Molecular Profiling Data Research Articles

Abstract 3525: Molecular determinants of the anticancer efficacy of indenoisoquinolines

Abstract Topoisomerase I (top1) is a validated target for the anticancer alkaloid camptothecin and its analogs (CPTs). CPTs are the only class of topoisomerase I (top1) inhibitors approved for cancer treatment in clinic. However, CPTs have pharmacologic and clinical limitations. This leads to the development of noncamptothecins, such as indenoisoquinolines (INDs), as novel top1 inhibitors. Currently three INDs have been selected as leads for clinical development by the National Cancer Institute [1]. INDs are endowed with favorable characteristics. Persistence of top1 cleavage complexes induced by INDs is higher than that of induced by CPTs. The top1 cleavage sites trapped by the INDs have different genomic locations, implying differential targeting of cancer cell genomes. Presently, we demonstrate that molecular dynamics and bioinformatics are valuable tools for the study of molecular determinants of anticancer drugs. Our previous molecular dynamics studies revealed that the most persistent CPTs have higher calculated free-energy barriers for drug dissociation from the flanking base pairs [2]. We have recently extended our studies to INDs. We found the INDs have higher calculated free-energy barriers when compared with that of CPTs. This may explain why INDs experimentally produce more persistent top1 cleavage complexes when compared with that of CPTs [3]. The National Cancer Institute (NCI)-60 panel consists of 60 human tumor cell lines established for screening anticancer efficacy of a larger number of molecules, including INDs. To deciphering the relationships between anticancer efficacy of INDs and the characteristics of the cancer cells, the molecular profile data on the NCI-60 were downloaded from the CellMiner database [4]. Integromic and bioinformatics analyses were carried out on the markers that correlated with the responses of INDs-treatments. Same and unique markers, when compared to that of CPTs, were identified for INDs. Further studies are required for the validation of these predictive markers.

CellMiner: a relational database and query tool for the NCI-60 cancer cell lines

BackgroundAdvances in the high-throughput omic technologies have made it possible to profile cells in a large number of ways at the DNA, RNA, protein, chromosomal, functional, and pharmacological levels. A persistent problem is that some classes of molecular data are labeled with gene identifiers, others with transcript or protein identifiers, and still others with chromosomal locations. What has lagged behind is the ability to integrate the resulting data to uncover complex relationships and patterns. Those issues are reflected in full form by molecular profile data on the panel of 60 diverse human cancer cell lines (the NCI-60) used since 1990 by the U.S. National Cancer Institute to screen compounds for anticancer activity. To our knowledge, CellMiner is the first online database resource for integration of the diverse molecular types of NCI-60 and related meta data.DescriptionCellMiner enables scientists to perform advanced querying of molecular information on NCI-60 (and additional types) through a single web interface. CellMiner is a freely available tool that organizes and stores raw and normalized data that represent multiple types of molecular characterizations at the DNA, RNA, protein, and pharmacological levels. Annotations for each project, along with associated metadata on the samples and datasets, are stored in a MySQL database and linked to the molecular profile data. Data can be queried and downloaded along with comprehensive information on experimental and analytic methods for each data set. A Data Intersection tool allows selection of a list of genes (proteins) in common between two or more data sets and outputs the data for those genes (proteins) in the respective sets. In addition to its role as an integrative resource for the NCI-60, the CellMiner package also serves as a shell for incorporation of molecular profile data on other cell or tissue sample types.ConclusionCellMiner is a relational database tool for storing, querying, integrating, and downloading molecular profile data on the NCI-60 and other cancer cell types. More broadly, it provides a template to use in providing such functionality for other molecular profile data generated by academic institutions, public projects, or the private sector. CellMiner is available online at .

Dynamic network topology changes in functional modules predict responses to oxidative stress in yeast

In response to environmental challenges, biological systems respond with dynamic adaptive changes in order to maintain the functionality of the system. Such adaptations may lead to cumulative stress over time, possibly leading to global failure of the system. When studying such systems responses, it is therefore important to understand them in system-wide and dynamic context. Here we hypothesize that dynamic changes in the topology of functional modules of integrated biological networks reflect their activity under specific environmental challenges. We introduce topological enrichment analysis of functional subnetworks (TEAFS), a method for the analysis of integrated molecular profile and interactome data, which we validated by comprehensive metabolomic analysis of dynamic yeast response under oxidative stress. TEAFS identified activation of multiple stress response related mechanisms, such as lipid metabolism and phospholipid biosynthesis. We identified, among others, a fatty acid elongase IFA38 as a hub protein which was absent at all time points under oxidative stress conditions. The deletion mutant of the IFA38 encoding gene is known for the accumulation of ceramides. By applying a comprehensive metabolomic analysis, we confirmed the increased concentrations over time of ceramides and palmitic acid, a precursor of de novo ceramide biosynthesis. Our results imply that the connectivity of the system is being dynamically modulated in response to oxidative stress, progressively leading to the accumulation of (lipo)toxic lipids such as ceramides. Studies of local network topology dynamics can be used to investigate as well as predict the activity of biological processes and the system's responses to environmental challenges and interventions.

Gene expression profiling of non‐alcoholic steatohepatitis using gene set enrichment analysis

Non-alcoholic steatohepatitis (NASH) is a subset of non-alcoholic fatty liver disease (NAFLD) and sometimes progresses to cirrhosis and liver failure. In this study we analyzed the expression profile of genes and biological pathways involved in NASH in comparison with non-NASH by gene set enrichment analysis (GSEA) employing a DNA microarray technique. mRNA from liver biopsy specimens was collected from a group of NASH patients and a group of non-NASH patients. We analyzed the relative abundance of mRNA using high-density oligonucleotide microarrays containing probes for 54 675 known genes, and investigated the pathogenetic mechanisms of NASH by means of a powerful technique for analyzing molecular profiling data, GSEA. The results showed that the level of expression of 27 gene sets was significantly higher and the level of expression of 25 gene sets was significantly lower in the NASH samples than in the non-NASH samples. Based on these results we created an online, publicly available, searchable database containing the data for the gene expression profiles of the NASH patients (http://www2.genome.rcast.u-tokyo.ac.jp/___/NASH/NASH_GSEA2/). Our data revealed differences in expression of many gene sets that are involved in the pathogenesis of NASH.

Future of Molecular Profiling of Human Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a fatal disease occurring worldwide and developing mainly in chronic liver diseased patients. Despite routine screening of individuals at high risk, most of the patients are diagnosed at late stages of HCC. In addition, the recurrence rate after surgical resection of small tumors is high. Molecular profiling, including expression analysis, comparative genomics and proteomics, provides powerful tools to gain insight into the molecular mechanisms underlying carcinogenesis. Advances in bioinformatics have also allowed for the evaluation of large data sets. Therefore, molecular profiling of HCC using a Biological Expression Network Discovery (BLEND) strategy that integrates global molecular profiling data, including mRNA, miRNA, DNA methylation and DNA copy numbers from both the tumor and the surrounding microenvironment, along with mechanistic studies, may improve the diagnosis, treatment and prognosis of HCC patients. Such an approach will provide mechanistic insight into the pathogenesis of HCC, potentially leading to personalized medicine and the identification of new therapeutic targets.

<i>In vitro</i> Simulation Study of Individualized Chemotherapy in Lung Cancer

The primary aim of this in vitro simulation study was to evaluate the utility of gene expression profile analysis in predicting the effect of varying drug combinations for the treatment of lung cancer. Using 10 human cancer cell lines, we focused our gene expression analysis on a cohort of candidate sensitivity-prediction factors, previously reported using cDNA filter arrays, with a view to predicting the ability of a set of anti-cancer drugs commonly used to treat lung cancer, namely cisplatin, 5-fluorouracil (5FU), SN38, docetaxel, gemcitabine, and vinorelbine. Altered expression of genes for glutathione-S-transferase-pi, uridine phosphorylase, O-6-methylguanine-DNA methyltransferase, and multidrug resistance 1 was identified in lung cancer cell lines. Drug sensitivity testing, in the form of methylthiotetrazol analysis, was performed using these six anti-cancer drugs against the panel of 10 lung cancer cell lines. We compared the predicted chemosensitivity based on the gene expression pattern of 19 well-known sensitivity-related genes with the cytotoxic activity of each of these anti-cancer drugs. Molecular profiling data predicted resistance to CDDP in LK-2 cells, 5FU in LK-2, PC7, A549, NCI-N231, Lu135 cells, irinitecan in PC9 cells, and VNR in PC7 cells. However, the prediction efficacy (number of predicted inactive drugs by gene expression analysis/number of inactive drugs by methylthiotetrazol assay) was 21.6% (8 of 37). No false-positive findings in relation to sensitivity-related genes were obtained on the basis of this molecular analysis. Thus, prediction of sensitivity to lung cancer by molecular analysis appears possible. With elucidation of additional drug sensitivity factors, selection of appropriate anticancer drugs by gene expression profiling may make it possible to increase the response rate in lung cancer chemotherapy.

Novel integrative genomics strategies to identify genes for complex traits

Forward genetics is a common approach to dissecting complex traits like common human diseases. The ultimate aim of this approach was the identification of genes that are causal for disease or other phenotypes of interest. However, the forward genetics approach is by definition restricted to the identification of genes that have incurred mutations over the course of evolution or that incurred mutations as a result of chemical mutagenesis, and that as a result lead to disease or to variations in other phenotypes of interest. Genes that harbour no such mutations, but that play key roles in parts of the biological network that lead to disease, are systematically missed by this class of approaches. Recently, a class of novel integrative genomics approaches has been devised to elucidate the complexity of common human diseases by intersecting genotypic, molecular profiling, and clinical data in segregating populations. These novel approaches take a more holistic view of biological systems and leverage the vast network of gene–gene interactions, in combination with DNA variation data, to establish causal relationships among molecular profiling traits and Fbetween molecular profiling and disease (or other classic phenotypes). A number of novel genes for disease phenotypes have been identified as a result of these approaches, highlighting the utility of integrating orthogonal sources of data to get at the underlying causes of disease.

Identification of genes linked to gefitinib treatment in prostate cancer cell lines with or without resistance to androgen: A clue to application of gefitinib to hormone-resistant prostate cancer

Understanding the molecular action of gefitinib, an epidermal growth factor receptor tyrosine kinase inhibitor, might allow us to perform more effective therapies for hormone-independent advanced prostate cancer. A DNA microarray study was undertaken to comprehensively analyze the alteration of levels of 1,081 genes after gefitinib treatment in androgen-independent PC3 and DU145 cells and androgen-dependent LNCaP cells. The proliferation of PC3, DU145 and LNCaP cells was significantly inhibited by 50.2%, 83.8% and 55.2%, respectively, 6 days after 10 microM gefitinib administration. Of the above 1,081 genes, we identified 23, 13 and 33 genes with significantly different expression in PC3, DU145 and LNCaP cells, respectively, 24 h after 10 microM-gefitinib exposure. Among the identified genes, only Quiescin Q6, a negative cell cycle regulator, was increased after gefitinib treatment in all three cell lines regardless of gefitinib sensitivity. Except for Quiescin Q6, there were no overlapping genes between PC3 and DU145 cells. However, levels of several oncogenes or proliferation-related genes were changed after gefitinib treatment in the 2 androgen-independent cell lines. We also identified 7 unique genes [glycyl-tRNA synthetase, interferon, alpha-inducible protein, stratifin, nuclear factor of kappa light polypeptide gene enhancer in B-cells 1, dual specificity phosphatase 9, guanine nucleotide binding protein (G protein) beta polypeptide 2, neural retina leucine zipper] whose levels were altered exclusively after gefitinib administration in gefitinib-resistant PC3 and LNCaP cells, but not in DU145 cells, suggesting that these 7 genes could be targets for overcoming gefitinib resistance. Collectively, our molecular profiling data will serve as a framework for understanding the molecular action of gefitinib for prostate cancer.

MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data

New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis. MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.

Exploiting naturally occurring DNA variation and molecular profiling data to dissect disease and drug response traits

Identifying the key drivers of common human diseases and associated signaling pathways remains one of the primary objectives in the biomedical and life sciences. In this respect, common inbred strains of mice have played a crucial role, and recent advances in the development of genomics and bioinformatics tools have significantly enhanced their utility for this purpose. These advances have enabled a more holistic, network-oriented view of biological systems that facilitates elucidation of the underlying causes of disease and the best ways to target them. Success in reconstructing gene networks underlying disease traits (or other complex traits like drug response) and identifying the key drivers of these traits now largely rests on integrative approaches that combine data from multiple different sources. Such integrative genomics approaches that take into account genotypic, molecular profiling and clinical data in segregating mouse populations have recently been developed. Key to this integration has been the development and application of sophisticated algorithms to mine the diversity of data.

Estrogen Receptor Analysis for Breast Cancer

The estrogen receptor (ER) is a regulator of cellular growth, proliferation, and differentiation. In addition to having prognostic value, ER is the most important biologic marker of therapeutic response in breast cancer. Some level of measurable ER protein is expressed in 70-80% of human breast. Immunohistochemistry is the current method of choice for ER assessment, and its predictive value has been shown to be superior to that of biochemically based assays. Although accurate ER protein assessment is critical for optimal treatment of patients with breast cancer, studies have demonstrated inter-laboratory variability in ER detection. False-negative results for tumors with low ER protein levels have been a subject of recent concern. Lack of standardization for immunohistochemistry between laboratories is thought to be the major reason for testing errors, although variability in scoring methods and reporting practices, which can affect results, also plays a role. In this article, we review studies addressing interlaboratory variability and recommend optimal testing techniques and reporting procedures for ER testing, with the goal of increasing interlaboratory standardization for ER analysis by immunohistochemistry. We additionally highlight recent biologic, molecular, and gene expression profiling data related to ER in breast cancer.

Text-based analysis of genes, proteins, aging, and cancer

The diverse nature of cancer- and aging-related genes presents a challenge for large-scale studies based on molecular sequence and profiling data. An underexplored source of data for modeling and analysis is the textual descriptions and annotations present in curated gene-centered biomedical corpora. Here, 450 genes designated by surveys of the scientific literature as being associated with cancer and aging were analyzed using two complementary approaches. The first, ensemble attribute profile clustering, is a recently formulated, text-based, semi-automated data interpretation strategy that exploits ideas from statistical information retrieval to discover and characterize groups of genes with common structural and functional properties. Groups of genes with shared and unique Gene Ontology terms and protein domains were defined and examined. Human homologs of a group of known Drosphila aging-related genes are candidates for genes that may influence lifespan ( hep/MAPK2K7, bsk/MAPK8, puc/LOC285193). These JNK pathway-associated proteins may specify a molecular hub that coordinates and integrates multiple intra- and extracellular processes via space- and time-dependent interactions with proteins in other pathways. The second approach, a qualitative examination of the chromosomal locations of 311 human cancer- and aging-related genes, provides anecdotal evidence for a “phenotype position effect”: genes that are proximal in the linear genome often encode proteins involved in the same phenomenon. Comparative genomics was employed to enhance understanding of several genes, including open reading frames, identified as new candidates for genes with roles in aging or cancer. Overall, the results highlight fundamental molecular and mechanistic connections between progenitor/stem cell lineage determination, embryonic morphogenesis, cancer, and aging. Despite diversity in the nature of the molecular and cellular processes associated with these phenomena, they seem related to the architectural hub of tissue polarity and a need to generate and control this property in a timely manner.

β4 integrin is involved in statin-induced endothelial cell death

HMG-CoA reductase inhibitors (statins) have been shown to inhibit angiogenesis. The molecular mechanism mediating the anti-endothelial activities of statins remains unclear. The present study demonstrated that the antiangiogenic effect of atorvastatin (ATV) was associated with endothelial death. Molecular profiling data identified a 29-fold upregulation of β4 integrin mRNA. Western blot and flow cytometry confirmed robust increases of total and cell-surface β4 integrin. Blockage of β4 integrin activity by antagonizing antibody abrogated ATV-induced endothelial death. The endothelial death and β4 integrin upregulation by ATV could be reversed by intermediate metabilites of the HMG-CoA reductase pathway mevalonate or GGPP, but not by FPP, suggesting that these effects were results of specific inhibition of the pathway. These data indicate that the HMG-CoA reductase might represent an important survival pathway in angiogenic endothelial cells and thus, a potential target for antiangiogenic therapy.

Molecular profiling approaches for identifying novel biomarkers

An unprecedented interest in biomarker development has arisen from the increasing use of genomic information and high-throughput technologies in the field of drug development. Monitoring global cellular responses to perturbation due to disease, drug treatment or toxicity is achieved using molecular profiling methods such as DNA microarrays, proteomics and metabonomics. Unique fingerprints composed of molecular changes are captured and subjected to interpretation with the goal of class discovery, comparison or prediction. Each fingerprint reflects a cumulative response of complex molecular interactions, and if these interactions can be significantly correlated to an end point, the molecular fingerprint may be qualified as a predictive biomarker. Furthermore, in cases where the predictive power of any single response or set of responses is statistically significant, a molecular fingerprint can provide novel information related to the underlying disease biology or mechanism of toxicity. There is an acute need for effective biomarkers in every phase of drug development, from discovery, to preclinical studies, through to clinical trials. The context in which these molecular biomarkers are used will depend upon the nature of the biological problem being addressed. This review will summarise experimental and computational efforts in the field of molecular profiling and discuss the significant challenges in interpreting molecular profiling data and qualifying novel transcriptional biomarkers.

Molecular profiling approaches for identifying novel biomarkers.

An unprecedented interest in biomarker development has arisen from the increasing use of genomic information and high-throughput technologies in the field of drug development. Monitoring global cellular responses to perturbation due to disease, drug treatment or toxicity is achieved using molecular profiling methods such as DNA microarrays, proteomics and metabonomics. Unique fingerprints composed of molecular changes are captured and subjected to interpretation with the goal of class discovery, comparison or prediction. Each fingerprint reflects a cumulative response of complex molecular interactions, and if these interactions can be significantly correlated to an end point, the molecular fingerprint may be qualified as a predictive biomarker. Furthermore, in cases where the predictive power of any single response or set of responses is statistically significant, a molecular fingerprint can provide novel information related to the underlying disease biology or mechanism of toxicity. There is an acute need for effective biomarkers in every phase of drug development, from discovery, to preclinical studies, through to clinical trials. The context in which these molecular biomarkers are used will depend upon the nature of the biological problem being addressed. This review will summarise experimental and computational efforts in the field of molecular profiling and discuss the significant challenges in interpreting molecular profiling data and qualifying novel transcriptional biomarkers.

The genetics and genomics of cancer.

The past decade has seen great strides in our understanding of the genetic basis of human disease. Arguably, the most profound impact has been in the area of cancer genetics, where the explosion of genomic sequence and molecular profiling data has illustrated the complexity of human malignancies. In a tumor cell, dozens of different genes may be aberrant in structure or copy number, and hundreds or thousands of genes may be differentially expressed. A number of familial cancer genes with high-penetrance mutations have been identified, but the contribution of low-penetrance genetic variants or polymorphisms to the risk of sporadic cancer development remains unclear. Studies of the complex somatic genetic events that take place in the emerging cancer cell may aid the search for the more elusive germline variants that confer increased susceptibility. Insights into the molecular pathogenesis of cancer have provided new strategies for treatment, but a deeper understanding of this disease will require new statistical and computational approaches for analysis of the genetic and signaling networks that orchestrate individual cancer susceptibility and tumor behavior.

Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data

Molecular profiling technologies monitor many thousands of transcripts, proteins, metabolites or other species concurrently in a biological sample of interest. Given such high-dimensional data for different types of samples, classification methods aim to assign specimens to known categories. Relevant feature identification methods seek to define a subset of molecules that differentiate the samples. This work describes L IKNON, a specific implementation of a statistical approach for creating a classifier and identifying a small number of relevant features simultaneously. Given two-class data, L IKNON estimates a sparse linear classifier by exploiting the simple and well-known property that minimising an L 1 norm (via linear programming) yields a sparse hyperplane. It performs well when used for retrospective analysis of three cancer biology profiling data sets, (i) small, round, blue cell tumour transcript profiles from tumour biopsies and cell lines, (ii) sporadic breast carcinoma transcript profiles from patients with distant metastases <5 years and those with no distant metastases ⩾5 years and (iii) serum sample protein profiles from unaffected and ovarian cancer patients. Computationally, L IKNON is less demanding than the prevailing filter-wrapper strategy; this approach generates many feature subsets and equates relevant features with the subset yielding a classifier with the lowest generalisation error. Biologically, the results suggest a role for the cellular microenvironment in influencing disease outcome and its importance in developing clinical decision support systems.

Integrated analysis of transcript profiling and protein sequence data

Transcript profiling can be used to elucidate the molecular and cellular mechanisms involved in ageing and cancer. A recent study of human gastrointestinal stromal tumours (GISTs) with mutations in the KIT gene, Cancer Res. 61 (2001) 8624 exemplifies a common type of investigation. cDNA microarrays were used to generate measurements for 1987 clones in two types of tissues: 13 KIT mutation-positive GISTs and 6 spindle cell tumours from locations outside the gastrointestinal tract. Statistical problems associated with such two-class, high-dimensional profiling data include simultaneous classification and relevant feature identification, probabilistic clustering and protein sequence family modelling. Here, the GIST data were reexamined using specific solutions to these problems, namely sparse hyperplanes, naı̈ve Bayes models and profile hidden Markov models respectively. The integrated analysis of molecular profiling and sequence data highlighted 6 clones that may be of clinical and experimental interest. The protein encoded by one of these putative biomarkers defined a novel protein family present in diverse eucarya. The family may be involved in chromosome segregation and/or stability. One family member is a potential biomarker identified recently from a retrospective analysis of transcript profiles for sporadic breast cancer samples from patients with poor and good prognosis, Signal Process. (in press).

Analysis of molecular profile data using generative and discriminative methods.

A modular framework is proposed for modeling and understanding the relationships between molecular profile data and other domain knowledge using a combination of generative (here, graphical models) and discriminative [Support Vector Machines (SVMs)] methods. As illustration, naive Bayes models, simple graphical models, and SVMs were applied to published transcription profile data for 1,988 genes in 62 colon adenocarcinoma tissue specimens labeled as tumor or nontumor. These unsupervised and supervised learning methods identified three classes or subtypes of specimens, assigned tumor or nontumor labels to new specimens and detected six potentially mislabeled specimens. The probability parameters of the three classes were utilized to develop a novel gene relevance, ranking, and selection method. SVMs trained to discriminate nontumor from tumor specimens using only the 50-200 top-ranked genes had the same or better generalization performance than the full repertoire of 1,988 genes. Approximately 90 marker genes were pinpointed for use in understanding the basic biology of colon adenocarcinoma, defining targets for therapeutic intervention and developing diagnostic tools. These potential markers highlight the importance of tissue biology in the etiology of cancer. Comparative analysis of molecular profile data is proposed as a mechanism for predicting the physiological function of genes in instances when comparative sequence analysis proves uninformative, such as with human and yeast translationally controlled tumour protein. Graphical models and SVMs hold promise as the foundations for developing decision support systems for diagnosis, prognosis, and monitoring as well as inferring biological networks.

Molecular Profiling of Clinical Tissue Specimens: Feasibility and Applications

The relationship between gene expression profiles and cellular behavior in humans is largely unknown. Expression patterns of individual cell types have yet to be precisely measured, and, at present, we know or can predict the function of a relatively small percentage of genes. However, biomedical research is in the midst of an informational and technological revolution with the potential to increase dramatically our understanding of how expression modulates cellular phenotype and response to the environment. The entire sequence of the human genome will be known by the year 2003 or earlier. 1, 2 In concert, the pace of efforts to complete identification and full-length cDNA sequencing of all genes has accelerated, and these goals will be attained within the next few years. 3, 4, 5, 6, 7 Accompanying the expanding base of genetic information are several new technologies capable of global gene expression measurements. 8, 9, 10, 11, 12, 13, 14, 15, 16 Taken together, the expanding genetic database and developing expression technologies are leading to an exciting new paradigm in biomedical research known as molecular profiling.