Long non-coding RNAs as novel components in the TP53 pathway

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Comparison of RNAi efficiency mediated by tetracycline-responsive H1 and U6 promoter variants in mammalian cell lines

Conditional expression of short hairpin RNAs (shRNAs) to knock down target genes is a powerful tool to study gene function. The most common inducible expression systems are based on tetracycline-regulated RNA polymerase III promoters. During the last years, several tetracycline-inducible U6 and H1 promoter variants have been reported in different experimental settings showing variable efficiencies. In this study, we compare the most common variants of these promoters in several mammalian cell lines. For all cell lines tested, we find that several inducible U6 and H1 promoters containing single tetracycline operator (tetO) sequences show high-transcriptional background in the non-induced state. Promoter variants containing two tetO sequences show tight suppression of transcription in the non-induced state, and high tet responsiveness and high gene knockdown efficiency upon induction in all cell lines tested. We report a variant of the H1 promoter containing two O2-type tetO sequences flanking the TATA box that shows little transcriptional background in the non-induced state and up to 90% target knockdown when the inducer molecule (dox–doxycycline) is added. This inducible system for RNAi-based gene silencing is a good candidate for use both in basic research on gene function and for potential therapeutic applications

Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

<p>Abstract</p> <p>Background</p> <p>Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system. Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastoma therapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients and demonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping among signatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy to merge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble (MuSE)-classifier of neuroblastoma (NB) patients outcome.</p> <p>Methods</p> <p>Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets, were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty three signatures were evaluated for patients' outcome prediction using 22 classification algorithms each and generating 726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated on an independent dataset and the 20 signatures performing with an accuracy > = 80% were retained.</p> <p>Results</p> <p>We combined the 20 predictions associated to the corresponding signatures through the selection of the best performing algorithm into a single outcome predictor. The best performance was obtained by the Decision Table algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%. Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by the NB-MuSE-classifier have a significantly different survival (p < 0.0001). Survival curves constructed on subgroups of patients divided on the bases of known prognostic marker suggested an excellent stratification of localized and stage 4s tumors but more data are needed to prove this point.</p> <p>Conclusions</p> <p>The NB-MuSE-classifier is based on an ensemble approach that merges twenty heterogeneous, neuroblastoma-related gene signatures to blend their discriminating power, rather than numeric values, into a single, highly accurate patients' outcome predictor. The novelty of our approach derives from the way to integrate the gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into a single classifier. This model can be exported to other types of cancer and to diseases for which dedicated databases exist.</p

A biology-driven approach identifies the hypoxia gene signature as a predictor of the outcome of neuroblastoma patients

Background Hypoxia is a condition of low oxygen tension occurring in the tumor microenvironment and it is related to poor prognosis in human cancer. To examine the relationship between hypoxia and neuroblastoma, we generated and tested an in vitro derived hypoxia gene signature for its ability to predict patients' outcome. Results We obtained the gene expression profile of 11 hypoxic neuroblastoma cell lines and we derived a robust 62 probesets signature (NB-hypo) taking advantage of the strong discriminating power of the l1-l2 feature selection technique combined with the analysis of differential gene expression. We profiled gene expression of the tumors of 88 neuroblastoma patients and divided them according to the NB-hypo expression values by K-means clustering. The NB-hypo successfully stratifies the neuroblastoma patients into good and poor prognosis groups. Multivariate Cox analysis revealed that the NB-hypo is a significant independent predictor after controlling for commonly used risk factors including the amplification of MYCN oncogene. NB-hypo increases the resolution of the MYCN stratification by dividing patients with MYCN not amplified tumors in good and poor outcome suggesting that hypoxia is associated with the aggressiveness of neuroblastoma tumor independently from MYCN amplification. Conclusions Our results demonstrate that the NB-hypo is a novel and independent prognostic factor for neuroblastoma and support the view that hypoxia is negatively correlated with tumors' outcome. We show the power of the biology-driven approach in defining hypoxia as a critical molecular program in neuroblastoma and the potential for improvement in the current criteria for risk stratification.Foundation KiKaChildren's Neuroblastoma Cancer FoundationSKK FoundationDutch Cancer Societ

ERBB3 is a marker of a ganglioneuroblastoma/ganglioneuroma-like expression profile in neuroblastic tumours

Background: Neuroblastoma (NB) tumours are commonly divided into three cytogenetic subgroups. However, by unsupervised principal components analysis of gene expression profiles we recently identified four distinct subgroups, r1-r4. In the current study we characterized these different subgroups in more detail, with a specific focus on the fourth divergent tumour subgroup (r4). Methods: Expression microarray data from four international studies corresponding to 148 neuroblastic tumour cases were subject to division into four expression subgroups using a previously described 6-gene signature. Differentially expressed genes between groups were identified using Significance Analysis of Microarray (SAM). Next, gene expression network modelling was performed to map signalling pathways and cellular processes representing each subgroup. Findings were validated at the protein level by immunohistochemistry and immunoblot analyses. Results: We identified several significantly up-regulated genes in the r4 subgroup of which the tyrosine kinase receptor ERBB3 was most prominent (fold change: 132–240). By gene set enrichment analysis (GSEA) the constructed gene network of ERBB3 (n = 38 network partners) was significantly enriched in the r4 subgroup in all four independent data sets. ERBB3 was also positively correlated to the ErbB family members EGFR and ERBB2 in all data sets, and a concurrent overexpression was seen in the r4 subgroup. Further studies of histopathology categories using a fifth data set of 110 neuroblastic tumours, showed a striking similarity between the expression profile of r4 to ganglioneuroblastoma (GNB) and ganglioneuroma (GN) tumours. In contrast, the NB histopathological subtype was dominated by mitotic regulating genes, characterizing unfavourable NB subgroups in particular. The high ErbB3 expression in GN tumour types was verified at the protein level, and showed mainly expression in the mature ganglion cells. Conclusions: Conclusively, this study demonstrates the importance of performing unsupervised clustering and subtype discovery of data sets prior to analyses to avoid a mixture of tumour subtypes, which may otherwise give distorted results and lead to incorrect conclusions. The current study identifies ERBB3 as a clear-cut marker of a GNB/GN-like expression profile, and we suggest a 7-gene expression signature (including ERBB3) as a complement to histopathology analysis of neuroblastic tumours. Further studies of ErbB3 and other ErbB family members and their role in neuroblastic differentiation and pathogenesis are warranted

Allelic loss of chromosome 1p as a predictor of unfavorable outcome in patients with neuroblastoma

Background. Neuroblastoma is a childhood tumor derived from cells of the neural crest, with a widely variable outcome. Differences in the behavior and prognosis of the tumor suggest that neuroblastoma can be divided into several biologic subgroups. We evaluated the most frequent genetic abnormalities in neuroblastoma to determine their prognostic value. Methods. We used Southern blot analysis to study the allelic loss of chromosomes 1p, 4p, 11q, and 14q, the duplication of chromosome 17q, and the amplification of the N-myc oncogene in 89 neuroblastomas. We also determined the nuclear DNA content of the tumor cells. Results. Allelic loss of chromosome 1p, N-myc amplification, and extra copies of chromosome 17q were significantly associated with unfavorable outcomes. In a multivariate analysis, loss of chromosome 1p was the most powerful prognostic factor. It provided strong prognostic information when it was included in multivariate models containing the prognostic factors of age and stage or serum ferritin level and stage. Among the patients with stage I, II, or IVS disease, the mean (±SD) three-year event-free survival was 100 percent in those without allelic loss of chromosome 1p and 34±15 percent in those with such loss; the rates of three- year event-free survival among the patients with stage III and stage IV disease were 53±10 percent and 0 percent, respectively. Conclusions. The loss of chromosome 1p is a strong prognostic factor in patients with neuroblastoma, independently of age and stage. It reliably identifies patients at high risk in stages I, II, and IVS, which are otherwise clinically favorable. More intensive therapy may be considered in these patients. Patients in stages III and IV with allelic loss of chromosome 1p have a very poor outlook, whereas those without such loss are at moderate risk

Robust selection of cancer survival signatures from high-throughput genomic data using two-fold subsampling

Identifying relevant signatures for clinical patient outcome is a fundamental task in high-throughput studies. Signatures, composed of features such as mRNAs, miRNAs, SNPs or other molecular variables, are often non-overlapping, even though they have been identified from similar experiments considering samples with the same type of disease. The lack of a consensus is mostly due to the fact that sample sizes are far smaller than the numbers of candidate features to be considered, and therefore signature selection suffers from large variation. We propose a robust signature selection method that enhances the selection stability of penalized regression algorithms for predicting survival risk. Our method is based on an aggregation of multiple, possibly unstable, signatures obtained with the preconditioned lasso algorithm applied to random (internal) subsamples of a given cohort data, where the aggregated signature is shrunken by a simple thresholding strategy. The resulting method, RS-PL, is conceptually simple and easy to apply, relying on parameters automatically tuned by cross validation. Robust signature selection using RS-PL operates within an (external) subsampling framework to estimate the selection probabilities of features in multiple trials of RS-PL. These probabilities are used for identifying reliable features to be included in a signature. Our method was evaluated on microarray data sets from neuroblastoma, lung adenocarcinoma, and breast cancer patients, extracting robust and relevant signatures for predicting survival risk. Signatures obtained by our method achieved high prediction performance and robustness, consistently over the three data sets. Genes with high selection probability in our robust signatures have been reported as cancer-relevant. The ordering of predictor coefficients associated with signatures was well-preserved across multiple trials of RS-PL, demonstrating the capability of our method for identifying a transferable consensus signature. The software is available as an R package rsig at CRAN (http://cran.r-project.org)

Glycerophosphodiesterase GDE2 Promotes Neuroblastoma Differentiation through Glypican Release and Is a Marker of Clinical Outcome

Neuroblastoma is a pediatric embryonal malignancy characterized by impaired neuronal differentiation. A better understanding of neuroblastoma differentiation is essential for developing new therapeutic approaches. GDE2 (encoded by GDPD5) is a six-transmembrane-domain glycerophosphodiesterase that promotes embryonic neurogenesis. We find that high GDPD5 expression is strongly associated with favorable outcome in neuroblastoma. GDE2 induces differentiation of neuroblastoma cells, suppresses cell motility, and opposes RhoA-driven neurite retraction. GDE2 alters the Rac-RhoA activity balance and the expression of multiple differentiation-associated genes. Mechanistically, GDE2 acts by cleaving (in cis) and releasing glycosylphosphatidylinositol-anchored glypican-6, a putative co-receptor. A single point mutation in the ectodomain abolishes GDE2 function. Our results reveal GDE2 as a cell-autonomous inducer of neuroblastoma differentiation with prognostic significance and potential therapeutic value.</p

A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities.

Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value