Report of the National Oncology Research and Developement Consortium, 2003

Consorcial projects focused on 5 cancer types, breast-, colorectal-, head and neck- and pediatric cancers, and malignant melanoma. Breast cancer studies revealed unique splicing mechanisms concerning BRCA1. In sporadic breast cancers the involvement of DNA-repair genes was proved to be dependent on the histological type. Bone-metastatic tumors have been characterized by decreased NM23 and increased c-met and p53 expressions. C-erbB2 genotype of the primary tumor was not maintained frequently in bone metastases. Application of DNA-microarray and quantitative PCR technologies improved the prediction of therapeutic sensitivity of breast cancers. Colorectal cancer studies revealed regional inhomogenities (clusters) in various geographical regions of Hungary, which were distinct in the case of colonic and rectal cancers. To increase the sensitivity of fecal blood test of colorectal cancer screening, a new double-antibody test was developed and tested in a large cohort of patients. Genetic analysis revealed that hypermethylation is a significant factor in microsatellite instability which, and plays a role in silencing of APC and E-cadherin genes as well. The Hungarian pattern of TS polymorphism was also determined and was correlated not only with the efficacy of 5-FU treatment but with the progression of the disease as well. Population-based studies have been carried out in head and neck cancer patients (HNC) and smokers as well to reveal the genetic background of increasing tumor incidence. These studies revealed polymorphism in XRCC1/3 methylation enzyme gene which has preventive role. Other studies found frequent local immunosuppression in HNC patients. Studies indicated that the success of irradiation in this cancer type is dependent on the anti-vascular effects. Pediatric cancer studies determined the parameters of neuroblastoma screening based on VMA measurements. New splice variants of the WT1 gene involved in the monitoring of MRD of ALL patients was also described this year. We also obtained positive experimental data for the retinoic acid therapy of ALL. Melanoma studies extensively used DNA-microarray technology which identified 4 melanoma-specific and 2 melanoma progression-specific genes. In experimental human melanoma xenograft models we have identified 3 anti-metastatic agents: low molecular weight heparin, 2-methoxyestradiol and erythropoietin-alpha, where the later was characterized by specific effects on tumor vasculature.     

Classifying human cancer by analysis of gene expression

With the development and application of DNA microarrays, the expression of almost all human genes can now be systematically examined in human malignancies. This can lead to the identification of candidate molecular targets for therapeutic intervention and biomarkers for early detection of these diseases. However, perhaps the most exciting result to come from this research has been the demonstration that patterns of gene expression can distinguish between tumors of different anatomical origin, and define new subgroups of cancer with similar histological appearance, but distinct molecular profiles. Some of these new molecular subclasses of tumor appear to correlate with clinical behavior. If substantiated in larger studies, this might form a basis for stratifying patients so that they receive optimal therapeutic treatment and follow-up.     

Identifying splits with clear separation: a new class discovery method for gene expression data

We present a new class discovery method for microarray gene expression data. Based on a collection of gene expression profiles from different tissue samples, the method searches for binary class distinctions in the set of samples that show clear separation in the expression levels of specific subsets of genes. Several mutually independent class distinctions may be found, which is difficult to obtain from most commonly used clustering algorithms. Each class distinction can be biologically interpreted in terms of its supporting genes. The mathematical characterization of the favored class distinctions is based on statistical concepts. By analyzing three data sets from cancer gene expression studies, we demonstrate that our method is able to detect biologically relevant structures, for example cancer subtypes, in an unsupervised fashion.     

Inferring pathway activity toward precise disease classification

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease.     

DNA-Microarray Technology: Comparison of Methodological Factors of Recent Technique Towards Gene Expression Profiling

DNA-microarray technology is a powerful tool to explore the potentials of functional genomics. Almost a decade back, it was not so easy to trace the molecular pathway process in the pathogenesis of diseases. Today DNA-microarray technology allows identifying candidate genes that are implicated in the development and progression of diseases. Because this technology is new, it remains a challenge to perform DNA-microarray experiments in the laboratory. All the technical factors associated with DNA-microarray experiments have a strong impact on the results and therefore, all parts/steps of the protocol, in particular sample preparation (i.e., RNA isolation, RNA preamplification, and cDNA labeling), hybridization, washing, and scanning settings need to be checked and optimized to obtain reliable results from DNA-microarray experiments.     

Potential Therapeutic Targets for Oral Cancer: ADM,TP53, EGFR,LYN, CTLA4, SKIL,CTGF, CD70

In India, oral cancer has consistently ranked among top three causes of cancer-related deaths, and it has emerged as a top cause for the cancer-related deaths among men. Lack of effective therapeutic options is one of the main challenges in clinical management of oral cancer patients. We interrogated large pool of samples from oral cancer gene expression studies to identify potential therapeutic targets that are involved in multiple cancer hallmark events. Therapeutic strategies directed towards such targets can be expected to effectively control cancer cells. Datasets from different gene expression studies were integrated by removing batch-effects and was used for downstream analyses, including differential expression analysis. Dependency network analysis was done to identify genes that undergo marked topological changes in oral cancer samples when compared with control samples. Causal reasoning analysis was carried out to identify significant hypotheses, which can explain gene expression profiles observed in oral cancer samples. Text-mining based approach was used to detect cancer hallmarks associated with genes significantly expressed in oral cancer. In all, 2365 genes were detected to be differentially expressed genes, which includes some of the highly differentially expressed genes like matrix metalloproteinases (MMP-1/3/10/13), chemokine (C-X-C motif) ligands (IL8, CXCL-10/-11), PTHLH, SERPINE1, NELL2, S100A7A, MAL, CRNN, TGM3, CLCA4, keratins (KRT-3/4/13/76/78), SERPINB11 and serine peptidase inhibitors (SPINK-5/7). XIST, TCEAL2, NRAS and FGFR2 are some of the important genes detected by dependency and causal network analysis. Literature mining analysis annotated 1014 genes, out of which 841 genes were statistically significantly annotated. The integration of output of various analyses, resulted in the list of potential therapeutic targets for oral cancer, which included targets such as ADM, TP53, EGFR, LYN, CTLA4, SKIL, CTGF and CD70.     

Quantitative expression profile of distinct functional regions in the adult mouse brain

The adult mammalian brain is composed of distinct regions with specialized roles including regulation of circadian clocks, feeding, sleep/awake, and seasonal rhythms. To find quantitative differences of expression among such various brain regions, we conducted the BrainStars (B*) project, in which we profiled the genome-wide expression of ～50 small brain regions, including sensory centers, and centers for motion, time, memory, fear, and feeding. To avoid confounds from temporal differences in gene expression, we sampled each region every 4 hours for 24 hours, and pooled the samples for DNA-microarray assays. Therefore, we focused on spatial differences in gene expression. We used informatics to identify candidate genes with expression changes showing high or low expression in specific regions. We also identified candidate genes with stable expression across brain regions that can be used as new internal control genes, and ligand-receptor interactions of neurohormones and neurotransmitters. Through these analyses, we found 8,159 multi-state genes, 2,212 regional marker gene candidates for 44 small brain regions, 915 internal control gene candidates, and 23,864 inferred ligand-receptor interactions. We also found that these sets include well-known genes as well as novel candidate genes that might be related to specific functions in brain regions. We used our findings to develop an integrated database (http://brainstars.org/) for exploring genome-wide expression in the adult mouse brain, and have made this database openly accessible. These new resources will help accelerate the functional analysis of the mammalian brain and the elucidation of its regulatory network systems.     

生信分析筛选心衰氧化应激相关关键[]基因并预测候选药物

为探究氧化应激相关基因在心力衰竭发生发展中的作用,并发现核心基因进行靶基因药物预测。从GEO数据库下载GSE120895基因表达图谱,通过GEO2R筛选差异表达基因,将差异表达基因与GeneCard数据库中筛选的氧化应激相关基因取交集,得到心力衰竭氧化应激相关差异表达基因,利用R软件对差异表达基因进行GO及KEGG分析,利用Cytoscape进行PPI网络的模块以及关键基因的筛选。之后在GSE17800基因表达图谱中验证关键基因的表达,并针对关键基因进行相互作用药物预测。差异表达基因与氧化应激相关基因取交集后,共筛选出52个上调的氧化应激相关差异表达基因,在此基础上,筛选出ACTB,STAT3,FN1,EDN1,CAT共5个关键基因,在GSE17800基因表达图谱中验证后,针对4个关键基因预测了19个靶基因潜在药物。总之,本研究通过生物信息学方法鉴定关键基因,并预测潜在治疗药物,从而为了解心力衰竭的分子机制及其诊治方法提供新的见解。     

Multidimensional analysis and functional assignment of DNA-microarray transcription profiles of genes involved in adipogenesis

The results of two independent DNA-microarray experiments concerning adipogenesis in the murine preadipocyte 3T3-L1 cell line, which covered the first two days after the induction of differentiation, were analyzed using the multidimensional scaling (MDS) method. In both data arrays, the first three scaling components accounted for 73.5–73.8% of the total dispersion. This result implies that both arrays of the gene expression profiles are in fact three-dimensional and each component reflects a definite principal process involved in one of the three early stages of adipogenesis: (i) determination of the fibroblast-like stem cells, (ii) clonal expansion of adipoblasts, and (iii) preadipocyte conversion into a mature adipocyte phenotype. Each profile of the gene expression is characterized by coefficients of correlation with the first three scaling components. The functional annotation in terms of the Gene Ontology database profiles (sorted according to the correlations with each component) generally corresponds to a regular change of elementary biological processes during the three early stages of adipogenesis. Analysis of correlations with the principal scaling components for the genes previously classified as subject to differential expression in the course of adipogenesis in mice suggests a complicated role of these genes in early adipogenesis (in some cases, described in the literature). The MDS analysis of the gene expression profiles and the analysis of correlations between these profiles and the main scaling components provides a deeper insight into the fine role of these genes and makes possible the search for new biomarkers of various differentiation stages.

     

Gene expression signature of human HepG2 cell line

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide and is associated with various clinico-pathological characteristics such as genetic mutations and viral infections. Therefore, numerous laboratories look out for identifying always new putative markers for the improvement of HCC diagnosis/prognosis. Many molecular profiling studies investigated gene expression changes related to HCC. HepG2 represents a pure cell line of human liver carcinoma, often used as HCC model due to the absence of viral infection. In this study we compare gene expression profiles associated with HepG2 (as HCC model) and normal hepatocyte cells by microarray technology. Hierarchical cluster analysis of genes evidenced that 2646 genes significantly down-regulated in HepG2 cells compared to hepatocytes whereas a further 3586 genes significantly up-regulated. By using the Ingenuity Pathway Analysis (IPA) program, we have classified the genes that were differently expressed and studied the functional networks correlating these genes in the complete human interactome. Moreover, to confirm the differentially expressed genes as well as the reliability of our microarray data, we performed a quantitative Real time RT-PCR analysis on 9 up-regulated and 11 down-regulated genes, respectively. In conclusion this work i) provides a gene signature of human hepatoma cells showing genes that change their expression as a consequence of liver cancer in the absence of any genetic mutations or viral infection, ii) evidences new differently expressed genes found in our signature compared to previous published studies and iii) suggests some genes on which to focus future studies to understand if they can be used to improve the HCC prognosis/diagnosis.     

Differential gene expression analysis by DNA microarrays technology and its application in molecular oncology

Accumulation of genetic and epigenetic aberrations leads to malignant transformation of normal cells. Functional studies of cancer using genomic and proteomic tools will help to reveal the true complexity of the processes leading to cancer development in humans. Until recently, diagnosis and prognosis of cancer was based on conventional pathologic criteria and epidemiological evidence. Certain tumors were divided only into relatively broad histological and morphological subcategories. Rapidly developing methods of differential gene expression analysis promote the search for clinically relevant genes changing their expression levels during malignant transformation. DNA microarrays offer a unique possibility to rapidly assess the global expression picture of thousands genes in any given time point and compare the detailed combinatory analysis results of global expression profiles for normal and malignant cells at various functional stages or separate experimental conditions. Acquisition of such "genetic portraits" allows searching for regularity and difference in expression patterns of certain genes, understanding their function and pathological importance, and ultimately developing the "molecular nosology" of cancer. This review describes the basis of DNA microarray technology and methodology, and focuses on their applications in molecular classification of tumors, drug sensitivity and resistance studies, and identification of biological markers of cancer.     

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.     

Identification of retinoblastoma related genes with shortest path in a protein-protein interaction network

This paper presents a new method for identifying retinoblastoma related genes by integrating gene expression profile and shortest path in a functional linkage graph. With the existing protein-protein interaction data from STRING, a weighted functional linkage graph is constructed. 119 consistently differentially expressed genes between retinoblastoma and normal retina were obtained from the overlap of two gene expression studies of retinoblastoma. Then the shortest paths between each pair of these 119 genes were determined with Dijkstra's algorithm. Finally, all the genes present on the shortest paths were extracted and ranked according to their betweenness and the 119 shortest genes with a betweenness greater than 100 and with a p-value less than 0.05 were selected for further analysis. We also identified 53 retinoblastoma related miRNAs from published miRNA array data and most of the 238 (119 consistently differentially expressed genes and 119 shortest path genes) retinoblastoma genes were shown to be target genes of these 53 miRNAs. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network included more cancer genes than did the genes identified from the gene expression profiles alone. In addition, these genes also had greater functional similarity to the reported cancer genes than did the genes identified from the gene expression profiles alone. This study shows promising results and proves the efficiency of the proposed methods.     

Monitoring cellular responses to Listeria monocytogenes with oligonucleotide arrays

Listeria monocytogenes is a pathogenic intracellular microorganism whose infection induces pleiotropic biological changes associated with host cell gene expression regulation. Here we define the gene expression profiles of the human promyelocytic THP1 cell line before and after L. monocytogenes infection. Gene expression was measured on a large scale via oligonucleotide microarrays with probe sets corresponding to 6,800 human genes. We assessed and discussed the reproducibility of the hybridization signatures. In addition to oligonucleotide arrays, we also performed the large scale gene expression measurement with two high-density membranes, assaying for 588 and 18,376 human genes, respectively. This work allowed the reproducible identification of 74 up-regulated RNAs and 23 down-regulated RNAs as a consequence of L. monocytogenes infection of THP1. The reliability of these data was reinforced by performing independent infections. Some of these detected RNAs were consistent with previous results, while some newly identified RNAs encode gene products that may play key roles in L. monocytogenes infection. These findings will undoubtedly enhance the understanding of L. monocytogenes molecular physiology and may help identify new therapeutic targets.     

Identification of gene expression signature modulated by nicotinamide in a mouse bladder cancer model

Background

Urinary bladder cancer is often a result of exposure to chemical carcinogens such as cigarette smoking. Because of histological similarity, chemically-induced rodent cancer model was largely used for human bladder cancer studies. Previous investigations have suggested that nicotinamide, water-soluble vitamin B3, may play a key role in cancer prevention through its activities in cellular repair. However, to date, evidence towards identifying the genetic alterations of nicotinamide in cancer prevention has not been provided. Here, we search for the molecular signatures of cancer prevention by nicotinamide using a N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN)-induced urinary bladder cancer model in mice.

Methodology/Principal Findings

Via microarray gene expression profiling of 20 mice and 233 human bladder samples, we performed various statistical analyses and immunohistochemical staining for validation. The expression patterns of 893 genes associated with nicotinamide activity in cancer prevention were identified by microarray data analysis. Gene network analyses of these 893 genes revealed that the Myc and its associated genes may be the most important regulator of bladder cancer prevention, and the gene expression signature correlated well with protein expression data. Comparison of gene expression between human and mouse revealed that BBN-induced mouse bladder cancers exhibited gene expression profiles that were more similar to those of invasive human bladder cancers than to those of non-invasive human bladder cancers.

Conclusions/Significance

This study demonstrates that nicotinamide plays an important role as a chemo-preventive and therapeutic agent in bladder cancer through the regulation of the Myc oncogenic signature. Nicotinamide may represent a promising therapeutic modality in patients with muscle-invasive bladder cancer.     

Aberrant expression of N-acetylglucosaminyltransferase-IVa and IVb (GnT-IVa and b) in pancreatic cancer

The goal of this study was to identify glycosyltransferases that are specifically expressed in pancreatic cancer. To investigate the gene expression of glycosyltransferases between pancreatic cancer and normal pancreatic tissues, we performed DNA-microarray (involving about 1000 oligosaccharide-related genes) using RNA mixtures of pancreatic cancer cells and normal pancreatic tissues. Eighty-six genes were up-regulated and thirty-two were down-regulated in pancreatic cancer, compared to normal pancreatic tissue. Among these changes, it is noteworthy that the expression of GnT-IVa was decreased and the expression of GnT-IVb was increased in pancreatic cancer, compared to normal pancreatic tissues. Although GnT-IVa and -IVb are involved in the same reaction as a glycosyltransferase, their chromosomal localization is different. When 5 cases of pancreatic cancer tissues were examined using the real-time RT-PCR method, the expression of GnT-IVb was dominant in tumor tissues and the expression of GnT-IVa was dominant in the surrounding normal tissues. The expression of GnT-IVa was increased in all 3 cell lines that had been treated with 5-aza-C and butyrate. These results suggest that the down-regulation of GnT-IVa in pancreatic cancer cells is due to an epigenetic abnormality in the gene.     

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.