Technologies and resources used in biomarker discovery and development

Biomarker development has served as an indicator of disease progression and response to therapeutic intervention. The conventional approach involved the study of molecules (considered as biomarker) in regulatory pathways but with the advent of technologies and resources, the development and biomarker discovery gotten easier (Tong & Li, 2016).

Importance of technologies and resources in biomarker discovery

The first step of biomarker development pipeline is to discover the potential candidate. However, several methods can be used to identify the potential biomarker candidate. Furthermore, over the years there has been rapid growth in technologies that allow understanding the cellular processes. These include mRNAs (transcriptome), proteins (proteome), sequence and structural variations (genomics), metabolites (metabolomics) and interactions (interactome) (Tong & Li, 2016). Furthermore, on the basis of utility, different types of biomarkers exist.

Biomarker types based on their utility (Tong & Li, 2016)

Biomarker types based on their utility (Tong & Li, 2016)

However, techniques and technologies like DNA microarrays and protein microarrays allow identification of chromosomal alterations, chromosomal copy, identification of proteins and characteristic metastatic cancer present in serum or plasma. Moreover, these technologies are used in a minimum amount, where the sample is sufficient for the assays and sample can be collected using non-invasive techniques (González-González et al., 2013).

Common technologies used in biomarker discovery

Over the last decade, there has been a dramatic evolution in technologies that remove the barrier of time consumption. With recent development, high throughput technologies enable non-invasive sample collection, simultaneous measurement, thousands of molecules. These technologies include; genomic technology, proteomic and epigenomic technology.

Genomic technology

The major milestone of genomic technologies is Human Genome Project (HGP), during human genome sequencing many technologies were developed which are applicable to molecular diagnostics which led to biomarker discovery (Jain, 2010). Genomic technologies focus on an analysis of gene that could be involved in the therapeutic response. These technologies also analyze gene expression which is important to tell if the patient is at risk of a disease. Gene expression analysis helps researchers to survey and quantify gene activity in disease population and disease stages (González-González et al., 2013).

Proteomic technology

Proteomic technologies provide complete assessment protein content in a cell or tissue, determining protein’s nature and quantity. Protein profiling plays an important role in understanding the phenotype of an organism (Wu et al., 2010). The goal of proteomics is to identify protein changes linked with the disease development such as cancer.

The discovery of such proteins can be used as biomarkers for diagnosis, prognosis, treatment and therapeutic monitoring. Furthermore, the proteomic approach is also useful for determining the serum markers in blood sample, which might be helpful in diagnosing recurrence and prognosis of the disease. The development in protein technologies such as capillary electrophoresis, high-performance liquid chromatography (HPLC), matrix-assisted laser desorption/ionization (MALDI) and mass spectrometry has also advanced the proteomic studies which have now become an important tool for clinical trials (Wright, 2012).

Epigenomic technology

Epigenomic technology is recent and promising technology in biomarker discovery. The study of heritable changes in gene expression that are not because of alteration in the primary DNA sequence. They are important for gene transcription, development and differentiation of cells is known as epigenetics (Sandoval, Peiró-Chova, Pallardó, & García-Giménez, 2013). Genetic basis of molecular mechanism responsible for disease progression and treatment resistance have notably revealed by advances in DNA and RNA sequencing (Valdés-Mora & Clark, 2015). Consequently, epigenomic technologies have also majorly contributed to biomarker research. Three main mechanisms are involved in epigenetic regulations:

  1. DNA methylation,
  2. miRNA expression and lastly
  3. histone post-translational modifications (PTMs).

Classification

A classification of methods is given in table for all three biomarker approaches (table).

Technology Methods
Genomic (Jain, 2010)
  1. Genome-wide methods Microarrays
  2. whole-genome expression array
  3. Serial analysis of gene expression (SAGE) Expressed sequence tags (ESTs) analysis
  4. Gene expression profiling based on alternative RNA splicing
  5. Tangerine expression profiling
  6. Individual sequences
  7. Real-time RT-PCR
  8. Competitive RT-PCR
  9. RNase protection assay
  10. Magnetic resonance imaging (MRI)
Proteomic (Wu et al., 2010)
  1. 2-D gel electrophoresis
  2. Liquid chromatography or capillary electrophoresis based separation of protein/digested peptides followed by mass spectrometry (MS)
  3. Matrix assisted laser desorption/ionization- MS (MALDI-MS)
  4. Immunoassay based microarray technologies (immunohistochemistry)
Epigenomic (Sandoval et al., 2013)
  1. Bisulfite genomic sequencing
  2. Pyrosequencing
  3. Methyl specific Polymerase chain reaction
  4. MALDI- time of flight

Resources useful for biomarker discovery

Reliable biomarker databases and integration of information expands the research area on etiology, disease risk prediction and prevention of environmental disease (Jain, 2010). A biomarker database allows accurate and convenient comparison of all of the existing related studies which might be extremely helpful in pharmacological research and development. Database like Genomics of Drug Sensitivity in Cancer (GDSC) provides information on drug sensitivity in cancer cells and molecular markers of drug response. In addition, GDSC currently contains data for almost 75000 experiments for drug sensitivity, describing response to 138 anticancer drugs across ~700 cancer cell lines.

Urinary Protein Biomarker Database allows researchers to do comparative study and to discover new relationships between diseases and proteins by reanalyzing data (Shao, 2015). Biobanking also helps in expanding biomarker research and development. Biobanking includes a wide range of specimen types and sample collection designs, which ranges from population-based biobanking of specimens from healthy subjects to specific biobanking of diseased tissues obtained in the course of therapeutic intervention. Thus, tissue repositories play a crucial role in the biomarker development and drug target discovery through the procurement of annotated specimens to innovative research programs.

Thus, there has been a great improvement in “-omics” technologies in the past 10 years. DNA microarrays and protein microarrays are advance techniques that reveal metastatic characteristics and chromosomal alteration/copy. In addition, the advancement of high-throughput technologies enables the identification of specific biomarkers in lesser time and with non-invasive techniques that could have a great impact on prevention, diagnostics, prognosis, and treatment of many human diseases.

References

  • González-González, M., Garcia, J. G., Montero, J. A. A., Fernandez, L. M. G., Bengoechea, O., Muñez, O. B., … Fuentes, M. (2013). Genomics and proteomics approaches for biomarker discovery in sporadic colorectal cancer with metastasis. Cancer Genomics & Proteomics, 10(1), 19–25. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/23382583.
  • Jain, K. K. (2010). The handbook of biomarkers. The Handbook of Biomarkers. http://doi.org/10.1007/978-1-60761-685-6.
  • Sandoval, J., Peiró-Chova, L., Pallardó, F. V, & García-Giménez, J. L. (2013). Epigenetic biomarkers in laboratory diagnostics: emerging approaches and opportunities. Expert Review of Molecular Diagnostics, 13(5), 457–471. http://doi.org/10.1586/erm.13.37.
  • Shao, C. (2015). Urinary Protein Biomarker Database: A Useful Tool for Biomarker Discovery. In Advances in experimental medicine and biology (Vol. 845, pp. 195–203). http://doi.org/10.1007/978-94-017-9523-4_19.
  • Tong, P., & Li, H. (2016). Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms. In Big Data Analytics in Genomics (pp. 337–355). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-41279-5_10.
  • Valdés-Mora, F., & Clark, S. J. (2015). Prostate cancer epigenetic biomarkers: next-generation technologies. Oncogene, 34(13), 1609–1618. http://doi.org/10.1038/onc.2014.111.
  • Wright, P. (2012). A review of current proteomics technologies with a survey on their widespread use in reproductive biology investigations. Theriogenology, 77(4), 738–765.
  • Wu, J.-Y., Yi, C., Chung, H.-R., Wang, D.-J., Chang, W.-C., Lee, S.-Y., … Yang, W.-C. V. (2010). Potential biomarkers in saliva for oral squamous cell carcinoma. Oral Oncology, 46(4), 226–231. http://doi.org/10.1016/j.oraloncology.2010.01.007.
Diksha Tomer

Diksha Tomer

Research Analyst at Project Guru
She is a bachelor in Botany honours from Miranda House (Delhi University) and is pursuing her master’s in Plant Biotechnology at TERI SAS. She is a recipient of Hemendra Kothari Scholarship and her interests lie in the field of Bioinformatics and Biotechnology.
Diksha Tomer

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