In silico analysis of cancer through the Cancer Genome Anatomy Project

Bioinformatics Tools and Resources for Cancer Immunotherapy Study

In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.

Chromosome-centric Human Proteome Project (C-HPP): Chromosome 12

Following an official announcement of the Chromosome-centric Human Proteome Project (C-HPP), the Chromosome 12 (Ch12) Consortium has been established by five representative teams from five Asian countries including Thailand (Siriraj Hospital, Mahidol University), Singapore (National University of Singapore), Taiwan (Academia Sinica), Hong Kong (The Chinese University of Hong Kong), and India (Institute of Bioinformatics). We have worked closely together to extensively and systematically analyze all missing and known proteins encoded by Ch12 for their tissue/cellular/subcellular localizations. The target organs/tissues/cells include kidney, brain, gastrointestinal tissues, blood/immune cells, and stem cells. In the later phase, post-translational modifications and functional significance of Ch12-encoded proteins as well as their associations with human diseases (i.e., immune diseases, metabolic disorders, and cancers) will be defined. We have collaborated with other chromosome teams, Human Kidney and Urine Proteome Project (HKUPP), AOHUPO Membrane Proteomics Initiative, and other existing HUPO initiatives in the Biology/Disease-Based Human Proteome Project (B/D-HPP) to delineate functional roles and medical implications of Ch12-encoded proteins. The data set to be obtained from this multicountry consortium will be an important piece of the jigsaw puzzle to fulfill the missions and goals of the C-HPP and the global Human Proteome Project (HPP).

The UCSC Genome Browser

The University of California Santa Cruz (UCSC) Genome Browser is a popular Web-based tool for quickly displaying a requested portion of a genome at any scale, accompanied by a series of aligned annotation "tracks." The annotations generated by the UCSC Genome Bioinformatics Group and external collaborators include gene predictions, mRNA and expressed sequence tag alignments, simple nucleotide polymorphisms, expression and regulatory data, phenotype and variation data, and pairwise and multiple-species comparative genomics data. All information relevant to a region is presented in one window, facilitating biological analysis and interpretation. The database tables underlying the Genome Browser tracks can be viewed, downloaded, and manipulated using another Web-based application, the UCSC Table Browser. Users can upload personal datasets in a wide variety of formats as custom annotation tracks in both browsers for research or educational purposes. This unit describes how to use the Genome Browser and Table Browser for genome analysis, download the underlying database tables, and create and display custom annotation tracks.

G-A variant in miR-200c binding site of EFNA1 alters susceptibility to gastric cancer

MicroRNAs (miRNAs) post-transcriptionally modulate gene expression by binding to complementary sites at 3'-untranslated regions (3'UTRs) of their target messenger RNAs (mRNAs). Genetic variations in miRNA binding sites may alter individual susceptibilities to many cancers. However, whether miRNA binding site single nucleotide polymorphisms (SNPs) interfere with gastric cancer (GC) susceptibility has not been reported. In this case-control study including 525 GC patients and 501 controls, we selected three miRNA binding site SNPs located in 3'UTRs of genes involved in GC to investigated their associations with GC susceptibility. We identified that rs12904 in ephrin-A1 (EFNA1) gene was significantly associated with risk of GC, with the OR for carrying AG or GG genotype being 0.65 (P = 0.002, OR 0.65; 95% CI, 0.50-0.85) compared with AA genotype. Specifically, we found that rs12904 is in strong linkage disequilibrium (LD) with rs4072037, a susceptibility variant reported by previous genome-wide association study (GWAS). No significant associations were observed for the other two SNPs (rs699517 in TYMS and rs1042542 in BIRC5). Furthermore, luciferase assays indicated EFNA1 as the target of hsa-miR-200c and rs12904 G > A change resulted in altered regulation of luciferase expression. In addition, rs12904 AA genotype was associated with increased expression of EFNA1 mRNA compared with AG or GG genotype in the cancer tissues from 48 patients. Taken together, these findings indicate that the miR-200c binding site SNP (rs12904 G > A) in the 3'UTR of EFNA1 can modulate EFNA1 expression and is associated with GC susceptibility. Larger replication studies are needed to confirm our findings.

The use of EST expression matrixes for the quality control of gene expression data

EST expression profiling provides an attractive tool for studying differential gene expression, but cDNA libraries' origins and EST data quality are not always known or reported. Libraries may originate from pooled or mixed tissues; EST clustering, EST counts, library annotations and analysis algorithms may contain errors. Traditional data analysis methods, including research into tissue-specific gene expression, assume EST counts to be correct and libraries to be correctly annotated, which is not always the case. Therefore, a method capable of assessing the quality of expression data based on that data alone would be invaluable for assessing the quality of EST data and determining their suitability for mRNA expression analysis. Here we report an approach to the selection of a small generic subset of 244 UniGene clusters suitable for identification of the tissue of origin for EST libraries and quality control of the expression data using EST expression information alone. We created a small expression matrix of UniGene IDs using two rounds of selection followed by two rounds of optimisation. Our selection procedures differ from traditional approaches to finding "tissue-specific" genes and our matrix yields consistency high positive correlation values for libraries with confirmed tissues of origin and can be applied for tissue typing and quality control of libraries as small as just a few hundred total ESTs. Furthermore, we can pick up tissue correlations between related tissues e.g. brain and peripheral nervous tissue, heart and muscle tissues and identify tissue origins for a few libraries of uncharacterised tissue identity. It was possible to confirm tissue identity for some libraries which have been derived from cancer tissues or have been normalised. Tissue matching is affected strongly by cancer progression or library normalisation and our approach may potentially be applied for elucidating the stage of normalisation in normalised libraries or for cancer staging.

Toward an integrated knowledge environment to support modern oncology

Around the world, teams of researchers continue to develop a wide range of systems to capture, store, and analyze data including treatment, patient outcomes, tumor registries, next-generation sequencing, single-nucleotide polymorphism, copy number, gene expression, drug chemistry, drug safety, and toxicity. Scientists mine, curate, and manually annotate growing mountains of data to produce high-quality databases, while clinical information is aggregated in distant systems. Databases are currently scattered, and relationships between variables coded in disparate datasets are frequently invisible. The challenge is to evolve oncology informatics from a "systems" orientation of standalone platforms and silos into an "integrated knowledge environments" that will connect "knowable" research data with patient clinical information. The aim of this article is to review progress toward an integrated knowledge environment to support modern oncology with a focus on supporting scientific discovery and improving cancer care.

The UCSC Genome Browser

The University of California Santa Cruz (UCSC) Genome Browser is a popular Web-based tool for quickly displaying a requested portion of a genome at any scale, accompanied by a series of aligned annotation "tracks." The annotations generated by the UCSC Genome Bioinformatics Group and external collaborators include gene predictions, mRNA and expressed sequence tag alignments, simple nucleotide polymorphisms, expression and regulatory data, phenotype and variation data, and pairwise and multiple-species comparative genomics data. All information relevant to a region is presented in one window, facilitating biological analysis and interpretation. The database tables underlying the Genome Browser tracks can be viewed, downloaded, and manipulated using another Web-based application, the UCSC Table Browser. Users can upload personal datasets in a wide variety of formats as custom annotation tracks in both browsers for research or educational purposes. This unit describes how to use the Genome Browser and Table Browser for genome analysis, download the underlying database tables, and create and display custom annotation tracks.

The (non)malignancy of cancerous amino acidic substitutions

The process of natural selection acts both on individual organisms within a population and on individual cells within an organism as they develop into cancer. In this work, we have taken a first step toward understanding the differences in selection pressures exerted on the human genome under these disparate circumstances. Focusing on single amino acid substitutions, we have found that cancer-related mutations (CRMs) are frequent in evolutionarily conserved sites, whereas single amino acid polymorphisms (SAPs) tend to appear in sites having a more relaxed evolutionary pressure. Those CRMs classed as cancer driver mutations show greater enrichment for conserved sites than passenger mutations. Consistent with this, driver mutations are enriched for sites annotated as key functional residues and their neighbors, and are more likely to be located on the surface of proteins than expected by chance. Overall the pattern of CRM and polymorphism is remarkably similar, but we do see a clear signal indicative of diversifying selection for disruptive amino acid substitutions in the cancer driver mutations. The ultimate consequence of the appearance of those mutations must be advantageous for the tumor cell, leading to cell population-growth and migration events similar to those seen in natural ecosystems.

The UCSC Genome Browser

The University of California Santa Cruz (UCSC) Genome Browser is a popular Web-based tool for quickly displaying a requested portion of a genome at any scale, accompanied by a series of aligned annotation "tracks." The annotations-generated by the UCSC Genome Bioinformatics Group and external collaborators-display gene predictions, mRNA and expressed sequence tag alignments, simple nucleotide polymorphisms, expression and regulatory data, phenotype and variation data, and pairwise and multiple-species comparative genomics data. All information relevant to a region is presented in one window, facilitating biological analysis and interpretation. The database tables underlying the Genome Browser tracks can be viewed, downloaded, and manipulated using another Web-based application, the UCSC Table Browser. Users can upload data as custom annotation tracks in both browsers for research or educational use. This unit describes how to use the Genome Browser and Table Browser for genome analysis, download the underlying database tables, and create and display custom annotation tracks.

Identification of candidate cancer genes involved in human retinoblastoma by data mining

OBJECTIVE

The objective of this study was to discover potential cancer-related genes involved in retinoblastoma (RB) tumorigenesis.

MATERIALS AND METHODS

Using a data-mining tool called cDNA Digital Gene Expression Displayer (DGED) and serial analysis of gene expression DGED from the Cancer Genome Anatomy Project (CGAP) database, eight cDNA libraries and five serial analysis of gene expression libraries from retinoblastoma (RB) solid tumors and normal retina tissues were analyzed. The deregulated genes were classified into major families using information from Gene Ontology. Several candidate cancer-related genes were analyzed by real-time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) on tissue microarrays (TMA) of RB and human normal retina samples.

RESULTS

A total of 260 genes with deregulated expression emerged when examined by DGED from the CGAP database. Functional classification of these genes not only provided an interesting insight into RB tumorigenesis but also facilitated target identification for RB therapeutics. Several candidate genes were confirmed by real-time RT-PCR and IHC analysis on TMA and were found to be associated with RB genesis through text-mining in Information Hyperlinked over Proteins. The results also implicated MCM7 and WIF1 as promising therapeutic targets for RB, but further validation is needed.

The UCSC Genome Browser

The University of California Santa Cruz (UCSC) Genome Browser (genome.ucsc.edu) is a popular Web-based tool for quickly displaying a requested portion of a genome at any scale, accompanied by a series of aligned annotation "tracks". The annotations-generated by the UCSC Genome Bioinformatics Group and external collaborators-display gene predictions, mRNA and expressed sequence tag alignments, simple nucleotide polymorphisms, expression and regulatory data, and pairwise and multiple-species comparative genomics data. All information relevant to a region is presented in one window, facilitating biological analysis and interpretation. The database tables underlying the Genome Browser tracks can be viewed, downloaded, and manipulated using another Web-based application, the UCSC Table Browser. Users can upload personal data as custom annotation tracks in both browsers for research or educational use. This unit describes how to use the Genome Browser and Table Browser for genome analysis, download the underlying database tables, and create and display custom annotation tracks.

Evaluation of combining several statistical methods with a flexible cutoff for identifying differentially expressed genes in pairwise comparison of EST sets

The detection of differentially expressed genes from EST data is of importance for the discovery of potential biological or pharmaceutical targets, especially when studying biological processes in less characterized organisms and where large-scale microarrays are not an option. We present a comparison of five different statistical methods for identifying up-regulated genes through pairwise comparison of EST sets, where one of the sets is generated from a treatment and the other one serves as a control. In addition, we specifically address situations where the sets are relatively small (~2,000–10,000 ESTs) and may differ in size. The methods were tested on both simulated and experimentally derived data, and compared to a collection of cold stress induced genes identified by microarrays. We found that combining the method proposed by Audic and Claverie with Fisher’s exact test and a method based on calculating the difference in relative frequency was the best combination for maximizing the detection of up-regulated genes. We also introduced the use of a flexible cutoff, which takes the size of the EST sets into consideration. This could be considered as an alternative to a static cutoff. Finally, the detected genes showed a low overlap with those identified by microarrays, which indicates, as in previous studies, low overall concordance between the two platforms.

Expression levels of 63 p53-related genes add up to similar values in 24 different tissues and are unified in cancer

The expression patterns of 62 genes interacting with p53 have been investigated in 24 normal and cancerous tissues using NIH's dbEST library. The expression levels of individual genes, such as the TTP53 gene itself, but also other genes, vary up to 33-fold among the 24 different tissues and no consistent pattern can be recognized. However, when expression levels for all 63 genes are summed, these "cumulated levels" are surprisingly constant over the 24 investigated normal tissues. In cancers, the variation is further reduced. Essentially, the cumulated expression levels in cancer are independent of those in normal tissue. We furthermore constructed a linear statistical classifier, i.e., a weighted sum of gene expression levels, which robustly distinguishes normal from cancer tissue independent of the particular kind of tissue. Thus, despite very large differences for individual genes and considerable changes during carcinogenesis, the cumulated expressions have narrowly defined levels.

caCORE version 3: Implementation of a model driven, service-oriented architecture for semantic interoperability

One of the requirements for a federated information system is interoperability, the ability of one computer system to access and use the resources of another system. This feature is particularly important in biomedical research systems, which need to coordinate a variety of disparate types of data. In order to meet this need, the National Cancer Institute Center for Bioinformatics (NCICB) has created the cancer Common Ontologic Representation Environment (caCORE), an interoperability infrastructure based on Model Driven Architecture. The caCORE infrastructure provides a mechanism to create interoperable biomedical information systems. Systems built using the caCORE paradigm address both aspects of interoperability: the ability to access data (syntactic interoperability) and understand the data once retrieved (semantic interoperability). This infrastructure consists of an integrated set of three major components: a controlled terminology service (Enterprise Vocabulary Services), a standards-based metadata repository (the cancer Data Standards Repository) and an information system with an Application Programming Interface (API) based on Domain Model Driven Architecture. This infrastructure is being leveraged to create a Semantic Service-Oriented Architecture (SSOA) for cancer research by the National Cancer Institute's cancer Biomedical Informatics Grid (caBIG).

Mining the oocyte transcriptome

Mammalian folliculogenesis and oocyte physiology are complex and not fully understood. However, major advances over the past 15 years in our ability to create and study in vivo models have improved our understanding of these essential physiological processes. More recently, the availability of vast arrays of DNA sequence information in the forms of "complete" genomes, expressed sequence tag libraries and microarray data from reproductive tissues have stimulated the discovery of new information through genome scanning, prediction programs and in silico screening techniques. These technological improvements will help to expand our understanding of folliculogenesis and oocyte physiology and improve human reproductive health.

HPtaa database-potential target genes for clinical diagnosis and immunotherapy of human carcinoma

Tumor-associated antigens (TAAs) have been the most actively employed targets in the clinical diagnosis and treatment of human carcinoma, such as PSA in the diagnosis of prostate cancer and NY-ESO-1 in the immunotherapy of melanoma and other cancers. However, identification of TAAs has often been hampered by the complicated and laborsome laboratory procedures. In order to accelerate the process of tumor antigen discovery, and thereby improve diagnosis and treatment of human carcinoma, we have made an effort to establish a publicly available Human Potential Tumor Associated Antigen database (HPtaa) with potential TAAs identified by in silico computing (). Tumor specificity was chosen as the core of tumor antigen evaluation, together with other relevant clues. Various platforms of gene expression, including microarray, expressed sequence tag and SAGE data, were processed and integrated by several penalty algorithms. A total of 3518 potential TAAs have been included in the database, which is freely available to academic users. As far as we know, this database is the first one addressing human potential TAAs, and the first one integrating various kinds of expression platforms for one purpose.

Ubiquitous cancer genes: Multipurpose molecules for protein micro-arrays

Multipurpose genes in the human genome which are over-expressed in a large variety of different cancers have been identified. Forty-two of the 19,016 human genes annotated to date (0.2%) are ubiquitously over-expressed in half or more of the 36 investigated human cancers. Of these genes, 15 are involved in protein biosynthesis and folding, six of them in glycolysis. A group of 13 solid tumours over-express almost all (39-42 of 42) ubiquitous cancer genes, suggesting a common mechanism underlying these cancers. Others, such as endocrine cancers, have only a few over-expressed ubiquitous cancer genes. The proteins for which these genes code or the corresponding antibodies are candidates for small protein microarrays aiming at maximum information with only a limited number of proteins. Since the over-expression pattern varies from cancer to cancer, distinction between different cancer classes is possible using one single set of protein or antibody molecules.

Proteomic analysis of formalin-fixed prostate cancer tissue

Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue would enable retrospective biomarker investigations of this vast archive of pathologically characterized clinical samples that exist worldwide. These FFPE tissues are, however, refractory to proteomic investigations utilizing many state of the art methodologies largely due to the high level of covalently cross-linked proteins arising from formalin fixation. A novel tissue microdissection technique has been developed and combined with a method to extract soluble peptides directly from FFPE tissue for mass spectral analysis of prostate cancer (PCa) and benign prostate hyperplasia (BPH). Hundreds of proteins from PCa and BPH tissue were identified, including several known PCa markers such as prostate-specific antigen, prostatic acid phosphatase, and macrophage inhibitory cytokine-1. Quantitative proteomic profiling utilizing stable isotope labeling confirmed similar expression levels of prostate-specific antigen and prostatic acid phosphatase in BPH and PCa cells, whereas the expression of macrophage inhibitory cytokine-1 was found to be greater in PCa as compared with BPH cells.

Detecting false expression signals in high-density oligonucleotide arrays by an in silico approach

High-density oligonucleotide arrays have become a popular assay for concurrent measurement of mRNA expression at the genome scale. Much effort has been devoted to the development of statistical analysis tools aimed at reducing experimental noise and normalizing experimental variation in gene expression analysis. However, these investigations do not detect or catalog systematic problems associated with specific oligonucleotide probes. Here, we present an investigation of problematic probes that yield consistent but inaccurate signals across multiple experiments. By evaluating data integrity among gene, probe sequence, and genomic structure we identified a total of 20,696 (10.5%) nonspecific probes that could cross-hybridize to multiple genes and a total of 18,363 (9.3%) probes that miss the target transcript sequences on the Affymetrix GeneChip U95A/Av2 array. The numbers of nonspecific and mistargeted probes on the U133A array are 29,405 (12.1%) and 19,717 (8.0%), respectively. The poor performance of the mistargeted probes was confirmed in two GeneChip experiments, in which these probes showed a 20-30% decrease in detecting present signals compared with normal probes. Comparison of qualitative expression signals obtained from SAGE and EST data with those from GeneChip arrays showed that the consistency of the two platforms is 30% lower in problematic probes than in normal probes. A Web application was developed to apply our results for improving the accuracy of expression analysis.

Genes of glycolysis are ubiquitously overexpressed in 24 cancer classes

Using NIH's public database dbEST for expression of genes and ESTs, genes of the glycolysis pathway have been found to be overexpressed in a set of 24 cancers representing more than 70% of human cancer cases worldwide. Genes can be classified as those that are almost ubiquitously overexpressed, particularly glyceraldehyde-3-phosphate dehydrogenase, enolase 1, and also pyruvate kinase, and those that are overexpressed in less than 50% of the investigated cancers. Cancers can be classified as those with overexpression of the majority of the glycolysis genes, particularly lymph node, prostate, and brain cancer, in which essentially all glycolysis genes are overexpressed, and those with only sporadic overexpression, particularly cancers of the cartilage or bone marrow. This classification may be useful when cancer therapies aimed at the Warburg effect are designed.

Proteome-wide analysis of head and neck squamous cell carcinomas using laser-capture microdissection and tandem mass spectrometry

Remarkable progress has been made to identify genes expressed in squamous cell carcinomas of the head and neck (HNSCC). However, limited information is available on their corresponding protein products, whose expression, post-translational modifications, and activity are ultimately responsible for the malignant behavior of this tumor type. We have combined laser-capture microdissection (LCM) with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify proteins expressed in histologically normal squamous epithelium and matching SCC. The protein fraction from approximately 10,000-15,000 normal and tumor cells was solubilized, digested with trypsin, and the resulting peptides were analyzed by LC-MS/MS. Database searching of the resulting sequence information identified 30-55 proteins per sample. Keratins were the most abundant proteins in both normal and tumor tissues. Among the proteins differentially expressed, keratin 13 was much lower in tumors, whereas heat-shock (Hsp) family members were highly expressed in neoplastic cells. Wnt-6 and Wnt-14 were identified in both normal and tumor tissues, respectively, and placental growth factor (PIGF) was detected only in tumors. Immunohistochemical analysis of HNSCC tissues revealed lack of keratin 13 in tumor tissues, and strong staining in normal epithelia, and high expression of Hsp90 in tumors. Our study, by combining LCM and proteomic technologies, underscores the advantages of this approach to investigate complex changes at the protein level in HNSCC, thus complementing existing and emerging genomic technologies. These efforts may likely result in the identification of new biomarkers for HNSCC that can be used to diagnose disease, predict susceptibility, and monitor progression in individual patients.

Using DNA microarray analyses to elucidate the effects of genistein in androgen-responsive prostate cancer cells: identification of novel targets

Many studies have correlated the consumption of soy-rich diets with a decreased risk of developing hormone-dependent cancers, including prostate cancer. Genistein is a candidate prostate cancer preventive phytochemical found at high levels in soybean and soy foods. To better understand the molecular mechanisms underlying the beneficial effects of genistein on prostate cancer prevention, we used a DNA microarray approach to examine the effects of genistein at concentrations in the physiologic range on global gene expression patterns in androgen-responsive cancer cells. Microarray analyses were performed on androgen-responsive LNCaP human prostate cancer cells exposed to 0, 1, 5, or 25 microM genistein. We found a concentration-dependent modulation of multiple cellular pathways that are important in prostate carcinogenesis. Interestingly, the androgen receptor (AR)-mediated pathways, in particular, appeared to be modulated by genistein at the lowest concentrations. Based on these results, we propose that the regulation of AR-mediated pathways is potentially the most relevant chemopreventive mechanism for genistein administered at physiologic levels.

Comprehensive EST analysis of tomato and comparative genomics of fruit ripening

A large tomato expressed sequence tag (EST) dataset (152 635 total) was analyzed to gain insights into differential gene expression among diverse plant tissues representing a range of developmental programs and biological responses. These ESTs were clustered and assembled to a total of 31 012 unique gene sequences. To better understand tomato gene expression at a plant system level and to identify differentially expressed and tissue-specific genes, we developed and implemented a digital expression analysis protocol. By clustering genes according to their relative abundance in the various EST libraries, expression patterns of genes across various tissues were generated and genes with similar patterns were grouped. In addition, tissues themselves were clustered for relatedness based on relative gene expression as a means of validating the integrity of the EST data as representative of relative gene expression. Arabidopsis and grape EST collections were also characterized to facilitate cross-species comparisons where possible. Tomato fruit digital expression data was specifically compared with publicly available grape EST data to gain insight into molecular manifestation of ripening processes across diverse taxa and resulted in identification of common transcription factors not previously associated with ripening.

Promising directions for the diagnosis and management of gynecological cancers

Diagnosis and management of cancer requires tools with both high sensitivity and specificity. The minimally invasive cervical smear has demonstrated how a test, even one with low specificity, can change the public health profile of a cancer from a late stage deadly disease to early diagnosis with rare tumor-related deaths. The benefit of such a test is best demonstrated by the low frequency of cervix cancer and its good outcome in countries where this test is readily available and used with appropriate secondary follow up. Early and specific symptoms, and identification and prevention for high risk groups has had similar impact for endometrial cancer. Neither a robust test, nor reliable or specific early symptoms are available for ovarian cancer, making clinical and scientific advances in this area a critical world-wide need. Current approaches testing one protein or gene marker at a time will not address this crisis expeditiously. New sensitive, specific, accurate, and reliable technologies that can be implemented using high throughput mechanisms are needed at as low a cost as possible. Ideally, these technologies should be focused on readily available patient resources, such as blood or urine, or as in the case of cervix cancer, minimally invasive informative approaches such as cervical smears. Techniques that allow data mining from a large input database overcome the slow advances of one protein-one gene investigation, and further address the multi-faceted carcinogenesis process occurring even in germ line mutation-associated malignancy. Proteomics, the study of the cellular proteins and their activation states, has led the progress in biomarker development for ovarian and other cancers and is being applied to management assessment. Amenable to high throughput, internet interface, and representative of the proteome spectrum, proteomic technology is the newest and most promising direction for translational developments in gynecologic cancers.

Bioinformatics Tools for Single Nucleotide Polymorphism Discovery and Analysis

Single nucleotide polymorphisms (SNPs) are a valuable resource for investigating the genetic basis of disease. These variants can serve as markers for fine-scale genetic mapping experiments and genome-wide association studies. Certain of these nucleotide polymorphisms may predispose individuals to illnesses such as diabetes, hypertension, or cancer, or affect disease progression. Bioinformatics techniques can play an important role in SNP discovery and analysis. We use computational methods to identify SNPs and to predict whether they are likely to be neutral or deleterious. We also use informatics to annotate genes that contain SNPs. To make this information available to the research community, we provide a variety of Internet-accessible tools for data access and display. These tools allow researchers to retrieve data about SNPs based on gene of interest, genetic or physical map location, or expression pattern.

Kallikrein gene downregulation in breast cancer

Recent evidence suggests that many members of the human kallikrein gene family are differentially regulated in breast cancer and other endocrine-related malignancies. In this study, we utilised the serial analysis of gene expression (SAGE) and expressed sequence tag (EST) databases of the Cancer Genome Anatomy Project (CGAP) to perform in silico analyses of the expression pattern of the 15 human kallikrein genes in normal and cancerous breast tissues and cell lines using different analytical tools such as Virtual Northern blotting, Digital Differential Display and X-profiler. Our results indicate that at least four kallikrein genes (KLK5, 6, 8, 10) are downregulated in breast cancer. Probing eight normal and 24 breast cancer SAGE libraries with gene-specific tags for each of the above kallikreins indicated moderate-to-high expression densities in normal breast (27–319 tags per million; tpm, in two to five out of eight libraries), compared to no or low expression (0 – 34 tpm in zero to two libraries out of 24) in breast cancer. These data were verified by screening the EST databases, where all mRNA clones isolated for these genes, except for one in each, were from normal breast libraries, with no clones detected from breast cancer tissues or cell lines (with the exception of KLK8). X-profiler comparison of two pools of normal and breast cancer libraries further verified the presence of significant downregulation of expression levels of 4 of the kallikreins genes (KLK5, 6, 10, 12). We experimentally verified the downregulation of these four kallikreins (KLK5, 6, 8, 10 and 12) by RT – PCR analysis.

Universal tumor antigens as targets for immunotherapy

BACKGROUND

Clinically successful Ag-specific cancer immunotherapy depends on the identification of tumor-rejection Ags. Historically, tumor Ags have been identified by analyzing cancer patients' own T-cell or Ab responses.

METHODS

The unveiling of the human genome and optimized immunological analytical tools, particularly 'reverse immunology', have made it possible to screen any given protein for immunogenic epitopes. These advances enable the immunological characterization of universal tumor-associated gene products that mediate critical functions for tumor growth and development.

RESULTS

Four examples of candidate universal tumor Ags reviewed here include the telomerase reverse transcriptase (hTERT), the inhibitor of apoptosis survivin, the p53-interacting protein MDM2, and the cytochrome P450 isoform 1B1--each at various levels of preclinical and clinical development.

DISCUSSION

The cardinal feature of universal TAA is that they are expressed in (nearly) all tumors and in no normal tissues. They are directly involved in the malignant phenotype of the tumor. Certain peptides derived from such Ags are expressed on the tumor-cell surface, as evidenced by Ag-specific, MHC-restricted T-cell anti-tumor reactivity in vitro. It is hoped that these features imply a pre-existing, high-affinity T-cell pool that can be activated in vivo in patients, without immunoselection of variant tumor cells no longer expressing the Ag of choice.

Tumor vasculature directed drug targeting: applying new technologies and knowledge to the development of clinically relevant therapies

Recognition of the dependence of solid tumor growth on the formation of new blood vessels has ignited an enormous research effort aimed at the development of new therapeutic strategies for cancer. Besides direct application of drugs inhibiting endothelial cell function during angiogenesis, tumor vasculature directed drug-targeting strategies have been investigated for this purpose. In animal models of disease, proof of principle regarding the potential of selective interference with tumor blood flow as a powerful tumor therapy has been generated to its full extent. The challenge for the coming years will be to develop these strategies into clinically applicable ones. New insights into the molecular mechanisms prevailing in the endothelium during angiogenesis and into the mechanism(s) of action of drugs with anti-angiogenic activities, as well as new techniques to identify useful tumor endothelium specific target epitopes have in recent years been exploited to meet this challenge. This review summarizes vasculature directed therapeutic strategies proven to be successful in pre-clinical models and new (drug targeting) technologies enabling the development of more effective therapeutics for the treatment of cancer.

Cancer Molecular Analysis Project: weaving a rich cancer research tapestry

The Cancer Molecular Analysis Project (CMAP) of the NCI is integrating diverse cancer research data to elucidate fundamental etiologic processes, enable development of novel therapeutic approaches, and facilitate the bridging of basic and clinical science.

Online genomics facilities in the new millennium

The review begins by providing a brief typology of biological databases on the Internet, illustrated by examples of the most influential resources of each kind. We then take an insider look at one typical on-line genomic resource -- the yeast genome database hosted at the Munich Information Center for Protein Sequences (MIPS) -- and explain how and why it has evolved from a basic sequence repository to a multidomain knowledge base. The role of community efforts in curating and annotating genome data is discussed. The crucial role of data integration and interoperability in developing next-generation genomic facilities is underscored.