Chapter 16 Clinical cancer proteomics

Chapter 16

Clinical cancer proteomics Ayodele Alaiya and Stig Linder

16.1

INTRODUCTION

Tumors result from uncontrolled cell proliferation due to breakdown of the normal regulatory mechanisms of cell division. The loss of proliferation control is a consequence of the accumulation of genetic changes in key regulatory genes; either gain of function of dominantly acting proto-oncogenes or loss of function of tumor suppressor genes [1]. Despite the enormous advances in the field of cancer research with respect to understanding molecular changes underlying tumor progression, the overall mortality rates in most cancer diseases remain essentially unchanged [2]. A major reason for the slow progress in improving cancer therapy is the complexity of the disease; each tumor type (breast, colon, lung cancer) consists of a large number of subtypes that differ with regard to their spectrum of genetic alterations. Each molecular subtype leads to a distinct clinical behavior with regard to progression, metastasis and treatment response. Proteomics technology is attracting great interest with regard to applications in translational cancer research [3]. Among the goals of clinical cancer proteomics includes development of more efficient biomarkers for early disease detection and accurate diagnosis, better prediction of disease prognosis and more effective and individually tailored therapies [4] (Box 16.1). The quest to accelerate the translation of basic discoveries into patient care has resulted in several studies reporting discovery of potential biomarkers, and many more studies have demonstrated the proof of the principle of capacity of clinical proteomics in biomedicine. Even though these studies are promising, studies involving larger patient materials are needed for applications in clinical routine. Furthermore, validations of many of these potential biomarkers are needed before the potential of clinical proteomics could be fully realized. Comprehensive Analytical Chemistry 46 Marko-Varga (Ed) Volume 46 ISSN: 0166-526X DOI: 10.1016/S0166-526X(05)46016-X r 2005 Elsevier B.V. All rights reserved.

597

A. Alaiya and S. Linder

Box 16.1 Goals of clinical cancer proteomics 1. Cancer risk evaluation and early tumor diagnosis a. Discovery of biomarkers for cancer risk assessment b. Biomarkers for early cancer diagnosis 2. Accurate and objective diagnosis a. Development of biomarkers for accurate cancer diagnosis b. Artificial cancer diagnosis 3. Tumor prognosis a. Better prediction of tumor behavior b. Prediction of disease recurrence 4. Treatment monitoring a. Selection of patients for specific treatment modalities b. Monitoring of treatment response/efficacy

16.2

ON THE USE OF BIOMARKERS IN TUMOR DIAGNOSIS

The vast majority of cancer diagnoses are based on microscopical assessments of morphologic alterations of cells and tissues [5]. Benign tumors are composed of highly differentiated cell populations, whereas malignant tumors are aggressive, may invade the surrounding tissues and have the potential to metastasize to distant sites. In most instances, the distinction between benign and malignant tumors is clear. However, in a substantial number of tumors, more detailed examination and biomarker analysis are required before an accurate diagnosis can be made. Biomarker determinations are central in tumor pathology. Diagnostic markers are used to aid histopathological tumor classification. Accurate classification of tumor is not only of academic interest but is necessary to make optimal treatment choices. Prognostic markers provide information about the malignant potential, information which is instrumental for further treatment decisions. The adequate evaluation 598

Cancer proteomics updates

of the risk of metastatic spread is of particular importance with regard to these decisions. Examples of prognostic markers include hormone receptors, proliferation markers, proteases, markers of angiogenesis, growth factor receptors (HER-2/neu), p53, etc. Predictive markers are used to choose between different alternative treatment modalities. For instance, breast cancer patients that exhibit estrogen receptor positive tumors are usually treated with anti-estrogen compounds such as tamoxifen, whereas estrogen receptor negative tumor patients are treated with chemotherapy. Immunohistochemistry is the standard method for routine pathological diagnostics, due to the surge in commercially available antibodies directed against tumor biomarkers and the introduction of automated slide staining instruments.

16.3

CANCER PROTEOMICS

The term ‘‘Proteome’’ describes the PROTEin complement of a genOME [6,7]. The additional complexity of analysis of the proteome is not just a matter of numerical complexity in terms of variants of gene products that can arise from a single gene, but also protein–protein interactions and targeting of proteins to specific subcellular compartments and structures. Large-scale global analysis of the levels of different expressed proteins within a cell or tissue at a particular time and space is commonly referred to as ‘‘expression proteomics’’ [8]. The aim is to identify polypeptides that significantly differ in their concentrations between cells, tissues or extracellular fluids, reflecting different disease conditions. ‘‘Cancer proteomics’’ is the analysis of molecular pathogenesis of cancer by comparing global protein expression changes of tumor cells and cells from normal tissue [4]. The ultimate goal of cancer proteome analysis is to use this complex information as a basis for individually tailored therapy. Ideally, such markers should be highly sensitive, specific and possible to analyze at an affordable cost. The complex and dynamic nature of proteins makes proteome studies quite challenging. There is currently no single proteome analysis strategy that can sufficiently address all levels of the organization of the proteome. This is in contrast to measurements of gene transcript levels using nucleic acid-based methods such as cDNA arrays, SAGE and other methods, where technologies are becoming standardized and 599

A. Alaiya and S. Linder

a routine. Measurements of transcript levels do, however, not necessarily predict the level of the corresponding proteins. The exact number of proteins in human cells is not known, but it is estimated that 50,000–400,000 proteins are produced in human cells if each gene produces 2–10 proteins. Many genes produce multiple variants of the same protein due to alternative RNA splicing and various forms of post-translational modifications. The correlation between mRNA transcript profiles and corresponding protein abundance has been reported to be modest [9]. Cellular structural gene products show a high correlation between mRNA and protein levels, whereas the correlation between protein and mRNA levels is less prominent for other gene products [10]. In terms of expression profiling, these differences may be of limited practical importance since a very good correlation has been reported between alterations in transcript levels and protein levels recorded between non-invasive and invasive bladder tumors [11]. 16.4 16.4.1

PROTEOMICS ANALYSIS PLATFORMS Instrumentation and technology platforms

High-resolution two-dimensional gel electrophoresis (2-DE) is a wellestablished protein separation method capable of resolving thousands of polypeptides [12,13] (Table 16.1). Even though alternative methods are being taken into use, 2-DE remains a standard tool for expression proteomics. 2-DE has been greatly improved over the years by the introduction of immobilized pH gradient (IPG) strips for the first-dimensional isoelectric focusing (IEF) step. The IPG system allows more protein to be loaded and significantly improves gel reproducibility and enhances inter-laboratory gel comparison. Substantial improvements in image analysis, data-mining and image storage capabilities have encouraged investigators to continue to apply 2-DE for the analysis of complex samples. Finally, the potential of 2-DE as a tool for biomarker discovery has been greatly improved with the development of more sensitive mass spectrometry (MS) methods for identification of 2-DE spots. Despite the high resolving power and improvements in 2-DE, there are inherent drawbacks that need to be considered. First, 2-DE does not detect all proteins present in a sample. Many proteins are expressed at low levels (o1000 copies/cell) and may elude detection 600

Cancer proteomics updates TABLE 16.1 Brief comparison of selected proteomic technologies Methods Protein separation 2-D electrophoresis

Advantages

Limitations

Global differential display analysis High resolution of proteins

Limited dynamic range of protein detection Poor solubility of some proteins Comprehensive image analysis remains a moderate bottleneck Low-throughput capability Labor intensive and requires skill

Reveals physical protein characteristics; Mr/pI Analysis of post-translational protein modifications Absolute quantitation Suitable for biomarker discovery 1-DE/LC-MS/MS

Highly sensitive Efficient in analysis of lowand high-molecular-weight biomarkers Analysis of post-translational protein modifications Scalable to automation

2-D-LC-MS/MS (MudPit)

Large data file for analysis

Good resolution

Multi-step procedure

Wide dynamic range than 2DE Enhanced number of peptides for identification

Large data file for analysis Less suitable for differential display unlike 2-DE Requires comprehensive genome sequence database

Amenable to automation

ICAT

Moderate protein resolution Admixture of proteins on a single band

Sensitive

Requires cysteine residues for labeling (continued)

601

A. Alaiya and S. Linder TABLE 16.1 (continued ) Methods

Advantages Suitable for low-abundant proteins Analysis of wide range of protein classes Accurate relative quantitation No radioactivity or metabolic labeling, no gel electrophoresis Possible sequence identification

Protein identification MS (MALDI-TOFMS)

Highly sensitive Can give sequence information

Limitations

Peptide ionization may be impeded by the ICAT tags Careful mass spectral analysis and data interpretation Low-throughput capability

Expensive instrumentation Requires sequence databases for protein identification

Molecular mass determination Characterize posttranslational modifications High-throughput capability Scalable to automation Minimal sample required Immunoaffinity

Ideal to study protein–antibody complexes

Requires highly specific affinity May be expensive May require protein purification

Proteomics patterns analysis SELDI-TOF-MS

Selective surfaces retains specific protein properties

Not yet fully reproducible

Reduces sample complexity

Not yet fully standardized (continued)

602

Cancer proteomics updates TABLE 16.1 (continued ) Methods

Advantages High-throughput capability Minimal sample handling and processing steps Suitable for screening

Limitations Poor quantitation No specific protein IDs, only pattern recognition analysis

Suitable for routine clinical application Scalable to automation Optimal for profiling of lowmolecular-weight proteins Protein arrays

Selective antibodies retains specific protein properties Minimal sample handling and processing steps Suitable for screening

Requires specific antibodies Expensive Labor intensive to make specific antibodies for thousands of proteins

High-throughput capability Suitable for routine clinical application Scalable to automation 1-DE: one-dimensional gel electrophoresis; 2-DE: two-dimensional gel electrophoresis; 2D-LC/MS/ MS: two-dimensional liquid chromatography/tandem mass spectrometry; ICAT: isotope-coated affinity tag; LC: liquid chromatography; LC/MS/MS: liquid chromatography/tandem mass spectrometry; MALDI-TOF-MS: matrix-assisted laser desorption/ionization time-of-flight mass spectrometry; MS: mass spectrometry; SELDI-MS: surfaced-enhanced laser desorption/ionization mass spectrometry; MudPit: Multi dimensional Protein identification technique

by 2-DE. Several of these low-abundant proteins are of functional significance in disease processes. The use of narrow pH-range IPG strips and/or fractionation of samples can enhance the detection of low-abundant proteins. Many clinical samples (obtained from small tumors), do unfortunately not contain sufficient amounts of protein to be analyzed on multiple 2-DE gels. Other proteins are not soluble in the detergent/ urea buffer used in the first dimension, and yet other proteins will comigrate with highly abundant proteins and therefore not be detected. Another problem is that the throughput of the procedure is relatively 603

A. Alaiya and S. Linder

low. Most laboratories have not automated the electrophoresis process, and image analysis requires data editing. Differential in-gel electrophoresis (2D-DIGE) may overcome some of the above limitations [14]. The system allows complex protein extracts to be covalently labeled prior to electrophoresis. Three fluorescent dyes (Cy2, Cy3 and Cy5) presently are commercially available and the introduction of more dyes will improve sample throughput. The multiple labeled samples are mixed and subjected to 2-DE. The gel is scanned at different emission wavelengths and multiple images corresponding to different samples are generated. This methodology significantly improves sample throughput and greatly enhances gel reproducibility [15–17]. A number of 2-DE image analysis software programs are available, which are capable of quantifying the levels of proteins resolved on 2-DE gels. Most of the commercially available 2-DE image analysis programs such as PDQuest, Melanie 3, Investigator 2D and Phoretix 2D have incorporated excellent spot detection algorithms and features that facilitate gel alignment and matching. However, despite these features, the inherent methodological variability necessitates some degree of manual work-up for accurate spot matching. An important aspect of global expression data is the subsequent analysis of multivariate data. Gene expression patterns are commonly analyzed using principal component analysis, hierarchical clustering analysis or similar methods, features often included in 2-DE image analysis programs. Mass spectrometry is currently the most effective method in identifing proteins separated by 2-DE. Protein spots can be routinely identified by MALDI-TOF-MS (matrix-assisted laser desorption/ionization time-of-flight MS) analysis. Following ionization, the time-of-flight of the molecules from the source to the detector is measured. This method allows high-throughput protein identification. Other MS techniques such as electrospray ionization (ESI-MS-MS) have become standard methods in proteomic workflows. The utility of MS has been dependent on the development of comprehensive sequence databases and EST databases that allowed protein identification by correlation of database sequence information with MS-generated data [18–21]. Despite great improvement made in gel-based to proteome methods, attempts are being made to develop alternative methods (Table 16.1). One such method is multidimensional protein identification (MudPIT)[22]. This method involves tryptic digestion of protein mixture 604

Cancer proteomics updates

followed by reverse-phase ion-exchange multidimensional liquid chromatography. The isotope-coated affinity tag (ICAT) method is another evolving proteomic technology that is capable of comparing proteomes of two samples simultaneously [23]. The principle of ICAT is that protein mixtures are reacted with reagents with specificity toward sulfhydryl groups. These reagents also contain a linker of variable size and a biotin affinity tag. Samples are separately reacted with reagents with linkers of two distinct sizes. The same peptides present in the two samples will differ slightly in molecular weight and can be separated by MS. Wall et al. [24] developed a two-dimensional all liquid-phase method combined with MS for protein profile analysis. With this method, proteins are fractionated by pI using IEF in the Rotofor cell and then further separated by hydrophobicity using reverse-phase HPLC in the second dimension.

16.5

PROTEIN ARRAYS AND HIGH-THROUGHPUT TECHNIQUES

Most investigators in the proteome field agree that the low throughput of 2-DE calls for the development of new proteomics tools. These methods are either based on protein separation (by MS or chromatography), or by array technologies. Examples of such methods are SELDI-TOF (surface-enhanced laser description/ionization time-offlight), integrated affinity chromatography procedures, protein arrays and other microfluidic technologies [25]. It has been possible to covalently attach proteins onto chemically derivatized glass slides at high spatial densities. The proteins attached to the slide surface retained their ability to interact specifically with other proteins, or with small molecules, in solution [26,27]. Such arrays can be used for screening of protein–protein interactions, to identify the protein targets of small molecules and also have other applications. Antibody microarrays have enormous potential for characterization of molecular mixtures similar to DNA microarray technologies [28,29]. However, antibody array methods will depend on efficient protein solubilization, which is difficult/impossible to obtain for some proteins under conditions that allow antibody binding (i.e. without the use of ionic detergents or very high salt concentrations). The Protein Chip System (Ciphergen, Fremont, CA, USA), based on SELDI, is an alternative chip system which has the advantage 605

A. Alaiya and S. Linder

of integration with MS [30]. Using these protein chips, proteins are captured directly on a modified MALDI plate. The current platform, based on nine different capture agents per chip, is claimed to achieve comprehensive coverage of the proteome. As discussed above, methods based on separation (electrophoresis or MS) are limited by the fact that different proteins are present at widely different concentrations in tissues and fluids, leading to difficulties in detecting minor peaks in areas containing very abundant components.

16.6

SAMPLE PREPARATION PROTOCOLS

The quality and reproducibility of 2-DE gels largely depends on adequate sample preparation procedures. Initial sample handling, preparation and storage prior to detailed proteomic analysis are of paramount importance. Tumor tissue biopsies are commonly preserved in formalin-fixed paraffin-embedded. This allows retrospective studies of tumor materials where the patient’s outcome is known. However, the quality of protein extracts derived from tissue samples preserved in formalin-fixed and paraffin-embedded is not suitable for most protein analysis [31]. Major improvements have been achieved in solubilizing cells and tissues using cocktails of detergents and/or sequential extraction protocols. There is, however, no standard sample preparation protocol prior to proteome analysis, although sample preparation extraction kits are commercially available. The use of fresh tissue samples has been reported to give rise to better resolution 2-DE patterns compared to frozen tissue [32]. Analysis of whole tissue samples often results in admixture of tumor cells with various other cells present in tumors (stromal cells, lymphocytes, red blood cells, macrophages, etc.). Serum proteins (albumin, haptoglobin, immunoglobulins) can be the major components in the protein profiles of whole tissue samples. The extent of contamination by proteins from these other sources will vary across different samples and could significantly affect result interpretation (i.e. in the work-up of data it is difficult to express the levels of a specific protein as amount/mg loaded protein if serum proteins may constitute a significant but varying part). The successful purification of tumor epithelial cells using antibody-coated magnetic beads or Dynabeads has been recently described [33]. Our group has used various simple protocols, including fine-needle aspiration, scraping and squeezing to enrich tumor cells 606

Cancer proteomics updates

[32]. This routinely results in high-quality 2-DE gels (Fig. 16.1). The purity of cell preparations was checked by microscopy before analysis and internal markers such as PCNA and tropomyosin-1 could be used to validate the absence of contaminated cells. Laser capture microscopy (LCM) can be used to procure highly representative subpopulation of cells from complex heterogeneous tissue samples [34] and can produce sufficient tumor cells for 2-DE analysis [35,36] or antibody arrays [37]. Using post-labeling techniques, proteomics analysis is possible from 10,000 cells collected by LCM [38]. LCM has been used to study the proteome of prostate tumors [39,40], ovarian [41], breast [38] and pancreatic tumors [42]. Tumor heterogeneity is always a concern in studies of markers in tumor tissue. Our studies have shown that the protein profiles of tumor cell populations collected from different areas of the same breast tumors are surprisingly similar [43]. This result could be due to ‘‘clonal dominance’’, i.e. large tumors are dominated by a rapidly growing clone of cells [44]. One approach used to overcome tumor heterogeneity is to study short-term primary cell cultures. Although such cultures may be argued to resemble the parent tissue, it is impossible to replace the in vivo natural environment of the primary source. A comparison between human prostate cell lines with tumor cells from the same patients showed significant altered protein profiles [39]. Similarly, several changes in gene expression was observed after explantation of transitional cell carcinomas (TCCs) into cultures [45]. The value of shortterm primary cultures can therefore be questioned.

16.7

SHORT OVERVIEW OF APPLIED CLINICAL CANCER PROTEOMICS

Clinical cancer proteomics can be divided into different areas: (i) projects aimed to identify new diagnostic/prognostic/predictive markers; and (ii) projects aimed to use multivariate analysis (‘‘profiling’’) to distinguish between tumor subtypes. With regard to searches for novel biomarkers, proteomics has the obvious advantage of measuring primarily high abundance proteins, which are ideal tumor biomarkers since they can be easily measured and targeted. Proteomics does not, however, show any obvious advantages over cDNA microarrays (‘‘transcriptomics’’) if the aim is simply profile gene expression. Both methodologies require similar amounts of material, and have the capacity to 607

608 Protein identification

Post translationalmodifications e.g.: Protein fingerprint

phosphorylation, glycosylation Calreticulin Global differential expression

Spot quantitation

Actins

Serum albumins

A. Alaiya and S. Linder

HSP 70

Cancer proteomics updates

generate large data sets. However, proteomics have unique potentials in protein profiling since it can be used to analyze the state of protein phosphorylation (using phospho-specific antibodies and other techniques). Information with regard to the state of activation of kinase pathways potentially, is extremely valuable in predicting metastasis and apoptosis sensitivity. One example is the AKT kinase, which has been implicated in clinical resistance to therapy [46]. An overview of clinical proteomics strategies is presented in Fig. 16.2. We will not attempt to review all literature in this field, and we will restrict ourselves to investigation of human tumor material (i.e. not cell lines in vitro), and do not attempt to cover all published works. For other reviews of the field, see [3,47,48]. 16.7.1

Differential cancer diagnosis

Metastatic cancer of unknown primary site (CUP) accounts for approximately 3% of all malignant neoplasms and is therefore one of the 10 most frequent cancer diagnoses in man. These cases are subjected to extensive investigations (primarily using immunohistochemistry); however, the primary site remains unknown in most patients, even after autopsy. The most frequently detected primary tumors are carcinomas hidden in the lung or pancreas. Lung is a very common site of metastasis, and the possibility that a lung tumor is not a primary must therefore always be considered. Also when the origin is clear, differential diagnosis between tumor types can be difficult. One example is ovarian cancer, where serous papillary ovarian cancer and uterine serous papillary carcinoma are histologically similar. However, these tumors exhibit distinct clinical behavior and response to chemotherapeutic agents. In general, the ability to make the right diagnosis is of importance for the choice of therapy and subsequent follow-up assessment. One useful diagnostic marker identified by 2-DE is the aspartyl protease napsin. The protein was originally observed by Franze´n et al. [49] in lung adenocarcinoma and was proposed to be a useful marker Fig. 16.1. Two-dimensional gel electrophoresis. A representative 2-D gel derived from ovarian cancer is shown to illustrate some potentials of 2-DE for both qualitative and quantitative analysis, as well as analysis of post translational protein modifications. Spots of interest were excised for mass spectrometric protein identification.

609

610 Sample collection /preparation Whole cell lysate, LCM Fractionation, FACS, Enzymatic/mechanical preparation Protein separation 1D/2D/2D-DIGE MudPit, LC CE, Tissue imaging

Sample Sources/Types Experimental design/ sample selection

Core Proteomics platforms 2-D Electrophoresis /image analysis

Protein identification Spot
/ In-gel digestion MALDI-TOF,(mass fingerprinting) Tandem MS; MS/MS (Sequencing) Database search Bioinformatics Disease Database LIMS Biomarker Discovery MS Protein identification Biomarker validation IHC, ELISA Development of diagnostic kit HTP analysis Protein arrays

Clinical application

A. Alaiya and S. Linder

Visualization, Image & data Analysis Fluorescent/ Colorimetric dyes Differential analysis Multivariate data analysis

Tissues, Body fluids Cells, etc

Cancer proteomics updates

for this type of tumor. Later studies have shown that napsin is a prosurfactant convertase in type II alveolar cells [50] and that this marker is indeed a very useful marker for primary lung adenocarcinoma [51,52]. Molecular profiling technologies have the potential to offer solutions to problems associated with tumors of unknown primary etiology, as demonstrated in a recent study using data available from public databases [53]. Sixty-one candidate markers with tissue-specific expression were identified; 11 genes were tested by RT-PCR in primary adenocarcinomas from a range of sites, and seven were found to be siterestricted. 16.7.2

Tumor grading and prognosis

A very large number of studies have been carried out in order to identify prognostic markers in various malignancies. We will discuss here some of these studies, and then discuss some common themes and future perspectives. The term ‘‘borderline malignancy’’ is used to classify a group of ovarian carcinomas with a significantly more favorable prognosis. The diagnosis of borderline tumors can be problematic, so different criteria have been suggested [54]. Alaiya and coworkers analyzed 2-DE profiles from 22 ovarian tumors, and described markers that could be used to differentiate between benign, borderline and malignant tumors [55]. Jones and colleagues analyzed five microdissected ovarian tumors and described 23 proteins that were differentially expressed in malignant and borderline tumors [41]. Principal components analysis and hierarchical clustering analysis was used for classification of ovarian tumors [56,57]. The pattern of expression of 170 polypeptides was used to construct a training model which was tested on a separate set of 18 tumors. A clear separation between carcinomas and benign/borderline tumors was observed, whereas benign and borderline tumors were Fig. 16.2. Overview of clinical proteomics strategies. Schematic illustration of an overview of clinical cancer proteomics strategies. This is a typical proteomics platform including two-dimensional gel electrophoresis and mass spectrometry (2DE/MS) as the core technologies. Gel separated proteins will be visualized and subjected to computer assisted image analysis. Differentially expressed protein spots of interest will then be excised and identified using mass spectrometry (MALDI-MS, LCMS/MS). Potential identified biomarkers will be validated using immunohistochemistry (IHC) or enzyme linked immunosorbent assay (ELISA). 611

A. Alaiya and S. Linder

more difficult to separate [56]. Hierarchical clustering analysis produced similar results, showing that benign and borderline tumors have similar protein profiles [57]. Celis and coworkers have performed a number of studies on human bladder cancer (reviewed in [3,58]). These studies defined changes in the expression of several proteins during bladder tumor progression. In one study, 150 fresh bladder tumors were analyzed by 2-DE/microsequencing/MS. Tumors with a low degree of differentiation did not express keratin 10 and were characterized by a decreased expression of keratin 14, psoriasin, PA-FABP, galectin 7 and stratifin (14-3-3 sigma) [59,60]. The results of proteome analyses have been verified by immunohistochemistry for some markers. Intricate changes in the expression of keratins were shown to be useful for defining different types of bladder lesions [61]. The bladder cancer proteome database is available on the Internet (http://proteomics.cancer.dk). Several studies have described differences in protein expression profiles between benign and malignant tumors of the breast [35,38,62–67]. Some of these studies identified a large number of proteins (300–400) using MS and also validated the expression of some proteins in normal and tumor tissues using immunohistochemistry [35,66]. These studies have identified a number of proteins with different levels of expression in normal breast tissue/benign tumors relative to malignant breast tumors, including heat-shock proteins, tropomyosins and 14-3-3 sigma. A 2-DE database of human breast carcinoma is available at http://www.bio-mol.unisi.it/2d/2d.html. Lung cancer is a common form of malignancy with a very poor 5-year survival. A number of proteins have been described to be overexpressed in lung adenocarcinoma, including the antioxidant enzyme AOE372 and glutathione-S-transferase M4, glucose-regulated 58 kDa protein, prolyl 4-hydroxylase beta subunit and triosephosphate isomerase [68]. Potential prognostic markers were described by the same laboratory [69] in a study of 90 lung adenocarcinomas. Elevated levels of one of these markers, phosphoglycerate kinase 1 were associated with survival. Interestingly, elevated serum levels of this enzyme also correlated to poor patient outcome. Yanagisawa et al. used MALDI-MS from frozen tissue sections to classify lung tumors. Using data from differentially expressed proteins it was possible to generate models to perfectly classify lung cancer histologies, distinguish primary tumors from metastases to the lung from other sites and classify nodal involvement with 85% accuracy [70]. 612

Cancer proteomics updates

A final example of a clinical application of proteome analysis is the work by Voss and coworkers [71] on chronic lymphocytic leukemia (B-CLL). B-CLL tumor cells from patients with shorter survival times were found to exhibit altered levels of redox enzymes, heat-shock protein 27 and protein disulfide isomerase. Many of the proteome studies of clinical tumor materials performed so far suffer from various shortcomings. First, it is in most instances unclear how tumors were selected for analysis. Not all tumors can be analyzed by 2-DE or similar methods and there are a number of tumor characteristics that will lead to under-representation of certain types of tumors. Small tumors and tumors very rich in stroma may not yield sufficient material for analysis, and necrotic tumors may produce protein profiles of poor quality. Studies of prognostic markers should ideally be based on population-based materials (i.e. not extreme cases of malignant tumors compared to benign counterparts or normal tissue). The value of tumor materials that do not accurately reflect the naturally occurring spectrum of tumors can be questioned. Second, published studies often described very small materials; the statistical power of investigations of 10–20 tumors is extremely low. Third, in only a few instances were markers identified by proteome analysis, validated by immunohistochemistry in tissue sections; in even fewer were a sufficiently large number of tumors used for such a validation. Most investigators in the prognostic marker field would agree that results may be affected by statistical fluctuations in materials of 50 tumors, and that population-based series of 4200 tumors should be used. Despite these shortcomings, interesting results have been gained, and several independent studies consistently point to the involvement of some markers (such as Hsp27) in malignant transformation in vivo. However, it would be desirable if proteome laboratories collaborated with clinical pathologists and biostatisticians to clarify the usefulness of candidate prognostic markers. Another issue is whether proteome analysis can be used in clinical routine, or if these methods only are useful for biomarker discovery studies. 2-DE has been adapted to automation, primarily by Large Scale Biology Corporation. Their ProGExTM system is automated from sample preparation to protein identification (see www.lsbc.com). This type of industrial application of 2-DE is not possible to implement in routine hospital laboratories, but the proteome analysis process does not necessarily need to be as sophisticated for clinical applications. We have shown that breast tumors can be successfully analyzed and 613

A. Alaiya and S. Linder

classified by mini-gels that resolve only 350 proteins [67]. This technology requires small amounts of material and is simple and rapid. Finally, it is possible to use breast tissue samples collected by core biopsy for 2-DE analysis [72]. We therefore believe that it is possible to develop a simplified, automated system for proteome analysis that will be very useful in routine clinical work.

16.8

PROTEIN PROFILING OF SERUM AND OTHER BODY FLUIDS

Body fluids such as plasma, serum, saliva or urine are rich sources of biomarker discovery. Tumor-associated and tumor-specific molecules will be released into the blood and other fluids where they can be detected by proteome analysis. Body fluids are much more accessible for study than tissue samples. In terms of biology, however, they present with additional levels of complexity. Proteins can be secreted from cells into the circulation, or they can be released from dying cells (by necrosis and/or apoptosis [73]). Proteins that reach the circulation may have different turnover rates in different patients (dependent on liver and kidney function). Despite recent excitement in the area of serum proteomics, any marker or marker concept will always show a lack of sensitivity since some tumors will be too small to release detectable amounts of protein. Furthermore, other diseases than cancer will lead to the appearance of various markers in the blood (i.e. liver disease, infections, etc.) and specificity will therefore never be 100%. It is not an easy task to measure proteins released from tumor cells. Some plasma proteins occur in very high concentrations, the most abundant being serum albumin (44 mg/ml). If one assumes that tumorassociated proteins are present at concentrations in the range of a few picograms/ml, this will be 1010-fold lower than serum albumin. In terms of a practical separation experiment, this means that if a tumor marker gives rise to a peak of 1 mm in a scanned gel image, serum albumin will generate a peak of 10,000 km (twice the distance of the United States, coast to coast). Looking for a needle in a haystack will be like looking for a Boeing 747 in a parking lot in comparison. Obviously, even very rare degradation products of serum albumin will disturb the detection of interesting markers if they have the same or similar molecular mass. In addition to the difficulty of finding markers in mixtures of proteins present at such different concentrations, commonly 614

Cancer proteomics updates

used dyes such as silver or Coomassie stains exhibit dynamic detection range of around 2–3 orders of magnitudes. Therefore, serum or plasma samples that are to be analyzed by electrophoresis or MS require extensive pre-analysis cleanup. Human serum albumin removal protocols such as the Affi-Gel Blue or Aurum serum protein mini kit [74] or immunoaffinity-based protein subtraction chromatography (IASC) have been successfully used to deplete serum sample prior to 2-DE [75]. The introduction of fluorescent dyes has improved the linear range of detection and more efficient protein quantitation of both high and low abundance proteins. The SELDI protein chip platform is based on the principle that proteins from crude mixtures are selectively attracted to specific biochemical surfaces. Potential biomarkers may show a higher binding affinity to certain surfaces than serum albumin, haptoglobin and other abundant serum proteins. However, such preferential binding is unlikely to be absolute, and competition may still occur. Pieper et al. [75] fractionated serum samples using a combination of methods including immunoaffinity chromatography, sequential anion exchange and size-exclusion chromatography. Different fractions were subjected to 2-DE and approximately 3700 distinct protein spots were resolved. A total of 1800 serum protein spots representing 325 distinct proteins were identified by MS. Interestingly, some relatively lowabundant proteins, present at o10 ng/ml, could be detected, highlighting the potential of 2-D/MS in profiling of the human serum proteome. Poon and Johnson [76] searched for potential tumor markers in undepleted sera of patients with hepatocellular carcinoma (HCC). Despite the presence of serum albumin and immunoglobulin they could observe differentially expressed proteins and identified isoforms of hepatocellular cancer specific-a-fetoprotein. In another study, sera from colon cancer patients were fractionated using Con A-Sepharose chromatography followed by 2D-PAGE. Low-abundant proteins were observed in serum samples from cancer patients and matched control samples [77]. Surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) has been suggested to be useful for ovarian cancer screening [78]. A model for detecting ovarian cancer was established by analysis of serum from 50 patients and 50 unaffected women. This model was then tested on an independent set of 116 serum samples. The sensitivity of detection of ovarian cancer was reported to be 100% and the specificity 95%. These findings have been questioned 615

A. Alaiya and S. Linder

by other investigators [79,80] arguing that the data set available on the internet does not support the conclusions made [81,82]. Another group of investigators have used SELDI for similar applications (plasma protein profiles of normal and ovarian cancer patients) but with less conspicuous results [83]. A set of four markers was identified by SELDI and was found to improve the detection of ovarian cancer when used in combination with serum CA-125. The sensitivity and specificity of SELDI-TOF analysis in detection of ovarian cancer in serum samples is therefore unclear, and the original data need to be reproduced by other laboratories. Several factors such as differences in patient selection, sample collection and processing may be contributing factors. Koopmann et al. [84] analyzed serum samples from 60 patients with pancreatic adenocarcinoma and appropriate age- and sex-matched controls by SELDI-MS. The two most discriminating markers differentiated patients with pancreatic cancer from healthy controls with a sensitivity of 78% and specificity of 97% and performed significantly better than the serum marker CA19-9. It is important to realize that extremely high sensitivities and specificities are required for justification of general screening. A specificity of detection of ovarian cancer of 95% by SELDI analysis of serum will result in a large number of false-positive results, since ovarian cancer is not a common disease (the predictive value will be 0.8% in a population where the frequency of ovarian cancer is 1:2500 [85,86]). Urine is an interesting source of biomarkers. The protein profile of human urine changes as a result of disease or drug toxicity. Fractionation procedures leading to excellent protein spot resolution of urine samples have been described, and around 400 urinary spots are identified [87]. Several of these spots represented post-translational modifications and proteolytic products. Urine is routinely used for detection of urinary bladder cancer, and several biomarkers are available (for reviews, see [88–90]). Vlahou et al. [91] explored the use of SELDITOF-MS for the diagnosis of bladder TCC. The detection rates ranged from 43 to 70% and specificities from 70 to 86% using individual markers, and were higher when combinations of markers were used (sensitivity 87%, specificity 66%). Cerebrospinal fluid (CSF) is another source of protein biomarkers. Two isoforms of a(2)-Heremans-Schmid glycoprotein (AHSG) were identified and demonstrated in higher levels the CSF of patients with low-grade gliomas compared with a control group using 2-DE and 616

Cancer proteomics updates

MALDI-TOF [92]. In a study of CSF from patients with primary brain tumors, the N-myc oncoprotein and low-molecular-weight caldesmon were identified by 2-DE and MALDI-TOF-MS [93]. SELDI-TOF-MS is suitable as a potential screening and/or diagnostic tool because it requires minimal sample preparation and only very small amount of sample. Two groups have both independently reported analyses of complex spectra derived from SELDI-TOF-MS experiments on nipple fluid aspirates. These experiments were reported to result in the identification of several potential biomarkers for early detection of breast cancer [94,95]. Recent studies have described the potential of proteomic pattern recognition in disease diagnosis. The proteomic pattern approach is based on the analysis of large amount of mass spectrometric data derived from complex protein mixtures and do not per se requires that the proteins involved be identified. Even though, these results are promising, the identification and validation of the potential biomarkers used in the proteomic patterns analysis is called for.

16.9

FUTURE PERSPECTIVES AND CHALLENGES

A number of potential biomarkers have been identified by proteome studies. It is very important to validate these markers using other methods such as immunohistochemistry. Clinicians can only be convinced to use markers that have been shown to have prognostic or treatment predictive value in consecutive patient’s materials (i.e. all patients that received the diagnosis during a particular time period). A bias toward large tumors is not acceptable, since tumor size is known to be associated with poor prognosis in most cancer diseases. Proteome data need to be translated into artificial learning models which can be used for prognosis and treatment prediction. In our own studies [56,57], we found that using the entire data set obtained from 2-DE gels (usually around 1500 spots) does not yield good discrimination between benign and malignant tumors, and that sets of markers have to be selected. How such model building should be coordinated between different laboratories to reach a consensus model is not easy to foresee at present. However, analysis of proteome data have an enormous potential to be further developed into a ‘‘proteome scanner’’, i.e. an artificial intelligence tool capable of assisting clinical judgments in establishing a more accurate diagnosis and prognosis. 617

A. Alaiya and S. Linder

ACKNOWLEDGMENTS We thank Cancerfo¨reningen in Stockholm for funding.

REFERENCES D. Hanahan and R.A. Weinberg, The hallmarks of cancer, Cell, 100 (2000) 57–70. 2 H.K. Weir, M.J. Thun, B.F. Hankey, L.A. Ries, H.L. Howe, P.A. Wingo, A. Jemal, E. Ward, R.N. Anderson and B.K. Edwards, Annual report to the nation on the status of cancer, 1975–2000, featuring the uses of surveillance data for cancer prevention and control, J. Natl. Cancer Inst., 95 (2003) 1276–1299. 3 J.E. Celis and P. Gromov, Proteomics in translational cancer research: toward an integrated approach, Cancer Cell, 3 (2003) 9–15. 4 A.A. Alaiya, B. Franzen, G. Auer and S. Linder, Cancer proteomics: from identification of novel markers to creation of artificial learning models for tumor classification [In Process Citation], Electrophoresis, 21 (2000) 1210–1217. 5 R. Fox and M. Hull, Ultrasound diagnosis of polycystic ovaries, Ann. NY Acad. Sci., 687 (1993) 217–223. 6 P. Kahn, From genome to proteome: looking at a cell’s proteins, Science, 270 (1995) 369–370. 7 M.R. Wilkins, J.C. Sanchez, A.A. Gooley, R.D. Appel, I. Humphery-Smith, D.F. Hochstrasser and K.L. Williams, Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it, Biotechnol. Genet. Eng. Rev., 13 (1996) 19–50. 8 W.P. Blackstock and M.P. Weir, Proteomics: quantitative and physical mapping of cellular proteins, Trends Biotechnol., 17 (1999) 121–127. 9 L. Anderson and J. Seilhamer, A comparison of selected mRNA and protein abundancies in human liver, Electrophoresis, 18 (1997) 533–537. 10 S. Nishizuka, L. Charboneau, L. Young, S. Major, W.C. Reinhold, M. Waltham, H. Kouros-Mehr, K.J. Bussey, J.K. Lee, V. Espina, P.J. Munson, E. Petricoin, III, L.A. Liotta and J.N. Weinstein, Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays, Proc. Natl. Acad. Sci. USA, 100 (2003) 14229–14234. 11 T.F. Orntoft, T. Thykjaer, F.M. Waldman, H. Wolf and J.E. Celis, Genome-wide study of gene copy numbers, transcripts, and protein levels in pairs of non-invasive and invasive human transitional cell carcinomas, Mol. Cell Proteomics, 1 (2002) 37–45. 12 P.H. O’Farrell, High resolution two-dimensional electrophoresis of proteins, J. Biol. Chem., 250 (1975) 4007–4021. 1

618

Cancer proteomics updates 13

14

15

16

17

18

19

20

21 22

23

24

J. Klose, Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues. A novel approach to testing for induced point mutations in mammals, Humangenetik, 26 (1975) 231–243. M. Unlu, M.E. Morgan and J.S. Minden, Difference gel electrophoresis: a single gel method for detecting changes in protein extracts, Electrophoresis, 18 (1997) 2071–2077. F. Von Eggeling, A. Gawriljuk, W. Fiedler, G. Ernst, U. Claussen, J. Klose and I. Romer, Fluorescent dual colour 2D-protein gel electrophoresis for rapid detection of differences in protein pattern with standard image analysis software, Int. J. Mol. Med., 8 (2001) 373–377. R. Tonge, J. Shaw, B. Middleton, R. Rowlinson, S. Rayner, J. Young, F. Pognan, E. Hawkins, I. Currie and M. Davison, Validation and development of fluorescence two-dimensional differential gel electrophoresis proteomics technology, Proteomics, 1 (2001) 377–396. G. Zhou, H. Li, D. DeCamp, S. Chen, H. Shu, Y. Gong, M. Flaig, J.W. Gillespie, N. Hu, P.R. Taylor, M.R. Emmert-Buck, L.A. Liotta, E.F. Petricoin, III and Y. Zhao, 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers, Mol. Cell Proteomics, 1 (2002) 117–124. M. Mann, P. Hojrup and P. Roepstorff, Use of mass spectrometric molecular weight information to identify proteins in sequence databases, Biol. Mass Spectrom., 22 (1993) 338–345. E. Mortz, P.B. O’Connor, P. Roepstorff, N.L. Kelleher, T.D. Wood, F.W. McLafferty and M. Mann, Sequence tag identification of intact proteins by matching tandem mass spectral data against sequence data bases, Proc. Natl. Acad. Sci. USA, 93 (1996) 8264–8267. J.R. Yates, III, J.K. Eng and A.L. McCormack, Mining genomes: correlating tandem mass spectra of modified and unmodified peptides to sequences in nucleotide databases, Anal. Chem., 67 (1995) 3202–3210. D.J. Pappin, P. Hojrup and A.J. Bleasby, Rapid identification of proteins by peptide-mass fingerprinting, Curr. Biol., 3 (1993) 327–332. M.P. Washburn, D. Wolters and J.R. Yates, III, Large-scale analysis of the yeast proteome by multidimensional protein identification technology, Nat. Biotechnol., 19 (2001) 242–247. S.P. Gygi, D.K. Han, A.C. Gingras, N. Sonenberg and R. Aebersold, Protein analysis by mass spectrometry and sequence database searching: tools for cancer research in the post-genomic era, Electrophoresis, 20 (1999) 310–319. D.B. Wall, M.T. Kachman, S. Gong, R. Hinderer, S. Parus, D.E. Misek, S.M. Hanash and D.M. Lubman, Isoelectric focusing nonporous RP HPLC: a two-dimensional liquid-phase separation method for mapping of cellular proteins with identification using MALDI-TOF mass spectrometry, Anal. Chem., 72 (2000) 1099–1111.

619

A. Alaiya and S. Linder 25 26 27

28 29 30

31

32

33

34

35

36

37

620

H. Zhou, S. Roy, H. Schulman and M.J. Natan, Solution and chip arrays in protein profiling, Trends Biotechnol., 19 (2001) S34–S39. G. MacBeath and S.L. Schreiber, Printing proteins as microarrays for high-throughput function determination, Science, 289 (2000) 1760–1763. B.B. Haab, M.J. Dunham and P.O. Brown, Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions, Genome Biol., 2 (2001) 1–13 RESEARCH0004. P.F. Predki, Functional protein microarrays: ripe for discovery, Curr. Opin. Chem. Biol., 8 (2004) 8–13. W. Kusnezow and J.D. Hoheisel, Antibody microarrays: promises and problems, Biotechniques, (Suppl)(2002) 14–23. E.T. Fung, V. Thulasiraman, S.R. Weinberger and E.A. Dalmasso, Protein biochips for differential profiling, Curr. Opin. Biotechnol., 12 (2001) 65–69. M. Ahram, M.J. Flaig, J.W. Gillespie, P.H. Duray, W.M. Linehan, D.K. Ornstein, S. Niu, Y. Zhao, E.F. Petricoin III and M.R. Emmert-Buck, Evaluation of ethanol-fixed, paraffin-embedded tissues for proteomic applications, Proteomics, 3 (2003) 413–421. B. Franzen, S. Linder, K. Okuzawa, H. Kato and G. Auer, Nonenzymatic extraction of cells from clinical tumor material for analysis of gene expression by two-dimensional polyacrylamide gel electrophoresis, Electrophoresis, 14 (1993) 1045–1053. M.J. Page, B. Amess, R.R. Townsend, R. Parekh, A. Herath, L. Brusten, M.J. Zvelebil, R.C. Stein, M.D. Waterfield, S.C. Davies and M.J. O’Hare, Proteomic definition of normal human luminal and myoepithelial breast cells purified from reduction mammoplasties, Proc. Natl. Acad. Sci. USA, 96 (1999) 12589–12594. M.R. Emmert-Buck, R.F. Bonner, P.D. Smith, R.F. Chuaqui, Z. Zhuang, S.R. Goldstein, R.A. Weiss and L.A. Liotta, Laser capture microdissection, Science, 274 (1996) 998–1001. J.D. Wulfkuhle, D.C. Sgroi, H. Krutzsch, K. McLean, K. McGarvey, M. Knowlton, S. Chen, H. Shu, A. Sahin, R. Kurek, D. Wallwiener, M.J. Merino, E.F. Petricoin III, Y. Zhao and P.S. Steeg, Proteomics of human breast ductal carcinoma in situ, Cancer Res., 62 (2002) 6740–6749. R.A. Craven, N. Totty, P. Harnden, P.J. Selby and R.E. Banks, Laser capture microdissection and two-dimensional polyacrylamide gel electrophoresis: evaluation of tissue preparation and sample limitations, Am. J. Pathol., 160 (2002) 815–822. V. Knezevic, C. Leethanakul, V.E. Bichsel, J.M. Worth, V.V. Prabhu, J.S. Gutkind, L.A. Liotta, P.J. Munson, E.F. Petricoin, III and D.B. Krizman, Proteomic profiling of the cancer microenvironment by antibody arrays, Proteomics, 1 (2001) 1271–1278.

Cancer proteomics updates 38

39

40

41

42

43

44

45

46

47 48

L. Zang, D.P. Toy, W.S. Hancock, D.C. Sgroi and B.L. Karger, Proteomic analysis of ductal carcinoma of the breast using laser capture microdissection, LC-MS, and 16O/18O isotopic labeling, J. Proteome. Res., 3 (2004) 604–612. D.K. Ornstein, J.W. Gillespie, C.P. Paweletz, P.H. Duray, J. Herring, C.D. Vocke, S.L. Topalian, D.G. Bostwick, W.M. Linehan, E.F. Petricoin, III and M.R. Emmert-Buck, Proteomic analysis of laser capture microdissected human prostate cancer and in vitro prostate cell lines, Electrophoresis, 21 (2000) 2235–2242. L.C. Lawrie, S. Curran, H.L. McLeod, J.E. Fothergill and G.I. Murray, Application of laser capture microdissection and proteomics in colon cancer, Mol. Pathol., 54 (2001) 253–258. M.B. Jones, H. Krutzsch, H. Shu, Y. Zhao, L.A. Liotta, E.C. Kohn and E.F. Petricoin III, Proteomic analysis and identification of new biomarkers and therapeutic targets for invasive ovarian cancer, Proteomics, 2 (2002) 76–84. A.R. Shekouh, C.C. Thompson, W. Prime, F. Campbell, J. Hamlett, C.S. Herrington, N.R. Lemoine, T. Crnogorac-Jurcevic, M.W. Buechler, H. Friess, J.P. Neoptolemos, S.R. Pennington and E. Costello, Application of laser capture microdissection combined with two-dimensional electrophoresis for the discovery of differentially regulated proteins in pancreatic ductal adenocarcinoma, Proteomics, 3 (2003) 1988–2001. B. Franze´n, G. Auer, A.A. Alaiya, E. Eriksson, K. Uryu, T. Hirano, K. Okuzawa, H. Kato and S. Linder, Assessment of homogeneity in polypeptide expression shows highly variable expression in high malignant breast carcinomas, Int. J. Cancer, 69 (1996) 408–414. R.S. Kerbel, C. Waghorne, B. Korczak, A. Lagarde and M.L. Breitman, Clonal dominance of primary tumors by metastatic cells: genetic analysis and biological implications, Cancer Surv., 7 (1988) 597–630. A. Celis, H.H. Rasmussen, P. Celis, B. Basse, J.B. Lauridsen, G. Ratz, B. Hein, M. Ostergaard, H. Wolf, T. Orntoft and J.E. Celis, Short-term culturing of low-grade superficial bladder transitional cell carcinomas leads to changes in the expression levels of several proteins involved in key cellular activities, Electrophoresis, 20 (1999) 355–361. J.A. Fresno Vara, E. Casado, J. de Castro, P. Cejas, C. Belda-Iniesta and M. Gonzalez-Baron, PI3K/Akt signalling pathway and cancer, Cancer Treat. Rev., 30 (2004) 193–204. S. Hanash, Disease proteomics, Nature, 422 (2003) 226–232. H. Hondermarck, A.S. Vercoutter-Edouart, F. Revillion, J. Lemoine, I. elYazidi-Belkoura, V. Nurcombe and J.P. Peyrat, Proteomics of breast cancer for marker discovery and signal pathway profiling, Proteomics, 1 (2001) 1216–1232.

621

A. Alaiya and S. Linder 49

50

51

52

53

54

55

56

57

58

59

60

622

K. Okuzawa, B. Franze´n, J. Lindholm, S. Linder, T. Hirano, T. Bergman, Y. Ebihara, H. Kato and G. Auer, Characterization of gene expression in clinical lung cancer materials by two-dimensional polyacrylamide gel electrophoresis, Electrophoresis, 15 (1994) 382–390. T. Ueno, S. Linder, C.L. Na, W.R. Rice, J. Johansson and T.E. Weaver, Processing of pulmonary surfactant protein B by napsin and cathepsin H, J. Biol. Chem., 279 (2004) 16178–16184. T. Hirano, Y. Gong, K. Yoshida, Y. Kato, K. Yashima, M. Maeda, A. Nakagawa, K. Fujioka, T. Ohira, N. Ikeda, Y. Ebihara, G. Auer and H. Kato, Usefulness of TA02 (napsin A) to distinguish primary lung adenocarcinoma from metastatic lung adenocarcinoma, Lung Cancer, 41 (2003) 155–162. T. Ueno, S. Linder and G. Elmberger, Aspartic proteinase napsin is a useful marker for diagnosis of primary lung adenocarcinoma, Br. J. Cancer, 88 (2003) 1229–1233. J.L. Dennis, J.K. Vass, E.C. Wit, W.N. Keith and K.A. Oien, Identification from public data of molecular markers of adenocarcinoma characteristic of the site of origin, Cancer Res., 62 (2002) 5999–6005. W.D. Lawrence, The borderland between benign and malignant surface epithelial ovarian tumors. Current controversy over the nature and nomenclature of ‘‘borderline’’ ovarian tumors, Cancer, 76 (1995) 2138–2142. A.A. Alaiya, B. Franzen, K. Fujioka, B. Moberger, K. Schedvins, C. Silfversvard, S. Linder and G. Auer, Phenotypic analysis of ovarian carcinoma: polypeptide expression in benign, borderline and malignant tumors, Int. J. Cancer, 73 (1997) 678–683. A.A. Alaiya, B. Franzen, A. Hagman, C. Silfversward, B. Moberger, S. Linder and G. Auer, Classification of human ovarian tumors using multivariate data analysis of polypeptide expression patterns, Int. J. Cancer, 86 (2000) 731–736. A.A. Alaiya, B. Franzen, A. Hagman, B. Dysvik, U.J. Roblick, S. Becker, B. Moberger, G. Auer and S. Linder, Molecular classification of borderline ovarian tumors using hierarchical cluster analysis of protein expression profiles, Int. J. Cancer, 98 (2002) 895–899. J.E. Celis, I. Gromova, J.M. Moreira, T. Cabezon and P. Gromov, Impact of proteomics on bladder cancer research, Pharmacogenomics, 5 (2004) 381–394. M. Ostergaard, H.H. Rasmussen, H.V. Nielsen, H. Vorum, T.F. Orntoft, H. Wolf and J.E. Celis, Proteome profiling of bladder squamous cell carcinomas: identification of markers that define their degree of differentiation, Cancer Res., 57 (1997) 4111–4117. J.M. Moreira, P. Gromov and J.E. Celis, Expression of the tumor suppressor protein 14-3-3 sigma is down-regulated in invasive transitional cell carcinomas of the urinary bladder undergoing epithelial-to-mesenchymal transition, Mol. Cell Proteomics, 3 (2004) 410–419.

Cancer proteomics updates 61

62

63

64

65

66

67

68

69

70

J.E. Celis, P. Celis, M. Ostergaard, B. Basse, J.B. Lauridsen, G. Ratz, H.H. Rasmussen, T.F. Orntoft, B. Hein, H. Wolf and A. Celis, Proteomics and immunohistochemistry define some of the steps involved in the squamous differentiation of the bladder transitional epithelium: a novel strategy for identifying metaplastic lesions, Cancer Res., 59 (1999) 3003–3009. B. Franze´n, S. Linder, K. Uryu, A.A. Alaiya, T. Hirano, K. Kato and G. Auer, Expression of tropomyosin isoforms in benign and malignant human breast lesions, Br. J. Cancer, 73 (1996) 909–913. B. Franze´n, G. Auer, A.A. Alaiya, E. Eriksson, K. Uryu, T. Hirano, K. Okuzawa and S. Linder, Analysis of polypeptide expression in benign and malignant human breast lesions: down-regulation of cytokeratins, Br. J. Cancer, 73 (1996) 1632–1638. L. Bini, B. Magi, B. Marzocchi, F. Arcuri, S. Tripodi, M. Cintorino, J.C. Sanchez, S. Frutiger, G. Hughes, V. Pallini, D.F. Hochstrasser and P. Tosi, Protein expression profiles in human breast ductal carcinoma and histologically normal tissue, Electrophoresis, 18 (1997) 2832–2841. A.C. Bergman, T. Benjamin, A. Alaiya, M. Waltham, K. Sakaguchi, B. Franzen, S. Linder, T. Bergman, G. Auer, E. Appella, P.J. Wirth and H. Jornvall, Identification of gel-separated tumor marker proteins by mass spectrometry, Electrophoresis, 21 (2000) 679–686. R.I. Somiari, A. Sullivan, S. Russell, S. Somiari, H. Hu, R. Jordan, A. George, R. Katenhusen, A. Buchowiecka, C. Arciero, H. Brzeski, J. Hooke and C. Shriver, High-throughput proteomic analysis of human infiltrating ductal carcinoma of the breast, Proteomics, 3 (2003) 1863–1873. M.V. Dwek and A.A. Alaiya, Proteome analysis enables separate clustering of normal breast, benignn breast and breast cancer tissues, Br. J. Cancer, 89 (2003) 305–307. G. Chen, T.G. Gharib, C.C. Huang, D.G. Thomas, K.A. Shedden, J.M. Taylor, S.L. Kardia, D.E. Misek, T.J. Giordano, M.D. Iannettoni, M.B. Orringer, S.M. Hanash and D.G. Beer, Proteomic analysis of lung adenocarcinoma: identification of a highly expressed set of proteins in tumors, Clin. Cancer Res., 8 (2002) 2298–2305. G. Chen, T.G. Gharib, H. Wang, C.C. Huang, R. Kuick, D.G. Thomas, K.A. Shedden, D.E. Misek, J.M. Taylor, T.J. Giordano, S.L. Kardia, M.D. Iannettoni, J. Yee, P.J. Hogg, M.B. Orringer, S.M. Hanash and D.G. Beer, Protein profiles associated with survival in lung adenocarcinoma, Proc. Natl. Acad. Sci. USA, 100 (2003) 13537–13542. K. Yanagisawa, Y. Shyr, B.J. Xu, P.P. Massion, P.H. Larsen, B.C. White, J.R. Roberts, M. Edgerton, A. Gonzalez, S. Nadaf, J.H. Moore, R.M. Caprioli and D.P. Carbone, Proteomic patterns of tumour subsets in nonsmall-cell lung cancer, Lancet, 362 (2003) 433–439.

623

A. Alaiya and S. Linder 71

72

73

74

75

76

77

78

79 80 81 82

83

624

T. Voss, H. Ahorn, P. Haberl, H. Dohner and K. Wilgenbus, Correlation of clinical data with proteomics profiles in 24 patients with B-cell chronic lymphocytic leukemia, Int. J. Cancer, 91 (2001) 180–186. A. Bisca, C. D’Ambrosio, A. Scaloni, F. Puglisi, G. Aprile, A. Piga, C. Zuiani, M. Bazzocchi, C. Di Loreto, I. Paron, G. Tell and G. Damante, Proteomic evaluation of core biopsy specimens from breast lesions, Cancer Lett., 204 (2004) 79–86. G. Kramer, H. Erdal, H.J. Mertens, M. Nap, J. Mauermann, G. Steiner, M. Marberger, K. Biven, M.C. Shoshan and S. Linder, Differentiation between cell death modes using measurements of different soluble forms of extracellular cytokeratin 18, Cancer Res., 64 (2004) 1751–1756. N. Ahmed, G. Barker, K. Oliva, D. Garfin, K. Talmadge, H. Georgiou, M. Quinn and G. Rice, An approach to remove albumin for the proteomic analysis of low abundance biomarkers in human serum, Proteomics, 3 (2003) 1980–1987. R. Pieper, Q. Su, C.L. Gatlin, S.T. Huang, N.L. Anderson and S. Steiner, Multi-component immunoaffinity subtraction chromatography: an innovative step towards a comprehensive survey of the human plasma proteome, Proteomics, 3 (2003) 422–432. T.C. Poon and P.J. Johnson, Proteome analysis and its impact on the discovery of serological tumor markers, Clin. Chim. Acta., 313 (2001) 231–239. A.M. Rodriguez-Pineiro, D. Ayude, F.J. Rodriguez-Berrocal and M. Paez de la Cadena, Concanavalin A chromatography coupled to two-dimensional gel electrophoresis improves protein expression studies of the serum proteome, J. Chromatogr. B: Anal. Technol. Biomed. Life Sci., 803 (2004) 337–343. E.F. Petricoin, A.M. Ardekani, B.A. Hitt, P.J. Levine, V.A. Fusaro, S.M. Steinberg, G.B. Mills, C. Simone, D.A. Fishman, E.C. Kohn and L.A. Liotta, Use of proteomic patterns in serum to identify ovarian cancer, Lancet, 359 (2002) 572–577. Nature, c.i. Proteomic diagnostics tested, Nature, 429 (2004) 487. E.P. Diamandis, OvaCheck: doubts voiced soon after publication, Nature, 430 (2004) 611. J.M. Sorace and M. Zhan, A data review and re-assessment of ovarian cancer serum proteomic profiling, BMC Bioinformatics, 4 (2003) 24. K.A. Baggerly, J.S. Morris and K.R. Coombes, Reproducibility of SELDITOF protein patterns in serum: comparing datasets from different experiments, Bioinformatics, 20 (2004) 777–785. A.J. Rai, Z. Zhang, J. Rosenzweig, M. Shih Ie, T. Pham, E.T. Fung, L.J. Sokoll and D.W. Chan, Proteomic approaches to tumor marker discovery, Arch. Pathol. Lab. Med., 126 (2002) 1518–1526.

Cancer proteomics updates 84

85 86 87

88 89

90 91

92

93

94

95

J. Koopmann, Z. Zhang, N. White, J. Rosenzweig, N. Fedarko, S. Jagannath, M.I. Canto, C.J. Yeo, D.W. Chan and M. Goggins, Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry, Clin. Cancer Res., 10 (2004) 860–868. D.C. Pearl, Proteomic patterns in serum and identification of ovarian cancer, Lancet, 360 (2002) 169–170 author reply 170–171. B. Rockhill, Proteomic patterns in serum and identification of ovarian cancer, Lancet, 360 (2002) 169 author reply 170–171. R. Pieper, C.L. Gatlin, A.M. McGrath, A.J. Makusky, M. Mondal, M. Seonarain, E. Field, C.R. Schatz, M.A. Estock, N. Ahmed, N.G. Anderson and S. Steiner, Characterization of the human urinary proteome: a method for high-resolution display of urinary proteins on two-dimensional electrophoresis gels with a yield of nearly 1400 distinct protein spots, Proteomics, 4 (2004) 1159–1174. M. Muller, Telomerase: its clinical relevance in the diagnosis of bladder cancer, Oncogene, 21 (2002) 650–655. A.S. Glas, D. Roos, M. Deutekom, A.H. Zwinderman, P.M. Bossuyt and K.H. Kurth, Tumor markers in the diagnosis of primary bladder cancer. A systematic review, J. Urol., 169 (2003) 1975–1982. P. Dey, Urinary markers of bladder carcinoma, Clin. Chim. Acta., 340 (2004) 57–65. A. Vlahou, P.F. Schellhammer, S. Mendrinos, K. Patel, F.I. Kondylis, L. Gong and S. Nasim, G.L. Wright Jr. Development of a novel proteomic approach for the detection of transitional cell carcinoma of the bladder in urine, Am. J. Pathol., 158 (2001) 1491–1502. D. Ribom, A. Westman-Brinkmalm, A. Smits and P. Davidsson, Elevated levels of alpha-2-Heremans-Schmid glycoprotein in CSF of patients with low-grade gliomas, Tumour Biol., 24 (2003) 94–99. P.P. Zheng, T.M. Luider, R. Pieters, C.J. Avezaat, M.J. van den Bent, P.A. Sillevis Smitt and J.M. Kros, Identification of tumor-related proteins by proteomic analysis of cerebrospinal fluid from patients with primary brain tumors, J. Neuropathol Exp. Neurol., 62 (2003) 855–862. C.P. Paweletz, B. Trock, M. Pennanen, T. Tsangaris, C. Magnant, L.A. Liotta and E.F. Petricoin, III, Proteomic patterns of nipple aspirate fluids obtained by SELDI-TOF: potential for new biomarkers to aid in the diagnosis of breast cancer, Dis. Markers, 17 (2001) 301–307. E.R. Sauter, W. Zhu, X.J. Fan, R.P. Wassell, I. Chervoneva and G.C. Du Bois, Proteomic analysis of nipple aspirate fluid to detect biologic markers of breast cancer, Br. J. Cancer, 86 (2002) 1440–1443.

625