MALDI mass spectrometry in prostate cancer biomarker discovery

Biochimica et Biophysica Acta 1844 (2014) 940–949

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Review

MALDI mass spectrometry in prostate cancer biomarker discovery☆ Brian Flatley a,b, Peter Malone b, Rainer Cramer a,⁎ a b

Department of Chemistry, University of Reading, Reading, UK Urology Research Department, Royal Berkshire Hospital, Reading, UK

a r t i c l e

i n f o

Article history: Received 16 April 2013 Received in revised form 23 May 2013 Accepted 20 June 2013 Available online 2 July 2013 Keywords: Prostate cancer Biomarkers MALDI MS MS profiling MS imaging

a b s t r a c t Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) is a highly versatile and sensitive analytical technique, which is known for its soft ionisation of biomolecules such as peptides and proteins. Generally, MALDI MS analysis requires little sample preparation, and in some cases like MS profiling it can be automated through the use of robotic liquid-handling systems. For more than a decade now, MALDI MS has been extensively utilised in the search for biomarkers that could aid clinicians in diagnosis, prognosis, and treatment decision making. This review examines the various MALDI-based MS techniques like MS imaging, MS profiling and proteomics in-depth analysis where MALDI MS follows fractionation and separation methods such as gel electrophoresis, and how these have contributed to prostate cancer biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge. © 2013 Elsevier B.V. All rights reserved.

1. Introduction

conditions to invasive confirmation tests such as tissue needle biopsy [6].

1.1. Prostate cancer 1.2. Biomarker discovery in prostate cancer Prostate cancer (PCa) is after lung cancer the most frequently diagnosed malignancy in men worldwide. In 2008, an estimated N900,000 new PCa cases were recorded [1]. A more in-depth analysis of the statistics shows that the incidence rate of PCa is highest in developed countries while the mortality rate is far higher in developing countries [2]. The introduction of diagnostic tests where the level of prostate specific antigen (PSA) is measured in serum, in conjunction with a digital rectal examination (DRE) of the prostate, has been the most valuable approach in early detection of PCa [3,4]. This method of testing was adopted much earlier by developed countries, putatively explaining the high incidence rate amongst these countries. Whilst testing for high PSA levels has greatly increased the number of men diagnosed with PCa it does have its limitations. It is not sufficiently specific to distinguish PCa from benign prostate conditions such as benign prostate hyperplasia (BPH) or prostatitis, nor is it sensitive enough to detect some cases of clinically significant PCa with low levels of PSA, i.e. b10 ng/ml [5]. In conclusion, while PSA has been very useful for detecting PCa and reducing the mortality of the disease, the disadvantages are low sensitivity and even lower specificity, over-treating indolent PCa and subjecting sufferers of benign

☆ This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge. ⁎ Corresponding author at: Department of Chemistry, University of Reading, Whiteknights, Reading RG6 6AD, UK. Tel.: +44 1183784550. E-mail address: [email protected] (R. Cramer). 1570-9639/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.bbapap.2013.06.015

According to Atkinson et al. [7] a biomarker can be best defined as “any characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”. There are a number of stages along the clinical diagnosis and subsequent treatment of PCa where clinicians would welcome additional biomarkers that would aid in their decision making. Important measures for a biomarker are its sensitivity and specificity. A newly proposed diagnostic biomarker for PCa should be sufficiently sensitive to detect the disease ideally in its early stages and specific enough to distinguish the disease from symptomatically similar benign diseases such as BPH or prostatitis. Once the disease status has been established a prognostic marker would provide a measure of the disease progression (staging), and from that the probable course of the disease, e.g. metastasis [7]. A therapeutic/pharmaceutical marker is useful for clinicians to monitor the response to a particular intervention such as hormone therapy for the treatment of PCa where eventually the disease cessation will be overcome and the cancer will stop responding to all hormone therapy (androgen-independent or hormone refractory PCa) [8]. Historically, biomarkers were established from well-known empirically found clinical markers. For example, blood pressure is one of the simplest biomarkers, qualified by the Food and Drug authority (FDA) as a surrogate endpoint that can be used in a clinical trial [9]. Arguably, the most obvious biomarkers (the low-hanging fruits) have

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been found and established, making it increasingly more difficult to discover the next generation of biomarkers. In addition, as technology improves so does the expectation and demand for improved diagnostic tests, mainly with respect to their accuracy (specificity and sensitivity). Drug and medicine approval agencies such as the FDA or the European Medicines Agency (EMA) require newly proposed biomarkers to exceed the specificity and sensitivity of the current diagnostic test for the disease under investigation before approval will be granted. Stricter biomarker qualification criteria have been put in place with global collaborations between the different approval agencies [10]. The evolution of more sensitive modern mass spectrometric analytical techniques such as in-depth proteomic analysis of complex biological samples or the identification of a specific molecule modification that is unique to a disease state, has meant that mass spectrometry (MS) is playing a key role in biomarker discovery and evaluation studies. PCa biomarker discovery and research methods are multifaceted and typically encompass genetic, proteomic and metabolomic approaches [11]. A protein biomarker discovery platform with matrix-assisted laser desorption/ionisation (MALDI) MS is often an essential part of a multitude of associated techniques, of which most have been applied to PCa biomarker discovery. In the following review a number of those techniques will be discussed, and the published literature, where it has been applied to PCa, will be examined. 2. MALDI mass spectrometry MALDI was introduced in the late 1980's when Hillenkamp and Karas outlined a novel ionisation technique that enabled the introduction of larger biomolecules such as proteins into the mass spectrometer [12]. In its simplest form a concentrated matrix solution is mixed with the analyte solution and allowed to dry on a MALDI target plate to produce a matrix/analyte crystalline spot with high molar excess of the matrix. The matrix-dominated sample crystals absorb the pulsed laser energy, leading to matrix/analyte desorption and ionisation by a sample volume disintegration process. A typical matrix for use with UV-lasers is an aromatic acid with a chromophore that strongly absorbs light at the laser wavelength. For the MALDI MS analysis of peptides and proteins using UV-laser irradiation, the most commonly employed matrices are α-cyano-4-hydroxycinnamic acid (CHCA), 2,5-dihydroxybenzoic acid (DHB) and sinapinic acid (SA). The choice of matrix plays an important role in the type of ions produced [13,14]. MALDI MS has been used in proteomic methods, from global proteome profiling to imaging of intact tissue slices. It has advantages over other soft ionisation techniques such as electrospray ionisation (ESI), which include its robustness to contamination and in general its easy and rapid (off-line) sample preparation [15]. Traditional MALDI produces mainly singly charged ions, providing a less complex analyte ion profile than ESI where the occurrence of multiply charged ions from the same analyte tends to crowd the spectrum and renders interpretation difficult. A major challenge for any technique in biomarker discovery is to bring the proposed candidate marker from the discovery stage right through to its use in the clinical laboratory. MALDI MS has significantly contributed to the discovery process and already entered clinical laboratories, particularly in clinical microbiology, where it is used in the identification and classification (biotyping) of clinically important microorganisms based on MALDI-TOF MS profiling [16]. 2.1. MALDI mass spectrometry imaging In the 1990's, Spengler et al. [17] and Caprioli et al. [18] established MALDI MS imaging (MSI), which can be employed for the simultaneous in-situ or on-tissue visualisation and spatial mapping of various classes of molecules, from drug metabolites [19] to large proteins [20]. These research groups realised that MALDI with its high spatial resolution when tightly focussed laser beams are used and its high speed acquisition

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would be well suited for imaging applications. A general workflow for an MSI experiment is outlined in Fig. 1. There is a wealth of publications, both original research and review articles, on MALDI imaging and the methods involved in a successful acquisition of tissue MS images [20–23]. Thus, the basics of MALDI MSI will not be discussed at length in this review. In brief, MSI is the acquisition of individual mass spectra from precise spatial coordinates on a (tissue) sample surface. Then, using vendorsupplied, freeware or in-house produced software, the ion signals can be disentangled and ion maps representing the relative intensity and distribution of specific ions across the surface can be mapped. Initially, MALDI MSI was performed primarily on fresh frozen tissue slices (between 5 and 15 μm in thickness) since formalin-fixed and paraffin-embedded (FFPE) samples provided little analyte release putatively due to the complex effects of preservation reactions and protein cross-linking which are still not well understood. However, this problem has more recently been circumvented through washing the FFPE tissue slice with xylene to remove the wax and subsequently rehydrating it with ethanol [24–26], thus releasing the proteins from the fixed structure and increasing their availability for ionisation. Irrespective of the types of tissue samples that are accessible for MSI (either larger FFPE sample slices or surplus biopsy cores taken specifically for research), there should be areas of both normal and malignant cells (of the same cellular origin) across the imaged surface so that direct comparison can be made between the pathologically different cells. Schwamborn et al. were amongst the first to publish on the application of MSI in the hunt for PCa biomarkers. They collected and snap-froze tissue removed from men undergoing radical prostatectomy [27]. In this study there were 11 samples with immuno-histology-confirmed PCa (Gleason score range between 6 and 9) and 11 with benign conditions. Using vendor-supplied software, the collected data was processed and two class-differentiating algorithms were compared – genetic algorithm (GA) and support vector machine (SVM). SVM was found to have the best sensitivity (85.21%) and specificity (90.74%) for distinguishing tissue areas with malignant cells from areas with normal cells. Cazares et al. also looked at a similar sample cohort, i.e. samples from men undergoing radical prostatectomy, and again used the same software and GA to analyse and classify the data. In contrast to the 22 biomolecular signatures (peaks) within the class-differentiating GA used by Schwamborn et al. only three of these were incorporated into their GA to separate cancer from benign tissue areas [28]. Cazares' application of GA correctly classified 85% of the samples in the discovery set and 81% of samples in the validation set. Although this classification accuracy is superior to the GA application by Schwamborn et al., which showed a sensitivity of 70% and a specificity of 84%, it was inferior to their SVM results (vide supra). More recently, Chuang et al. used a fusion of texture-analysis and MSI to obtain both physical and biochemical information for the construction of a PCa region prediction model [29]. Texture-analysis uses a computer-aided diagnostic system to automatically analyse a pathology image and predict the presence of PCa or estimate the Gleason grade of the cancer based on image colour, texture and morphometric information. For this study, MSI was carried out on a consecutive tissue slice to the one used for texture-analysis. Following data acquisition and processing, a feature-selection algorithm was used to select a compact set of m/z values at which the signal intensity from the underlying biomolecule best differentiated between cancer and non-cancer tissue regions. To evaluate the overall performance of the proposed system they used a threefold cross-validation system, which involved dividing all the available data into three parts. Two thirds of the dataset were then used to create a classification model, whilst one third was used to test the proposed model. This procedure was repeated three times, each time a different third of the dataset was used for testing the model. When the probabilities of correct area identification from both texture analysis and MSI were combined and tested the best overall

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Fig. 1. Annotated illustration of the steps involved in sample preparation and data acquisition for MALDI MS imaging. Adapted from reference [85].

sensitivity and specificity achieved was 80.39% and 93.09% respectively [29]. The identification of biomolecules responsible for the differentially abundant peaks between two sample classes is an important part in the validation and acceptance of a potential biomarker. In MSI, protein identification can be done either through a bottom-up or top-down approach. In a typical bottom-up approach the tissue section is sprayed with a protease enzyme such as trypsin and the proteolytic peptides are sequenced for precursor identification, whereas in a top-down experiment, identification is achieved by directly fragmenting the MALDI MSI-generated ions of interest as they are desorbed from the tissue with ion fragmentation techniques like collision-induced dissociation (CID), post-source decay (PSD) or in-source decay (ISD), using the most suitable matrix such as 1,5-diaminonaphthalene (1,5 DAN) for ISD [30]. A bottom-up approach was employed by Bonnel et al. on FFPE prostate tissue sections, from where they identified ions corresponding to actin and numerous fragments from the histone family as more abundant in malignant cells [26]. Cazares et al. identified in their MSI study the biomolecule in the PCa cells' that was responsible for the significant MS signal intensity difference at m/z 4,355 by tandem mass spectrometry (MS/MS) as a peptide fragment of the mitogen-activated protein kinase/extracellular signal-regulated kinase kinase 2 protein and confirmed the result using immunohistochemistry. The signal at m/z 4,355 was one of the three biomolecular features used in the GA algorithm. The advantage of MSI for biomarker discovery is that it requires very little sample. A single cryostat tissue slice mounted on an electrically conductive glass slide will allow both MSI and histology staining [31]. The proteins are analysed in-situ, allowing much better differential analysis of heterogeneous tissue sections that contain benign and malignant areas, which, if subject to global protein extraction, are often (partially) mixed, thus potentially masking expression differences. The possibility to get both the ion signal intensity distribution and the histology information on the same slice allows direct comparison of different ion distributions on the tissue with knowledge of the cell type at that exact location, leading to a more detailed protein expression result [31].

2.2. MALDI mass spectrometry for biomolecular profiling During the progression of PCa, biomolecular changes will occur that potentially lead to cancer-specific efflux of peptides and proteins (or fragments of them) from the tumour directly or indirectly into body fluids such as blood or urine. These additional biomolecules will lead to a different biomolecular profile for samples derived from patients with PCa when compared to samples from patients with benign conditions. Classical techniques for the analysis of the biomolecular content of clinical samples are largely based on targeted approaches such as immunoassays. There are also genomic profiling techniques but these are not suitable for the profiling of the proteomic content of a sample. The recording of the proteomic profiles can be achieved by MALDI MS analysis, where numerous samples can be spotted on a standard MALDI target plate and analysed in an automated way [32]. Alternatively, the samples can be applied to a surface-modified target plate. In one approach, different binding substrates are used that enhance the plate's affinity for specific, targeted compound classes. This approach is known as surface-enhanced laser desorption/ionisation (SELDI). Techniques like MALDI or SELDI are well suited to MS profiling experiments for various reasons. For instance, high-throughput MS analysis of samples is possible with up to several thousand samples a day and the sample preparation workflow, including sample clean-up, for MALDI MS samples can be automated through the use of robotic liquid handling systems [33], and unlike immunoassays MALDI MS analysis is fundamentally non-discriminate in analyte detection. Despite a flux of research published in the early part of the new millennium, success for SELDI profiling has been limited. A number of disadvantages of SELDI such as low sensitivity, low resolution, and high cost of protein binding chip-plates [34], perhaps have limited the progression of SELDI to a useful clinical diagnostic test. Fig. 2 outlines a general workflow for both MALDI and SELDI sample preparation, illustrating the process up to the point of MS data acquisition. From here onwards in the review no further differentiation between the two techniques need to be made. The post-data acquisition stage involves the processing of the spectra, extracting peak lists and applying some type of (multi)variate analysis to

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Fig. 2. A general sample preparation workflow for MALDI (left) and SELDI (right) MS profiling, from initial sample treatment to co-crystallisation with the matrix on a MALDI target plate.

identify pattern variations between the different sample classes. In a recommended biomarker (pattern) discovery approach, the samples are divided into two sets – a training set and a testing set. Samples in the training set are subdivided into their respective disease class and various models are applied until the best discriminating peaks and/or algorithm are identified. The value of this model is evaluated using the blinded test set. Both of these steps are part of the discovery stage for potential biomarkers. In order for any potential disease profile recognition test to be implemented into a clinical laboratory, it will require further validation. Fig. 3 depicts the pathway of establishing a differentiating biomarker/ profile from discovery to validation. The experimental design and processes that underpin MS profiling, e.g. the robustness and reliability of the biomolecular features (peaks) used in a classification model [35], the reproducibility of the data acquisition and processing methods [36], and the pre-analytical variables like sample collection and storage conditions [37] were examined in other cancer research areas. The overall conclusion one can draw from these papers is that strict standard operating protocols (SOPs) have to be adhered to – something that is difficult to introduce into the daily clinical workflows and requires appropriate skills and training [33]. These examples also show that lessons need to be learnt from the early efforts before further validation studies and thus biomarker discovery efforts end in failure. Some of the PCa serum profiling experiments that showed good sensitivity and specificity in the discovery phase have yet to undergo rigorous validation [38–41]. Others with promising discovery results

[42,43] went on to the validation step in subsequent research [44] supported by the National Cancer Institute Early Detection Research Network (EDRN). From this study, a number of follow-up publications emerged such as studies looking at analytical reproducibility, along with the performance of the classification algorithm used in the discovery stage [45] and unfortunately studies where the validation steps failed [46,47]. The final conclusion from this set of studies is that the ability of the proposed model to progress from discovery to successful validation was severely compromised by an amalgamation of biases in the serum specimens from the initial discovery studies, such as different storage times, different freeze-thaw cycles and times of sample collection [46]. Removing the samples compromised by such bias from the sample cohort and redesigning the model to separate the disease classes, however, did not result in a successful classification model [47]. As well as looking at different clinical stages of cancer progression, numerous biomaterials have been investigated for PCa biomarker identification, such as serum [38,39,42,43,48,49], urine [50,51] and tissue [52–54]. These references are by no means exhaustive and there are many more articles on the application of MALDI MS profiling in PCa biomarker discovery. However, as the quality of these articles and their published data vary, the review focuses on those articles that are relevant for the discussion of the techniques and methodologies rather than the varying biomedical data resulting from the many, often low-quality applications in the field. In MALDI MS profiling of human urine samples, Calvano et al. examined the optimisation of the sample preparation steps for this analysis

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Fig. 3. The different stages a candidate biomarker (individual MS ion signal or profile pattern) has to go through in order to progress from discovery experiments in a mass spectrometry laboratory to clinical utility. The initial training data are subjected to (multi)variate analysis such as principal component analysis or hierarchical clustering in order to identify the best class-discriminating biomolecular feature(s). Once identified and a suitable model has been created, test data can be employed to check the sensitivity and specificity of the model. At this stage the model can be refined if improvements are possible before it progresses to the independent validation stage. Alternatively, it will be deemed unsuitable and a return to the discovery stage will follow. The candidate biomarker discovered has to show sufficient analytical potential (and cost-effectiveness) at the test stage before it will progress to the validation stage, as this is the most expensive and extended stage of the biomarker discovery process.

[55] and concluded that the best protocol for low-molecular weight profiling was based on solid-phase extraction using HLB microtubes and CHCA as the MALDI matrix to give the highest number of ions in the mass range of interest. They evaluated their optimised method using urine samples from healthy individuals. Once satisfied with the performance of the method, it was used to analyse urine samples from PCa patients. The resulting MS profiles from the healthy and the PCa groups were compared for any differentiating peaks or patterns that reflect any existing biomolecular differences. This comparison revealed a number of biomolecular features (peaks) with different abundances in one or the other of the two groups. A number of these features were also found differentially abundant in other publications looking at MS profiling of PCa and benign urine samples. For instance, Calvano et al. found that the signal intensity at m/z 1433 was higher for the urine samples of PCa patients than for the benign cases, which correlates with the results from M'Koma et al. in their MALDI MS profiling study of urine [51]. There was a misinterpretation of M'Koma's report, which resulted in Calvano et al. incorrectly attributing this molecular feature to a fragment of semenogelin I isoform b preproprotein. However, M'Koma only tentatively reported a match to a fragment of semenogelin I isoform b preproprotein at m/z 1374, not m/z 1433. Additionally, Calvano et al. found that the signal intensity at m/z 10,722 was lower for PCa urine samples when compared to healthy individuals. In their literature search

they found that a peak at m/z 10,788 was identified by Okamoto et al. with different intensity for the urine samples of PCa patients when compared to urine samples from healthy individuals [50]. Calvano et al. compared these two results but incorrectly stated that in the results from Okamoto et al. the peak at m/z 10,788 was reported as higher for urine samples from PCa patients. As in fact Okamoto et al. found that the signal intensity at m/z 10,788 was lower for urine samples from PCa patients, it could be possible that the underlying biomolecular ions are actually the same polypeptide, providing that the 16-Da mass difference can be either explained by pre-analytical molecular changes, low-quality data acquisition or closely related quasimolecular ions due to reactions during sample preparation, adduct ion formation or the loss of smaller functional groups. Consequently, comparisons like these must be done with caution (as also pointed out by Calvano et al.), especially without access to the necessary raw data and metadata. Where a significant difference in peak intensity at a specific m/z value is found the biomolecule responsible for the mass spectral peak should ideally be identified. This can help in the verification process by cross-checking the identified polypeptide's disease involvement using published information and will also provide useful information for potential validation assays such as ELISAs as well as for further targeted research, offering an insight into the biological pathways affected by tumorigenesis. For instance, the underlying protein of a

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significantly decreased peak in MALDI MS profiles of sera from men with metastatic PCa when compared to sera from benign patients and patients with lower grade PCa was identified as platelet factor 4 (PF4) [56]. In this study, a number of peaks were found with differential intensities, from which the authors chose the peak (at m/z 7771) with the most significant difference as the primary candidate for further analysis. Due to the complex nature of crude serum samples, the identification of the biomolecule responsible for this peak was done using a variety of separation and fractionation steps to isolate and purify the sample prior to MS analysis. The progress after each separation step was monitored using MALDI MS, whereby each fraction was analysed and only the fraction still containing the mass of interest progressed to the next stage of purification. Two-dimensional (2D) gel electrophoresis (GE) served as the final separation step. The band closest to the target mass of interest (as measured from a low molecular weight reference set also separated by 2D-GE) was excised, destained and digested before using reflectron MALDI-TOF MS peptide mass fingerprinting (PMF), to identify it as PF4 [56]. 3. MALDI MS and hyphenated techniques for biomarker analysis Along with the direct application of MALDI MS to complex biological samples such as tissue or plasma as discussed in Section 2, it can also be employed to identify peptides and proteins that have been already separated and purified using techniques such as liquid chromatography (LC) or GE. GE methods are still the most common front-end protein separation technique for MALDI MS analysis of proteins. Proteins extracted from tissue or biological fluids like serum or urine are separated on gel-slabs based on physico-chemical properties like molecular weight and/or pI values. Following staining and visualisation of the separated proteins in the gel-slabs, protein spots with differential abundances are identified and excised, and the protein spot is usually subjected to some form of enzymatic digestion procedure such as trypsin digestion. Following this digestion MALDI MS can then be used in a number of different ways to aid in protein identification. A generic workflow for approaches using hyphenated techniques is outlined in Fig. 4, and the approach of peptide mass fingerprinting (PMF) and MS/MS are also described in the following sections. 3.1. Peptide mass fingerprinting (PMF) Once the separated proteins are excised and digested, a MALDI MS spectrum of the resulting digest solution is acquired [57]. Using this spectrum, a peak list is generated and searched against theoretical peptide ion masses generated from protein sequence databases to look for the best matching sequence. PMF is a very useful technique for the rapid identification of a well-separated isolated protein. However, this approach can be problematic if the sample complexity is high and/or contaminants such as keratin are also in the sample, as they can cause extraneous peaks and make the interpretation of the results more difficult due to the higher percentage of protein-non-specific peaks, resulting in lower probabilities in the above-mentioned database search. In these cases a tandem mass spectrometry (MS/MS) approach is more successful for high-confidence identification. 3.2. Tandem mass spectrometry (MS/MS) MALDI MS/MS analysis exploits the fragmentation of selected precursor peptide ions. The recorded MS/MS fragment ion information as well as the precursor ion's mass are used in searching databases that contain in-silico digested and fragmented protein sequences. It typically leads to higher probability values and thus greater confidence in protein identification. The mass analyser coupled to the MALDI source determines the type of MS/MS analysis that can be carried out. A detailed discussion on the mechanisms, merits and disadvantages of different fragmentation methods and mass analysers is beyond the scope of this

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review. However, techniques like post-source decay (PSD) [58], in particular in connection with the so-called LIFT technology [59], and collisioninduced dissociation (CID) on TOF/TOF and Q-TOF instruments [60] are two common options in MALDI MS/MS analysis. 3.3. PMF and MS/MS applied to prostate cancer biomarker discovery Many of the above techniques have been applied as part of a larger workflow to the identification of PCa candidate biomarkers. Rehman et al. identified caligranulin B (also known as MRP-14 or S100-A9) as a potential novel marker for PCa after they found that it was over-expressed in the urine of prostate cancer patients when compared to patients with benign prostate hyperplasia (BPH) [61]. However, the use of S100-A9 as a PCa biomarker is questionable after subsequent research compared its performance as a PCa biomarker against PSA and found that it fell short as a candidate biomarker for the discrimination of PCa from benign prostate conditions [62]. No improvements in sensitivity or specificity were found when using S100-A9 serum levels as a diagnostic test for PCa, and more interestingly, in this study it was found to be over-expressed in benign group rather than the PCa group. Two studies employed very similar methods of 2D-GE separation of proteins extracted from prostate tissue biopsies and used MALDI-TOF/ TOF with CID precursor ion fragmentation for the identification of the differential spots [63,64]. Between these two studies there was a difference in the staining methods used for the visualisation of the gel spots, but foregoing this technical difference, there was limited correlation between the two studies with respect to the protein signals found to be different in benign compared to cancer tissue samples. Ummanni et al. particularly focused on the cell cycle regulatory protein prohibitin as protein of higher abundance in tumour samples [63]. This protein and its greater abundance was also recognised by Lin et al. [64] but not further investigated. Ummanni et al. identified prohibitin from a tissue extract and subsequently performed RNA extraction and measurement of its transcription using quantitative real-time PCR, and also used immunohistochemistry to study the distribution of prohibitin in 21 other prostatectomy specimens. They found a clear over-expression of prohibitin at the transcriptional level in PCa tissue, and the immunohistochemistry results confirmed that the expression of prohibitin is higher in prostatic intraepithelial neoplasia as in prostatic carcinoma but not in benign prostatic hyperplasia or inflammatory atrophy. In a later publication Ummanni et al. used 2D difference gel electrophoresis (DIGE) along with PMF to identify proteins that showed differential 2D-DIGE spot intensities in a comparison of tumour tissue with benign adjacent tissue [65]. Interestingly, only one protein was common between the three studies, dimethylarginine dimethylaminohydrolase 1 (DDAH1), which was found to be up-regulated in all three studies. 4. Validation of MALDI MS-discovered biomarkers After the discovery of a potential biomarker, the next step is candidate verification and validation. The large attrition rate (cf. above described EDRN study) and the large cost involved in the validation of a biomarker and its clinical use mean that priority should be given to biomarkers where their discovery is with regard to all analytical aspects (incl. pre-analytical biases and statistics) sound and their biological context convincing and disease-relevant. Biomarkers discovered using MALDI MS techniques like MS imaging and profiling sometimes turn out to be inflammatory markers and therefore warrant some further investigation into their specificity as a PCa biomarker. Well-established techniques such as western blotting or immunohistochemistry using specific antibodies against the candidate biomarker can be used for initial verification. Certainly for MALDI MSI, validation should include a comparison of the proposed biomarker's MS signal distribution across the tissue surface with traditional immunohistochemistry methods [26,66]. Where MALDI MS has been used as an identification tool either in a profiling approach or in combination with separation techniques,

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Fig. 4. This diagram outlines the options in MALDI MS analysis in combination with a pre-separation technique like gel electrophoresis for the identification of differentially abundant proteins. Following in-gel digestion the resulting peptides can either be subjected to simple MS analysis and the underlying protein is identified by peptide mass fingerprinting; or precursor ions can be selected from the initial MS analysis and subjected to further fragmentation and MS/MS analysis. Using various available software packages for data processing and sequence database searching the statistically most probable protein matches are returned, based on the measured ion fragment data along with the precursor ion mass-to-charge ratio.

immunoassays such as ELISAs can be used for continuing investigations into the biomarker's clinical utility using a larger patient cohort and ideally a multi-centre (sample collection and analysis) study. Makawita and Diamandis examined MS techniques like multiple reaction monitoring (MRM) MS as suitable alternatives to immunoassays for the evaluation and quantification of proposed biomarkers [67], and concluded that although it is unlikely that MRM MS will replace the traditional immunoassay in the near future, it does hold great promise as a fast reliable method for quantitative assessment of a large number of candidate cancer biomarkers. A recent publication from Calligaris et al. used high resolution MALDI-FTICR for reaction monitoring in MSI experiments [68], demonstrating a novel combination of MSI and ISD for localisation and reliable identification of potential biomarkers present in tissue sections that can complement traditional histopathology analysis. Pepe et al. suggested different phases in the biomarker development pipeline that can be used for early detection of cancer [69]. These guidelines from working groups within the EDRN guided the design by Grizzle et al. for the earlier mentioned SELDI serum profiling study [44]. However, the validation step is still a

stumbling block along the path from bench to bedside in MALDI MS profiling, and to-date, no MALDI MS-discovered PCa biomarkers have been approved by a regulatory body for clinical use. 5. Perspectives There has been a plethora of published research over the past 15 years reporting the application of MALDI MS to all stages of the biomarker process. Though many teething problems and pitfalls were identified and still need to be satisfactorily addressed, it has been shown that MALDI MS analysis has great potential in biomarker discovery and application, from cancer research to the biotyping of microorganisms. The variations in sample collection and storage are a recurring problem in the biomarker discovery pathway. Perhaps one solution to the problem is the utilisation of blood spot collection cards. Benefits of dried blood spot analysis include ease of collection, the low volume of sample required, and the long-term storage capability (for several months) without significant loss of sample stability. These make DBS an attractive alternative to traditional methods as one can use simple

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prick sampling of as little as a few microlitres for each spot as opposed to a minimum of 0.5 ml for venous sampling [70]. The first application of DBS, using simple filter papers, was in the early 1960s when they were successfully employed as a sampling method for the detection of phenylketonuria from neonatal blood [71]. More recently, DBS has been successfully used in the monitoring of small drug molecules in samples from pre-clinical trials [72,73]. Albeit it has proven to-date more difficult to extract proteins and peptides from DBS, identification of haemoglobin variants has already been demonstrated with success using surface sampling methods [74] and perhaps as the technique further develops it will be possible to extract more proteins directly from the blood spot and use them for MALDI MS biomarker research. Recent advancements in the wider field of mass spectrometry includes the introduction of more atmospheric pressure (AP) ionisation techniques such as desorption electrospray ionisation (DESI) [75], laser ablation electrospray ionisation (LAESI) [76] and matrix-assisted laser desorption electrospray ionisation (MALDESI) [77], which, although in their infancy when compared to established techniques like vacuum MALDI or SELDI, offer great promise for MS analysis without the need for a high vacuum system. One exciting development is real-time mass spectrometric analysis of tissue being excised during surgery [78]. Here, mass spectra are generated from ions produced at the surgical site. Ionisation is believed to be a result of the formation of charged droplets during tissue evaporation when using certain surgical cutting implements e.g. electro- or laser-surgical cutting devices. The ions are then carried via PEEK tubing from the area under surgery cutting device to a mass analyser, where a mass spectrum is recorded. Classification of the specific area of tissue being cut is done using classification models that were created from previously acquired spectra from tissues of known biological and pathological origin. Although rapid evaporative ionisation mass spectrometry (REIMS), as this technique is known, is a different ionisation technique to the main focus of this review, many of the underlying principles are the same as for MALDI MSI and in the case of using laser surgery it can be argued that MALDI-like processes are exploited, depending on the physico-chemical properties of the biological matrix at the laser wavelength employed. In any case, the data generated in the operating room, with or without MSI data from histology samples, can also offer clinicians fast continuous feedback on sub-cellular level activity and disease progression. In general, emerging methods such as those that enable MALDIgenerated multiple-charged ions [79] or the desorption/ionisation of analytes in vacuum without the application of an external energy source [80], in addition to improved informatics approaches to MALDI MS data processing [81] and further analysis of MALDI MS data [82], and the continuing efforts in addressing (previous) mistakes and best practice for the future [83,84] present a great pool of opportunities for MALDI MS in clinical biomarker discovery and analysis, including clinical research of PCa. 6. Conclusions The contribution of MALDI MS to the bioanalytical field, in particular with respect to cancer research, cannot be overstated. It has been used in many areas since its inception, including the identification of potential clinical markers for a range of different diseases. The ease of sample preparation, user-friendliness, speed, high sensitivity and easy-to-interpret spectra mean that MALDI is one of the most suitable MS ionisation techniques for the clinical laboratory. The exciting area of MALDI MS imaging has opened up a whole realm of new possibilities for oncology research. Access to archived FFPE samples for research should allow much larger sample numbers to be included in the initial discovery phase adding better statistical weight to the data. MALDI MS profiling has recently become extremely successful through its use in clinical microbiology, biotyping microorganisms from blood culture. However, some applications of MALDI

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MS such as disease profiling and biomarker discovery have not delivered on their initial promise. Despite the plethora of published literature on the subject, the search for credible and validated MALDI MS-based biomarkers or biomarker patterns for prostate cancer continues. Those embarking on MALDI MS profiling experiments should research previously published literature carefully and learn from their findings. Yet throughout the work for this review highly contradictory results and repeat mistakes were discovered. Until standard operating protocols (SOPs) that have been published and reviewed [33] are employed and much more rigorous, multi-centre studies are undertaken, carefully analysed and correctly reported, MALDI MS profiling will lag behind other techniques as a viable biomarker discovery and detection method. 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