HuR/ELAVL1 drives malignant peripheral nerve sheath tumor growth and metastasis

Cancer cells can develop a strong addiction to discrete molecular regulators, which control the aberrant gene expression programs that drive and maintain the cancer phenotype. Here, we report the identification of the RNA-binding protein HuR/ELAVL1 as a central oncogenic driver for malignant peripheral nerve sheath tumors (MPNSTs), which are highly aggressive sarcomas that originate from cells of the Schwann cell lineage. HuR was found to be highly elevated and bound to a multitude of cancer-associated transcripts in human MPNST samples. Accordingly, genetic and pharmacological inhibition of HuR had potent cytostatic and cytotoxic effects on tumor growth, and strongly suppressed metastatic capacity in vivo. Importantly, we linked the profound tumorigenic function of HuR to its ability to simultaneously regulate multiple essential oncogenic pathways in MPNST cells, including the Wnt/β-catenin, YAP/TAZ, RB/E2F, and BET pathways, which converge on key transcriptional networks. Given the exceptional dependency of MPNST cells on HuR for survival, proliferation, and dissemination, we propose that HuR represents a promising therapeutic target for MPNST treatment.

Epigenetic Networks Regulate the Transcriptional Program in Memory and Terminally Differentiated CD8+ T Cells

Epigenetic mechanisms play a critical role during differentiation of T cells by contributing to the formation of stable and heritable transcriptional patterns. To better understand the mechanisms of memory maintenance in CD8⁺ T cells, we performed genome-wide analysis of DNA methylation, histone marking (acetylated lysine 9 in histone H3 and trimethylated lysine 9 in histone), and gene-expression profiles in naive, effector memory (EM), and terminally differentiated EM (TEMRA) cells. Our results indicate that DNA demethylation and histone acetylation are coordinated to generate the transcriptional program associated with memory cells. Conversely, EM and TEMRA cells share a very similar epigenetic landscape. Nonetheless, the TEMRA transcriptional program predicts an innate immunity phenotype associated with genes never reported in these cells, including several mediators of NK cell activation (VAV3 and LYN) and a large array of NK receptors (e.g., KIR2DL3, KIR2DL4, KIR2DL1, KIR3DL1, KIR2DS5). In addition, we identified up to 161 genes that encode transcriptional regulators, some of unknown function in CD8⁺ T cells, and that were differentially expressed in the course of differentiation. Overall, these results provide new insights into the regulatory networks involved in memory CD8⁺ T cell maintenance and T cell terminal differentiation.

TRIB3 suppresses tumorigenesis by controlling mTORC2/AKT/FOXO signaling

In a recent article, we found that Tribbles pseudokinase 3 (TRIB3) plays a tumor suppressor role and that this effect relies on the dysregulation of the phosphorylation of v-akt murine thymoma viral oncogene homolog (AKT) by the mammalian target of rapamycin complex 2 (mTORC2 complex), and the subsequent hyperphosphorylation and inactivation of the transcription factor Forkhead box O3 (FOXO3).

Regulation of the transcriptional program by DNA methylation during human αβ T-cell development

Thymocyte differentiation is a complex process involving well-defined sequential developmental stages that ultimately result in the generation of mature T-cells. In this study, we analyzed DNA methylation and gene expression profiles at successive human thymus developmental stages. Gain and loss of methylation occurred during thymocyte differentiation, but DNA demethylation was much more frequent than de novo methylation and more strongly correlated with gene expression. These changes took place in CpG-poor regions and were closely associated with T-cell differentiation and TCR function. Up to 88 genes that encode transcriptional regulators, some of whose functions in T-cell development are as yet unknown, were differentially methylated during differentiation. Interestingly, no reversion of accumulated DNA methylation changes was observed as differentiation progressed, except in a very small subset of key genes (RAG1, RAG2, CD8A, PTCRA, etc.), indicating that methylation changes are mostly unique and irreversible events. Our study explores the contribution of DNA methylation to T-cell lymphopoiesis and provides a fine-scale map of differentially methylated regions associated with gene expression changes. These can lay the molecular foundations for a better interpretation of the regulatory networks driving human thymopoiesis.

Loss of Tribbles pseudokinase-3 promotes Akt-driven tumorigenesis via FOXO inactivation

Tribbles pseudokinase-3 (TRIB3) has been proposed to act as an inhibitor of AKT although the precise molecular basis of this activity and whether the loss of TRIB3 contributes to cancer initiation and progression remain to be clarified. In this study, by using a wide array of in vitro and in vivo approaches, including a Trib3 knockout mouse, we demonstrate that TRIB3 has a tumor-suppressing role. We also find that the mechanism by which TRIB3 loss enhances tumorigenesis relies on the dysregulation of the phosphorylation of AKT by the mTORC2 complex, which leads to an enhanced phosphorylation of AKT on Ser473 and the subsequent hyperphosphorylation and inactivation of the transcription factor FOXO3. These observations support the notion that loss of TRIB3 is associated with a more aggressive phenotype in various types of tumors by enhancing the activity of the mTORC2/AKT/FOXO axis.

Bivariate segmentation of SNP-array data for allele-specific copy number analysis in tumour samples

Background

SNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation.

Results

In the present work, we introduce a generic framework for bivariate segmentation of SNP array data for ASCN analysis. For the matter, we discuss the characteristics of the typically applied BAF transformation and how they affect segmentation, introduce concepts of multivariate time series analysis that are of concern in this field and discuss the appropriate formulation of the problem. The framework is implemented in a method named CnaStruct, the bivariate form of the structural change model (SCM), which has been successfully applied to transcriptome mapping and aCGH.

Conclusions

On a comprehensive synthetic dataset, we show that CnaStruct outperforms the segmentation of existing ASCN analysis methods. Furthermore, CnaStruct can be integrated into the workflows of several ASCN analysis tools in order to improve their performance, specially on tumour samples highly contaminated by normal cells.

Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data

Background

The detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. A number of tools have been recently developed but their performance needs yet to be thoroughly assessed. To this aim, a comprehensive model that integrates the factors of normal cell contamination and intra-tumour heterogeneity and that can be translated to synthetic data on which to perform benchmarks is indispensable.

Results

We propose such model and implement it in an R package called CnaGen to synthetically generate a wide range of alterations under different normal cell contamination levels. Six recently published methods for CNA and loss of heterozygosity (LOH) detection on tumour samples were assessed on this synthetic data and on a dilution series of a breast cancer cell-line: ASCAT, GAP, GenoCNA, GPHMM, MixHMM and OncoSNP. We report the recall rates in terms of normal cell contamination levels and alteration characteristics: length, copy number and LOH state, as well as the false discovery rate distribution for each copy number under different normal cell contamination levels.

Assessed methods are in general better at detecting alterations with low copy number and under a little normal cell contamination levels. All methods except GPHMM, which failed to recognize the alteration pattern in the cell-line samples, provided similar results for the synthetic and cell-line sample sets. MixHMM and GenoCNA are the poorliest performing methods, while GAP generally performed better. This supports the viability of approaches other than the common hidden Markov model (HMM)-based.

Conclusions

We devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays. The validity of the model is supported by the similarity of the results obtained with synthetic and real data. Based on these results and on the software implementation of the methods, we recommend GAP for advanced users and GPHMM for a fully driven analysis.