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.     

A method to improve selection of molecular targets by circumventing the ADME pharmacokinetic system utilizing PharmArray DNA microarrays

DNA microarrays may be used to identify potential molecular targets for drug discovery. Yet, DNA microarray experiments provide massive amounts of data. To limit the choice of potential molecular targets, it may be desirable to eliminate genes coincidentally up-regulated in tissues implicated in absorption, distribution, metabolism, and excretion (ADME) pharmacokinetics. DNA microarray experiments were performed to demonstrate a gene-exclusion approach using as an example RNA samples of neural origin, i.e., a human neuroblastoma cell line (SK-N-SH) and brain tissue, as the intended hypothetical site(s) of drug action. Biomarkers were identified using PharmArray DNA microarrays. The lists of neuroblastoma and neural biomarkers were constrained by limiting selection to the subset of genes that were not highly expressed in three transformed cell lines from liver, colon, and kidney (HepG2, Caco-2, and 786-O, respectively) that are routinely used as representatives of the ADME system during in vitro pharmacology and toxicology experiments. Principal component analysis methods with likelihood ratio-related bioinformatic tools were utilized to identify robust potential biomarker genes for the three ADME-related cell lines, neuroblastoma, and normal brain. Biomarkers of each sample were identified and selected genes were validated by qRT-PCR. Hundreds of biomarkers of the three ADME-related cell types, representing hepatocytes, kidney epithelium, and gastrointestinal tract, may now be used as a valuable database to restrict selection of biomarkers as potential molecular targets from the intended samples (e.g., neuroblastoma in this work). In addition to biomarker discovery per se, this demonstration suggests that our model method may be viable to help restrict gene lists during selection of potential molecular targets for subsequent drug discovery.     

Biomarkers of human skin cells identified using DermArray DNA arrays and new bioinformatics methods

Biomarker genes of human skin-derived cells were identified by new simple bioinformatic methods and DNA microarray analysis utilizing in vitro cultures of normal neonatal human epidermal keratinocytes, melanocytes, and dermal fibroblasts. A survey of 4405 human cDNAs was performed using DermArray DNA microarrays. Biomarkers were rank ordered by "likelihood ratio" algorithms and stringent selection criteria that have general applicability for analyzing a minimum of three RNA samples. Signature biomarker genes (up-regulated in one cell type) and anti-signature biomarker genes (down-regulated in one cell type) were determined for the three major skin cell types. Many of the signature genes are known biomarkers for these cell types. In addition, 17 signature genes were identified as ESTs, and 22 anti-signature biomarkers were discovered. Quantitative RT-PCR was used to verify nine signature biomarker genes. A total of 158 biomarkers of normal human skin cells were identified, many of which may be valuable in diagnostic applications and as molecular targets for drug discovery and therapeutic intervention.     

Identification of blood biomarkers of rheumatoid arthritis by transcript profiling of peripheral blood mononuclear cells from the rat collagen-induced arthritis model

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disease that results in joint destruction and subsequent loss of function. To better understand its pathogenesis and to facilitate the search for novel RA therapeutics, we profiled the rat model of collagen-induced arthritis (CIA) to discover and characterize blood biomarkers for RA. Peripheral blood mononuclear cells (PBMCs) were purified using a Ficoll gradient at various time points after type II collagen immunization for RNA preparation. Total RNA was processed for a microarray analysis using Affymetrix GeneChip technology. Statistical comparison analyses identified differentially expressed genes that distinguished CIA from control rats. Clustering analyses indicated that gene expression patterns correlated with laboratory indices of disease progression. A set of 28 probe sets showed significant differences in expression between blood from arthritic rats and that from controls at the earliest time after induction, and the difference persisted for the entire time course. Gene Ontology comparison of the present study with previous published murine microarray studies showed conserved Biological Processes during disease induction between the local joint and PBMC responses. Genes known to be involved in autoimmune response and arthritis, such as those encoding Galectin-3, Versican, and Socs3, were identified and validated by quantitative TaqMan RT-PCR analysis using independent blood samples. Finally, immunoblot analysis confirmed that Galectin-3 was secreted over time in plasma as well as in supernatant of cultured tissue synoviocytes of the arthritic rats, which is consistent with disease progression. Our data indicate that gene expression in PBMCs from the CIA model can be utilized to identify candidate blood biomarkers for RA.     

Identification of blood biomarkers of rheumatoid arthritis by transcript profiling of peripheral blood mononuclear cells from the rat collagen-induced arthritis model

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disease that results in joint destruction and subsequent loss of function. To better understand its pathogenesis and to facilitate the search for novel RA therapeutics, we profiled the rat model of collagen-induced arthritis (CIA) to discover and characterize blood biomarkers for RA. Peripheral blood mononuclear cells (PBMCs) were purified using a Ficoll gradient at various time points after type II collagen immunization for RNA preparation. Total RNA was processed for a microarray analysis using Affymetrix GeneChip technology. Statistical comparison analyses identified differentially expressed genes that distinguished CIA from control rats. Clustering analyses indicated that gene expression patterns correlated with laboratory indices of disease progression. A set of 28 probe sets showed significant differences in expression between blood from arthritic rats and that from controls at the earliest time after induction, and the difference persisted for the entire time course. Gene Ontology comparison of the present study with previous published murine microarray studies showed conserved Biological Processes during disease induction between the local joint and PBMC responses. Genes known to be involved in autoimmune response and arthritis, such as those encoding Galectin-3, Versican, and Socs3, were identified and validated by quantitative TaqMan RT-PCR analysis using independent blood samples. Finally, immunoblot analysis confirmed that Galectin-3 was secreted over time in plasma as well as in supernatant of cultured tissue synoviocytes of the arthritic rats, which is consistent with disease progression. Our data indicate that gene expression in PBMCs from the CIA model can be utilized to identify candidate blood biomarkers for RA.     

An Integrated Peptide-Antigen Microarray on Plasmonic Gold Films for Sensitive Human Antibody Profiling

High-throughput screening for interactions of peptides with a variety of antibody targets could greatly facilitate proteomic analysis for epitope mapping, enzyme profiling, drug discovery and biomarker identification. Peptide microarrays are suited for such undertaking because of their high-throughput capability. However, existing peptide microarrays lack the sensitivity needed for detecting low abundance proteins or low affinity peptide-protein interactions. This work presents a new peptide microarray platform constructed on nanostructured plasmonic gold substrates capable of metal enhanced NIR fluorescence enhancement (NIR-FE) by hundreds of folds for screening peptide-antibody interactions with ultrahigh sensitivity. Further, an integrated histone peptide and whole antigen array is developed on the same plasmonic gold chip for profiling human antibodies in the sera of systemic lupus erythematosus (SLE) patients, revealing that collectively a panel of biomarkers against unmodified and post-translationally modified histone peptides and several whole antigens allow more accurate differentiation of SLE patients from healthy individuals than profiling biomarkers against peptides or whole antigens alone.     

Cell-specific aptamer probes for membrane protein elucidation in cancer cells

Disease biomarkers play critical roles in the management of various pathological conditions of diseases. This involves diagnosing diseases, predicting disease progression and monitoring the efficacy of treatment modalities. While efforts to identify specific disease biomarkers using a variety of technologies has increased the number of biomarkers or augmented information about them, the effective use of disease-specific biomarkers is still scarce. Here, we report that a high expression of protein tyrosine kinase 7 (PTK7), a transmembrane receptor protein tyrosine kinase-like molecule, was discovered in a series of leukemia cell lines using whole cell aptamer selection. With the implementation of a two-step strategy (aptamer selection and biomarker discovery), combined with mass spectrometry, PTK7 was ultimately identified as a potential biomarker for T-cell acute lymphoblastic leukemia (T-ALL). Specifically, the aptamers for T-ALL cells were selected using the cell-SELEX process, without any prior knowledge of the cell biomarker population, conjugated with magnetic beads and then used to capture and purify their binding targets on the leukemia cell surface. This demonstrates that a panel of molecular aptamers can be easily generated for a specific type of diseased cells. It further demonstrates that this two-step strategy, that is, first selecting cancer cell-specific aptamers and then identifying their binding target proteins, has major clinical implications in that the technique promises to substantially improve the overall effectiveness of biomarker discovery. Specifically, our strategy will enable efficient discovery of new malignancy-related biomarkers, facilitate the development of diagnostic tools and therapeutic approaches to cancer, and markedly improve our understanding of cancer biology.     

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.     

Improved microRNA quantification in total RNA from clinical samples

microRNAs are small regulatory RNAs that are currently emerging as new biomarkers for cancer and other diseases. In order for biomarkers to be useful in clinical settings, they should be accurately and reliably detected in clinical samples such as formalin fixed paraffin embedded (FFPE) sections and blood serum or plasma. These types of samples represent a challenge in terms of microRNA quantification. A newly developed method for microRNA qPCR using Locked Nucleic Acid (LNA?)-enhanced primers enables accurate and reproducible quantification of microRNAs in scarce clinical samples. Here we show that LNA?-based microRNA qPCR enables biomarker screening using very low amounts of total RNA from FFPE samples and the results are compared to microarray analysis data. We also present evidence that the addition of a small carrier RNA prior to total RNA extraction, improves microRNA quantification in blood plasma and laser capture microdissected (LCM) sections of FFPE samples.     

Biomarkers of human cutaneous squamous cell carcinoma from tissues and cell lines identified by DNA microarrays and qRT-PCR

Squamous cell carcinoma (SCC) is the second most common form of skin cancer in Caucasians. Here we report on the identification of biomarkers of human cutaneous SCC cell lines in vitro and tissue samples in vivo using DermArray and PharmArray DNA microarrays, consisting of ca. 7400 unique human cDNAs. Differentially expressed genes were identified in two facial skin SCC cell lines (SCC 12 and SCC 13) compared to normal keratinocytes, and three cutaneous SCC tissue samples compared to normal skin. Quantitative validations of up- and down-regulated biomarkers were performed by qRT-PCR on 23 biomarker genes for the cell lines and 20 biomarker genes for the tumor tissues. In addition, three oral SCC cell lines were also included in the qRT-PCR validations for comparison, and the biomarker profiles were highly similar between the cutaneous and the oral SCC cell lines for all 23 biomarkers examined. The expression profiles for a variety of non-cutaneous SCC types, such as head-and-neck, oral, and lung, have been previously published. This report is the first to describe biomarkers for cutaneous SCC in two contexts, in vitro and in vivo. Although there was minimal overlap between the two different contexts using DNA microarrays, five genes were found common to both the cell lines and tissues, namely fibronectin 1, annexin A5, glyceraldehyde 3-phosphate dehydrogenase, zinc-finger protein 254, and huntingtin-associated protein interacting protein. Some of our previously published biomarkers of normal keratinocytes were down-regulated in SCC, suggestive of the dedifferentiated status of the transformed cells. While recent reports have identified some of the same genes as SCC biomarkers, for instance in head-and-neck cancer, thereby validating our approach, we have identified some novel biomarkers for cutaneous disease. These biomarker lists may be useful in molecular diagnostics of non-melanoma skin cancer, and a subset of the biomarkers might serve as suitable targets for drug discovery efforts of therapies for SCC.     

Protein biomarker discovery and validation: the long and uncertain path to clinical utility

Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests.     

Integrated expressional analysis: application to the drug discovery process

Microarray technology enables high-throughput testing of gene expression to investigate various neuroscience related questions. This in turn creates a demand for scalable methods to confirm microarray results and the opportunity to use this information to discover and test novel pathways and therapeutic applications. Discovery of new central nervous system (CNS) treatments requires a comprehensive understanding of multiple aspects including the biology of a target, the pathophysiology of a disease/disorder, and the selection of successful lead compounds as well as efficient biomarker and drug disposition strategies such as absorption (how a drug is absorbed), distribution (how a drug spreads through an organism), metabolism (chemical conversion of a drug, if any, and into which substances), and elimination (how is a drug eliminated) (ADME). Understanding of the toxicity is also of paramount importance. These approaches, in turn, require novel high-content integrative assay technologies that provide thorough information about changes in cell biology. To increase efficiency of profiling, characterization, and validation, we established a new screening strategy that combines high-content image-based testing on Array Scan (Cellomics) with a confocal system and the multiplexed TaqMan RT-PCR method for quantitative mRNA expression analysis. This approach could serve as an interface between high-throughput microarray testing and specific application of markers discovered in the course of a microarray experiment. Markers could pinpoint activation or inhibition of a molecular pathway related, for instance, to neuronal viability. We demonstrate the successful testing of the same cell population in an image-based translocational assay followed by poly(A) mRNA capture and multiplexed single tube RT-PCR. In addition, Ciphergen ProteinChip analysis can be performed on the supernatant, thus allowing significant complementarity in the data output and interpretation by also including the capture and initial analysis of proteins in the integrative approach presented. We have determined various conditions including the number of cells, RT and PCR optimization, which are necessary for successful detection and consequent assay integration. We also show the successful convergence of various different approaches and multiplexing of different targets within a single real-time PCR tube. This novel integrative technological approach has utility for CNS drug discovery, target and biomarker identification, selection and characterization as well as for the study of toxicity- and adverse event-associated molecular mechanisms.     

Assessing an Improved Protocol for Plasma microRNA Extraction

The first step in biomarkers discovery is to identify the best protocols for their purification and analysis. This issue is critical when considering peripheral blood samples (plasma and serum) that are clinically interesting but meet several methodological problems, mainly complexity and low biomarker concentration. Analysis of small molecules, such as circulating microRNAs, should overcome these disadvantages. The present study describes an optimal RNA extraction method of microRNAs from human plasma samples. Different reagents and commercially available kits have been analyzed, identifying also the best pre-analytical conditions for plasma isolation. Between all of them, the column-based approaches were shown to be the most effective. In this context, miRNeasy Serum/Plasma Kit (from Qiagen) rendered more concentrated RNA, that was better suited for microarrays studies and did not require extra purification steps for sample concentration and purification than phenol based extraction methods. We also present evidences that the addition of low doses of an RNA carrier before starting the extraction process improves microRNA purification while an already published carrier dose can result in significant bias over microRNA profiles. Quality controls for best protocol selection were developed by spectrophotometry measurement of contaminants and microfluidics electrophoresis (Agilent 2100 Bioanalyzer) for RNA integrity. Selected donor and patient plasma samples and matched biopsies were tested by Affymetrix microarray technology to compare differentially expressed microRNAs. In summary, this study defines an optimized protocol for microRNA purification from human blood samples, increasing the performance of assays and shedding light over the best way to discover and use these biomarkers in clinical practice.