Prediction of drug efficacy for cancer treatment based on comparative analysis of chemosensitivity and gene expression data

The NCI60 database is the largest available collection of compounds with measured anti-cancer activity. The strengths and limitations for using the NCI60 database as a source of new anti-cancer agents are explored and discussed in relation to previous studies. We selected a sub-set of 2333 compounds with reliable experimental half maximum growth inhibitions (GI(50)) values for 30 cell lines from the NCI60 data set and evaluated their growth inhibitory effect (chemosensitivity) with respect to tissue of origin. This was done by identifying natural clusters in the chemosensitivity data set and in a data set of expression profiles of 1901 genes for the corresponding tumor cell lines. Five clusters were identified based on the gene expression data using self-organizing maps (SOM), comprising leukemia, melanoma, ovarian and prostate, basal breast, and luminal breast cancer cells, respectively. The strong difference in gene expression between basal and luminal breast cancer cells was reflected clearly in the chemosensitivity data. Although most compounds in the data set were of low potency, high efficacy compounds that showed specificity with respect to tissue of origin could be found. Furthermore, eight potential topoisomerase II inhibitors were identified using a structural similarity search. Finally, a set of genes with expression profiles that were significantly correlated with anti-cancer drug activity was identified. Our study demonstrates that the combined data sets, which provide comprehensive information on drug activity and gene expression profiles of tumor cell lines studied, are useful for identifying potential new active compounds.     

Epigenetic regulation of genes during development： A conserved theme from flies to mammals

Eukaryotic genome is organized in form of chromatin within the nucleus. This organization is important for compaction of DNA as well as for the proper expression of the genes. During early embryonic development, genomic packaging receives variety of signals to eventually set up cell type specific expression patterns of genes. This process of regulated chromatinization leads to "cell type specific epigenomes". The expression states attained during differentiation process need to be maintained subsequently throughout the life of the organism. Epigenetie modifications are responsible for chromatin dependent regulatory mechanism and play a key role in maintenance of the expression state-a process referred to as cellular memory. Another key feature in the packaging of the genome is formation of chro- matin domains that are thought to be structural as well as functional units of the higher order chromatin organization. Boundary elements that function to define such domains set the limits of regulatory elements and that of epigenetie modifications. This connection of epige- netic modification, chromatin structure and genome organization has emerged from several studies. Hox genes are among the best studied in this context and have led to the significant understanding of the epigenetic regulation during development. Here we discuss the evolu- tionarily conserved features of epigenetic mechanisms emerged from studies on homeotic gene clusters.     

Match/X,A Gene Expression Pattern Recognition Algorithm Used to Identify Genes Which May be Related to CDC2 Function and Cell Cycle Regulation

Large-scale microarray gene expression studies can provide insight into complex genetic networks and biological pathways. A comprehensive gene expression database was constructed using Affymetrix GeneChip microarrays and RNA isolated from more than 6,400 distinct normal and diseased human tissues. These individual patient samples were grouped into over 700 sample sets based on common tissue and disease morphologies, and each set contained averaged expression data for over 45,000 gene probe sets representing more than 33,000 known human genes. Sample sets were compared to each other in more than 750 normal vs. disease pairwise comparisons. Relative up or down-regulation patterns of genes across these pairwise comparisons provided unique expression fingerprints that could be compared and matched to a gene of interest using the Match/X algorithm. This algorithm uses the kappa statistic to compute correlations between genes and calculate a distance score between a gene of interest and all other genes in the database. Using cdc2 as a query gene, we identified several hundred genes that had similar expression patterns and highly correlated distance scores. Most of these genes were known components of the cell cycle involved in G2/M progression, spindle function or chromosome arrangement. Some of the identified genes had unknown biological functions but may be related to cdc2 mediated mechanism based on their closely correlated distance scores. This algorithm may provide novel insights into unknown gene function based on correlation to expression profiles of known genes and can identify elements of cellular pathways and gene interactions in a high throughput fashion.     

Beyond C4: Analysis of the complement gene pathway shows enrichment for IQ in patients with psychotic disorders and healthy controls

Variation in cognitive performance, which strongly predicts functional outcome in schizophrenia (SZ), has been associated with multiple immune‐relevant genetic loci. These loci include complement component 4 (C4A), structural variation at which was recently associated with SZ risk and synaptic pruning during neurodevelopment and cognitive function. Here, we test whether this genetic association with cognition and SZ risk is specific to C4A, or extends more broadly to genes related to the complement system. Using a gene‐set with an identified role in “complement” function (excluding C4A), we used MAGMA to test if this gene‐set was enriched for genes associated with human intelligence and SZ risk, using genome‐wide association summary statistics (IQ; N = 269 867, SZ; N = 105 318). We followed up this gene‐set analysis with a complement gene‐set polygenic score (PGS) regression analysis in an independent data set of patients with psychotic disorders and healthy participants with cognitive and genomic data (N = 1000). Enrichment analysis suggested that genes within the complement pathway were significantly enriched for genes associated with IQ, but not SZ. In a gene‐based analysis of 90 genes, SERPING1 was the most enriched gene for the phenotype of IQ. In a PGS regression analysis, we found that a complement pathway PGS associated with IQ genome‐wide association studies statistics also predicted variation in IQ in our independent sample. This association (observed across both patients and controls) remained significant after controlling for the relationship between C4A and cognition. These results suggest a robust association between the complement system and cognitive function, extending beyond structural variation at C4A.     

Meta-Analysis of Large-Scale Toxicogenomic Data Finds Neuronal Regeneration Related Protein and Cathepsin D to Be Novel Biomarkers of Drug-Induced Toxicity

Undesirable toxicity is one of the main reasons for withdrawing drugs from the market or eliminating them as candidates in clinical trials. Although numerous studies have attempted to identify biomarkers capable of predicting pharmacotoxicity, few have attempted to discover robust biomarkers that are coherent across various species and experimental settings. To identify such biomarkers, we conducted meta-analyses of massive gene expression profiles for 6,567 in vivo rat samples and 453 compounds. After applying rigorous feature reduction procedures, our analyses identified 18 genes to be related with toxicity upon comparisons of untreated versus treated and innocuous versus toxic specimens of kidney, liver and heart tissue. We then independently validated these genes in human cell lines. In doing so, we found several of these genes to be coherently regulated in both in vivo rat specimens and in human cell lines. Specifically, mRNA expression of neuronal regeneration-related protein was robustly down-regulated in both liver and kidney cells, while mRNA expression of cathepsin D was commonly up-regulated in liver cells after exposure to toxic concentrations of chemical compounds. Use of these novel toxicity biomarkers may enhance the efficiency of screening for safe lead compounds in early-phase drug development prior to animal testing.     

Accurate cancer classification using expressions of very few genes

We aim at finding the smallest set of genes that can ensure highly accurate classification of cancers from microarray data by using supervised machine learning algorithms. The significance of finding the minimum gene subsets is three-fold: 1) it greatly reduces the computational burden and "noise" arising from irrelevant genes. In the examples studied in this paper, finding the minimum gene subsets even allows for extraction of simple diagnostic rules which lead to accurate diagnosis without the need for any classifiers, 2) it simplifies gene expression tests to include only a very small number of genes rather than thousands of genes, which can bring down the cost for cancer testing significantly, 3) it calls for further investigation into the possible biological relationship between these small numbers of genes and cancer development and treatment. Our simple yet very effective method involves two steps. In the first step, we choose some important genes using a feature importance ranking scheme. In the second step, we test the classification capability of all simple combinations of those important genes by using a good classifier. For three "small" and "simple" data sets with two, three, and four cancer (sub)types, our approach obtained very high accuracy with only two or three genes. For a "large" and "complex" data set with 14 cancer types, we divided the whole problem into a group of binary classification problems and applied the 2-step approach to each of these binary classification problems. Through this "divide-and-conquer" approach, we obtained accuracy comparable to previously reported results but with only 28 genes rather than 16,063 genes. In general, our method can significantly reduce the number of genes required for highly reliable diagnosis     

Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network

The vast majority (>95%) of single-gene mutations in yeast affect not only the expression of the mutant gene, but also the expression of many other genes. These data suggest the presence of a previously uncharacterized "gene expression network"--a set of interactions between genes which dictate gene expression in the native cell environment. Here, we quantitatively analyze the gene expression network revealed by microarray expression data from 273 different yeast gene deletion mutants.(1) We find that gene expression interactions form a robust, error-tolerant "scale-free" network, similar to metabolic pathways(2) and artificial networks such as power grids and the internet.(3-5) Because the connectivity between genes in the gene expression network is unevenly distributed, a scale-free organization helps make organisms resistant to the deleterious effects of mutation, and is thus highly adaptive. The existence of a gene expression network poses practical considerations for the study of gene function, since most mutant phenotypes are the result of changes in the expression of many genes. Using principles of scale-free network topology, we propose that fragmenting the gene expression network via "genome-engineering" may be a viable and practical approach to isolating gene function.     

Pre-clinical drug prioritization via prognosis-guided genetic interaction networks

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call 'synergistic outcome determination' (SOD), a concept similar to 'Synthetic Lethality'. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.     

Selecting informative genes for discriminant analysis using multigene expression profiles

Background

Gene expression data extracted from microarray experiments have been used to study the difference between mRNA abundance of genes under different conditions. In one of such experiments, thousands of genes are measured simultaneously, which provides a high-dimensional feature space for discriminating between different sample classes. However, most of these dimensions are not informative about the between-class difference, and add noises to the discriminant analysis.

Results

In this paper we propose and study feature selection methods that evaluate the "informativeness" of a set of genes. Two measures of information based on multigene expression profiles are considered for a backward information-driven screening approach for selecting important gene features. By considering multigene expression profiles, we are able to utilize interaction information among these genes. Using a breast cancer data, we illustrate our methods and compare them to the performance of existing methods.

Conclusion

We illustrate in this paper that methods considering gene-gene interactions have better classification power in gene expression analysis. In our results, we identify important genes with relative large p-values from single gene tests. This indicates that these are genes with weak marginal information but strong interaction information, which will be overlooked by strategies that only examine individual genes.

     

Relationships between gene expression and brain wiring in the adult rodent brain

We studied the global relationship between gene expression and neuroanatomical connectivity in the adult rodent brain. We utilized a large data set of the rat brain "connectome" from the Brain Architecture Management System (942 brain regions and over 5000 connections) and used statistical approaches to relate the data to the gene expression signatures of 17,530 genes in 142 anatomical regions from the Allen Brain Atlas. Our analysis shows that adult gene expression signatures have a statistically significant relationship to connectivity. In particular, brain regions that have similar expression profiles tend to have similar connectivity profiles, and this effect is not entirely attributable to spatial correlations. In addition, brain regions which are connected have more similar expression patterns. Using a simple optimization approach, we identified a set of genes most correlated with neuroanatomical connectivity, and find that this set is enriched for genes involved in neuronal development and axon guidance. A number of the genes have been implicated in neurodevelopmental disorders such as autistic spectrum disorder. Our results have the potential to shed light on the role of gene expression patterns in influencing neuronal activity and connectivity, with potential applications to our understanding of brain disorders. Supplementary data are available at http://www.chibi.ubc.ca/ABAMS.     

The entire nogalamycin biosynthetic gene cluster of Streptomyces nogalater: characterization of a 20-kb DNA region and generation of hybrid structures

Fragments spanning 20 kb of Streptomyces nogalater genomic DNA were characterized to elucidate the molecular genetic basis of the biosynthetic pathway of the anthracycline antibiotic nogalamycin. Structural analysis of the products obtained by expression of the fragments in S. galilaeus and S. peucetius mutants producing aclacinomycin and daunomycin metabolites, respectively, revealed hybrid compounds in which either the aglycone or the sugar moiety was modified. Subsequent sequence analysis revealed twenty ORFs involved in nogalamycin biosynthesis, of which eleven could be assigned to the deoxysugar pathway, four to aglycone biosynthesis, while the remaining five express products with unknown function. On the basis of sequence similarity and experimental data, the functions of the products of the newly discovered genes were determined. The results suggest that the entire biosynthetic gene cluster for nogalamycin is now known. Furthermore, the compounds obtained by heterologous expression of the genes show that it is possible to use the genes in combinatorial biosynthesis to create novel chemical structures for drug screening purposes.     

Gene expression profiling and its practice in drug development

The availability of sequenced genomes of human and many experimental animals necessitated the development of new technologies and powerful computational tools that are capable of exploiting these genomic data and ask intriguing questions about complex nature of biological processes. This gave impetus for developing whole genome approaches that can produce functional information of genes in the form of expression profiles and unscramble the relationships between variation in gene expression and the resulting physiological outcome. These profiles represent genetic fingerprints or catalogue of genes that characterize the cell or tissue being studied and provide a basis from which to begin an investigation of the underlying biology. Among the most powerful and versatile tools are high-density DNA microarrays to analyze the expression patterns of large numbers of genes across different tissues or within the same tissue under a variety of experimental conditions or even between species. The wide spread use of microarray technologies is generating large sets of data that is stimulating the development of better analytical tools so that functions can be predicted for novel genes. In this review, the authors discuss how these profiles are being used at various stages of the drug discovery process and help in the identification of new drug targets, predict the function of novel genes, and understand individual variability in response to drugs.     

Identification of selective estrogen receptor modulators by their gene expression fingerprints

Clinical studies have shown that estrogen replacement therapy (ERT) reduces the incidence and severity of osteoporosis and cardiovascular disease in postmenopausal women. However, long term estrogen treatment also increases the risk of endometrial and breast cancer. The selective estrogen receptor (ER) modulators (SERMs) tamoxifen and raloxifene, cause antagonistic and agonistic responses when bound to the ER. Their predominantly antagonistic actions in the mammary gland form the rationale for their therapeutic utility in estrogen-responsive breast cancer, while their agonistic estrogen-like effects in bone and the cardiovascular system make them candidates for ERT regimens. Of these two SERMs, raloxifene is preferred because it has markedly less uterine-stimulatory activity than either estrogen or tamoxifen. To identify additional SERMs, a method to classify compounds based on differential gene expression modulation was developed. By analysis of 24 different combinations of genes and cells, a selected set of assays that permitted discrimination between estrogen, tamoxifen, raloxifene, and the pure ER antagonist ICI164384 was generated. This assay panel was employed to measure the activity of 38 compounds, and the gene expression fingerprints (GEFs) obtained for each compound were used to classify all compounds into eight groups. The compound's GEF predicted its uterine-stimulatory activity. One group of compounds was evaluated for activity in attenuating bone loss in ovariectomized rats. Most compounds with similar GEFs had similar in vivo activities, thereby suggesting that GEF-based screens could be useful in predicting a compound's in vivo pharmacological profile.