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JSM Biomarkers

Biobank as an Important Tool for Biomarker Discovery and Validation

Editorial | Open Access | Volume 1 | Issue 1

  • 1. Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, USA
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Corresponding Authors
Angen Liu, Biospecimen Repository, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 417 North Caroline Street, Room 302, Baltimore, MD 21287, USA
Citation

Liu A (2014) Biobank as an Important Tool for Biomarker Discovery and Validation. JSM Biomar 1(1): 1001.

EDITORIAL

A biomarker is a measurable indicator, such as a protein, genetic alteration, or metabolite, that is found in tissue, blood, or other body fluids. Identification of biomarkers that are specifically associated with particular medical conditions such as cancer, cardiovascular disease, and neurological disorders is useful for early detection, prevention, and therapy of the diseases. The availability of biomarkers for disease diagnosis, prognosis, and prediction of response to therapy can advance personalized medicine, significantly improve clinical outcomes, and enhance patient care. The biomarker pipeline consists of a series of phases: biomarker discovery, verification, and clinical validation, which require the availability of high-quality and well-characterized patient samples [1, 2]. Therefore, it is critical to have a large collection of patient samples with patient clinical, pathologic, and outcome data for biomarker discovery and validation.

A biobank is an entity that collects, processes, stores, and distributes biospecimens and associated patient information for use in future research. The principle of biospecimen collection and storage is relatively straightforward, but the practicalities of biobanking are quite complicated and require significant collaborative efforts. Expertise in standardization, quality control, clinical and pathological practices, and information technology are generally required for biobank operation and management. Biospecimens include blood, tissue, bone marrow, urine and other body fluids, etc. Blood is one of the most easily accessible biospecimens and has been the most used biomarker discovery matrix to date. Biomarker investigators have discovered and assessed biomarkers in the blood for a variety of diseases, including cancer and non-cancer diseases such as Alzheimer’s disease and cardiovascular disease [3–5]. Collected blood samples should be divided into fractions such as plasma, serum, white blood cells, and red blood cells and stored separately to maximize their value in biomarker studies. Plasma and buffy coat are collected in a tube with an anticoagulant such as ethylenediamine tetra-acetate (EDTA), citrate, or heparin. EDTA-coated collection tubes are suitable for a wide range of DNA and protein-based arrays [6], while plasma from heparin-stimulated blood is often used for metabolomics studies [6, 7]. Citrate produces a higher yield of lymphocytes; therefore, citrate-dextrose-coated tubes are used for harvest of peripheral

blood lymphocytes [6, 8]. Serum is collected in a tube that contains clot accelerators such as silica and thrombin and is useful for certain assays in clinical biochemistry and metabolomics studies [6]. Serum samples are generally collected in 30–60 minutes at room temperature for a clot to form; longer than 60 minutes are likely to cause the lysis of cells in the clot and the release of cellular components in serum [9]. Buffy coat can be used as a long-term biobank specimen for DNA and RNA in lieu of immediately isolating those biomolecules at the time of blood collection. Buffy coat is one of the best DNA sources for methylation assays in cancer risk biomarker studies [10]. Urine is another easily obtained biospecimen and has been widely used for biomarker research [11]. Urine can be aliquoted and frozen intact or enrich the cellular content by centrifuging the urine and storing the cell pellet separately from the clear supernatant. Other body fluids, such as cerebrospinal fluid (CSF), have demonstrated a high value for biomarker studies in neurological diseases [12]. CSF must be processed within 30–60 minutes of collection and stored at -80? °C for biobanking.

In addition to body fluids, tissue is another key requirement for biomarker identification and validation. Fresh frozen tissue collected for biobanking should be aliquoted and stored at -80? °C to avoid freeze-thaw cycles. Because of the heterogeneous nature of solid tissue, subsequent morphological analysis by a pathologist is required to determine the percentage of tumor and normal adjacent tissue and the presence of inflammatory and necrotic cells. To reduce microheterogeneity within tissues, manual macrodissection or laser capture microdissection is often required to select specific cells to enrich the purity of cells of interest (e.g., tumor cells) prior to RNA, DNA, and protein extraction for downstream biomarker analysis [13]. In parallel, tissue microarray combined with immunohistochemistry or in situ hybridization offers a high-throughput screening tool for biomarker studies.

The most important aspect of a successful biobank for biomarker discovery and validation is the quality of biospecimens. Investigators studying low-quality biospecimens will likely generate erroneous and misleading data. Proper biospecimen handling is crucial for high-quality biobank development.

Biospecimens should be processed and banked, ideally within 30 minutes of collection, to reduce ischemic time. Patient

Information associated with the biospecimens should be centrally stored on a computer-based database with a secure method and should be backed up frequently. A section should be routinely generated from a tissue sample for quality control purposes. The morphological details of the tissue section need to be documented by a pathologist [14]. The quality of DNA, RNA, and proteins that are extracted from banked tissue samples can be assessed by the Agilent TapeStation system and spectrophotometry such as Nanodrop. DNA and RNA isolated from FFPE tissue samples often require PCR analysis to determine their quality [14]. DNA and RNA extracted from non-tissue biospecimens such as blood and blood fractions can be measured in the same way to determine the quality. The quality assurance of bodily fluids primarily involves selecting the parameters of collection, processing, and storage and receiving investigator feedback. It is important to process and freeze the bodily fluids as soon as possible within one hour of collection. The College of American Pathologists has recently set up detailed biorepository accreditation requirements to insure that quality assurance and quality control procedures are implemented in biobanks [15].

The infrastructure and accessibility of biobanks have a direct impact on biomarker research. Long-term institutional support and chargeback mechanisms should be in place in order to ensure the long-term sustainability of biobanks. Identification and validation of candidate biomarkers for disease diagnosis, prognosis, and prediction of response to therapy promise personalized medicine. Biobanks have been proven to be an invaluable scientific resource for the development of novel personalized medicines that significantly improve clinical outcomes and enhance patient care.

REFERENCES

 1. Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol. 2006; 24: 971-983. 

2. Parker CE, Borchers CH. Mass spectrometry based biomarker discovery, verification, and validation - Quality assurance and control of protein biomarker assays. Mol Oncol. 2014.

3. Reumer A, Maes E, Mertens I, Cho WC, Landuyt B, Valkenborg D, et al. Colorectal cancer biomarker discovery and validation using LC-MS/ MS-based proteomics in blood: truth or dare? Expert Rev Proteomics. 2014;.

4. Snyder HM, Carrillo MC, Grodstein F, Henriksen K, Jeromin A, Lovestone S, et al. Developing novel blood-based biomarkers for Alzheimer’s disease. Alzheimers Dement. 2014; 10: 109-114.

5. Yin X, Subramanian S, Hwang SJ, O’Donnell CJ, Fox CS, Courchesne P, et al. Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative. Arterioscler Thromb Vasc Biol. 2014; 34: 939-945.

6. Elliott P, Peakman TC, UK Biobank. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int J Epidemiol. 2008; 37: 234-244.

7. Shabihkhani M, Lucey GM, Wei B, Mareninov S, Lou JJ, Vinters HV, et al. The procurement, storage, and quality assurance of frozen blood and tissue biospecimens in pathology, biorepository, and biobank settings. Clin Biochem. 2014; 47: 258-266

8. Holland NT, Smith MT, Eskenazi B, Bastaki M. Biological sample collection and processing for molecular epidemiological studies. Mutat Res. 2003; 543: 217-234.

9. Timms JF, Arslan-Low E, Gentry-Maharaj A, Luo Z, T’Jampens D, Podust VN, et al. Preanalytic influence of sample handling on SELDITOF serum protein profiles. Clin Chem. 2007; 53: 645-656.

10.van Bemmel D, Lenz P, Liao LM, Baris D, Sternberg LR, Warner A, et al. Correlation of LINE-1 methylation levels in patient-matched buffy coat, serum, buccal cell, and bladder tumor tissue DNA samples. Cancer Epidemiol Biomarkers Prev. 2012; 21:1143-1148.

11.Rodríguez-Suárez E, Siwy J, Zürbig P, Mischak H. Urine as a source for clinical proteome analysis: from discovery to clinical application. Biochim Biophys Acta. 2014; 1844: 884-898.

12.Teunissen CE, Tumani H, Engelborghs S, Mollenhauer B. Biobanking of CSF: international standardization to optimize biomarker development. Clin Biochem. 2014; 47: 288-292.

13.Liu A. Laser capture microdissection in the tissue biorepository. J Biomol Tech. 2010; 21: 120-125.

14.Liu A. Developing an institutional cancer biorepository for personalized medicine. Clin Biochem. 2014; 47: 293-299.

15.College of American Pathologits. Biorepository Checklist CAP Accreditation Program. 2013

Liu, A. (2014) Biobank as an Important Tool for Biomarker Discovery and Validation. JSM Biomar 1(1): 1001.

Received : 07 Apr 2014
Accepted : 07 Jun 2014
Published : 09 Jun 2014
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