Clinicopathological-Associated Regulatory Network of Deregulated circRNAs in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers worldwide. Here, we present a novel strategy to identify key circRNA signatures of clinically relevant co-expressed circRNA-mRNA networks in pertinent cancer-pathways that modulate prognosis of HCC patients, by integrating clinic-pathological features, circRNA and mRNA expression profiles. Through further integration with miRNA expression profiles, clinically relevant competing-endogenous-RNA (ceRNA) networks of circRNA-miRNA-mRNAs were constructed. At least five clinically relevant nodal-circRNAs, co-expressed with numerous genes, were identified from the circRNA-mRNA networks. These nodal circRNAs upregulated proliferation (except circRaly) and transformation in cells. The most upregulated nodal-circRNA, circGPC3, associated with higher-grade tumors and co-expressed with 33 genes, competes with 11 mRNAs for two shared miRNAs. circGPC3 was experimentally demonstrated to upregulate cell-cycle and migration/invasion in both transformed and non-transformed liver cell-lines. circGPC3 was further shown to act as a sponge of miR-378a-3p to regulate APSM (Abnormal spindle-like microcephaly associated) expression and modulate cell transformation. This study identifies 5 key nodal master circRNAs in a clinically relevant circRNA-centric network that are significantly associated with poorer prognosis of HCC patients and promotes tumorigenesis in cell-lines. The identification and characterization of these key circRNAs in clinically relevant circRNA-mRNA and ceRNA networks may facilitate the design of novel strategies targeting these important regulators for better HCC prognosis.

Mutational interactions define novel cancer subgroups.

Large-scale genomic data highlight the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues can guide drug repositioning as well as the design of targeted treatments. Here, we develop an improved Bayesian network model for tumour mutational profiles and apply it to 8198 patient samples across 22 cancer types from TCGA. For each cancer type, we identify the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we find genes which interact both within and across cancer types. To detach cancer classification from the tissue type we perform de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We find 22 novel clusters which significantly improve survival prediction beyond clinical information. The models highlight key gene interactions for each cluster potentially allowing genomic stratification for clinical trials and identifying drug targets.

SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics.

BACKGROUND: Molecular precision oncology is an emerging practice to improve cancer therapy by decreasing the risk of choosing treatments that lack efficacy or cause adverse events. However, the challenges of integrating molecular profiling into routine clinical care are manifold. From a computational perspective these include the importance of a short analysis turnaround time, the interpretation of complex drug-gene and gene-gene interactions, and the necessity of standardized high-quality workflows. In addition, difficulties faced when integrating molecular diagnostics into clinical practice are ethical concerns, legal requirements, and limited availability of treatment options beyond standard of care as well as the overall lack of awareness of their existence. METHODS: To the best of our knowledge, we are the first group in Switzerland that established a workflow for personalized diagnostics based on comprehensive high-throughput sequencing of tumors at the clinic. Our workflow, named SwissMTB (Swiss Molecular Tumor Board), links genetic tumor alterations and gene expression to therapeutic options and clinical trial opportunities. The resulting treatment recommendations are summarized in a clinical report and discussed in a molecular tumor board at the clinic to support therapy decisions. RESULTS: Here we present results from an observational pilot study including 22 late-stage cancer patients. In this study we were able to identify actionable variants and corresponding therapies for 19 patients. Half of the patients were analyzed retrospectively. In two patients we identified resistance-associated variants explaining lack of therapy response. For five out of eleven patients analyzed before treatment the SwissMTB diagnostic influenced treatment decision. CONCLUSIONS: SwissMTB enables the analysis and clinical interpretation of large numbers of potentially actionable molecular targets. Thus, our workflow paves the way towards a more frequent use of comprehensive molecular diagnostics in Swiss hospitals.

NGS-pipe: a flexible, easily extendable and highly configurable framework for NGS analysis.

Motivation: Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility and documentation are missing. Results: We developed NGS-pipe, a flexible, transparent and easy-to-use framework for the design of pipelines to analyze whole-exome, whole-genome and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, documentation, reproducibility, parallelization and easy adaptation to other NGS experiments. Availability and implementation: https://github.com/cbg-ethz/NGS-pipe. Contact: niko.beerenwinkel@bsse.ethz.ch.

Genomic variant annotation workflow for clinical applications.

Annotation and interpretation of DNA aberrations identified through next-generation sequencing is becoming an increasingly important task. Even more so in the context of data analysis pipelines for medical applications, where genomic aberrations are associated with phenotypic and clinical features. Here we describe a workflow to identify potential gene targets in aberrated genes or pathways and their corresponding drugs. To this end, we provide the R/Bioconductor package rDGIdb, an R wrapper to query the drug-gene interaction database (DGIdb). DGIdb accumulates drug-gene interaction data from 15 different resources and allows filtering on different levels. The rDGIdb package makes these resources and tools available to R users. Moreover, rDGIdb queries can be automated through incorporation of the rDGIdb package into NGS sequencing pipelines.

Global re-wiring of p53 transcription regulation by the hepatitis B virus X protein.

BACKGROUND: The tumour suppressor p53 is a central player in transcription regulation and cell fate determination. By interacting with p53 and altering its sequence-specific binding to the response elements, the hepatitis B virus X protein (HBx) was reported to re-direct p53 regulation of some genes. RESULTS: Coupling massively parallel deep sequencing with p53 chromatin immunoprecipitation, we demonstrate that HBx modulates global p53 site selection and that this was strongly influenced by altered interaction with transcription co-factors/co-regulators as well as post-translational modifications. Specifically, HBx attenuated p53-TBP-RB1 transcription complex recruitment and interaction and this was associated with hyper-phosphorylation of p53 at serine 315 by HBx. Concurrently, HBx enhanced p53 DNA occupancy to other response elements either alone by displacing specific transcription factors such as CEBPB and NFkB1, or in complex with distinct interacting co-factors Sp1, JUN and E2F1. Importantly, re-wiring of p53 transcription regulation by HBx was linked to the deregulation of genes involved in cell proliferation and death, suggesting a role of HBx in errant cell fate determination mediated by altered p53 site selection of target genes. CONCLUSIONS: Our study thus presents first evidence of global modes of p53 transcription alteration by HBx and provides new insights to understand and potentially curtail the viral oncoprotein.

Differentially Expressed miRNAs in Hepatocellular Carcinoma Target Genes in the Genetic Information Processing and Metabolism Pathways.

To date, studies of the roles of microRNAs (miRNAs) in hepatocellular carcinoma (HCC) have either focused on specific individual miRNAs and a small number of suspected targets or simply reported a list of differentially expressed miRNAs based on expression profiling. Here, we seek a more in-depth understanding of the roles of miRNAs and their targets in HCC by integrating the miRNA and messenger RNA (mRNA) expression profiles of tumorous and adjacent non-tumorous liver tissues of 100 HCC patients. We assessed the levels of 829 mature miRNAs, of which 32 were significantly differentially expressed. Statistical analysis indicates that six of these miRNAs regulate a significant proportion of their in silico predicted target mRNAs. Three of these miRNAs (miR-26a, miR-122, and miR-130a) were down-regulated in HCC, and their up-regulated gene targets are primarily associated with aberrant cell proliferation that involves DNA replication, transcription and nucleotide metabolism. The other three miRNAs (miR-21, miR-93, and miR-221) were up-regulated in HCC, and their down-regulated gene targets are primarily involved in metabolism and immune system processes. We further found evidence for a coordinated miRNA-induced regulation of important cellular processes, a finding to be considered when designing therapeutic applications based on miRNAs.

Methylation profiles reveal distinct subgroup of hepatocellular carcinoma patients with poor prognosis.

Hepatocellular Carcinoma (HCC) is one of the leading causes of cancer-associated mortality worldwide. However, the role of epigenetic changes such as aberrant DNA methylation in hepatocarcinogenesis remains largely unclear. In this study, we examined the methylation profiles of 59 HCC patients. Using consensus hierarchical clustering with feature selection, we identified three tumor subgroups based on their methylation profiles and correlated these subgroups with clinicopathological parameters. Interestingly, one tumor subgroup is different from the other 2 subgroups and the methylation profile of this subgroup is the most distinctly different from the non-tumorous liver tissues. Significantly, this subgroup of patients was found to be associated with poor overall as well as disease-free survival. To further understand the pathways modulated by the deregulation of methylation in HCC patients, we integrated data from both the methylation as well as the gene expression profiles of these 59 HCC patients. In these patients, while 4416 CpG sites were differentially methylated between the tumors compared to the adjacent non-tumorous tissues, only 536 of these CpG sites were associated with differences in the expression of their associated genes. Pathway analysis revealed that forty-four percent of the most significant upstream regulators of these 536 genes were involved in inflammation-related NFκB pathway. These data suggest that inflammation via the NFκB pathway play an important role in modulating gene expression of HCC patients through methylation. Overall, our analysis provides an understanding on aberrant methylation profile in HCC patients.

Population estimation of human embryonic stem cell cultures.

Traditionally, the population of human embryonic stem cell (hESC) culture is estimated through haemacytometer counts, which include harvesting the cells and manually analyzing a fraction of an entire population. Obviously, through this highly invasive method, it is not possible to preserve any spatial information on the cell population. The goal of this study is to identify a fast and consistent method for in situ automated hESC population estimation to quantitatively estimate the cell growth. Therefore, cell cultures were fixed, stained, and their nuclei imaged through high-resolution microscopy, and the images were processed with different image analysis techniques. The proposed method first identifies signal and background by computing an image specific threshold for image segmentation. By applying a morphological operator (watershed), we split most physically overlapping nuclei, leading to a pixel area distribution of isolated signal areas on the image. On the basis of this distribution, we derive a nucleus area model, describing the distribution of the area of cell debris, single nuclei, and small groups of connected nuclei. Through the model, we can give a quantitative estimation of the population. The focus of this study is on low-density human embryonic stem cell populations; hence cultures were measured at days 2-3 after seeding. Compared with manual cell counts, the automatic method achieved higher accuracy with <6% error.