Biobanking and risk assessment: a comprehensive typology of risks for an adaptive risk governance

Scalable summary-statistics-based heritability estimation method with individual genotype level accuracy

SNP heritability, the proportion of phenotypic variation explained by genotyped SNPs, is an important parameter in understanding the genetic architecture underlying various diseases and traits. Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale data sets and by the restrictions in access to individual-level data. These limitations have motivated the development of methods that only require summary statistics. Although the availability of publicly accessible summary statistics makes them widely applicable, these methods lack the accuracy of methods that utilize individual genotypes. Here we present a SUMmary-statistics-based Randomized Haseman-Elston regression (SUM-RHE), a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes, while exclusively relying on summary statistics. SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population, which can be efficiently estimated and readily shared for public use. Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared with other summary statistic methods and on par with methods that rely on individual-level data.

Risk mapping for better governance in biobanking: the case of biobank.cy

Introduction: Risk governance is central for the successful and ethical operation of biobanks and the continued social license for being custodians of samples and data. Risks in biobanking are often framed as risks for participants, whereas the biobank's risks are often considered as technical ones. Risk governance relies on identifying, assessing, mitigating and communicating all risks based on technical and standardized procedures. However, within such processes, biobank staff are often involved tangentially. In this study, the aim has been to conduct a risk mapping exercise bringing biobank staff as key actors into the process, making better sense of emerging structure of biobanks. Methods: Based on the qualitative research method of situational analysis as well as the card-based discussion and stakeholder engagement processes, risk mapping was conducted at the biobank setting as an interactive engagement exercise. The analyzed material comprises mainly of moderated group discussions. Results: The findings from the risk mapping activity are framed through an organismic metaphor: the biobank as a growing, living organism in a changing environment, where trust and sustainability are cross-cutting elements in making sense of the risks. Focusing on the situatedness of the dynamics within biobanking activity highlights the importance of prioritizing relations at the core of risk governance and promoting ethicality in the biobanking process by expanding the repertoire of considered risks. Conclusion: With the organismic metaphor, the research brings the diverse group of biobank staff to the central stage for risk governance, highlighting how accounting for such diversity and interdependencies at the biobank setting is a prerequisite for an adaptive risk governance.

Unlocking the potential of big data and AI in medicine: insights from biobanking

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

Post-identifiability in changing sociotechnological genomic data environments

Data practices in biomedical research often rely on standards that build on normative assumptions regarding privacy and involve 'ethics work.' In an increasingly datafied research environment, identifiability gains a new temporal and spatial dimension, especially in regard to genomic data. In this paper, we analyze how genomic identifiability is considered as a specific data issue in a recent controversial case: publication of the genome sequence of the HeLa cell line. Considering developments in the sociotechnological and data environment, such as big data, biomedical, recreational, and research uses of genomics, our analysis highlights what it means to be (re-)identifiable in the postgenomic era. By showing how the risk of genomic identifiability is not a specificity of the HeLa controversy, but rather a systematic data issue, we argue that a new conceptualization is needed. With the notion of post-identifiability as a sociotechnological situation, we show how past assumptions and ideas about future possibilities come together in the case of genomic identifiability. We conclude by discussing how kinship, temporality, and openness are subject to renewed negotiations along with the changing understandings and expectations of identifiability and status of genomic data.

Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.

Paediatric biobanking for health: The ethical, legal, and societal landscape

Biobanks play a central role in pediatric translational research, which deals primarily with genetic data from sample-based research. However, participation of children in biobanking has received only limited attention in the literature, even though research in general and in clinical trials in particular have a long history in involving minors. So, we resolved to explore specific challenging ethical, legal, and societal issues (ELSI) in the current pediatric biobanking landscape to propose a way forward for biobanking with children as partners in research. Methodologically, we first established the accessibility and utilization of pediatric biobanks, mainly in Europe. This was supported by a literature review related to children's participation, taking into account not only academic papers but also relevant guidelines and best-practices. Our findings are discussed under five themes: general vulnerability; ethical issues-balancing risks and benefits, right to an open future, return of results including secondary findings; legal issues-capacity and legal majority; societal issues-public awareness and empowerment; and responsible research with children. Ultimately, we observed an on-going shift from the parents'/guardians' consent being a sine-qua-non condition to the positive minor's agreement: confirming that the minor is the participant, not the parent(s)/guardian(s). This ethical rethinking is paving the way toward age-appropriate, dynamic and participatory models of involving minors in decision-making. However, we identified a requirement for dynamic tools to assess maturity, a lack of co-produced engagement tools and paucity of shared best practices. We highlight the need to provide empowerment and capability settings to support researchers and biobankers, and back this with practical examples. In conclusion, equipping children and adults with appropriate tools, and ensuring children's participation is at the forefront of responsible pediatric biobanking, is an ethical obligation, and a cornerstone for research integrity.

Risk management in biobanking

Risk management is a key aspect of the organization and management of biobanks, which is part of the overall quality management system aimed at early detection, analysis and minimization of events, that can lead to negative consequences for the biobank, as well as affect the quality of biosamples and related data. The article presents the biobanking risk classification with the description of each category.

Aim. To develop and implement the methodology for identification, analysis, evaluation and development of risk management measures for the biobanking process in the biobank of the National Medical Research Center for Therapy and Preventive Medicine.

Material and methods. We present the methodology of the risk management process developed on the basis of the literary data, world experience and experience of the biobank of the National Medical Research Center for Therapy and Preventive Medicine.

Results. The biobanking risk management procedure was developed and implemented in the biobank of the National Medical Research Center for Therapy and Preventive Medicine in 2020. The work carried out made it possible to identify, analyze and evaluate a wide range of potential negative events and actions that could lead to biobank damage, both in the form of financial losses and ethical and technical issues related to the biobanking process. A significant reduction in the frequency of emergency events and the high stability of the biobank operation under the influence of various external factors prove the effectiveness of the approach used.

Conclusion. The creation and maintenance of a risk management system in the biobank allows, in combination with other measures, to ensure the safety and high quality of the procedures for collecting, processing and long-term storage of biomaterial and related data by creating an environment that rules out or minimizes the impact of various risks.

Identification and Assessment of Risks in Biobanking: The Case of the Cancer Institute of Bari

Simple Summary

Risk assessment is one of the requirements for all activities involving the management of human biological samples within the framework of the new ISO 20387:2018. Although some theoretical approaches are available for preparing risk assessments in general, there is no evidence in the literature of examples of listed insurable risks for cancer biobanks. To fill this gap and to provide an overview of the survey performed in our cancer Biobank, we have assessed potential exposures to insurable risks. After an analysis of the Biobank structure and focusing on natural catastrophe risks, we produced a summary map of risk scenarios. In addition to implementing security awareness, this also lays the foundation for transferring the residual risk arising from Biobank activities to the insurance market.

Abstract

Although research biobanks are among the most promising tools to fight disease and improve public health, there are a range of risks biobanks may face that mainly need to be assessed in an attempt to be relieved. We conducted a strategic insurance review of an institutional cancer biobank with the aim of both identifying the insurable risks of our own Biobank and gathering useful evidence of primary exposure to insurable risks. In this practical scenario, risks have been outlined and categorized into inherent and residual risks, along with their possible impact on biobank maintenance. Results at the Biobank of the Cancer Institute of Bari showed evidence of potentially significant and intrinsic risk due to highly relevant threats, along with already implemented improvements that significantly reduce risks to a range of relative acceptability.