Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

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.     

Gene classification using parameter-free semi-supervised manifold learning

A new manifold learning method, called parameter-free semi-supervised local Fisher discriminant analysis (pSELF), is proposed to map the gene expression data into a low-dimensional space for tumor classification. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, which can be computed efficiently by eigen decomposition. Experimental results on synthetic data and SRBCT, DLBCL, and Brain Tumor gene expression data sets demonstrate the effectiveness of the proposed method.     

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.     

Classification of large microarray datasets using fast random forest construction

Random forest is an ensemble classification algorithm. It performs well when most predictive variables are noisy and can be used when the number of variables is much larger than the number of observations. The use of bootstrap samples and restricted subsets of attributes makes it more powerful than simple ensembles of trees. The main advantage of a random forest classifier is its explanatory power: it measures variable importance or impact of each factor on a predicted class label. These characteristics make the algorithm ideal for microarray data. It was shown to build models with high accuracy when tested on high-dimensional microarray datasets. Current implementations of random forest in the machine learning and statistics community, however, limit its usability for mining over large datasets, as they require that the entire dataset remains permanently in memory. We propose a new framework, an optimized implementation of a random forest classifier, which addresses specific properties of microarray data, takes computational complexity of a decision tree algorithm into consideration, and shows excellent computing performance while preserving predictive accuracy. The implementation is based on reducing overlapping computations and eliminating dependency on the size of main memory. The implementation's excellent computational performance makes the algorithm useful for interactive data analyses and data mining.     

Biomarker discovery for arsenic exposure using functional data. Analysis and feature learning of mass spectrometry proteomic data

Plasma biomarkers of exposure to environmental contaminants play an important role in early detection of disease. The emerging field of proteomics presents an attractive opportunity for candidate biomarker discovery, as it simultaneously measures and analyzes a large number of proteins. This article presents a case study for measuring arsenic concentrations in a population residing in an As-endemic region of Bangladesh using plasma protein expressions measured by SELDI-TOF mass spectrometry. We analyze the data using a unified statistical method based on functional learning to preprocess mass spectra and extract mass spectrometry (MS) features and to associate the selected MS features with arsenic exposure measurements. The task is challenging due to several factors, the high dimensionality of mass spectrometry data, complicated error structures, and a multiple comparison problem. We use nonparametric functional regression techniques for MS modeling, peak detection based on the significant zero-downcrossing method, and peak alignment using a warping algorithm. Our results show significant associations of arsenic exposure to either under- or overexpressions of 20 proteins.     

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.     

Gene discovery in neuropharmacological and behavioral studies using Affymetrix microarray data

We describe methods and software tools for doing data analysis based on Affymetrix microarray data, emphasizing often neglected issues. In our experience with neuroscience studies, experimental design and quality assessment are vital. We also describe in detail the pre-processing methods we have found useful for Affymetrix data. Finally, we summarize the statistical literature and describe some pitfalls in the post-processing analysis.     

Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods

We present a web resource MEM (Multi-Experiment Matrix) for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection, characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at .     

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.     

Independence and reproducibility across microarray platforms

Microarrays have been widely used for the analysis of gene expression, but the issue of reproducibility across platforms has yet to be fully resolved. To address this apparent problem, we compared gene expression between two microarray platforms: the short oligonucleotide Affymetrix Mouse Genome 430 2.0 GeneChip and a spotted cDNA array using a mouse model of angiotensin II-induced hypertension. RNA extracted from treated mice was analyzed using Affymetrix and cDNA platforms and then by quantitative RT-PCR (qRT-PCR) for validation of specific genes. For the 11,710 genes present on both arrays, we assessed the relative impact of experimental treatment and platform on measured expression and found that biological treatment had a far greater impact on measured expression than did platform for more than 90% of genes, a result validated by qRT-PCR. In the small number of cases in which platforms yielded discrepant results, qRT-PCR generally did not confirm either set of data, suggesting that sequence-specific effects may make expression predictions difficult to make using any technique.     

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems' blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.     

Optimized blood cell profiling method for genomic biomarker discovery using high-density microarray

High-quality biomarkers for disease progression, drug efficacy and toxicity liability are essential for improving the efficiency of drug discovery and development. The identification of drug-activity biomarkers is often limited by access to and the quantity of target tissue. Peripheral blood has increasingly become an attractive alternative to tissue samples from organs as source for biomarker discovery, especially during early clinical studies. However, given the heterogeneous blood cell population, possible artifacts from ex vivo activations, and technical difficulties associated with overall performance of the assay, it is challenging to profile peripheral blood cells directly for biomarker discovery. In the present study, Applied BioSystems’ blood collection system was evaluated for its ability to isolate RNA suitable for use on the Affymetrix microarray platform. Blood was collected in a TEMPUS tube and RNA extracted using an ABI-6100 semi-automated workstation. Using human and rat whole blood samples, it was demonstrated that the RNA isolated using this approach was stable, of high quality and was suitable for Affymetrix microarray applications. The microarray data were statistically analysed and compared with other blood protocols. Minimal haemoglobin interference with RNA labelling efficiency and chip hybridization was found using the TEMPUS tube and extraction method. The RNA quality, stability and ease of handling requirement make the TEMPUS tube protocol an attractive approach for expression profiling of whole blood to support target and biomarker discovery.