

OTB

10

4

12

13922

1559

Funding source

Target demographic

*9% institutional, 86% Academic researchers. Moving grant, 5% access fees toward also supporting industry researchers. *95% institutional, 5% Academic researchers. May access fees provide to industry researchers under defined circumstances. *75% institutional, Academic and industry 25% access fees researchers are both fully supported.

*Biobanks: Alberta Cancer Research Biorepository, The BC Cancer Agency Tumor Tissue Repository, The Ontario Tumour Bank; **‘‘Collection nodes’’ is the number of participating hospitals where patients are recruited; ^Number of paid FTEs (Full Time Equivalent employees) at the time of publication; {‘‘Total accrual’’ is the sum of all cases (i.e., patients or donors) collected for the full duration of the biobank; {Represents the average accrual of cases over the last 5 years of operation.

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Table 2. Cost and Distribution Parameters Among the Three Biobanks Criteria

Average (range)

Predicted cost per case (intended budget/intended accrual, post ramp up) Total cost per case (over full duration of biobank) Ongoing cost per case (average over past 5 years) Predicted % distribution, (utilization, post ramp up) % distribution (utilization, cases accessed vs collected) - 5yr % distribution (utilization, aliquots stored vs released) - 5yr Distribution to academic researchers Distribution to industry researchers Price extended to academic researchers (% of ongoing cost) Price extended to industry researchers (% of ongoing cost) % cost recovery (average over past 5 years)

44%*, 26%*, 91%*, 9%*,

$608 $1231 $828 76% 39%** 19%** 86%** 14%** 20% 125% 11%

($592–$808) ($716–$1888) ($562–$1086) (74–80%) (24–59%) (4–49%) (75–100%) (0–25%) (10–25%) (100–150%) (5–20%)

Cost and distribution values were derived from information from each biobank’s budgets and historical business cases. Values are represented as averages. *Unweighted (sum of average values per biobank divided by number of biobanks); **Weighted (weighted by total collection and/or distribution per biobank).

cost per case collected approaches $1250, but if start-up costs are excluded, current (averaged over 5 years) costs/ case are significantly lower (Table 2) at around $850. Over the most recent 5 years, almost 40% of all cases collected (nearly 8990) have been distributed, in whole or in part, to researchers of whom 86% were ‘‘academic’’ (Table 2) (where two of the three biobanks distributed 100% to academics and the third 75%). The collection of multiple aliquots/case means that 80% of collected material remains available for future research (Table 2).

Fees and cost recovery All three biobanks heavily ‘‘discount’’ prices to academic researchers, who contribute only 10%–25% of the cost/ sample, with 86% of nearly 8900 cases distributed in whole or in part to academics over a 5-year period. A rough estimate suggests these three bio-repositories have ‘‘subsidized’’ academic research in excess of $5.1M over the past 5 years or $7.6M if the total cost/case is used (data from Table 2, calculated as total number of cases distributed over 5 years*cost*ave %distribution to academic researchers*(1ave%subsidy), that is, 8990*$829*0.86*(1-0.2) = $5.1M).

Testing assumptions Only one biobank aimed prospectively to be self-sustaining, but the data collected informs the potential for financial self-

sustainability. In general, the biobanks achieved lower collection rates than estimated in the original plan conceived before initiation of operation, which affects the cost per case through economies of scale ($608 estimated vs $828 actual) (Table 2). None experienced the level of utilization estimated (Table 2).

Cost recovery potential Select aggregate values captured in Table 2 became the inputs for a very simple model to ascertain the cost recovery potential under current constraints as experienced by the three biobanks (Table 3). Even at 100% distribution, where every case collected is dispensed, under current operating constraints only 35% cost recovery is forecast. At 39% distribution (by case), the average experienced in practice, 14% cost recovery is forecast, which is remarkably close to the 13% experienced. The values for each individual biobank resulted in predicted cost recoveries within 5% of actual performance (not shown), confirming the utility of this tool for estimating potential cost recovery.

Sensitivity analysis A number of values were manipulated (e.g., percent support to academics, etc.) to determine the potential for self-sustainability through cost recovery (Table 3). Extreme scenarios were devised to test the boundaries and some

Table 3. Cost Recovery Potential at Current Parameters Percent distribution (cases accessed, utilization) Cases collected Cost to program ($827.65 per case) Distribution number (# cases accessed, utilization) Revenue from academics (86% at an 80% subsidy) Revenue from industry (14% at a 25% margin) Cost recovery (total revenue) % cost recovery (total revenue/cost to program)

15%

25%

39%

50%

75%

100%

200%

1,000 $827,646 150 $21,379 $21,752 $43,131 5%

1,000 $827,646 250 $35,632 $36,253 $71,885 9%

1,000 $827,646 390 $55,586 $56,555 $112,141 14%

1,000 $827,646 500 $71,264 $72,507 $143,771 17%

1,000 $827,646 750 $106,896 $108,760 $215,656 26%

1,000 $827,646 1,000 $142,528 $145,014 $287,542 35%

1,000 $827,646 2,000 $285,056 $290,028 $575,083 69%

Values from TABLE 2 (cost per case, utilization, distribution ratio, and pricing) were input into this simple model. This scenario reflects the current state of the three biobanks on average and the potential for cost recovery at different levels of utilization (‘‘percent distribution’’). The distribution level on average was 39% and this column represents the actual cost recovery predicted for the biobanks on average.

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Table 4. Scenario Modeling (Sensitivity Analysis) to Demonstrate Potential Cost Recovery Scenario Actual (baseline) Utilization increased to 100% All users are100% academic All users are 100% industry 100% industry and 80% utilization Raise price to industry 100% sustainability (example 1) 100% sustainability (example 2) 100% sustainability (example 3)

Cost to program

% distribution (utilization)

Price to academics

Price to industry

% to academics

% to industry

% cost recovery

828 828 828 828 828 828 828 112 828

39% 100% 39% 39% 80% 39% 50% 39% 75%

166 166 166 166 166 166 166 166 166

1035 1035 1035 1035 1035 1655 1655 1035 1655

86% 86% 100% 0% 0% 86% 0% 86% 37%

14% 14% 0% 100% 100% 14% 100% 14% 63%

14% 35% 8% 49% 100% 18% 100% 100% 100%

The first row (‘‘actual’’) is a duplication of values from Table 3, column 3 ‘‘39%’’. Italicized values indicate changed (scenario) from baseline (actual). By affecting specific variables, the potential for cost recovery can be predicted for if those values were to be true (e.g., if the biobank were able to dispense 100% of its materials, or if 100% of its users were academic, or if it were to raise prices).

balanced multi-factorial scenarios were created to understand the relational outcome if many parameters were varied simultaneously (Table 4). No realistic scenario tested led to financial self-sustainability, even when the primary goal of supporting academic research was excluded.

Discussion Our study, based on data from three comparatively mature and highly successful (with respect to quality, accrual, and distribution) cancer biobanks in Canada, demonstrates that financial self-sustainability is not attainable and is not a realistic expectation for the operating plan for operating a large poly-user biobank when the primary objective is to provide materials primarily to academic researchers. Simultaneously, these models provide some direct metrics of the success of these biobanking initiatives: almost 11,000 total cases have been distributed with estimated indirect subsidies to academic researchers between $6.3 and $9.3 million Canadian dollars. Therefore both directly through sample supply, and indirectly through financial subsidy, these biobanks have provided significant support to academic research over the past 5 years. Further testing with the model shows that when values are arbitrarily manipulated in a sensitivity analysis, 100% selfsustainability through academic user fees is unachievable in practice, even if there were a shift to support more industry users. For example, based on current operational costs, if all current users were secured from industry alone, only 49% cost recovery is forecast, which is unlikely given that, despite years of efforts, the one biobank in this study actively pursuing industry clients has only been able to achieve a 25% distribution ratio to industry. Utilization would have to increase to 80% to achieve 100% financial sustainability through industry user fees alone (excluding academics; Table 4). Pragmatically, given current goals to support academic research a cost recovery ranging from 5%–25% is realistic. The reader is able to enter their own present or desired parameters into the formula to test assumptions around cost recovery potential. The model itself is a very basic tool for revenue forecasting that effectively divides total revenue over total costs. While information from only three biobanks was used to populate the tables and calculations, the model is applicable to information from virtually any biobank that charges ac-

cess fees. As data emerges from large surveys (e.g., NCI BBRB survey),18 it can populate this formula with broaderbased statistics and from across the world to learn the range of sustainability potential and better understand the effect of jurisdictional differences. Individual biobanks can input values from their own experiences to gain a better understanding of cost recovery potential, which inform funding levels needed for medium to long term sustainability. Pertinent to this discussion is an exploration into why this reality exists—what lies behind these values: Cost of biobanking. The participating biobanks have a collection cost/case of $828 on average. This is within the range reported by other tumor biobanks, when all costs are accounted for. For example, the U.S. Army’s Clinical Breast Care Project reports $861 USD per case.8 A common misconception amongst researchers is that when biospecimens are donated by patients, they should be practically free, implying that biobanks either overcharge or are mismanaged. In reality the costs of consenting donors, processing, storage, quality control, data capture, and management are real, significant, and are unlikely to change much once maximum efficiencies are reached. But even these costs directly supported by biobanks do not include every cost associated with the biobanking process (as noted below). Indirect costs, not included in the above analyses, reflect the broad support for biobanks provided by the community and are not reflected in the biobanking costs. Surgeons, pathologists, nursing, and administrative support staff help to identify donors, resect tissue, draw blood, etc.—in most cases because they see the value in what biobanks achieve. If these activities were solicited to primarily support nonacademic research or a for-profit collection, then it is unlikely that these services would be contributed pro-bono and the cost per case would increase significantly. The collection cost per case is tied to the number of cases a biobank can accrue within existing resources. Creating efficiencies and sharing administrative support among collection sites can decrease the cost/case to an extent. There will be a limit as to the lowest cost a biobank is able to achieve without compromising its ability to deliver on quality. Utilization. Part of the answer lies with utilization (percent distribution) and the size of the present market. 70% of biobanks report concerns about underutilization.19 Utilization relates to such parameters as the purpose and utility of the biobank, the size of the market, and ease of access.

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During development, when a biobank does not yet have the numbers or types of materials needed to meet specific research aims, it will struggle to support researchers. How many cases are needed to achieve critical mass within a single biobank or coordinated network to become broadly useful to the research community is not simply a question of having sufficient representation of organ types but also of disease subtypes. The inventory of an open-ended biobank or coordinated network may need to be quite sizable before it can fulfil the breadth of potential study aims from multiple researchers. Willingness or ability to pay. The biobanks in this study have heard numerous concerns from academic researchers regarding price, even despite the high subsidies (75%–90%) currently in place. Many researchers are unprepared for even the comparatively small cost burden and will typically either abandon the intention of using human specimens in favor of more affordable options, such as cell culture or mouse models, or obtain specimens from unvalidated sources. Reasons behind this are likely multifactorial, and may relate to the lack of internalization of full research-related costs experienced in an academic setting, such as labor and equipment, which can lead researchers to underestimate costs. Whatever the root cause, the clear message is price remains a barrier. This further reduces the potential for biobanks to recover costs by reducing subsidies to academic researchers. Conversely, our experience is that industry researchers, coming from an unsubsidized sector, clearly understand the true costs and value of biobanking and are unsurprised at a price representing full cost. Recovery of full collection costs from this demographic, including a margin to offset subsidy to academics, is wholly realistic. There are ethical concerns in requesting fees higher than the per sample costs. As such, it is also unlikely that further cost recovery can be realized through price increases to industry researchers, at least without a better understanding of the full costs of biobanking per useable sample as justification. A better understanding of the costs of biobanking and the market reality will support future planning for sustainability. In the absence of sustained funding, biobanks will need to adjust their scope to fit within funding constraints, reducing capacity or services, and thereby support to the research community. At present, a failure to recognize the true cost and value of biobanks is likely to result in the closure or decay in integrity of many biobanks and lost potential for improved patient care based on biomedical and scientific advances. Unlike research projects, which have a beginning, middle, and end, biobanks exist in perpetuity so long as the repository remains relevant to research needs. Current funding streams meet the needs of short-term research projects—not biobanks. Some may suggest that the retrospective biobank is a failed model. We argue, however, that the retrospective biobank is not a failed model, but rather an under-rated and underfunded one. This is particularity relevant given that the costs of biobanking are not fully understood and are chronically underestimated, biospecimens must be held and maintained by the repository even if they go unused in research, and that a large enough inventory to support broad research needs can take many years to build. Despite these challenges, retrospective biobanks have the capability of accelerating research through the immediate provision of biospecimens and follow-up data to researchers upon request that would otherwise require considerable time

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to collect prospectively. We demonstrate that, for the biobanks in this study, there is strong evidence that they provide essential support to researchers through both access to biospecimens (*11,000 cases total, in whole or in part) and indirect financial support (est. $6.3–9.3 million total). Direct evidence of the value of biobanks to research is provided by the publications by TCGA3,20–22 and others that demonstrate the academic value provided by biobanks. We argue that too much emphasis has been incorrectly and unrealistically placed on cost recovery, thereby devaluing the true worth of biobanking to society: to support new health discoveries that benefit mankind and improve healthcare. To sustain biomedical research, funders need to be apprised and educated of the long-term needs of biobanks. Sufficient funding is needed both at a core level and through grants to researchers to accommodate true-to-cost access fees. It has long been accepted that animal facilities are required infrastructure for biomedical research. It is clear that biobanks also are required infrastructure that must be adequately funded to ensure availability of human tissues for biomedical research in the future.

Conclusion Evidence compiled from the experience of three major and comparatively successful biobanks suggests that complete self-sustainability would be difficult or impossible to achieve in today’s market. Inflated expectations around cost recovery result in underfunding and threaten their existence. Funders and biobankers alike hopefully will not lose sight of the true value in biobanks, which play a major role in the advancement of medical research, and thereby serving the public and ensuring the advancement of biomedical discovery to ultimately improve human health.

Acknowledgments The authors would like to thank Nancy Ahlan (OTB, OICR), Sindy Babinszky (BC-TTR), Joseph Roberts (ACRB), and Kathryn Graham (ACRB) for compiling raw data for this study.

Author Disclosure Statement There are no conflicts of financial interest to disclose.

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Address correspondence to: Dr. John Bartlett Ontario Institute for Cancer Research MaRS Centre 661 University Avenue, Suite 510 Toronto, ON M5G 0A3 Canada E-mail: [email protected]

Biobank bootstrapping: is biobank sustainability possible through cost recovery?

The pre-eminent goal of biobanks is to accelerate scientific discovery and support improvements in healthcare through the supply of high quality biosp...
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