Large scale statistical inference of signaling pathways from RNAi and microarray data

<p>Abstract</p> <p>Background</p> <p>The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway.</p> <p>Results</p> <p>In this paper we address this challenging problem by extending previous work by Markowetz <it>et al</it>., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on <it>p</it>-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks.</p> <p>Conclusion</p> <p>Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-<it>α </it>pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability.</p> <p>The code for the module network inference method is available in the latest version of the <it>R</it>-package <it>nem</it>, which can be obtained from the Bioconductor homepage.</p

Computation times (s) for the module network (white) and the simulated annealing (gray) approach

<p><b>Copyright information:</b></p><p>Taken from "Large scale statistical inference of signaling pathways from RNAi and microarray data"</p><p>http://www.biomedcentral.com/1471-2105/8/386</p><p>BMC Bioinformatics 2007;8():386-386.</p><p>Published online 15 Oct 2007</p><p>PMCID:PMC2241646.</p><p></p

miR-449a Repression Leads to Enhanced NOTCH Signaling in TMPRSS2:ERG Fusion Positive Prostate Cancer Cells

Simple Summary The reason for the frequent presence of the TMPRSS2:ERG (T2E) gene fusion in prostate cancer (PCa) is not fully understood. We were interested in investigating epigenomic alterations associated with the T2E gene fusion in PCa cells. Making use of publicly available genome-wide data and in vitro analyses using an LNCaP cell line model with inducible T2E expression, we uncovered a molecular network driving NOTCH signaling in T2E-positive PCa. We show that ERG directly activates NOTCH1 and HES1 expression by interacting with their promoter regions. Furthermore, NOTCH is activated by downregulation of miR-449a in ERG overexpressing cells. Experimental NOTCH pathway inhibition as well as HES1 knockdown reduced oncogenic capacities in vitro, suggesting that the NOTCH pathway triggers oncogenic processes in TMPRSS2:ERG-positive PCa. HES1 and ERG expression are correlated in tissue samples from PCa patients. Our data suggest a novel epigenomic network driving NOTCH signaling in T2E+ PCa cells. About 50% of prostate cancer (PCa) tumors are TMPRSS2:ERG (T2E) fusion-positive (T2E+), but the role of T2E in PCa progression is not fully understood. We were interested in investigating epigenomic alterations associated with T2E+ PCa. Using different sequencing cohorts, we found several transcripts of the miR-449 cluster to be repressed in T2E+ PCa. This repression correlated strongly with enhanced expression of NOTCH and several of its target genes in TCGA and ICGC PCa RNA-seq data. We corroborated these findings using a cellular model with inducible T2E expression. Overexpression of miR-449a in vitro led to silencing of genes associated with NOTCH signaling (NOTCH1, HES1) and HDAC1. Interestingly, HDAC1 overexpression led to the repression of HES6, a negative regulator of the transcription factor HES1, the primary effector of NOTCH signaling, and promoted cell proliferation by repressing the cell cycle inhibitor p21. Inhibition of NOTCH as well as knockdown of HES1 reduced the oncogenic properties of PCa cell lines. Using tissue microarray analysis encompassing 533 human PCa cores, ERG-positive areas exhibited significantly increased HES1 expression. Taken together, our data suggest that an epigenomic regulatory network enhances NOTCH signaling and thereby contributes to the oncogenic properties of T2E+ PCa

QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments

Genome-wide enrichment of methylated DNA followed by sequencing (MeDIP-seq) offers a reasonable compromise between experimental costs and genomic coverage. However, the computational analysis of these experiments is complex, and quantification of the enrichment signals in terms of absolute levels of methylation requires specific transformation. In this work, we present QSEA, Quantitative Sequence Enrichment Analysis, a comprehensive workflow for the modelling and subsequent quantification of MeDIP-seq data. As the central part of the workflow we have developed a Bayesian statistical model that transforms the enrichment read counts to absolute levels of methylation and, thus, enhances interpretability and facilitates comparison with other methylation assays. We suggest several calibration strategies for the critical parameters of the model, either using additional data or fairly general assumptions. By comparing the results with bisulfite sequencing (BS) validation data, we show the improvement of QSEA over existing methods. Additionally, we generated a clinically relevant benchmark data set consisting of methylation enrichment experiments (MeDIP-seq), BS-based validation experiments (Methyl-seq) aswell as gene expression experiments (RNA-seq) derived from non-small cell lung cancer patients, and show that the workflow retrieves well-known lung tumour methylation markers that are causative for gene expression changes, demonstrating the applicability of QSEA for clinical studies. QSEA is implemented in R and available from the Bioconductor repository 3.4 (www.bioconductor.org/packages/qsea)

Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Background: Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide and is primarily treated with radiation, surgery, and platinum-based drugs like cisplatin and carboplatin. The major challenge in the treatment of NSCLC patients is intrinsic or acquired resistance to chemotherapy. Molecular markers predicting the outcome of the patients are urgently needed. Methods: Here, we employed patient-derived xenografts (PDXs) to detect predictive methylation biomarkers for platin-based therapies. We used MeDIP-Seq to generate genome-wide DNA methylation profiles of 22 PDXs, their parental primary NSCLC, and their corresponding normal tissues and complemented the data with gene expression analyses of the same tissues. Candidate biomarkers were validated with quantitative methylation-specific PCRs (qMSP) in an independent cohort. Results: Comprehensive analyses revealed that differential methylation patterns are highly similar, enriched in PDXs and lung tumor-specific when comparing differences in methylation between PDXs versus primary NSCLC. We identified a set of 40 candidate regions with methylation correlated to carboplatin response and corresponding inverse gene expression pattern even before therapy. This analysis led to the identification of a promoter CpG island methylation of LDL receptor-related protein 12 (LRP12) associated with increased resistance to carboplatin. Validation in an independent patient cohort (n = 35) confirmed that LRP12 methylation status is predictive for therapeutic response of NSCLC patients to platin therapy with a sensitivity of 80% and a specificity of 84% (p < 0.01). Similarly, we find a shorter survival time for patients with LRP12 hypermethylation in the TCGA data set for NSCLC (lung adenocarcinoma). Conclusions: Using an epigenome-wide sequencing approach, we find differential methylation patterns from primary lung cancer and PDX-derived cancers to be very similar, albeit with a lower degree of differential methylation in primary tumors. We identify LRP12 DNA methylation as a powerful predictive marker for carboplatin resistance. These findings outline a platform for the identification of epigenetic therapy resistance biomarkers based on PDX NSCLC models