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Mass-encoded, synthetic biomarkers and multiplexed urinary monitoring: new frontiers in disease monitoring.

In his treatise on the psychology of science, Abraham H. Maslow noted that when the only tool you have is a hammer you tend to think of every problem as a nail (1). One group that appears to personify this principle more so than others are the hi-tech-minded practitioners of applied analytical sciences, in particular those interested in disease biomarkers. Depending on the class of molecules targeted for specific analyses, for example nucleic acids, proteins, or small molecules, the biomarker field can be categorized in subspecialties such as genomics, proteomics, and metabolomics, to just name the most prominent ones. It is not surprising that each specialty has its advocates, often using arguments based solely on available expertise. A telling example of the hammer-and-nail theory can occasionally be witnessed at proteomics gatherings when the need is stressed to prioritize analysis of proteins because "they carry out all the work" in a cell whereas a gene/transcript is merely a genetic blueprint. True, except that in the protein biomarker field the emphasis has been almost exclusively on measuring disease-related changes in protein concentrations and modifications, using immunohistochemistry (IHC) [2] or mass spectrometry (MS), and not on differential biological activities.

Prospective usage in blood-based testing has been one of the key criteria that guided most searches for the perfect disease biomarkers during the past decennia. Aside from a few early hits, such as CA125 and CEA, it does not appear that said IHC/MS approach has been much of a success (2), mainly because the complexity of the blood proteome precludes detection at the lowand sub-nanogram-per-milliliter levels without prefractionation of proteins or peptides. Clearly, this field is in need of new ideas or should revive some older ones, such as the use of protein (i.e., enzymatic) activities, termed activity biomarkers. In fact, one of the earliest markers for prostate cancer, reported in 1938, was a rise of "acid" phosphatase activity in serum.

A survey of the human plasma proteome in the latest version of the PeptideAtlas (http://www.peptideatlas. org/) indicates the presence of 60 out of the approximately 570 human proteases that are listed in the MEROPS peptidase database (http://merops.sanger.ac.uk/), which makes them one of the largest enzyme families detectable in blood. Proteases share a principal functionality, the lysis of peptide bonds, but their biological roles differ greatly. They have also been implicated in disease, notably in cancer, where they may promote both tumor progression and suppression. The earliest observation of blood-based protease activities and prospective uses for disease monitoring was made half a century ago. Since then, sporadic reports on deregulated protease activities in cancer patients have appeared in a variety of journals, without, however, having had any influence on clinical practice.

Blood proteases gained some inadvertent notoriety 10 years ago when reports appeared on the use of serum peptidome patterns to diagnose ovarian and other cancers. Although the rise and fall of the ill-fated SELDI profiling platform were well documented at that time in several scientific journal news updates, the underlying reasons for its failure were not really understood. It was only later revealed that most serum peptides sort into nested sets of sequences, generated by exopeptidase activities superimposed on the proteolytic events of the ex vivo coagulation and complement degradation pathways (3), thus explaining the erratic measurements when preanalytical conditions are not exceedingly well controlled. Building on these observations, robust blood-protease activity assays have since been developed as a new type of diagnostic technique, which uses labeled peptide substrates and nondegradable reference peptides for reliable, quantitative measurements, with the potential to monitor cancer recurrence (4).

An inherent problem with the profiling of in vitro plasma or serum protease activity (or any type of blood protein profiling) is that the minute amounts of enzyme (or protein) secreted/shed from a small tumor are infinitely diluted in the bloodstream and degraded and/or cleared by the renal system, and therefore they elude detection, even with the use of state-of-the-art assays (or MS analysis) (5). Secondly, in vitro tests can not monitor activities of proteases aberrantly expressed on the surface of cancer cells and stromal cells in the tumor microenvironment. A recent report by Kwong et al. on mass-encoded synthetic biomarkers describes a new, innovative approach to the measurement of protease activities in vivo that takes an important step toward eliminating those 2 major shortcomings of in vitro assays (6).

The new method also uses synthetic peptides as proteolytic substrates, but they are first attached to nanoparticles ("nanoworms") and administered intravenously. Nanoworms appear to shuttle the peptides to the sites of disease, be it fibrotic areas of the liver or fenestrated angiogenic tumor vessels, while also preventing renal clearance until they are proteolytically cleaved. Free peptide is then readily recovered from urine and quantitated by MS, a process facilitated by the renal concentration of plasma peptides (from 5 L blood to 300 mL urine void volume) and the comparatively lower complexity of the urinary proteome/peptidome. In an application using mouse models of metastatic colorectal cancer, tumors as small as approximately 150 [mm.sup.1] could be detected by nanoworm infusion and urine analysis, whereas tumor sizes > 1300 [mm.sup.3] were required for detection based on serum CEA.

The design of the synthetic biomarkers and technical details of this new method are fairly sophisticated, because the authors had to find ingenious ways of dealing with some obstacles imposed by vertebrate physiology and to optimally make use of renal function. For instance, in addition to being coupled to nanoworms, the degradable peptide substrates have a nondegradable reporter peptide extension. The extension is modeled after fibrinopeptide B to facilitate renal filtration, contains D-amino acids to prevent proteolytic breakdown, and can eventually be released from the partially degraded substrates by photocleavage to generate a reporter with fixed molecular mass. This avoids MS analysis and interpretation of extremely complex peptide mixtures that might otherwise result from degradation by the omnipresent exopeptidases. In addition, the reporter is isotopically labeled with heavy (e.g., [sup.13]C or [sup.15]N) amino acids in various positions to yield intact peptides with identical mass but with distinct fragment ions that can be monitored by tandem MS, a technique termed mass encoding. Mass encoding enables multiplexed quantification, akin to a technique known in proteomics circles as iTRAQ (isobaric tags for relative and absolute quantitation), to monitor degradation of 10 different substrates simultaneously.

Although synthetic biomarkers and the new method that makes use of them have shown clear promise in simple animal models of cancer, it will be a long and difficult road before the whole idea can be taken to the clinic and used to address some unmet, specific diagnostic or prognostic needs or as a simple means to monitor recurrence of disease. A major shortcoming at this time, as is also the case for the peptide-based in vitro assays (4), is a total lack of information regarding which proteases degrade what synthetic biomarkers. For example, although the 10 substrates described in the report by Kwong et al. (6) appear to be excellent targets for cleavage by matrix metalloproteinase 9, they could predictably just as well be degraded by trypsin and chymotrypsin. On the positive side, mass encoding may allow careful tweaking of the precise protease substrate sequences and assemble them in unique peptide-nanoworm biomarker panels that can be empirically optimized for use as highly sensitive cancer tests or even as cancer type-specific tests.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, oranalysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: No authors declared any potential conflicts of interest.

References

(1.) Maslow AH. The psychology of science: a reconnaissance. New York: HarperCollins; 1966. p 15.

(2.) Rifai N, Gillette MA, Carr, SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 2006; 24: 971-83.

(3.) Villanueva J, Shaffer DR, Philip J, Chaparro CA, Erdjument-Bromage H, Olshen AB, et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J Clin Invest 2006; 116: 271-84.

(4.) Villanueva J, Nazarian A, Lawlor K, Yi SS, Robbins RJ, Tempst P. A sequence-specific exopeptidase activity test (SSEAT) for functional biomarker discovery. Mol Cell Proteomics 2008; 7:509-18.

(5.) Hori SS, Gambhir SS. Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations. Sci Transl Med 2011; 3: 109ra116.

(6.) Kwong GA, von Maltzahn G, Murugappan G, Abudayyeh O, Mo S, Papayannopoulos IA, et al. Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease. Nat Biotechnol 2013; 31:64-71.

Paul Tempst [1] *

[1] Memorial Sloan-Kettering Cancer Center, New York, NY.

[2] Nonstandard abbreviations: IHC, immunohistochemistry; MS, mass spectrometry.

* Address correspondence to this author at: Memorial Sloan-Kettering Cancer Center, New York, NY 10065. E-mail p-tempst@mskcc.org.

Received July 1, 2013; accepted July 8, 2013.

Previously published online at DOI: 10.1373/clinchem.2013.205443
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Title Annotation:Perspective
Author:Tempst, Paul
Publication:Clinical Chemistry
Geographic Code:1USA
Date:Dec 1, 2013
Words:1516
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