Briefings in Functional Genomics and Proteomics Advance Access published online on October 4, 2007
Briefings in Functional Genomics and Proteomics, doi:10.1093/bfgp/elm023
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Biobanks: transnational, European and global networks
Corresponding author. Kurt Zatloukal, Institute of Pathology, Medical University of Graz, Auenbruggerplatz 25, A-8036 Graz, Austria. Tel: +43-316-380-4404; Fax: +43-316-384-329; E-mail: kurt.zatloukal{at}meduni-graz.at
 |
ABSTRACT |
|---|
TOP
ABSTRACT
INTRODUCTIONBiobanks contain biological samples and associated information that are essential raw materials for advancement of biotechnology, human health, and research and development in life sciences. Population-based and disease-oriented biobanks are major biobank formats to establish the disease relevance of human genes and provide opportunities to elucidate their interaction with environment and lifestyle. The developments in personalized medicine require molecular definition of new disease subentities and biomarkers for identification of relevant patient subgroups for drug development. These emerging demands can only be met if biobanks cooperate at the transnational or even global scale. Establishment of common standards and strategies to cope with the heterogeneous legal and ethical landscape in different countries are seen as major challenges for biobank networks. The Central Research Infrastructure for Molecular Pathology (CRIP), the concept for a pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI), and the Organization for Economic Co-operation and Development (OECD) global Biological Resources Centres network are examples for transnational, European and global biobank networks that are described in this article.
Keywords: biobank, personalized medicine, biomarkers, CRIP, ESFRI, OECD Biological Resource Centres
|
INTRODUCTION |
|---|
After sequencing of the human genome, biomedical and health research has progressed from studying rare monogenic diseases to common multifactorial diseases. The latter are caused by a large number of small, often additive, effects, ranging from genetic predisposition to lifestyle and exposure to different environments. Innovative high-throughput technologies are widely expected to enable a better dissection of these complex, causally heterogeneous diseases into more homogeneous subgroups. Discovery of the disease-triggering effects, i.e. discriminating disease-specific effects from general alterations, will critically depend on the study of large collections of biological material such as tissues, blood or other body fluids from a large number of patients and healthy individuals, which are annotated with well-documented, up-to-date information on the sample donor including the clinical course of the disease. These collections, so-called biobanks, are essential resources to understand the function and medical relevance of human genes and their products as well as to explore the biological networks in which they are operating. This knowledge is also a prerequisite for the development of more effective drugs for specific patient groups in the context of personalized medicine [1–3].
The current concept for biobanks has been established by the rising demands for access to biological material from large populations, particularly fostered by the development of a broad spectrum of high-throughput ‘omics’ technologies such as DNA microarrays, massive parallel DNA sequencing and mass spectrometry, which enable detailed and affordable analyses [1, 3, 4].
|
BIOBANK FORMATS |
|---|
Two major formats of biobanks with several subtypes can be distinguished, each with distinct and complementary scientific value (Table 1).
|
Population-based biobanks
The most common format is the longitudinal population-based biobank with biological samples and data from randomly selected individuals of a general population. Typically, blood or isolated DNA together with data about family history, lifestyle, environmental exposure, etc., are collected at the entry time point into the study and at certain time points during follow-up [5, 6]. This prospective approach can be established best in the context of preventive medical programs. The specific strength of this format is the assessment of the natural frequency of occurrence and progression of common diseases in an a priori healthy population, with special emphasis on putative predisposing genetic variants and environmental risk factors. A further advantage is the possibility to study biomarkers that are predictive for the onset of diseases in not yet diseased individuals, which makes prospective population-based biobanks also an important complementation to disease-oriented biobanks (see subsequent text) in biomarker development. The population cohorts provide a setting to estimate the quantitative and qualitative impact of biomarkers on health and disease in the population. A major drawback of this biobank format is that most studies can be initiated at the earliest after 10–15 years of follow up, when a sufficient number of diseases will have occurred in the individuals of the cohort [7]. For instance, in a genome scan for a genetic polymorphism associated with a certain disease, DNA of about 10 000 diseased individuals should be analysed. The need of such large numbers of incident disease cases clearly demonstrates how the potential of these biobanks, particularly for the study of rare diseases, can be increased if biobanks cooperate internationally so that cases from different biobanks can be combined.
Geographically separated cohorts, which may be specific ethnic groups or population isolates due to other reasons and for which family history can typically be traced for several generations, have unique advantages for the identification of genetic risk profiles, since the genetic makeup and environmental exposure implies significantly fewer variables [8]. Another specific format is twin registries preferentially containing approximately equal numbers of monozygotic and dizygotic twins. This allows the parallel dissection of the effect of genetic variation in a homogeneous environment with dizygotic twins and of environmental effects against an identical genetic background with monozygotic twins [9].
Disease-oriented biobanks
In contrast, in disease-oriented biobanks, which may contain tissue, isolated cells, blood or other body fluids, specimens which are collected from an individual in the context of medical diagnosis and treatment. The specific strength is their high number of represented diseases. The ability to compare different disease stages and/or forms of treatment at a molecular level is instrumental for finding biomarkers for diagnosis of a disease or prediction of disease progression, e.g. mortality, and response to therapy. Furthermore, identification of pathways involved in disease progression may lead to new molecular targets for more specific drugs, which in combination with corresponding biomarkers for patient selection, are key developments for the advancement of personalized medicine [10, 11]. Disease-oriented biobanks are also critically needed to establish the human disease relevance of discoveries made in various model organisms, such as yeast, Caenorhabditis elegans, drosophila or mice.
A specific format of disease-oriented biobanks is the case–control study containing about equal numbers of samples and data from diseased and healthy individuals. The comparison between patients and controls in these studies can be used to recognize disease profiles or specific features, such as a mutated gene against a background of naturally occurring variations [12]. However, the appropriate control population is difficult to define and it is a matter of debate whether independent longitudinal population-based biobanks can serve as proper controls in the context of clinical case–control settings. An optimal scenario would be if samples (e.g. blood or DNA) collected from randomly selected individuals before onset of a given disease were combined with samples and data collected after onset of disease (e.g. blood or tissue), thus demonstrating the synergies of longitudinal population-based and disease-oriented biobank formats.
Another specific format is the tissue bank, collecting various diseased human tissue specimens, annotated with detailed information on the existing disease and, at least for a fraction of cases, information on response to therapy as well as final disease outcome. A specific advantage is that tissue permits investigation of localized diseases, such as cancer, inflammation or the organ-specific manifestations of systemic diseases. Most tissue banks contain, in addition to diseased, also corresponding non-diseased tissue from the same patient, which allows direct comparison of acquired disease-related alterations within the genetic background of the affected individual.
Exceptional large numbers of diseased tissues have been collected in the context of routine histopathological diagnosis and are stored in the archives of institutes of pathology or hospitals. However, these archives cannot automatically be considered as tissue banks since the requirements for tissue banks, which are specifically designed to fulfil the needs of modern research, are much more far-reaching than the demands on medical archives for diagnostic services [4, 13–15].
|
SPECIFIC FEATURES OF TISSUE BANKS |
|---|
Most tissue banks contain large numbers of formalin-fixed, paraffin-embedded tissues (FFPE), whereas frozen tissues are only available in limited quantities. The relevance of FFPE tissue for modern genome research has been underestimated until recently. FFPE tissue was mainly considered to be suitable for morphological analysis and immunohistochemistry. However, novel sample preparation and analysis techniques now allow the quantitative recovery of DNA and even RNA fragments as well as of proteins of more than 100 kDa [16, 17]. Since a broad variety of biomolecules have been shown to be well preserved in FFPE material and can be extracted, modern technologies can now be applied to old archived material, i.e. to diseased tissue that has been collected decades ago. Diseased tissue annotated with long-term follow-up data is particularly important to evaluate the natural course of various diseases or different treatment effects. An emerging important relevance of tissue banks is that diseased and corresponding non-diseased tissue are a critical resource to develop and validate biomarkers or gene signatures for prediction of the individual outcome of cancer and for a more accurate selection of patients for specific therapeutic regimens [18, 19].
Although the range of use of FFPE material has been markedly improved, several high-throughput ‘omics’ technologies require high integrity of RNA, DNA, proteins or metabolites and, therefore, still rely on high-quality cryopreserved material. Therefore, many academic institutions are establishing collections of fresh frozen tissues in parallel to FFPE samples. In contrast to FFPE material, which is collected in the context of routine medical service, the cryopreservation of material requires a different and expensive process. For instance, marked effects of ischaemia time on gene expression profiles have been observed in different studies and all authors recommend cryopreservation of tissue as rapidly as possible, realistically within 15–30 min [20, 21]. Because of the essentially very short time window between surgical resection and cryopreservation of tissue, the collection process has to be well coordinated between surgery or other clinical departments, pathology and the tissue bank. This requires trained pathologists to be available on a stand by basis, guaranteeing optimal preservation of representative cryospecimens for research, without impeding the diagnostic process.
One key prerequisite of tissue banks for proper use of samples and the basis for cooperation with other biobanks is the compliance with common standards. Standard operating procedures (SOPs) should define the whole process of sample acquisition, sample processing and preservation as well as storage and retrieval [22]. SOPs should consider specific requirements of analysis platforms and of the biological questions to be addressed. However, SOPs can only be applied for prospectively collected samples, whereas for old archived samples, processing has often not been documented in sufficient detail and did not follow specific SOPs. In the context of quality management, the scientific use of this material therefore requires special analyses to assess the proper preservation of biomolecules.
Standardization and quality control have to consider parameters such as varying ischaemia times, preservation technique (snap-freezing, fixation and embedding) and storage. Furthermore, factors that cannot be standardized since they are directed by patient needs, such as medication during surgery or different surgical procedures, also influence sample quality and should be documented in detail [21]. Another aspect is that quality of a tissue sample can only be specified in the context of its intended use. Hence, different quality control parameters have to be established for morphological, genomic, transcriptomic or metabolomic analyses [16, 22]. For DNA and RNA, most relevant parameters are total yield and extractable fragment length. Protein quality depends on sustained antigenicity or preservation of biological activity as well as post-translational modifications such as phosphorylation, ubiquitination, glycosylation and methylation [14, 23].
The scientific value of tissue for research markedly depends on the annotation with detailed information describing the specimen itself (e.g. cellular composition of tissue or which disease aspect is actually represented in a given sample), medical information on the sample donor and the outcome of the disease. The majority of these data is accessible in various medical databases of hospitals. Data structured at least to a certain extent can be retrieved using parsers. Data mining tools help to transform non-structured free text into structured information, as a prerequisite for further statistical analysis [24, 25].
The combination of experimental—especially genetic—data with detailed patient data poses further requirements for data protection, as even in the absence of personal data, patient re-identification through the combination of large datasets cannot be excluded [13, 26]. Therefore, particularly when comprehensive patient-related data will be shared with the scientific community outside the hospital environment in which the patient has been treated, the implementation of more far-reaching anonymization tools is necessary. One possible approach is based on the concept of k-anonymity, employing the combination of data in such a way that a sufficient number of data twins is always available, which prevents the indirect identification of individuals by seemingly ‘innocent’ parameters [27].
|
BIOBANKS IN DRUG DISCOVERY AND DEVELOPMENT |
|---|
Biomarker discovery and drug development programs rely on access to high-quality biospecimens. Therefore, biobanks, in particular tissue banks, are expected to become a key resource for the pharmaceutical industry [14].
At present, the high attrition rate in drug development is a major cause of increasing health care costs. As an important measure to cope with this problem, the role of biomarkers for early detection of drug safety and efficacy has been highlighted in the strategic research agenda of the innovative medicines initiative [28], http://ec.europa.eu/research/health/imi/index_en.html.
For instance, comprehensive profiling of drug target expression of diseased and non-diseased tissue might allow predicting drug side effects that might occur in different organ systems. Furthermore, systematic investigation of tissues from hundreds to thousands of patients provides an overview on the heterogeneity of a patient population, which can then be translated into clinical trial designs, increasing the effectiveness of the trial while reducing the costs.
In addition, drug discovery requires animal models for preclinical testing, which have been validated for their disease relevance at the molecular level, particularly considering the broad molecular heterogeneity of human diseases. In this context, biobanks are essential to validate animal models for their human disease relevance, for example whether a target or pathway of interest is properly preserved in the model organism.
In summary, biobanks can be used to support the whole drug discovery and development processes if they are properly designed and contain the required samples and data and are associated with validated model systems.
|
BIOBANKS IN SYSTEMS BIOLOGY |
|---|
A systems biology approach for a specific disease aims at describing alterations of distinct cellular components and their direct interaction partners in the context of the functional and regulatory networks in which they are operating. Systems biology becomes increasingly important since the medical relevance of disease-associated alterations of genes, proteins or metabolites serving as biomarkers, as well as the functional implication of altered proteins serving as drug targets, is not sufficiently specified by the altered cellular component itself but is strongly dependent on the actual context in which it operates. To properly describe the functional state of a diseased organ, the complexity of alterations at the gene, protein or metabolite level including their specific interactions have to be analysed employing a broad spectrum of ‘omics’ technologies. Individual diseases can be seen as defined states of a biological system. Thus, data can be used to generate computational models of diseases [29] by systematic comparison of different disease situations. This approach relies on access to samples of extraordinarily high quality (suitable for different omics analysis platforms) as well as on detailed information on the sample donor's disease.
|
WHY NETWORKS OF BIOBANKS? |
|---|
Because of the huge number of biological and medical parameters (e.g. type of disease, treatment, genetic polymorphisms, accompanying disease, lifestyle, etc.) that influence and characterize the disease of individual patients and split classical diseases into several new subentities, hundreds to thousands of samples have to be investigated to cope with the biological/medical diversity. Even big institutions can hardly provide the sample number required to achieve statistical significance in biomarker validation studies. This is even more relevant when rare diseases are to be investigated or if molecular signatures consisting of multiple parameters have to be validated [30].
Another important aspect is that pharmaceutical companies are operating globally, so that samples and data from populations of different ethnic origin have to be investigated. This requirement for global cooperation of biobanks has far-reaching social, ethical and political–legal implications. A specific challenge is to find ways on how to cooperate even in the context of heterogeneous ethical and legal frameworks in which national biobanks are being established [3, 31].
The importance of networking of biobanks has been emphasized by many authors and there are several major biobank networking initiatives worldwide, such as cancer Biomedical Informatics Grid (CaBIG) (https://cabig.nci.nih.gov), Public Population Project in Genomics (P3G) (www.p3gconsortium.org), EuroBioBank (www.eurobiobank.org), EPIC, GenomEUtwin (www.genomeutwin.org) and TuBaFrost (www.tubafrost.org) [2, 32–34].
In the subsequent text, some recent examples of networking initiatives at European and global scale are described in more detail.
|
CRIP—CENTRAL RESEARCH INFRASTRUCTURE FOR MOLECULAR PATHOLOGY |
|---|
CRIP is an example for transnational networking between tissue banks in Europe. CRIP was originally established at the RZPD (German Resource Center for Genome Research, Berlin, Germany) and has just recently been transferred to the Fraunhofer Institute for Biomedical Engineering (IBMT, Potsdam-Golm, Germany). CRIP provides an Internet portal to facilitate research projects that rely on investigation of diseased and normal human tissues (https://crip.fraunhofer.de/). The Institutes of Pathology from the Charité Campus Benjamin Franklin and from Charité Campus Mitte in Berlin, Germany, and the Institute of Pathology at the Medical University of Graz, Austria, are currently partners of CRIP and provide an anonymized inventory of available tissues and associated data. The specific medical and scientific strengths of these three institutes complement each other so that a broad spectrum of cancers as well non-neoplastic diseases is represented [35]. The consortium members host tissue archives containing more than 5 million FFPE samples as well as approximately 50 000 frozen tissue samples.
Access to the CRIP portal is free of charge for researchers from academia and industry but users have to register in order to perform queries in the CRIP database. For data protection reasons, only the data related to tissue samples from defined disease groups are shown, whereas information on individual patients cannot be accessed. This information should provide researchers with a rapid overview on available tissue samples and sufficient details on associated histopathological, medical and molecular data to foster definition of joint research projects. Specific research projects will then be agreed between the CRIP partner institutions and researchers from academia or industry (an overview on workflow is shown in Figure 1).
|
CRIP can be seen as a pilot project to solve technical, legal and ethical problems of transnational collation of human tissues and patient-related data. For instance, IT-tools for sample tracking, data mining, storage and retrieval and solutions for protection of patient confidentiality have been developed. Data formats have been harmonized and sample collection and retrieval follows SOPs. Ethical, legal and societal governance is provided by an Advisory Board with experts from the fields of medicine, data protection, sociology, ethics and law.
|
PAN-EUROPEAN RESEARCH INFRASTRUCTURE FOR BIOBANKING AND BIOMOLECULAR RESOURCES |
|---|
The European Strategy Forum on Research Infrastructures (ESFRI) has developed a European roadmap for research infrastructures, which includes a biobanking and biomolecular resources research infrastructure (BBMRI) (http://cordis.europa.eu/esfri/roadmap.htm). The first call for the European Framework Program 7 invited to submit proposals for the preparatory phase of research infrastructures of the ESFRI road map, including BBMRI.
During the ESFRI process, key features of BBMRI have been specified and evaluated by an international expert panel (see report of the ESFRI Biological and Medical Sciences Road Map Working Group; http://cordis.europa.eu/esfri/publications-reports.htm). BBMRI addresses the emerging needs of access to well-characterized and standardized human biological samples and associated data as well as to molecular tools required for their analysis. The infrastructure should comprise different biobank formats (i.e. population-based and disease-oriented) as well as a variety of biomolecular resources, such as ORF-clone collections, siRNA libraries, antibody collections and affinity binders, collection of recombinant proteins, molecular tools to decipher protein–protein interactions and cell lines (for the general concept of BBMRI, see www.biobanks.eu). All these components are critical resources to establish the disease relevance of the human genome and proteome, to unravel environment–gene interactions and to promote advancements in drug development and personalized medicine.
Although existing biobank initiatives and established biomolecular resources are a unique European strength, the benefit for academia and industry is limited because of their fragmentation in the scientific community resulting in the lack of common standards, absence of a general inventory and variable access rules. This hampers the combination of biological samples and data from different biobanks in order to provide a large number of study cases so that sufficient statistical power can be achieved. This is a requirement in particular for the investigation of new disease subentities in the context of personalized medicine and for research on rare diseases. Moreover, the current fragmentation of the biobank community results in duplication of efforts and lack of long-term funding solutions.
BBMRI should overcome these limitations by integrating existing as well as emerging resources into a pan-European network of biobanks and biomolecular resource centres. To fulfil the pan-European scope and allow proper response to upcoming developments, a flexible network architecture is required. The foreseen distributed hub structure of BBMRI facilitates the integration of new members or creation of new domain hubs (e.g. for a specific biobank format or resource) at any time so that the infrastructure can rapidly respond to technical developments and emerging needs (Figure 2). Hubs coordinate and harmonize activities of the various domains, including collection, exchange and analysis of samples and data. Biological samples and data will be stored in a decentralized fashion at biobanks or biomolecular resource centres that are members of BBMRI. Furthermore, a variety of public or private partners (e.g. universities, hospitals and companies), which provide biological samples, data, technologies or services, may be associated with certain BBMRI members. The complex network of hubs, members and partners will be integrated into a single virtual research infrastructure by IT infrastructure and biocomputing solutions that employ federated database architecture and grid computing technology [36].
|
A broad spectrum of high-end analysis platforms (high-throughput sequencing, genotyping, gene expression profiling technologies, proteomics and metabolomics platforms, tissue microarray technology, etc.) is planned to be associated with BBMRI. On the one hand, the integration of analysis platforms should guarantee that the quality of biological samples meets the requirements of latest analysis technologies. On the other hand, the high-end analysis platforms should allow for sample analysis under optimal conditions and for the data generated to be readily shared within the scientific community. This is seen as an important measure to achieve the most efficient use of non-renewable biological materials, as well as to avoid redundant analyses.
A specific challenge in establishing BBMRI is the implementation of common standards, which is complicated by the existing heterogeneous legal and ethical frameworks within Europe. Therefore, the ethical, legal and societal guidance will be a central activity in the preparatory phase. Major emphasis will also be placed on the development of incentives and benefit sharing policies for sample and data providers, which properly consider the needs of the broad stakeholder community (patients, medical professionals, funding organizations, scientists, industry, etc.).
The research infrastructure should further develop the potential of a broad spectrum of biological resources and facilitate the access for academia and industry of all EU member states by providing a pan-European inventory. BBMRI should offer possibilities for biomedical research that exceed the capacities of individual facilities and that are too cost intensive and laborious to be generated by individual institutions.
|
THE OECD GLOBAL NETWORK OF BIOLOGICAL RESOURCE CENTRES |
|---|
The Organization for Economic Co-operation and Development (OECD) highlighted in its report on Biological Resource Centres in 2001 ‘Underpinning the Future of Life Sciences and Biotechnology’ (ISBN92-64-18690-5, OECD, 2001; http://wdcm.nig.ac.jp/brc.pdf) that biological resources and related information are the essential raw materials for advancement of biotechnology, human health and research and development in life sciences. To cope with the increasing demand for biological materials, the establishment of Biological Resource Centres (BRCs), which comply with internationally harmonized standards and are integrated into a global BRC network, was proposed. The OECD member countries (Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland, Turkey, United Kingdom and United States of America) endorsed the recommendations of that report and subsequently, guidelines for the operation of BRCs have been elaborated.
The OECD Committee of Science and Technology Policy (CSTP) declassified a report on best practice guidelines on BRCs in March 2007 (http://www.oecd.org/dataoecd/7/13/38777417.pdf). The report comprises four sets of guidelines dealing with (i) general quality aspects, (ii) biosecurity-related issues, (iii) specific guidelines for BRCs holding and supplying microorganisms and (iv) specific guidelines for BRCs holding human-derived materials. In particular, the guidelines describe key quality parameters of biological materials and procedures for collection, storage, retrieval and sample tracking that should become a common basis for international cooperation and future certification of BRCs. The guidelines also address the problem of possible misuse of biological materials containing dangerous pathogens in the context of bioterrorism. Therefore, several measures to prevent loss or theft of biological materials or possible ‘dual use’ are outlined. No implementation plans for the establishment of national BRCs or the global BRC network are available to date. However, the concept of BRCs has been approved at the OECD countries’ Ministerial level and guidelines for the operation of BRCs have achieved broad consensus.
Key Points
|
|
ACKNOWLEDGEMENTS |
|---|
We thank Dr Christina Schröder for stimulating discussion and Karin Bonvecchio and Dr Peter Abuja for critically reading of the manuscript. This work has been supported by the Austria Genome Program GEN-AU.
|
FOOTNOTES |
|---|
Martin Asslaber is in residence for pathologist at the Institute of Pathology, Medical University of Graz, Austria. His main research interest is cancer development, with special emphasis on breast and colorectal cancer.
Kurt Zatloukal is professor of pathology at the Institute of Pathology, Medical University of Graz, Austria. His research interests are molecular pathology of metabolic liver diseases and hepatocellular cancer as well as the development of biobanks and associated technologies.
|
REFERENCES |
|---|
- Hirtzlin I, Dubreuil C, Preaubert N, et al. An empirical survey on biobanking of human genetic material and data in six EU countries. Eur J Hum Genet (2003) 11:475–88.[CrossRef][Web of Science][Medline]
- Hagen HE, Carlstedt-Duke J. Building global networks for human diseases: genes and populations. Nat Med (2004) 10:665–67.[CrossRef][Web of Science][Medline]
- Cambon-Thomsen A. The social and ethical issues of post-genomic human biobanks. Nat Rev Genet (2004) 5:866–73.[Web of Science][Medline]
- Kaiser J. Biobanks. Population databases boom, from Iceland to the U.S. Science (2002) 298:1158–61.
[Abstract/Free Full Text] - Ollier W, Sprosen T, Peakman T. UK Biobank: from concept to reality. Pharmacogenomics (2005) 6:639–46.[CrossRef][Web of Science][Medline]
- Sutrop M, Simm K. The Estonian healthcare system and the genetic database project: from limited resources to big hopes. Camb Q Healthc Ethics (2004) 13:254–62.[Web of Science][Medline]
- Manolio TA, Bailey-Wilson JE, Collins FS. Genes, environment and the value of prospective cohort studies. Nat Rev Genet (2006) 7:812–20.[CrossRef][Web of Science][Medline]
- Hakonarson H, Gulcher JR, Stefansson K. deCODE genetics, Inc. Pharmacogenomics (2003) 4:209–15.[CrossRef][Web of Science][Medline]
- Peltonen L. GenomEUtwin: a strategy to identify genetic influences on health and disease. Twin Res (2003) 6:354–60.[CrossRef][Web of Science][Medline]
- Sotiriou C, Piccart MJ. Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer (2007) 7:545–53.[CrossRef][Web of Science][Medline]
- Molnar MJ, Bencsik P. Establishing a Neurological-Psychiatric biobank: banking, informatics, ethics. Cell Immunol (2006) 244:101–4.[CrossRef][Web of Science][Medline]
- van't Veer PV, Kok FJ. Human studies to substantiate health effects of antioxidants. What is needed? Free Radic Res (2000) 33(Suppl):S109–15.[Web of Science][Medline]
- Godard B, Schmidtke J, Cassiman JJ, et al. Data storage and DNA banking for biomedical research: informed consent, confidentiality, quality issues, ownership, return of benefits. A professional perspective. Eur J Hum Genet (2003) 11(Suppl 2):S88–22.[CrossRef][Web of Science][Medline]
- Mahan S, Ardlie KG, Krenitsky KF, et al. Collaborative design for automated DNA storage that allows for rapid, accurate, large-scale studies. Assay Drug Dev Technol (2004) 2:683–9.[CrossRef][Web of Science][Medline]
- Knox K, Kerr DJ. Establishing a national tissue bank for surgically harvested cancer tissue. Br J Surg (2004) 91:134–6.[CrossRef][Web of Science][Medline]
- Shi SR, Liu C, Balgley BM, et al. Protein extraction from formalin-fixed, paraffin-embedded tissue sections: quality evaluation by mass spectrometry. J Histochem Cytochem (2006) 54:739–43.
[Abstract/Free Full Text] - Becker KF, Schott C, Hipp S, et al. Quantitative protein analysis from formalin-fixed tissues: implications for translational clinical research and nanoscale molecular diagnosis. J Pathol (2007) 211:370–8.[CrossRef][Web of Science][Medline]
- Hwang SI, Thumar J, Lundgren DH, et al. Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues. Oncogene (2007) 26:65–76.[CrossRef][Web of Science][Medline]
- Foekens JA, Atkins D, Zhang Y, et al. Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol (2006) 24:1665–71.
[Abstract/Free Full Text] - Spruessel A, Steimann G, Jung M, et al. Tissue ischemia time affects gene and protein expression patterns within minutes following surgical tumor excision. Biotechniques (2004) 36:1030–7.[Web of Science][Medline]
- Dash A, Maine IP, Varambally S, et al. Changes in differential gene expression because of warm ischemia time of radical prostatectomy specimens. Am J Pathol (2002) 161:1743–8.
[Abstract/Free Full Text] - Morente MM, Mager R, Alonso S, et al. TuBaFrost 2: standardising tissue collection and quality control procedures for a European virtual frozen tissue bank network. Eur J Cancer (2006) 42:2684–91.[CrossRef][Web of Science][Medline]
- Jewell SD, Srinivasan M, McCart LM, et al. Analysis of the molecular quality of human tissues: an experience from the Cooperative Human Tissue Network. Am J Clin Pathol (2002) 118:733–41.
[Abstract/Free Full Text] - Gilbertson JR, Gupta R, Nie Y, et al. Automated clinical annotation of tissue bank specimens. Medinfo (2004) 11:607–10.[Medline]
- Isabelle M, Teodorovic I, Morente MM, et al. TuBaFrost 5: multifunctional central database application for a European tumor bank. Eur J Cancer (2006) 42:3103–9.[CrossRef][Web of Science][Medline]
- van Veen EB, Riegman PH, Dinjens WN, et al. TuBaFrost 3: regulatory and ethical issues on the exchange of residual tissue for research across Europe. Eur J Cancer (2006) 42:2914–23.[CrossRef][Web of Science][Medline]
- Stark K, Eder H, Zatloukal K. Priority-based k-anonymity accomplished by weighted generalisation structures. Computer Sci (2006) 4081:394–404.
- Halban PA, Ferrannini E, Nerup J. Diabetes research investment in the European Union. Nat Med (2006) 12:70–2.[CrossRef][Web of Science][Medline]
- Brazma A, Krestyaninova M, Sarkans U. Standards for systems biology. Nat Rev Genet (2006) 7:593–605.[CrossRef][Web of Science][Medline]
- Luscombe NM, Babu MM, Yu H, et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature (2004) 431:308–12.[CrossRef][Medline]
- Webster A, Martin P, Lewis G, et al. Integrating pharmacogenetics into society: in search of a model. Nat Rev Genet (2004) 5:663–9.[CrossRef][Web of Science][Medline]
- Bouchie A. Coming soon: a global grid for cancer research. Nat Biotechnol (2004) 22:1071–3.[CrossRef][Web of Science][Medline]
- Bingham S, Riboli E. Diet and cancer—the European Prospective Investigation into Cancer and Nutrition. Nat Rev Cancer (2004) 4:206–15.[CrossRef][Web of Science][Medline]
- Riegman PH, Dinjens WN, Oomen MH, et al. TuBaFrost 1: uniting local frozen tumour banks into a European network: an overview. Eur J Cancer (2006) 42:2678–83.[CrossRef][Web of Science][Medline]
- Asslaber M, Abuja PM, Stark K, et al. The Genome Austria Tissue Bank (GATiB). Pathobiology (2007) 74:251–8.[CrossRef][Web of Science][Medline]
- Muilu J, Peltonen L, Litton JE. The federated database–a basis for biobank-based post-genome studies, integrating phenome and genome data from 600,000 twin pairs in Europe. Eur J Hum Genet (2007) 15:718–23.[CrossRef][Web of Science][Medline]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  D. Gurwitz, I. Fortier, J. E. Lunshof, and B. M. Knoppers Children and Population Biobanks Science, August 14, 2009; 325(5942): 818 - 819. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B Hofmann Broadening consent--and diluting ethics? J. Med. Ethics, February 1, 2009; 35(2): 125 - 129. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||