Tumor-specific serum peptidome patterns.
Authors: Paul Tempst, Josep Villanueva, David R. Shaffer, Andrew Martorella, Mark E. Robson, Kevin Lawlor, John Philip, Martin Fleisher, Hans Lilja, Eric C. Holland, Richard J. Robbins, Carlos Cordon-Cardo, Howard I. Scher
Journal: Cancer Research (2006)

Proc Amer Assoc Cancer Res, Volume 47, 2006

SY05-01 

Most anti-cancer strategies rely on early detection, followed by close monitoring for early relapse so that therapies can be appropriately adjusted. There is optimism, however, that recent advances in genomics and proteomics may more readily lead to new and improved approaches in molecular diagnostics, capable of classifying patients into subgroups based on predicted response to individual treatment. Biomarker based screens should be minimally invasive and reproducible. For instance, a simple blood or urine test that detects molecules specific to tumor tissues would be ideal. In addition, screening technology should be sufficiently sensitive to detect early cancers but specific enough to classify individuals without cancer as being free of disease.

While genes contain the hereditary information, including genetic predisposition to cancer and other diseases, it is their products that confer the actual phenotypes of living organisms and, in case of disease, normal versus pathological states. Since there are many post-translational events that can modify the structure, function, and degradation of proteins, the knowledge of genes alone does not even begin to describe the full complexity of biological systems. From a screening perspective, it is also mainly the proteins that are secreted or otherwise released from tissues into the blood stream. Yet, despite an intensive search during the past decade(s), only a small number of identified cancer biomarkers, all plasma proteins, have proven clinically useful, often in combination with other diagnostic tools, for the prognosis of response to therapy, relapse and survival, for defining the rate of progression and monitoring of treatment, but less so for broad based population screening. Those proteins are typically present in plasma or serum at sub-nanomolar concentrations and require individual immuno-assays for detection and quantitation. New and improved cancer biomarkers and facile detection methods are clearly on order but have so far eluded discovery and implementation. Even the most recent approaches, using identity-based proteomics that involves digesting (e.g., with trypsin) complex protein mixtures into peptides for mass spectrometric (MS) analysis, have yet to translate into any practical applications.

As cancer involves the transformation and proliferation of altered cell types that produce high levels of specific proteins and enzymes such as proteases (e.g., PSA and PSMA), it will not only modify the array of existing serum proteins (serum proteome) but also their metabolic products, i.e. peptides (serum peptidome). It is well established that human serum contains thousands of proteolytically derived peptides, yet it remains unclear to date whether this complex peptidome may provide a correlate of some biological events occurring in the entire organism. As advances in MS permit the display of hundreds of small to medium sized peptides using microliter volumes of serum, several researchers have advocated the use of MS-based serum peptide profiling to determine qualitative and quantitative patterns, often referred to as signatures or barcodes, that indicate the presence / absence of disease. However, this work has come under criticism as growing evidence indicated that uncontrolled variables related to both clinical and analytical chemistry, and/or signal processing artifacts, may have tainted the published results. In any case, the identities of only a few putative markers have been established so far. Yet, the proof of the potential value of this new approach will be in the ability of several laboratories to independently show that the discriminatory peptides have the same amino acid sequences.

Working towards this goal, we have developed an automated procedure for the simultaneous measurement of peptides in serum that utilizes magnetic, reversed-phase beads for analyte capture and a MALDI-TOF MS read-out. This system is more sensitive than surface capture on chips as spherical particles have larger combined surface areas, and therefore higher binding capacity, than small-diameter spots. Coupled to high-resolution MS and MS/MS, hundreds of peptides can be detected in a droplet of serum, many of which can be readily identified without further fractionation. The automation facilitates throughput and ensures reproducibility. Furthermore, we have developed a minimal entropy-based algorithm that simplifies and improves alignment of spectra and subsequent statistical analysis. With these tools in hand, we have sought to determine if selected patterns of serum peptides with known sequences can (i) separate cancer from non-cancer, (ii) distinguish between different types of solid tumors, and (iii) allow class prediction with an independent validation set. We used peptide-ion relative intensity comparisons, statistical analysis and visual inspection of spectral overlays to sort through hundreds of features obtained by rigorous peptide profiling of 106 serum samples from patients with advanced prostate, or bladder or breast cancer, and from healthy controls, to identify several that are most predictive of outcome. It appeared that reduction in the number of key peptides to only a few (i.e. the signatures or barcodes) that are easily recognized between samples did not adversely affect class predictions. Targeted MS/MS-based sequence-identification of 61 barcode-peptides indicated that all were breakdown products, many related, of abundant proteins in the blood. By correlating the proteolytic patterns with disease groups and controls, we show that exoprotease activities, superimposed on the ex-vivo coagulation and complement-degradation pathways, contribute to generation of not only cancer-specific but also cancer type-specific serum peptides. This small but robust set of marker peptides then enabled highly accurate class prediction for an external validation set of prostate cancer samples. Our study therefore provides a direct link between peptide marker profiles of disease and differential protease activity. The patterns we describe may have clinical utility as surrogate markers for detection and classification of cancer. Our findings have also important implications for future peptide biomarker discovery efforts.