A robust biomarker discovery pipeline for high-performance mass spectrometry data

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.     

Sampling and analytical strategies for biomarker discovery using mass spectrometry

There is an often unspoken truth behind the course of scientific investigation that involves not what is necessarily academically worthy of study, but rather what is scientifically worthy in the eyes of funding agencies. The perception of worthy research is, as cost is driven in the simplest sense in economics, often driven by demand. Presently, the demand for novel diagnostic and therapeutic protein biomarkers that possess high sensitivity and specificity is placing major impact on the field of proteomics. The focal discovery technology that is being relied on is mass spectrometry (MS), whereas the challenge of biomarker discovery often lies not in the application of MS but in the underlying proteome sampling and bioinformatic processing strategies. Although biomarker discovery research has been historically technology-driven, it is clear from the meager success in generating validated biomarkers that increasing attention must be placed at the pre-analytic stage, such as sample retrieval and preparation. As diseases vary, so do the combinations of sampling and sample analyses necessary to discover novel biomarkers. In this review, we highlight different strategies used toward biomarker discovery and discuss them in terms of their reliance on technology and methodology.     

Imaging mass spectrometry in biomarker discovery and validation

Biomarker discovery and validation involves the consideration of many issues and challenges in order to be effectively used for translation from bench to bedside. Imaging mass spectrometry (IMS) is a new technology to assess spatial molecular arrangements in tissue sections, going far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. The possibility to correlate distribution maps of multiple analytes with histological and clinical features makes it an ideal tool to discover diagnostic and prognostic markers of diseases. Some recently published studies that show the usefulness and advantages of this technology in the field of cancer research are highlighted.     

Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry

We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.     

MALDI mass spectrometry in prostate cancer biomarker discovery

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.     

Evaluation of multiprotein immunoaffinity subtraction for plasma proteomics and candidate biomarker discovery using mass spectrometry

Strategies for removal of high abundance proteins have been increasingly utilized in proteomic studies of serum/plasma and other body fluids to enhance the detection of low abundance proteins and achieve broader proteome coverage; however, both the reproducibility and specificity of the high abundance protein depletion process still represent common concerns. Here we report a detailed evaluation of immunoaffinity subtraction performed applying the ProteomeLab IgY-12 system that is commonly used in human serum/plasma proteome characterization in combination with high resolution LC-MS/MS. Plasma samples were repeatedly processed using this approach, and the resulting flow-through fractions and bound fractions were individually analyzed for comparison. The removal of target proteins by the immunoaffinity subtraction system and the overall process was highly reproducible. Non-target proteins, including one spiked protein standard (rabbit glyceraldehyde-3-phosphate dehydrogenase), were also observed to bind to the column at different levels but also in a reproducible manner. The results suggest that multiprotein immunoaffinity subtraction systems can be readily integrated into quantitative strategies to enhance detection of low abundance proteins in biomarker discovery studies.     

MALDI mass spectrometry for direct tissue analysis: a new tool for biomarker discovery

The direct analysis of tissue sections by MALDI mass spectrometry holds tremendous potential for biomarker discovery. This technology routinely allows many hundreds of proteins to be detected over a mass range of approximately 2000-70 000 Da while maintaining the spatial localization of the proteins detected. This technology has been applied to a wide range of tissue samples, including human glioma tissue and human lung tumor tissue. In many cases, biostatistical analyses of the resulting protein profiles revealed patterns that correlated with disease state and/or clinical endpoints. This work serves as a review of recent applications and summarizes the current state of technology.     

Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery

The low molecular weight plasma proteome and its biological relevance are not well defined; therefore, experiments were conducted to directly sequence and identify peptides observed in plasma and serum protein profiles. Protein fractionation, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) profiling, and liquid-chromatography coupled to MALDI tandem mass spectrometry (MS/MS) sequencing were used to analyze the low molecular weight proteome of heparinized plasma. Four fractionation techniques using functionally derivatized 96-well plates were used to extract peptides from plasma. Tandem TOF was successful for identifying peptides up to m/z 5500 with no prior knowledge of the sequence and was also used to verify the sequence assignments for larger ion signals. The peptides (n>250) sequenced in these profiles came from a surprisingly small number of proteins (n approximately 20), which were all common to plasma, including fibrinogen, complement components, antiproteases, and carrier proteins. The cleavage patterns were consistent with those of known plasma proteases, including initial cleavages by thrombin, plasmin and complement proteins, followed by aminopeptidase and carboxypeptidase activity. On the basis of these data, we discuss limitations in biomarker discovery in the low molecular weight plasma or serum proteome using crude fractionation coupled to MALDI-MS profiling.     

Biomarker discovery from body fluids using mass spectrometry

Systems analysis of body fluids by mass spectrometry (MS) is an upcoming field of biomarker research. This approach is extremely attractive because it does not require biomarker candidates and the sample preparation is simple. However, during the development of the technique strong critical comments were made on the poor reproducibility, the special characteristics of blood as a source of peptides and on the frequent non-adequate statistical analysis of the data. Here we discuss the efforts made in the last few years to develop suitable protocols, which could lead to biomarker discovery from body fluids by mass spectrometry. Our review focuses on the systems analysis of non-digested blood serum or plasma samples by MALDI-TOF and SELDI-TOF.     

Endometrial carcinoma biomarker discovery and verification using differentially tagged clinical samples with multidimensional liquid chromatography and tandem mass spectrometry

The utility of differentially expressed proteins discovered and identified in an earlier study (DeSouza, L., Diehl, G., Rodrigues, M. J., Guo, J., Romaschin, A. D., Colgan, T. J., and Siu, K. W. M. (2005) Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cleavable ICAT with multidimensional liquid chromatography and tandem mass spectrometry. J. Proteome Res. 4, 377-386) to discriminate malignant and benign endometrial tissue samples was verified in a 40-sample iTRAQ (isobaric tags for relative and absolute quantitation) labeling study involving normal proliferative and secretory samples and Types I and II endometrial cancer samples. None of these proteins had the sensitivity and specificity to be used individually to discriminate between normal and cancer samples. However, a panel of pyruvate kinase, chaperonin 10, and alpha1-antitrypsin achieved the best results with a sensitivity, specificity, predictive value, and positive predictive value of 0.95 each in a logistic regression analysis. In addition, three new potential markers were discovered, whereas two other proteins showed promising trends but were not detected in sufficient numbers of samples to permit statistical validation. Differential expressions of some of these candidate biomarkers were independently verified using immunohistochemistry.     

Proteomic analysis and discovery using affinity proteomics and mass spectrometry

Antibody-based microarrays are a rapidly evolving affinity-proteomic methodology that recently has shown great promise in clinical applications. The resolution of these proteomic analyses is, however, directly related to the number of data-points, i.e. antibodies, included on the array. Currently, this is a key bottleneck because of limited availability of numerous highly characterized antibodies. Here, we present a conceptually new method, denoted global proteome survey, opening up the possibility to probe any proteome in a species-independent manner while still using a limited set of antibodies. We use context-independent-motif-specific antibodies directed against short amino acid motifs, where each motif is present in up to a few hundred different proteins. First, the digested proteome is exposed to these antibodies, whereby motif-containing peptides are enriched, which then are detected and identified by mass spectrometry. In this study, we profiled extracts from human colon tissue, yeast cells lysate, and mouse liver tissue to demonstrate proof-of-concept.     

Biomarker discovery by imaging mass spectrometry: transthyretin is a biomarker for gentamicin-induced nephrotoxicity in rat

Adverse drug effects are often associated with pathological changes in tissue. An accurate depiction of the undesired affected area, possibly supported by mechanistic data, is important to classify the effects with regard to relevance for human patients. MALDI imaging MS represents a new analytical tool to directly provide the spatial distribution and the relative abundance of proteins in tissue. Here we evaluate this technique to investigate potential toxicity biomarkers in kidneys of rats that were administered gentamicin, a well known nephrotoxicant. Differential analysis of the mass spectrum profiles revealed a spectral feature at 12,959 Da that strongly correlates with histopathology alterations of the kidney. We unambiguously identified this spectral feature as transthyretin (Ser(28)-Gln(146)) using an innovative combination of tissue microextraction and fractionation by reverse-phase liquid chromatography followed by a top-down tandem mass spectrometric approach. Our findings clearly demonstrate the emerging role of imaging MS in the discovery of toxicity biomarkers and in obtaining mechanistic insights concerning toxicity mechanisms.     

SNP and mutation discovery using base-specific cleavage and MALDI-TOF mass spectrometry

MOTIVATION: Single Nucleotide Polymorphisms (SNPs) are believed to contribute strongly to the genetic variability in living beings, in particular their disease or drug side effect predispositions. Mutation-induced sequence variations are playing an important role in the development of cancer, among others. From this, it is clear that SNP and mutation discovery is of great interest in today's Life Sciences. Currently, such discovery is often performed utilizing electrophoresis-based Sanger Sequencing. Discovery of SNPs can also be performed by multiple sequence alignment of publicly available sequence data, but recent studies indicate that only a small percentage of SNPs can be discovered using this approach and, in particular, that SNPs with low frequency are often missed. Other SNP discovery methods only indicate the presence of a SNP in a sample region, but fail to resolve its characterization and localization. RESULTS: We present a method to discover mutations and SNPs using base-specific cleavage and mass spectrometry. An amplicon of known reference sequence with length usually between 100 and 1000 nt is amplified, transcribed, and cleaved using base-specific endonucleases such as RNAse A or T1. The resulting cleavage products (or fragments) are analyzed by MALDI-TOF mass spectrometry and, comparing the measured spectra with those predicted in-silico, the goal is to discover and pinpoint sequence variations of the sample sequence compared to the reference sequence. A time-efficient algorithm for discovering sequence variations is presented that enables fast analysis of such variations even if the sample sequence differs significantly from the reference sequence.     

Simulating multiplexed SNP discovery rates using base-specific cleavage and mass spectrometry

MOTIVATION: Single Nucleotide Polymorphisms (SNPs) are believed to contribute strongly to the genetic variability in living beings, and SNP and mutation discovery are of great interest in today's Life Sciences. A comparatively new method to discover such polymorphisms is based on base-specific cleavage, where resulting cleavage products are analyzed by mass spectrometry (MS). One particular advantage of this method is the possibility of multiplexing the biochemical reactions, i.e. examining multiple genomic regions in parallel. Simulations can help estimating the performance of a method for polymorphism discovery, and allow us to evaluate the influence of method parameters on the discovery rate, and also to investigate whether the method is well suited for a certain genomic region. RESULTS: We show how to efficiently conduct such simulations for polymorphism discovery using base-specific cleavage and MS. Simulating multiplexed polymorphism discovery leads us to the problem of uniformly drawing a multiplex. Given a multiset of natural numbers we want to uniformly draw a subset of fixed cardinality so that the elements sum up to some fixed total length. We show how to enumerate multiplex layouts using dynamic programming, which allows us to uniformly draw a multiplex.     

Targeted mass spectrometry approaches for protein biomarker verification

The search for protein biomarkers has been a highly pursued topic in the proteomics community in the last decade. This relentless search is due to the constant need for validated biomarkers that could facilitate disease risk stratification, disease diagnosis, prognosis, monitoring as well as drug development, which ultimately would improve our quality of life. The recent development of proteomic technologies including the advancement of mass spectrometers with high sensitivity and speed has greatly advanced the discovery of potential biomarkers. One of the bottlenecks lies in the development of well-established verification assays to screen the biomarker candidates identified in the discovery stage. Recently, absolute quantitation using multiple-reaction monitoring mass spectrometry (MRM-MS) in combination with isotope-labeled internal standards has been extensively investigated as a tool for high-throughput protein biomarker verification. In this review, we describe and discuss recent developments and applications of MRM-MS methods for biomarker verification.     

Targeted proteomics by selected reaction monitoring mass spectrometry: applications to systems biology and biomarker discovery

Mass Spectrometry-based proteomics is now considered a relatively established strategy for protein analysis, ranging from global expression profiling to the identification of protein complexes and specific post-translational modifications. Recently, Selected Reaction Monitoring Mass Spectrometry (SRM-MS) has become increasingly popular in proteome research for the targeted quantification of proteins and post-translational modifications. Using triple quadrupole instrumentation (QqQ), specific analyte molecules are targeted in a data-directed mode. Used routinely for the quantitative analysis of small molecular compounds for at least three decades, the technology is now experiencing broadened application in the proteomics community. In the current review, we will provide a detailed summary of current developments in targeted proteomics, including some of the recent applications to biological research and biomarker discovery.     

Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents

In this paper we try to identify potential biomarkers for early stroke diagnosis using surface-enhanced laser desorption/ionization mass spectrometry coupled with analysis tools from machine learning and data mining. Data consist of 42 specimen samples, i.e., mass spectra divided in two big categories, stroke and control specimens. Among the stroke specimens two further categories exist that correspond to ischemic and hemorrhagic stroke; in this paper we limit our data analysis to discriminating between control and stroke specimens. We performed two suites of experiments. In the first one we simply applied a number of different machine learning algorithms; in the second one we have chosen the best performing algorithm as it was determined from the first phase and coupled it with a number of different feature selection methods. The reason for this was 2-fold, first to establish whether feature selection can indeed improve performance, which in our case it did not seem to confirm, but more importantly to acquire a small list of potentially interesting biomarkers. Of the different methods explored the most promising one was support vector machines which gave us high levels of sensitivity and specificity. Finally, by analyzing the models constructed by support vector machines we produced a small set of 13 features that could be used as potential biomarkers, and which exhibited good performance both in terms of sensitivity, specificity and model stability.