Differential expression of toll-like receptor genes: sepsis compared with sterile inflammation 1 day before sepsis diagnosis.

Toll-like receptors (TLRs) are critical components of innate immunity. This study was designed to evaluate differential expression of genes for TLR and associated signal transduction molecules in critically ill patients developing sepsis compared with those with sterile inflammation. Uninfected critically ill patients with systemic inflammatory response syndrome were prospectively followed daily for development of sepsis. They were divided into two groups and compared in a case-control manner: (a) preseptic patients (n = 45) who subsequently developed sepsis, and (b) uninfected systemic inflammatory response syndrome patients (n = 45) who remained uninfected. Whole blood RNA was collected (PAXGene tube) at study entry and 1, 2, and 3 days before clinical sepsis diagnosis (or time-matched uninfected control) and analyzed via Affymetrix Hg_U133 Plus 2.0 microarrays. Genes were considered differentially expressed if they met univariate significance controlled for multiple comparisons at P < 0.005. Differentially expressed probes were uploaded into the Database for Annotation, Visualization and Integrated Discovery. The TLR pathway (Kyoto Encyclopedia of Genes and Genomes-KEGG) significance was determined via Expression Analysis Systematic Explorer (EASE) scoring. A total of 2,974 Affymetrix probes representing 2,190 unique genes were differentially expressed 1 day before sepsis diagnosis. Thirty-six probes representing 25 genes were annotated to the TLR pathway (KEGG) via the Database for Annotation, Visualization and Integrated Discovery with an EASE score at P < 0.0004. Notable TLR genes demonstrating increased expression include TLR-4 (median, 1.43-fold change), TLR-5 (2.08-fold change), and MAPK14 (1.90-fold change). An additional 11 unique genes were manually annotated into the TLR pathway based on known relevance such as TLR-8 (1.54-fold change). The total 36 genes contained 28 showing increased expression and 8 showing decreased expression. Differential gene expression was noted for TLR receptors (eight genes), TLR intracellular signal transduction cascade molecules (27 genes), and TLR-related effector molecules (one gene). The TLR and downstream signaling genes are differentially expressed in critically ill patients developing sepsis compared with those with sterile inflammation. These expression differences occur before phenotypic-based diagnosis of clinical sepsis.

Credentialing preclinical pediatric xenograft models using gene expression and tissue microarray analysis.

Human tumor xenografts have been used extensively for rapid screening of the efficacy of anticancer drugs for the past 35 years. The selection of appropriate xenograft models for drug testing has been largely empirical and has not incorporated a similarity to the tumor type of origin at the molecular level. This study is the first comprehensive analysis of the transcriptome of a large set of pediatric xenografts, which are currently used for preclinical drug testing. Suitable models representing the tumor type of origin were identified. It was found that the characteristic expression patterns of the primary tumors were maintained in the corresponding xenografts for the majority of samples. Because a prerequisite for developing rationally designed drugs is that the target is expressed at the protein level, we developed tissue arrays from these xenografts and corroborated that high mRNA levels yielded high protein levels for two tested genes. The web database and availability of tissue arrays will allow for the rapid confirmation of the expression of potential targets at both the mRNA and the protein level for molecularly targeted agents. The database will facilitate the identification of tumor markers predictive of response to tested agents as well as the discovery of new molecular targets.

BBC3 mediates fenretinide-induced cell death in neuroblastoma.

Fenretinide (4-HPR) is a synthetic retinoid whose apoptosis-inducing effects have been demonstrated in many tumor types. The precise mechanism of its apoptotic action is not fully understood. To further study the mechanism by which 4-HPR exerts its biological effects in neuroblastoma (NB) and to identify the genes that contribute to the induction of apoptosis, we determined the sensitivity of eight NB cell lines to 4-HPR. Additionally, cDNA microarray analysis was performed on a 4-HPR-sensitive cell line to investigate the temporal changes in gene expression, primarily focusing on the induction of proapoptotic genes. BBC3, a transcriptionally regulated proapoptotic member of the BCL2 family, was the most highly induced proapoptotic gene. Western analysis confirmed the induction of BBC3 protein by 4-HPR. Furthermore, the induction of BBC3 was associated with the sensitivity to this agent in the cell lines tested. Finally we demonstrated that BBC3 alone is sufficient to induce cell death in the 4-HPR-sensitive and resistant NB cell lines, and that siRNA against BBC3 significantly decreases apoptosis induced by 4-HPR. Our results indicate that BBC3 mediates cell death in NB cells in response to 4-HPR.

Database of mRNA gene expression profiles of multiple human organs.

Genome-wide expression profiling of normal tissue may facilitate our understanding of the etiology of diseased organs and augment the development of new targeted therapeutics. Here, we have developed a high-density gene expression database of 18,927 unique genes for 158 normal human samples from 19 different organs of 30 different individuals using DNA microarrays. We report four main findings. First, despite very diverse sample parameters (e.g., age, ethnicity, sex, and postmortem interval), the expression profiles belonging to the same organs cluster together, demonstrating internal stability of the database. Second, the gene expression profiles reflect major organ-specific functions on the molecular level, indicating consistency of our database with known biology. Third, we demonstrate that any small (i.e., n approximately 100), randomly selected subset of genes can approximately reproduce the hierarchical clustering of the full data set, suggesting that the observed differential expression of >90% of the probed genes is of biological origin. Fourth, we demonstrate a potential application of this database to cancer research by identifying 19 tumor-specific genes in neuroblastoma. The selected genes are relatively underexpressed in all of the organs examined and belong to therapeutically relevant pathways, making them potential novel diagnostic markers and targets for therapy. We expect this database will be of utility for developing rationally designed molecularly targeted therapeutics in diseases such as cancer, as well as for exploring the functions of genes.

Altered expression of cell cycle genes distinguishes aggressive neuroblastoma.

In this study, gene expression profiling was performed on 103 neuroblastoma (NB) tumors, stages 1-4 with and without MYCN amplification, using cDNA microarrays containing 42,578 elements. Using principal component analysis (PCA) to analyse the relationships among these samples, we confirm that the global patterns of gene expression reflect the phenotype of the tumors. To explore the biological processes that may contribute to increasing aggressive phenotype of the tumors, we utilized a statistical approach based on PCA. We identified a specific subset of the cell cycle and/or chromosome segregation genes that distinguish stage 4 NB tumors from all lower stage tumors, including stage 3. Furthermore, the control of the kinetochore assembly emerges from the Gene Ontology analysis as one of the key biological processes associated with an aggressive NB phenotype. Finally, we establish that these genes are further upregulated in the most aggressive MYCN-amplified tumors.

Detection of low level genomic alterations by comparative genomic hybridization based on cDNA micro-arrays.

MOTIVATION: The accumulation of genomic alterations is an important process in tumor formation and progression. Comparative genomic hybridization performed on cDNA arrays (cDNA aCGH) is a common method to investigate the genomic alterations on a genome-wide scale. However, when detecting low-level DNA copy number changes this technology requires the use of noise reduction strategies due to a low signal to noise ratio. RESULTS: Currently a running average smoothing filter is the most frequently used noise reduction strategy. We analyzed this strategy theoretically and experimentally and found that it is not sensitive to very low level genomic alterations. The presence of systematic errors in the data is one of the main reasons for this failure. We developed a novel algorithm which efficiently reduces systematic noise and allows for the detection of low-level genomic alterations. The algorithm is based on comparison of the biological relevant data to data from so-called self-self hybridizations, additional experiments which contain no biological information but contain systematic errors. We find that with our algorithm the effective resolution for +/-1 DNA copy number changes is about 2 Mb. For copy number changes larger than three the effective resolution is on the level of single genes.

Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma.

Currently, patients with neuroblastoma are classified into risk groups (e.g., according to the Children's Oncology Group risk-stratification) to guide physicians in the choice of the most appropriate therapy. Despite this careful stratification, the survival rate for patients with high-risk neuroblastoma remains <30%, and it is not possible to predict which of these high-risk patients will survive or succumb to the disease. Therefore, we have performed gene expression profiling using cDNA microarrays containing 42,578 clones and used artificial neural networks to develop an accurate predictor of survival for each individual patient with neuroblastoma. Using principal component analysis we found that neuroblastoma tumors exhibited inherent prognostic specific gene expression profiles. Subsequent artificial neural network-based prognosis prediction using expression levels of all 37,920 good-quality clones achieved 88% accuracy. Moreover, using an artificial neural network-based gene minimization strategy in a separate analysis we identified 19 genes, including 2 prognostic markers reported previously, MYCN and CD44, which correctly predicted outcome for 98% of these patients. In addition, these 19 predictor genes were able to additionally partition Children's Oncology Group-stratified high-risk patients into two subgroups according to their survival status (P = 0.0005). Our findings provide evidence of a gene expression signature that can predict prognosis independent of currently known risk factors and could assist physicians in the individual management of patients with high-risk neuroblastoma.

cDNA array-CGH profiling identifies genomic alterations specific to stage and MYCN-amplification in neuroblastoma.

BACKGROUND: Recurrent non-random genomic alterations are the hallmarks of cancer and the characterization of these imbalances is critical to our understanding of tumorigenesis and cancer progression. RESULTS: We performed array-comparative genomic hybridization (A-CGH) on cDNA microarrays containing 42,000 elements in neuroblastoma (NB). We found that only two chromosomes (2p and 12q) had gene amplifications and all were in the MYCN amplified samples. There were 6 independent non-contiguous amplicons (10.4-69.4 Mb) on chromosome 2, and the largest contiguous region was 1.7 Mb bounded by NAG and an EST (clone: 757451); the smallest region was 27 Kb including an EST (clone: 241343), NCYM, and MYCN. Using a probabilistic approach to identify single copy number changes, we systemically investigated the genomic alterations occurring in Stage 1 and Stage 4 NBs with and without MYCN amplification (stage 1-, 4-, and 4+). We have not found genomic alterations universally present in all (100%) three subgroups of NBs. However we identified both common and unique patterns of genomic imbalance in NB including gain of 7q32, 17q21, 17q23-24 and loss of 3p21 were common to all three categories. Finally we confirm that the most frequent specific changes in Stage 4+ tumors were the loss of 1p36 with gain of 2p24-25 and they had fewer genomic alterations compared to either stage 1 or 4-, indicating that for this subgroup of poor risk NB requires a smaller number of genomic changes are required to develop the malignant phenotype. CONCLUSIONS: cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. Furthermore we were able to detect single copy number changes using our probabilistic approach and identified genomic alterations specific to stage and MYCN amplification.

Prognostic classification of relapsing favorable histology Wilms tumor using cDNA microarray expression profiling and support vector machines.

Treatment of Wilms tumor has a high success rate, with some 85% of patients achieving long-term survival. However, late effects of treatment and management of relapse remain significant clinical problems. If accurate prognostic methods were available, effective risk-adapted therapies could be tailored to individual patients at diagnosis. Few molecular prognostic markers for Wilms tumor are currently defined, though previous studies have linked allele loss on 1p or 16q, genomic gain of 1q, and overexpression from 1q with an increased risk of relapse. To identify specific patterns of gene expression that are predictive of relapse, we used high-density (30 k) cDNA microarrays to analyze RNA samples from 27 favorable histology Wilms tumors taken from primary nephrectomies at the time of initial diagnosis. Thirteen of these tumors relapsed within 2 years. Genes differentially expressed between the relapsing and nonrelapsing tumor classes were identified by statistical scoring (t test). These genes encode proteins with diverse molecular functions, including transcription factors, developmental regulators, apoptotic factors, and signaling molecules. Use of a support vector machine classifier, feature selection, and test evaluation using cross-validation led to identification of a generalizable expression signature, a small subset of genes whose expression potentially can be used to predict tumor outcome in new samples. Similar methods were used to identify genes that are differentially expressed between tumors with and without genomic 1q gain. This set of discriminators was highly enriched in genes on 1q, indicating close agreement between data obtained from expression profiling with data from genomic copy number analyses.