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Editorials |
NCI/FDA Clinical Proteomics Program, Office of the Director, Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, MD 20892
NCI/FDA Clinical Proteomics Program, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892
aAuthor for correspondence.
Advances in mass spectrometry-based diagnostics could ignite a revolution in the field of molecular medicine. This platform has the potential to become the practical clinical analyzer of the future for nucleic acids and proteins. Mass spectrometry-based diagnostics are an example of a "disruptive" or "nonlinear" technology (1)(2). Such disruptive technologies are by their very nature polarizing, causing a dynamic dichotomy of excitement (3) as well as anxiety(4) in the clinical diagnostic community because this technology can potentially outperform traditional measurement and detection systems. In this issue of Clinical Chemistry, Bonk et al. (5) demonstrate the advantages of mass spectrometry compared with traditional electrophoretic methodologies. Bonk et al. used mass spectrometry as a "sensor" to detect the amplified product from a PCR amplification reaction. The ultimate clinical application for this study is the early detection of colon cancer, a topic of obvious public health importance.
Although major advances have been made in elucidating the genetic underpinnings of cancer, especially colorectal cancer, diagnostic methodologies for routine clinical detection and monitoring of important cancer genetic derangements have lagged behind. Microsatellite instability (MSI), caused by mismatch repair gene silencing, is predicted to be an important early event in cancer progression (6)(7)(8).
In this issue, Bonk et al. (5) report on a study in which they use time-of-flight-based mass spectrometry to detect MSI. Unlike previous studies, in which chromatography, electrophoresis, and traditional DNA sequencing methodologies were used to detect the presence of MSI, these authors have chosen a technology that has many distinct advantages over these more traditional methods. As pointed out by the authors, these traditional methods are really not amenable to high-throughput diagnostics because they are low throughput, costly, and suffer from poor sensitivity. Mass spectrometry, on the other hand, is extremely rapid (the entire process can occur in less than 1 min), has tremendous cycle time (hundreds of samples can be analyzed sequentially, one after another without pause), and can achieve sensitivities in the femtomolar range (9)(10)(11). After PCR amplification, Bonk et al. (5) used matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry to directly detect the PCR products.
Mass spectrometry-based detection of surrogates for disease detection is a timely topic. Mass spectrometry has recently generated excitement (and anxiety) as a platform for protein-based biomarker profiling. The most reliable, sensitive, and widely available tests for the detection of cancer today are protein–ligand assays, such as ELISA systems. These tests are robust, linear, accurate, and have reasonable throughput. However, the use of an ELISA system to test for the presence of disease requires a single, meticulously validated analyte for detection as well as an extremely well-characterized, high-affinity antibody for detection of the analyte. This requires a tremendous amount of time and effort. Traditionally, the search for cancer-related biomarkers for early disease detection, aggressiveness, and therapeutic response has been a one-at-a-time approach looking for overexpressed proteins in blood that are shed into the circulation as a consequence of the disease process (12)(13)(14). Unfortunately, this approach is laborious and time-consuming because there are potentially thousands of intact and cleaved proteins in the human serum proteome that would need to be identified, and antibodies would need to be developed, validated, and checked for specificity and sensitivity. Finding the single disease-related protein is like searching for a needle in a haystack, requiring the separation and identification of these entities one by one. Moreover, it is likely that discovery and use of the elusive single biomarkers for early detection of cancer will be nonexistent because clinical applications would be applied to a human population characterized by vast heterogeneity not only in their proteomes, but also in the underlying cancer genetics.
Within the past year, a new type of protein-based diagnostic paradigm has been described: proteomic pattern diagnostics (15). With this approach, a single droplet of blood is placed on a specially treated surface that contains chemical bait molecules that bind with proteins in the blood. The surface is washed and subjected to MALDI-TOF analysis. The readout is a proteomic "barcode" pattern of the complex protein milieu that sticks to the surface. A typical low-resolution MALDI-TOF proteomic profile will have up to 15 500 data points that comprise the recordings of data between m/z 500 and 20 000, with higher resolution mass spectrometry instruments generating as many as 400 000 data points for m/z 500 to 12 000.
Proteomic pattern diagnostics use new types of pattern recognition systems to sift through this huge amount of data to find diagnostic patterns of protein expression. These artificial-intelligence-based bioinformatic systems are vigilant and powerful, and enable the analysis of these large, complex datastreams. These types of informatic algorithms have the special attribute to learn, adapt, and gain experience over time and are uniquely suited for proteomic data analysis because of the huge dimensionality of the proteome itself. The artificial intelligence tool learns, adapts, and gains experience through constant vigilant retraining—meaning that it can start to recognize a unique and new phenotype that the system had not been trained to recognize or even seen before. This is extremely important when one considers clinical applications involving the screening of hundreds of thousands of patients. In fact, it is possible to generate not just one, but multiple combinations of proteomic patterns with diagnostic potential (16). With this approach, knowledge of the underlying identities of the individual components of the pattern is not necessary for its use as a potential diagnostic and sentinel for the presence of the disease: the pattern itself is the diagnostic.
Mass spectrometry-based proteomic pattern diagnostics have been used for ovarian cancer detection, and the value of this paradigm has been confirmed in other diseases, including breast (17) and prostate cancer (18)(19). What this means is that the unique tumor–host microenvironment can set off amplification cascades that may be specific to the disease process, and yet the signatures for the presence of cancer, even at its earliest stages, may be composed of untold numbers of combinations of slight but significant shifts in protein–protein interactions, protein folding, and protein abundances that are reflected in mass spectrometry-based protein profiles.
Mass spectrometry platforms, already capable of reporting tens of thousands of events in less than a few minutes from a microliter of blood, are advancing rapidly in speed, throughput, sensitivity, and "on-the-fly" protein identification. Semiquantitative MSI profiling, as described in this issue, represents an additional new and exciting component of the repertoire of mass spectrometry’s diagnostic potential.
The coupling of advances in mass spectrometry with adaptive and vigilant bioinformatic pattern recognition tools may dramatically change how disease is detected and monitored. The result will be a rich source of information to aid the clinician in patient management. On the basis of these nonlinear technologic advances, the clinical diagnostic landscape is now shifting dramatically. Like it or not, we may be moving beyond existing immunoassay-based (for proteins) and electrophoretic (nucleic acid) approaches. Rapid, low-cost mass spectrometry-based alternatives with much higher clinical sensitivity and specificity for the detection and monitoring of disease may eventually dominate clinical diagnostics. The utility and validity of this vision will be answered over the next several months to few years. Clinical trials for these diagnostic tests are ongoing for early detection of cancer, individualization of therapy, and monitoring of relapse and drug-induced toxicity. As a demonstration of the commercial interest in the expansion of mass spectrometry-based diagnostics (20), large reference laboratories have begun serious evaluation of the eventual implementation of proteomic pattern diagnostics in their routine practice (21).
References
The following articles in journals at HighWire Press have cited this article:
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  D. H. Lee, J. W. Kim, S. Y. Jeon, B. K. Park, and B. G. Han Proteomic Analysis of the Effect of Storage Temperature on Human Serum Ann. Clin. Lab. Sci., January 1, 2010; 40(1): 61 - 70. [Abstract] [Full Text] [PDF]
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 P. de Valpine, H.-M. Bitter, M. P. S. Brown, and J. Heller A simulation-approximation approach to sample size planning for high-dimensional classification studies Biostat., July 1, 2009; 10(3): 424 - 435. [Abstract] [Full Text] [PDF]
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 Y. Saeys, I. Inza, and P. Larranaga A review of feature selection techniques in bioinformatics Bioinformatics, October 1, 2007; 23(19): 2507 - 2517. [Abstract] [Full Text] [PDF]
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 J. Shen, C. Behrens, I. I. Wistuba, L. Feng, J. J. Lee, W. K. Hong, and R. Lotan Identification and Validation of Differences in Protein Levels in Normal, Premalignant, and Malignant Lung Cells and Tissues Using High-Throughput Western Array and Immunohistochemistry Cancer Res., December 1, 2006; 66(23): 11194 - 11206. [Abstract] [Full Text] [PDF]
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E. P. Diamandis Serum Proteomic Profiling by Matrix-Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry for Cancer Diagnosis: Next Steps Cancer Res., June 1, 2006; 66(11): 5540 - 5541. [Full Text] [PDF]
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N. Jeffries Algorithms for alignment of mass spectrometry proteomic data Bioinformatics, July 15, 2005; 21(14): 3066 - 3073. [Abstract] [Full Text] [PDF]
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J. S. Yu, S. Ongarello, R. Fiedler, X. W. Chen, G. Toffolo, C. Cobelli, and Z. Trajanoski Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data Bioinformatics, May 15, 2005; 21(10): 2200 - 2209. [Abstract] [Full Text] [PDF]
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 J. Listgarten and A. Emili Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry Mol. Cell. Proteomics, April 1, 2005; 4(4): 419 - 434. [Abstract] [Full Text] [PDF]
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 G. L. Hortin Can Mass Spectrometric Protein Profiling Meet Desired Standards of Clinical Laboratory Practice? Clin. Chem., January 1, 2005; 51(1): 3 - 5. [Full Text] [PDF]
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D. Radulovic, S. Jelveh, S. Ryu, T. G. Hamilton, E. Foss, Y. Mao, and A. Emili Informatics Platform for Global Proteomic Profiling and Biomarker Discovery Using Liquid Chromatography-Tandem Mass Spectrometry Mol. Cell. Proteomics, October 1, 2004; 3(10): 984 - 997. [Abstract] [Full Text] [PDF]
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 D. H. Geho, E. F. Petricoin, and L. A. Liotta Blasting into the Microworld of Tissue Proteomics: A New Window on Cancer: Commentary re S. A. Schwartz et al., Protein Profiling in Brain Tumors Using Mass Spectrometry: Feasibility of a New Technique for the Analysis of Protein Expression. Clin. Cancer Res., 10: 981-987, 2004. Clin. Cancer Res., February 1, 2004; 10(3): 825 - 827. [Full Text] [PDF]
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 E. F. Petricoin, V. Rajapaske, E. H. Herman, A. M. Arekani, S. Ross, D. Johann, A. Knapton, J. Zhang, B. A. Hitt, T. P. Conrads, et al. Toxicoproteomics: Serum Proteomic Pattern Diagnostics for Early Detection of Drug Induced Cardiac Toxicities and Cardioprotection Toxicol Pathol, January 1, 2004; 32(1_suppl): 122 - 130. [Abstract] [PDF]
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E. Petricoin III and L. A. Liotta The Vision for a New Diagnostic Paradigm Clin. Chem., August 1, 2003; 49(8): 1276 - 1278. [Full Text] [PDF]
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D. H. Chace, E. F. Petricoin, and L. A. Liotta Mass Spectrometry-based Diagnostics: The Upcoming Revolution in Disease Detection Has Already Arrived * Drs. Petricoin and Liotta respond: Clin. Chem., July 1, 2003; 49(7): 1227 - 1229. [Full Text] [PDF]
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