'Omic' and hypothesis-driven research in the molecular pharmacology of cancer

Survey for Computer-Aided Tools and Databases in Metabolomics

Metabolomics has advanced from innovation and functional genomics tools and is currently a basis in the big data-led precision medicine era. Metabolomics is promising in the pharmaceutical field and clinical research. However, due to the complexity and high throughput data generated from such experiments, data mining and analysis are significant challenges for researchers in the field. Therefore, several efforts were made to develop a complete workflow that helps researchers analyze data. This paper introduces a review of the state-of-the-art computer-aided tools and databases in metabolomics established in recent years. The paper provides computational tools and resources based on functionality and accessibility and provides hyperlinks to web pages to download or use. This review aims to present the latest computer-aided tools, databases, and resources to the metabolomics community in one place.

Expression profiling of maternal plasma and placenta microRNAs in preeclamptic pregnancies by microarray technology

Preeclampsia (PE) is one of the leading causes of maternal and fetal morbidity and mortality, occurring usually in the second half of pregnancy and affecting approximately 5-8% of pregnancies in the world. miRNAs play critical role in the regulation of placental development processes. We aimed to determine specific novel miRNAs for early diagnosis of preeclampsia which is one of the most dangerous pregnancy diseases. In this study 72 samples, maternal age 22 ≤ and ≤36, have been analyzed; maternal plasma and placental miRNAs were isolated from 18 severe preeclampsia (sPE) patients and 18 controls, respectively. Profiling of human miRNAs (1368 probe) was performed in samples with Agilent v16 microarrays for detection of the differences in miRNA expression between two groups. The results were validated by using TaqMan RT-qPCR method. The analysis indicated that 406 of these miRNAs in all placentas and 42 of these miRNAs in all maternal plasma were expressed. The relative expression analysis has shown that 12 miRNAs (p < 0.05 and >2-fold) in maternal plasma were differentially expressed in PE and control group. However, five miRNAs were validated by qRT-PCR. Once validated miRNAs have been searched in databases for their target genes and function, it has been shown that there are some preeclampsia related pathways as a target such as angiogenesis, cardiovascular, hypertension, placental abruption and preeclampsia disorders. Differentially expressed and validated plasma miRNAs might be used as notable biomarkers for non-invasive early diagnosis of preeclampsia and treatment of disease.

Pathway Analysis: State of the Art

Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms. Despite their wide employment, pathway analysis foundations and overall background may not be fully understood, leading to misinterpretation of analysis results. This review attempts to comprise the fundamental knowledge to take into consideration when using pathway analysis as a hypothesis generation tool. We discuss the key elements that are part of these methodologies, their capabilities and current deficiencies. We also present an overview of current and all-time popular methods, highlighting different classes across them. In doing so, we show the exploding diversity of methods that pathway analysis encompasses, point out commonly overlooked caveats, and direct attention to a potential new class of methods that attempt to zoom the analysis scope to the sample scale.

Toward the Broad Adoption of 3D Tumor Models in the Cancer Drug Pipeline

Despite a cost of approximately $1 billion to develop a new cancer drug, about 90% of drugs that enter clinical trials fail. A tremendous opportunity exists to streamline the drug selection and testing process, and innovative approaches promise to reduce the burdensome cost of health care for those suffering from cancer. There is great potential for 3D models of human tumors to complement more traditional testing methods; however, the shift from 2D to 3D assays at early stages of the drug discovery and development process is far from widely accepted. 3D platforms range from simple tumor spheroids to more complex microfluidic hydrogels that better mimic the tumor microenvironment. While several companies have developed and patented advanced high-throughput 3D platforms for drug screening, their cost and complexity have limited their adoption as an industry standard. In this review, we will highlight the various tumor platforms that have been developed, emphasizing the approaches that have successfully led to commercial products. We will then consider potential directions toward more relevant tumor models, advantages of the adoption of such platforms within the drug development and screening process, and new opportunities in personalized medicine that such platforms will uniquely enable.

Implementation strategies of Systems Medicine in clinical research and home care for cardiovascular disease patients

Insights from the "-omics" science have recently emphasized the need to implement an overall strategy in medical research. Here, the development of Systems Medicine has been indicated as a potential tool for clinical translation of basic research discoveries. Systems Medicine also gives the opportunity of improving different steps in medical practice, from diagnosis to healthcare management, including clinical research. The development of Systems Medicine is still hampered however by several challenges, the main one being the development of computational tools adequate to record, analyze and share a large amount of disparate data. In addition, available informatics tools appear not yet fully suitable for the challenge because they are not standardized, not universally available, or with ethical/legal concerns. Cardiovascular diseases (CVD) are a very promising area for translating Systems Medicine into clinical practice. By developing clinically applied technologies, the collection and analysis of data may improve CV risk stratification and prediction. Standardized models for data recording and analysis can also greatly broaden data exchange, thus promoting a uniform management of CVD patients also useful for clinical research. This advance however requires a great organizational effort by both physicians and health institutions, as well as the overcoming of ethical problems. This narrative review aims at providing an update on the state-of-art knowledge in the area of Systems Medicine as applied to CVD, focusing on current critical issues, providing a road map for its practical implementation.

High resolution copy number variation data in the NCI-60 cancer cell lines from whole genome microarrays accessible through CellMiner

Array-based comparative genomic hybridization (aCGH) is a powerful technique for detecting gene copy number variation. It is generally considered to be robust and convenient since it measures DNA rather than RNA. In the current study, we combine copy number estimates from four different platforms (Agilent 44 K, NimbleGen 385 K, Affymetrix 500 K and Illumina Human1Mv1_C) to compute a reliable, high-resolution, easy to understand output for the measure of copy number changes in the 60 cancer cells of the NCI-DTP (the NCI-60). We then relate the results to gene expression. We explain how to access that database using our CellMiner web-tool and provide an example of the ease of comparison with transcript expression, whole exome sequencing, microRNA expression and response to 20,000 drugs and other chemical compounds. We then demonstrate how the data can be analyzed integratively with transcript expression data for the whole genome (26,065 genes). Comparison of copy number and expression levels shows an overall medium high correlation (median r = 0.247), with significantly higher correlations (median r = 0.408) for the known tumor suppressor genes. That observation is consistent with the hypothesis that gene loss is an important mechanism for tumor suppressor inactivation. An integrated analysis of concurrent DNA copy number and gene expression change is presented. Limiting attention to focal DNA gains or losses, we identify and reveal novel candidate tumor suppressors with matching alterations in transcript level.

Recapitulation of tumor heterogeneity and molecular signatures in a 3D brain cancer model with decreased sensitivity to histone deacetylase inhibition

Introduction

Physiologically relevant pre-clinical ex vivo models recapitulating CNS tumor micro-environmental complexity will aid development of biologically-targeted agents. We present comprehensive characterization of tumor aggregates generated using the 3D Rotary Cell Culture System (RCCS).

Methods

CNS cancer cell lines were grown in conventional 2D cultures and the RCCS and comparison with a cohort of 53 pediatric high grade gliomas conducted by genome wide gene expression and microRNA arrays, coupled with immunohistochemistry, ex vivo magnetic resonance spectroscopy and drug sensitivity evaluation using the histone deacetylase inhibitor, Vorinostat.

Results

Macroscopic RCCS aggregates recapitulated the heterogeneous morphology of brain tumors with a distinct proliferating rim, necrotic core and oxygen tension gradient. Gene expression and microRNA analyses revealed significant differences with 3D expression intermediate to 2D cultures and primary brain tumors. Metabolic profiling revealed differential profiles, with an increase in tumor specific metabolites in 3D. To evaluate the potential of the RCCS as a drug testing tool, we determined the efficacy of Vorinostat against aggregates of U87 and KNS42 glioblastoma cells. Both lines demonstrated markedly reduced sensitivity when assaying in 3D culture conditions compared to classical 2D drug screen approaches.

Conclusions

Our comprehensive characterization demonstrates that 3D RCCS culture of high grade brain tumor cells has profound effects on the genetic, epigenetic and metabolic profiles of cultured cells, with these cells residing as an intermediate phenotype between that of 2D cultures and primary tumors. There is a discrepancy between 2D culture and tumor molecular profiles, and RCCS partially re-capitulates tissue specific features, allowing drug testing in a more relevant ex vivo system.

The placenta in preeclampsia

The root cause of preeclampsia is the placenta. Preeclampsia begins to abate with the delivery of the placenta and can occur in the absence of a fetus but with the presence of trophoblast tissue with hydatidiform moles. In view of this, study of the placenta should provide insight into the pathophysiology of preeclampsia. In this presentation we examine placental pathological and pathophysiological changes with preeclampsia and fetal growth restriction (FGR). It would seem that this comparison should be illuminating as both conditions are associated with similarly abnormal placentation yet only in preeclampsia is there a maternal pathophysiological syndrome. Similar insights about early and late onset preeclampsia should also be provided by such information.We report that the placental abnormalities in preeclampsia are what would be predicted in a setting of reduced perfusion and oxidative stress. However, the differences from FGR are inconsistent. The most striking differences between the two conditions are found in areas that have been the least studied. There are differences between the placental findings in early and late onset preeclampsia but whether these are qualitative, indicating different diseases, or simply quantitative differences within the same disease is difficult to determine.We attempt to decipher the true differences, seek an explanation for the disparate results and provide recommendations that we hope may help resolve these issues in future studies.

Platinum sensitivity-related germline polymorphism discovered via a cell-based approach and analysis of its association with outcome in ovarian cancer patients

PURPOSE

Cell-based approaches were used to identify genetic markers predictive of patients' risk for poor response prior to chemotherapy.

EXPERIMENTAL DESIGN

We conducted genome-wide association studies (GWAS) to identify single-nucleotide polymorphisms (SNP) associated with cellular sensitivity to carboplatin through their effects on mRNA expression using International HapMap lymphoblastoid cell lines (LCL) and replicated them in additional LCLs. SNPs passing both stages of the cell-based study were tested for association with progression-free survival (PFS) in patients. Phase 1 validation was based on 377 ovarian cancer patients receiving at least four cycles of carboplatin and paclitaxel from the Australian Ovarian Cancer Study (AOCS). Positive associations were then assessed in phase 2 validation analysis of 1,326 patients from the Ovarian Cancer Association Consortium and The Cancer Genome Atlas.

RESULTS

In the initial GWAS, 342 SNPs were associated with carboplatin-induced cytotoxicity, of which 18 unique SNPs were retained after assessing their association with gene expression. One SNP (rs1649942) was replicated in an independent LCL set (Bonferroni adjusted P < 0.05). It was found to be significantly associated with decreased PFS in phase 1 AOCS patients (P(per-allele) = 2 × 10(-2)), with a stronger effect in the subset of women with optimally debulked tumors (P(per-allele) = 4 × 10(-3)). rs1649942 was also associated with poorer overall survival in women with optimally debulked tumors (P(per-allele) = 9 × 10(-3)). However, this SNP was not significant in phase 2 validation analysis with patients from numerous cohorts.

CONCLUSION

This study shows the potential of cell-based, genome-wide approaches to identify germline predictors of treatment outcome and highlights the need for extensive validation in patients to assess their clinical effect.

The reductionist paradox: are the laws of chemistry and physics sufficient for the discovery of new drugs?

Reductionism is alive and well in drug-discovery research. In that tradition, we continually improve experimental and computational methods for studying smaller and smaller aspects of biological systems. Although significant improvements continue to be made, are our efforts too narrowly focused? Suppose all error could be removed from these methods, would we then understand biological systems sufficiently well to design effective drugs? Currently, almost all drug research focuses on single targets. Should the process be expanded to include multiple targets? Recent efforts in this direction have lead to the emerging field of polypharmacology. This appears to be a move in the right direction, but how much polypharmacology is enough? As the complexity of the processes underlying polypharmacology increase will we be able to understand them and their inter-relationships? Is "new" mathematics unfamiliar in much of physics and chemistry research needed to accomplish this task? A number of these questions will be addressed in this paper, which focuses on issues and questions not answers to the drug-discovery conundrum.

On the intrinsic inevitability of cancer: from foetal to fatal attraction

The cracks in the paradigm of oncogenic mutations and somatic evolution as driving force of tumorigenesis, lucidly exposed by the dynamic heterogeneity of "cancer stem cells" or the diffuse results of cancer genome sequencing projects, indicate the need for a more encompassing theory of cancer that reaches beyond the current proximate explanations based on individual genetic pathways. One such integrative concept, derived from first principles of the dynamics of gene regulatory networks, is that cancerous cell states are attractor states, just like normal cell types are. Here we extend the concept of cancer attractors to illuminate a more profound property of cancer initiation: its inherent inevitability in the light of metazoan evolution. Using Waddington's Epigenetic Landscape as a conceptual aid, for which we present a mathematical and evolutionary foundation, we propose that cancer is intrinsically linked to ontogenesis and phylogenesis. This explanatory rather than enumerating review uses a formal argumentation structure that is atypical in modern experimental biology but may hopefully offer a new coherent perspective to reconcile many conflicts between new findings and the old thinking in the categories of linear oncogenic pathways.

Comprehensive evaluation of the contribution of X chromosome genes to platinum sensitivity

Using a genome-wide gene expression data set generated from Affymetrix GeneChip Human Exon 1.0ST array, we comprehensively surveyed the role of 322 X chromosome gene expression traits on cellular sensitivity to cisplatin and carboplatin. We identified 31 and 17 X chromosome genes whose expression levels are significantly correlated (after multiple testing correction) with sensitivity to carboplatin and cisplatin, respectively, in the combined HapMap CEU (Utah residents with ancestry from northern and western Europe) and YRI (Yoruba in Ibahan, Nigeria) populations (false discovery rate, FDR < 0.05). Of those, 14 overlap for both cisplatin and carboplatin. Using an independent gene expression quantification method, the Illumina Sentrix Human-6 Expression BeadChip, measured on the same HapMap cell lines, we found that 4 and 2 of these genes are significantly associated with carboplatin and cisplatin sensitivity, respectively, in both analyses. Two genes, CTPS2 and DLG3, were identified by both genome-wide gene expression analyses as correlated with cellular sensitivity to both platinating agents. The expression of DLG3 gene was also found to correlate with cellular sensitivity to platinating agents in NCI-60 cancer cell lines. In addition, we evaluated whether the expression of X chromosome genes contributed to the observed differences in sensitivity to the platinums between CEU and YRI-derived cell lines. Of the 34 distinct genes significantly correlated with either carboplatin or cisplatin sensitivity, 14 are differentially expressed (defined as P < 0.05) between CEU and YRI. Thus, sex chromosome genes play a role in cellular sensitivity to platinating agents and differences in the expression level of these genes are an important source of variation that should be included in comprehensive pharmacogenomic studies.

iCOD: an integrated clinical omics database based on the systems-pathology view of disease

BACKGROUND

Variety of information relating between genome and the pathological findings in disease will yield a wealth of clues to discover new function, the role of genes and pathways, and future medicine. In addition to molecular information such as gene expression and genome copy number, detailed clinical information is essential for such systematic omics analysis.

RESULTS

In order to provide a basic platform to realize a future medicine based on the integration of molecular and clinico-pathological information of disease, we have developed an integrated clinical omics database (iCOD) in which comprehensive disease information of the patients is collected, including not only molecular omics data such as CGH (Comparative Genomic Hybridization) and gene expression profiles but also comprehensive clinical information such as clinical manifestations, medical images (CT, X-ray, ultrasounds, etc), laboratory tests, drug histories, pathological findings and even life-style/environmental information. The iCOD is developed to combine the molecular and clinico-pathological information of the patients to provide the holistic understanding of the disease. Furthermore, we developed several kinds of integrated view maps of disease in the iCOD, which summarize the comprehensive patient data to provide the information for the interrelation between the molecular omics data and clinico-pathological findings as well as estimation for the disease pathways, such as three layer-linked disease map, disease pathway map, and pathome-genome map.

CONCLUSIONS

With these utilities, our iCOD aims to contribute to provide the omics basis of the disease as well as to promote the pathway-directed disease view. The iCOD database is available online, containing 140 patient cases of hepatocellular carcinoma, with raw data of each case as supplemental data set to download. The iCOD and supplemental data can be accessed at http://omics.tmd.ac.jp/icod_pub_eng.

Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery

OBJECTIVE

Biomarkers for preterm labor (PTL) and delivery can be discovered through the analysis of the transcriptome (transcriptomics) and protein composition (proteomics). Characterization of the global changes in low-molecular weight compounds which constitute the 'metabolic network' of cells (metabolome) is now possible by using a 'metabolomics' approach. Metabolomic profiling has special advantages over transcriptomics and proteomics since the metabolic network is downstream from gene expression and protein synthesis, and thus more closely reflects cell activity at a functional level. This study was conducted to determine if metabolomic profiling of the amniotic fluid can identify women with spontaneous PTL at risk for preterm delivery, regardless of the presence or absence of intraamniotic infection/inflammation (IAI).

STUDY DESIGN

Two retrospective cross-sectional studies were conducted, including three groups of pregnant women with spontaneous PTL and intact membranes: (1) PTL who delivered at term; (2) PTL without IAI who delivered preterm; and (3) PTL with IAI who delivered preterm. The first was an exploratory study that included 16, 19, and 20 patients in groups 1, 2, and 3, respectively. The second study included 40, 33, and 40 patients in groups 1, 2, and 3, respectively. Amniotic fluid metabolic profiling was performed by combining chemical separation (with gas and liquid chromatography) and mass spectrometry. Compounds were identified using authentic standards. The data were analyzed using discriminant analysis for the first study and Random Forest for the second.

RESULTS

(1) In the first study, metabolomic profiling of the amniotic fluid was able to identify patients as belonging to the correct clinical group with an overall 96.3% (53/55) accuracy; 15 of 16 patients with PTL who delivered at term were correctly classified; all patients with PTL without IAI who delivered preterm neonates were correctly identified as such (19/19), while 19/20 patients with PTL and IAI were correctly classified. (2) In the second study, metabolomic profiling was able to identify patients as belonging to the correct clinical group with an accuracy of 88.5% (100/113); 39 of 40 patients with PTL who delivered at term were correctly classified; 29 of 33 patients with PTL without IAI who delivered preterm neonates were correctly classified. Among patients with PTL and IAI, 32/40 were correctly classified. The metabolites responsible for the classification of patients in different clinical groups were identified. A preliminary draft of the human amniotic fluid metabolome was generated and found to contain products of the intermediate metabolism of mammalian cells and xenobiotic compounds (e.g. bacterial products and Salicylamide).

CONCLUSION

Among patients with spontaneous PTL with intact membranes, metabolic profiling of the amniotic fluid can be used to assess the risk of preterm delivery in the presence or absence of infection/inflammation.

Multifactorial regulation of E-cadherin expression: an integrative study

E-cadherin (E-cad) is an adhesion molecule associated with tumor invasion and metastasis. Its down-regulation is associated with poor prognosis for many epithelial tumor types. We have profiled E-cad in the NCI-60 cancer cell lines at the DNA, RNA, and protein levels using six different microarray platforms plus bisulfite sequencing. Here we consider the effects on E-cad expression of eight potential regulatory factors: E-cad promoter DNA methylation, the transcript levels of six transcriptional repressors (SNAI1, SNAI2, TCF3, TCF8, TWIST1, and ZFHX1B), and E-cad DNA copy number. Combined bioinformatic and pharmacological analyses indicate the following ranking of influence on E-cad expression: (1) E-cad promoter methylation appears predominant, is strongly correlated with E-cad expression, and shows a 20% to 30% threshold above which E-cad expression is silenced; (2) TCF8 expression levels correlate with (-0.62) and predict (P < 0.00001) E-cad expression; (3) SNAI2 and ZFHX1B expression levels correlate positively with each other (+0.83) and also correlate with (-0.32 and -0.30, respectively) and predict (P = 0.03 and 0.01, respectively) E-cad expression; (4) TWIST1 correlates with (-0.34) but does not predict E-cad expression; and (5) SNAI1 expression, TCF3 expression, and E-cad DNA copy number do not correlate with or predict E-cad expression. Predictions of E-cad regulation based on the above factors were tested and verified by demethylation studies using 5-aza-2'-deoxycytidine treatment; siRNA knock-down of TCF8, SNAI2, or ZFHX1B expression; and combined treatment with 5-aza-2'-deoxycytidine and TCF8 siRNA. Finally, levels of cellular E-cad expression are associated with levels of cell-cell adhesion and response to drug treatment.

Experimental validation for quantitative protein network models

Cellular responses are the consequence of complex reactions of protein networks. The complexity should ultimately be described by a set of formulas in a quantitative fashion, in which each formula defines the reactions in response to given types of input. However, testing these formulas has not been a simple task because of the lack of appropriate means for experimental validation. 'Reverse-phase' lysate microarrays have been proved to be powerful for such requirements and thus can be a good resource for providing an experimental reference point for the theoretical biology of protein networks.

Integrated data analysis for genome-wide research

Integrated data analysis is introduced as the intermediate level of a systems biology approach to analyse different 'omics' datasets, i.e., genome-wide measurements of transcripts, protein levels or protein-protein interactions, and metabolite levels aiming at generating a coherent understanding of biological function. In this chapter we focus on different methods of correlation analyses ranging from simple pairwise correlation to kernel canonical correlation which were recently applied in molecular biology. Several examples are presented to illustrate their application. The input data for this analysis frequently originate from different experimental platforms. Therefore, preprocessing steps such as data normalisation and missing value estimation are inherent to this approach. The corresponding procedures, potential pitfalls and biases, and available software solutions are reviewed. The multiplicity of observations obtained in omics-profiling experiments necessitates the application of multiple testing correction techniques.

MicroRNA expression profiles for the NCI-60 cancer cell panel

Advances in the understanding of cancer cell biology and response to drug treatment have benefited from new molecular technologies and methods for integrating information from multiple sources. The NCI-60, a panel of 60 diverse human cancer cell lines, has been used by the National Cancer Institute to screen >100,000 chemical compounds and natural product extracts for anticancer activity. The NCI-60 has also been profiled for mRNA and protein expression, mutational status, chromosomal aberrations, and DNA copy number, generating an unparalleled public resource for integrated chemogenomic studies. Recently, microRNAs have been shown to target particular sets of mRNAs, thereby preventing translation or accelerating mRNA turnover. To complement the existing NCI-60 data sets, we have measured expression levels of microRNAs in the NCI-60 and incorporated the resulting data into the CellMiner program package for integrative analysis. Cell line groupings based on microRNA expression were generally consistent with tissue type and with cell line clustering based on mRNA expression. However, mRNA expression seemed to be somewhat more informative for discriminating among tissue types than was microRNA expression. In addition, we found that there does not seem to be a significant correlation between microRNA expression patterns and those of known target transcripts. Comparison of microRNA expression patterns and compound potency patterns showed significant correlations, suggesting that microRNAs may play a role in chemoresistance. Combined with gene expression and other biological data using multivariate analysis, microRNA expression profiles may provide a critical link for understanding mechanisms involved in chemosensitivity and chemoresistance.

The use of high-dimensional biology (genomics, transcriptomics, proteomics, and metabolomics) to understand the preterm parturition syndrome

High-dimensional biology (HDB) refers to the simultaneous study of the genetic variants (DNA variation), transcription (messenger RNA [mRNA]), peptides and proteins, and metabolites of an organ, tissue, or an organism in health and disease. The fundamental premise is that the evolutionary complexity of biological systems renders them difficult to comprehensively understand using only a reductionist approach. Such complexity can become tractable with the use of "omics" research. This term refers to the study of entities in aggregate. The current nomenclature of "omics" sciences includes genomics for DNA variants, transcriptomics for mRNA, proteomics for proteins, and metabolomics for intermediate products of metabolism. Another discipline relevant to medicine is pharmacogenomics. The two major advances that have made HDB possible are technological breakthroughs that allow simultaneous examination of thousands of genes, transcripts, and proteins, etc., with high-throughput techniques and analytical tools to extract information. What is conventionally considered hypothesis-driven research and discovery-driven research (through "omic" methodologies) are complementary and synergistic. Here we review data which have been derived from: 1) genomics to examine predisposing factors for preterm birth; 2) transcriptomics to determine changes in mRNA in reproductive tissues associated with preterm labour and preterm prelabour rupture of membranes; 3) proteomics to identify differentially expressed proteins in amniotic fluid of women with preterm labour; and 4) metabolomics to identify the metabolic footprints of women with preterm labour likely to deliver preterm and those who will deliver at term. The complementary nature of discovery science and HDB is emphasised.

The promise of a virtual lab in drug discovery

To date, the life sciences 'omics' revolution has not lived up to the expectation of boosting the drug discovery process. The major obstacle is dealing with the volume and diversity of data generated. An enhanced-science (e-science) approach based on remote collaboration, reuse of data and methods, and supported by a virtual laboratory (VL) environment promises to get the drug discovery process afloat. The creation, use and preservation of information in formalized knowledge spaces is essential to the e-science approach. VLs include Grid computation and data communication as well as generic and domain-specific tools and methods for information management, knowledge extraction and data analysis. Problem-solving environments (PSEs) are the domain-specific experimental environments of VLs. Thus, VL-PSEs can support virtual organizations, based on the changing partnerships characteristic of successful drug discovery enterprises.

AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies

Background

Monoclonal antibodies are used extensively throughout the biomedical sciences for detection of antigens, either in vitro or in vivo. We, for example, have used them for quantitation of proteins on "reverse-phase" protein lysate arrays. For those studies, we quality-controlled > 600 available monoclonal antibodies and also needed to develop precise information on the genes that encode their antigens. Translation among the various protein and gene identifier types proved non-trivial because of one-to-many and many-to-one relationships. To organize the antibody, protein, and gene information, we initially developed a relational database in Filemaker for our own use. When it became apparent that the information would be useful to many other researchers faced with the need to choose or characterize antibodies, we developed it further as AbMiner, a fully relational web-based database under MySQL, programmed in Java.

Description

AbMiner is a user-friendly, web-based relational database of information on > 600 commercially available antibodies that we validated by Western blot for protein microarray studies. It includes many types of information on the antibody, the immunogen, the vendor, the antigen, and the antigen's gene. Multiple gene and protein identifier types provide links to corresponding entries in a variety of other public databases, including resources for phosphorylation-specific antibodies. AbMiner also includes our quality-control data against a pool of 60 diverse cancer cell types (the NCI-60) and also protein expression levels for the NCI-60 cells measured using our high-density "reverse-phase" protein lysate microarrays for a selection of the listed antibodies. Some other available database resources give information on antibody specificity for one or a couple of cell types. In contrast, the data in AbMiner indicate specificity with respect to the antigens in a pool of 60 diverse cell types from nine different tissues of origin.

Conclusion

AbMiner is a relational database that provides extensive information from our own laboratory and other sources on more than 600 available antibodies and the genes that encode the antibodies' antigens. The data will be made freely available at

Neuroinformatics in clinical practice: are computers going to help neurological patients and their physicians?

Neuroinformatics is the field that merges the power of computational analysis with neuroscience. It is a discipline that has evolved from the original use of computers for data organization to the current development and application of sophisticated computational tools for large-scale data and image management, analysis and modeling of brain function in health and disease. Neuroinformatics has the potential to be a powerful instrument in the discovery of biological markers of neurological diseases, as well as in the development of new and more effective therapies. Owing to the exponential growth in size and complexity of the information available in the neurosciences, neuroinformatic methods are becoming indispensable in modern neurological research. We predict that, in the near future, they will also be essential at the bedside.

Utilizing logical relationships in genomic data to decipher cellular processes

The wealth of available genomic data has spawned a corresponding interest in computational methods that can impart biological meaning and context to these experiments. Traditional computational methods have drawn relationships between pairs of proteins or genes based on notions of equality or similarity between their patterns of occurrence or behavior. For example, two genes displaying similar variation in expression, over a number of experiments, may be predicted to be functionally related. We have introduced a natural extension of these approaches, instead identifying logical relationships involving triplets of proteins. Triplets provide for various discrete kinds of logic relationships, leading to detailed inferences about biological associations. For instance, a protein C might be encoded within an organism if, and only if, two other proteins A and B are also both encoded within the organism, thus suggesting that gene C is functionally related to genes A and B. The method has been applied fruitfully to both phylogenetic and microarray expression data, and has been used to associate logical combinations of protein activity with disease state phenotypes, revealing previously unknown ternary relationships among proteins, and illustrating the inherent complexities that arise in biological data.

Biomarkers in cancer staging, prognosis and treatment selection

Advances in genomics, proteomics and molecular pathology have generated many candidate biomarkers with potential clinical value. Their use for cancer staging and personalization of therapy at the time of diagnosis could improve patient care. However, translation from bench to bedside outside of the research setting has proved more difficult than might have been expected. Understanding how and when biomarkers can be integrated into clinical care is crucial if we want to translate the promise into reality.

Genetic dissection of neurodegeneration and CNS inflammation

Inflammation and neurodegeneration characterize multiple sclerosis, as well as many other diseases of the central nervous system (CNS). The understanding of the molecular pathways that regulate these processes is of fundamental importance for the development of new therapies. Nerve lesions paradigms in animals can serve as important tools to dissect central features of human CNS disease and by using these models certain key regulators have also been identified. However, our knowledge of how aspects of neurodegeneration and CNS inflammation are regulated on a genomic level is very limited. Such knowledge may help to unravel disease mechanisms. By using a standardized nerve trauma model, ventral root avulsion (VRA), in a series of inbred rat strains we here demonstrate a potent genetic regulation of the degree of neuron death and glial activation. Genome wide mapping of these phenotypes in experimental rat strain crosses identifies several quantitative trait loci (QTLs) controlling nerve lesion-induced nerve cell death, local T cell accumulation and expression of MHC class II on microglia. This approach may lead to the identification of evolutionary conserved genetic polymorphisms in key controlling genes, which can serve as prime candidates for association studies in several human CNS diseases.

Data integration: challenges for drug discovery

The effective integration of data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key element of conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realizing economies of scale in large enterprises. However, viewing data integration as simply an 'IT problem' underestimates the novel and serious scientific and management challenges it embodies - challenges that could require significant methodological and even cultural changes in our approach to data.

Transcriptomic analysis of the NCI-60 cancer cell lines

Pharmacogenomics aims at molecular subsetting of patients for more effective therapy. Transcriptomic profiling of the 60 human cancer cell lines (the NCI-60) used by the US National Cancer Institute serves that aim because the cells have been treated with > 100,000 chemical compounds over the last 13 years. Patterns of potency can be mapped into molecular structures of the compounds or into molecular characteristics of the cells. We discuss conceptual and experimental aspects of the profiling, as well as a number of bioinformatic computer programs that we have developed for biological interpretation of the profiles.

Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays

Because most potential molecular markers and targets are proteins, proteomic profiling is expected to yield more direct answers to functional and pharmacological questions than does transcriptional profiling. To aid in such studies, we have developed a protocol for making reverse-phase protein lysate microarrays with larger numbers of spots than previously feasible. Our first application of these arrays was to profiling of the 60 human cancer cell lines (NCI-60) used by the National Cancer Institute to screen compounds for anticancer activity. Each glass slide microarray included 648 lysate spots representing the NCI-60 cell lines plus controls, each at 10 two-fold serial dilutions to provide a wide dynamic range. Mouse monoclonal antibodies and the catalyzed signal amplification system were used for immunoquantitation. The signal levels from the >30,000 data points for our first 52 antibodies were analyzed by using p-scan and a quantitative dose interpolation method. Clustered image maps revealed biologically interpretable patterns of protein expression. Among the principal early findings from these arrays were two promising pathological markers for distinguishing colon from ovarian adenocarcinomas. When we compared the patterns of protein expression with those we had obtained for the same genes at the mRNA level by using both cDNA and oligonucleotide arrays, a striking regularity appeared: cell-structure-related proteins almost invariably showed a high correlation between mRNA and protein levels across the NCI-60 cell lines, whereas non-cell-structure-related proteins showed poor correlation.

What is the relevance of bioinformatics to pharmacology?

Although bioinformatics achieved prominence because of its central role in genome data storage, management and analysis, its focus has shifted as the life sciences exploit these data. In pharmacology, genomic, transcriptomic and proteomic data are being used in the quest for drugs that fulfill unmet medical needs, are disease modifying or curative and are more effective and safer than current drugs. Bioinformatics is used in drug target identification and validation and in the development of biomarkers and toxicogenomic and pharmacogenomic tools to maximize the therapeutic benefit of drugs. Now that the 'parts list' of cellular signalling pathways is available, integrated computational and experimental programmes are being developed, with the goal of enabling in silico pharmacology by linking the genome, transcriptome and proteome to cellular pathophysiology.

What is next in pharmacogenomics? Translating it to clinical practice

Pharmacogenomics (PG) holds promise for transforming medical therapeutics but the details of how the promise will become reality are still vague. In this article, we focus on the role that laboratory medicine, as a discipline, might play in transitioning the application of pharmacogenomics into the healthcare system and begin to frame a perspective on how PG may be viewed in this context. Development of clinical diagnostic tests usually evolves as a continuum of information starting with the discovery of a potential biological marker through to its routine use in clinical practice. This process has traditionally been rooted in the practice of laboratory medicine and, importantly, includes the development of testing strategies to optimize the predictive value of single or a combination of biological markers. In this context, we also discuss a perspective on some future strategies that may prove useful in advancing the application of PG, including the need for an evidenced-based approach and the potential role of proteomics as a means to drive more comprehensive strategies.

GoMiner: a resource for biological interpretation of genomic and proteomic data

We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources.

MatchMiner: a tool for batch navigation among gene and gene product identifiers

MatchMiner is a freely available program package for batch navigation among gene and gene product identifier types commonly encountered in microarray studies and other forms of 'omic' research. The user inputs a list of gene identifiers and then uses the Merge function to find the overlap with a second list of identifiers of either the same or a different type or uses the LookUp function to find corresponding identifiers.