Proteomics biomarkers for non-small cell lung cancer

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PBA-9674; No. of Pages 10

Journal of Pharmaceutical and Biomedical Analysis xxx (2014) xxx–xxx

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Review

Proteomics biomarkers for non-small cell lung cancer Joanna Kisluk a,∗ , Michal Ciborowski b , Magdalena Niemira b , Adam Kretowski b , Jacek Niklinski a a b

Department of Clinical Molecular Biology, Medical University of Bialystok, Poland Clinical Research Centre, Medical University of Bialystok, Poland

a r t i c l e

i n f o

Article history: Received 15 May 2014 Received in revised form 29 July 2014 Accepted 31 July 2014 Available online xxx Keywords: NSCLC proteomic biomarkers Non-small cell lung cancer Proteomic biomarkers Lung cancer proteomics.

a b s t r a c t In the last decade, proteomic analysis has become an integral tool for investigation of tumor biology, complementing the genetic analysis. The idea of proteomics is to characterize proteins by evaluation of their expressions, functions, and interactions. Proteomics may also provide information about posttranslational modifications of proteins and evaluate their value as specific disease biomarkers. The major purpose of clinical proteomics studies is to improve diagnostic procedures including the precise evaluation of biological features of tumor cells and to understand the molecular pathogenesis of cancers to invent novel therapeutic strategies and targets. This review briefly describes the latest reports in proteomic studies of NSCLC. It contains a summary of the methods used to detect proteomic markers in different types of biological material and their clinical application as diagnostic, prognostic, and predictive biomarkers compiled on the basis of the most recent literature and our own experience. © 2014 Elsevier B.V. All rights reserved.

Contents 1. 2. 3.

4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proteomic Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Patients material for biomarker detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Blood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Pleural Effusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Urine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Tissue Interstitial Fluid (TIF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. Saliva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical Significance of Proteomic Biomarkers in NSCLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Diagnostic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1. Protein diagnostic biomarkers detected in NSCLC patient’s tissue samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2. Protein diagnostic biomarkers detected in NSCLC patients’ fluid sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3. Proteomic signatures for histological types of NSCLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Prognostic Biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Predictive Biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction ∗ Corresponding author at: Department of Clinical Molecular Biology, Medical University of Bialystok, Polan, Waszyngtona 13, 15-269 Bialystok, Poland. Tel.: +48 7485935; fax: +48 7485988. E-mail address: [email protected] (J. Kisluk).

Lung cancer is the most common cause of cancer-related mortality worldwide and its incidence is steadily increasing. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancers. Most NSCLC patients are diagnosed in advanced,

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Please cite this article in press as: J. Kisluk, et al., Proteomics biomarkers for non-small cell lung cancer, J. Pharm. Biomed. Anal. (2014), http://dx.doi.org/10.1016/j.jpba.2014.07.038

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Fig. 1. Diversity of methods to analyze genes, transcripts, and their final productprotein level and function. Legend: Genome studies may be performed by analyzing DNA mutations, deletions, or copy number variations. Transcriptome studies are performed by analyzing changes in mRNA expression level. Proteomic analysis may provide protein sequences stored in genes and also allows for detection of posttranslational modifications, especially important in tumor cells biology.

incurable stage, with local and/or distant metastases. Lack of relevant early stage detection is related with high risk of metastasizing and results in unsatisfactory treatment—the 5-year survival rate for all stages jointly is only 15%. Despite better understanding of NSCLC biology and mechanism of carcinogenesis through genomic studies, universal tools for more optimal diagnosing, staging, and predicting of patients are still missing. Moreover, there are currently no biomarkers enabling reliable screening for NSCLC. All of this results from the diversity between transcript sequence in genes and their final product protein level and function. Translational and post-translational modifications regulate the protein expression in tumor cells in particular. Changing levels and functions of various proteins add another level of complexity in the regulation of cancer cells biology for sustaining proliferative signaling and evading growth suppressors [1]. As it can be seen in Fig. 1, different types of analyses may be performed at the genome, transcriptome, and proteome level. In the last decade, proteomic analysis has become an integral tool for investigation of tumor biology, complementing the genetic analysis. The role of proteins in physiological and pathological processes remains inestimable and every year brings deep understanding of these biological macromolecules and their impact. It is now recognized that proteomic studies are fundamental to obtain an accurate recognition of cellular physiology and to finally clarify the pathogenesis of various types of malignant neoplasm. The major purpose of clinical proteomics is to enhance diagnostic capabilities, provide accurate prognostic information and/or predict the outcome of patients treated with specific therapies. A close correlation between diagnostic, prognostic, and predictive proteomic biomarkers allows for better efficiency of early started therapy and may lead to increased survival in patients. Diagram of the relationship between the types of clinically useful biomarkers is shown in Fig. 2. Biomarkers can be used as indicators of many different disease states. Any biomarker is defined as a specific that is objectively measured and evaluated as an indicator of normal physiological processes, pathogenic processes and diseases or pharmacological responses to a specified therapeutic intervention. This review briefly describes the latest reports in proteomic studies of NSCLC. It contains a summary of the methods used to detect proteomic markers in different types of biological material

Fig. 2. Relationship between the types of clinically useful biomarkers.

and their clinical application as diagnostic, prognostic, and predictive biomarkers compiled on the basis of the most recent literature and our own experience. 2. Proteomic Techniques The idea of proteomics is to characterize proteins by evaluation of their expression, functions, and interactions. Proteomics may also provide information about post-translational modifications of proteins and evaluate their value as specific disease biomarkers [2]. Proteomics studies have started with the approach called now the “first generation” proteomics. In this approach complex protein mixtures are separated at high resolution by twodimensional gel electrophoresis (2DE) into reproducible patterns giving the opportunity to diagnose quantitative and qualitative differences in the proteins composition [3]. Samples are separated on a polyacrylamide gel electrophoresis (SDS-PAGE) medium based on isoelectric point (1st dimension) and later based on molecular weight (2nd dimension) [4,5]. However, the use of 2DE without reliable tools for the identification of the separated protein species limited its utility in molecular biology research. However, it has improved again when mass spectrometry (MS) methods were used for the identification of proteins separated by 2DE. In particular, such ionization methods such as matrix-assisted laser desorption ionization (MALDI) or electrospray ionization (ESI) combined with time-of-flight (TOF) mass spectrometry or tandem mass spectrometry (MS/MS) detection have become gold standard methods used for proteins identification. Typically, these methods involved excision of gel bands, in gel trypsin digestion of the proteins contained in the band and finally mass spectrometric analysis of the peptides produced. Since a single band contains several proteins, separation by liquid chromatography is substantial before identification and quantitation of peptides by MS. Digested proteins produce peptides, which elute at different time points from the reverse phase column depending on their hydrophobicity. In a typical LC–MS/MS workflow in the first scan, the mass and intensity of all eluting peptides is recorded while in the next scans (over a dozen, but the exact number depends on the equipment used) the most abundant precursor ions are selected for MS/MS fragmentation. Identification of the protein is performed based on the amino acid sequence obtained from MS/MS spectra (Fig. 3) while label-free quantitation can be obtained based on spectral counts, ion intensities, or chromatographic peak area [5]. Application of MS for identification of proteins significantly increased proteome coverage and improved

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Fig. 3. The example of MALDI-MS/MS spectrum obtained for trypsin-digested peptide. Legend: Spectra were searched in NCBI no. database by the Mascot software. Ions assigned to particular amino acids sequences (PL, PLN, PLLAD, and LGPLNI) are marked on the spectrum. Ions ascribed to the main fragmentation series, y (C-terminal series) and b (N-terminal series) have also been labeled. C-terminal fragment ions corresponding to the loss of ammonia (−17 Da) are indicated by an asterisk (*).

quality of proteomics data obtained. However, due to utilization of gel electrophoresis for protein separation this approach remains a low throughput and requires relatively large amount of sample [2]. Biological samples used in proteomics studies contain thousands of proteins with high dynamic range (difference in abundance of six or more orders of magnitude), therefore ideal proteomics approach should allow for adequate separation of proteins mixture with exhaustive characterization of even the lowest abundant proteins [3]. These demands seem to be fulfilled by LC–MSbased multidimensional protein/peptide separation and analysis. The most commonly used is 2D LC–MS/MS technology, in which digested peptides are separated on a strong cation exchange and reverse-phase chromatography with MS/MS platform for the detection of peptides to achieve identification and quantification of thousands of proteins with femtomolar or even subfemtomolar sensitivity [4]. This type of approach is referred to as “second generation” proteomics. Especially the ability to conduct tandem MS has revolutionized proteomics since amino acid sequence can be obtained based on fragmentation spectra. Fragmentation occurs predominantly at the peptide bonds, therefore MS can identify amino acids by measuring the precise differences in the molecular weights of these fragments and the sequence of the peptide can then be deduced from the resulting fragments [2]. Predictability of MS/MS peptides fragmentation and the progress in bioinformatics tools development allowed for automation of peptides identification. Currently, protein identification is achieved mainly through a database search to match MS/MS spectra with peptide/protein sequences by computer software [4]. For functional and sequencebased databases, UniProt is one of the most comprehensive. It consists of several classifications such as Swiss-Prot and TrEMBL, which contain sequence and functional information about proteins, and UniRef and UniParc contain sequence and archived sequence records and supporting data such as literature references and crossreferenced databases (if available). Public databases such as UniProt can be searched by computer programs (e.g., Mascot, SEQUEST, or X!Tandem) by FASTA protein sequences [6,7]. As mentioned above, quantitative information can be obtained based on spectral counts, ion intensities, or chromatographic peak area [5], however, this information does not constitute a good indicator of the amount of protein in the sample. More accurate quantitation can be achieved by isotopic labeling methods. Popular labeling methods include isotope-coded affinity tags (ICAT), isobaric tagging for relative and absolute quantification (iTRAQ), tandem mass tags (TMT), and stable isotope labeling by amino acids in cell culture (SILAC) [4,5]. In the first two approaches proteins are labeled with the structurally identical, but of different molecular weight tags on thiol- (ICAT) or amine- (iTRAQ) containing residues, which in turn allows for comparison of peptides identical in

sequence but different in mass. In SILAC method labeled amino acids are added to the cell culture medium and are metabolically incorporated into the proteins serving as internal standard controls for subsequent quantification. All methods allow for accurate and reliable quantification of proteins by comparison of relative abundances of labeled peptides, however, obtained information may be falsified if proteins were subjected to post-translational modifications or proteolysis [4]. Despite undoubted progress in proteomics research attained by combination of multidimensional LC in conjunction with automated, data-dependent tandem mass spectrometry, and sequence data base searching as well as selective labeling of proteins for better quantitation, complete proteome coverage of complex samples such as plasma remains complicated. From thousands of proteins detected in human plasma, 22 with the highest abundance (including albumin, immunoglobulin, transferrin, and haptoglobin) comprise 99% of total plasma protein abundance [8]. Complexity of plasma proteome results from proteins leaking from entire body tissues, complex post-translational modifications of proteins, as well as the existence of various forms of proteins for each expressed gene. At the same time, these proteins present an outstanding dynamic range (>10 orders of magnitude) in protein concentrations (the most abundant albumin has concentration of approximately 45 mg/mL while cytokines of around 1–10 pg/mL or lower) [7]. One of the options to manage such diverse sample is elimination of the most abundant proteins. To increase the depth of proteome identification in unbiased discovery and to increase sensitivity for targeted analyses of specific proteins, the step of depletion of abundant plasma proteins was implemented [9]. This idea was introduced for the first time in 2003, when antibody columns were used to remove albumin, immunoglobulin G, immunoglobulin A, transferrin, haptoglobin, ␣-1-antitrypsin, hemopexin, transthyretin, ␣-2-HS glycoprotein, ␣-1-acid glycoprotein, ␣-2-macroglobulin, and fibrinogen from human plasma samples [10]. Currently several immune-depletion systems are available (e.g., MARS, IgY-12, Proteoprep 20), which remove up to 20 of the most abundant plasma proteins [7,11]. However, it has been reported that depleted fraction of proteins may contain important plasma proteins, which have been indicated to be relevant as disease biomarkers [11]. One of the workflows currently utilized in plasma proteomics, which provides the highest proteome coverage [7], is presented in Fig. 4. Although such innovations such as multidimensional chromatography, labeling methods, depletion techniques, or development of bioinformatics tools for identification and pathway analysis improved proteomics workflow significantly, complete proteome analyses (especially for complex samples such as plasma) remains challenging.

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Fig. 4. Diagram representing one of the workflows currently utilized in plasma proteomics. Legend: The most abundant proteins present in plasma sample (colored in red and blue) are depleted by use of immune-depletion system. Subsequently proteins undergo trypsin digestion and isotopic labeling of obtained peptides is performed. Labeled peptides are analyzed by 2D-LC–MS/MS. Identification and quantitation of peptides and proteins is performed by use of sophisticated computer software.

3. Patients material for biomarker detection 3.1. Tissue In order to identify potential NSCLC tissue biomarkers differences in protein expression levels between tumor, adjacent, and distant tissue samples were mostly studied. Lung tissue samples obtained from surgical resections with pathologically confirmed NSCLC, precise disease stage, and pathological tumornode-metastasis classification (pTNM classification) are used. The most targeted method enabling separate analysis of tumor cells and tumor stromal components in tumor tissue is laser capture microdissection (LCM). However, one of the drawbacks concerning this method is an excessive loss of tumor mass and consequently high risk of insufficient amount of sample for multiple analyses. Due to limited amount of material after LCM, it is impossible to perform extensive pre-fractionation of the sample, often needed for large proteome coverage (MS-based or gel analysis). Routinely collected in the clinical setting formalin-fixed paraffin-embedded (FFPE) tumor samples are not recommended for proteome analysis in view of long-term storage which may influence protein composition, especially in terms of integrity of post-translational modifications. Frozen tissue sections are used for MALDI analysis [12]. Fresh tumor material, as opposed to frozen or FFPE specimens, is likely prerequisite to obtain good results using proteomic methods [12,13]. De Petris et al. [13] have established a reproducible method to prepare freshly collected lung tumors for proteomics analysis, aiming at tumor cell enrichment and reduction of blood/plasma protein contamination. The use of this method results in larger proteome coverage as compared to the direct analysis of frozen samples. It can be successfully implemented to perform biomarker discovery on NSCLC tissue samples with in-depth proteomics analysis (mass spectrometry based) [14].

predict response to therapy [16]. In practice, detection of lowabundance proteins in serum or plasma creates difficulties because of the vast protein composition. In order to reduce this complexity every protein analysis in this sample type should be preceded by depletion of non-specific proteins. Remaining serum or plasma proteins need to be separated according to their characteristics (glycosylation, phosphorylation, hydrophilicity, hydrophobicity, ion charges, or molecular weights) by use of chromatographic methods [17]. Additionally, blood samples for proteomics studies are not adequate for long-time storage since polypeptides can undergo proteolysis through proteases activation. 3.3. Pleural Effusion Pleural fluid is secreted by the parietal layer of the pleura and reabsorbed by the lymphatic in the most dependent parts of the parietal pleura, primarily the diaphragmatic and mediastinal regions. Fluid that accumulates in the pleural cavity due to trauma or disease is called pleural effusion [18]. It remains a valuable patient’s material in view of secreted or membrane-shed potential protein biomarkers. The protein profile determined by the composition of pleural effusion opens a new window of opportunities for discovering, otherwise undetectable, low-abundance biomarkers [19]. This fluid, when in contact with the affected organ, may be a new source of markers derived from the tumor, hence more specific for lung cancer. Pleural effusion depletion to remove highabundance proteins (albumin, IgG, antitrypsin, IgA, transferrin, and haptoglobin) that could be masking potential disease biomarkers is typically performed by dilution (1:25) in buffer and centrifugation [19]. Pleural effusion contains proteins ultrafiltered from plasma, therefore identification of the proteins detected in this fluid is always performed as for normal human plasma [18]. 3.4. Urine

3.2. Blood Blood proteomic analysis may have a great advantage over proteomics conducted in lung cancer tissue because blood samples are more readily accessible. The Human Proteome Organization (HUPO) has recommended that blood should be examined as plasma rather than as serum and established standardized sample collection protocols [15]. Theoretically, proteins circulating in blood are generated in the whole organism in response to disease and can be assumed as biomarkers produced by tumor tissue. Many reports suggest that the low molecular weight (LMW) protein/peptides in serum and/or plasma (e.g., peptide hormones or small secreted proteins) are correlated with pathological conditions, which makes them candidate biomarkers with potential clinical utility for diagnosis and prognosis of the disease or to

Urine seems to be more attractive for proteomic analysis than blood because it contains fewer proteins. It is due to the fact, that only a few organs are located directly along the path of urine production and excretion [20]. It contains highly soluble proteins and polypeptides of lower molecular mass (<30 kDa), which can be analyzed in their natural state without the need for additional procedure. Interesting type of protein transporters detected in urine are urinary exosomes, a low-density solid component secreted by renal epithelial cells, accounts for about 3% of the total protein in normal urine. Proteins in urinary exosomes are derived from the urinary and circulatory system [21]. Quantitative and qualitative study of urinary exosomes proteins composition may reflect the pathological process also in whole organism and not only in urinary system [22]. Another advantage of urinary proteomics is relatively

Please cite this article in press as: J. Kisluk, et al., Proteomics biomarkers for non-small cell lung cancer, J. Pharm. Biomed. Anal. (2014), http://dx.doi.org/10.1016/j.jpba.2014.07.038

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good stability of the samples, which can be stored up to 3 days at 4 ◦ C, or up to 6 h at room temperature [23]. 3.5. Tissue Interstitial Fluid (TIF) Tissue interstitial fluid (TIF) is another interesting sample for evaluation of proteomic biomarkers. TIF is the main component of the extracellular fluid, which also includes plasma and transcellular fluid. The interstitial fluid is found in the interstitial space. TIF is directly associated with the tumor, so it is a great source of tumorspecific biomarkers. In comparison to TIF and non-tumor, Li et al. compared TIF and non-tumor adjacent tissue (NAT) and identified 24 tumor-associated proteins (11 upregulated and 13 downregulated). Among them peroxideroxin 1 (PRDX1), whose level was six-fold elevated, was significantly related to lymph node metastasis and tumor differentiation [24]. 3.6. Saliva Human saliva is a biofluid especially useful for early detection of various oral and systemic pathological conditions. Noninvasive way of collection and containing of large array of proteins leads to widespread use of this type of sample to diagnosis disorders which may affect salivary glands. Xiao et al. in their first proof-of-concept report demonstrated significant role of three salivary proteins (calprotectin, haptoglobin hp2, and zinc alpha2-glycoprotein—shown in Table 2), which could be potentially used as discriminatory biomarkers to differentiate patients with early NSCLC from healthy control subjects. Further validation in large sample set is necessary for definitive validation that selected salivary proteins could be used in clinical practice [25]. 4. Clinical Significance of Proteomic Biomarkers in NSCLC

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cancer development and progression. Zeng et al. [28] have established that SELENBP1 is decreased in early LSCC carcinogenesis and its expression level is able to discriminate between normal bronchial epithelium, preneoplastic lesions, and invasive LSCC. Transgelin is one of several proteins that bind actin and subsequently cross-link and bundle filaments into stress fibers. Its expression was found decreased in prostate, breast, and colon cancers [29]. Rho et al. [30] described TAGLN as a marker of active stromal remodeling in the vicinity of invasive cancer, which participates in tumor–stroma interaction and could be used for early diagnosis, treatment guidance, and treatment response monitoring. Peroxiredoxins are a ubiquitous family of antioxidant enzymes that also control cytokine-induced peroxide levels and thereby mediate signal transduction in mammalian cells. Expression of peroxiredoxins is elevated in several lung diseases including lung cancer, mesothelioma, and sarcoidosis, however, the mechanism for these alterations is not known [31]. Park et al. [32] described PRDX1 functions as an Nrf2-dependent inducible tumor suppressant in K-ras-driven lung adenocarcinogenesis by opposing ROS/ERK/cyclin D1 pathway activation. Park et al. [33], in their research performed on tissue extracts of lung cancer patients by use of one-dimensional electrophoresis and immunohistochemistry methods, revealed that upregulation of PRDX1 and PRDX3 in adenocarcinoma tissue may represent an attempt by tumor cells to adjust to the microenvironment in a manner that is advantageous to survival and proliferation. Tan et al. [34] described that PRDX2, protein participating in cellular antioxidant defense, was downregulated in lung SCC tumors. List of 20 selected reactive protein spots identified by MALDI-TOF MS in SCC tumors presented by Yang et al. [35] includes PRDX6 as one of important diagnostics biomarkers for this histological NSCLC subtype. Detailed characteristic of diagnostic biomarkers for NSCLC detected in tissue samples is shown in Table 1.

4.1. Diagnostic Diagnostic biomarker must be directly correlated with the presence of the disease and should be the most specific and sensitive at early stage of the disease. Early detection has a high priority in order to decrease mortality related to NSCLC. Currently, in highrisk populations, radiographic screening through spiral computed tomography (CT) is a promising tool for the first-phase tumor detection. Protein biomarkers detected in blood, urine, pleural effusion, or tissue interstitial fluid might complement diagnosis by CT for the most optimal early detection [26]. 4.1.1. Protein diagnostic biomarkers detected in NSCLC patient’s tissue samples The identification of diagnostic lung cancer biomarkers in postoperative tissue samples is performed based on the evaluation of the protein levels in tumor samples and normal lung tissue samples. Proteomic studies have identified a great number of proteins whose expression was diversified between normal lung and cancer tissue. Regardless of histological subtypes of NSCLC in tissue samples, the selenium-binding protein 1 (SELENBP1), HSP20-like protein, transgelin (TAGLN), and carbonic anhydrases (CA) were found under expressed [27]. In postoperative material caclynin (S100A6), MIF, peroxideroxin 1 and 3 (PRDX1, PRDX3), thioredoxin (TXN), TMSB4, TMSB10, enolase (ENO1), thymopoietin (TMPO), ribosomal protein L39 and S30, histone H2A.2, and coatomer protein complex subunit gamma (COPG) were found overexpressed [12]. SELENBP1, a member of selenoproteins family, binds and mediate intracellular transport of selenium. Many studies have shown that deficiency of selenium in daily diet is associated with an increased risk of epithelial cancers including lung cancer [28]. Downregulation of SELENBP1 plays a major role in regulation of

4.1.2. Protein diagnostic biomarkers detected in NSCLC patients’ fluid sample Studies on other types of specimens revealed another important diagnostic biomarkers. Regardless of histological subtypes of NSCLC, many studies reported elevation of serum haptoglobin (HP) in comparison to controls [36,37]. Park et al. [38] underlined a specific diagnostic role of haptoglobin subunit alpha and evidenced that HP alpha chain is a more prospective biomarker to diagnose NSCLC than other subunits. High levels of serum amyloid alpha (SAA) in NSCLC patients were also found in proteomics studies [39,40]. The acute phase reactant and inflammatory marker belonging to the family of apolipoproteins may be elevated even up to 1000-fold in states of tissue damage or inflammation. It could be also used as a non-specific biomarker for many cancer types [41]. Sung et al. [42] emphasized that elevated levels of SAA contribute to cell lung cancer. Increased production of SAA by lung cancer cells is associated with high levels of cytokines (IL1, IL6) and TNF-␣ in the nearby environment of lung cancer as a result of interactions between cancer cells and immune cells. SAA also seems to be involved in lung cancer metastasis by effects on extracellular matrix (EMC) and inducing expression of matrix metallopeptidase-9 (MMP-9) [42]. Under-expression of tissue metalloproteinase inhibitor 2 (TIMP2) was observed in fluid samples prom NSCLC patients [19]. This protein may be critical to the maintenance of tissue homeostasis by suppressing the proliferation of quiescent tissues in response to angiogenic factors, and by inhibiting protease activity in tissues undergoing remodeling of the extracellular matrix. It is also related to disorganization of lung parenchyma [43]. Levels of pigment epithelium-derived factor (PEDF; SERPINF1) were found significantly different between NSCLC patients and

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Table 1 Proteomic diagnostic biomarkers detected in tissue samples. Specificity

Biomarker

Proteomic technique

Ref.

NSCLC (without division on histological types)

Peroxiredoxin: PRDX1, PRDX3 Heat shock protein HSP20-like Thymosin ␤4, ␤10 SOD2 (superoxide dismutase 2)

2D-PAGE 2D-PAGE, MALDI-TOF-MS MALDI-MS 2D-PAGE, MALDI-TOF-TOF, 2D-PAGE, MALDI-TOF-MS 2D-DIGE, MALDI-TOF-TOF MALDI-TOF-MS

[23,29] [23] [12] [31,32]

2D-PAGE, MALDI-TOF-MS MALDI-TOF-MS, IMAC, LC–MS/MS

[34,35] [14,35,36]

2D-PAGE, MALDI-TOF-MS, Q-TOF-MS/MS 2D-PAGE, LC–MS/MS 2D-DIGE, LC–MS/MS 2D-PAGE, MALDI-MS

[32]

2D-PAGE, ESI-Q-TOF-MS/MS

[37]

2D-PAGE, MALD-TOF-MS 2D-PAGE, MALDI-TOF-MS MALDI-TOF-MS, LC–Q-MS/MS 2D-PAGE, MALDI-TOF-MS

[31,35] [30,31] [38] [31,39]

2D-PAGE, MALDI-TOF-MS, LC–Q-MS/MS LC–Q-MS/MS

[31,38,39]

2D-PAGE, MALDI-TOF-MS

[39]

14-3-3 ␴ (stratifin-SNF) SELENBP1 (selenium binding protein 1) TIM (triose-phosphate isomerase) MIF (macrophage migration inhibitory factor) Adenocarcinoma

Annexin ANXA1 TAGLN (transgelin) Cyclophilin A (CyP-A) UCH-L1 (ubiquitin carboxy-terminal hydrolase L1) CFL1 (cofilin-1)

Squamous cell carcinoma

ENO1 (alpha enolase) Peroxiredoxin: PRDX2, PRDX6 Protein S10 A6, S100A8, S100A9 Annexin: ANXA2, ANXA3, ANXA5, ANXA6 Heat shock protein: HSP70, HSP60, HSP27 SCCA1 (squamous cell carcinoma antigen 1) ACTG1 (gamma actin)

benign-disease controls (pneumonia, tuberculosis), both in the pleural effusion and the serum samples [44]. PEDF is a member of serine protease inhibitor superfamily. It is a multifunctional secreted protein that has anti-angiogenic, anti-tumorigenic, and neurotrophic functions. Zhang et al. reported reduction of PEDF at both protein and mRNA level in NSCLC tumors in comparison to normal lung tissues. This reduction was associated with an increase in microvessel density in tumors and significantly associated with TNM stage, tumor size, and the overall survival [44]. Calprotectin (S100A8/A9; MRP8/14), a heterodimer of the two calcium-binding proteins S100A8 and S100A9, was detected in various human cancers, presenting high expression in cancer cells and tumor-infiltrating immune cells. Ohri et al. [45] have examined expression of MRP8/14 (S100A8/A9) in the non-macrophage (NM) cells in surgically resected NSCLC specimens. Expression of NM-MRP 8/14 was increased in the tumor islets of extender survival versus poor survival patients. This suggests that these proteins play an important role in tumor immunology and cancer progression. Overexpression of this molecule has also a prognostic impact on NSCLC [46]. Higher levels of leucine-rich alpha-2-glycoprotein (LRG1) were found in urine samples of cancer patients in comparison to healthy subjects. LRG1 has been shown to be involved in promoting neovascularization (new blood vessel growth) by switching transforming growth factor beta (TGFbeta) signaling in endothelial cells. LRG1 binds to the accessory receptor endoglin and promotes signaling via the ALK1-Smad1/5/8 pathway. Li et al. [22] reported that high LRG1 expression in urinary exosomes of NSCLC patients may be derived from tumor tissues, and therefore LRG1 may be a candidate urine biomarker for NSCLC-related tumors [22]. Gelsolin is an actin-binding protein that is a key regulator of actin filament assembly and disassembly. Many scientific groups created the hypothesis that gelsolin acts in a complex manner in the development and progression of NSCLC [47–50]. According to Yang et al. [49] high level of gelsolin expression was significantly associated with death risk of NSCLC patients. High gelsolin expression can facilitate tumor dissemination and metastasis by promoting tumor

[33] [23,32]

[25,26] [14,26] [34]

[38]

cell locomotion, which can subsequently translate into poor clinical outcomes. Summary of proteomic markers detected in fluid sample from NSCLC patients is shown in Table 2. 4.1.3. Proteomic signatures for histological types of NSCLC Histological type is one of the most important clinical features of NSCLC. Each of the histological subtypes: adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC) correlates with specific clinical features. Histology of tumor is important to establish a therapeutic strategy, but NSCLC often shows histological heterogenity (ADC and SCC component in one tumor). The molecular profile and proteomic pattern correlating with NSCLC histological variation is still unclear. The vast majority of previously known diagnostic proteomic biomarkers can be divided according to the histological type of lung cancer. Using distinct proteomic techniques several diagnostic markers of adenocarcinoma were identified. Seike et al. [51] have found several proteins highly associated with histological type of tumor by use of two-dimensional difference gel electrophoresis (2D DIGE) technique. In this study, fatty acid-binding protein 5 (FABP5) was found useful for discrimination of ADCs from SCC. Kikuchi et al. [1] established 25 upregulated proteins listed for each NSCLC histology group. Six of these proteins were unique to ADC (chromogranin B, calcitonin-related polypeptide Ralpha, IPI00911047, nerve growth factor inducible, proprotein convertase, subtilisin/kexin type 1) and two were found only in SCC (visinin-like 1 and matrix metalloproteinase 10). 4.2. Prognostic Biomarkers Prognostic protein biomarker should correlate with patient survival. Expression of protein molecule has a prognostic value when correlating with natural history of the disease and tumor stage. Proteomic examinations can also be useful to define metastatic potential in variable groups of patients with different clinical stages of disease. The presence or the absence of such

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Table 2 Diagnostic biomarkers in fluid samples. Specificity

Biomarker

Type of fluid sample

Proteomic technique

Ref.

NSCLC (without division on histological types)

Gelsolin

Urine, serum, plasma, pleural effusion

[56,18],[52,18]

LRG1 (leucine-rich alpha-2-glycoprotein) SAA (serum amyloid A) Calprotectin

Urine

1D-PAGE, HPLC–MS/MS 2D-DIGE, MALDI-TOF-MS, LC–MS/MS 2D-DIGE, MALDI-TOF-MS 1D-PAGE, HPLC–MS/MS

[46] [24]

HP (haptoglobin)

Serum, saliva

Protein S100A8, S100A9 PEDF (pigment epithelium-derived factor, SERPINF1) Peroxideroxin PRDX1 IDH1 (isocitrate dehydrogenase 1) AZGP (zinc Alpha2glycoprotein)

Serum, pleural effusion Pleural effusion

LC–MS/MS 2D-DIGE, LC–MS/MS, MALDI-TOF MS/MS 2D-DIGE, MALDI-TOF-TOF, MALDI-TOF-MS, MALDI-TOF-PMF 2D-DIGE, LC–MS/MS, MALDI-TOF MS/MS 2D-DIGE MALDI-TOF-MS 2D-DIGE, MALDI-TOF-MS

Plasma Plasma

2D-PAGE 2D-DIGE, MALDI-TOF-TOF

[58] [30]

Saliva

2D-DIGE, LC–MS/MS, MALDI-TOF MS/MS

[24]

Gelsolin

Plasma, pelural effusion

[18,52]

SAA (serum amyloid A) TTR (transthyretin) Apolipoprotein A1 A1AT (␣-1-antitrypsin) Cystatin C3 CK8 (cytokeratin8)

Serum

2D-DIGE, MALDI-TOF-MS, MALDI-TOF-TOF, LC–MS/MS 2D-PAGE, MALDI-TOF-PMF

Serum Serum Plasma Plasma, pleural effusion Plasma, pleural effusion

2D-PAGE, MALDI-TOF-PMF 2D-PAGE, MALDI-TOF-PMF MALDI-TOF-TOF, LC–MS/MS MALDI-TOF-TOF, LC–MS/MS MALDI-TOF-TOF, LC–MS/MS

[59] [59] [52] [52] [52]

CLU (clusterin) SAA (serum amyloid A) C3 complement component, C3c Apolipoprotein A4 AHSG (alpha-2-HSglycoprotein)

Serum Serum Serum

2D-DIGE, MALDI-TOF-MS 2D-DIGE, MALDI-TOF-MS 2D-DIGE, MALDI-TOF-MS

[60] [14,60] [60]

Serum Serum

2D-DIGE, MALDI-TOF-MS 2D-DIGE, MALDI-TOF-MS

[60] [60]

Adenocarcinoma

Squamous cell carcinoma

prognostic marker can be useful for the qualification of the patients for certain treatment, but does not predict the response to this treatment. According to a NIH Consensus Conference, a clinically useful prognostic marker must be a proven independent significant factor, which is easy to determine, interpret, and has therapeutic consequences [52]. Prognostic biomarkers for relapse after local treatment are needed for better patient selection for adjuvant treatment strategies. Postoperative tissue were proven to be the most useful when discovering the range of prognostic biomarkers for NSCLC patients. Many studies have focused on finding proteomic biomarkers that can distinguish patients with good or poor prognosis after resection of primary tumor. Acting according to this approach, it was assessed that levels of proteins defined as members of S100 family have been directly implicated in metastasis potential. It has been shown that this small calcium-binding proteins family is involved in many cellular processes (cell cycle progression, differentiation, inflammation) and is related to prognosis and risk of metastasis in several tumor types [53], including gastric cancer [54,55], clear renal carcinoma [56], colorectal cancer [57]. Immunohistochemical analysis and MALDI-TOF/MS and MS/MS methods validated by RT-PCR and western blot [58] have shown that metastatic phenotype of lung cancer cells seems to be related to upregulation of S100A11. High S100A11 expression is correlated with higher TNM stage and lymph nodes metastasis [58]. Another protein from S100 family—S100A6—also might be an important molecule in lung cancer progression. Determination of S100A6 protein level in various samples (cell lysates, plasma, pleural effusion) by

Serum Saliva

[51,56,57]

[40,41,42] [24]

[18] [18]

[14,59]

surface-enhanced laser desorption ionization time-of-flight mass spectrometry method (SELDI TOF-MS) indicated longer patient’s survival in S100A6-negative cases. Over 90% of deaths from lung cancer are caused by metastases; therefore proteins involved in this process are the most considerable as prognostic markers. It has been shown that other protein with prognostic potential for NSCLC patients (adenocarcinoma type) is annexin A3 (ANXA3). This member of calcium-dependent phospholipid-binding protein family plays a role in the regulation of cellular growth and in signal transduction pathways. It is also related to advanced clinical stage, lymph nodes, and distant metastasis, decreased overall survival, and high relapse rate [59]. Seeking for SCC prognostic biomarkers was performed by comparing cancer tissues with and without lymph nodes metastasis. Results of these analyses showed overexpression of ANXA2, HSP27, and CK17, and under-expression of SNF (14-3-3 ␴) [60]. Annexin A2 is a calcium-regulated membrane-binding protein. It was established that expression of this protein in tumor tissue is correlated with clinical stage and tumor differentiation. HSP27 expression was correlated with histopathological differentiation, lymph node metastasis, and clinical stage. So it is possible for HSP27 to serve as a potential target for gene therapy in HSP27-positive lung squamous cancer. Blockage of HSP27 or HSP27 signaling might prevent lung squamous cancer cells from invasion and metastasis. Similar association with poor overall survival was identified in isocitrate dehydrogenase 1 (IDH1) [34,61]. The cytoskeleton reorganization is one common cellular event underlying all the steps of the metastatic cascade in every cancer

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Table 3 Prognostic biomarkers found in tissue samples. Specificity

Biomarker

Proteomic technique

Ref.

NSCLC

Protein S100A6; S100A9 Thymosin ␤ 4 and ␤10 CFL1(cofilin-1)

SELDI-TOF-MS MALDI-MS, MALDI-TOF-MS 2D-PAGE, ESI-Q-TOF-MS/MS

[70] [12,57] [37]

Adenocarcinoma

CFL1(cofilin-1) Cytokeratin CK7, CK8, CK18, CK19 Heat shock protein HSP70 Annexin ANXA1, ANXA2, ANXA3

2D-PAGE, ESI-Q-TOF-MS/MS, MALDI-TOF-MS 2D-PAGE, MALDI-TOF-MS 2D-PAGE, MALDI-MS 2D-DIGE, MALDI-TOF-PMF

[37,57] [71] [34] [69]

Squamous cell carcinoma

IDH1 (isocitrate dehydrogenase 1) Cytokeratin CK19 Heat shock protein HSP27 Annexin ANXA2 SNF (14-3-3 ␴)

2D-DIGE, MALDI-TOF-TOF 2D-DIGE, MALDI-TOF-MS 2D-DIGE, MALDI-TOF-MS 2D-DIGE, MALDI-TOF-MS 2D-DIGE, MALDI-TOF-MS, MALDI-TOF-PMF

[30] [33] [33] [33] [33,72]

type. Many studies have focused on construction of the effective mechanisms to block this central process, which could abrogate the generation of metastasis. Expression of many proteins involved in cytoskeletal rearrangement has been proposed as potential prognostic markers in various cancers. Cytokeratins are intermediate filament structural proteins found in the cytoskeleton of epithelial tissue. They are detected as partially degraded, single protein fragments or complexes, but no intact molecules. Elevation of cytokeratins is observed in lung cancer of all histologic types of NSCLC. CYFRA 21-1 (fragment of cytokeratin 19) has been demonstrated as clinically useful in prognostication and monitoring of the patients with lung cancer. Elevated pretreatment values of CYFRA 21-1 in NSCLC patients reported to be associated with unfavorable prognosis [62]. Decreasing concentrations of CYFRA 21-1 in NSCLC predict objective response to treatment in advanced disease [63]. Many studies have also shown that expression of cytokeratin 18 is NSCLC strong prognostic factor [64]. Poor prognosis for patients with stage I of NSCLC is also correlated with positive expression of vimentin and nonmuscle myosin IIA. Both proteins are upregulated in many metastasis cell lines, and when overexpressed, enhance metastatic behavior by regulating cells motility. These two molecules identified by proteomic analysis might reflect the cellular functions of metastasis and the mechanism of recurrence of malignant neoplasms [65]. Other groups of prognostic markers include proteins involved in cytoskeleton regulation such as thymosin B4, thymosin B10, calmodulin, and cofilin-1. Thymosin ␤4 is involved in cell proliferation, migration, and differentiation. It is a protein which plays a role in regulation of actin polymerization. Gene expression of thymosin B4 was suggested as prognostic factor for NSCLC. Overexpression of this protein seems to stimulate lung cancer metastasis by angiogenesis ability and activating cells migration. Thymosin B10 is encoded by the TMSB10 gene. It binds to sequester actin monomers (G actin) and therefore inhibits actin polymerization by similarity. Calmodulin regulates death-associated protein kinases activity (DAPK) and participates in cytoskeletal conversions during cell death. Cofilin-1 also regulates actin dynamics and is associated with NSCLC patient’s survival. Cofilin-1 expression is directly related to tumor invasion, intravasation, and metastasis properties [66]. In Table 3 a detailed list of prognostic biomarkers adequate to NSCLC tissue sample is shown. 4.3. Predictive Biomarkers Biomarkers for treatment response are useful to indicate how well a given disease treatment is working and to predict positive or negative clinical outcome. It is especially important in cancers, when chemotherapy treatments are not equally effective in all individuals. Establishing variability of the treatment effects is principal

for clinicians to effectively select individuals, who will benefit from a given treatment and avoid unnecessary or harmful procedures. A predictive biomarker can also be a target for the therapy. Lung cancer proteomics studies related to predictive markers have focused on the response to EGFR inhibitors in NSCLC patients. Gefitinib and erlotinib are currently used selective inhibitors of EGFR in NSCLC treatment. Most of the studies tried to identify protein signatures to select candidate patients, who are likely to benefit from the treatment with these inhibitors. During analysis of different responses of lung adenocarcinoma patients to gefitinib treatment it was noted that various levels of heart fatty acid-binding protein (H-FABP) were correlated with complete and partial reaction [32]. Physiological role of this protein concentrates around long-chain fatty acids metabolism (transport and storage) and it may also be responsible for the modulation of cell growth and proliferation. MALDI-MS study on serum from NSCLC patients treated with gefitinib and erlotinib exposed an 8-peak profile able to predict benefit of the treatment. Based on these results a commercial product Veristrat® (Biodesix, Broom field, CO, USA) was created, which is being validated in phase III clinical trial [67,68]. 5. Conclusions Advanced technologies are available for sequencing of the entire genome or transcriptome allowing for identification of potentially important failures and targetable lesions involved in carcinogenesis. The results of gene function can be regulated by various mechanisms. The major purpose of clinical proteomics studies is to improve diagnostic procedures including the precise evaluation of biological features of tumor cells and to understand the molecular pathogenesis of cancers to invent novel therapeutic strategies and targets. Biomarkers can be used as indicators of many different disease states. Any biomarker is defined as a specific that is objectively measured and evaluated as an indicator of normal physiological processes, pathogenic processes and diseases or pharmacological responses to a specified therapeutic intervention. Before introduction of any biomarker into routine laboratory tests several studies performed on diversified, large-scale populations is mandatory. Additionally, laboratory techniques used to measure this molecule should be adequately validated. Although several, mainly tissue-related, NSCLC biomarkers have been already discovered, further research is needed in order to find prognostic and treatment predicting biomarkers, especially of plasma origin. Notwithstanding, an arresting divergence exists between the effort directed toward biomarker discovery and the number of markers that make it into daily clinical practice. At this moment, any proteomic biomarker for NSCLC is not listed on Food and Drug Administration (FDA)-approved protein tumor markers currently used in clinical practice [69]. This suggests the

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need to conduct deeper research on existing discovered proteomic biomarkers and their real usefulness.

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