Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study

We used microarray analysis to investigate associations between genotypic expression profiles and survival phenotypes in patients with primary glioblastoma (GBM). Tumor samples from 7 long-term glioblastoma survivors (>24 months) and 13 short-term survivors (<9 months) were analyzed to detect differential patterns of gene expression between these groups and to identify genotypic subclasses of glioblastomas that correlate with survival phenotypes. Five unsupervised and three supervised clustering algorithms consistently and accurately grouped the tumors into genotypic subgroups corresponding to the two clinical survival phenotypes. Three unique prospective mathematical classification algorithms were subsequently trained to use expression data to stratify unknown glioblastomas between survival groups and performed this task with 100% accuracy in validation studies. A set of 1478 genes with significant differential expression (p<0.01) between long-term and short-term survivors was identified, and additional mathematical filtering was used to isolate a 43-gene "fingerprint" that distinguished survival phenotypes. Differential regulation of a subset of these genes was confirmed using RT-PCR. Gene ontology analysis of the fingerprint demonstrated pathophysiologic functions for the gene products that are consistent with current models of tumor biology, suggesting that differential expression of these genes may contribute etiologically to the observed differences in survival. These results demonstrate that unique expression profiles characterize genotypic subsets of primary GBMs associated with differential survival phenotypes, and these profiles can be used in a prospective fashion to assign unknown tumors to survival groups. Future efforts will focus on building more robust classifiers and identifying additional subclasses of gliomas with phenotypic significance.     

Distinctive features of plant organs characterized by global analysis of gene expression in Arabidopsis.

The distinctive features of plant organs are primarily determined by organ-specific gene expression. We analyzed the expression specificity of 8809 genes in 7 organs of Arabidopsis using a cDNA macroarray system. Using relative expression (RE) values between organs, many known and unknown genes specifically expressed in each organ were identified. We also analyzed the organ specificity of various gene groups using the GRE (group relative expression) value, the average of the REs of all genes in a group. Consequently, we found that many gene groups even ribosomal protein genes, have strong organ-specific expression. Clustering of the expression profiles revealed that the 8809 genes were classified into 9 major categories. Although 3451 genes were clustered into the largest category, which showed constitutive gene expression, 266 and 1005 genes were found to be root- and silique-specific genes, respectively. By this clustering, particular gene groups which showed multi-organ-specific expression profiles, such as bud-flower-specific, stem-silique-specific or bud-flower-root-specific profiles, could be effectively identified. From these results, major features of plant organs could be characterized by their distinct profiles of global gene expression. These data of organ-specific gene expression are available at our web site: Arabidopsis thaliana Tissue-Specific Expression Database, ATTED (http://www.atted.bio.titech.ac.jp/).     

A ten-microRNA expression signature predicts survival in glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n=222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR]=2.4; 95% CI=1.4-3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR=1.7; 95% CI=1.1-2.8; p=0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR=2.0; 95% CI=1.4-2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR=1.120; 95% CI=1.04-1.20; p=0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.     

Peripheral immune cell gene expression predicts survival of patients with non-small cell lung cancer

Prediction of cancer recurrence in patients with non-small cell lung cancer (NSCLC) currently relies on the assessment of clinical characteristics including age, tumor stage, and smoking history. A better prediction of early stage cancer patients with poorer survival and late stage patients with better survival is needed to design patient-tailored treatment protocols. We analyzed gene expression in RNA from peripheral blood mononuclear cells (PBMC) of NSCLC patients to identify signatures predictive of overall patient survival. We find that PBMC gene expression patterns from NSCLC patients, like patterns from tumors, have information predictive of patient outcomes. We identify and validate a 26 gene prognostic panel that is independent of clinical stage. Many additional prognostic genes are specific to myeloid cells and are more highly expressed in patients with shorter survival. We also observe that significant numbers of prognostic genes change expression levels in PBMC collected after tumor resection. These post-surgery gene expression profiles may provide a means to re-evaluate prognosis over time. These studies further suggest that patient outcomes are not solely determined by tumor gene expression profiles but can also be influenced by the immune response as reflected in peripheral immune cells.     

Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data

MOTIVATION: An important application of microarray technology is to relate gene expression profiles to various clinical phenotypes of patients. Success has been demonstrated in molecular classification of cancer in which the gene expression data serve as predictors and different types of cancer serve as a categorical outcome variable. However, there has been less research in linking gene expression profiles to the censored survival data such as patients' overall survival time or time to cancer relapse. It would be desirable to have models with good prediction accuracy and parsimony property. RESULTS: We propose to use the L(1) penalized estimation for the Cox model to select genes that are relevant to patients' survival and to build a predictive model for future prediction. The computational difficulty associated with the estimation in the high-dimensional and low-sample size settings can be efficiently solved by using the recently developed least-angle regression (LARS) method. Our simulation studies and application to real datasets on predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed procedure, which we call the LARS-Cox procedure, can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. The LARS-Cox regression gives better predictive performance than the L(2) penalized regression and a few other dimension-reduction based methods. CONCLUSIONS: We conclude that the proposed LARS-Cox procedure can be very useful in identifying genes relevant to survival phenotypes and in building a parsimonious predictive model that can be used for classifying future patients into clinically relevant high- and low-risk groups based on the gene expression profile and survival times of previous patients.     

Supervised harvesting of expression trees

Background

We propose a new method for supervised learning from gene expression data. We call it 'tree harvesting'. This technique starts with a hierarchical clustering of genes, then models the outcome variable as a sum of the average expression profiles of chosen clusters and their products. It can be applied to many different kinds of outcome measures such as censored survival times, or a response falling in two or more classes (for example, cancer classes). The method can discover genes that have strong effects on their own, and genes that interact with other genes.

Results

We illustrate the method on data from a lymphoma study, and on a dataset containing samples from eight different cancers. It identified some potentially interesting gene clusters. In simulation studies we found that the procedure may require a large number of experimental samples to successfully discover interactions.

Conclusions

Tree harvesting is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worthy of further investigation.     

Quantitative trait associated microarray gene expression data analysis

Selection on phenotypes may cause genetic change. To understand the relationship between phenotype and gene expression from an evolutionary viewpoint, it is important to study the concordance between gene expression and profiles of phenotypes. In this study, we use a novel method of clustering to identify genes whose expression profiles are related to a quantitative phenotype. Cluster analysis of gene expression data aims at classifying genes into several different groups based on the similarity of their expression profiles across multiple conditions. The hope is that genes that are classified into the same clusters may share underlying regulatory elements or may be a part of the same metabolic pathways. Current methods for examining the association between phenotype and gene expression are limited to linear association measured by the correlation between individual gene expression values and phenotype. Genes may be associated with the phenotype in a nonlinear fashion. In addition, groups of genes that share a particular pattern in their relationship to phenotype may be of evolutionary interest. In this study, we develop a method to group genes based on orthogonal polynomials under a multivariate Gaussian mixture model. The effect of each expressed gene on the phenotype is partitioned into a cluster mean and a random deviation from the mean. Genes can also be clustered based on a time series. Parameters are estimated using the expectation-maximization algorithm and implemented in SAS. The method is verified with simulated data and demonstrated with experimental data from 2 studies, one clusters with respect to severity of disease in Alzheimer's patients and another clusters data for a rat fracture healing study over time. We find significant evidence of nonlinear associations in both studies and successfully describe these patterns with our method. We give detailed instructions and provide a working program that allows others to directly implement this method in their own analyses.     

Variation in gene expression patterns in human gastric cancers

Gastric cancer is the world's second most common cause of cancer death. We analyzed gene expression patterns in 90 primary gastric cancers, 14 metastatic gastric cancers, and 22 nonneoplastic gastric tissues, using cDNA microarrays representing approximately 30,300 genes. Gastric cancers were distinguished from nonneoplastic gastric tissues by characteristic differences in their gene expression patterns. We found a diversity of gene expression patterns in gastric cancer, reflecting variation in intrinsic properties of tumor and normal cells and variation in the cellular composition of these complex tissues. We identified several genes whose expression levels were significantly correlated with patient survival. The variations in gene expression patterns among cancers in different patients suggest differences in pathogenetic pathways and potential therapeutic strategies.     

Directed indices for exploring gene expression data

MOTIVATION: Large expression studies with clinical outcome data are becoming available for analysis. An important goal is to identify genes or clusters of genes where expression is related to patient outcome. While clustering methods are useful data exploration tools, they do not directly allow one to relate the expression data to clinical outcome. Alternatively, methods which rank genes based on their univariate significance do not incorporate gene function or relationships to genes that have been previously identified. In addition, after sifting through potentially thousands of genes, summary estimates (e.g. regression coefficients or error rates) algorithms should address the potentially large bias introduced by gene selection. RESULTS: We developed a gene index technique that generalizes methods that rank genes by their univariate associations to patient outcome. Genes are ordered based on simultaneously linking their expression both to patient outcome and to a specific gene of interest. The technique can also be used to suggest profiles of gene expression related to patient outcome. A cross-validation method is shown to be important for reducing bias due to adaptive gene selection. The methods are illustrated on a recently collected gene expression data set based on 160 patients with diffuse large cell lymphoma (DLCL).     

Gene expression profile during the life cycle of the urochordate Ciona intestinalis

Recent whole-genome studies and in-depth expressed sequence tag (EST) analyses have identified most of the developmentally relevant genes in the urochordate, Ciona intestinalis. In this study, we made use of a large-scale oligo-DNA microarray to further investigate and identify genes with specific or correlated expression profiles, and we report global gene expression profiles for about 66% of all the C. intestinalis genes that are expressed during its life cycle. We succeeded in categorizing the data set into 5 large clusters and 49 sub-clusters based on the expression profile of each gene. This revealed the higher order of gene expression profiles during the developmental and aging stages. Furthermore, a combined analysis of microarray data with the EST database revealed the gene groups that were expressed at a specific stage or in a specific organ of the adult. This study provides insights into the complex structure of ascidian gene expression, identifies co-expressed gene groups and marker genes and makes predictions for the biological roles of many uncharacterized genes. This large-scale oligo-DNA microarray for C. intestinalis should facilitate the understanding of global gene expression and gene networks during the development and aging of a basal chordate.     

Novel gene expression profiles define the metabolic and physiological processes characteristic of wood and its extractive formation in a hardwood tree species,Robinia pseudoacacia

Wood is of critical importance to humans as a primary feedstock for biofuel, fiber, solid wood products, and various natural compounds including pharmaceuticals. The trunk wood of most tree species has two distinctly different regions: sapwood and heartwood. In addition to the major constituents, wood contains extraneous chemicals that can be removed by extraction with various solvents. The composition and the content of the extractives vary depending on such factors as, species, growth conditions, and time of year when the tree is cut. Despite the great commercial and keen scientific interest, little is known about the tree-specific biology of the formation of heartwood and its extractives. In order to gain insight on the molecular regulations of heartwood and its extractive formation, we carried out global examination of gene expression profiles across the trunk wood of black locust (Robinia pseudoacacia L.) trees. Of the 2,915 expressed sequenced tags (ESTs) that were generated and analyzed in the current study, 55.3% showed no match to known sequences. Cluster analysis of the ESTs identified a total of 2278 unigene sets, which were used to construct cDNA microarrays. Microarray hybridization analyses were then performed to survey the changes in gene expression profiles of trunk wood. The gene expression profiles of wood formation differ according to the region of trunk wood sampled, with highly expressed genes defining the metabolic and physiological processes characteristic of each region. For example, the gene encoding sugar transport had the highest expression in the sapwood, while the structural genes for flavonoid biosynthesis were up-regulated in the sapwood-heartwood transition zone. This analysis also established the expression patterns of 341 previously unknown genes.     

Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction

Background

Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.

Methodology and Principal Findings

A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts.

Conclusions

The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.     

Gene-engineered rigidification of membrane lipids enhances the cold inducibility of gene expression in synechocystis

A sudden decrease in ambient temperature induces the expression of a number of genes in poikilothermic organisms. We report here that the cold inducibility of gene expression in Synechocystis sp. PCC 6803 was enhanced by the rigidification of membrane lipids that was engineered by disruption of genes for fatty acid desaturases. DNA microarray analysis revealed that cold-inducible genes could be divided into three groups according to the effects of the rigidification of membrane lipids. The first group included genes whose expression was not induced by cold in wild-type cells but became strongly cold-inducible upon rigidification of membrane lipids. This group included certain heat-shock genes, genes for subunits of the sulfate transport system, and the hik34 gene for a histidine kinase. The second group consisted of genes whose cold inducibility was moderately enhanced by the rigidification of membrane lipids. Most genes in this group encoded proteins of as yet unknown function. The third group consisted of genes whose cold inducibility was unaffected by the rigidification of membrane lipids. This group included genes for an RNA helicase and an RNA-binding protein. DNA microarray analysis also indicated that the rigidification of membrane lipids had no effect on the heat inducibility of gene expression. Hik33, a cold-sensing histidine kinase, regulated the expression of most genes in the second and third groups but of only a small number of genes in the first group, an observation that suggests that the cold-inducible expression of genes in the first group might be regulated by a cold sensor that remains to be identified.     

Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning

Hepatocellular carcinoma (HCC) is one of the most common and aggressive human malignancies. Its high mortality rate is mainly a result of intra-hepatic metastases. We analyzed the expression profiles of HCC samples without or with intra-hepatic metastases. Using a supervised machine-learning algorithm, we generated for the first time a molecular signature that can classify metastatic HCC patients and identified genes that were relevant to metastasis and patient survival. We found that the gene expression signature of primary HCCs with accompanying metastasis was very similar to that of their corresponding metastases, implying that genes favoring metastasis progression were initiated in the primary tumors. Osteopontin, which was identified as a lead gene in the signature, was over-expressed in metastatic HCC; an osteopontin-specific antibody effectively blocked HCC cell invasion in vitro and inhibited pulmonary metastasis of HCC cells in nude mice. Thus, osteopontin acts as both a diagnostic marker and a potential therapeutic target for metastatic HCC.     

Genome trees from conservation profiles

The concept of the genome tree depends on the potential evolutionary significance in the clustering of species according to similarities in the gene content of their genomes. In this respect, genome trees have often been identified with species trees. With the rapid expansion of genome sequence data it becomes of increasing importance to develop accurate methods for grasping global trends for the phylogenetic signals that mutually link the various genomes. We therefore derive here the methodological concept of genome trees based on protein conservation profiles in multiple species. The basic idea in this derivation is that the multi-component "presence-absence" protein conservation profiles permit tracking of common evolutionary histories of genes across multiple genomes. We show that a significant reduction in informational redundancy is achieved by considering only the subset of distinct conservation profiles. Beyond these basic ideas, we point out various pitfalls and limitations associated with the data handling, paving the way for further improvements. As an illustration for the methods, we analyze a genome tree based on the above principles, along with a series of other trees derived from the same data and based on pair-wise comparisons (ancestral duplication-conservation and shared orthologs). In all trees we observe a sharp discrimination between the three primary domains of life: Bacteria, Archaea, and Eukarya. The new genome tree, based on conservation profiles, displays a significant correspondence with classically recognized taxonomical groupings, along with a series of departures from such conventional clusterings.     

Large scale gene expression analysis of cholesterol-loaded macrophages

We conducted large scale gene expression analysis of the response of macrophages to exposure to oxidized low density lipoprotein (Ox-LDL). Much of the vessel wall lesion of atherosclerosis is composed of macrophages that have become engorged with cholesterol. These resulting "foam cells" contribute to the progression of vascular disease through several pathways. As a potential model of foam cell formation, we treated THP-1 cells with 12-O-tetradecanoylphorbol 13-acetate to differentiate them into a macrophage-like phenotype and subsequently treated them with oxidized low density lipoprotein for various time periods. RNA from Ox-LDL treated and time-matched control untreated cells was hybridized to microarrays containing 9808 human genes. 268 genes were found to be at least 2-fold regulated at one or more time points. These regulation patterns were classified into seven clusters of expression profiles. The data is discussed in terms of the overall pattern of gene expression, the thematic classification of the responding genes, and the clustering of functional groups in distinct expression patterns. The magnitude and the temporal patterns of gene expression identified known and novel molecular components of the cellular response that are implicated in the growth, survival, migratory, inflammatory, and matrix remodeling activity of vessel wall macrophages. In particular, the role of nuclear receptors in mediating the gene expression modulation by Ox-LDL is highlighted.