Identification of common pipeline for biomarker discovery

As discussed in the previous article the biomarkers are any substance of biological origin, which is used as an indicator for certain medical condition within the biological system. Biomarker discovery comprises of a process of discovering biomarkers and is a multistep process that leads to clinical applications.

Introduction to biomarker discovery

Biomarkers-based studies have always been on:

  1. Hypothesis
  2. Discovery

However, biomarker identification based on hypothesis approach uses mechanistic realization of the disease processes. In contrast, discovery based methods have emphasized on recognizing changes in the relative abundance or presence of molecular species (McDermott et al., 2013).

Need and importance of biomarker discovery

Ever since the inception of biomarkers, various facets of medical practice largely rely upon it. With developments in the field of medical sciences, especially with the arrival of molecular biology and the discovery of central dogma, researchers look for  distinctive molecular markers that are associated with the disease processes. In order to identify the diseases in early stages, asserting prognosis, observing the response to a specific therapy or choosing treatments probable to be effective (Catchpole, 2013).

Changes in levels of biomarkers predict the clinical outcomes immediately that otherwise would take longer, sometimes months or years to detect conservatively in the clinic. Biomarker observation can be crucial in controlling the treatment course by providing interim signs of disease progression and outcomes of on-going therapy. However, in clinical trials it helps to state the abundance of the biomarker to substitute a customary clinical endpoint, often with greater accuracy and more qualitatively (McDermott et al., 2013).

Regardless of the importance of biomarkers, the discovery of biomarker has progressed reasonably slow. Due to heterogeneity among individuals and the complex biological pathways, a single biomarker cannot be a trustworthy diagnostic tool. Therefore, more discoveries and findings are required in this field (Catchpole, 2013).

Classification and description of biomarker discovery technologies

Biomarker discovery technologies are categorized based on technology. For every type of biomarker discovery technology, the methodology and the process is different.

Classification of Biomarkers technology (Catchpole, 2013)

Classification of Biomarkers technology (Catchpole, 2013)

Genomic technology

The genomic technology involves the use of human genome sequence (Catchpole, 2013). The human genome sequencing used in molecular diagnostics in the discovery of biomarkers. Genome-wide methods include the following techniques:

  • Microarrays involves whole-genome expression array,
  • Serial analysis of gene expression (SAGE),
  • Expressed sequence tags analysis (EST),
  • Polymerase chain reaction (PCR),
  • Monitoring in vivo gene expression etc.

Epigenomic Technologies

Epigenetics is the change in the gene expression without a change in the nucleotide sequence, the epigenomics too has arisen in the essence of HGP (Catchpole, 2013). Therefore, the HEP (Human Epigenome Project) map the sites of DNA methylation all over the human genome, sorting more information about the human epigenome can provide proofs and evidence in cancer and other diseases that what goes wrong. Through epigenomics technique the discovery of biomarker is accomplished.

Proteomic Technologies

Recent development in the technologies has opened up the way for the analysis of protein for diagnostic purposes. However, new analytical tools allow the concurrent analysis of a huge number of proteins in biological samples such as plasma and serum (McDermott et al., 2013). Furthermore, several proteomics approaches have been used to identify novel biomarkers, such as:

  • 2-D gel,
  • ICAT,
  • mass spectrometry,
  • MALDI MS,
  • liquid chromatography-MS,
  • protein tomography,
  • microarrays,
  • SELDI-TOF,
  • an antibody-based biomarker.

Metabolomics Technology

Metabolomics is used in the biomarker discoveries due to its significance (Zhang, 2015). Therefore, metabolic pathways in diseased condition pathways provide information to the pathological understanding of diseases and hence used as a marker in recognition of diseases.

Lipidomics

Lipidomics is the study of lipids involves the use of multi-dimensional lipid analysis tool. Furthermore, they provide investigators with a prospect to quantify lipids on an unparalleled scale (Jain, 2010). In addition, the advancement in this field augmented by the introduction of systems biology and progresses in the related areas helps in biomarkers discovery.

Others

However, other technologies that used in order to discover the biomarkers include the molecular imaging technologies, bioinformatics, NMR, nanobiotechnology etc (Jain, 2010).

Pipeline for biomarker discovery

Various stages help to discover new biomarkers. A biomarker can identify using numerous technologies, and the basic idea remains the same. The initial stage in the development of biomarker determines suitable contenders. Two common methods are used. The first approach employs a rational reasoning to recognize contenders based on a pre-existing understanding of the disease pathophysiology. The other approach is “unbiased”, using molecular techniques or proteomics. In addition, helps to identify putative candidate biomarkers based on their distinctive expression among diseased and normal states.

Proteomic technologies used to compare the expression of protein between samples of tissues acquired from diseased and normal subjects by employing mass spectrometer. In addition, it can result in a huge collection of putative proteins biomarkers without any contamination.  They arise due to confounding effects of various pathologic conditions. Furthermore, this approach analyses numbers of analytes in rather a small number of samples, hence several “candidate biomarkers” may just signify “false discoveries”. In these circumstances the difference in the abundance of protein likely to reflect inter-individual disparity and is not because of the underlying process of disease (Gerszten, Asnani, and Carr, 2011; Frangogiannis, 2012).

Flow chart for the biomarker development pipeline

Flowchart for the biomarker development pipeline

Common framework to simplify biomarker research

Biomarkers are substances of biological origin. Since the inception of biomarker has taken place, it becomes an integral part of the diagnosis of many diseases because it can immediately give the result. Discovery of biomarkers is accomplished by using several techniques on the basis of the target of discovery and the application required. However, a common pipeline is maintained for the identification and segmentation of biomarker research. Every type of technology for biomarker discovered undergoes 4 different stages and every technology and manual clinical methods follow the same pipeline. Common pipeline thereby provides a common framework for all researchers and specialists to study and research on the same platform.

References

  • Alavi, A. and Axford, J. S. (2008) ‘Glyco-biomarkers: potential determinants of cellular physiology and pathology.’, Disease markers, 25(4–5), pp. 193–205. Available at: http://www.ncbi.nlm.nih.gov/pubmed/19126964.
  • Catchpole, N. S. and K. (2013) Biomarker discovery: the need for new generation peptideprotein microarrays. Available at: http://www.ddw-online.com/personalised-medicine/p216810-biomaker-discovery:-the-need-for-new-generation-peptideprotein-microarrays.html (Accessed: 22 March 2018).
  • Frangogiannis, N. G. (2012) ‘Biomarkers: Hopes and challenges in the path from discovery to clinical practice’, Translational Research, pp. 197–204. doi: 10.1016/j.trsl.2012.01.023.
  • Gerszten, R. E., Asnani, A. and Carr, S. A. (2011) ‘Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics’, Circulation Research, pp. 463–474. doi: 10.1161/CIRCRESAHA.110.225003.
  • Jain, K. K. (2010) The handbook of biomarkers, The Handbook of Biomarkers. doi: 10.1007/978-1-60761-685-6.
  • McDermott, J. E. et al. (2013) ‘Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data.’, Expert opinion on medical diagnostics, 7(1), pp. 37–51. doi: 10.1517/17530059.2012.718329.
  • Zhang A, Sun H, Yan G, Wang P, and Wang X, “Metabolomics for Biomarker Discovery: Moving to the Clinic,” BioMed Research International, vol. 2015, Article ID 354671, 6 pages, 2015. doi:10.1155/2015/354671.
Mohammad Shadab Ali

Mohammad Shadab Ali

Research Analyst at Project Guru
Shadab has envisioned an intellectually stimulating and professionally satisfying research career for himself. He has completed his masters (M.Sc.) in Biosciences from Jamia Millia Islamia University, He did his bachelors in microbiology from University of Delhi. His interest and field of study has always been in virology. He had the privilege of working in the field of virology for Jamia millia islamia during M.Sc. dissertation. He has worked for acadecraft pvt. Ltd. as a Subject Matter Expert in biology, and has experience in teaching as well. His hobbies are playing and watching soccer and cricket, he also played cricket at the district level.
Mohammad Shadab Ali

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