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Li S, Looby N, Chandran V, Kulasingam V. Challenges in the Metabolomics-Based Biomarker Validation Pipeline. Metabolites 2024; 14:200. [PMID: 38668328 PMCID: PMC11051909 DOI: 10.3390/metabo14040200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/28/2024] Open
Abstract
As end-products of the intersection between the genome and environmental influences, metabolites represent a promising approach to the discovery of novel biomarkers for diseases. However, many potential biomarker candidates identified by metabolomics studies fail to progress beyond analytical validation for routine implementation in clinics. Awareness of the challenges present can facilitate the development and advancement of innovative strategies that allow improved and more efficient applications of metabolite-based markers in clinical settings. This minireview provides a comprehensive summary of the pre-analytical factors, required analytical validation studies, and kit development challenges that must be resolved before the successful translation of novel metabolite biomarkers originating from research. We discuss the necessity for strict protocols for sample collection, storage, and the regulatory requirements to be fulfilled for a bioanalytical method to be considered as analytically validated. We focus especially on the blood as a biological matrix and liquid chromatography coupled with tandem mass spectrometry as the analytical platform for biomarker validation. Furthermore, we examine the challenges of developing a commercially viable metabolomics kit for distribution. To bridge the gap between the research lab and clinical implementation and utility of relevant metabolites, the understanding of the translational challenges for a biomarker panel is crucial for more efficient development of metabolomics-based precision medicine.
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Affiliation(s)
- Shenghan Li
- Division of Rheumatology, Psoriatic Arthritis Program, Schroeder Arthritis Program, University Health Network, Toronto, ON M5T 0S8, Canada; (S.L.); (N.L.)
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A1, Canada
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Nikita Looby
- Division of Rheumatology, Psoriatic Arthritis Program, Schroeder Arthritis Program, University Health Network, Toronto, ON M5T 0S8, Canada; (S.L.); (N.L.)
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Division of Orthopaedic Surgery, Osteoarthritis Research Program, Schroeder Arthritis Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Vinod Chandran
- Division of Rheumatology, Psoriatic Arthritis Program, Schroeder Arthritis Program, University Health Network, Toronto, ON M5T 0S8, Canada; (S.L.); (N.L.)
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A1, Canada
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Division of Rheumatology, Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Vathany Kulasingam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A1, Canada
- Division of Clinical Biochemistry, Laboratory Medicine Program, University Health Network, Toronto, ON M5G 2C4, Canada
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Xiong W, Anthony DC, Anthony S, Ho TBT, Louis E, Satsangi J, Radford-Smith DE. Sodium fluoride preserves blood metabolite integrity for biomarker discovery in large-scale, multi-site metabolomics investigations. Analyst 2024; 149:1238-1249. [PMID: 38224241 DOI: 10.1039/d3an01359f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Background: Metabolite profiling of blood by nuclear magnetic resonance (NMR) is invaluable to clinical biomarker discovery. To ensure robustness, biomarkers require validation in large cohorts and across multiple centres. However, collection procedures are known to impact on the stability of biofluids that may, in turn, degrade biomarker signals. We trialled three blood collection tubes with the aim of solving technical challenges due to preanalytical variation in blood metabolite levels that are common in cohort studies. Methods: We first investigated global NMR-based metabolite variability between biobanks, including the large-scale UK Biobank and TwinsUK biobank of the general UK population, and more targeted biobanks derived from multicentre clinical trials relating to inflammatory bowel disease. We then compared the blood metabolome of 12 healthy adult volunteers when collected into either sodium fluoride/potassium oxalate, lithium heparin, or serum blood tubes using different pre-processing parameters. Results: Preanalytical variation in the method of blood collection strongly influences metabolite composition within and between biobanks. This variability can largely be attributed to glucose and lactate. In the healthy control cohort, the fluoride oxalate collection tube prevented fluctuation in glucose and lactate levels for 24 hours at either 4 °C or room temperature (20 °C). Conclusions: Blood collection into a fluoride oxalate collection tube appears to preserve the blood metabolome with delayed processing up to 24 hours at 4 °C. This method may be considered as an alternative when rapid processing is not feasible.
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Affiliation(s)
- Wenzheng Xiong
- Department of Chemistry, University of Oxford, Oxford, UK.
- Department of Pharmacology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Daniel C Anthony
- Department of Pharmacology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Suzie Anthony
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Thi Bao Tien Ho
- Department of Pharmacology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Edouard Louis
- Department of Gastroenterology, University Hospital CHU of Liège, Liège, Belgium
| | - Jack Satsangi
- Translational Gastroenterology Unit, Nuffield Department of Experimental Medicine, University of Oxford, Oxford, UK
| | - Daniel E Radford-Smith
- Department of Chemistry, University of Oxford, Oxford, UK.
- Department of Pharmacology, Medical Sciences Division, University of Oxford, Oxford, UK
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Playdon MC, Tinker LF, Prentice RL, Loftfield E, Hayden KM, Van Horn L, Sampson JN, Stolzenberg-Solomon R, Lampe JW, Neuhouser ML, Moore SC. Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake. Am J Clin Nutr 2024; 119:511-526. [PMID: 38212160 PMCID: PMC10884612 DOI: 10.1016/j.ajcnut.2023.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/12/2023] [Accepted: 10/11/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Metabolomics has the potential to enhance dietary assessment by revealing objective measures of many aspects of human food intake. Although metabolomics studies indicate that hundreds of metabolites are associated with dietary intake, correlations have been modest (e.g., r < 0.50), and few have been evaluated in controlled feeding studies. OBJECTIVES The aim of this study was to evaluate associations between metabolites and weighed food and beverage intake in a controlled feeding study of habitual diet. METHODS Healthy postmenopausal females from the Women's Health Initiative (N = 153) were provided with a customized 2-wk controlled diet designed to emulate their usual diet. Metabolites were measured by liquid chromatography tandem mass spectrometry in end-of-study 24-h urine and fasting serum samples (1293 urine metabolites; 1113 serum metabolites). We calculated partial Pearson correlations between these metabolites and intake of 65 food groups, beverages, and supplements during the feeding study. The threshold for significance was Bonferroni-adjusted to account for multiple testing (5.94 × 10-07 for urine metabolites; 6.91 × 10-07 for serum metabolites). RESULTS Significant diet-metabolite correlations were identified for 23 distinct foods, beverages, and supplements (171 distinct metabolites). Among foods, strong metabolite correlations (r ≥ 0.60) were evident for citrus (highest r = 0.80), dairy (r = 0.65), and broccoli (r = 0.63). Among beverages and supplements, strong correlations were evident for coffee (r = 0.86), alcohol (r = 0.69), multivitamins (r = 0.69), and vitamin E supplements (r = 0.65). Moderate correlations (r = 0.50-0.60) were also observed for avocado, fish, garlic, grains, onion, poultry, and black tea. Correlations were specific; each metabolite correlated with one food, beverage, or supplement, except for metabolites correlated with juice or multivitamins. CONCLUSIONS Metabolite levels had moderate to strong correlations with weighed intake of habitually consumed foods, beverages, and supplements. These findings exceed in magnitude those previously observed in population studies and exemplify the strong potential of metabolomics to contribute to nutrition research. The Women's Health Initiative is registered at clinicaltrials.gov as NCT00000611.
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Affiliation(s)
- Mary C Playdon
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT; Department of Population Health Sciences, University of Utah, Salt Lake City, UT; Cancer Control and Population Sciences Division, Huntsman Cancer Institute, Salt Lake City, UT; Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | - Lesley F Tinker
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Ross L Prentice
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Erikka Loftfield
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | - Kathleen M Hayden
- School of Medicine, Wake Forest Baptist Medical Center, Winston-Salem, NC
| | - Linda Van Horn
- Feinberg School of Medicine, Northwestern University, Chicago IL
| | - Joshua N Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | | | - Johanna W Lampe
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Marian L Neuhouser
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD.
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His M, Gunter MJ, Keski-Rahkonen P, Rinaldi S. Application of Metabolomics to Epidemiologic Studies of Breast Cancer: New Perspectives for Etiology and Prevention. J Clin Oncol 2024; 42:103-115. [PMID: 37944067 DOI: 10.1200/jco.22.02754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 07/24/2023] [Accepted: 09/11/2023] [Indexed: 11/12/2023] Open
Abstract
PURPOSE To provide an overview on how the application of metabolomics (high-throughput characterization of metabolites from cells, organs, tissues, or biofluids) to population-based studies may inform our understanding of breast cancer etiology. METHODS We evaluated studies that applied metabolomic analyses to prediagnostic blood samples from prospective epidemiologic studies to identify circulating metabolites associated with breast cancer risk, overall and by breast cancer subtype and menopausal status. We provide some important considerations for the application and interpretation of metabolomics approaches in this context. RESULTS Overall, specific lipids and amino acids were indicated as the most common metabolite classes associated with breast cancer development. However, comparison of results across studies is challenging because of heterogeneity in laboratory techniques, analytical methods, sample size, and applied statistical methods. CONCLUSION Metabolomics is being increasingly applied to population-based studies for the identification of new etiologic hypotheses and/or mechanisms related to breast cancer development. Despite its success in applications to epidemiology, studies of larger sample size with detailed information on menopausal status, breast cancer subtypes, and repeated biologic samples collected over time are needed to improve comparison of results between studies and enhance validation of results, allowing potential clinical translation of findings.
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Affiliation(s)
- Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
- Prevention Cancer Environment Department, Centre Léon Bérard, Lyon, France
- Inserm, U1296 Unit, "Radiation: Defense, Health and Environment", Centre Léon Bérard, Lyon, France
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
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Michels KA, Weinstein SJ, Albert PS, Black A, Brotzman M, Diaz-Mayoral NA, Gerlanc N, Huang WY, Sampson JN, Shreves A, Ueland PM, Wyatt K, Wentzensen N, Abnet CC. The Influence of Preanalytical Biospecimen Handling on the Measurement of B Vitamers, Amino Acids, and Other Metabolites in Blood. Biopreserv Biobank 2023; 21:467-476. [PMID: 36622937 PMCID: PMC10616936 DOI: 10.1089/bio.2022.0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Introduction: Sample handling can influence biomarker measurement and introduce variability when combining data from multiple studies or study sites. To inform the development of blood collection protocols within a multisite cohort study, we directly quantified concentrations of 54 biomarkers in blood samples subjected to different handling conditions. Materials and Methods: We obtained serum, lithium heparin plasma, and EDTA plasma from 20 adult volunteers. Tubes of chilled whole blood were either centrifuged and processed within 2 hours of collection (the "reference standard") or were stored with cool packs for 24 or 48 hours; centrifuged before and/or after this delay; or collected in tubes with/without gel separators. We used linear mixed models with random intercepts to estimate geometric mean concentrations and relative percent differences across the conditions. Results: Compared to the reference standard tubes, concentrations of many biomarkers changed after processing delays, but changes were often small. In serum, we observed large differences for B vitamers, glutamic acid (37% and 73% increases with 24- and 48-hour delays, respectively), glycine (12% and 23% increases), serine (16% and 27% increases), and acetoacetate (-19% and -26% decreases). Centrifugation timing and separator tube use did not affect concentrations of most biomarkers. Conclusion: Sample handling should be consistent across samples within an analysis. The length of processing delays should be recorded and accounted for when this is not feasible.
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Affiliation(s)
- Kara A. Michels
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Stephanie J. Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Paul S. Albert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Amanda Black
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Michelle Brotzman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Norma A. Diaz-Mayoral
- BioProcessing Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | - Nicole Gerlanc
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Joshua N. Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Alaina Shreves
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Kathleen Wyatt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Christian C. Abnet
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
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McClain KM, Sampson JN, Petrick JL, Mazzilli KM, Gerszten RE, Clish CB, Purdue MP, Lipworth L, Moore SC. Metabolomic Analysis of Renal Cell Carcinoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Metabolites 2022; 12:metabo12121189. [PMID: 36557227 PMCID: PMC9785244 DOI: 10.3390/metabo12121189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 12/02/2022] Open
Abstract
Background: In the US in 2021, 76,080 kidney cancers are expected and >80% are renal cell carcinomas (RCCs). Along with excess fat, metabolic dysfunction is implicated in RCC etiology. To identify RCC-associated metabolites, we conducted a 1:1 matched case−control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Methods: We measured 522 serum metabolites in 267 cases/control pairs. Cases were followed for a median 7.1 years from blood draw to diagnosis. Using conditional logistic regression, we computed adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing risk between 90th and 10th percentiles of log metabolite intensity, with the significance threshold at a false discovery rate <0.20. Results: Four metabolites were inversely associated with risk of RCC during follow-up—C38:4 PI, C34:0 PC, C14:0 SM, and C16:1 SM (ORs ranging from 0.33−0.44). Two were positively associated with RCC risk—C3-DC-CH3 carnitine and C5 carnitine (ORs = 2.84 and 2.83, respectively). These results were robust when further adjusted for metabolic risk factors (body mass index (BMI), physical activity, diabetes/hypertension history). Metabolites associated with RCC had weak correlations (|r| < 0.2) with risk factors of BMI, physical activity, smoking, alcohol, and diabetes/hypertension history. In mutually adjusted models, three metabolites (C38:4 PI, C14:0 SM, and C3-DC-CH3 carnitine) were independently associated with RCC risk. Conclusions: Serum concentrations of six metabolites were associated with RCC risk, and three of these had independent associations from the mutually adjusted model. These metabolites may point toward new biological pathways of relevance to this malignancy.
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Affiliation(s)
- Kathleen M. McClain
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
- Correspondence: ; Tel.: +240-276-6317
| | - Joshua N. Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | | | - Kaitlyn M. Mazzilli
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Clary B. Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mark P. Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Loren Lipworth
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Steven C. Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
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7
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Mouttham L, Castelhano MG, Akey JM, Benton B, Borenstein E, Castelhano MG, Coleman AE, Creevy KE, Crowder K, Dunbar MD, Ernst HR, Fajt VR, Fitzpatrick AL, Garrison SJ, Herndon RS, Jaramilla D, Jeffery U, Jonlin EC, Kaeberlein M, Karlsson EK, Kerr KF, Levine JM, Ma J, McClelland RL, Prescott JO, Promislow DEL, Ruple A, Schwartz SM, Shrager S, Snyder-Mackler N, Tinkle AK, Tolbert MK, Urfer SR, Wilfond BS. Purpose, Partnership, and Possibilities: The Implementation of the Dog Aging Project Biobank. Biomark Insights 2022; 17:11772719221137217. [PMID: 36468152 PMCID: PMC9716607 DOI: 10.1177/11772719221137217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/18/2022] [Indexed: 12/02/2022] Open
Abstract
Background: Biobanks have been supporting longitudinal prospective and retrospective studies by providing standardized services for the acquisition, transport, processing, storage, and distribution of high-quality biological material and associated data. Here, we describe how the Dog Aging Project (DAP), a large-scale longitudinal study of the domestic dog ( Canis familiaris) with translational applications for humans, developed a biobank of canine biospecimens and associated data. Design and methods: This was accomplished by working with the Cornell Veterinary Biobank, the first biobank in the world to receive accreditation to ISO 20387:2018—General Requirements for Biobanking. The biobank research team was involved in the early collection stages of the DAP, contributing to the development of appropriate workflows and processing fit-for-purpose biospecimens. In support of a dynamic strategy for real-time adjustment of processes, a pilot phase was implemented to develop, test, and optimize the biospecimen workflows, followed by an early phase of collection, processing, and banking of specimens from DAP participants. Results: During the pilot and early phases of collection, the DAP Biobank stored 164 aliquots of whole blood, 273 aliquots of peripheral blood mononuclear cells, 130 aliquots of plasma, and 70 aliquots of serum, and extracted high molecular weight genomic DNA suitable for whole-genome sequencing from 109 whole blood specimens. These specimens, along with their associated preanalytical data, have been made available for distribution to researchers. Conclusion: We discuss the challenges and opportunities encountered during the implementation of the DAP Biobank, along with novel strategies for promoting biobanking sustainability such as partnering with a DAP quality assurance manager and a DAP marketing and communication specialist and developing a pilot grant structure to fund small innovative research projects.
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Affiliation(s)
- Lara Mouttham
- Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Marta G Castelhano
- Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Joshua M Akey
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Brooke Benton
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Elhanan Borenstein
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Santa Fe Institute, Santa Fe, NM, USA
| | - Marta G Castelhano
- Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Amanda E Coleman
- Department of Small Animal Medicine and Surgery, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Kate E Creevy
- Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA
| | - Kyle Crowder
- Department of Sociology, University of Washington, Seattle, WA, USA
- Center for Studies in Demography and Ecology, University of Washington, Seattle, WA, USA
| | - Matthew D Dunbar
- Center for Studies in Demography and Ecology, University of Washington, Seattle, WA, USA
| | - Holley R Ernst
- Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA
| | - Virginia R Fajt
- Department of Small Animal Medicine and Surgery, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Annette L Fitzpatrick
- Department of Family Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Susan J Garrison
- Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Reba S Herndon
- Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Debra Jaramilla
- Cornell Veterinary Biobank, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Unity Jeffery
- Department of Veterinary Pathobiology, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA
| | - Erica C Jonlin
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jonathan M Levine
- Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA
| | - Jing Ma
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Jena O Prescott
- Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA
| | - Daniel EL Promislow
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Biology, University of Washington, Seattle, WA, USA
| | - Audrey Ruple
- Department of Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA
| | - Stephen M Schwartz
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Epidemiology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Sandi Shrager
- Collaborative Health Studies Coordinating Center, Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Noah Snyder-Mackler
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
- School for Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA
| | - Amanda K Tinkle
- Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA
| | - M Katherine Tolbert
- Department of Small Animal Clinical Sciences, Texas A&M University College of Veterinary Medicine & Biomedical Sciences, College Station, TX, USA
| | - Silvan R Urfer
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Benjamin S Wilfond
- Treuman Katz Center for Pediatric Bioethics, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, Division of Bioethics and Palliative Care, University of Washington School of Medicine, Seattle, WA, USA
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