Differential expression of apoptotic genes PDIA3 and MAP3K5 distinguishes between low- and high-risk prostate cancer.

BACKGROUND: Despite recent progress in the identification of genetic and molecular alterations in prostate cancer, markers associated with tumor progression are scarce. Therefore precise diagnosis of patients and prognosis of the disease remain difficult. This study investigated novel molecular markers discriminating between low and highly aggressive types of prostate cancer. RESULTS: Using 52 microdissected cell populations of low- and high-risk prostate tumors, we identified via global cDNA microarrays analysis almost 1200 genes being differentially expressed among these groups. These genes were analyzed by statistical, pathway and gene enrichment methods. Twenty selected candidate genes were verified by quantitative real time PCR and immunohistochemistry. In concordance with the mRNA levels, two genes MAP3K5 and PDIA3 exposed differential protein expression. Functional characterization of PDIA3 revealed a pro-apoptotic role of this gene in PC3 prostate cancer cells. CONCLUSIONS: Our analyses provide deeper insights into the molecular changes occurring during prostate cancer progression. The genes MAP3K5 and PDIA3 are associated with malignant stages of prostate cancer and therefore provide novel potential biomarkers.

Down-regulation of HLA Class I and NKG2D ligands through a concerted action of MAPK and DNA methyltransferases in colorectal cancer cells.

Most malignant features of cancer cells are triggered by activated oncogenes and the loss of tumor suppressors due to mutation or epigenetic inactivation. It is still unclear, to what extend the escape of emerging cancer cells from recognition and elimination by the immune system is determined by similar mechanisms. We compared the transcriptomes of HCT116 colorectal cancer cells deficient in DNA methyltransferases (DNMTs) and of cells, in which the RAS pathway as the major growth-promoting signaling system is blocked by inhibition of MAPK. We identified the MHC Class I genes HLA-A1/A2 and the ULBP2 gene encoding 1 of the 8 known ligands of the activating NK receptor NKG2D among a cluster of immune genes up-regulated under the conditions of both DNMT-deficiency and MEK-inhibition. Bisulphite sequencing analyses of HCT116 with DNMT deficiency or after MEK-inhibition showed that de-methylation of the ULPB2 promoter correlated with its enhanced surface expression. The HLA-A promoters were not methylated indicating that components of the HLA assembly machinery were also suppressed in DNMT-deficient and MEK-inhibited cells. Increased HLA-A2 surface expression was correlated with enhanced recognition and lysis by A2-specific CTL. On the contrary, elevated ULBP2 expression was not reflected by enhanced recognition and lysis by NK cells. Cosuppression of HLA Class I and NKG2D ligands and genes encoding peptide transporters or proteasomal genes mediates a strong functional link between RAS activation, DNMT activity and disruption of the antigen presenting system controlling immune recognition in colorectal cancer cells.

Classification across gene expression microarray studies.

BACKGROUND: The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing. RESULTS: For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios. CONCLUSIONS: We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.

Global gene expression analysis reveals specific patterns of cell junctions in non-small cell lung cancer subtypes.

Non-small cell lung cancer (NSCLC) can be classified into the major subtypes adenocarcinoma (AC) and squamous cell carcinoma (SCC). Although explicit molecular, histological and clinical characteristics have been reported for both subtypes, no specific therapy exists so far. However, the characterization of suitable molecular targets holds great promises to develop novel therapies in NSCLC. In the present study, global gene expression profiling of 58 human NSCLC specimens revealed large transcriptomic differences between AC and SCC subtypes: more than 1700 genes were found to be differentially expressed. The assignment of these genes to biological processes pointed to the deregulation of distinct sets of genes coding for cell junctions in both tumor subtypes. We focused on 17 cell adhesion genes and 11 reported marker genes for epithelial-mesenchymal transition (EMT), and investigated their expression in matched tumor-normal specimens by quantitative real-time PCR. The majority of the cell adhesion genes was significantly up-regulated in at least one tumor subtype compared to normal tissue, predominantly desmosomes and gap junctions in SCC, and tight junctions in AC. The higher expression of EMT marker transcripts in tumor specimens suggested a large potential for invasion and migration processes in NSCLC. Our results indicate that AC and SCC in the lung are characterized by the expression of distinct sets of cell adhesion molecules which may represent promising targets for novel specific therapies.

Molecular cancer phenotype in normal prostate tissue.

BACKGROUND: Insufficient sensitivity and specificity of prostate biopsies for cancer detection. OBJECTIVES: Based on evidence from our microarray analyses, we hypothesized that considerable molecular changes precede morphologically detectable malignant transformation of prostate epithelial tissues. The identification of such changes could lead to novel strategies in the clinical management of prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: Histologically normal, fresh prostate tissue from prostate cancer patients, healthy donors, and cancer suspect patients with continuous negative biopsies were analyzed. MEASUREMENTS: To identify molecular changes between 29 tumor-free prostate tissues from healthy donors and 27 patients with proven prostate cancer, we performed a global microarray screening. Based on this screening as well as literature data, we selected a subset of 29 genes for validation by arrayed real-time reverse transcription-polymerase chain reaction (RT-PCR) using histologically tumor-free biopsy samples from 114 patients representing three prostate cancer risk groups. RESULTS AND LIMITATIONS: We identified five genes (FOS, EGR1, MYC, TFRC, and FOLH1), which displayed significant differential expression between morphologically normal prostate tissues from men of each of the three risk groups. These results were independent from age, prostate-specific antigen (PSA), frequency and timing of previous prostate biopsies, tissue composition, tumor stage, and tumor grade. In univariate logistic regression analyses, the transcript levels of these genes were found to be highly indicative for the presence or absence of cancer in the entire prostate. The study was designed as a proof of principle. The clinical relevance of our results has to be evaluated in a larger clinical setting. CONCLUSIONS: Our results suggest a measurable molecular cancer phenotype in histologically normal prostate tissue indicating the presence of prostate cancer elsewhere in the organ.

Genomic analysis reveals poor separation of human cardiomyopathies of ischemic and nonischemic etiologies.

Clinically, the differentiation between ischemic (ICM) and nonischemic (NICM) human cardiomyopathies is highly relevant, because ICM and NICM differ with respect to prognosis and certain aspects of pharmacological therapy, despite a common final phenotype characterized by ventricular dilatation and reduced contractility. So far, it is unclear whether microarray-based signatures can be used to infer the etiology of heart failure. Using three different classification algorithms, we independently analyzed one cDNA and two publicly available high-density oligonucleotide microarray studies comprising a total of 279 end-stage human heart failure samples. When classifiers identified in a single study were applied to the remaining studies, misclassification rates >25% for ICM and NICM specimens were noted, indicating poor separation of both etiologies. However, data mining of 458 classifier genes that were concordantly identified in at least two of the three data sets points to different biological processes in ICM vs. NICM. Consistent with the underlying ischemia, cytokine signaling pathways and immediate-early response genes were overrepresented in ICM samples, whereas NICM samples displayed a deregulation of cytoskeletal transcripts, genes encoding for the major histocompatibility complex, and antigen processing and presentation pathways, potentially pointing to immunologic processes in NICM. Overall, our results suggest that ICM and NICM exhibit substantial heterogeneity at the transcriptomic level. Prospective studies are required to test whether etiology-specific gene expression patterns are present at earlier disease stages or in subsets of both etiologies.

Contact spotting of protein microarrays coupled with spike-in of normalizer protein permits time-resolved analysis of ERBB receptor signaling.

Protein microarrays allow highly accurate comparison and quantification of numerous biological samples in parallel while requiring only little material. This qualifies protein arrays for systems biology and clinical research where only limited sample material is available, but a precise readout is required. With the introduction of signal normalization steps to monitor the drop size of manually contact-spotted RP protein arrays, the usefulness of normalizer proteins to ensure a high-throughput but inexpensive protein analysis was demonstrated. This approach was applied for the analysis of signaling through ERBB receptor activated kinases in the breast cancer cell line MCF-7. Activation of ERK1/2 and AKT by ERBB1 (EGFR), ERRB2 (HER2/neu), and ERBB3-4 was monitored in a time-resolved manner. Analysis of pathway activation by stimulation with epidermal growth factor and heregulin, or inhibition by blocking with gefitinib or herceptin allowed a characterization of the distinct signaling properties of the different ERBB receptor subtypes.

Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer.

MOTIVATION: In cancer, chromosomal imbalances like amplifications and deletions, or changes in epigenetic mechanisms like DNA methylation influence the transcriptional activity. These alterations are often not limited to a single gene but affect several genes of the genomic region and may be relevant for the disease status. For example, the ERBB2 amplicon (17q21) in breast cancer is associated with poor patient prognosis. We present a general, unsupervised method for genome-wide gene expression data to systematically detect tumor patients with chromosomal regions of distinct transcriptional activity. The method aims to find expression patterns of adjacent genes with a consistently decreased or increased level of gene expression in tumor samples. Such patterns have been found to be associated with chromosomal aberrations and clinical parameters like tumor grading and thus can be useful for risk stratification or therapy. RESULTS: Our approach was applied to 12 independent human breast cancer microarray studies comprising 1422 tumor samples. We prioritized chromosomal regions and genes predominantly found across all studies. The result highlighted not only regions which are well known to be amplified like 17q21 and 11q13, but also others like 8q24 (distal to MYC) and 17q24-q25 which may harbor novel putative oncogenes. Since our approach can be applied to any microarray study it may become a valuable tool for the exploration of transcriptional changes in diverse disease types. AVAILABILITY: The R source codes which implement the method and an exemplary analysis are available at http://www.dkfz.de/mga2/people/buness/CTP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification and meta-analysis of a small gene expression signature for the diagnosis of estrogen receptor status in invasive ductal breast cancer.

In breast cancer, the determination of estrogen receptor (ER) expression is crucial for the decision on therapeutic strategies. Current ER expression analysis is based on immunohistochemical (IHC) staining of ER on formalin fixed tissue sections. However, low levels of ER expression frequently escape detection because of varying sensitivities of routine histopathological laboratories. Moreover, in estimating ER by IHC the receptor protein only is tested instead of the complex underlying ER pathway, which reflects its biological activity. To overcome this limitation, we have used the microarray technology to study 56 samples of invasive ductal carcinoma. We infer a robust and reliable signature of 10 genes, which is associated with ER expression and presumably therapeutically relevant biological processes. In a meta-analysis, the signature was tested on 3 further independent microarray gene expression data sets, covering different laboratories, array platforms, and clinics. The classification based on the signature showed a very low misclassification rate. In summary, the expression of few genes is sufficient to determine ER status. Future decisions on antiestrogen based therapy in breast cancer could be based on this signature rather than on immunostaining alone.

Identification of a common gene expression signature in dilated cardiomyopathy across independent microarray studies.

OBJECTIVES: This study was designed to identify a common gene expression signature in dilated cardiomyopathy (DCM) across different microarray studies. BACKGROUND: Dilated cardiomyopathy is a common cause of heart failure in Western countries. Although gene expression arrays have emerged as a powerful tool for delineating complex disease patterns, differences in platform technology, tissue heterogeneity, and small sample sizes obscure the underlying pathophysiologic events and hamper a comprehensive interpretation of different microarray studies in heart failure. METHODS: We accounted for tissue heterogeneity and technical aspects by performing 2 genome-wide expression studies based on cDNA and short-oligonucleotide microarray platforms which comprised independent septal and left ventricular tissue samples from nonfailing (NF) (n = 20) and DCM (n = 20) hearts. RESULTS: Concordant results emerged for major gene ontology classes between cDNA and oligonucleotide microarrays. Notably, immune response processes displayed the most pronounced down-regulation on both microarray types, linking this functional gene class to the pathogenesis of end-stage DCM. Furthermore, a robust set of 27 genes was identified that classified DCM and NF samples with >90% accuracy in a total of 108 myocardial samples from our cDNA and oligonucleotide microarray studies as well as 2 publicly available datasets. CONCLUSIONS: For the first time, independent microarray datasets pointed to significant involvement of immune response processes in end-stage DCM. Moreover, based on 4 independent microarray datasets, we present a robust gene expression signature of DCM, encouraging future prospective studies for the implementation of disease biomarkers in the management of patients with heart failure.

Analysis of pigmented villonodular synovitis with genome-wide complementary DNA microarray and tissue array technology reveals insight into potential novel therapeutic approaches.

OBJECTIVE: To characterize the gene expression profile and determine potential diagnostic markers and therapeutic targets in pigmented villonodular synovitis (PVNS). METHODS: Gene expression patterns in 11 patients with PVNS, 18 patients with rheumatoid arthritis (RA), and 19 patients with osteoarthritis (OA) were investigated using genome-wide complementary DNA microarrays. Validation of differentially expressed genes was performed by real-time quantitative polymerase chain reaction and immunohistochemical analysis on tissue arrays (80 patients with PVNS, 51 patients with RA, and 20 patients with OA). RESULTS: The gene expression profile in PVNS was clearly distinct from those in RA and OA. One hundred forty-one up-regulated genes and 47 down-regulated genes were found in PVNS compared with RA, and 153 up-regulated genes and 89 down-regulated genes were found in PVNS compared with OA (fold change > or = 1.5; Q < or = 0.001). Genes differentially expressed in PVNS were involved in apoptosis regulation, matrix degradation, and inflammation (ALOX5AP, ATP6V1B2, CD53, CHI3L1, CTSL, CXCR4, HSPA8, HSPCA, LAPTM5, MMP9, MOAP1, and SPP1). CONCLUSION: The gene expression signature in PVNS is similar to that of activated macrophages and is consistent with the local destructive course of the disease. The gene and protein expression patterns suggest that the ongoing proliferation in PVNS is sustained by apoptosis resistance. This result suggests the possibility of a potential novel therapeutic intervention against PVNS.

Functional profiling: from microarrays via cell-based assays to novel tumor relevant modulators of the cell cycle.

Cancer transcription microarray studies commonly deliver long lists of "candidate" genes that are putatively associated with the respective disease. For many of these genes, no functional information, even less their relevance in pathologic conditions, is established as they were identified in large-scale genomics approaches. Strategies and tools are thus needed to distinguish genes and proteins with mere tumor association from those causally related to cancer. Here, we describe a functional profiling approach, where we analyzed 103 previously uncharacterized genes in cancer relevant assays that probed their effects on DNA replication (cell proliferation). The genes had previously been identified as differentially expressed in genome-wide microarray studies of tumors. Using an automated high-throughput assay with single-cell resolution, we discovered seven activators and nine repressors of DNA replication. These were further characterized for effects on extracellular signal-regulated kinase 1/2 (ERK1/2) signaling (G1-S transition) and anchorage-independent growth (tumorigenicity). One activator and one inhibitor protein of ERK1/2 activation and three repressors of anchorage-independent growth were identified. Data from tumor and functional profiling make these proteins novel prime candidates for further in-depth study of their roles in cancer development and progression. We have established a novel functional profiling strategy that links genomics to cell biology and showed its potential for discerning cancer relevant modulators of the cell cycle in the candidate lists from microarray studies.

A compendium to ensure computational reproducibility in high-dimensional classification tasks.

We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).