Biopreservation and BiobankingVol. 21, No. 1 Brief ReportsPriorities in Biobanking Research: A Report on the 2021 ISBER Round TableJennifer A. Byrne, Anastazia T. Banaszak, Jane E. Carpenter, Steven L. Carroll, Marta G. Castelhano, Paula S. Espinal, Marianne K. Henderson, Anusha Hettiaratchi, Mantombi Maseme, Wayne Ng, Kirtika Patel, Iuliana Popescu, Sergio I. Prada, William S. Schleif, Miranda Smith, Shirley Wee, Carol J. Weil, and Katherine WoodsJennifer A. ByrneAddress correspondence to: Jennifer A. Byrne, PhD, New South Wales Health Pathology, Camperdown 2050, Australia E-mail Address: jennifer.byrne@health.nsw.gov.auhttps://orcid.org/0000-0002-8923-0587New South Wales Health Pathology, Camperdown, Australia.School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.Search for more papers by this author, Anastazia T. BanaszakInstitute of Ocean Sciences and Limnology, National Autonomous University of Mexico, Mexico City, Mexico.Search for more papers by this author, Jane E. CarpenterNew South Wales Health Pathology, Camperdown, Australia.Search for more papers by this author, Steven L. CarrollDepartment of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, USA.Search for more papers by this author, Marta G. Castelhanohttps://orcid.org/0000-0003-2497-1939Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA.Search for more papers by this author, Paula S. EspinalNicklaus Children's Biobank, Research Institute, Nicklaus Children's Hospital, Miami, Florida, USA.Search for more papers by this author, Marianne K. HendersonNational Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.Search for more papers by this author, Anusha HettiaratchiMark Wainwright Analytical Centre, UNSW, Sydney, Australia.Search for more papers by this author, Mantombi MasemeNational Health Laboratory Service, Biobank, Constitution Hill, Johannesburg, South Africa.Search for more papers by this author, Wayne NgVictorian Cancer Biobank, Melbourne, Australia.Search for more papers by this author, Kirtika PatelMoi University, School of Medicine, College of Health Science, Eldoret, Kenya.Search for more papers by this author, Iuliana PopescuBarnstable Brown Diabetes Center, University of Kentucky, College of Medicine, Lexington, Kentucky, USA.Search for more papers by this author, Sergio I. Pradahttps://orcid.org/0000-0001-7986-0959Fundación Valle del Lili, Centro de Investigaciones Clínicas, Cali, Colombia.Centro PROESA, Universidad Icesi, Cali, Colombia.Search for more papers by this author, William S. SchleifJohns Hopkins All Children's Pediatric Biorepository, Johns Hopkins All Children's Hospital, St. Petersburg, Florida, USA.Pediatric Biospecimen Science Program, Johns Hopkins All Children's Institute for Clinical and Translational Research, St. Petersburg, Florida, USA.Search for more papers by this author, Miranda SmithThe Peter Doherty Institute for Infection and Immunity, Melbourne, Australia.Search for more papers by this author, Shirley WeeMenzies Health Institute Queensland, Griffith University, Southport, Australia.Search for more papers by this author, Carol J. WeilNational Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.Search for more papers by this author, and Katherine WoodsSt Vincent's Biobank, NRL, St Vincent's Institute of Medical Research, Fitzroy, Australia.Search for more papers by this authorPublished Online:14 Feb 2023https://doi.org/10.1089/bio.2021.0178AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 21Issue 1Feb 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Jennifer A. Byrne, Anastazia T. Banaszak, Jane E. Carpenter, Steven L. Carroll, Marta G. Castelhano, Paula S. Espinal, Marianne K. Henderson, Anusha Hettiaratchi, Mantombi Maseme, Wayne Ng, Kirtika Patel, Iuliana Popescu, Sergio I. Prada, William S. Schleif, Miranda Smith, Shirley Wee, Carol J. Weil, and Katherine Woods.Priorities in Biobanking Research: A Report on the 2021 ISBER Round Table.Biopreservation and Biobanking.Feb 2023.111-113.http://doi.org/10.1089/bio.2021.0178Published in Volume: 21 Issue 1: February 14, 2023Online Ahead of Print:May 4, 2022PDF download

Mindfulness training (MT) has been shown to benefit sustained attention and mind wandering. Yet, few studies have examined whether benefits differ as a function of individual differences in mind wandering. The sustained attention to response task (SART) measured task accuracy (A′), response time variability (response time intraindividual coefficient of variation, ICV), and self-reported mind wandering in participants before (T1) and after (T2) a 1-month MT retreat (n = 56), as well as in a control group (n = 32) who received no MT. Only the retreat group demonstrated increased A′ and reduced mind wandering, but no change in ICV, from T1 to T2. Retreat participants demonstrated reduced ICV compared to the control group only when considering individual differences in mind wandering such that retreat participants with higher levels of mind wandering at T1 demonstrated greater benefits to ICV over time. These findings suggest MT may benefit sustained attention, as seen in changes on both objective and subjective measures, and that considering baseline individual differences may increase our understanding of MT’s benefits to sustained attention.

Human health biobanks are forms of research infrastructure that supply biospecimens and associated data to researchers, and therefore juxtapose the activities of clinical care and biomedical research. The discipline of biobanking has existed for over 20 years and is supported by several international professional societies and dedicated academic journals. However, despite both rising research demand for human biospecimens, and the growth of biobanking as an academic discipline, many individual biobanks continue to experience sustainability challenges. This commentary will summarize how the COVID-19 pandemic is creating new challenges and opportunities for both the health biobanking sector and the supporting discipline of biobanking. While the challenges for biobanks may be numerous and acute, there are opportunities for both individual biobanks and the discipline of biobanking to embrace change such that biobanks can continue to support and drive biomedical research. We will therefore describe numerous practical steps that individual biobanks and/or the discipline of biobanking can take to survive and possibly thrive in response to the COVID-19 pandemic.

The SHARC (SHAring Reward & Credit) interest group (IG) is an interdisciplinary group set up in the framework of RDA (Research Data Alliance) to improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Notably, one of the objectives is to promote data sharing activities in research assessment schemes at national and European levels. To this aim, the RDA-SHARC IG is developing assessment grids using criteria to establish if data are compliant to the FAIR principles (findable /accessible / interoperable / reusable). The grid is aiming to be extensive, generic and trans-disciplinary. It is meant to be used by evaluators to assess the quality of the sharing practice of the researcher/scientist over a given period, taking into account the means & support available over that period. The grid displays a mind-mapped tree-graph structure based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2016; Wilkinson et al., 2018; and E.U.Guidelines about FAIRness Data Management Plans). The criteria used are based on the work from FORCE 11*, and the Open Science Career Assessment Matrix designed by the EC Working group on Rewards under Open science. The criteria are organised in 5 clusters: ‘Motivations for sharing’; ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’. For each criterion, 4 graduations are proposed (‘Never / Not Assessable’; ‘If mandatory’; ‘Sometimes’; ‘Always’). Only one value must be selected per criterion. Evaluation should be done by cluster; the final overall assessment will be based on the sum of the number of each ticked value / total number of criteria in each cluster; the ‘motivations for sharing’ should be appreciated qualitatively in the final interpretation. The final goals are to develop a graduated assessment of the researcher FAIRness literacy and help identifying needs to build FAIRness guidelines to improve the sharing capacity of researchers.

Standardization and sustainability are ideals within the biobanking world, and the demand for high-quality well-annotated specimens is growing just as rapidly as the ever-increasing precision and throughput of today's high-tech scientific methods. In the state of New South Wales (NSW) in Australia, the state government has allocated significant funding toward this requirement in recent years, with the launch of the NSW Health Statewide Biobank in central Sydney in 2017, and the introduction of the voluntary NSW Biobank Certification Program, and Consent Toolkit. For new and established biobanks, the influence of these new resources has been twofold: first they have provided valuable guidance for moving toward standardized practices and raising the bar for biobanking quality standards; second, they have brought to the forefront the challenges of sustainability and transitioning to a certification standard of biobanking. In Westmead, similar to 20 km from Sydney's central business district, the Westmead Research Hub has responded to these challenges with a collaborative biobanking project initiated in 2015. As the site of almost 30 individual biobanks, and to inform a pilot project of central biobank services, a questionnaire was developed and administered to all of the biobanks. This article reports on the results from the questionnaire and the rationale for subsequent initiation of a core biobanking facility.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

There are increasing concerns that research regulatory requirements exceed those required to manage risks, particularly for low- and negligible-risk research projects. In particular, inconsistent documentation requirements across research sites can delay the conduct of multi-site projects. For a one-year, negligible-risk project examining biobank operations conducted at three separate Australian institutions, we found that the researcher time required to meet regulatory requirements was eight times greater than that required for the approved research activity (60 hours versus 7.5 hours respectively). In total, 76 business days (almost four months) were required to obtain the necessary approvals, and site-specific processes required twice as long (52 business days/approximately 10 weeks) as primary Human Research Ethics Committee and Research Governance Office processes (24 business days/ approximately five weeks). We describe the impact of this administrative load on the conduct of a one-year, externally-funded research project, and identify a shared set of application requirements that could be used to streamline and harmonise research governance review of low- and negligible-risk research projects.

Indexed identifier ? Identification Are each data/dataset identified by an indexed and independant identifier ? Persistent metadata / data link ? Metadata traceability Are the metadata linked to the dataset through a persistent identifier? Metadata & authority linked ? Metadata traceability Are the metadata of each dataset linked to a unique authority (responsible for the datasets at a given time)? Unique, global, persistent ID? Identification Are the data identifiers unique, global and persistent ? Are the data identifiers unique, global and persistent ? Datasets linked to authority ? Metadata traceability Are all datasets linked to an authority (legal entity) through a unique and persistent identifier over time (e.g. institution, association or established body)? In case of a legal reuse restriction (such as personal data, state and public security, national defense secret, confidentiality of external relations, information systems security, secrets in industrial and commercial matters) , is the restriction properly justified?

Ongoing quality management is an essential part of biobank operations and the creation of high quality biospecimen resources. Adhering to the standards of a national biobanking network is a way to reduce variability between individual biobank processes, resulting in cross biobank compatibility and more consistent support for health researchers. The Canadian Tissue Repository Network (CTRNet) implemented a set of required operational practices (ROPs) in 2011 and these serve as the standards and basis for the CTRNet biobank certification program. A review of these 13 ROPs covering 314 directives was conducted after 5 years to identify areas for revision and update, leading to changes to 7/314 directives (2.3%). A review of all internal controlled documents (including policies, standard operating procedures and guides, and forms for actions and processes) used by the BC Cancer Agency's Tumor Tissue Repository (BCCA-TTR) to conform to these ROPs was then conducted. Changes were made to 20/106 (19%) of BCCA-TTR documents. We conclude that a substantial fraction of internal controlled documents require updates at regular intervals to accommodate changes in best practices. Reviewing documentation is an essential aspect of keeping up to date with best practices and ensuring the quality of biospecimens and data managed by biobanks.

Biobanks face increasing demands for research materials of consistent quality, which can be used in collaborative studies. Several countries and some international agencies have made formal efforts to standardize biobank operations and outputs. These include the establishment of best practice guidelines for collection management, and certification programs. Such guidelines and programs increase biobanks' opportunities for participation in high impact research and funding. However, they also impose economic and time costs, which may burden biobanks. This study aimed to estimate the costs of gaining certification and maintaining certification (i.e., committing extra resources to continue standards) for three cancer biobanks participating in a biobank certification program in New South Wales, Australia. To gather cost data for a range of cancer biobanks, we recruited three with different full time equivalent (FTE) staff levels (1.0-3.0), recognizing FTE staff level as an indicator of resources and operating scale. In extended interviews with staff, we gathered biobanks' expected costs in obtaining and annually maintaining certification. The biobank with the highest staff level reported the lowest expected costs in gaining certification, due to the strong prealignment of its present operations with certification requirements. The other biobanks expected higher costs as their operations required greater adjustments. Overall, relative costs of gaining certification were between 2% and 6% of current total annual wage costs. To the authors' knowledge, this is the first such costing study of a biobank certification program. Supplementary Data include the interview schedule that other biobanks may use to estimate their own economic certification costs.

The RDA-SHARC (SHAring Reward & Credit) interest group is an interdisciplinary volunteer member-based group set up as part of RDA (Research Data Alliance) to unpack and improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Background and objectives of this group are reported here. Notably, one of the objectives is to promote the inclusion of data sharing activities in the research (& researchers) assessment scheme at national and European levels. To this aim, the RDA-SHARC-IG is developing two assessment grids using criteria to establish if data are compliant to the F.A.I.R principles (findable /accessible / interoperable / reusable) based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2018; and E.U.Guidelines*): 1/ The self-assessment grid to be used by a scientist as a ‘checklist’ to identify her/his own activities and to pinpoint the hurdles that hinder efficient sharing and reuse of his/her data by all potential users. 2/ The two-level grid (quick/extensive) to be used by the evaluator to assess the quality of the researcher/scientist sharing practice, over a given period, taking into account the means & support available over that period. Assessment criteria are classified according their importance with regards to FAIRness (essential / recommended / desirable) meanwhile good practices are recommended for critical steps. To implement a highly fair assessment of the sharing process, appropriate criteria must be selected in order to design optimal generic assessment grids. This process requires participation, time and input from volunteer scientists data producers/users from various fields.

Medical Journal of AustraliaVolume 206, Issue 7 p. 325-326 Letter Adaptation of a biobank certification program for Australia Jane E Carpenter, Corresponding Author Jane E Carpenter jane.carpenter1@health.nsw.gov.au Biobanking Services, New South Wales Health Pathology, Sydney, NSWCorrespondence: jane.carpenter1@health.nsw.gov.auSearch for more papers by this authorAmanda Rush, Amanda Rush Children's Hospital at Westmead, Sydney, NSWSearch for more papers by this authorCandace Carter, Candace Carter Biobanking Services, New South Wales Health Pathology, Sydney, NSWSearch for more papers by this author Jane E Carpenter, Corresponding Author Jane E Carpenter jane.carpenter1@health.nsw.gov.au Biobanking Services, New South Wales Health Pathology, Sydney, NSWCorrespondence: jane.carpenter1@health.nsw.gov.auSearch for more papers by this authorAmanda Rush, Amanda Rush Children's Hospital at Westmead, Sydney, NSWSearch for more papers by this authorCandace Carter, Candace Carter Biobanking Services, New South Wales Health Pathology, Sydney, NSWSearch for more papers by this author First published: 17 April 2017 https://doi.org/10.5694/mja16.01147Citations: 6Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume206, Issue7April 2017Pages 325-326 RelatedInformation

The main goal of this study was to investigate the occurrence of porcine reproductive and respiratory syndrome virus (PRRSV)-specific genotypes in swine sites in Ontario (Canada) using molecular, spatial and network data from a porcine reproductive and respiratory syndrome (PRRS) regional control project. For each site, location, animal movement service provider (truck companies), PRRSV status and sequencing data of the open reading frame 5 (ORF5) were obtained. Three-kilometre buffers were created to evaluate neighbourhood characteristics for each site. Social network analysis was conducted on swine sites and trucking companies to assemble the network and define network components. Three different PRRSV genotypes were used as outcomes for statistical analysis based on the region's phylogenetic tree of the ORF5. Multivariable exact logistic regression was conducted to investigate the association between being positive for a specific genotype and two main exposures of interest: (i) having at least one neighbour within three km also positive for the same genotype outside the production system and (ii) having at least one positive site for the same genotype in the same truck network component outside the production system. Results showed that the importance of area spread and truck network on PRRSV occurrence differed according to genotype. Additionally, the Ontario PRRS database appears suitable for conducting regional disease investigations. Finally, the use of relatively new tools available for network, spatial and molecular analysis could be useful in investigation, control and prevention of endemic infectious diseases in animal populations.

The objective of this study was to develop a discrete event agent-based stochastic model to explore the likelihood of the occurrence of porcine reproductive and respiratory syndrome (PRRS) outbreaks in swine herds with different PRRS control measures in place. The control measures evaluated included vaccination with a modified-live attenuated vaccine and live-virus inoculation of gilts, and both were compared to a baseline scenario where no control measures were in place. A typical North American 1,000-sow farrow-to-wean swine herd was used as a model, with production and disease parameters estimated from the literature and expert opinion. The model constructed herein was not only able to capture individual animal heterogeneity in immunity to and shedding of the PRRS virus, but also the dynamic animal flow and contact structure typical in such herds under field conditions. The model outcomes included maximum number of females infected per simulation, and time at which that happened and the incidence of infected weaned piglets during the first year of challenge-virus introduction. Results showed that the baseline scenario produced a larger percentage of simulations resulting in outbreaks compared to the control scenarios, and interestingly some of the outbreaks occurred over long periods after virus introduction. The live-virus inoculation scenario showed promising results, with fewer simulations resulting in outbreaks than the other scenarios, but the negative impacts of maintaining a PRRS-positive population should be considered. Finally, under the assumptions of the current model, neither of the control strategies prevented the infection from spreading to the piglet population, which highlights the importance of maintaining internal biosecurity practices at the farrowing room level.

Distinct subtypes of triple negative (TN) breast cancer have been identified by tumor expression profiling. However, little is known about the relationship between histopathologic features of TN tumors, which reflect aspects of both tumor behavior and tumor microenvironment, and molecular TN subtypes. The histopathologic features of TN tumors were assessed by central review and 593 TN tumors were subjected to whole genome expression profiling using the Illumina Whole Genome DASL array. TN molecular subtypes were defined based on gene expression data associated with histopathologic features of TN tumors. Gene expression analysis yielded signatures for four TN subtypes (basal-like, androgen receptor positive, immune, and stromal) consistent with previous studies. Expression analysis also identified genes significantly associated with the 12 histological features of TN tumors. Development of signatures using these markers of histopathological features resulted in six distinct TN subtype signatures, including an additional basal-like and stromal signature. The additional basal-like subtype was distinguished by elevated expression of cell motility and glucose metabolism genes and reduced expression of immune signaling genes, whereas the additional stromal subtype was distinguished by elevated expression of immunomodulatory pathway genes. Histopathologic features that reflect heterogeneity in tumor architecture, cell structure, and tumor microenvironment are related to TN subtype. Accounting for histopathologic features in the development of gene expression signatures, six major subtypes of TN breast cancer were identified.

Background The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10 −5 ), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10 −8 ) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.

Common variants in 94 loci have been associated with breast cancer including 15 loci with genome-wide significant associations ( P <5 × 10 −8 ) with oestrogen receptor (ER)-negative breast cancer and BRCA1 -associated breast cancer risk. In this study, to identify new ER-negative susceptibility loci, we performed a meta-analysis of 11 genome-wide association studies (GWAS) consisting of 4,939 ER-negative cases and 14,352 controls, combined with 7,333 ER-negative cases and 42,468 controls and 15,252 BRCA1 mutation carriers genotyped on the iCOGS array. We identify four previously unidentified loci including two loci at 13q22 near KLF5 , a 2p23.2 locus near WDR43 and a 2q33 locus near PPIL3 that display genome-wide significant associations with ER-negative breast cancer. In addition, 19 known breast cancer risk loci have genome-wide significant associations and 40 had moderate associations ( P <0.05) with ER-negative disease. Using functional and eQTL studies we implicate TRMT61B and WDR43 at 2p23.2 and PPIL3 at 2q33 in ER-negative breast cancer aetiology. All ER-negative loci combined account for ∼11% of familial relative risk for ER-negative disease and may contribute to improved ER-negative and BRCA1 breast cancer risk prediction.

Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk.