BBMRI-ERIC's contributions to research and knowledge exchange on COVID-19

N-glycan profiling of tissue samples to aid breast cancer subtyping

Breast cancer is a highly heterogeneous disease. Its intrinsic subtype classification for diagnosis and choice of therapy traditionally relies on the presence of characteristic receptors. Unfortunately, this classification is often not sufficient for precise prediction of disease prognosis and treatment efficacy. The N-glycan profiles of 145 tumors and 10 healthy breast tissues were determined using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry. The tumor samples were classified into Mucinous, Lobular, No-Special-Type, Human Epidermal Growth Factor 2 + , and Triple-Negative Breast Cancer subtypes. Statistical analysis was conducted using the reproducibility-optimized test statistic software package in R, and the Wilcoxon rank sum test with continuity correction. In total, 92 N-glycans were detected and quantified, with 59 consistently observed in over half of the samples. Significant variations in N-glycan signals were found among subtypes. Mucinous tumor samples exhibited the most distinct changes, with 28 significantly altered N-glycan signals. Increased levels of tri- and tetra-antennary N-glycans were notably present in this subtype. Triple-Negative Breast Cancer showed more N-glycans with additional mannose units, a factor associated with cancer progression. Individual N-glycans differentiated Human Epidermal Growth Factor 2 + , No-Special-Type, and Lobular cancers, whereas lower fucosylation and branching levels were found in N-glycans significantly increased in Luminal subtypes (Lobular and No-Special-Type tumors). Clinically normal breast tissues featured a higher abundance of signals corresponding to N-glycans with bisecting moiety. This research confirms that histologically distinct breast cancer subtypes have a quantitatively unique set of N-glycans linked to clinical parameters like tumor size, proliferative rate, lymphovascular invasion, and metastases to lymph nodes. The presented results provide novel information that N-glycan profiling could accurately classify human breast cancer samples, offer stratification of patients, and ongoing disease monitoring.

FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries

Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice. In the context of COVID-19, there has been a rush to share data marked by an explosion of population-specific and discipline-specific resources for collecting, curating, and disseminating participant-level data. We conducted a scoping review and cross-sectional survey to identify and describe COVID-19-related platforms and registries that harmonise and share participant-level clinical, omics (eg, genomic and metabolomic data), imaging data, and metadata. We assess how these initiatives map to the best practices for the ethical and equitable management of data and the findable, accessible, interoperable, and reusable (FAIR) principles for data resources. We review gaps and redundancies in COVID-19 data-sharing efforts and provide recommendations to build on existing synergies that align with frameworks for effective and equitable data reuse. We identified 44 COVID-19-related registries and 20 platforms from the scoping review. Data-sharing resources were concentrated in high-income countries and siloed by comorbidity, body system, and data type. Resources for harmonising and sharing clinical data were less likely to implement FAIR principles than those sharing omics or imaging data. Our findings are that more data sharing does not equate to better data sharing, and the semantic and technical interoperability of platforms and registries harmonising and sharing COVID-19-related participant-level data needs to improve to facilitate the global collaboration required to address the COVID-19 crisis.

No time for complacency: The CoVaRR-Net Biobank is an essential element of laboratory preparedness for infectious disease outbreaks

The SARS-CoV-2 pandemic highlighted the need for rapid, collaborative, and population-centric research to define health impact, develop health care policies and establish reliable diagnostic and surveillance tests. Critical for these objectives were in-depth clinical data collected in standardized fashion and large numbers of various types of human samples prior and post-viral encounter. As the pandemic evolved with the emergence of new variants of concern (VOCs), access to samples and data from infected and vaccinated individuals were needed to monitor immune durability, the possibility of increased transmissibility and virulence, and vaccine protection against new and emerging VOCs. Therefore, essential to the pandemic response is a strong laboratory and data research component, supported by effective biobanking and data sharing. Critically important to the speed of the research response is the rapid access to biobanked samples. To address critical challenges brought to light by the pandemic, the Coronavirus Variants Rapid Response Network (CoVaRR-Net), funded by the Canadian Institutes of Health Research, was established to coordinate research efforts to provide rapid evidence-based responses to emerging VOCs. The purpose of this paper is to introduce the CoVaRR-Net Biobank and define its contribution to pandemic preparedness.

Establishment of the large-scale longitudinal multi-omics dataset in COVID-19 patients: data profile and biospecimen

Understanding and monitoring virus-mediated infections has gained importance since the global outbreak of the coronavirus disease 2019 (COVID-19) pandemic. Studies of high-throughput omics-based immune profiling of COVID-19 patients can help manage the current pandemic and future virus-mediated pandemics. Although COVID-19 is being studied since past 2 years, detailed mechanisms of the initial induction of dynamic immune responses or the molecular mechanisms that characterize disease progression remains unclear. This study involved comprehensively collected biospecimens and longitudinal multi-omics data of 300 COVID-19 patients and 120 healthy controls, including whole genome sequencing (WGS), single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA(+scTCR/BCR)-seq), bulk BCR and TCR sequencing (bulk TCR/BCR-seq), and cytokine profiling. Clinical data were also collected from hospitalized COVID-19 patients, and HLA typing, laboratory characteristics, and COVID-19 viral genome sequencing were performed during the initial diagnosis. The entire set of biospecimens and multi-omics data generated in this project can be accessed by researchers from the National Biobank of Korea with prior approval. This distribution of largescale multi-omics data of COVID-19 patients can facilitate the understanding of biological crosstalk involved in COVID-19 infection and contribute to the development of potential methodologies for its diagnosis and treatment. [BMB Reports 2022; 55(9): 465-471].

The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19

With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.

Study protocol for the St James's Hospital, Tallaght University Hospital, Trinity College Dublin Allied Researchers' (STTAR) Bioresource for COVID-19

Background: The current coronavirus disease 2019 (COVID-19) pandemic began in Ireland with the first confirmed positive case in March 2020. In the early stages of the pandemic clinicians and researchers in two affiliated Dublin hospitals identified the need for a COVID-19 biobanking initiative to support and enhance research into the disease. Through large scale analysis of clinical, regional, and genetic characteristics of COVID-19 patients, biobanks have helped identify, and so protect, at risk patient groups The STTAR Bioresource has been created to collect and store data and linked biological samples from patients with SARS-CoV-2 infection and healthy and disease controls. Aim: The primary objective of this study is to build a biobank, to understand the clinical characteristics and natural history of COVID-19 infection with the long-term goal of research into improved disease understanding, diagnostic tests and treatments. Methods: This is a prospective dual-site cohort study across two tertiary acute university teaching hospitals. Patients are recruited from inpatient wards or outpatient clinics. Patients with confirmed COVID-19 infection as well as healthy and specific disease control groups are recruited.  Biological samples are collected and a case report form detailing demographic and medical background is entered into the bespoke secure online Dendrite database. Impact: The results of this study will be used to inform national and international strategy on health service provision and disease management related to COVID-19. In common with other biobanks, study end points  evolve over time as new research questions emerge. They currently include patient survival, occurrence of severe complications of the disease or its therapy, occurrence of persistent symptoms following recovery from the acute illness and vaccine responses.

Pandemic Response Requires Research Samples: A U.S. Safety-Net Hospital's Experience and Call for National Action

Biorepositories provide a critical resource for gaining knowledge of emerging infectious diseases and offer a mechanism to rapidly respond to outbreaks; the emergence of the novel coronavirus, SARS-CoV-2, has proved their importance. During the COVID-19 pandemic, the absence of centralized, national biorepository efforts meant that the onus fell on individual institutions to establish sample repositories. As a safety-net hospital, Boston Medical Center (BMC) recognized the importance of creating a COVID-19 biorepository to both support critical science at BMC and ensure representation in research for its urban patient population, most of whom are from underserved communities. This article offers a realistic overview of the authors' experience in establishing this biorepository at the onset of the COVID-19 pandemic during the height of the first surge of cases in Boston, Massachusetts, with the hope that the challenges and solutions described are useful to other institutions. Going forward, funders, policymakers, and infectious disease and public health communities must support biorepository implementation as an essential element of future pandemic preparedness.

A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement

Background: Precision medicine focuses on the identification of therapeutic strategies that are effective for a group of patients based on similar unifying characteristics. The recent success of precision medicine in non-critical care settings has resulted from the confluence of large clinical and biospecimen repositories, innovative bioinformatics, and novel trial designs. Similar advances for precision medicine in sepsis and in the acute respiratory distress syndrome (ARDS) are possible but will require further investigation and significant investment in infrastructure. Methods: This project was funded by the American Thoracic Society Board of Directors. A multidisciplinary and diverse working group reviewed the available literature, established a conceptual framework, and iteratively developed recommendations for the Precision Medicine Research Agenda for Sepsis and ARDS. Results: The following six priority recommendations were developed by the working group: 1) the creation of large richly phenotyped and harmonized knowledge networks of clinical, imaging, and multianalyte molecular data for sepsis and ARDS; 2) the implementation of novel trial designs, including adaptive designs, and embedding trial procedures in the electronic health record; 3) continued innovation in the data science and engineering methods required to identify heterogeneity of treatment effect; 4) further development of the tools necessary for the real-time application of precision medicine approaches; 5) work to ensure that precision medicine strategies are applicable and available to a broad range of patients varying across differing racial, ethnic, socioeconomic, and demographic groups; and 6) the securement and maintenance of adequate and sustainable funding for precision medicine efforts. Conclusions: Precision medicine approaches that incorporate variability in genomic, biologic, and environmental factors may provide a path forward for better individualizing the delivery of therapies and improving care for patients with sepsis and ARDS.

Finding the Value in Biobanks: Enhancing the CTRNet Locator

Biobanks are a critical piece of Research Infrastructure (RI). However, biobanks need to accept the reality of a life cycle for RIs. Until recently, strategies to sustain biobanks have been commonly focused on ways to maintain current operational models. However, sustaining biobanks as they exist today may be increasingly challenging in the face of the disruption in health and research priorities caused by the COVID-19 pandemic. In this opinion article, we review the current and emerging future drivers of biobank value for their researchers, institutions, and funders, highlighting utilization and impact of research performed using the biobank as key measures of future value. While biobanks can only indirectly influence the specific impact of the research performed, they can transform themselves to more actively redefine utilization to their advantage. Utilization means more than the balance of samples and data in versus out. Utilization means redirecting expertise to best support end users, and importantly, closing the operating gap between biobanks and their end users who seek to find the right biospecimens and data to pursue their research. We discuss the specific role of locators (those created by public investment) in closing this gap and the need for additional tools for researchers, before and subsequent to connecting with locators. For the former, we specifically propose that more support is needed to assist researchers in the decision as to how to best obtain biospecimens and navigate the options as to whether finding existing biospecimens and data held by a biobank is the optimal solution for a given project, or whether the optimal solution is either contracting with a biobank to collect samples or creating a new biobank. We believe this type of biospecimen navigator platform will help to maximize utilization of current biobank resources, and also promote the services and expertise in biobanks to better serve researchers' needs.

Biomarker Research and Development for Coronavirus Disease 2019 (COVID-19): European Medical Research Infrastructures Call for Global Coordination

An effective response to the coronavirus disease 2019 (COVID-19) pandemic requires a better understanding of the biology of the infection and the identification of validated biomarker profiles that would increase the availability, accuracy, and speed of COVID-19 testing. Here, we describe the strategic objectives and action lines of the European Alliance of Medical Research Infrastructures (AMRI), established to improve the research process and tackle challenges related to diagnostic tests and biomarker development. Recommendations include: the creation of a European taskforce for validation of novel diagnostic products, the definition and promotion of criteria for COVID-19 samples biobanking, the identification and validation of biomarkers as clinical endpoints for clinical trials, and the definition of immune biomarker signatures at different stages of the disease. An effective management of the COVID-19 pandemic is possible only if there is a high level of knowledge and coordination between the public and private sectors within a robust quality framework.

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.

Considerations for diagnostic COVID-19 tests

During the early phase of the coronavirus disease 2019 (COVID-19) pandemic, design, development, validation, verification and implementation of diagnostic tests were actively addressed by a large number of diagnostic test manufacturers. Hundreds of molecular tests and immunoassays were rapidly developed, albeit many still await clinical validation and formal approval. In this Review, we summarize the crucial role of diagnostic tests during the first global wave of COVID-19. We explore the technical and implementation problems encountered during this early phase in the pandemic, and try to define future directions for the progressive and better use of (syndromic) diagnostics during a possible resurgence of COVID-19 in future global waves or regional outbreaks. Continuous global improvement in diagnostic test preparedness is essential for more rapid detection of patients, possibly at the point of care, and for optimized prevention and treatment, in both industrialized countries and low-resource settings.

Rapid establishment of a COVID-19 perinatal biorepository: early lessons from the first 100 women enrolled

BACKGROUND

Collection of biospecimens is a critical first step to understanding the impact of COVID-19 on pregnant women and newborns - vulnerable populations that are challenging to enroll and at risk of exclusion from research. We describe the establishment of a COVID-19 perinatal biorepository, the unique challenges imposed by the COVID-19 pandemic, and strategies used to overcome them.

METHODS

A transdisciplinary approach was developed to maximize the enrollment of pregnant women and their newborns into a COVID-19 prospective cohort and tissue biorepository, established on March 19, 2020 at Massachusetts General Hospital (MGH). The first SARS-CoV-2 positive pregnant woman was enrolled on April 2, and enrollment was expanded to SARS-CoV-2 negative controls on April 20. A unified enrollment strategy with a single consent process for pregnant women and newborns was implemented on May 4. SARS-CoV-2 status was determined by viral detection on RT-PCR of a nasopharyngeal swab. Wide-ranging and pregnancy-specific samples were collected from maternal participants during pregnancy and postpartum. Newborn samples were collected during the initial hospitalization.

RESULTS

Between April 2 and June 9, 100 women and 78 newborns were enrolled in the MGH COVID-19 biorepository. The rate of dyad enrollment and number of samples collected per woman significantly increased after changes to enrollment strategy (from 5 to over 8 dyads/week, P < 0.0001, and from 7 to 9 samples, P < 0.01). The number of samples collected per woman was higher in SARS-CoV-2 negative than positive women (9 vs 7 samples, P = 0.0007). The highest sample yield was for placenta (96%), umbilical cord blood (93%), urine (99%), and maternal blood (91%). The lowest-yield sample types were maternal stool (30%) and breastmilk (22%). Of the 61 delivered women who also enrolled their newborns, fewer women agreed to neonatal blood compared to cord blood (39 vs 58, P < 0.0001).

CONCLUSIONS

Establishing a COVID-19 perinatal biorepository required patient advocacy, transdisciplinary collaboration and creative solutions to unique challenges. This biorepository is unique in its comprehensive sample collection and the inclusion of a control population. It serves as an important resource for research into the impact of COVID-19 on pregnant women and newborns and provides lessons for future biorepository efforts.