Proteomics has been ushered in with the hope that the use of high throughput technologies to characterize complex biological matrices will lead to the discovery of a plethora of novel disease-specific biomarkers. Unfortunately, the lack of true success stories of biomarker discovery, despite the considerable intellectual and financial resources currently invested in the use of conventional proteomic technologies, gives one pause. It is probable that a vast majority of disease states do not manifest themselves in such a manner that a single recognizable change in a protein can be used to diagnose it with high accuracy. For example, both CA 125 for ovarian cancer, and PSA for prostate cancer, have low PPV over a large population. When the complexity of an individual cell and the aberrations caused by such disease states as cancer are considered, a vast number of differences between the protein character of healthy and diseased tissues would be expected. Obviously one of the main reasons that the discovery of disease-specific biomarkers has been so elusive is that for a diagnostic marker to be clinically relevant it should be assayed from a sample that can be relatively noninvasively obtained in sufficient quantity from patients, therefore, the search for biomarkers has largely focused on plasma and serum. While serum constantly perfuses tissues, hence potentially endowing an archive of disease relevant information, this information is comprised not only of the expected circulatory proteins in serum such as immunoglobulins but also of peptides and proteins that are secreted into the blood and species shed from diseased, dying, or dead cells present throughout the body [12]. This background matrix within biofluids, such as serum, represents a complex milieu in which, low-abundance, disease-specific biomarkers may be discovered. While the identification of a reliable biomarker relies on the comparison of samples from thousands of healthy and disease-stricken individuals, the comparison of even two distinct serum samples is incredibly laborious using conventional proteomic technologies. More to the point, in the comparison of just two serum samples, a multitude of changes in protein abundances are observed due simply to differences in age, gender, lifestyle, etc., making the assumption that a particular difference is a result of a specific disease state tenuous at best.

Disease diagnosis using proteomic patterns has emerged as a potentially revolutionary new method to use proteomic technology for the early detection of diseases such as cancer. A major criticism of the disease diagnosis using proteomic patterns is that the identity of the proteins or peptides giving rise to the diagnostic features within each spectrum is not known. It is debatable as to whether it is worth the effort to identify these features, since their identity may provide little novel information regarding the mechanism of cancer progression or aid in the development of an alternative diagnostic platform. Most of the key features within the proteomic patterns are of low m/z (i.e., <10,000 Da), therefore it is likely that these could be from fragment species generated from larger proteins that are proteolyzed either within the circulatory system or in the tumor/host microenvironment. It would be difficult to generate an affinity reagent with specificity to a peptide fragment without considerable cross-reactivity to its parent protein. In addition, there are many tools in medicine today in which physicians rely solely on a pattern to base their diagnosis, such as an electroencephalogram. Even the identification of a specific biomarker may not provide any direct insight into how a disease may arise or progress. Take, for instance, PSA, which is used to indicate the possible presence of a prostatic tumor. Its role in cancer development, however, remains unclear. Conversely, the likelihood that these key features may represent proteins that provide exciting insights to the manifestation and progression of cancer still exists. Therefore, their identification is most likely a worthwhile effort, although the advancement of disease diagnostics using proteomic patterns should not be hindered by this exercise.

Disease diagnostics using proteomic patterns has rapidly emerged as a potentially revolutionary tool to detect and monitor disease progression or therapeutic response. It represents a complete about face in proteomic analysis. While the trend in proteomic technology has been to identify and characterize an increasing number of proteins from a particular clinical sample in order to find a disease-specific biomarker, proteomic patterns rely simply on a crude proteomic survey that provides all of the necessary diagnostic information. Although the potential is great, much still needs to be learned. The concept of using a proteomic pattern as a diagnostic tool is in its infancy, therefore every step in this analytical process requires optimization. This optimization process will include such aspects as sample acquisition and processing, pattern acquisition, and data analysis. Since the diagnostic power of proteomic patterns relies heavily on the use of bioinformatics, it is important to discover the biological basis behind the mathematical solution. While the identification of key peaks that are called out by the bioinformatic analysis may not provide any clues as to the manifestation or progression of the disease, the hope is they can at least validate the results being provided. While many critics still abound, one simple fact cannot be ignored: the diagnostic models generated from proteomic patterns continue to provide highly sensitive and specific results in testing and blind validation studies, even as the number of samples being analyzed continues to increase.

The next few years will be critical in the validation of the use of proteomic patterns in disease detection. While currently the information present in pro-teomic patterns may provide an extremely powerful complementary tool to assist physicians in disease diagnosis, the impact of proteomics in disease diagnosis is even greater. The niche that proteomics will fill within the field of diagnostic medicine remains to be determined. The most obvious benefit of using proteomic patterns to diagnose disease states is in screening large populations to detect diseases, such as cancer, at earlier stages, to enable more effective medical intervention. The ease by which proteomic patterns can be acquired makes it feasible to screen populations at high risk for a variety of different cancers. If the sensitivity and specificity of diagnosing cancer using proteomic patterns can approach 100%, its use may revolutionize diagnostic medicine. Even if this level of sensitivity and specificity is not achieved, proteomic patterns will still provide an invaluable complement to determine the need for a patient biopsy or response to therapy.

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