Classification and biomarker identification using gene network modules and support vector machines

<p>Abstract</p> <p>Background</p> <p>Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.</p> <p>We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)</p> <p>Results</p> <p>Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form <it>n </it>clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.</p> <p>Conclusion</p> <p>More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.</p> <p>The Matlab version of SVM-RNE can be downloaded from <url>http://web.macam.ac.il/~myousef</url></p

Molecular Insights into the Pathogenesis of Alzheimer's Disease and Its Relationship to Normal Aging

Alzheimer's disease (AD) is a complex neurodegenerative disorder that diverges from the process of normal brain aging by unknown mechanisms. We analyzed the global structure of age- and disease-dependent gene expression patterns in three regions from more than 600 brains. Gene expression variation could be almost completely explained by four transcriptional biomarkers that we named BioAge (biological age), Alz (Alzheimer), Inflame (inflammation), and NdStress (neurodegenerative stress). BioAge captures the first principal component of variation and includes genes statistically associated with neuronal loss, glial activation, and lipid metabolism. Normally BioAge increases with chronological age, but in AD it is prematurely expressed as if some of the subjects were 140 years old. A component of BioAge, Lipa, contains the AD risk factor APOE and reflects an apparent early disturbance in lipid metabolism. The rate of biological aging in AD patients, which cannot be explained by BioAge, is associated instead with NdStress, which includes genes related to protein folding and metabolism. Inflame, comprised of inflammatory cytokines and microglial genes, is broadly activated and appears early in the disease process. In contrast, the disease-specific biomarker Alz was selectively present only in the affected areas of the AD brain, appears later in pathogenesis, and is enriched in genes associated with the signaling and cell adhesion changes during the epithelial to mesenchymal (EMT) transition. Together these biomarkers provide detailed description of the aging process and its contribution to Alzheimer's disease progression

Malignant inflammation in cutaneous T-cell lymphoma: a hostile takeover

Cutaneous T-cell lymphomas (CTCL) are characterized by the presence of chronically inflamed skin lesions containing malignant T cells. Early disease presents as limited skin patches or plaques and exhibits an indolent behavior. For many patients, the disease never progresses beyond this stage, but in approximately one third of patients, the disease becomes progressive, and the skin lesions start to expand and evolve. Eventually, overt tumors develop and the malignant T cells may disseminate to the blood, lymph nodes, bone marrow, and visceral organs, often with a fatal outcome. The transition from early indolent to progressive and advanced disease is accompanied by a significant shift in the nature of the tumor-associated inflammation. This shift does not appear to be an epiphenomenon but rather a critical step in disease progression. Emerging evidence supports that the malignant T cells take control of the inflammatory environment, suppressing cellular immunity and anti-tumor responses while promoting a chronic inflammatory milieu that fuels their own expansion. Here, we review the inflammatory changes associated with disease progression in CTCL and point to their wider relevance in other cancer contexts. We further define the term "malignant inflammation" as a pro-tumorigenic inflammatory environment orchestrated by the tumor cells and discuss some of the mechanisms driving the development of malignant inflammation in CTCL

CD164 identifies CD4+ T cells highly expressing genes associated with malignancy in Sézary syndrome: the Sézary signature genes, FCRL3, Tox, and miR-214

Sézary syndrome (SS), a leukemic variant of cutaneous T-cell lymphoma (CTCL), is associated with a significantly shorter life expectancy compared to skin-restricted mycosis fungoides. Early diagnosis of SS is, therefore, key to achieving enhanced therapeutic responses. However, the lack of a biomarker(s) highly specific for malignant CD4+ T cells in SS patients has been a serious obstacle in making an early diagnosis. We recently demonstrated the high expression of CD164 on CD4+ T cells from Sézary syndrome patients with a wide range of circulating tumor burdens. To further characterize CD164 as a potential biomarker for malignant CD4+ T cells, CD164+ and CD164-CD4+ T cells isolated from patients with high-circulating tumor burden, B2 stage, and medium/low tumor burden, B1-B0 stage, were assessed for the expression of genes reported to differentiate SS from normal controls, and associated with malignancy and poor prognosis. The expression of Sézary signature genes: T plastin, GATA-3, along with FCRL3, Tox, and miR-214, was significantly higher, whereas STAT-4 was lower, in CD164+ compared with CD164-CD4+ T cells. While Tox was highly expressed in both B2 and B1-B0 patients, the expression of Sézary signature genes, FCRL3, and miR-214 was associated predominantly with advanced B2 disease. High expression of CD164 mRNA and protein was also detected in skin from CTCL patients. CD164 was co-expressed with KIR3DL2 on circulating CD4+ T cells from high tumor burden SS patients, further providing strong support for CD164 as a disease relevant surface biomarker

Chromatin Profiling of Epstein-Barr Virus Latency Control Region▿

Epstein-Barr virus (EBV) escapes host immunity by the reversible and epigenetic silencing of immunogenic viral genes. We previously presented evidence that a dynamic chromatin domain, which we have referred to as the latency control region (LCR), contributes to the reversible repression of EBNA2 and LMP1 gene transcription. We now explore the protein-DNA interaction profiles for a few known regulatory factors and histone modifications that regulate LCR structure and activity. A chromatin immunoprecipitation assay combined with real-time PCR analysis was used to analyze protein-DNA interactions at ∼500-bp intervals across the first 60,000 bp of the EBV genome. We compared the binding patterns of EBNA1 with those of the origin recognition complex protein ORC2, the chromatin boundary factor CTCF, the linker histone H1, and several histone modifications. We analyzed three EBV-positive cell lines (MutuI, Raji, and LCL3459) with distinct transcription patterns reflecting different latency types. Our findings suggest that histone modification patterns within the LCR are complex but reflect differences in each latency type. The most striking finding was the identification of CTCF sites immediately upstream of the Qp, Cp, and EBER transcription initiation regions in all three cell types. In transient assays, CTCF facilitated EBNA1-dependent transcription activation of Cp, suggesting that CTCF coordinates interactions between different chromatin domains. We also found that histone H3 methyl K4 clustered with CTCF and EBNA1 at sites of active transcription or DNA replication initiation. Our findings support a model where CTCF delineates multiple domains within the LCR and regulates interactions between these domains that correlate with changes in gene expression

A Composite Gene Expression Signature Optimizes Prediction of Colorectal Cancer Metastasis and Outcome.

PURPOSE: We previously found that an epithelial-to-mesenchymal transition (EMT)-based gene expression signature was highly correlated with the first principal component (PC1) of 326 colorectal cancer tumors and was prognostic. This study was designed to improve these signatures for better prediction of metastasis and outcome. EXPERIMENTAL DESIGN: A total of 468 colorectal cancer tumors including all stages (I-IV) and metastatic lesions were used to develop a new prognostic score (ΔPC1.EMT) by subtracting the EMT signature score from its correlated PC1 signature score. The score was validated on six other independent datasets with a total of 3,697 tumors. RESULTS: ΔPC1.EMT was found to be far more predictive of metastasis and outcome than its parent scores. It performed well in stages I to III, among microsatellite instability subtypes, and across multiple mutation-based subclasses, demonstrating a refined capacity to predict distant metastatic potential even in tumors with a "good" prognosis. For example, in the PETACC-3 clinical trial dataset, it predicted worse overall survival in an adjusted multivariable model for stage III patients (HR standardized by interquartile range [IQR] = 1.50; 95% confidence interval, 1.25-1.81; P = 0.000016, N = 644). The improved performance of ΔPC1.EMT was related to its propensity to identify epithelial-like subpopulations as well as mesenchymal-like subpopulations. Biologically, the signature was correlated positively with RAS signaling but negatively with mitochondrial metabolism. ΔPC1.EMT was a "best of assessed" prognostic score when compared with 10 other known prognostic signatures. CONCLUSIONS: The study developed a prognostic signature score with a propensity to detect non-EMT features, including epithelial cancer stem cell-related properties, thereby improving its potential to predict metastasis and poorer outcome in stage I-III patients