Over the past 100 years, biobanking has evolved from haphazard biospecimen collection and storage to automated, standardized processes. There are now many different kinds of biorepositories, including disease-centric, population-based, project-driven and virtual biobanks 1. This biobanking evolution has greatly improved biospecimen and data quality. It has also made these resources more available to biomedical researchers.
Despite these advances, many biorepositories face significant challenges, such as maximizing sample use and maintaining financial viability 2. However, not every biorepository faces the same challenges. A biobank classification system may make it easier to understand the unique problems in different types of biorepositories. It may also help researchers find biospecimens 3.
A Proposed Biorepository Classification System
A group from the University of Leeds interviewed 33 biorepositories in 9 countries. The researchers then used this data to classify biobanks based on the types of biospecimens they store, the type of research they support, how they store samples and whether they are part of the private or public sector4. This paper suggests the following biobank classification:
In storage networks, groups of researchers come together to form a shared biorepository facility. This approach lowers storage costs and improves biospecimen quality, compared with each group storing their own biospecimens. These networks typically do not share samples with external researchers. Example, Peter MacCallum tissue bank.
Bring and Share Networks
Similar to storage networks, except these facilities offer researchers a lower storage fee if they are willing to share their biospecimens with other researchers. Example, Biological Bank and Cell Factory, Italy.
Catalogue networks make their samples available to external researchers. They also have a freely-available biospecimen catalogue or database that can be searched by external researchers. Example, Australian Brain Bank Network.
Similar to storage networks, partnership networks involve a consortium of research groups working together to lower biospecimen storage costs. However, unlike storage networks, partnership networks may use several storage sites, instead of one central biorepository. A partnership network may link many previously independent biorepositories under one umbrella organization. Example, The Australasian Leukaemia and Lymphoma Group Price Waterhouse Cooper Tissue Bank.
Conceptual Classification Approach
In a separate paper3, Peter Watson and Rebecca Barnes propose a different way to classify biorepositories. They used the following criteria:
1. Biospecimen donor
2. Collection methods and design, for example:
- Are the specimens prospective or retrospective?
- Are the specimens managed and collected directly for the primary purpose of research or indirectly during a medical procedure
- Is it a small collection for a single study or a large multi-study, multi-use collection
3. Biospecimen type e.g. blood, tissue, fresh or fixed etc.
4. Brand and intended users. Will the specimens be used by a single group or institution or multiple users?
They proposed a schema for classifying biobanks into 3 groups (mono-, oligo-, and poly-user), primarily based upon biospecimen access policies. The study used that research to further classify biobanks concluding that Poly-user biobanks employed significantly more full-time equivalent staff, and were significantly more likely to have a website, share staff between biobanks, access governance support, utilize quality control measures, be aware of biobanking best practice documents, and offer staff training. Mono-/oligo-user biobanks were significantly more likely to seek advice from other biobanks.
There are no widely-accepted guidelines to classify biorepositories. In this post, we have shared two options. Please let us know in the comments below, whether you think the community should work towards a universal biorepository classification system.
1. De Souza and Greenspan. Biobanking Past, Present and Future: Responsibilities and Benefits. AIDS. 2013
2. Brown et al. How Biobanks Are Assessing and Measuring Their Financial Sustainability. Biopreserv. and Biobank. 2017
3. Watson and Barnes. A Proposed Schema for Classifying Human Research Biobanks. Biopreserv. And Biobank. 2011.
4. Shickle et al. Inter- and Intra-Biobank Networks: Classification of Biobanks. Pathobiology. 2010
Written by: Srikanth Adiga, CEO, Krishagni
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