Strategy for encoding and comparison of gene expression signatures

EXALT (EXpression signature AnaLysis Tool) is a computational system enabling comparisons of microarray data across experimental platforms and different laboratories . An essential feature of EXALT is a database holding thousands of gene expression signatures extracted from the Gene Expression Omnibus, and encoded in a searchable format. This novel approach to performing global comparisons of shared microarray data may have enormous value when coupled directly with a shared data repository.     

Mining gene expression profiles: expression signatures as cancer phenotypes

Many examples highlight the power of gene expression profiles, or signatures, to inform an understanding of biological phenotypes. This is perhaps best seen in the context of cancer, where expression signatures have tremendous power to identify new subtypes and to predict clinical outcomes. Although the ability to interpret the meaning of the individual genes in these signatures remains a challenge, this does not diminish the power of the signature to characterize biological states. The use of these signatures as surrogate phenotypes has been particularly important, linking diverse experimental systems that dissect the complexity of biological systems with the in vivo setting in a way that was not previously feasible.     

Web-based interrogation of gene expression signatures using EXALT

Background  

Widespread use of high-throughput techniques such as microarrays to monitor gene expression levels has resulted in an explosive growth of data sets in public domains. Integration and exploration of these complex and heterogeneous data have become a major challenge.     

DNA methylation-based biomarkers in serum of patients with breast cancer

Alterations of genetic and epigenetic features can provide important insights into the natural history of breast cancer. Although DNA methylation analysis is a rapidly developing field, a reproducible epigenetic blood-based assay for diagnosis and follow-up of breast cancer has yet to be successfully developed into a routine clinical test. The aim of this study was to review multiple serum DNA methylation assays and to highlight the value of those novel biomarkers in diagnosis, prognosis and prediction of therapeutic outcome. Serum is readily accessible for molecular diagnosis in all individuals from a peripheral blood sample. The list of hypermethylated genes in breast cancer is heterogeneous and no single gene is methylated in all breast cancer types. There is increasing evidence that a panel of epigenetic markers is essential to achieve a higher sensitivity and specificity in breast cancer detection. However, the reported percentages of methylation are highly variable, which can be partly explained by the different sensitivities and the different intra-/inter-assay coefficients of variability of the analysis methods. Moreover, there is a striking lack of receiver operating characteristic (ROC) curves of the proposed biomarkers. Another point of criticism is the fact that 'normal' patterns of DNA methylation of some tumor suppressor and other cancer-related genes are influenced by several factors and are often poorly characterized. A relatively frequent methylation of those genes has been observed in high-risk asymptomatic women. Finally, there is a call for larger prospective cohort studies to determine methylation patterns during treatment and follow-up. Identification of patterns specific for a differential response to therapeutic interventions should be useful. Only in this way, it will be possible to evaluate the predictive and prognostic characteristics of those novel promising biomarkers.     

In vitro analysis of integrated global high-resolution DNA methylation profiling with genomic imbalance and gene expression in osteosarcoma

Genetic and epigenetic changes contribute to deregulation of gene expression and development of human cancer. Changes in DNA methylation are key epigenetic factors regulating gene expression and genomic stability. Recent progress in microarray technologies resulted in developments of high resolution platforms for profiling of genetic, epigenetic and gene expression changes. OS is a pediatric bone tumor with characteristically high level of numerical and structural chromosomal changes. Furthermore, little is known about DNA methylation changes in OS. Our objective was to develop an integrative approach for analysis of high-resolution epigenomic, genomic, and gene expression profiles in order to identify functional epi/genomic differences between OS cell lines and normal human osteoblasts. A combination of Affymetrix Promoter Tilling Arrays for DNA methylation, Agilent array-CGH platform for genomic imbalance and Affymetrix Gene 1.0 platform for gene expression analysis was used. As a result, an integrative high-resolution approach for interrogation of genome-wide tumour-specific changes in DNA methylation was developed. This approach was used to provide the first genomic DNA methylation maps, and to identify and validate genes with aberrant DNA methylation in OS cell lines. This first integrative analysis of global cancer-related changes in DNA methylation, genomic imbalance, and gene expression has provided comprehensive evidence of the cumulative roles of epigenetic and genetic mechanisms in deregulation of gene expression networks.     

Molecular subsets in the gene expression signatures of scleroderma skin

Background

Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production.

Methodology and Findings

We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc) with diffuse scleroderma (dSSc), 7 patients with SSc with limited scleroderma (lSSc), 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of ‘intrinsic’ genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p<0.001) and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud''s phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc.

Conclusions and Significance

Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of patients with scleroderma.     

DNA methylation-based diagnostic,prognostic, and predictive biomarkers in colorectal cancer

DNA methylation is an epigenetic mechanism regulating gene expression. Changes in DNA methylation were suggested to be useful biomarkers for diagnosis, and for the determination of prognosis and treatment response. Here, we provide an overview of methylation-based biomarkers in colorectal cancer.First, we start with the two methylation-based diagnostic biomarkers already approved for colorectal cancer, SEPT9 and the combination of NDRG4 and BMP3. Then, we provide a list-based overview of new biomarker candidates depending on the sample source including plasma, stool, urine, and surgically removed tumor tissues. The most often identified markers like SDC2, VIM, APC, MGMT, SFRP1, SFRP2, and NDRG4 have distinct functions previously linked to tumor progression.Although numerous studies have identified tumor-specific methylation changes, most of these alterations were observed in a single study only. The lack of validation in independent samples means low reproducibility and is a major limitation. The genome-wide determination of methylation status (methylome) can provide data to solve these issues. In the third section of the review, methylome studies focusing on different aspects related to CRC, including precancerous lesions, CRC-specific changes, molecular subtypes, aging, and chemotherapy response are summarized. Notably, techniques simultaneously analyzing a large set of regions can also uncover epigenetic regulation of genes which have not yet been associated with tumorigenesis previously.A remaining constraint of studies published to date is the low patient number utilized in these preventing the identification of clinically valuable biomarker candidates. Either future large-scale studies or the integration of already available methylome-level data will be necessary to uncover biomarkers sufficiently robust for clinical application.     

Distinct gene expression signatures during development of distant metastasis

Using cDNA microarrays, we have conducted a systematic characterization of global gene expression in v-raf or v-raf/v-myc transformed rat liver epithelial (RLE) cell lines exhibiting both non-metastatic and metastatic phenotypes. Seven transformed cell lines were compared with the non-transformed parental RLE cell. The hierarchical clustering analysis of gene expression profiles revealed two groups reflecting the in vivo metastatic potential of the cells. Surprisingly, one non-metastatic cell line T1 was co-clustered with metastatic cell lines, suggesting that T1 underwent significant genetic changes. The T1 cell line was further compared against all the metastatic cell lines in order to reveal the critical genes required for metastatic conversion but not expressed in the T1. These data demonstrated that expression of genes involved in apoptosis and immune cell homing were altered in all metastatic cell lines. Survival of both intravasated cells in circulation systems and extravasated cells in a new tissue environment might be critical for the final step in the metastatic process. Our study provides gene expression signatures consistent with two critical events in the metastatic process, namely, the acquisition of early homing capacity and increased survival potential of the tumor cells.     

DNA polymorphisms detect ancient barriers to gene flow in Basques

This work features the first district-by-district analysis of all provinces in the Iberian Peninsula with an autochthonous Basque population, and indicates the existence of genetic heterogeneity. The populations cluster in three groups arising from processes of genetic drift which probably occurred in pre-Mesolithic times, and were probably those which repopulated the southern areas of the Basque Country after the Last Glacial Maximum. It seems that from that period onwards, the population settled in three major groups (West Basques, Central Basques, and East Basques), along geographical axes which appear substantial in the maintaining of each population unit. This genetic structure is probably reflected in other aspects such as the existence of ancient tribes and the dialects of the Basque language, the boundaries of which may be related at origin and which are quite similar to those detected in this work. Our results indicate that the populations of the Basque Country are genetically close to other neighboring populations, such as that of Aragon, which may indicate an outgoing gene flow from the Basque area down the River Ebro towards the Mediterranean seaboard. While our short tandem repeat data suggest that population structure within the Basques dates back to the Mesolithic, our findings are also consistent with the hypothesis that patterns of modern European genetic diversity have been shaped mainly during the Neolithic.     

HIV gp120 induced gene expression signatures in vaginal epithelial cells

Gp120 is the envelope protein of HIV which binds to CD4 independent proteins on vaginal epithelial cells. HIV-gp120 has been reported to modulate gene expression in several cell types. How this interaction may alter the physiologic vaginal milieu during the earliest stages of vaginal transmission of HIV, is currently unknown. Vaginal epithelial cells were treated with HIV-gp120, and a global snapshot of changes in gene expression profiles, were unraveled by microarray analysis. The differentially expressed genes were involved in diverse cellular functions. Genes of immunomodulatory processes and induction of proteases were highly enriched. We propose that the induction of inflammation and proteases may act in concert to weaken the vaginal epithelium, making it more permeable to viral entry. Identification of the gene signatures involved in vaginal-HIV dialogue would aid in understanding the environ induced by HIV itself, as the virus invades and gains entry into its host.     

Drug response prediction by ensemble learning and drug-induced gene expression signatures

Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in addition to cytotoxicity databases. These large sets of data provided an opportunity to apply machine learning methods to predict drug activity. However, due to the complexity of cancer drug mechanisms, none of the existing methods is perfect. In this paper, we propose a novel ensemble learning method to predict drug response. In addition, we attempt to use the drug screen data together with two novel signatures produced from the drug-induced gene expression profiles of cancer cell lines. Finally, we evaluate predictions by in vitro experiments in addition to the tests on data sets. The predictions of the methods, the signatures and the software are available from http://mtan.etu.edu.tr/drug-response-prediction/.     

DNA methylation-based diagnostic and prognostic biomarkers of nonsmoking lung adenocarcinoma patients

Currently, there are few studies on patients with nonsmoking lung adenocarcinoma, and the pathogenesis is still unclear. The role of DNA methylation in the pathogenesis of cancer is gradually being recognized. The purpose of this study was to determine the abnormal methylation genes and pathways involved in nonsmoking lung adenocarcinoma patients. Gene expression microarray data (GSE10072, GSE43458) and gene methylation microarray data (GSE62948) were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes were obtained through GEO2R. Next, we analyzed the function and enrichment of the selected genes using Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) networks were constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized in Cytoscape. Finally, we performed module analysis of the PPI network using Molecular Complex Detection. And we obtained 10 hub genes by Cytoscape Centiscape. We analyzed the independent prognostic value of each hub gene in nonsmoking nonsmall cell lung cancer patients through Kaplan-Meier plotter. Seven hub genes (CXCL12, CDH1, CASP3, CREB1, COL1A1, ERBB2, and ENO2) were closely related to the overall survival time. This study provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of nonsmoking lung adenocarcinoma patients. Hub genes with prognostic value could be selected as effective biomarkers for timely diagnosis and prognostic of nonsmoking lung adenocarcinoma patients.     

A consensus prognostic gene expression classifier for ER positive breast cancer

Background  

A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer.