Biomedical subjects

Daniel R Rhodes

Publications and source records attributed to Daniel R Rhodes.

13 recordsLinked to original sources

PubMed2005

Integrative analysis of the cancer transcriptome.

DNA microarrays have been widely applied to the study of human cancer, delineating myriad molecular subtypes of cancer, many of which are associated with distinct biological underpinnings, disease progression and treatment response. These primary analyses have begun to decipher the molecular heterogeneity of cancer, but integrative analyses that evaluate cancer transcriptome data in the context of other data sources are often capable of extracting deeper biological insight from the data. Here we discuss several such integrative computational and analytical approaches, including meta-analysis, functional enrichment analysis, interactome analysis, transcriptional network analysis and integrative model system analysis.

Animals↗

PubMed2005

Mining for regulatory programs in the cancer transcriptome.

DNA microarrays have been widely applied to cancer transcriptome analysis. The Oncomine database contains a large collection of such data, as well as hundreds of derived gene-expression signatures. We studied the regulatory mechanisms responsible for gene deregulation in these cancer signatures by searching for the coordinate regulation of genes with common transcription factor binding sites. We found that genes with binding sites for the archetypal cancer transcription factor, E2F, were disproportionately overexpressed in a wide variety of cancers, whereas genes with binding sites for other transcription factors, such as Myc-Max, c-Rel and ATF, were disproportionately overexpressed in specific cancer types. These results suggest that alterations in pathways activating these transcription factors may be responsible for the observed gene deregulation and cancer pathogenesis.

Binding Sites↗

PubMed2004

Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.

Cell Transformation, Neoplastic↗

PubMed2004

Humoral immune response to alpha-methylacyl-CoA racemase and prostate cancer.

BACKGROUND: Although prostate-specific antigen (PSA) is a prototypic biomarker for prostate cancer, it has poor specificity. Expression of alpha-methylacyl-CoA racemase (AMACR), which is involved in the conversion of R-stereoisomers of branched-chain fatty acids to S-stereoisomers, has been shown to be specifically increased in prostate cancer epithelia. However, attempts to detect AMACR in circulation have not been successful. Hence, we determined whether an immune response to AMACR could be used as a serum biomarker for prostate cancer. METHODS: Sera from patients with biopsy-proven prostate cancer and from control subjects were screened for a humoral immune response to selected tumor antigens, including AMACR, by using protein microarrays (46 patients, 28 control subjects). Humoral immune response to AMACR was then validated using high-throughput immunoblot analysis (151 patients, 259 control subjects) and enzyme-linked immunosorbent assay (ELISA) (54 patients, 55 control subjects). Receiver operating characteristic curves were used to determine the sensitivity and specificity of the immune response to AMACR to detect prostate cancer. RESULTS: Immunoreactivity against AMACR was statistically significantly higher in sera from patients with prostate cancer than in control subjects by all three techniques (P(protein microarray) =.009, P(immunoblot)<.001, P(ELISA) =.011). High-throughput immunoblot analysis revealed that, in subjects with intermediate PSA levels (4-10 ng/mL), the immune response against AMACR was more sensitive and specific than was PSA in distinguishing sera from prostate cancer patients relative to control subjects (sensitivity and specificity of 77.8% and 80.6% versus 45.6% and 50%, respectively; area under the curve of 0.789 versus 0.492; P<.001). CONCLUSION: Assays to detect a humoral immune response against AMACR may have the potential to supplement PSA screening in identifying patients with clinically significant prostate cancer, especially those with intermediate PSA levels.

Adult↗

PubMed2004

Overexpression, amplification, and androgen regulation of TPD52 in prostate cancer.

Gains in the long arm of chromosome 8 (8q) are believed to be associated with poor outcome and the development of hormone-refractory prostate cancer. Based on a meta-analysis of gene expression microarray data from multiple prostate cancer studies (D. R. Rhodes et al., Cancer Res 2002;62:4427-33), a candidate oncogene, Tumor Protein D52 (TPD52), was identified in the 8q21 amplicon. TPD52 is a coiled-coil motif-bearing protein, potentially involved in vesicle trafficking. Both mRNA and protein levels of TPD52 were highly elevated in prostate cancer tissues. Array comparative genomic hybridization and amplification analysis using single nucleotide polymorphism arrays demonstrated increased DNA copy number in the region encompassing TPD52. Fluorescence in situ hybridization on tissue microarrays confirmed TPD52 amplification in prostate cancer epithelia. Furthermore, our studies suggest that TPD52 protein levels may be regulated by androgens, consistent with the presence of androgen response elements in the upstream promoter of TPD52. In summary, these findings suggest that dysregulation of TPD52 by genomic amplification and androgen induction may play a role in prostate cancer progression.

Adult↗

PubMed2004

Bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers.

The DNA microarray has revolutionized cancer research. Now, scientists can obtain a genome-wide perspective of cancer gene expression. One potential application of this technology is the discovery of novel cancer biomarkers for more accurate diagnosis and prognosis, and potentially for the earlier detection of disease or the monitoring of treatment effectiveness. Because microarray experiments generate a tremendous amount of data and because the number of laboratories generating microarray data is rapidly growing, new bioinformatics strategies that promote the maximum utilization of such data are necessary. Here, we describe a method to validate multiple microarray data sets, a Web-based cancer microarray database for biomarker discovery, and methods for integrating gene ontology annotations with microarray data to improve candidate biomarker selection.

Biomarkers, Tumor↗

PubMed2003

Multiplex biomarker approach for determining risk of prostate-specific antigen-defined recurrence of prostate cancer.

BACKGROUND: Molecular signatures in cancer tissue may be useful for diagnosis and are associated with survival. We used results from high-density tissue microarrays (TMAs) to define combinations of candidate biomarkers associated with the rate of prostate cancer progression after radical prostatectomy that could identify patients at high risk for recurrence. METHODS: Fourteen candidate biomarkers for prostate cancer for which antibodies are available included hepsin, pim-1 kinase, E-cadherin (ECAD; cell adhesion molecule), alpha-methylacyl-coenzyme A racemase, and EZH2 (enhancer of zeste homolog 2, a transcriptional repressor). TMAs containing more than 2000 tumor samples from 259 patients who underwent radical prostatectomy for localized prostate cancer were studied with these antibodies. Immunohistochemistry results were evaluated in conjunction with clinical parameters associated with prostate cancer progression, including tumor stage, Gleason score, and prostate-specific antigen (PSA) level. Recurrence was defined as a postsurgery PSA level of more than 0.2 ng/mL. All statistical tests were two-sided. RESULTS: Moderate or strong expression of EZH2 coupled with at most moderate expression of ECAD (i.e., a positive EZH2:ECAD status) was the biomarker combination that was most strongly associated with the recurrence of prostate cancer. EZH2:ECAD status was statistically significantly associated with prostate cancer recurrence in a training set of 103 patients (relative risk [RR] = 2.52, 95% confidence interval [CI] = 1.09 to 5.81; P =.021), in a validation set of 80 patients (RR = 3.72, 95% CI = 1.27 to 10.91; P =.009), and in the combined set of 183 patients (RR = 2.96, 95% CI = 1.56 to 5.61; P<.001). EZH2:ECAD status was statistically significantly associated with disease recurrence even after adjusting for clinical parameters, such as tumor stage, Gleason score, and PSA level (hazard ratio = 3.19, 95% CI = 1.50 to 6.77; P =.003). CONCLUSION: EZH2:ECAD status was statistically significantly associated with prostate cancer recurrence after radical prostatectomy and may be useful in defining a cohort of high-risk patients.

Adult↗

PubMed2003

Dysregulation of the annexin family protein family is associated with prostate cancer progression.

Hormone refractory prostate cancer (PCa) is invariably lethal despite aggressive clinical treatment strategies. Detection strategies are needed to identify aggressive PCa before it becomes widely disseminated. Recently, two studies identified annexin 1 and 7 as potential biomarkers in the development of PCa progression. The annexins are a group of calcium-binding structural proteins that may play a role in the regulation of membrane trafficking, cellular adhesion, and cell signaling. Therefore the goal of this study is to simultaneously characterize the multiple members of the annexin family of genes in advanced PCa. Prostate samples from men with advanced hormone refractory PCa were compared to samples of hormone-naïve PCa and noncancerous prostate tissue. Samples from 15 patients with advanced hormone refractory PCa were used. To examine the annexin family, gene expression profiles from 21 noncancerous prostate tissues, 16 clinically localized PCas, and 20 hormone refractory PCa samples were used. By cDNA microarray analysis, annexins 1, 2, 4, 7, and 11 were significantly decreased in hormone refractory PCa when compared to localized hormone-naïve PCa with 2.2-, 1.5-, 1.3-, 1.4-, and 1.8-fold decreases, respectively (all P values <0.05). Interstudy validation of annexin family transcript expression was performed by meta-analysis of three other published prostate profiling studies. High-density tissue microarrays were used to validate a subset of annexins at the protein level by immunohistochemistry. Tissue microarray analysis revealed a significant decrease in protein expression for annexins 1, 2, 4, 7, and 11 in hormone refractory PCa as compared to localized PCa with 1.68-, 2.46-, 2.52-, and 3.01-fold decreases, respectively (Kruskal Wallis test, all P values P < 0.05). However, no significant differences were detected between the clinically localized PCa and noncancerous prostate tissues. These findings suggest that down-regulation of several members of the annexin family may contribute to PCa tumorigenesis. Annexins 1, 2, 4, 7, and 11 may play a role in tumor progression through distinct mechanisms or, alternatively, they may have redundant tumor suppressor activities. This study also suggests that a meta-analysis of existing gene expression data is useful in confirming findings from individual studies. Finally, down-regulation of several annexin family members may play a role in the development of the lethal PCa phenotype.

Adult↗

PubMed2003

Prostate cancer biomarkers: a current perspective.

Genomic- and proteomic-based studies have led to the identification of a large number of candidate biomarkers, as well as signature patterns of multiple markers for prostate cancer diagnosis, disease progression and prediction of survival. While these candidates include the usual suspects, including oncogenes, proliferation markers and cytoskeletal proteins, there are many additional unexpected molecules such as those involved in processes such as transcriptional repression and fatty acid metabolism. Patterns of expression serving as useful biomarkers is a new and, as yet, clinically untested concept which promises to permit a consideration of the complex milieu of cancer. Exciting as these developments are, clinical application will have to await careful validation of these candidates by independent biochemical approaches over large and diverse samples.

Biomarkers, Tumor↗

PubMed2002

Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.

The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer profiling studies. To extract maximum value from the accumulating mass of publicly available cancer gene expression data, methods are needed to evaluate, integrate, and intervalidate multiple datasets. Here we demonstrate a statistical model for performing meta-analysis of independent microarray datasets. Implementation of this model revealed that four prostate cancer gene expression datasets shared significantly similar results, independent of the method and technology used (i.e., spotted cDNA versus oligonucleotide). This interstudy cross-validation approach generated a cohort of genes that were consistently and significantly dysregulated in prostate cancer. Bioinformatic investigation of these genes revealed a synchronous network of transcriptional regulation in the polyamine and purine biosynthesis pathways. Beyond the specific implications for prostate cancer, this work establishes a much-needed model for the evaluation, cross-validation, and comparison of multiple cancer profiling studies.

Adenosine Monophosphate↗

PubMed2002

CIT: identification of differentially expressed clusters of genes from microarray data.

Cluster Identification Tool (CIT) is a microarray analysis program that identifies differentially expressed genes. Following division of experimental samples based on a parameter of interest, CIT uses a statistical discrimination metric and permutation analysis to identify clusters of genes or individual genes that best differentiate between the experimental groups. CIT integrates with the freely available CLUSTER and TREEVIEW programs to form a more complete microarray analysis package.

Algorithms↗

PubMedDate not supplied

ONCOMINE: a cancer microarray database and integrated data-mining platform.

DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.

Databases, Genetic↗

PubMedDate not supplied

DNA microarrays: implications for clinical medicine.

DNA microarrays are capable of measuring the expression of thousands of genes in a single assay. This technology has led to an explosion in global gene expression profiling studies, allowing researchers to more fully explore a multitude of complex biological systems ranging from the yeast cell cycle to the progression of cancer. By analyzing collections of these global profiles, the molecular underpinnings of biological phenomena are being unraveled. It is the goal of this review to address the basic principles of the technology and methodology employed in gene expression profiling with DNA microarrays, and then to highlight recent studies with particular importance for clinical medicine, focusing on cancer.

Gene Expression Regulation, Neoplastic↗