Biobanks in the era of big data: objectives, challenges, perspectives, and innovations for predictive, preventive, and personalised medicine

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

Breast cancer is a multifaceted disease and a leading cause of cancer morbidity and mortality in females across the globe. In 2020 alone, 2.3 million women were diagnosed and 685,000 died of breast cancer worldwide. With the number of diagnoses projected to increase to 3 million per year by 2040 it is essential that new methods of detection and disease stratification are sought to decrease this global cancer burden. Although significant improvements have been made in breast cancer diagnosis and treatment, the prognosis of breast cancer remains poor in some patient groups (i.e. triple negative breast cancer), necessitating research into better patient stratification, diagnosis and drug discovery. The UK Biobank, a comprehensive biomedical and epidemiological database with a wide variety of multiomics data (genomics, proteomics, metabolomics) offers huge potential to uncover groundbreaking discoveries in breast cancer research leading to improved patient stratification. Combining genomic, proteomic, and metabolic profiles of breast cancer in combination with histological classification, can aid treatment decisions through accurate diagnosis and prognosis prediction of tumor behaviour. Here, we systematically reviewed PubMed publications reporting the analysis of UK Biobank data in breast cancer research. Our analysis of UK Biobank studies in the past five years identified 125 publications, of which 76 focussed on genomic data analysis. Interestingly, only two studies reported the analysis of metabolomics and proteomics data, with none performing multiomics analysis of breast cancer. A meta-analysis of the 76 publications identified 2870 genetic variants associated with breast cancer across 445 genes. Subtype analysis revealed differential genetic alteration in 13 of the 445 genes and the identification of 59 well-established breast cancer genes. in differential pathways. Pathway interaction analyses illuminated their involvement in general cancer biomolecular pathways (e.g. DNA damage repair, Gene expression). While our meta-analysis only measured genetic differences in breast cancer due to current usage of UK Biobank data, minimal multi-omics analyses have been performed and the potential for harnessing multi-omics strategies within the UK Biobank cohort holds promise for unravelling the biological signatures of distinct breast cancer subtypes further in the future.

A review of big data technology and its application in cancer care

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

Genetic tests as the strongest motivator of cooperation between participants and biobanks-Findings from cross-sectional study

Introduction

The development of the scientific potential linked with biobanking and research on human biological material is highly dependent on the willingness of potential donors to cooperate with entities that collect the material. For this reason, it is crucial to identify the circumstances and factors that may encourage potential participants to donate their biological material. In particular, knowledge of the motivational factors that can be modified by the persons managing a biobank may prove notably important for shaping the organizational and communication policy of the biobank and other scientific institutions.

Material and methods

The research was carried out on a group of 1,100 people over 18 years of age representing the adult population of Poland in 2021.

Results

More than half of the respondents declared their willingness to donate a blood sample for research purposes to a biobank (57.8%). The most often indicated incentives among the factors supporting the donation of biological material were offers of: obtaining the results of genetic tests predicting the risk of diseases (77.1%), blood tests (71.3%), the possibility of obtaining a small remuneration (64.6%) and the carrying out of genetic ancestry tests (60.4%).

Conclusion

Offering the possibility of performing additional diagnostic tests, especially genetic tests, may significantly increase the willingness of potential donors to cooperate with biobanks and other entities collecting human biological material for the purpose of scientific research. However, attention should also be paid to the challenges and risks linked with respecting the privacy and autonomy of research participants.

Do biobanks need pharmacists? Support of pharmacy students to biobanking of human biological material for pharmaceutical research and development

Objectives

This study aimed to assess the biobank awareness among Polish pharmacy students and how it affects their support for biobank research.

Methods

A survey among 366 pharmacy students enrolled at two Polish medical universities: the Poznań University of Medical Sciences and Medical University of Lublin was conducted.

Results

Although most pharmacy students felt positivity about biobanking and expressed the willingness to donate their biospecimens for biomedical research, their awareness on research biobanks was low. Their willingness to participate was driven by the desire to benefit society, help advance science and develop new therapies. While students supported donation for most types of research, biobanks run by medical universities were the highest trusted research institutions. The primary factors associated with student's willingness to participate were religiosity and place of study. Notably, nonreligious students and those studying in Poznan exhibited more favourable attitudes toward donating for research and expressed greater support for the establishment of research biobanks in Poland.

Conclusion

Since biobank awareness among future pharmacists is inadequate incorporating biobank competency domains into education and training of pharmacists is required.

Update of the Minimum Information About BIobank Data Sharing (MIABIS) Core Terminology to the 3rd Version

Introduction: The Minimum Information About BIobank Data Sharing (MIABIS) is a biobank-specific terminology enabling the sharing of biobank-related data for different purposes across a wide range of database implementations. After 4 years in use and with the first version of the individual-level MIABIS component Sample, Sample donor, and Event, it was necessary to revise the terminology, especially to include biobanks that work more in the data domain than with samples. Materials & Methods: Nine use-cases representing different types of biobanks, studies, and networks participated in the development work. They represent types of data, specific sample types, or levels of organization that were not included earlier in MIABIS. To support our revision of the Biobank entity, we conducted a survey of European biobanks to chart the services they provide. An important stakeholder group for biobanks include researchers as the main users of biobanks. To be able to render MIABIS more researcher-friendly, we collected different sample/data requests to analyze the terminology adjustment needs in detail. During the update process, the Core terminology was iteratively reviewed by a large group of experts until a consensus was reached. Results: With this update, MIABIS was adjusted to encompass data-driven biobanks and to include data collections, while also describing the services and capabilities biobanks offer to their users, besides the retrospective samples. The terminology was also extended to accommodate sample and data collections of nonhuman origin. Additionally, a set of organizational attributes was compiled to describe networks. Discussion: The usability of MIABIS Core v3 was increased by extending it to cover more topics of the biobanking domain. Additionally, the focus was on a more general terminology and harmonization of attributes with the individual-level entities Sample, Sample donor, and Event to keep the overall terminology minimal. With this work, the internal semantics of the MIABIS terminology was improved.

Ethical navigation of biobanking establishment in Ukraine: learning from the experience of developing countries

Building a biobank network in developing countries is essential to foster genomic research and precision medicine for patients' benefit. However, there are serious barriers to establishing biobanks in low-income and middle-income countries (LMICs), including Ukraine. Here, we outline key barriers and essential milestones for the successful expansion of biobanks, genomic research and personalised medicine in Ukraine, drawing from the experience of other LMICs. A lack of legal and ethical governance in conjunction with limited awareness about biobanking and community distrust are the principal threats to establishing biobanks. The experiences of LMICs suggest that Ukraine urgently needs national guidelines covering ethical and legal aspects of biospecimen-related research. National guidelines must be consistent with international ethical recommendations for safeguarding participants' rights, welfare and privacy. Additionally, efforts to educate and engage physicians and patient communities are essential for achieving biobanking goals and benefits for precision medicine and future patients.

A leap of faith: building the trust in human biobanks

Background: Human biobanks are an essential resource for contemporary medical research, crucial in treating and preventing human diseases and improving health. Public trust in human biobanks is a vital social prerequisite for their continued operation and related research. Methods: Drawing on the "leap of faith" theory proposed by Georg Simmel and Guido Möllering, this paper first examines the relationship between public trust and human biobanks and the process through which such trust is established. Subsequently, based on the results of this analysis, targeted policy recommendations are put forward to consolidate or enhance public trust in human biobanks. Results: Public trust in human biobanks stems from certain "good reasons," through which uncertainty and vulnerability are "suspended" by faith, leading to a leap toward the "land of expectations." In this progress, the critical factors in building and enhancing public trust in human biobanks are the public's propensity to trust, the inherent trustworthiness of human biobanks, and the security and interactivity of the trust environment. Conclusion: Public trust in human biobanks cannot be determined by any universal formula, as it is influenced by many factors, including intangible elements such as faith that defy empirical understanding. Nonetheless, public trust in human biobanks can be enhanced through measures such as fostering the public's propensity to trust, enhancing the inherent trustworthiness of human biobanks, establishing structural safeguards for the trust environment through ethical norms, systems, and supervision, and promoting public participation.

EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics

Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.

Differences of survival benefits brought by various treatments in ovarian cancer patients with different tumor stages

PURPOSE

The current study aimed to explore the prognosis of ovarian cancer patients in different subgroup using three prognostic research indexes. The current study aimed to build a prognostic model for ovarian cancer patients.

METHODS

The study dataset was downloaded from Surveillance Epidemiology and End Results database. Accelerated Failure Time algorithm was used to construct a prognostic model for ovary cancer.

RESULTS

The mortality rate in the model group was 51.6% (9,314/18,056), while the mortality rate in the validation group was 52.1% (6,358/12,199). The current study constructed a prognostic model for ovarian cancer patients. The C indexes were 0.741 (95% confidence interval: 0.731-0.751) in model dataset and 0.738 (95% confidence interval: 0.726-0.750) in validation dataset. Brier score was 0.179 for model dataset and validation dataset. The C indexes were 0.741 (95% confidence interval: 0.733-0.749) in bootstrap internal validation dataset. Brier score was 0.178 for bootstrap internal validation dataset.

CONCLUSION

The current research indicated that there were significant differences in the survival benefits of treatments among ovarian cancer patients with different stages. The current research developed an individual mortality risk predictive system that could provide valuable predictive information for ovarian cancer patients.

Challenges on the Development of Biodiversity Biobanks: The Living Archives of Biodiversity

Biodiversity biobanks or ex situ biodiversity biorepositories tend to receive less attention compared with their biomedical counterparts. In this review, we highlight the necessity for these biorepositories by presenting their significant role in health, biodiversity, linking of big data, other translational research, and biodiversity conservation efforts. Moreover, the significant challenges in developing and maintaining biodiversity biobanks based on successful biobanks in some megadiverse developing countries are examined to provide insights into what needs to be done and what can be improved by up-and-coming biodiversity biobanks. These challenges include lack of financial support and political will; availability of experts; development of standard policies; and information management system. In addition, issues regarding access and benefit sharing and Digital Sequence Information must be addressed by biodiversity biobanks.

A Literature Review on Ethics for AI in Biomedical Research and Biobanking

BACKGROUND

Artificial Intelligence (AI) is becoming more and more important especially in datacentric fields, such as biomedical research and biobanking. However, AI does not only offer advantages and promising benefits, but brings about also ethical risks and perils. In recent years, there has been growing interest in AI ethics, as reflected by a huge number of (scientific) literature dealing with the topic of AI ethics. The main objectives of this review are: (1) to provide an overview about important (upcoming) AI ethics regulations and international recommendations as well as available AI ethics tools and frameworks relevant to biomedical research, (2) to identify what AI ethics can learn from findings in ethics of traditional biomedical research - in particular looking at ethics in the domain of biobanking, and (3) to provide an overview about the main research questions in the field of AI ethics in biomedical research.

METHODS

We adopted a modified thematic review approach focused on understanding AI ethics aspects relevant to biomedical research. For this review, four scientific literature databases at the cross-section of medical, technical, and ethics science literature were queried: PubMed, BMC Medical Ethics, IEEE Xplore, and Google Scholar. In addition, a grey literature search was conducted to identify current trends in legislation and standardization.

RESULTS

More than 2,500 potentially relevant publications were retrieved through the initial search and 57 documents were included in the final review. The review found many documents describing high-level principles of AI ethics, and some publications describing approaches for making AI ethics more actionable and bridging the principles-to-practice gap. Also, some ongoing regulatory and standardization initiatives related to AI ethics were identified. It was found that ethical aspects of AI implementation in biobanks are often like those in biomedical research, for example with regards to handling big data or tackling informed consent. The review revealed current 'hot' topics in AI ethics related to biomedical research. Furthermore, several published tools and methods aiming to support practical implementation of AI ethics, as well as tools and frameworks specifically addressing complete and transparent reporting of biomedical studies involving AI are described in the review results.

CONCLUSIONS

The review results provide a practically useful overview of research strands as well as regulations, guidelines, and tools regarding AI ethics in biomedical research. Furthermore, the review results show the need for an ethical-mindful and balanced approach to AI in biomedical research, and specifically reveal the need for AI ethics research focused on understanding and resolving practical problems arising from the use of AI in science and society.

Standard operating procedures for biobank in oncology

Biobanks are biorepositories that collect, process, store, catalog, and distribute human biological samples, and record the associated data. The role and action field of these strategic infrastructures for implementing precision medicine in translational research is continuously evolving. To ensure the optimal quality at all stages of biobanking, specific protocols are required and should be elaborated according to updated guidelines, recommendations, laws, and rules. This article illustrates the standard operating procedures, including protocols, troubleshooting, and quality controls, of a fully certified biobank in a referral Cancer Center. This model involves all clinical departments and research groups to support the dual mission of academic cancer centers, i.e. to provide high-quality care and high-quality research. All biobanking activities based on the type of biological specimens are detailed and the most tricky methodological aspects are discussed, from patients’ informed consent to specimen management.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review

Introduction: In biobanks, participants' biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI's role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in biobanking". Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle-Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study's goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.

Precision Health in Taiwan: A Data-Driven Diagnostic Platform for the Future of Disease Prevention

“Precision medicine” has revolutionized how we respond to diseases by using an individual’s genomic data and lifestyle and environment-related information to create an effective personalized treatment. However, issues surrounding regulations, medical insurance payments and the use of patients’ medical data, have delayed the development of precision medicine and made it difficult to achieve “true” personalization. We therefore recommend that precision medicine be transformed into precision health: a novel and generalized platform of tools and methods that could prevent, manage, and treat disease at a population level. “Precision health,” one of six core strategic industries highlighted in Taiwan’s vision for 2030, uses various physiological data, genomic data, and external factors, to develop unique “preventative” solutions or therapeutic strategies. For Taiwan to implement precision health, it has to address three challenges: (1) the high-cost issue of precision health; (2) the harmonization issues surrounding integration and transmission of specimen and data; (3) the legal issue of combining information and communications technology (ICT) with Artificial Intelligence (AI) for medical use. In this paper, we propose an innovative framework with six recommendations for facilitating the development of precision health in Taiwan, including a novel model of precise telemedicine with AI-aided technology. We then describe how these tools can be proactively applied in early response to the COVID-19 crisis. We believe that precision health represents an important shift to more proactive and preventive healthcare that enables people to lead healthier lives.

Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine

Background

Acupuncture is safe and effective for functional dyspepsia (FD), while its efficacy varies among individuals. Predicting the response of different FD patients to acupuncture treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). In the current study, the individual efficacy prediction models were developed based on the support vector machine (SVM) algorithm and routine clinical features, aiming to predict the efficacy of acupuncture in treating FD and identify the FD patients who were appropriate to acupuncture treatment.

Methods

A total of 745 FD patients were collected from two clinical trials. All the patients received a 4-week acupuncture treatment. Based on the demographic and baseline clinical features of 80% of patients in trial 1, the SVM models were established to predict the acupuncture response and improvements of symptoms and quality of life (QoL) at the end of treatment. Then, the left 20% of patients in trial 1 and 193 patients in trial 2 were respectively applied to evaluate the internal and external generalizations of these models.

Results

These models could predict the efficacy of acupuncture successfully. In the internal test set, models achieved an accuracy of 0.773 in predicting acupuncture response and an R ² of 0.446 and 0.413 in the prediction of QoL and symptoms improvements, respectively. Additionally, these models had well generalization in the independent validation set and could also predict, to a certain extent, the long-term efficacy of acupuncture at the 12-week follow-up. The gender, subtype of disease, and education level were finally identified as the critical predicting features.

Conclusion

Based on the SVM algorithm and routine clinical features, this study established the models to predict acupuncture efficacy for FD patients. The prediction models developed accordingly are promising to assist doctors in judging patients' responses to acupuncture in advance, so that they could tailor and adjust acupuncture treatment plans for different patients in a prospective rather than the reactive manner, which could greatly improve the clinical efficacy of acupuncture treatment for FD and save medical expenditures.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00271-8.

Of Screening, Stratification, and Scores

Technological innovations including risk-stratification algorithms and large databases of longitudinal population health data and genetic data are allowing us to develop a deeper understanding how individual behaviors, characteristics, and genetics are related to health risk. The clinical implementation of risk-stratified screening programmes that utilise risk scores to allocate patients into tiers of health risk is foreseeable in the future. Legal and ethical challenges associated with risk-stratified cancer care must, however, be addressed. Obtaining access to the rich health data that are required to perform risk-stratification, ensuring equitable access to risk-stratified care, ensuring that algorithms that perform risk-scoring are representative of human genetic diversity, and determining the appropriate follow-up to be provided to stratification participants to alert them to changes in their risk score are among the principal ethical and legal challenges. Accounting for the great burden that regulatory requirements could impose on access to risk-scoring technologies is another critical consideration.

DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project

PRIMAGE is a European Commission-financed project dealing with medical imaging and artificial intelligence aiming to create an imaging biobank in oncology. The project includes a task dedicated to the interoperability between imaging and standard biobanks. We aim at linking Digital imaging and Communications in Medicine (DICOM) metadata to the Minimum Information About BIobank data Sharing (MIABIS) standard of biobanking. A very first integration model based on the fusion of the two existing standards, MIABIS and DICOM, has been developed. The fundamental method was that of expanding the MIABIS core to the imaging field, adding DICOM metadata derived from CT scans of 18 paediatric patients with neuroblastoma. The model was developed with the relational database management system Structured Query Language. The integration data model has been built as an Entity Relationship Diagram, commonly used to organise data within databases. Five additional entities have been linked to the “Image Collection” subcategory in order to include the imaging metadata more specific to the particular type of data: Body Part Examined, Modality Information, Dataset Type, Image Analysis, and Registration Parameters. The model is a starting point for the expansion of MIABIS with further DICOM metadata, enabling the inclusion of imaging data in biorepositories.

Making a complex dental care tailored to the person: population health in focus of predictive, preventive and personalised (3P) medical approach

An evident underestimation of the targeted prevention of dental diseases is strongly supported by alarming epidemiologic statistics globally. For example, epidemiologists demonstrated 100% prevalence of dental caries in the Russian population followed by clinical manifestation of periodontal diseases. Inadequately provided oral health services in populations are caused by multi-factorial deficits including but not limited to low socio-economic status of affected individuals, lack of insurance in sub-populations, insufficient density of dedicated medical units. Another important aspect is the “participatory” medicine based on the active participation of population in maintaining oral health: healthcare will remain insufficient as long as the patient is not motivated and does not feel responsible for their oral health. To this end, nearly half of chronically diseased people do not comply with adequate medical services suffering from severely progressing pathologies. Noteworthy, the prominent risk factors and comorbidities linked to the severe disease course and poor outcomes in COVID-19-infected individuals, such as elderly, diabetes mellitus, hypertension and cardiovascular disease, are frequently associated with significantly altered oral microbiome profiles, systemic inflammatory processes and poor oral health. Suggested pathomechanisms consider potential preferences in the interaction between the viral particles and the host microbiota including oral cavity, the respiratory and gastrointestinal tracts. Since an aspiration of periodontopathic bacteria induces the expression of angiotensin-converting enzyme 2, the receptor for SARS-CoV-2, and production of inflammatory cytokines in the lower respiratory tract, poor oral hygiene and periodontal disease have been proposed as leading to COVID-19 aggravation. Consequently, the issue-dedicated expert recommendations are focused on the optimal oral hygiene as being crucial for improved individual outcomes and reduced morbidity under the COVID-19 pandemic condition. Current study demonstrated that age, gender, socio-economic status, quality of environment and life-style, oral hygiene quality, regularity of dental services requested, level of motivation and responsibility for own health status and corresponding behavioural patterns are the key parameters for the patient stratification considering person-tailored approach in a complex dental care in the population. Consequently, innovative screening programmes and adapted treatment schemes are crucial for the complex person-tailored dental care to improve individual outcomes and healthcare provided to the population.

Education for future biobankers - The state-of-the-art and outlook

Biobanking as a quickly growing branch of personalised medicine has undergone enormous progress during last two decades. Nowadays it is a well developed and structured multidisciplinary field that reflects developments and advances of biomedical research based on principles of predictive, preventive and personalised medicine (PPPM/3PM). All these trends in PPPM progress have to be translated into practice and education of new generation of scientists and healthcare givers. The importance of biobanks for multitasking research, personalised treatment, and health care systems was emphasised by many scientists and health care experts. As biobanking carries multidisciplinary character currently including more professionals than ten—twenty years ago, new generation of professional biobankers is urgently needed. To create new generation of biobankers who are fully competent to answer more and more scientific and practical questions, new study programmes, novel university curricula, and topic-dedicated courses are essential. The aim of the review is to present basic forms, trends of biobanking education offered by various biobanking related bodies and to highlight future needs. The first step is to cover all activities and duties of biobanks: acquiring, collecting, storageing and sharing biological samples and associated data, using adequate assessment for both - materials and data, taking into consideration ethical, legal, and societal issues (ELSI), responding to all stakeholder needs including pharmaceutical and other related industries, patient organisations and many other interested groups, emerging technologies and innovations as well as current and future requirements of health care systems. To compile educational programmes is a comprehensive task for all actors involved in the field of biobanking who contribute to the harmonised process of creating high educational level for future generation of biobankers. The exchange of experience involving extensive international collaboration is the way how to facilitate the process of creating optimal biobanking education.