Advanced Proteomic Technologies for Cancer Biomarker Discovery

Sze Chuen Cesar Wong; Charles Ming Lok Chan; Brigette Buig Yue Ma; Money Yan Yee Lam; Gigi Ching Gee Choi; Thomas Chi Chuen Au; Andrew Sai Kit Chan; Anthony Tak Cheung Chan


Expert Rev Proteomics. 2009;6(2):123-134. 

In This Article

Abstract and Introduction


Proteomic technologies have experienced major improvements in recent years. Such advances have facilitated the discovery of potential tumor markers with improved sensitivities and specificities for the diagnosis, prognosis and treatment monitoring of cancer patients. This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about and examples of their applications in cancer biomarker discovery will be presented in this review, so that readers can appreciate the immense progress in proteomic technologies from 1997 to 2008. Finally, a summary will be presented that discusses current hurdles faced by proteomic researchers, such as the wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems will be provided.


Cancer is a leading cause of mortality worldwide. According to the WHO, it accounted for 7.9 million deaths (13% of all deaths) in 2007. It is estimated that approximately a third of all cancer cases could be prevented if detected and treated early enough.[83] A common method used for cancer diagnosis, prognosis and monitoring involves the use of serum or tissue protein biomarkers. Hence, a sensitive detection of a specific marker using a cost-effective assay has important clinical applications. Unfortunately, currently used tumor markers, such as carcinoembryonic antigen for colorectal cancer,[1] α-fetoprotein for liver cancer and prostate-specific antigen for prostate cancer, have low sensitivities and specificities.[2] Therefore, there is an urgent need for the development of novel tumor markers that would be helpful in improving cancer diagnosis, prognosis and treatment.

The rapid development of proteomic technologies during the past 10 years has brought about a massive increase in the discovery of novel cancer biomarkers. Such biomarkers may have broad applications, such as for the detection of the presence of a disease, monitoring of disease clearance and/or progression, monitoring of treatment response and demonstration of drug targeting of a particular pathway and/or target. In general, proteomic approaches begin with the collection of biological specimens representing two different physiological conditions. One of the samples is usually obtained from cancer patients and the other from reference subjects, such as normal individuals, patients with benign conditions or patients with precancerous disease. Proteins or peptides from plasma, tissue or cell line samples are extracted and separated to reduce sample complexity. The protein or peptide profiles after separation are then obtained and compared against each other in order to detect differentially expressed proteins. Commonly, quantitative proteomics is mainly performed by protein separation using either 2DE- or liquid chromatography (LC)-based methods coupled with protein identification using mass spectrometry (MS). However, such methods have limitations, such as low reproducibility, inability to obtain protein profiles directly from tissue sections for correlation with tissue morphology, limited ability to analyze post-translational modifications (PTMs) and low capacity for high-throughput validation of identified markers. In recent years, major progress in proteomic technologies has been achieved, which has led to the development of 2D DIGE, MALDI imaging MS (IMS), electron transfer dissociation (ETD) MS, and reverse-phase protein array (RPA). This review will focus on these latest proteomic technologies and explore their applications for cancer biomarker research.


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