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CONVENTIONAL AND PROTEOMIC TECHNOLOGIES FOR THE DETECTION OF EARLY STAGE MALIGNANCIES: Markers for Ovarian Cancer

Posted on: Wednesday, 31 January 2007, 06:00 CST

By Lee, Catherine J; Ariztia, Edgardo V; Fishman, David A

Our understanding of the tumor microenvironment continues to evolve and allows for the identification of biomarkers that should detect the presence of early stage malignancies. Recent advances in computational analysis and biomedical technologies have come together to elucidate signatures associated with cancer and that are capable of identifying unique tumor-specific proteins. Within the tumor microenvironment, we continue to characterize the proteophysiology of the different steps associated with tumor progression. The urgent need for biomarkers accurately detecting early-stage epithelial ovarian cancer has prompted us, and others, to engage in a search for specific peptide signatures that may discriminate transformed cells from those of the normal ovarian microenvironment. This endeavor also provides new insights into the biology of the disease, which may not only be applicable to detection but may also help to initiate new therapies and optimize patient care.

Keywords Proteomics, mass spectrometry, serum-based proteomic patterns, protein microarray, low-molecular-weight proteins, peptide fragments.

Abbreviations CEA, carcinoembryonic antigen; CM, composite marker; 2D-PAGE, two-dimensional polyacrylamide gel electrophoresis; DIGE, difference gel electrophoresis; EGF, epidermal growth factor; ELISA, enzyme-linked immunosorbent assay; EMILIN, elastin microfibril interphase-located protein; EOC, epithelial ovarian carcinoma; ERK, extracellular signal-regulated kinase; hCG, human chorionic gonadotrophin; HK, human kallikrein; HPLC, high- performance liquid chromatography; HUPO, international human proteome organization; ICAT, isotope-coded affinity tags; IL, interleukin; IMAC, immobilized metal affinity chromatography; LCM, laser capture microdissection; LMW, low-molecular weight; LPA, lysophosphatidic acid; MALDI, matrix-assisted laser desorption ionization; MCP, monocyte chemoattractant protein; M-CSF, macrophage colony-stimulating factor; MDA, mixture discriminant analysis; MEK, mitogen-activated protein kinase kinase; MES, mesothelin; MS, mass spectrometry; MUC, mucin; Mud-PIT, multidimensional protein identification technology; OPN, osteopontin; OTOF, orthogonal time of flight; PPP, plasma proteome project; PSA, prostate specific antigen; RAF, a murine leukemia viral oncogene; RCA, rolling circle amplification; RDPA, recursive-descent partition analysis; RPA, reverse-phase array; RT-PCR, reverse-transcription polymerase chain reaction; SAA1, serum amyloid A1; sEGFR, soluble epidermal growth factor receptor; SELDI, surface-enhanced laser desorption ionization; SELAC, stable-isotope labeling of amino acids in culture; SMR, soluble mesothelin-related; TOF, time of flight; VEGF, vascular endothelial growth factor.

I. INTRODUCTION

The tumor microenvironment is a complex system that requires the interaction or cross-talk between tumor cells and normal tissue components. These communications may be reflected in changes in phosphorylation/dephosphorylation of proteins, enzymatic cleavages, shedding of membrane-bound molecules, shedding of microvesicles, increased or decreased levels of certain molecules, and other unexplored processes. Identification of such specific changes and correlation with early stage disease is imperative to the selection of biomarkers for the detection of early stage cancer.1 Conventional methods relying on gel-based protein separation techniques and high affinity well-validated antibodies have been time consuming and, ultimately, have not produced clinically dependable tumor markers. Recent studies have applied mass spectrometry as a means of detecting early changes in the microenvironment, which may lead to the detection of early stage cancer. Mass spectrometry (MS) has the ability to detect low-molecular-weight (LMW) proteins, in addition to changes in protein composition and abundance after certain stimuli.2 Our group has also reported on the detection of clinically significant low-molecular-weight serum proteins through mass spectrometry, and we have identified over 800 distinct LMW proteins that are normally in complex with high-abundance circulating carrier proteins, such as albumin; many of these proteins exist in specific stages of ovarian cancer.3 MS analysis of the changes induced by cancer cells in the tumor microenvironment has the potential to become one of the most useful techniques for both the basic and clinical sciences.

The present review will focus on the currently available techniques to detect the proteomic changes associated with early tumor progression. These techniques hold the promise to help us identify those specific changes in the tumor microenvironment that ultimately will lead to biomarkers for early detection. Special consideration will be given to the ongoing effort to identify and characterize useful markers for the detection of early-stage epithelial ovarian carcinoma (EOC).

II. THE EARLY DETECTION OF CANCER

The field of proteomics represents a relatively recent advancement in the study of disease. In its simplest definition, proteomics is the large-scale study of protein structure, expression, and function.4 This definition encompasses a field in which we have the ability to discover and characterize the proteins that represent and are a consequence of the pathophysiology of disease. Tumor cells interact with surrounding stroma, organ parenchyma, vasculature, and immune cell populations. The biochemical cross-talk between these components is hypothesized to include numerous proteins and molecules (such as lipids) that may act as sensitive and specific biomarkers. Moreover, these molecules may be subjected to alternative splicing, post-translational protein modifications, enzymatic cleavages, and additions that may influence their function, the course of disease, and the response to treatment. Even more significantly, these clipped and cleaved biological molecules may represent an ongoing biomarker record of the pathophysiology of the tumor-host microenvironment. This pool of protein information flows between diseased and normal cells, within the extracellular microenvironment, and, ultimately, enters the serum/plasma host macroenvironment.

The most advanced proteomic technology can detect these resulting fragments, which represent a rich source for LMW biomarkers. Conventional protein separation methods have not been able to detect these LMW proteins, as they have poor resolution and fractionation capability in this range of the proteome. Newly developed high- resolution MS based technologies can now identify unique serum proteomic patterns and proteins as indicators or reflections of the complex tumor microenvironment/host macroenvironment interaction. These patterns and peptides also have the capability to act as diagnostic fingerprints and prognostic tools for the presence and early detection of disease.5-8

The potential applications of proteomics in the laboratory revolve around: (i) identification of components of the proteome; (ii) comparing the expression of proteins between a normal or diseased organ, at certain stages of disease, or as a function of exposure to a drug, chemical, irradiation, or other stimulus; (iii) protein networking analysis to determine how proteins interact with each other in vivo; (iv) identification and characterization of how and where proteins are post-translationally modified; (v) structure and function of protein complexes at different sub-cellular sites and organelles to understand the organization of cells at the molecular level.9,10 The use of proteomic technology in the clinical laboratory and at the patient bedside offers a powerful and novel method in disease detection, monitoring, and management. Some of the major areas in which clinical proteomics are utilized include cancer, cardiovascular disease, Alzheimer's disease, infectious diseases, infertility, obstetrics, immune rejection following transplantation, and nutrition.4,9,11

Currently, there is a tremendous effort to translate our knowledge of the tumor microenvironment to clinical applications. Early cancer physiology can alter gene expression and lead to dysregulated signaling cascades and changes in transcription, translation, and post-translational modification that can alter the quality and quantity of the end products. Thus, early changes in the tumor microenvironment could possibly be detected and used as indicators for the presence of disease. This rationale has been used to detect biomarkers in a variety of cancers, such as α- fetoprotein (hepatoma, testicular cancer, immature teratoma, ovarian germ cell cancer), CEA (colon, breast, lung, pancreatic), PSA (prostate), hCG (testicular cancer, trophoblastic tumors, ovarian germ cell cancer, gestational trophoblastic disease), CA125 (ovarian), CA15.3 (breast), CA19.9 (gastrointestinal), immunoglobulins (B cell dyscrasias), and steroid hormone receptors (breast).12

III. CONVENTIONAL METHODOLOGIES FOR THE EARLY DETECTION OF OVARIAN CANCER

Despite the intense effort to discover and utilize biomarkers to improve clinical outcomes, the early detection of ovarian cancer has been challenging for several reasons. First, nucleoti\de and protein arrays have revealed increased molecular heterogeneity between different types of tumors. Second, certain pathophysiologic cancer events and non-cancer physiologic events are shared and can confound biomarker specificity. Third, low sensitivity exists because early- stage lesions are small in tissue volume, thus reducing concentrations of biomarkers and making detection difficult. A biomarker is clinically valuable when it can be measured in an easily accessible body fluid such as serum, urine, or saliva6 and has a high sensitivity and specificity. This is especially true in ovarian cancer, as unnecessary surgery carries a financial and emotional cost to the patient as well as exposure to operative hazards. A biomarker for ovarian cancer, for example, must have a minimum positive predictive value of 10%, a specificity of 99.6%, and a sensitivity of 100%,13 such that one operation out of 10 performed will find a cancer.

Conventional methods for the identification of tumor markers that reflect early changes in the tumor microenvironment have been sub- optimal. One assay reflecting these limitations is the cancer antigen 125 (CA-125) test. The CA-125 antigen was initially defined in 1981 using a murine monoclonal antibody (OC-125) to a surface glycoprotein on serous epithelial ovarian carcinoma cells. It is a high-molecular-weight glycoprotein existing in forms ranging from 220 to more than 1,000 kDa that is expressed by fetal amniotic and coelomic epithelium. CA-125 is normally present in the cells that line the fallopian tubes, endometrium, endocervix, peritoneum, pleura, pericardium, and bronchus. Little or no CA-125 can be detected in normal ovarian tissue, although the antigen is expressed in ovarian inclusion cysts, benign papillary excrescences, and tubal metaplasia. Currently, it is known that the CA125 antigen possesses two antigen domains: domain A binds to the monoclonal antibody OC125, and domain B binds to the monoclonal antibody M11. CA-125 levels exceeding 35 u/ml are considered significant and have been found in 80% of women with advanced stage disease.14 This tumor marker has been clinically useful for surveillance of persistent or recurrent disease after the diagnosis of EOC has been confirmed by surgery.14 However, the test lacks specificity, reaching only ~40% when detecting early stage disease. A modest rise in levels of CA125 can be found 10-12 months prior to diagnosis;15 however, at the time of conventional surgery, CA125 is elevated in 50% of patients with stage I disease.16 It is less reliable in pre-menopausal women, as elevated levels of CA125 occur during pregnancy, endometriosis, uterine fibroids, liver disease, benign ovarian cysts, and many other cancers.17

Other potential biomarkers have been identified using gene expression profiling, cDNA microarrays, and other transcriptional profiling techniques used to distinguish malignant from normal cells. HE4,18,19 prostasin,20,21 osteopontin,22,23 kallikreins,24,25 mesothelin,26 macrophage colony-stimulating factor,27,28 OVX1,29 inhibin,30 lysophosphatidic acid (LPA),31,32 p110 soluble epidermal growth factor receptor (sEGFR),33 and CA72-4(34) are a few that have been described in the ovarian cancer literature (Table 1). Comprehensive reviews of numerous candidate ovarian cancer biomarkers are available elsewhere.13,16,35

TABLE 1 Potential Ovarian Cancer Tumor Markers

Since the identification of single biomarkers, investigators have studied the utility of using combinations of markers, without and with CA125, for the early detection of EOC.16,36-39 To this end, Lu et al.37 have reported that several sets of potential markers, determined by recursive-descent partition analysis (RDPA), reverse- transcription polymerase chain reaction (RT-PCR), and immunohistochemistry, when used in different combinations, distinguished tumors of different histotypes from normal ovarian surface cells. This combination of markers would identify greater than 99% of all epithelial ovarian cancers despite their heterogeneity. Other investigators have determined that the serum analysis of four analytes (leptin, prolactin, osteopontin, and insulin-like growth factor-II), using the rolling circle amplification (RCA) antibody microarrays, can discriminate between disease-free and EOC patients, including patients diagnosed with stage I and II disease, with high efficiency (95%), better than any single analyte alone.39

Multiplexed techniques, such as Luminex LabMap profiling, have allowed the simultaneous measurement of multiple markers.40,41 From a panel of 24 cytokines and CA125, a combination of six markers [interleukin (IL)-6, IL-8, epidermal growth factor (EGF), vascular endothelial growth factor (VEGF),monocyte chemoattractant protein-1 (MCP-1), and CA-125] showed significant differences in serum concentrations between 44 patients with early-stage ovarian cancer and 45 healthy women, with a sensitivity of 84% and a specificity of 95% using classification tree analysis.41 A serum composite marker (CM) combining a previously described soluble mesothelin-related (SMR) marker with CA125 has also proven valuable as a clinical indicator of disease. Data analysis revealed that CM has the best sensitivity and specificity, equal to the performance of CA125. The role of CM in comparison to CA125 has yet to be determined in a longitudinal screening study.38

The expression of potential serum tumor markers in tissue lacking or having weak CA125 expression on immunoperoxidase staining has also been investigated.42 From 296 epithelial ovarian cancers, 65 (22%) tissue samples with little or no CA125 expression were assessed for the expression of 10 potential serum tumor markers. All specimens (100%) expressed human kallikrein 10 (HK10), human kallikrein 6 (HK6), osteopontin (OPN), and claudin 3. A smaller fraction of CA125-deficient ovarian cancers expressed DF3 (95%), VEGF (81%), MUCINl (MUC)1 (62%), mesothelin (MES) (34%), HE4 (32%), and CA19-9 (29%). When reactivity with normal tissues was considered, however, MES and HE4 showed the greatest specificity. Differences in expression levels were also found for HK10, OPN, DF3, and MUC1. These results demonstrate that a number of markers can complement CA125 at the tissue level and also encourage the search for complementary expression of these markers in serum.

These reports have spurred a great need to develop novel statistical tools, so that increased sensitivity while maintaining high specificity is achieved when using panels of multiple markers. Novel biostatistical methods such as logistic regression, classification tree, and mixture discriminant analysis (MDA) models have produced a 70% pre-operative early stage sensitivity with a combination of CA-125II, CA 72-4, and macrophage colony-stimulating factor (M-CSF) when compared to 45% with CA-125II alone, while maintaining 98% first-line specificity.43

Other recent reports have assessed the utility of gene expression profiles as a prognostic tool in addition to monitoring the progression of cancer and its pathologic response to therapeutic drugs.44-46 New multiplex technologies have also been developed for rapid, cost-effective, high-throughput detection of specific nucleic acid sequences that may help molecular laboratories define and validate disciminants of ovarian cancer (reviewed in Ref. 47). The Luminex xMAP(TM) system uses color-coded micro spheres, allowing the investigator to perform high-throughput nucleic acid assays on a single sample.47

The study of DNA and RNA arrays has been important in the identification of genetic alterations, such as changes in DNA copy number, nucleotide mutations, and altered transcripts. Although gene- related information is of great importance, DNA and mRNA are several layers of abstraction away from the physiologic events that determine health or disease because they are the information storage form of proteins. However, these methods cannot detect the changes that ultimately affect the protein level, composition, and function. It is the proteins that dictate cellular fates and govern metabolic processes. DNA and mRNA cannot explain physiologic protein interactions (such as proteinase activation) or post-translation modification events that are known to occur in cellular microenvironments.48

IV. EVOLUTION OF PROTEOMIC TECHNOLOGY AND ITS ROLE IN THE IDENTIFICATION OF TUMOR MARKERS AND THE EARLY DETECTION OF OVARIAN CANCER

A. Gel-Based Proteomics

The hallmark proteomic tool for the discovery of potential biomarkers has been two-dimensional polyacrylamide gel electrophoresis (2D-PAGE).6,10,49 2D-PAGE has been used to compare protein expression and identify differentially expressed proteins between normal and tumor tissue.6 Technical advancements made to the 2D-PAGE methodology include the incorporation of larger-format and higher-resolving gels, increasingly sensitive staining methods, and the addition of mass spectrometry to identify proteins.50 The specificity of 2D-PAGE has improved with the advent of laser capture microdissection (LCM).6,49,51-53 The introduction of LCM has been a powerful tool in the field of proteomics because it allows the procurement of pure cell populations from heterogeneous tissue.51,52 Clean separation of tissue consisting of malignant, in situ, and normal cell populations can be achieved and may facilitate the analysis and comparison of DNA, RNA, or protein content between those populations.49,52 Another advancement over the basic 2D-PAGE technology has been two-dimensional difference gel electrophoresis (DIGE), which has increased speed, reproducibility, sensitivity, and quantitative capabilities compared to standard 2D-PAGE.4,6,54 In this technique, two different extracts, the "test" and "reference," are differentially labeled with different fluorescent dyes and then are mixed and run/separated on the same 2D-gel.

These gel-based assays have been usefulfor comparing the presence of different proteins, which may serve as candidate biomarkers, between a test sample and reference sample. However, they cannot separate or detect proteins in the LMW range (<15,000 Daltons) of the circulatory proteome. This is unfortunate, as it is recognized that this ultra-low range may contain numerous peptides that have been proteolytically clipped as a consequence of normal or altered biological processes.7,8,55 New technologies using MS have been developed that perform optimally in this molecular-weight range, thereby revolutionizing the search for tumor markers for the early detection of cancer.4,6,80-10,12,13,50,56-61 Table 2 describes the methods, advantages, and disadvantages of several of these proteomic technologies.

TABLE 2 Description, Advantages, and Disadvatages of Various Proteomic Technologies

TABLE 2 Description, Advantages, and Disadvatages of Various Proteomic Technologies

B. MS-Based Quantitative Proteomics

Depending on the assay, mass spectrometry can be utilized to compare samples and identify potential biomarkers or to detect changes in spectral patterns as a means of cancer detection. One method for protein profiling using mass spectrometry is the isotope- coded affinity tags (ICAT) technology. In this assay, two samples of proteins (reference and test) are labeled using isotopically different ICAT reagents (i.e., 12C-light, 13C-heavy) that bind to cysteine-containing peptides. These two sample proteomes are then mixed, digested with trypsin, separated by high-performance liquid chromatography (HPLC), and, finally, identified by mass spectrometry. The presence or absence of discriminatory peaks between the two specimens can then be compared and identified as specific peptides or proteins.62,63

Also used for the identification of tumor markers is the stable- isotope labeling of amino acids in culture (SILAC) system. This more recently used method relies on the metabolic incorporation of differentially labeled amino acids in cell culture. As a result, all SILAC-labeled proteins are fully labeled, in contrast to chemical modification methods such as ICAT, which may not have 100% labeling efficiency. With labeled cells in SILAC, one can proceed to do sub- cellular purification of organellar structures of multi-protein complexes as they exist in their native forms. Mass spectrometry analysis can then be performed in a similar fashion to ICAT.64 Compared to the gel-based assays, these methods are more efficient for the comparison of proteins from different specimen samples because the two digested samples are mixed together and run subjected to spectrometry simultaneously, thus decreasing the time by half and removing technical error that would be introduced with separate runs.

C. MALDI/SELDI-TOF Mass Spectrometry

Proteomic pattern analysis via the use of matrix-assisted laser desorption ionization (MALDI) and surface-enhanced laser desorption ionization (SELDI)-time-of-flight (TOF) mass spectrometry are under evaluation as tools to help achieve early disease detection. They have the potential to rapidly identify LMW biomarkers and proteomic patterns or signatures using 1 l of serum. SELDI is accepted as having the highest throughput technology, meaning that large-scale data (15,500 to 400,000 data points per sample and upwards of 1-2 million data points per patient) can be analyzed and produced by MS in hours. The amount of starting material, the large-scale production of data, and the shorter assay time makes SELDI and the other ionization methods significantly more powerful than 2D-PAGE. Furthermore, it does not require knowledge of protein characteristics or validated capture agents (i.e., antibodies) for these protein profiles. MALDI and SELDI technology utilizes protein chip fractionation employing liquid and laser energy, respectively, causing proteins to ionize and take flight in a certain amount of time depending on their mass/charge ratio relative to a detector plate. The detector plate records the intensity of the signal at a given m/z value, and a spectrum of peaks is generated. This fingerprint contains high-dimensional data consisting of millions of data points and so can only be interpreted by pattern-recognition type algorithms. The usefulness of MALDI/SELDI-TOF for these endpoints has been described for many diseases, especially prostate cancer.5,58-61,65-67 The most recent advance in mass spectroscopy is the prOTOF(TM), a MALDI orthogonal time-of-flight (OTOF) analyzer with better mass discrimination and protein identification of peptides.68,69

D. Proteomics and Ovarian Cancer

The need for biomarkers to detect early-stage ovarian cancer is critical, especially since ovarian cancer is often diagnosed with widespread metastases (stage III or stage IV) when surgical treatments are not able to cure the disease. EOC is the leading cause of death among all gynecological cancers in the United States and the fourth leading cause of mortality in women in the United States and Europe.13,56 Current early detection tools using ultrasound and serum CA125 have had very limited success. As a result, more than 70-75% of women are diagnosed when interventional modalities have limited use. Today, the five-year survival rate for women with stage III/IV disease is 35-40%. However, when this disease is detected early (stage I), conventional therapy produces a 95% five-year survival rate.13,14,56 Effective strategies utilizing tools that analyze the genetic, molecular, and biochemical events that regulate carcinogenesis, invasion, and metastatic dissemination are needed for the early detection of EOC. The detection of true early-stage epithelial ovarian cancer will have a profound impact in female healthcare.

Initial analyses using MS investigated the direct mapping and imaging of biomolecules present in tissue sections procured by LCM. Proteomic images obtained by MALDI-MS demonstrated that these molecular portraits could distinguish between different tumor types or between primary and metastatic disease and could also identify early-stage pre-malignant lesions. These studies also demonstrated the presence of significant differences in the proteomic composition of the tumor cells themselves, especially in the LMW range (below 40,000 m/z). Based on these findings, we, as well as others, set out to test the hypothesis that the LMW-range of the circulatory proteome contained previously unrecognized potential diagnostic information. Earlier work by Goodacre et al.70 and Holland et al.71 indicated that it was possible to employ pattern-recognition methodology with mass spectral information to identify fingerprints that could discriminate bacterial species without prior knowledge of the molecules themselves. This approach provided a facile means to exploit mass spectral information for diagnostic purposes in this LMW region. Using a variety of different types of pattern- recognition methods, high-throughput mass spectrometry, and a range of disease states, we and others have generated data that indicate that discriminatory information can be found within the context of the study sets employed.

SELDI-TOF has been used for the detection of specific ovarian cancer signatures. This technique requires only 1l of unfractionated serum for each sample. A set of serum samples from 50 women with ovarian cancer and 50 healthy women was utilized to generate discriminatory patterns that could potentially distinguish them from each other. The proteomic pattern generated was used to test and identify for pattern-matching against a separate set of 116 serum samples in a blind study. The results yielded 100% sensitivity and 95% specificity.65 In an attempt to improve the specificity, a higher resolution form of mass spectrometry (ABI qStar quadrapole) with the SELDI protein chip interface was used. Four discriminatory patterns were identified that resulted in a sensitivity and specificity of 100%, including correct identification of 18 stage-I patients.72

This new approach also allowed the isolation and identification of each peak and feature through the use of peptide sequence analysis.49 Kozak et al.73 had earlier reported the potential use of three sets of biomarker panels containing four to five potential biomarkers when using a strong anion-exchange surface chip and a different statistical method than that used with the SELDI-TOF data.65 Each of these three panels independently and effectively separated benign and ovarian neoplasia with sensitivities and specificities ranging from 72% to 95%. Together, they correctly diagnosed 41 of the 44 blinded test samples: 21 of 22 malignant ovarian neoplasias [10 of 11 early-stage ovarian cancer (I/II) and 11 of 11 advanced-stage ovarian cancer (III/IV)], 6 of 6 low malignant potential, 5 of the 6 benign tumors, and 9 of 10 normal patient samples.73

Several groups of investigators have characterized key mass/ charge (m/z) peaks. Rai et al.74 used the immobilized metal affinity chromatography (IMAC)-Ni chip surface for ovarian cancer plasma profiling, selected seven biomarkers, and characterized three of them. These biomarkers were identified as transferrin (m/z 79,000), haptoglobin precursor fragment (m/z 9200), and the immunoglobulin heavy chain (m/z 54,000). Ye et al. reported a serum biomarker at a m/z of 11,700 using an IMAC-Cu chip surface. This was further identified as the alpha subunit of haptoglobin. An enzyme-linked immunosorbent assay (ELISA) indicated that this subunit was

Zhang et al. reported the identification of three biomarkers from a five-center case-control study encompassing 153 patients with invasive epithelial ovarian cancer, 42 with other ovarian cancers, 166 with benign pelvic masses, and 142 healthy women. These biomarkers were identified as apolipoprotein A1 (down-regulated in cancer), a truncated form of transthyretin (down-regulated), and a cleavage fragment of inter-alpha-trypsin inhibitor heavy chain H4 (up-regulated). These investigators also described their use of markers for the detection of early-stage invasive epithelial ovarian cancer. The combination of these three biomarkers in conjunction with CA125 revealed a higher sensitivity and specificity than CA125 alone.78 The most recent report described proteomic analysis on 61 serum samples from 32 ovarian cancer patients and 29 healthy subjects. The results revealed five potential biomarkers: 2085 Da, 5881 Da, 7564 Da, 9422 Da, and 6044 Da. These biomarkers, when combined, separated the ovarian cancer from the healthy samples with a sensitivity of 96.7%, a specificity of 96.7% and a positive predictive value of 96.7%.79

One of the challenges that has arisen using mass spectrometry has been the need to remove or separate high-abundance proteins that are in the form of a complex or mask other important proteins. It has been argued that high-abundance proteins in the serum decrease the dynamic range available for the identification and characterization of other serum proteins but also alter MS output.80 Immunoglobulins are one class of proteins that may confound the information content of the blood proteome.81 Pre-purification and separation techniques, such as cation-anion affinity-binding columns or reversed-phase separation by HPLC systems, concentrate and ultimately allow the analysis of some of the most informative proteins.81

We and others have also demonstrated that many of the LMW proteins in the circulation are in complex with high-abundance circulating carrier proteins, such as albumin.82-85 Non-covalent association of LMW biomarkers with circulating carrier proteins greatly amplifies the total serum/plasma concentration of the measurable biomarkers. This occurs because association with carrier proteins often extends the half-life of these LMW proteins. This phenomenon was demonstrated after the serum proteome was fractionated for albumin, which makes up ~57% of the serum proteome, and the weakly bound proteins and peptides were eluted from the high- molecular-weight carrier protein. MS analysis revealed a marked amplification in signals from the albumin fraction compared to total serum.

This initial experiment led to a study whereby 90 ovarian cancer serum samples were analyzed by MS to investigate potential LMW biomarkers in complex with albumin. Thirty samples were obtained from women with stage-I disease, 30 sera samples from women with stage-III-IV disease, and 30 from high-risk subjects who were followed for 5 years after the samples were obtained. Data revealed over 800 distinct proteins, the majority of which represented fragments of larger proteins and were previously unknown to exist in human sera. Among 618 novel candidates sequenced, 215 were found only in stage I, 127 were found only in stage III-IV, and 30 were found in all ovarian cancer stages.

While approximately half of the identified proteins currently have no known function, the remainder fall into a multitude of classes with proposed physiologic activities, indicating origins from a variety of cellular compartments (Figure 1). Sera from stage- I ovarian cancer patients, compared to high-risk or stage-III- disease subjects, appeared to have a higher total number of carrier protein-bound proteins, which fell into the following physiologic categories: cell-signaling proteins, protein receptors and channels, and proteins related to apoptosis. Ninety-six of the novel proteins that fell into putative functional categories related to cancer were identified by two or more peptide hits, indicating greater than 99% confidence of a correct identification. Examples of diese ovarian cancer-associated biomarkers included ALK tyrosine kinase receptor, Tau tubulin kinase, LHX-3 homeobox protein, G protein 7- transmembrane receptor, BRCA2 protein, MAGE1, MAGE2, MDM4, TBX6 T- box, Nesprin, and RhoGDI (see Table 3 for a list of stage-I- specific identities). We are now evaluating polyclonal antibodies that may detect the LMW fragments from proteins within this list.

Serum proteomic analysis has not been limited to ovarian cancer. Many reports concerning prostate cancer have been published.58,60,67,86-89 Moreover, SELDITOF-MS has been used for serum proteomic profiling in cancers of lung,90-93 cervix,94,95 bladder,96 breast,10,57,97,98 colon,99 stomach,100 head and neck,101 and pancreas.50,102 This technology has also been applied to study proteins from other diseases, including Alzheimer's,103,104 rheumatoid arthritis,105 and Creutzfeldt-Jacob disease.106

E. Validation of Proteomic Data

Once a certain proteomic profile is recognized as being a particular representative of a specific stage of cancer, its components must be identified. One relevant challenge consists in isolating a specific fragment of a peptide or a combination of fragments. Currently, there are antibodies that recognize epitopes present in peptides; however, one must consider that a specific peptide fragment might have a different secondary or tertiary structure when present alone or in concert with carrier proteins. There is then a need for the development of new testing techniques and reagents that can identify such fragments in serum or other fluids containing the proteomic signature of the disease being tested. Osteopontin has been proposed as a biomarker for the detection of ovarian cancer; mass-spectrometric analysis of urine has identified six peptide fragments from the COOH terminal region of this protein present in urine.107 The same study identified a hyperglycosylated form of eosinophil-derived neurotoxin in the urine of ovarian cancer patients. The development of specific assays that identify differentially glycosylated proteins and specific peptide fragments in a diagnostic test becomes imperative and represents a challenge to current technologies.

FIGURE 1 Ovarian cancer-specific albumin-bound candidate biomarkers. 215 novel serum biomarker candidates were found to be specific for stage I; 127 were found in stage III-IV. Those that were identified have been classified and grouped by proposed physiologic activity. The graphs represent the percentage values obtained for each category in samples corresponding to stage I (graph A) and stage III (graph B) disease. Individual values are shown at the top of each bar for comparison purposes. (Modified, with permission, from Ref. 3.)

TABLE 3 List of Ovarian Cancer Stage-I-Specific Albumin- Associated Peptides*

TABLE 3 List of Ovarian Cancer Stage-I-Specific Albumin- Associated Peptides*

TABLE 3 List of Ovarian Cancer Stage-I-Specific Albumin- Associated Peptides*

Validation of MS data has been performed with the use of antibodies in a western blot or ELISA platform, but these methods may require a large quantity of the specific antigen being identified. Another drawback is the lack of antibodies specific to small peptide fragments that do not cross-react with the larger "parental" peptide. Immuno-mass spectrometry is a technique that effectively can detect and quantify protein isoforms and specific peptide fragments.108 Reverse-phase protein microarray-based immunoassays can use as little as 10 nanoliters of serum for evaluation with specific analyte antibodies. QuadraSpec's Bio-CD system uses novel nanometrology technology to detect as many as 200 markers in a 10-20 l sample of serum without the need for complex reagent development and manipulation (For more, information about this new technology, visit www.quadraspec.com). Together, these technologies offer the needed specificity and multiplexing capabilities for testing and validating proteomic patterns associated with specific stages of disease.

F. Protein Microarrays

Changes in protein expression, composition, and activation have not only facilitated the discovery of biomarkers but have also encouraged the identification of therapeutic targets. Protein microarray biochips represent the most recent advancement in high- throughput analysis of proteins whereby changes in the bioactivity, post-translational modifications, protein-protein interaction, and expression of proteins in various signaling cascades secondary to cancer-induced dysregulation or pharmacotherapy can be detected.109 Protein microarrays can be divided into two formats. In the forward- phase approach, a capture molecule (e.g., antibody, drug) is immobilized on a solid matrix and captures the analyte(s) of interest from a small sample of tissue lysate or sample fluid.109,110 This approach allows the direct characterization of selective interactions between therapeutic targets and drugs. In the reverse-phase array (RPA), a series of protein spots (of different samples or replicates/dilutions of the same lysate) are printed on nitrocellulose (or other capture agent)-coated glass slides.111 Each array is incubated with one pre-validated detection protein. Probing multiple arrays containing identical lysate samples with different phosphospecific antibodies can generate a map of signaling pathways. The high sensitivity of this assay requires only a small amount of protein (e.g., nanograms) for each spot; low abundant phosphorylated isoforms can still be measured from a spotted lysate amount of less than 10 cell equivalents.109

Use of the microprotein array platform has allowed the conservation of tissue obtained from needle biopsies and the development of a reproducible approach. The applications of L\CM, phospho-specific antibodies, and signal amplification technology have facilitated the molecular and biochemical profiling of key signal-transduction pathways in isolated tumor and stroma cell populations and have only been limited by the availability and specificity of the required antibodies.109,112-114

A series of known connected phosphorylation substrates, such as the EGFR, c-erbB1, MEK, and ERK kinases have been analyzed by protein microarrays in the research setting.114 Protein microarrays have been used to demonstrate that phosphorylation and activation of Akt, a pro-survival signal molecule, is a critical early step in cancer progression.115 Akt may be a potential target for molecular targeted therapeutics.115

Similarly, the activation status of several key molecular "gates," including ERK, Akt, and glycogen synthase kinase 3-beta, involved in cell survival and proliferation signaling in human ovarian tumor tissue has been examined. Wulfkuhle et al. concluded that advanced-stage tumors had slightly higher levels of phosphorylated ERK1/2 compared to early stage tumors, whereas the activation status of Akt and glycogen synthase kinase 3-beta, key proteins and indicators of the state of the phosphatidylinositol 3- kinase/Akt pro-survival pathway, showed more variation within each histotype than between the histotypes studied.116

Researchers may need to reclassify human tumors based on their degree of malignancy and metastatic potential, and identify early- stage cancers based on the patient's molecular and biochemical profiles.112 Analysis of activated states of key signaling cascades in microdissected human epithelial ovarian tissue specimens has also revealed a vast heterogeneity in the activity of these signaling cascades within each patient.109 Therefore, analyzing the status of each individual's protein network may facilitate the selection of appropriate and customized therapeutic drugs that target several points of the signaling pathway.109 Combination therapy is the future of individualized therapy, as it promises high specificity and efficacy at lower concentrations, thereby decreasing unwanted toxic effects.

V. CONCLUSIONS

Proteomics is a novel and exciting technology whereby serological methods offer hope for the early diagnosis of cancer. We have postulated that candidate biomarkers emanate not only from tumor cells themselves but also from non-tumor molecules or the extracellular environment. Therefore, these sets of proteins/ peptides reflect the underlying tissue pathology, and the LMW proteome is a rich source for diagnostic profiling. However, further developments and validations are needed before it can be used in the clinical setting. Protein fractionation and enrichment techniques to concentrate low-abundance proteins, in addition to refined instrumentation and bioinformatics, will improve the overall diagnosis of early-stage cancer. Improvements in sample acquisition and storage methodologies will also be needed in order to control for biases and variations that are not directly a reflection of the sample itself.

In 2001 the International Human Proteome Organization (HUPO) was formed to foster global communication and collaboration among the premier proteomic groups so that advancements in proteomic technology, techniques, and the knowledge of the proteomes in human disease could be achieved. Since the formation of this consortium, several proteome projects have been initiated. In 2002 the HUPO Plasma Proteome Project (PPP) was launched to detect and identify proteins in human serum and plasma.117 These pilot studies addressed some important areas: (i) specimen stability and protein concentrations; (ii) protein identifications from 18 MS/MS datasets, including subproteome analyses; (iii) independent analyses from raw MS/MS spectra; (iv) search engine performance; (v) biological annotations and insights; (vi) antibody arrays; (vii) direct MS/ SELDI analyses. Future studies from the PPP aim to generate standardized operating procedures and to continue the development and analysis of datasets arising from groups within and outside of HUPO. These initiatives may ultimately facilitate the early detection, diagnosis, and prognosis of cancer and may also allow for pathways-based monitoring of targeted therapies so that the goal of personalized cancer care can become a reality.

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