Gene Expression Profiling Technologies Research Articles

Molecular Aspects of Breast Cancer Metastasis to the Brain

Our knowledge of the biology underlying the development of brain metastases (BM) from breast cancer has improved over the last decade due to large clinical epidemiological studies, animal models of metastasis, and the use of high-resolution gene expression profiling technologies. However, there are still major gaps in our understanding of the mechanisms utilized by breast cancer cells to colonize the brain microenvironment, thus our arsenal of therapies remains relatively nonspecific, and the prognosis for breast cancer patients with BM remains poor. Additional insights into these mechanisms are necessary to facilitate the development of new preventive and curative therapeutic regimens to block this fatal disease. This paper aims to provide a general overview for the readers of what has been achieved in this field of research and its translation into clinical practice to date and to highlight exciting new areas of research that promise to inform the development of new targeted therapies for BM.

Identifying genes related with non-small cell lung cancer via transcription factors-target genes relationship

The developments of gene expression profiling technology have deeply influenced the understanding of lung cancer biology. By microarray data, we constructed a regulation network to identify potential genes that are highly correlated to non-small cell lung cancer. Of the network, some transcription factors and target genes, including perturbation factors (PPARG), ETV4, TGFBR2 and ATP2A2, have been proved to be related to non-small cell lung cancer in previous study. We also found some new transcription factors and target genes may be related to non-small cell lung cancer, including SPI-1/PU.1, ETS2, and EGR1 and so on; these genes may be potential markers for non-small cell lung cancer. Key words: Non-small cell lung cancer, transcription factors, regulation network.

Modern Classification of Breast Cancer

Breast cancer represents a heterogeneous group of tumors with varied morphologic and biological features, behavior, and response to therapy. The present routine clinical management of breast cancer relies on the availability of robust prognostic and predictive factors to support decision making. Breast cancer patients are stratified into risk groups based on a combination of classical time-dependent prognostic variables (staging) and biological prognostic and predictive variables. Staging variables include tumor size, lymph node stage, and extent of tumor spread. Classical biological variables include morphologic variables such as tumor grade and molecular markers such as hormone receptor and human epidermal growth factor receptor 2 status. Although individual molecular markers were introduced in the field of breast cancer management many years ago, the concept of molecular classification was raised after the introduction of global gene expression profiling and the identification of multigene classifiers. Although there is no doubt that gene expression profiling technology has revolutionized the field of breast cancer research and have been widely expected to improve breast cancer prognostication, the unprecedented speed of progress and publicity associated with the introduction of these commercially-based multigene classifiers should not lead us to expect this technology to replace the classical classification systems. These multigene classifiers have the potential to complement traditional methods through provision of additional biological prognostic and predictive information in presently indeterminate risk groups. Here we present updated information on the present clinical value of classical clinicopathologic factors, molecular taxonomy, and multigene classifiers in routine patients management and provide some critical views and practical expectations.

Performance and Clinical Evaluation of the 92-Gene Real-Time PCR Assay for Tumor Classification

Accurate determination of cancer origin is necessary to guide optimal treatment but remains a diagnostic challenge. Gene expression profiling technologies have aided the classification of tumors and, therefore, could be applied in conjunction with clinicopathologic correlates to improve accuracy. We report an expanded version of the previously described 92-gene assay to classify 30 main tumor types and 54 histological subtypes, with coverage of ≥95% of all solid tumors based on incidence. Increased tissue coverage was achieved through expansion of a reference tumor database containing 2,206 specimens, with a median of 62 samples per main tumor type. The 92-gene classification algorithm demonstrated sensitivities of 87% and 85% for 30 main types and 54 histological subtypes, respectively, in leave-one-out cross validation, and 83% in a test set of 187 tumors representing 28 of the 30 main cancer types. These findings provide further support that broad and diverse tumor classification can be performed using a relatively compact gene set. An additional 300 consecutive cases submitted for clinical testing were profiled to characterize clinical utility in a real-world setting: the 92-gene assay confirmed 78% of samples having a single suspected primary tumor and provided a single molecular prediction in 74% of cases with two or more differential diagnoses. Further development of the 92-gene RT-PCR assay has resulted in a significant expansion in reportable tumor types and histological features with strong performance characteristics and supports the use of molecular classification as an objective standardized adjunct to current methods.

Genetics and genomics of Sjögren's syndrome: research provides clues to pathogenesis and novel therapies

Although the key inciting events that drive the progression from autoantibodies to clinical disease remain to be clarified, new light has been shed on the factors contributing to disease susceptibility and the role of genetic factors in determining Sjögren's syndrome (SS) disease phenotypes. The purpose of this article is to provide an update on the role of genetic markers in the susceptibility to and pathogenesis of SS. This article also discusses how genomic and proteomic technology can help in the design of specific therapeutics. Recent evidence suggests that inflammatory genes associated with interferon pathways, and specific regulatory genes that control the maturation and proliferation of B cells, contribute to the pathogenesis of SS. Both gene expression profiling technology and gene association studies have been used to identify these key biological pathways. Molecularly, defined subsets of pSS patients are also being revealed by these studies. Previously, identified gene loci that predispose to multiple autoimmune disorders have been confirmed supporting the paradigm of "general" autoimmune disease genes. Association of SS with many additional susceptibility loci are likely to be established through ongoing genome-wide association scans (GWAS). Clues from genetic studies suggest that targeting B cells will prove to be an effective way of reducing the systemic manifestations of pSS and are supported by early clinical trials. Genome-wide technologies are likely to identify new genes and molecular pathways in the pathogenesis of SS that will be useful not only to identify patients at risk for SS, but also to identify subsets of patients at risk for variable levels of disease severity. In the future, these studies could identify novel biomarkers that will lead to significant advances in management by providing the means to tailor therapeutic strategies to individual patients.

LEX-SVM: EXPLORING THE POTENTIAL OF EXON EXPRESSION PROFILING FOR DISEASE CLASSIFICATION

Exon expression profiling technologies, including exon arrays and RNA-Seq, measure the abundance of every exon in a gene. Compared with gene expression profiling technologies like 3' array, exon expression profiling technologies could detect alterations in both transcription and alternative splicing, therefore they are expected to be more sensitive in diagnosis. However, exon expression profiling also brings higher dimension, more redundancy, and significant correlation among features. Ignoring the correlation structure among exons of a gene, a popular classification method like L1-SVM selects exons individually from each gene and thus is vulnerable to noise. To overcome this limitation, we present in this paper a new variant of SVM named Lex-SVM to incorporate correlation structure among exons and known splicing patterns to promote classification performance. Specifically, we construct a new norm, ex-norm, including our prior knowledge on exon correlation structure to regularize the coefficients of a linear SVM. Lex-SVM can be solved efficiently using standard linear programming techniques. The advantage of Lex-SVM is that it can select features group-wisely, force features in a subgroup to take equal weihts and exclude the features that contradict the majority in the subgroup. Experimental results suggest that on exon expression profile, Lex-SVM is more accurate than existing methods. Lex-SVM also generates a more compact model and selects genes more consistently in cross-validation. Unlike L1-SVM selecting only one exon in a gene, Lex-SVM assigns equal weights to as many exons in a gene as possible, lending itself easier for further interpretation.

A comparison of analog and Next-Generation transcriptomic tools for mammalian studies

This review focuses on tools for studying a cell's transcriptome, the collection of all RNA transcripts produced at a specific time, and the tools available for determining how these changes in gene expression relate to the functional changes in an organism. While the microarray-based (analog) gene-expression profiling technology has dominated the 'omics' era, Next-Generation Sequencing based gene-expression profiling (RNA-Seq) is likely to replace this analog technology in the future. RNA-Seq shows much promise for transcriptomic studies as the genes of interest do not have to be known a priori, new classes of RNA, SNPs and alternative splice variants can be detected, and it is also theoretically possible to detect transcripts from all biologically relevant abundance classes. However, the technology also brings with it new issues to resolve: the specific technical properties of RNA-Seq data differ to those of analog data, leading to novel systematic biases which must be accounted for when analysing this type of data. Additionally, multireads and splice junctions can cause problems when mapping the sequences back to a genome, and concepts such as cloud computing may be required because of the massive amounts of data generated.

Early prediction of breast cancer

Between 40 to 60, especially after menopause, breast cancer have a higher incidence in women population. CA-153 ( carbohydrate antigen-153 ) is a tumor marker commonly used for monitoring breast cancer in clinic. However, CA-153 appears to have no clinical value for early diagnosis of breast cancer. Gene expression profiling technology becomes hot-spot through which some breast cancer related genes have been found. Moreover, the methods used for diagnosis of breast cancer also has certain shortcomings, and it is still uncertain that breast cancer is likely to be found at the early stage or be predicted in advance. Key words: Breast neoplasms; Genes; Early diagnosis

Variation of gene expression in Bacillus subtilis samples of fermentation replicates

The application of comprehensive gene expression profiling technologies to compare wild and mutated microorganism samples or to assess molecular differences between various treatments has been widely used. However, little is known about the normal variation of gene expression in microorganisms. In this study, an Agilent customized microarray representing 4,106 genes was used to quantify transcript levels of five-repeated flasks to assess normal variation in Bacillus subtilis gene expression. CV analysis and analysis of variance were employed to investigate the normal variance of genes and the components of variance, respectively. The results showed that above 80% of the total variation was caused by biological variance. For the 12 replicates, 451 of 4,106 genes exhibited variance with CV values over 10%. The functional category enrichment analysis demonstrated that these variable genes were mainly involved in cell type differentiation, cell type localization, cell cycle and DNA processing, and spore or cyst coat. Using power analysis, the minimal biological replicate number for a B. subtilis microarray experiment was determined to be six. The results contribute to the definition of the baseline level of variability in B. subtilis gene expression and emphasize the importance of replicate microarray experiments.

Gene expression profiling in soybean under aluminum stress: genes differentially expressed between Al-tolerant and Al-sensitive genotypes

It is well documented that aluminum (Al) toxicity is the most important constraint to crop production on acid soils and soybean is one of the most Al sensitive plant species. To advance our understanding of the molecular and genetic mechanisms of Al-tolerance in soybean we compared root tip (1 cm long) transcriptome profiles of an Al-tolerant (PI 416937) and Al-sensitive (Young) soybean genotypes using a combination of DNA microarrays and quantitative real-time PCR gene expression profiling technologies, in a time-course experiment (2, 12, 48, 72 h post Al treatment). We observed many genes differentially expressed between the two genotypes in constitutive and non-constitutive manner. The most likely candidate Al-tolerance genes expressed at high level include the previously reported transcription factors, auxin down regulated-like protein (ADR6-like) and, basic leucine zipper (bZIP 94), sulfur transmembrane transport protein and lipid transfer protein; and several novel genes that include rare cold inducible protein (RCI2B ), GPI-transamidase, malonyl-COA: Isoflavone 7-O-glucoside-6-O-malontransferase, a cell proliferation protein (WPP2), oleosin protein, pectinestrease inhibitor, and impaired sucrose induction1; whereas genes negatively correlated with Al-tolerance, namely cellulose synthase and calcium transporters were down regulated in Al-tolerant PI 416937 compared to the Al-sensitive Young. The possible mechanisms of how these genes contribute to Al-tolerance trait are discussed. In conclusion, transcriptome profile comparisons of Al-tolerant and Al-sensitive soybean genotypes revealed novel putative Al-tolerance genes. These genes deserve further functional characterization for eventual utilization in developing soybean germplasm adapted to high aluminum soils.

Expression profiling for bladder cancer: strategies to uncover prognostic factors

Despite being a common cancer worldwide, management of transitional cell carcinoma of the bladder currently relies primarily on clinical staging and histopathologic parameters. Assaying alterations in molecular pathways can contribute valuable information that can accurately predict outcome and chemotherapeutic response in individual patients with bladder cancer. Medium- to high-throughput gene-expression profiling technologies are now allowing multiplexed assessment of alterations responsible for the genesis and progression of bladder tumors. These investigations employ global or pathway-based approaches to define molecular signatures that can predict prognosis independent of traditional clinical performance metrics. Prognostic panels generated using these strategies can also elucidate the biology of tumor progression and identify potential therapeutic targets.

Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer

The development of reliable gene expression profiling technology is having an increasing impact on our understanding of lung cancer biology. Our study aimed to determine any correlation between the phenotypic heterogeneity and genetic diversity of lung cancer. Microarray analysis was performed on a set of 46 tumor samples and 45 paired nontumor samples of nonsmall cell lung cancer (NSCLC) samples to establish gene signatures in primary adenocarcinomas and squamous-cell carcinomas, determine differentially expressed gene sequences at different stages of the disease and identify sequences with biological significance for tumor progression. After the microarray analysis, the expression level of 92 selected genes was validated by qPCR and the robust Bonferroni test in an independent set of 70 samples composed of 48 tumor samples and 22 nontumor samples. Gene sequences were differentially expressed as a function of tumor type, stage and differentiation grade. High upregulation was observed for KRT15 and PKP1, which may be good markers to distinguish squamous-cell carcinoma samples. High downregulation was observed for DSG3 in stage IA adenocarcinomas.

An approach to evaluate the reliability of hybridization-based and sequencing-based gene expression profiling technologies

Hybridization-based and sequencing-based technologies have found a widespread application in gene expression profiling analysis but much ambiguity exists regarding their reliability. This study developed a framework based on three parameters: detection ability, repeatability, and accuracy to evaluate the reliability of gene expression profiling technologies. The fraction of coverage of detected transcript category, the degree of variance for the number of differentially expressed genes (DEGs), the consistency of DEG category, and suspected false discovery rate (sFDR) were first introduced as statistical indices. In order to validate the availability of these indices, based on the same RNA extract, the analysis was performed by comparing gene expression differences between wild-type and transgenic rice using deep sequencing and microarray. The results suggested that the parameters were available and showed advances in the determination of gene expression differences. Based on relative self-comparison design, suspected false positive genes were easily identified from all DEGs detected, which was difficult for quantitative real-time polymerase chain reaction (qRT-PCR) validation when the count of DEGs was enormous. In addition, sFDRs had advantages in the accuracy evaluation for previous datasets.

Study of gene expression profiles in TK6 human cells exposed to DNA-oxidizing agents

During the last decade, there has been clear progress in using threshold in risk assessment but its acceptance by scientists is still under debate. Contrary to indirect DNA-damaging agents, DNA-reactive agents have been assumed to have a non-threshold mode of action, as they directly induce DNA lesions that potentially can be converted into mutations. However, in recent years there is a growing number of data establishing threshold doses even for these DNA-reactive compounds. Indeed, there are several defence and repair mechanisms that provide protection and that may be responsible for genotoxic thresholds. In this context, we recently showed that DNA-oxidizing agents exhibit a thresholded dose–response in vitro with respect to chromosomal alterations. We have hypothesized the involvement of different cellular responses whose nature and efficiency depend on the stress level. The aim of this study was to develop a more complete understanding of these underlying mechanisms. We investigated global gene expression profiles of human lymphoblastoid TK6 cells after exposure to potassium bromate and hydrogen peroxide ( via glucose oxidase). Cells were treated for 1 h and mRNAs were isolated either immediately at the end of the treatment or after a 23-h recovery period. Our results showed that cells have developed elaborate cellular responses to oxidative stress in order to maintain genomic integrity. Many of altered genes were redox-sensitive transcription factors such as p53, NF-κB, AP-1 and Nrf2. Their downstream target genes and signalling pathways were subsequently activated leading mainly to the induction of antioxidant defenses, inflammation, cell cycle arrest, DNA repair and cell death. Overall, our study allowed the identification of key events involved in the thresholded response observed after DNA-oxidizing agents exposure and shows the usefulness of the combination of standard in vitro genotoxicity assays with gene expression profiling technology to determine modes of action, particularly for critical risk assessment.

Kinase targets in renal-cell carcinomas: reassessing the old and discovering the new

Renal-cell carcinoma is a heterogeneous group of tumours that arise in the adult kidneys. Irrespective of the type of renal tumour, traditional chemotherapeutic and radiation-based therapies have been largely ineffective at treating advanced tumours, with long-term survival being very low. Molecularly-targeted inhibitors of protein kinases are effective in delaying progression of advanced renal tumours. These therapies revolve around inhibition of the vascular endothelial growth factor receptor tyrosine kinase and the mammalian target of rapamycin serine or threonine kinase signalling pathways. The genetic complexity of renal tumours revealed by gene-expression profiling and other molecular-genetic technologies indicate that inhibition of additional kinase-associated pathways could also prevent renal tumour growth. In this review, we discuss the use of molecularly-targeted kinase inhibitors in the treatment of renal-cell carcinoma and identify the next generation of kinase inhibitors that show promise for treatment.

Radiation-Associated Breast Tumors Display a Distinct Gene Expression Profile

Women who received irradiation for Hodgkin's lymphoma have a strong increased risk for developing breast cancer. Approximately 90% of the breast cancers in these patients can be attributed to their radiation treatment, rendering such series extremely useful to determine whether a common radiation-associated cause underlies the carcinogenic process. In this study we used gene expression profiling technology to assess gene expression changes in radiation-associated breast tumors compared with a set of control breast tumors of women unexposed to radiation, diagnosed at the same age. RNA was obtained from fresh frozen tissue samples from 22 patients who developed breast cancer after Hodgkin's lymphoma (BfHL) and from 20 control breast tumors. Unsupervised hierarchical clustering of the profile data resulted in a clustering of the radiation-associated tumors separate from the control tumors (p < 0.001). Using a supervised class prediction tool, a nearest centroid classifier of 198 probes was identified. The BfHL tumors were often of the intrinsic basal breast tumor subtype, and they showed a chromosomal instability profile and a higher expression of the proliferation marker Ki-67. These results indicate that radiation-associated tumors are different from other breast tumors on the basis of their expression profile and that they are mainly of one specific cause that is characterized by high proliferation and a more aggressive tumor type.

Gene expression signatures in breast cancer distinguish phenotype characteristics, histologic subtypes, and tumor invasiveness

The development of reliable gene expression profiling technology is having an increasing impact on the understanding of breast cancer biology. In this study, microarray analysis was performed to establish gene signatures for different breast cancer phenotypes, to determine differentially expressed gene sequences at different stages of the disease, and to identify sequences with biologic significance for tumor progression. Samples were taken from patients before their treatment. After microarray analysis, the expression level of 153 selected genes was studied by real-time quantitative polymerase chain reaction analysis. Several gene sequences were expressed differentially in tumor samples versus control samples and also were associated with different breast cancer phenotypes, estrogen receptor status, tumor histology, and grade of tumor differentiation. In lymph node-negative tumors were identified a set of genes related to tumor differentiation grade. Several differentially expressed gene sequences were identified at different stages of breast cancer.

Overview of Gene Expression Profiling and Novel Translational Technologies in Breast Cancer.

Abstract Gene expression profiling has had an enormous impact on many aspects of breast cancer, including elucidation of the basic mechanisms of carcinogenesis and mammary development, prediction of outcome and response to treatment, and identification of new subtypes of disease. Importantly, gene expression profiling in breast cancer has allowed the development of biomarkers that are being used in the clinic and/or will become part of routine clinical care in the near future. Genomic profiling is accomplished nowadays by using genome-wide DNA microarray assays, which evaluate thousands of genes with relatively low sensitivity, or qRT-PCR assays, which evaluate small numbers of genes with high sensitivity. The majority of these assays focus on mRNA molecules; however, in recent years, DNA microarrays evaluating other functionally important classes of RNA molecules, such as microRNAs and non-coding RNAs, have been developed and will likely have an impact on our understanding of breast cancer biology. Nonetheless, DNA microarrays and qRT-PCR assays based on mRNA have provided robust platforms for the investigation of breast cancer transcriptomes, and each has resulted in the development of clinically useful prognostic tests (i.e. OncotypeDX and Mammaprint). However, as with all technologies, new methods and platforms are constantly being developed, offering higher speed, lower cost and increased accuracy. Importantly, many of these promising new technologies are compatible with RNA derived from archival formalin-fixed paraffin embedded materials. Therefore, it is likely that gene expression-based assays will continue to play a critical role in the development of novel biomarkers, and the discovery of new targets. In this session, we will cover the basics of DNA microarrays and microarray statistics, and discuss the next generation of technologies to further study the gene expression patterns of breast cancers. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr ES3-1.

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BackgroundHigh-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets.ResultsUsing time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1.ConclusionsMerging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression.

Glucocorticoid Receptor in Health and Disease

Glucocorticoid Receptor in Health and DiseaseGlucocorticoid hormones are essential for life, have a vital place in the treatment of inflammatory and autoimmune diseases and are increasingly implicated in the pathogenesis of a number of common disorders. Their action is mediated by an intracellular receptor protein, the glucocorticoid receptor (GR), functioning as a ligand-inducible transcription factor. Multiple synthetic glucocorticoids are used as potent antiinflammatory and immunosuppressive agents, but their therapeutic usefulness is limited by a wide range and severity of side-effects. One of the most important pharmaceutical goals has been to design steroidal and non-steroidal GR ligands with profound therapeutic efficacy and reduced unwanted effects. The therapeutic benefit of glucocorticoid agonists is frequently compromised by resistance to glucocorticoids, which may depend on: access of the hormones to target cells, steroid metabolism, expression level and isoform composition of the GR protein, mutations and polymorphisms in the GR gene and association of the receptor with chaperone proteins. The major breakthrough into the critical role of glucocorticoid signaling in the maintenance of homeostasis and pathogenesis of diseases, as well as into the molecular mechanisms underlying the therapeutic usefulness of antiinflammatory drugs acting through the GR is expected to result from the current progress in large-scale gene expression profiling technologies and computational biology.