An Approach to Evaluate the Costs and Outputs of Academic Biobanks.

Academic biobanks commonly report sustainability challenges, which may be exacerbated by a lack of information on biobank value. To better understand the costs and supported outputs that contribute to biobank value, we developed a systematic, generalizable methodology to determine biobank inputs and publications arising from biobank-supported research. We then tested this in a small cohort (n = 12) of academic cancer biobanks in New South Wales, Australia. A proforma was developed to capture monetary and in-kind biobank costing data from biobank managers and publicly available sources. Participating biobanks were grouped and compared according to the following two classifications: open- versus restricted-access and high versus low total annual costs. Our methodology provides a feasible approach for capturing comprehensive costing data for a defined period. Characterization of biobanks using this approach showed that median total costs, as well as median staffing and in-kind costs, were comparable for open- and restricted-access biobanks, as were the quantity and journal impact metrics of supported publications. High- and low-cost biobanks supported similar median numbers of publications; however, high-cost biobanks supported publications with higher median journal impact factor and Altmetric scores. Overall, 9 of 10 biobanks had higher Field-Weighted Citation Impact scores than the global average for similar publications. This is the first tested, generalizable approach to analyze the costs and publications arising from biobank-supported research. By determining explicit cost and output data, academic biobanks, funders, and policymakers can engage in or support informed redirection of resourcing and/or benchmark setting with the aim of improving biobank support of research.

MiRNAs from the Dlk1-Dio3 locus and miR-224/452 cluster contribute to glioblastoma tumor heterogeneity.

Glioblastoma is one of the most common and aggressive brain tumors and has seen few improvements in patient outcomes. Inter-tumor heterogeneity between tumors of different patients as well as intra-tumor heterogeneity of cells within the same tumor challenge the development of effective drugs. MiRNAs play an essential role throughout the developing brain and regulate many key genes involved in oncogenesis, yet their role in driving many of the processes underlying tumor heterogeneity remains unclear. In this study, we highlight miRNAs from the Dlk1-Dio3 and miR-224/452 clusters which may be expressed cell autonomously and have expression that is associated with cell state genes in glioblastoma, most prominently in neural progenitor-like and mesenchymal-like states respectively. These findings implicate these miRNA clusters as potential regulators of glioblastoma intra-tumoral heterogeneity and may serve as valuable biomarkers for cell state identification.

DROSHA regulates mesenchymal gene expression in Wilms tumor.

Wilms tumor, the most common pediatric kidney cancer, resembles embryonic renal progenitors. Currently, there are no ways to therapeutically target Wilms tumor driver mutations, such as in the microRNA processing gene DROSHA. Here we used a "multi-omics" approach to define the effects of DROSHA mutation in Wilms tumor. We categorized Wilms tumor mutations into four mutational subclasses with unique transcriptional effects: microRNA processing, MYCN activation, chromatin remodeling, and kidney developmental factors. In particular, we find that DROSHA mutations are correlated with de-repressing microRNA target genes that regulate differentiation and proliferation and a self-renewing, mesenchymal state. We model these findings by inhibiting DROSHA expression in a Wilms tumor cell line, which led to upregulation of the cell cycle regulator cyclin D2 (CCND2). Furthermore, we observed that DROSHA mutations in Wilms tumor and DROSHA silencing in vitro were associated with a mesenchymal state with aberrations in redox metabolism. Accordingly, we demonstrate that Wilms tumor cells lacking microRNAs are sensitized to ferroptotic cell death through inhibition of glutathione peroxidase 4 (GPX4), the enzyme that detoxifies lipid peroxides. Implications: This study reveals genotype-transcriptome relationships in Wilms tumor and points to ferroptosis as a potentially therapeutic vulnerability in one subset of Wilms tumor.

Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study.

BACKGROUND: Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users' feedback for future user-centred design and a better cognitive model of human-computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. OBJECTIVE: A usability study for the VR prototype--Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. METHODS: The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann-Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. RESULTS: The usability study investigated the feasibility of using VR for genomic data analysis in domain users' daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users' daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users.

Inferring actual treatment pathways from patient records.

OBJECTIVE: Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. The objective of this study is to develop a method for inferring actual treatment steps for a particular patient group from administrative health records - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research. METHODS: We introduce Defrag, a method for examining health records to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability, establish benchmarks, and compare against baselines. RESULTS: We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag inference method, and to compare Defrag to several baselines, where it significantly outperforms non-NN-based methods. CONCLUSIONS: Defrag offers an innovative and effective approach for inferring treatment pathways from complex health data. Defrag significantly outperforms several existing pathway-inference methods, but computationally-derived treatment pathways are still difficult to compare against clinical guidelines. Furthermore, the open-source code for Defrag and the testing framework are provided to encourage further research in this area.

Machine learning for administrative health records: A systematic review of techniques and applications.

Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease.

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future.

The increasing rates of breast cancer, particularly in emerging economies, have led to interest in scalable deep learning-based solutions that improve the accuracy and cost-effectiveness of mammographic screening. However, such tools require large volumes of high-quality training data, which can be challenging to obtain. This paper combines the experience of an AI startup with an analysis of the FAIR principles of the eight available datasets. It demonstrates that the datasets vary considerably, particularly in their interoperability, as each dataset is skewed towards a particular clinical use-case. Additionally, the mix of digital captures and scanned film compounds the problem of variability, along with differences in licensing terms, ease of access, labelling reliability, and file formats. Improving interoperability through adherence to standards such as the BIRADS criteria for labelling and annotation, and a consistent file format, could markedly improve access and use of larger amounts of standardized data. This, in turn, could be increased further by GAN-based synthetic data generation, paving the way towards better health outcomes for breast cancer.

A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer.

BACKGROUND: Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers. METHODS: We combined immunohistochemistry (IHC) with RNA sequencing and whole-genome sequencing across a diverse spectrum of high-risk paediatric cancers to develop an alternative, expression-based signature associated with CD8+ T-cell infiltration of the TIME. Furthermore, we explored transcriptional features of immune archetypes and T-cell receptor sequencing diversity, assessed the relationship between CD8+ and CD4+ abundance by IHC and deconvolution predictions and assessed the common adult biomarkers such as neoantigen load and TMB. RESULTS: A novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), was identified. Using this signature, we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. In addition, we showed that PD-L1 protein expression is poorly correlated with PD-L1 RNA expression and TMB and neoantigen load are not predictive of T-cell infiltration in paediatrics. Furthermore, deconvolution algorithms are only weakly correlated with IHC measurements of T-cells. CONCLUSIONS: Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME.

WILMS TUMOR MUTATIONAL SUBCLASSES CONVERGE TO DRIVE CCND2 OVEREXPRESSION.

Wilms tumor, the most common kidney cancer in pediatrics, arises from embryonic renal progenitors. Although many patients are cured with multimodal therapy, outcomes remain poor for those with high-risk features. Recent sequencing efforts have provided few biological or clinically actionable insights. Here, we performed DNA and RNA sequencing on 94 Wilms tumors to understand how Wilms tumor mutations transform the transcriptome to arrest differentiation and drive proliferation. We show that most Wilms tumor mutations fall into four classes, each with unique transcriptional signatures: microRNA processing, MYCN activation, chromatin remodeling, and kidney development. In particular, the microRNA processing enzyme DROSHA is one of the most commonly mutated genes in Wilms tumor. We show that DROSHA mutations impair pri-microRNA cleavage, de-repress microRNA target genes, halt differentiation, and overexpress cyclin D2 (CCND2). Several mutational classes converge to drive CCND2 overexpression, which could render them susceptible to cell-cycle inhibitors.

Tracking the clonal dynamics of SARS-CoV-2-specific T cells in children and adults with mild/asymptomatic COVID-19.

Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop less severe coronavirus disease 2019 (COVID-19) than adults. The mechanisms for the age-specific differences and the implications for infection-induced immunity are beginning to be uncovered. We show by longitudinal multimodal analysis that SARS-CoV-2 leaves a small footprint in the circulating T cell compartment in children with mild/asymptomatic COVID-19 compared to adult household contacts with the same disease severity who had more evidence of systemic T cell interferon activation, cytotoxicity and exhaustion. Children harbored diverse polyclonal SARS-CoV-2-specific naïve T cells whereas adults harbored clonally expanded SARS-CoV-2-specific memory T cells. A novel population of naïve interferon-activated T cells is expanded in acute COVID-19 and is recruited into the memory compartment during convalescence in adults but not children. This was associated with the development of robust CD4+ memory T cell responses in adults but not children. These data suggest that rapid clearance of SARS-CoV-2 in children may compromise their cellular immunity and ability to resist reinfection.

Exocentric and Egocentric Views for Biomedical Data Analytics in Virtual Environments-A Usability Study.

Biomedical datasets are usually large and complex, containing biological information about a disease. Computational analytics and the interactive visualisation of such data are essential decision-making tools for disease diagnosis and treatment. Oncology data models were observed in a virtual reality environment to analyse gene expression and clinical data from a cohort of cancer patients. The technology enables a new way to view information from the outside in (exocentric view) and the inside out (egocentric view), which is otherwise not possible on ordinary displays. This paper presents a usability study on the exocentric and egocentric views of biomedical data visualisation in virtual reality and their impact on usability on human behaviour and perception. Our study revealed that the performance time was faster in the exocentric view than in the egocentric view. The exocentric view also received higher ease-of-use scores than the egocentric view. However, the influence of usability on time performance was only evident in the egocentric view. The findings of this study could be used to guide future development and refinement of visualisation tools in virtual reality.

SLC7A8 coding for LAT2 is associated with early disease progression in osteosarcoma and transports doxorubicin.

Background: Despite (neo) adjuvant chemotherapy with cisplatin, doxorubicin and methotrexate, some patients with primary osteosarcoma progress during first-line systemic treatment and have a poor prognosis. In this study, we investigated whether patients with early disease progression (EDP), are characterized by a distinctive pharmacogenetic profile. Methods and Findings: Germline DNA from 287 Dutch high-grade osteosarcoma patients was genotyped using the DMET Plus array (containing 1,936 genetic markers in 231 drug metabolism and transporter genes). Associations between genetic variants and EDP were assessed using logistic regression models and associated variants (p <0.05) were validated in independent cohorts of 146 (Spain and United Kingdom) and 28 patients (Australia). In the association analyses, EDP was significantly associated with an SLC7A8 locus and was independently validated (meta-analysis validation cohorts: OR 0.19 [0.06-0.55], p = 0.002). The functional relevance of the top hits was explored by immunohistochemistry staining and an in vitro transport models. SLC7A8 encodes for the L-type amino acid transporter 2 (LAT2). Transport assays in HEK293 cells overexpressing LAT2 showed that doxorubicin, but not cisplatin and methotrexate, is a substrate for LAT2 (p < 0.0001). Finally, SLC7A8 mRNA expression analysis and LAT2 immunohistochemistry of osteosarcoma tissue showed that the lack of LAT2 expression is a prognostic factor of poor prognosis and reduced overall survival in patients without metastases (p = 0.0099 and p = 0.14, resp.). Conclusion: This study identified a novel locus in SLC7A8 to be associated with EDP in osteosarcoma. Functional studies indicate LAT2-mediates uptake of doxorubicin, which could give new opportunities to personalize treatment of osteosarcoma patients.

Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts.

The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.

Withdrawn: Variations in Altered Global Gene Expression caused by Topoisomerase II Poisons Assessed with Reference to Differences in DNA Binding

The article has been withdrawn at the request of the authors of the journal Current Molecular Pharmacology.Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.

There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour-normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.

Methotrexate-related central neurotoxicity: clinical characteristics, risk factors and genome-wide association study in children treated for acute lymphoblastic leukemia.

Symptomatic methotrexate-related central neurotoxicity (MTX neurotoxicity) is a severe toxicity experienced during acute lymphoblastic leukemia (ALL) therapy with potential long-term neurologic complications. Risk factors and long-term outcomes require further study. We conducted a systematic, retrospective review of 1,251 consecutive Australian children enrolled on Berlin-Frankfurt-Münster or Children's Oncology Group-based protocols between 1998-2013. Clinical risk predictors for MTX neurotoxicity were analyzed using regression. A genome-wide association study (GWAS) was performed on 48 cases and 537 controls. The incidence of MTX neurotoxicity was 7.6% (n=95 of 1,251), at a median of 4 months from ALL diagnosis and 8 days after intravenous or intrathecal MTX. Grade 3 elevation of serum aspartate aminotransferase (P=0.005, odds ratio 2.31 [range, 1.28-4.16]) in induction/consolidation was associated with MTX neurotoxicity, after accounting for the only established risk factor, age ≥10 years. Cumulative incidence of CNS relapse was increased in children where intrathecal MTX was omitted following symptomatic MTX neurotoxicity (n=48) compared to where intrathecal MTX was continued throughout therapy (n=1,174) (P=0.047). Five-year central nervous system relapse-free survival was 89.2 4.6% when intrathecal MTX was ceased compared to 95.4 0.6% when intrathecal MTX was continued. Recurrence of MTX neurotoxicity was low (12.9%) for patients whose intrathecal MTX was continued after their first episode. The GWAS identified single-nucletide polymorphism associated with MTX neurotoxicity near genes regulating neuronal growth, neuronal differentiation and cytoskeletal organization (P<1x10-6). In conclusion, increased serum aspartate aminotransferase and age ≥10 years at diagnosis were independent risk factors for MTX neurotoxicity. Our data do not support cessation of intrathecal MTX after a first MTX neurotoxicity event.

Rare germline variants in childhood cancer patients suspected of genetic predisposition to cancer.

Identification of cancer-predisposing germline variants in childhood cancer patients is important for therapeutic decisions, disease surveillance and risk assessment for patients, and potentially, also for family members. We investigated the spectrum and prevalence of pathogenic germline variants in selected childhood cancer patients with features suggestive of genetic predisposition to cancer. Germline DNA was subjected to exome sequencing to filter variants in 1048 genes of interest including 176 known cancer predisposition genes (CPGs). An enrichment burden analysis compared rare deleterious germline CPG variants in the patient cohort with those in a healthy aged control population. A subset of predicted deleterious variants in novel candidate CPGs was investigated further by examining matched tumor samples, and the functional impact of AXIN1 variants was analyzed in cultured cells. Twenty-two pathogenic/likely pathogenic (P/LP) germline variants detected in 13 CPGs were identified in 19 of 76 patients (25.0%). Unclear association with the diagnosed cancer types was observed in 11 of 19 patients carrying P/LP CPG variants. The burden of rare deleterious germline variants in autosomal dominant CPGs was significantly higher in study patients versus healthy aged controls. A novel AXIN1 frameshift variant (Ser321fs) may impact the regulation of ß-catenin levels. Selection of childhood cancer patients for germline testing based on features suggestive of an underlying genetic predisposition could help to identify carriers of clinically relevant germline CPG variants, and streamline the integration of germline genomic testing in the pediatric oncology clinic.

What Do Biomedical Researchers Want from Biobanks? Results of an Online Survey.

Aims: The purpose of biobanking is to provide biospecimens and associated data to researchers, yet the perspectives of biobank research users have been under-investigated. This study aimed to ascertain biobank research users' needs and opinions about biobanking services. Methods: An online survey was developed, which requested information about researcher demographics, localities of biobanks accessed, methods of sourcing biospecimens, and opinions on topics including but not limited to, application processes, data availability, access fees, and return of research results. There were 27 multiple choice/check box questions, 4 questions with a 10-point Likert scale, and 8 questions with provision for further comment. A web link for the survey was distributed to researchers in late 2019/early 2020 in four Australian states: New South Wales, Victoria, Western Australia, and South Australia. Results: Respondents were generally satisfied with biobank application processes and the fit for purpose of received biospecimens/data. Nonetheless, most researchers (n = 61/99, 62%) reported creating their own collections owing to gaps in sample availability and a perceived increase in efficiency. Most accessed biobanks (n = 58/74, 78%) were in close proximity (local or intrastate) to the researcher. Most researchers had limited the scope of their research owing to difficulty of obtaining biospecimens (n = 55/86, 64%) and/or data (n = 52/85, 60%), with the top three responses for additional types of data required being "more long term follow up data," "more clinical data," and "more linked government data." The top influence to use a particular biobank was cost, and the most frequently suggested improvement was reduced direct "cost of obtaining biospecimens." Conclusion: Biobanks that do not meet the needs of their end-users are unlikely to be optimally utilized or sustainable. This survey provides valuable insights to guide biobanks and other stakeholders, such as developing marketing and client engagement plans to encourage local research users and discouraging the creation of unnecessary new collections.