Application of MALDI/SELDI Mass Spectrometry to Cancer Biomarker Discovery and Validation
Farid E. Ahmed.
This article provides a focused perspective on the application of MALDI/SELDI mass spectrometry (MS)- based technologies to biomarker discovery and validation in various biological materials, which might eventually serve as the basis for new clinical tests to improve diagnosis, guide molecularly-targeted therapy, and monitor the activity and therapeutic response to various cancers. However, the contribution of MALDI/SELDI MS to these goals has been scant due partially to current unavailability of robust analytical methods and instruments to overcome the finite complexity of the proteome and peptidome. Moreover, with a coherent pipeline, the successful throughput from marker discovery to robust validation methods has been dismal. While the development of a faster and more sensitive MS methods will have a major impact on the ability to identify thousands of proteins in body fluids, improvement in sample preparation methods such as high abundance protein depletion and chromatography, and better sample processing methods have also played key roles. Further optimization of MS-based methods is expected to further improve their analytical performance. Because of the guarded skepticism voiced by several investigators about the ultimate value of the entire MS-based proteomics approach for biomarker discovery in complicated samples such as blood, exhaustive experimental methods that use multiple reaction monitoring targeted proteomic approaches, which include immunoassays coupled with MS spectrometry techniques need to be developed and standardized, initially for less complex body fluids, tissues, or even cells or tissue explants in animal models and later on translated to blood, in order to avoid the pitfalls of bias and/or overfitting that plagued earlier studies, and it is still hoped that eventually proteomic profiling in complex biological samples will become a fulfilled promise rather than just an unrealized hope for the scientific and clinical community.
Keywords: Bioinformatics, blood, CSF, diagnoses, plasma, statistics, urine
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