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.     

Biomarker discovery: proteome fractionation and separation in biological samples.

Proteomics, analogous with genomics, is the analysis of the protein complement present in a cell, organ, or organism at any given time. While the genome provides information about the theoretical status of the cellular proteins, the proteome describes the actual content, which ultimately determines the phenotype. The broad application of proteomic technologies in basic science and clinical medicine has the potential to accelerate our understanding of the molecular mechanisms underlying disease and may facilitate the discovery of new drug targets and diagnostic disease markers. Proteomics is a rapidly developing and changing scientific discipline, and the last 5 yr have seen major advances in the underlying techniques as well as expansion into new applications. Core technologies for the separation of proteins and/or peptides are one- and two-dimensional gel electrophoresis and one- and two-dimensional liquid chromatography, and these are coupled almost exclusively with mass spectrometry. Proteomic studies have shown that the most effective analysis of even simple biological samples requires subfractionation and/or enrichment before protein identification by mass spectrometry. Selection of the appropriate technology or combination of technologies to match the biological questions is essential for maximum coverage of the selected subproteome and to ensure both the full interpretation and the downstream utility of the data. In this review, we describe the current technologies for proteome fractionation and separation of biological samples, based on our lab workflow for biomarker discovery and validation.     

Biomarker discovery in urine by proteomics

A hypothesis was formed that it would be possible to isolate an adequate amount of protein from a patient, having normal renal function, to identify biological markers of a particular disease state using a variety of proteomics techniques. To support this hypothesis, three samples of urine were collected from a volunteer: first when healthy, later when experiencing acute inflammation due to a pilonidal abcess, and again later still after successful recovery from the condition. The urine from these samples was processed by solid-phase extraction to concentrate and desalt the endogenous proteins and peptides. The proteins and peptides from these urine samples were analyzed in three different experiments: (1) traditional two-dimensional gel electrophoresis followed by proteolysis and mass spectrometric identification of various protein spots, (2) whole mixture proteolysis followed by one-dimensional packed capillary liquid chromatography and tandem mass spectrometry, (3) whole mixture proteolysis followed by two-dimensional capillary liquid chromatography and tandem mass spectrometry. In all three cases, a set of proteins was identified representing putative biomarkers. Each of these proteins was then found to have been previously linked in the scientific literature to inflammation. One acute phase reactant in particular, orosomucoid, was readily observed in all three experiments to dramatically increase in abundance, thereby supporting the hypothesis.     

Biomarker discovery for kidney diseases by mass spectrometry

By the development of soft ionization such as matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI), mass spectrometry (MS) has become an indispensable technique to analyze proteins. The combination of protein separation and identification such as two-dimensional gel electrophoresis and MS, surface-enhanced laser desorption/ionization-MS, liquid chromatography/MS, and capillary electrophoresis/MS has been successfully applied for proteome analysis of urine and plasma to discover biomarkers of kidney diseases. Some urinary proteins and their proteolytic fragments have been identified as biomarker candidates for kidney diseases. This article reviews recent advances in the application of proteomics using MS to discover biomarkers for kidney diseases.     

Biomarker discovery in microarray gene expression data with Gaussian processes

MOTIVATION: In clinical practice, pathological phenotypes are often labelled with ordinal scales rather than binary, e.g. the Gleason grading system for tumour cell differentiation. However, in the literature of microarray analysis, these ordinal labels have been rarely treated in a principled way. This paper describes a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian inference framework. RESULTS: The usefulness of the proposed algorithm for ordinal labels is demonstrated by the gene expression signature associated with the Gleason score for prostate cancer data. Our results demonstrate how multi-gene markers that may be initially developed with a diagnostic or prognostic application in mind are also useful as an investigative tool to reveal associations between specific molecular and cellular events and features of tumour physiology. Our algorithm can also be applied to microarray data with binary labels with results comparable to other methods in the literature.     

CA 125 in biological fluids

CA 125 is not a specific tumor marker, and is synthesized by normal and malignant cells of different origin (mainly in tissues derived from the müllerian epithelia) in a similar proportion. Abnormal CA 125 levels may be found in fluids of different origin (ascites, pleura, pericardium, amniotic fluid, cyst fluid, bronchoalveolar fluid, etc.) and in serum from patients with these fluids. Differences in serum CA 125 found in malignant or benign diseases may be related to the number of cells that synthesize the marker, and are highly dependent on the access to serum, where the marker is normally determined. Moreover, CA 125 is a very good tumor marker in ovarian and lung cancer. The sensitivity of CA 125 in ovarian cancer is related to stage (40-95%), histological type (lower levels in mucinous adenocarcinoma), and the marker is useful in the early detection of recurrence (sensitivity 80%) and in therapy monitoring. It's sensitivity in lung cancer is lower than in ovarian cancer, 39% in locoregional malignancies and 69% in metastatic disease, but clearly related to stage and histology (mainly in adenocarcinomas and large cell lung cancer) and it is useful in prognosis and disease monitoring.     

Biomarker discovery and validation: technologies and integrative approaches

The emerging field of biomarkers has applications in the diagnosis, staging, prognosis and monitoring of disease progression, as well as in the monitoring of clinical responses to a therapeutic intervention and the development and delivery of personalized treatments to reduce attrition in clinical trials. Moreover, biomarkers have a positive impact on health economics. The word "biomarker" has been used extensively across therapeutic areas and many disciplines, and its nature takes into consideration clinical, physiological, biochemical, developmental, morphological and molecular measures. In drug trials, biomarkers have been proposed for use in efficacy determination and patient population stratification, in deducing pharmacokinetic-pharmacodynamic relationships and in safety monitoring. The interfacing and integration of different technologies for data collection and analysis are pivotal to biomarker identification, characterization, validation and application. "Integrative functional informatics" represents a novel direction in such technology integration.     

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

Background  

For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.     

Biomarker discovery from the plasma proteome using multidimensional fractionation proteomics

Because biomarkers are typically low in abundance, the crucial step of biomarker discovery is to efficiently separate clinically relevant sets of proteins that might define disease stages and/or predict disease development. It is anticipated that a multi-dimensional fractionation system (MDFS) will provide an efficient means of separating low abundance proteins from plasma proteins, resulting in the extension of the detection limit. However, when using an MDFS to analyze the plasma proteome it is important to consider how sample processing, yield, resolution and throughput potential may influence the detection limit. This review evaluates the recent advances in MDFS research with respect to '4RS criterion' (4R: resolution, reproducibility, recovery, and robustness; 4S: simplicity, speed, selectivity and sensitivity) and discusses perspectives for future plasma-derived biomarker discovery.     

Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.     

The determination of aminoketones in biological fluids

• 1.1. A new chromatographic procedure is described for the determination of aminoacetone and δ-aminolevulinic acid (after conversion to pyrroles) in both tissues and urine following their separation from porphobilinogen.
• 2.2. The method for measurement of both porphobilinogen and aminoketones involves only two chromatographic procedures, both utilizing Dowex-1 acetate resin. It permits concentration of pyrroles derived from both aminoketones, providing for the determination of as little as 20-mμmole quantities of these substances. Reproducibility and recoveries are excellent over a wide range of concentrations.
• 3.3. Aminoacetone pyrrole is retained by the resin primarily by adsorption; δ-aminolevulinic acid pyrrole is bound by an ionic mechanism.
• 4.4. Aminoacetone constitutes a significant portion of the aminoketones in nonporphyric urines.
• 5.5. The administration of relatively large doses of threonine results in an increased urinary excretion of aminoacetone.

     

Measurement of lonized magnesium in biological fluids

A method that provides for direct in vitro measurement at 37°C of ionized magnesium in biological fluids using the Abbot bichromatic analyzer, a fully automated instrument is described. The metallochromic dye Eriochrome blue SE was used to measure ionized magnesium levels in Mueller-Hinton agar fluids. Formulation of an appropriate blank was found necessary and was made by treating agar fluids with Chelex 100 to remove divalent cations. The influence of pH in the biological range was examined and poses a significant but manageable source of error. The method allowed an appropriate assessment of the role of ionized magnesium in the susceptibility testing of Pseudomonas aeruginosa on Mueller-Hinton agar with the aminoglycoside antibiotics.     

Biomarker discovery across annotated and unannotated microarray datasets using semi-supervised learning

The growing body of DNA microarray data has the potential to advance our understanding of the molecular basis of disease. However annotating microarray datasets with clinically useful information is not always possible, as this often requires access to detailed patient records. In this study we introduce GLAD, a new Semi-Supervised Learning (SSL) method for combining independent annotated datasets and unannotated datasets with the aim of identifying more robust sample classifiers. In our method, independent models are developed using subsets of genes for the annotated and unannotated datasets. These models are evaluated according to a scoring function that incorporates terms for classification accuracy on annotated data, and relative cluster separation in unannotated data. Improved models are iteratively generated using a genetic algorithm feature selection technique. Our results show that the addition of unannotated data into training, significantly improves classifier robustness.