Averaged Differential Expression for the Discovery of Biomarkers in the Blood of Patients with Prostate Cancer

<div><h3>Background</h3><p>The identification of a blood-based diagnostic marker is a goal in many areas of medicine, including the early diagnosis of prostate cancer. We describe the use of averaged differential display as an efficient mechanism for biomarker discovery in whole blood RNA. The process of averaging reduces the problem of clinical heterogeneity while simultaneously minimizing sample handling.</p> <h3>Methodology/Principal Findings</h3><p>RNA was isolated from the blood of prostate cancer patients and healthy controls. Samples were pooled and subjected to the averaged differential display process. Transcripts present at different levels between patients and controls were purified and sequenced for identification. Transcript levels in the blood of prostate cancer patients and controls were verified by quantitative RT-PCR. Means were compared using a t-test and a receiver-operating curve was generated. The Ring finger protein 19A (RNF19A) transcript was identified as having higher levels in prostate cancer patients compared to healthy men through the averaged differential display process. Quantitative RT-PCR analysis confirmed a more than 2-fold higher level of RNF19A mRNA levels in the blood of patients with prostate cancer than in healthy controls (p = 0.0066). The accuracy of distinguishing cancer patients from healthy men using RNF19A mRNA levels in blood as determined by the area under the receiving operator curve was 0.727.</p> <h3>Conclusions/Significance</h3><p>Averaged differential display offers a simplified approach for the comprehensive screening of body fluids, such as blood, to identify biomarkers in patients with prostate cancer. Furthermore, this proof-of-concept study warrants further analysis of RNF19A as a clinically relevant biomarker for prostate cancer detection.</p> </div

Global gene expression profiling of oral cavity cancers suggests molecular heterogeneity within anatomic subsites

<p>Abstract</p> <p>Background</p> <p>Oral squamous cell carcinoma (OSCC) is a frequent neoplasm, which is usually aggressive and has unpredictable biological behavior and unfavorable prognosis. The comprehension of the molecular basis of this variability should lead to the development of targeted therapies as well as to improvements in specificity and sensitivity of diagnosis.</p> <p>Results</p> <p>Samples of primary OSCCs and their corresponding surgical margins were obtained from male patients during surgery and their gene expression profiles were screened using whole-genome microarray technology. Hierarchical clustering and Principal Components Analysis were used for data visualization and One-way Analysis of Variance was used to identify differentially expressed genes. Samples clustered mostly according to disease subsite, suggesting molecular heterogeneity within tumor stages. In order to corroborate our results, two publicly available datasets of microarray experiments were assessed. We found significant molecular differences between OSCC anatomic subsites concerning groups of genes presently or potentially important for drug development, including mRNA processing, cytoskeleton organization and biogenesis, metabolic process, cell cycle and apoptosis.</p> <p>Conclusion</p> <p>Our results corroborate literature data on molecular heterogeneity of OSCCs. Differences between disease subsites and among samples belonging to the same TNM class highlight the importance of gene expression-based classification and challenge the development of targeted therapies.</p

Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling

<p>Abstract</p> <p>Background</p> <p>Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies.</p> <p>Methods</p> <p>116 lymph node biopsies diagnosed as RL, classical Hodgkin lymphoma (cHL), diffuse large B cell lymphoma (DLBCL) or follicular lymphoma (FL) were assayed by mRNA microarray. Three supervised classification strategies (global multi-class, local binary-class and global binary-class classifications) using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation. The independent error rate was then evaluated by testing the identified gene classifiers on an independent (test) set of array data.</p> <p>Results</p> <p>The binary classifications provided prediction accuracies, between a subtype of interest and the remaining samples, of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL, and RL respectively. Identified gene classifiers include LIM domain only-2 (<it>LMO2</it>), Chemokine (C-C motif) ligand 22 (<it>CCL22</it>) and Cyclin-dependent kinase inhibitor-3 (<it>CDK3</it>) specifically for FL, cHL and DLBCL subtypes respectively.</p> <p>Conclusions</p> <p>This study highlights the ability of gene expression profiling to distinguish lymphoma from reactive conditions and classify the major subtypes of lymphoma in a diagnostic setting. A cost-effective single platform "mini-chip" assay could, in principle, be developed to aid the quick diagnosis of lymph node biopsies with the potential to incorporate other pathological entities into such an assay.</p

A combinatorial extracellular matrix platform identifies cell-extracellular matrix interactions that correlate with metastasis

Extracellular matrix interactions have essential roles in normal physiology and many pathological processes. Although the importance of extracellular matrix interactions in metastasis is well documented, systematic approaches to identify their roles in distinct stages of tumorigenesis have not been described. Here we report a novel-screening platform capable of measuring phenotypic responses to combinations of extracellular matrix molecules. Using a genetic mouse model of lung adenocarcinoma, we measure the extracellular matrix-dependent adhesion of tumour-derived cells. Hierarchical clustering of the adhesion profiles differentiates metastatic cell lines from primary tumour lines. Furthermore, we uncovered that metastatic cells selectively associate with fibronectin when in combination with galectin-3, galectin-8 or laminin. We show that these molecules correlate with human disease and that their interactions are mediated in part by α3β1 integrin. Thus, our platform allowed us to interrogate interactions between metastatic cells and their microenvironments, and identified extracellular matrix and integrin interactions that could serve as therapeutic targets.National Institutes of Health (U.S.) (Grant K99-CA151968)National Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service AwardStand Up To Cancer (SU2C/AACR)David H. Koch Institute for Integrative Cancer Research at MIT (CTC Project)Harvard Stem Cell Institute (SG-0046-08-00)National Cancer Center (Postdoctoral Fellowship)National Cancer Institute (U.S.) (U54CA126515)National Cancer Institute (U.S.) (U54CA112967)Howard Hughes Medical InstituteMassachusetts Institute of Technology. Ludwig Center for Molecular Oncolog

Inferring Pathway Activity toward Precise Disease Classification

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease

Critical Role of IRF-5 in the Development of T helper 1 responses to Leishmania donovani infection

The transcription factor Interferon Regulatory Factor 5 (IRF-5) has been shown to be involved in the induction of proinflammatory cytokines in response to viral infections and TLR activation and to play an essential role in the innate inflammatory response. In this study, we used the experimental model of visceral leishmaniasis to investigate the role of IRF-5 in the generation of Th1 responses and in the formation of Th1-type liver granulomas in Leishmania donovani infected mice. We show that TLR7-mediated activation of IRF-5 is essential for the development of Th1 responses to L. donovani in the spleen during chronic infection. We also demonstrate that IRF-5 deficiency leads to the incapacity to control L. donovani infection in the liver and to the formation of smaller granulomas. Granulomas in Irf5-/- mice are characterized by an increased IL-4 and IL-10 response and concomitant low iNOS expression. Collectively, these results identify IRF-5 as a critical molecular switch for the development of Th1 immune responses following L. donovani infections and reveal an indirect role of IRF-5 in the regulation of iNOS expression

Critical Role of IRF-5 in the Development of T helper 1 responses to Leishmania donovani infection

The transcription factor Interferon Regulatory Factor 5 (IRF-5) has been shown to be involved in the induction of proinflammatory cytokines in response to viral infections and TLR activation and to play an essential role in the innate inflammatory response. In this study, we used the experimental model of visceral leishmaniasis to investigate the role of IRF-5 in the generation of Th1 responses and in the formation of Th1-type liver granulomas in Leishmania donovani infected mice. We show that TLR7-mediated activation of IRF-5 is essential for the development of Th1 responses to L. donovani in the spleen during chronic infection. We also demonstrate that IRF-5 deficiency leads to the incapacity to control L. donovani infection in the liver and to the formation of smaller granulomas. Granulomas in Irf5-/- mice are characterized by an increased IL-4 and IL-10 response and concomitant low iNOS expression. Collectively, these results identify IRF-5 as a critical molecular switch for the development of Th1 immune responses following L. donovani infections and reveal an indirect role of IRF-5 in the regulation of iNOS expression

Analysis of gene expression profiles in HeLa cells in response to overexpression or siRNA-mediated depletion of NASP

<p>Abstract</p> <p>Background</p> <p>NASP (Nuclear Autoantigenic Sperm Protein) is a linker histone chaperone required for normal cell division. Changes in NASP expression significantly affect cell growth and development; loss of gene function results in embryonic lethality. However, the mechanism by which NASP exerts its effects in the cell cycle is not understood. To understand the pathways and networks that may involve NASP function, we evaluated gene expression in HeLa cells in which NASP was either overexpressed or depleted by siRNA.</p> <p>Methods</p> <p>Total RNA from HeLa cells overexpressing NASP or depleted of NASP by siRNA treatment was converted to cRNA with incorporation of Cy5-CTP (experimental samples), or Cy3-CTP (control samples). The labeled cRNA samples were hybridized to whole human genome microarrays (Agilent Technologies, Wilmington, Delaware, USA). Various gene expression analysis techniques were employed: Significance Analysis of Microarrays (SAM), Expression Analysis Systematic Explorer (EASE), and Ingenuity Pathways Analysis (IPA).</p> <p>Results</p> <p>From approximately 36 thousand genes present in a total human genome microarray, we identified a set of 47 up-regulated and 7 down-regulated genes as a result of NASP overexpression. Similarly we identified a set of 56 up-regulated and 71 down-regulated genes as a result of NASP siRNA treatment. Gene ontology, molecular network and canonical pathway analysis of NASP overexpression demonstrated that the most significant changes were in proteins participating in organismal injury, immune response, and cellular growth and cancer pathways (major "hubs": TNF, FOS, EGR1, NFκB, IRF7, STAT1, IL6). Depletion of NASP elicited the changed expression of proteins involved in DNA replication, repair and development, followed by reproductive system disease, and cancer and cell cycle pathways (major "hubs": E2F8, TP53, FGF, FSH, FST, hCG, NFκB, TRAF6).</p> <p>Conclusion</p> <p>This study has demonstrated that NASP belongs to a network of genes and gene functions that are critical for cell survival. We have confirmed the previously reported interactions between NASP and HSP90, HSP70, histone H1, histone H3, and TRAF6. Overexpression and depletion of NASP identified overlapping networks that included TNF as a core protein, confirming that both high and low levels of NASP are detrimental to cell cycle progression. Networks with cancer-related functions had the highest significance, however reproductive networks containing follistatin and FSH were also significantly affected, which confirmed NASP's important role in reproductive tissues. This study revealed that, despite some overlap, each response was associated with a unique gene signature and placed NASP in important cell regulatory networks.</p

PrognoScan: a new database for meta-analysis of the prognostic value of genes

<p>Abstract</p> <p>Background</p> <p>In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform.</p> <p>Description</p> <p>To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum <it>P</it>-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets.</p> <p>Conclusion</p> <p>PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at <url>http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html</url>.</p

Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers

BACKGROUND: Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques. RESULTS: We describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. This kind of similarity could improve the diagnostic accuracies of individual studies when SOGL is incorporated into the support vector machine algorithm. In particular, we investigate the similarities among three ordered gene lists pertaining to mesothelioma survival, prostate recurrence and glioma survival. The similarity-driving genes are related to the outcomes of patients with lung cancer with a hazard ratio of 4.47 (p = 0.035). Many of these genes are involved in breakdown of EMC proteins regulating angiogenesis, and may be used for further research on prognostic markers and molecular targets of gene therapy for cancers. CONCLUSION: The proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles