Preclinical evaluation of the combination of mTOR and proteasome inhibitors with radiotherapy in malignant peripheral nerve sheath tumors.

About one half of malignant peripheral nerve sheath tumors (MPNST) have Neurofibromin 1 (NF1) mutations. NF1 is a tumor suppressor gene essential for negative regulation of RAS signaling. Survival for MPNST patients is poor and we sought to identify an effective combination therapy. Starting with the mTOR inhibitors rapamycin and everolimus, we screened for synergy in 542 FDA approved compounds using MPNST cells with a native NF1 loss in both alleles. We further analyzed the cell cycle and signal transduction. In vivo growth effects of the drug combination with local radiation therapy (RT) were assessed in MPNST xenografts. The synergistic combination of mTOR inhibitors with bortezomib yielded a reduction in MPNST cell proliferation. The combination of mTOR inhibitors and bortezomib also enhanced the anti-proliferative effect of radiation in vitro. In vivo, the combination of mTOR inhibitor (everolimus) and bortezomib with RT decreased tumor growth and proliferation, and augmented apoptosis. The combination of approved mTOR and proteasome inhibitors with radiation showed a significant reduction of tumor growth in an animal model and should be investigated and optimized further for MPNST therapy.

Investigation of chromosome 1q reveals differential expression of members of the S100 family in clinical subgroups of intracranial paediatric ependymoma.

Gain of 1q is one of the most common alterations in cancer and has been associated with adverse clinical behaviour in ependymoma. The aim of this study was to investigate this region to gain insight into the role of 1q genes in intracranial paediatric ependymoma. To address this issue we generated profiles of eleven ependymoma, including two relapse pairs and seven primary tumours, using comparative genome hybridisation and serial analysis of gene expression. Analysis of 656 SAGE tags mapping to 1q identified CHI3L1 and S100A10 as the most upregulated genes in the relapse pair with de novo 1q gain upon recurrence. Moreover, three more members of the S100 family had distinct gene expression profiles in ependymoma. Candidates (CHI3L1, S100A10, S100A4, S100A6 and S100A2) were validated using immunohistochemistry on a tissue microarray of 74 paediatric ependymoma. In necrotic cases, CHI3L1 demonstrated a distinct staining pattern in tumour cells adjacent to the areas of necrosis. S100A6 significantly correlated with supratentorial tumours (P<0.001) and S100A4 with patients under the age of 3 years at diagnosis (P=0.038). In conclusion, this study provides evidence that S100A6 and S100A4 are differentially expressed in clinically relevant subgroups, and also demonstrates a link between CHI3L1 protein expression and necrosis in intracranial paediatric ependymoma.

What can digital transcript profiling reveal about human cancers?

Important biological and clinical features of malignancy are reflected in its transcript pattern. Recent advances in gene expression technology and informatics have provided a powerful new means to obtain and interpret these expression patterns. A comprehensive approach to expression profiling is serial analysis of gene expression (SAGE), which provides digital information on transcript levels. SAGE works by counting transcripts and storing these digital values electronically, providing absolute gene expression levels that make historical comparisons possible. SAGE produces a comprehensive profile of gene expression and can be used to search for candidate tumor markers or antigens in a limited number of samples. The Cancer Genome Anatomy Project has created a SAGE database of human gene expression levels for many different tumors and normal reference tissues and provides online tools for viewing, comparing, and downloading expression profiles. Digital expression profiling using SAGE and informatics have been useful for identifying genes that have a role in tumor invasion and other aspects of tumor progression

What can digital transcript profiling reveal about human cancers?

Important biological and clinical features of malignancy are reflected in its transcript pattern. Recent advances in gene expression technology and informatics have provided a powerful new means to obtain and interpret these expression patterns. A comprehensive approach to expression profiling is serial analysis of gene expression (SAGE), which provides digital information on transcript levels. SAGE works by counting transcripts and storing these digital values electronically, providing absolute gene expression levels that make historical comparisons possible. SAGE produces a comprehensive profile of gene expression and can be used to search for candidate tumor markers or antigens in a limited number of samples. The Cancer Genome Anatomy Project has created a SAGE database of human gene expression levels for many different tumors and normal reference tissues and provides online tools for viewing, comparing, and downloading expression profiles. Digital expression profiling using SAGE and informatics have been useful for identifying genes that have a role in tumor invasion and other aspects of tumor progression.

An international database and integrated analysis tools for the study of cancer gene expression.

Researchers working collaboratively in Brazil and the United States have assembled an International Database of Cancer Gene Expression. Several strategies have been employed to generate gene expression data including expressed sequence tags (ESTs), serial analysis of gene expression (SAGE), and open reading-frame expressed sequence tags (ORESTES). The database contains six million gene tags that reflect the gene expression profiles in a wide variety of cancerous tissues and their normal counterparts. All sequences are deposited in the public databases, GenBank and SAGEmap. A suite of informatics tools was designed to facilitate in silico analysis of the gene expression datasets and are available through the NCI Cancer Genome Anatomy Project web site (http://cgap.nci.nih.gov).

Using Serial Analysis of Gene Expression to identify tumor markers and antigens.

Tumor markers and antigens are normally highly expressed in malignant tissue, but not in the surrounding normal tissue. Serial Analysis of Gene Expression (SAGE) is a technology that counts mRNA transcripts and can be used to find those genes most highly induced in malignant tissues. SAGE produces a comprehensive profile of gene expression and can be used to search for tumor biomarkers in a limited number of samples. Public sources of SAGE data, in particular through the Cancer Genome Anatomy Project, increase the value of this technology by making a large source of information on many tumors and normal tissues available for comparison. Although the perfect tumor-specific gene does not exist, the differences in gene expression between tumor and normal can be exploited for therapeutic or diagnostic purposes.

Transcriptional response to hypoxia in human tumors.

BACKGROUND: The presence of hypoxic regions within solid tumors is associated with a more malignant tumor phenotype and worse prognosis. To obtain a blood supply and protect against cellular damage and death, oxygen-deprived cells in tumors alter gene expression, resulting in resistance to therapy. To investigate the mechanisms by which cancer cells adapt to hypoxia, we looked for novel hypoxia-induced genes. METHODS: The transcriptional response to hypoxia in human glioblastoma cells was quantified with the use of serial analysis of gene expression. The time course of gene expression in response to hypoxia in a panel of various human tumor cell lines was measured by real-time polymerase chain reaction. Hypoxic regions of human carcinomas were chemically marked with pimonidazole. Immunohistochemistry and in situ hybridization were used to examine gene expression in the tumor's hypoxic regions. RESULTS: From the 24 504 unique transcripts expressed, 10 new hypoxia-regulated genes were detected-all induced, to a greater extent than vascular endothelial growth factor, a hypoxia-induced mitogen that promotes blood vessel growth. These genes also responded to hypoxia in breast and colon cancer cells and were activated by hypoxia-inducible factor 1, a key regulator of hypoxic responses. In tumors, gene expression was limited to hypoxic regions. Induced genes included hexabrachion (an extracellular matrix glycoprotein), stanniocalcin 1 (a calcium homeostasis protein), and an angiopoietin-related gene. CONCLUSIONS: We have identified the genes that are transcriptionally activated within hypoxic malignant cells, a crucial first step in understanding the complex interactions driving hypoxia response. Within our catalogue of hypoxia-responsive genes are novel candidates for hypoxia-driven angiogenesis.

Gene discovery using the serial analysis of gene expression technique: implications for cancer research.

Cancer is a genetic disease. As such, our understanding of the pathobiology of tumors derives from analyses of the genes whose mutations are responsible for those tumors. The cancer phenotype, however, likely reflects the changes in the expression patterns of hundreds or even thousands of genes that occur as a consequence of the primary mutation of an oncogene or a tumor suppressor gene. Recently developed functional genomic approaches, such as DNA microarrays and serial analysis of gene expression (SAGE), have enabled researchers to determine the expression level of every gene in a given cell population, which represents that cell population's entire transcriptome. The most attractive feature of SAGE is its ability to evaluate the expression pattern of thousands of genes in a quantitative manner without prior sequence information. This feature has been exploited in three extremely powerful applications of the technology: the definition of transcriptomes, the analysis of differences between the gene expression patterns of cancer cells and their normal counterparts, and the identification of downstream targets of oncogenes and tumor suppressor genes. Comprehensive analyses of gene expression not only will further understanding of growth regulatory pathways and the processes of tumorigenesis but also may identify new diagnostic and prognostic markers as well as potential targets for therapeutic intervention.

Ham56-immunoreactive macrophages in untreated infiltrating gliomas.

CONTEXT: Classic diagnostic neuropathologic teachings have cautioned against making the diagnosis of neoplasia in the presence of a macrophage population. The knowledge of macrophage distribution should prove useful when confronted with an infiltrating glioma containing macrophages. OBJECTIVE: To identify macrophages in untreated, infiltrating gliomas using the monoclonal antibody HAM56, and to confirm their presence in an untreated glioblastoma multiforme (GBM) with the serial analysis of gene expression (SAGE) method. METHODS: We evaluated the presence of macrophages in 16 cases of untreated, supratentorial infiltrating gliomas with the macrophage monoclonal antibody HAM56. We performed SAGE for one case of GBM and for normal brain tissue. RESULTS: In World Health Organization (WHO) grade II well-differentiated astrocytoma and oligodendroglioma, HAM56 reactivity was noted only in endothelial cells, and unequivocal macrophages were not identified. In WHO grade III anaplastic astrocytoma and anaplastic oligodendroglioma, rare HAM56-positive macrophages were noted in solid areas of tumor. In WHO grade IV GBM, HAM56-positive macrophages were identified in areas of solid tumor (mean labeling index, 8.6%). In all cases of GBM, nonquantitated HAM56-positive macrophages were identified in foci of pseudopalisading cells abutting necrosis and in foci of microvascular proliferations. In none of the cases were granulomas or microglial nodules found, and there was no prior history of surgical intervention, radiation therapy, chemotherapy, or head trauma in these cases. By SAGE, the macrophage-related proteins osteopontin and macrophage-capping protein were overexpressed 12-fold and eightfold, respectively, in one untreated GBM compared with normal brain tissue. In this case, numerous HAM56-positive macrophages (labeling index, 24.5%) were present in the solid portion of tumor, and abundant nonquantified macrophages were identified in foci of pseudopalisading cells abutting necrosis and in foci of microvascular proliferations. CONCLUSIONS: This study confirms the utility of the monoclonal antibody HAM56 in identifying macrophages within untreated infiltrating gliomas. The overexpression of macrophage-related proteins in one case of GBM as detected by SAGE signifies that macrophages may be present in untreated GBMs.

Genome and genetic resources from the Cancer Genome Anatomy Project.

The Cancer Genome Anatomy Project (CGAP) is a collaborative network of cancer researchers with a common goal: to decipher the genetic changes that occur during cancer formation and progression. The project brings together several recent technologies capable of high-throughput analysis to help achieve this goal. Automated sequencing of cDNA libraries is a primary focus and is geared towards providing a comprehensive and annotated set of human and mouse transcribed sequences. This effort includes full-length transcript sequence generated by CGAP's new Mammalian Gene Collection initiative. Single nucleotide polymorphisms (SNPs) within human gene sequences (Genetic Annotation Initiative) and chromosomal rearrangements within cancer cells (Cancer Chromosome Aberration Project) are also being cataloged as part of CGAP. Finally, to help determine gene expression patterns related to cancer, CGAP provides a quantitative catalog of data through its SAGEmap initiative. The genome and genetic analysis tools listed in this review are all freely distributed by CGAP (http://cgap.nci.nih.gov/) without restriction.

A database for regional gene expression in the human brain.

Alterations in gene expression levels have been widely studied for various neurological diseases, but few studies have sought to characterize genome-wide gene expression patterns from various regions of the normal brain. A sensitive method for quantifying transcript levels, Serial Analysis of Gene Expression (SAGE), was used to assay expression levels in white matter, thalamus, and cerebellum from the same normal brain, obtained by rapid autopsy. The complete dataset for these SAGE libraries are posted on the Cancer Genome Anatomy Project sponsored SAGEmap website where library comparisons can be made, or the data downloaded for local analysis. The expression of several region-specific genes--neurogenic differentiation 1 (cerebellum), cocaine- and amphetamine-regulated transcript (thalamus), and neurogranin (white matter)--was confirmed using quantitative fluorescent real-time RT-PCR. Further informatics analysis of the data yielded a list of brain-specific genes. The database formed by this analysis provides a means to investigate the expression status of genes involved in the region specific functions of the normal brain. These normal brain gene expression levels also are useful for comparison to pathological expression levels and a sensitive means to determine gene expression in a normal adult brain prior to formulating therapeutic strategies.

Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cancer.

Difficulties in the detection, diagnosis, and treatment of ovarian cancer result in an overall low survival rate of women with this disease. A better understanding of the pathways involved in ovarian tumorigenesis will likely provide new targets for early and effective intervention. Here, we have used serial analysis of gene expression (SAGE) to generate global gene expression profiles from various ovarian cell lines and tissues, including primary cancers, ovarian surface epithelia cells, and cystadenoma cells. The profiles were used to compare overall patterns of gene expression and to identify differentially expressed genes. We have sequenced a total of 385,000 tags, yielding >56,000 genes expressed in 10 different libraries derived from ovarian tissues. In general, ovarian cancer cell lines showed relatively high levels of similarity to libraries from other cancer cell lines, regardless of the tissue of origin (ovarian or colon), indicating that these lines had lost many of their tissue-specific expression patterns. In contrast, immortalized ovarian surface epithelia and ovarian cystadenoma cells showed much higher similarity to primary ovarian carcinomas than to primary colon carcinomas. Primary tissue specimens therefore appeared to be a better model for gene expression analyses. Using the expression profiles described above and stringent selection criteria, we have identified a number of genes highly differentially expressed between nontransformed ovarian epithelia and ovarian carcinomas. Some of the genes identified are already known to be overexpressed in ovarian cancer, but several represent novel candidates. Many of the genes up-regulated in ovarian cancer represent surface or secreted proteins such as claudin-3 and -4, HE4, mucin-1, epithelial cellular adhesion molecule, and mesothelin. Interestingly, both apolipoprotein E (ApoE) and ApoJ, two proteins involved in lipid homeostasis, are among the genes highly up-regulated in ovarian cancer. Selected serial analysis of gene expression results were further validated through immunohistochemical analysis of ApoJ, claudin-3, claudin-4, and epithelial cellular adhesion molecule in archival material. These experiments provided additional evidence of the relevance of our findings in vivo. The publicly available expression data reported here should stimulate and aid further research in the field of ovarian cancer.

Identifying potential tumor markers and antigens by database mining and rapid expression screening.

Genes expressed specifically in malignant tissue may have potential as therapeutic targets but have been difficult to locate for most cancers. The information hidden within certain public databases can reveal RNA transcripts specifically expressed in transformed tissue. To be useful, database information must be verified and a more complete pattern of tissue expression must be demonstrated. We tested database mining plus rapid screening by fluorescent-PCR expression comparison (F-PEC) as an approach to locate candidate brain tumor antigens. Cancer Genome Anatomy Project (CGAP) data was mined for genes highly expressed in glioblastoma multiforme. From 13 mined genes, seven showed potential as possible tumor markers or antigens as determined by further expression profiling. Now that large-scale expression information is readily available for many of the commonly occurring cancers, other candidate tumor markers or antigens could be located and evaluated with this approach.

Genes expressed in human tumor endothelium.

To gain a molecular understanding of tumor angiogenesis, we compared gene expression patterns of endothelial cells derived from blood vessels of normal and malignant colorectal tissues. Of over 170 transcripts predominantly expressed in the endothelium, 79 were differentially expressed, including 46 that were specifically elevated in tumor-associated endothelium. Several of these genes encode extracellular matrix proteins, but most are of unknown function. Most of these tumor endothelial markers were expressed in a wide range of tumor types, as well as in normal vessels associated with wound healing and corpus luteum formation. These studies demonstrate that tumor and normal endothelium are distinct at the molecular level, a finding that may have significant implications for the development of anti-angiogenic therapies.

SAGEmap: a public gene expression resource.

We have constructed a public gene expression data repository and online data access and analysis, WWW and FTP sites for serial analysis of gene expression (SAGE) data. The WWW and FTP components of this resource, SAGEmap, are located at http://www.ncbi.nlm.nih. gov/sage and ftp://ncbi.nlm.nih.gov/pub/sage, respectively. We herein describe SAGE data submission procedures, the construction and characteristics of SAGE tags to gene assignments, the derivation and use of a novel statistical test designed specifically for differential-type analyses of SAGE data, and the organization and use of this resource.

A public database for gene expression in human cancers.

A public database, SAGEmap, was created as a component of the Cancer Genome Anatomy Project to provide a central location for depositing, retrieving, and analyzing human gene expression data. This database uses serial analysis of gene expression to quantify transcript levels in both malignant and normal human tissues. By accessing SAGEmap (http://www.ncbi.nlm.nih.gov/SAGE) the user can compare transcript populations between any of the posted libraries. As an initial demonstration of the database's utility, gene expression in human glioblastomas was compared with that of normal brain white matter. Of the 47,174 unique transcripts expressed in these two tissues, 471 (1.0%) were differentially expressed by more than 5-fold (P<0.001). Classification of these genes revealed functions consistent with the biological properties of glioblastomas, in particular: angiogenesis, transcription, and cell cycle related genes.

Serial analysis of gene expression: probing transcriptomes for molecular targets.

Serial analysis of gene expression (SAGE) is a method to rapidly count expressed RNA transcripts in a population of cells. The basic approach is to isolate a small transcript tag, clone multiple tags into a sequencing vector and efficiently count the tags by automated sequencing. The result is the fractional representation of nearly every transcript (the transcriptome), in a digital format. These tag counts can be compared to other SAGE libraries yielding differentially expressed genes. Analysis of the differentially expressed genes has been used to determine which genes are involved in a disease process. The promise of this technology is that by understanding when genes are pathologically altered in expression, therapies can be formulated to target the appropriate gene or pathway.

Mutations of mitotic checkpoint genes in human cancers.

Genetic instability was one of the first characteristics to be postulated to underlie neoplasia. Such genetic instability occurs in two different forms. In a small fraction of colorectal and some other cancers, defective repair of mismatched bases results in an increased mutation rate at the nucleotide level and consequent widespread microsatellite instability. In most colorectal cancers, and probably in many other cancer types, a chromosomal instability (CIN) leading to an abnormal chromosome number (aneuploidy) is observed. The physiological and molecular bases of this pervasive abnormality are unknown. Here we show that CIN is consistently associated with the loss of function of a mitotic checkpoint. Moreover, in some cancers displaying CIN the loss of this checkpoint was associated with the mutational inactivation of a human homologue of the yeast BUB1 gene; BUB1 controls mitotic checkpoints and chromosome segregation in yeast. The normal mitotic checkpoints of cells displaying microsatellite instability become defective upon transfer of mutant hBUB1 alleles from either of two CIN cancers.

Frequency of Smad gene mutations in human cancers.

Much excitement has recently been generated by the discovery of the Smad genes, encoding proteins that transduce signals from the transforming growth factor beta family of cytokines. Here, we report the completion of cloning of the six known human Smads, providing novel sequences for Smad5 and Smad6. Previously, Smad4 and Smad2 were shown to be mutated in human cancers. However, analysis of the other four Smad genes revealed no mutations in a total of 167 tumors, including those from colon, breast, lung, and pancreas. These results suggest that the various Smad genes have different functions and demonstrate that mutations in these four genes do not, in general, account for the widespread resistance to transforming growth factor beta that is found in human tumors.