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U. S. Food and Drug Administration–National Cancer Institute Clinical Proteomics Program, * Office of the Director, Center for Biologic Evaluation and Research, U. S. Food and Drug Administration, Rockville, MD 20852 and Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
2 To whom correspondence should be addressed. E-mail: petricoin{at}cber.fda.gov.
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ABSTRACT |
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 TOP ABSTRACT LITERATURE CITED |
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KEY WORDS: • proteomics • pattern diagnostics • SELDI • protein microarrays • cell signaling
Molecular medicine is moving beyond genomics to proteomics. The function of proteins is closely tied to their cellular, tissue and physiological contexts and the protein-protein interactions that drive biological outcomes can be characterized as a fluctuating information flow within the cell and the organism through protein pathways and networks ( 1– 6). The ability to access and visualize the entire interconnecting intracellular and extracellular protein "circuitry" inside and outside a cell could have a profound effect on biology, understanding of disease mechanisms and rational drug design. The pathogenic signaling pathways are not confined to the cancer cell but rather extend to the tumor-host interface ( 7), and recognition that cancer is a product of the proteomic tissue microenvironment has important implications. The view of individual therapeutic targets as the focus of therapy is changed to the targeting of entire protein-signaling pathways both inside and outside the cancer cell. Moreover, the tumor-host interface can generate enzymatic cleavage, shedding and sharing of growth factors, so the microenvironment could be a source for biomarkers that would ultimately be shed into the serum proteome for early disease detection and therapeutic efficacy monitoring.
Clinical proteomics: early diagnosis
Currently, cancer is diagnosed and treated when it is too late: metastasis has already occurred and the success of therapeutic modalities is very limited. Detecting cancers when they are in the earliest stages (even in the premalignant state) ultimately translates into higher cure rates. Nowhere is this dilemma more apparent than for ovarian cancer. More than two-thirds of ovarian cancer cases are detected at an advanced stage, when the ovarian cancer cells have spread away from the ovary surface and disseminated throughout the peritoneal cavity ( 8). Although the disease at this stage is advanced, it rarely produces specific or diagnostic symptoms. Consequently, ovarian cancer is usually treated when it is at an advanced stage ( 9). The resulting 5-y survival rate is 35–40% for late-stage patients even with the best of treatment. Conversely, when ovarian cancer is detected early (stage I), conventional therapy produces a high rate (95%) for 5-y survival ( 9– 13). The lack of a specific symptom in early-stage ovarian cancer may provide a new approach for the discovery of early cancer biomarkers. For this reason, ovarian cancer has been a major focus of marker discovery.
To be effective, a clinically useful biomarker should be measurable in an accessible body fluid such as serum, urine or saliva. Because these body fluids are protein-rich information reservoirs that contain the traces of what the blood has encountered on its constant perfusion and percolation throughout the body, proteomics may offer the best chance of discovering these early stage changes. In the past, the search for cancer-related biomarkers for early disease detection was a methodical and laborious approach that involved searching for overexpressed proteins in blood that are shed into the circulation as a consequence of the disease process ( 14– 18). There are potentially thousands of intact and cleaved proteins in the human serum proteome; finding the single disease-related protein is like searching for a needle in a haystack and requires the separation and identification of each protein biomarker. Moreover, it is likely that the discovery and use of these elusive single biomarkers for early detection of cancer will not occur, because clinical applications would be applied to a human population constituted by vast heterogeneity not only in the respective proteomes but also in the underlying cancer itself.
Proteomic pattern diagnostics
Recently, we have developed serum-based proteomic pattern diagnostics, which is a new method of diagnosis and disease identification for ovarian cancer detection ( 19). The new concept presented in these findings is that the diagnostic endpoint for ovarian cancer detection is not a single analyte but a proteomic pattern that is composed of many individual proteins, each of which independently cannot differentiate diseased from healthy individuals. The hope is that these patterns can be used as a diagnostic test without prior knowledge of the proteins.
Two underlying biological questions are: 1) Where do these proteins come from? and 2) Are these proteins simply measuring some type of nonspecific epiphenomenon? The blood proteome is changing constantly as a consequence of the perfusion of the diseased organ, and this adds, subtracts or modifies the circulating proteome. These disease-related differences may be the result of proteins being overexpressed and/or abnormally shed and added to the serum proteome. Most likely, these differences arise from a specific process of clipping, degradation and/or proteolysis as a consequence of the disease process or even subtraction from the proteome due to abnormal proteolytic degradation pathway activation. Other effects due to disease-related protein-protein interactions and protein-complex formation also can modify and subtly change the serum proteome. Thus, even if the specific pattern is composed of products that are many degrees of separation removed from the actual disease, these products can retain the specificity for the disease because the process can arise from a specific type of biomarker amplification.
Proteomic pattern diagnostics requires only a small amount of material: a few microliters of raw unfractionated serum from patients can be analyzed by surface-enhanced laser desorption ionization–time-of-flight (SELDI-TOF)3 spectrometry to create a proteomic signature of the serum ( Fig. 1). Only a subset of the proteins in the serum bind to the chromatographic surface of the bar, and the unbound proteins are washed away. The adherent proteins are treated with acid (so that they can become ionized) and are then dried down onto the surface. The bait region that contains individual captured serum protein samples (that are dried down on a row of spots) is inserted into a vacuum chamber, and a laser beam is fired at each spot. The laser energy blasts off (desorbs) the ionized proteins, and the ionized proteins fly down the vacuum tube toward an oppositely charged electrode. The mass-to-charge (m/z) value of each ion is estimated from the time it takes for the launched ion to reach the electrode; small ions travel faster. Therefore, the spectrum provides a TOF signature of ions that is ordered by size. This serum proteomic signature is composed of thousands of protein ion features that require highly ordered data-mining operations for analysis. Many new types of bioinformatics data-mining systems are being developed, but most fall into two main types of approaches including 1) supervised systems that require a body of knowledge or data where outcome or classification is known ahead of time to train on [example approaches are linear regression models, nonlinear feed-forward neural networks (NLFN) and genetic algorithms (GA) ( 20– 27)] and 2) unsupervised systems that cluster or group records without previous knowledge of outcome or classification [example tools are K-means nearest-neighbor analysis, Euclidean distance-based nonlinear vector n-dimensional clustering methods, fuzzy pattern-matching methods and self-organizing mapping [(SOM) ( 28– 30)]. The problem, however, is the same for any approach used: to find the best pattern out of hundreds of trillions of possibilities. A typical low-resolution SELDI-TOF proteomic profile has up to 15,500 data points that comprise the recordings of data between 500 and 20,000 m/z, and a high-resolution mass spectrometer generates >400,000 data points. Artificial intelligence (AI)–based systems that learn, adapt and gain experience over time are uniquely suited for proteomic data analysis because of the huge dimensionality of the proteome itself.
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1.5 billion to 1.5 trillion trillion (1.524) patterns. To explore each of these combinations one at a time would take a computer performing 1 billion operations per second over 47 million years to identify the optimal discriminatory pattern. GA can find nearly optimal solutions to these massive sets in only a few days through iterative searching, remating and recombination of the data packets with "selective pressure" applied. The systems use a fitness test; in this instance, the fitness test is an unsupervised simple spheroid-shaped Euclidean-distance clustering–based adaptive program (
Fig. 2).
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Enabling technologies for microproteomic analysis
Most current therapeutics are directed toward modulating protein products. Although DNA is the information archive, it is the protein that does the work of the cell. The cellular proteome is constantly fluctuating depending on the cellular microenvironment. Consequently, proteomic changes in cell lines may have limited relevance to human disease. Moreover, tissues and the disease process itself are heterogeneous and composed of hundreds of different interacting cell populations and interconnected protein-protein interactions. New technology makes it possible to analyze diseased cells in the tissue section itself ( 31) or to physically separate the desired cells directly from the surrounding contaminating cells. Laser capture microdissection (LCM) is a key enabling tool that allows the direct procurement of pure cell populations from heterogeneous tissue sections under direct microscopic visualization ( 32). This technology was applied to discover hundreds of new protein targets that are differentially expressed as either a cause or consequence of the disease process ( 32– 40).
Entire portfolios of drug targets, imaging markers and early-detection biomarkers will arise from hypothesis-generating discovery-based proteomic platforms. In the past, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) was the gold-standard discovery-based tool for proteomics ( 41– 43). Unfortunately, even with the advent of "zoom gels" that use ultra-narrow isoelectric point (pI) gradients, the scientist can only visualize a small percentage of the entire proteome via 2D-PAGE. Now, newer technologies that can drill down much further into the lower-abundance region of the proteome are being developed. Most often (and not surprisingly), it is the low-abundance proteins that are the biomarkers or drug targets for most disease processes. New multiplexed in-line liquid chromatographic (LC) separation systems coupled directly to mass spectrometry (MS) using cellular lysates (LC-LC-LC-MS/MS) with affinity tagging are being developed and may someday replace gel-based systems such as 2D-PAGE ( 44– 50). Nevertheless, 2D-PAGE remains a reliable workhorse separation technology especially for the larger molecular weight region of the proteome (a region that cannot be adequately resolved and analyzed by even the more-advanced liquid separation systems and mass spectrometers). Now, 2D-PAGE methodology is being adapted to higher throughput and higher sensitivity applications and modifications, because it is a key and complementary proteomic technology. One such adaptation uses the same cy3/cy5 dual dye-labeling methodology that has been employed so successfully for cDNA and oligonucleotide arrays ( 51). In this format, LCM-based cellular lysates from patient-matched normal and tumor epithelia are differentially labeled, each with the different flourophore, and the lysates are mixed together after labeling and then run together on one gel. Because both lysates are run simultaneously on the same gel, a direct comparison between the two samples can be more easily performed.
Despite their sophistication, the new proteomic technologies have significant limitations when applied to tissue and blood samples. Discovery platforms such as 2-D gels, isotope-coded affinity tagging multidimensional LC-MS platforms and antibody arrays require large cellular input samples in orders of magnitude greater than the quantity procured during a clinical biopsy ( 44– 54); these specimens may only contain a few hundred cells as the starting point for analysis. Clinical proteomics, or the use of clinical trial material for proteomic analysis, requires the development of new technologies that can employ these small amounts of cellular material as a launch point for discovery and profiling. Then proteomic analysis can be applied for validation of those targets in the patient setting. A second limitation of the newer technologies is the requirement for denatured proteins. Because denaturation breaks apart protein complexes and erases 3-D protein conformation, these methods may not adequately probe the state of the cellular circuitry mediated by protein-protein interactions. Consequently, new microproteomic technologies need to be developed so that the clinical scientist can gain access to the information content of the cellular circuit networks, which may be targeted for therapeutic intervention.
Reverse-phase protein microarrays: applications at bedside
Protein microarrays represent the first new technology that can actually profile the state of a signaling pathway target even after the cell is lysed ( 55, 56). A new type of protein array, the reverse-phase protein array, has demonstrated ( 56) a unique ability to analyze signaling pathways using small numbers of human tissue cells that were microdissected from biopsy specimens procured during clinical trials ( Fig. 3). Employing this approach, LCM-procured pure-cell populations are taken from human biopsy specimens, and a protein lysate is arrayed onto nitrocellulose slides. Sets of key technological components of this method offer unique advantages over tissue arrays ( 57) or antibody arrays ( 38, 51, 52) including:
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Patient-tailored therapeutics: personalized medicine using proteomic monitoring
Evidence is emerging to support the concept that each patient's cancer has a unique complement of pathogenic molecular derangements. Thus, a given class of therapy may be effective for only a subset of patients who harbor tumors with susceptible molecular derangements. There is strong justification for the strategy to select from a menu of treatment choices or combinations that best match the individual tumor's molecular profile ( 60– 68). Molecular profiling using gene arrays has shown considerable potential for classification of patient populations according to disease stage or survival outcome ( 23, 24, 69). However, transcript profiling by itself may provide an incomplete picture, because the gene transcript level may bear no relationship to the phosphorylated or otherwise functional state of the encoded protein. Gene transcripts provide little information about protein-protein interactions and the state of the cellular circuitry; this information is inferred by correlative bioinformatic approaches. Applications of molecular profiling for selection of the appropriate treatment strategy must include a direct proteomic pathway analysis of the biopsy material. Currently, cancer therapy is directed at a single molecular target. In the future, we can imagine targeting an entire set of nodes all along the pathogenic signal pathway ( Fig. 5). Such an approach will theoretically achieve higher efficacy with lower toxicity.
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Proteomic signal pathways consist of an amplification cascade of enzymatic events. The conventional pharmacological approach was to select a single upstream target as the drug target. To completely shut down the entire pathway, it is necessary to treat the upstream target at a drug concentration that blocks the target with a high degree of efficiency (>85%). At this high concentration, the drug may be in the dose range that produces unwanted toxic side effects.
Combinatorial therapy, which is an alternative to single-agent therapy, offers the promise of higher specificity at lower treatment doses ( 81– 83). A correctly chosen series of inhibitors acting at several points along the length of the pathway can be employed at low concentration, yet the result can be a complete shutdown of the pathway. The advantage is realized because the inhibitors work in series at different points along the pathway. The output of one node in the pathway is inhibited before it reaches the next node. Consequently, a lower concentration of inhibitor is required at each successive level. With this concept in mind, a redefined goal of molecular profiling is to map the cellular circuit so as to define the optimal set of interconnected drug targets. The use of combinatorial therapy for increased efficacy also may yield a decrease in unwanted toxic side effects because each drug can be given at a lower treatment dose. However, this needs to be demonstrated and requires a higher degree of vigilance during implementation of the regime to monitor the combined toxic effects of the drugs on normal cell populations. Thus, use of clinical proteomic tools such as whole-body protein arrays becomes even more relevant to this emerging era of patient-tailored molecular medicine, and a priori can aid in the analysis of desired drug effects on the target pathways and unwanted toxic effects on the circuitry within normal cell populations. Additionally, serum proteomic pattern analysis can be used to monitor for patterns associated with occult drug-induced toxicity. Proteomic pattern analysis also can be used in the drug development, lead optimization process and preclinical phases, whereby serum proteomic patterns associated with known drug-induced toxicities can be matched against the experimental therapeutic and predictive correlates obtained to guide and select which compounds should be taken forward or shelved.
Clinical proteomics: a view to the near future
Clinical proteomics can have important direct bedside applications. In the future, the physician and pathologist will use these different proteomic analyses at many points of disease management. The paradigm shift will directly affect clinical practice by having an impact on all of the following critical elements of patient care and management: early detection of the disease using proteomic patterns of body fluid samples, diagnosis based on proteomic signatures as a complement to histopathology, individualized selection of therapeutic combinations that best target the patient's entire disease-specific protein network, real-time assessment of therapeutic efficacy and toxicity and rational redirection of therapy based on changes in the diseased protein network associated with drug resistance.
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FOOTNOTES |
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3 Abbreviations used: AI, artificial intelligence; 2D-PAGE, two-dimensional polyacrylamide gel electrophoresis; ERK, extracellular signal-related kinase; GA, genetic algorithm; LCM, laser capture microdissection; LC-MS, liquid chromatography–mass spectroscopy; NFLN, nonlinear feed-forward neural networks; SELDI-TOF, surface enhanced laser desorption ionization time-of-flight; SOM, self-organizing mapping.
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