RNA-based liquid biopsies for better clinical management of Barrett’s esophagus and esophageal adenocarcinoma

In the past decades the incidence of esophageal adenocarcinoma (EAC) has increased dramatically in most Western populations. Due to the lack of symptoms EAC is often detected in a late stage, contributing to a poor 5-year survival rate. The potential of RNA (coding and miRNA) as circulating biomarker in blood has already been shown for many cancer entities but requires further investigation for EAC. In this study we will explore several RNA types in blood, including microRNA, messenger RNA, long non-coding RNA and circular RNA as a potential liquid biomarker to facilitate early diagnosis, prognosis and monitoring of esophageal adenocarcinoma We have been collecting blood and tissue samples from patients with non-dysplastic Barrett’s esophagus (NDBE), high-grade dysplasia (HGD) and EAC. Currently, our biobank includes >5000 samples from 120 patients. A proof-of-concept study was conducted including 17 patients from three groups (EAC, HGD and NDBE). For each patient, biopsies from diseased tissue and healthy tissue as well as blood were collected and analyzed using small RNA and total RNA sequencing. Gene expression analysis was performed to identify differentially expressed genes across the three groups. The highest number of significantly differentially expressed m(i)RNAs were present in the tissues of EAC versus NDBE patients, while these differences were much lower or even absent in the plasma samples. Moreover, we have identified between 1500 and 7500 unique circular RNAs in individual EAC cancer patients’ plasma, indicating promising opportunities for a blood-based liquid biomarker for BE and EAC. Currently, we are collecting additional samples to significantly increase the power of the differential expression study as well as to verify the results of our proof-of-concept study

A detailed inventory of DNA copy number alterations in four commonly used Hodgkin's lymphoma cell lines

Background and Objectives Classical Hodgkin's lymphoma (cHL) is a common malignant lymphoma characterized by the presence of large, usually multinucleated malignant Hodgkin and Reed Sternberg (HRS) cells which are thought to be derived from germinal center B-cells. In cHL, the HRS cells constitute less than 1% of the tumor volume; consequently the profile of genetic aberrations in cHL is still poorly understood. Design and Methods In this study, we subjected four commonly used cHL cell lines to array comparative genomic hybridization (aCGH) in order to delineate known chromosomal aberrations in more detail and to search for small hitherto undetected genomic imbalances. Results The aCGH profiles of the four cell lines tested confirmed the complex patterns of rearrangements previously demonstrated with multicolor fluorescence in situ hybridization and chromosomal CGH (cCGH). Importantly, aCGH allowed a much more accurate delineation of imbalances as compared to previous studies performed at a chromosomal level of resolution. Furthermore, we detected 35 hitherto undetected aberrations including a homozygous deletion of chromosomal region 15q26.2 in the cell line HDLM2 encompasing RGMA and CHD2 and an amplification of the STAT6 gene in cell line L1236 leading to STAT6 overexpression. Finally, in cell line KM-H2 we found a 2.35 Mb deletion at 16q12.1 putatively defining a small critical region for the recurrent 16q deletion in cHL. This region contains the CYLD gene, a known suppressor gene of the NF-kappa B pathway. Interpretation and Conclusions aCGH was performed on four cHL cell lines leading to the improved delineation of known chromosomal imbalances and the detection of 35 hitherto undetected aberrations. More specifically, our results highlight STAT6 as a potential transcriptional target and identified RGMA, CHD2 and CYLD as candidate tumor suppressors in cHL

Early and late effects of pharmacological ALK inhibition on the neuroblastoma transcriptome

Background: Neuroblastoma is an aggressive childhood malignancy of the sympathetic nervous system. Despite multi-modal therapy, survival of high-risk patients remains disappointingly low, underscoring the need for novel treatment strategies. The discovery of ALK activating mutations opened the way to precision treatment in a subset of these patients. Previously, we investigated the transcriptional effects of pharmacological ALK inhibition on neuroblastoma cell lines, six hours after TAE684 administration, resulting in the 77-gene ALK signature, which was shown to gradually decrease from 120 minutes after TAE684 treatment, to gain deeper insight into the molecular effects of oncogenic ALK signaling. Aim: Here, we further dissected the transcriptional dynamic profiles of neuroblastoma cells upon TAE684 treatment in a detailed timeframe of ten minutes up to six hours after inhibition, in order to identify additional early targets for combination treatment. Results: We observed an unexpected initial upregulation of positively regulated MYCN target genes following subsequent downregulation of overall MYCN activity. In addition, we identified adrenomedullin (ADM), previously shown to be implicated in sunitinib resistance, as the earliest response gene upon ALK inhibition. Conclusions: We describe the early and late effects of ALK inhibitor TAE684 treatment on the neuroblastoma transcriptome. The observed unexpected upregulation of ADM warrants further investigation in relation to putative ALK resistance in neuroblastoma patients currently undergoing ALK inhibitor treatment

Zipper plot : visualizing transcriptional activity of genomic regions

Background: Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. Current state-of-the-art tools for long non-coding RNA (lncRNA) annotation are mainly based on evolutionary constraints, which may result in false negatives due to the overall limited conservation of lncRNAs. Results: To tackle this problem we have developed the Zipper plot, a novel visualization and analysis method that enables users to simultaneously interrogate thousands of human putative transcription start sites (TSSs) in relation to various features that are indicative for transcriptional activity. These include publicly available CAGE-sequencing, ChIP-sequencing and DNase-sequencing datasets. Our method only requires three tab-separated fields (chromosome, genomic coordinate of the TSS and strand) as input and generates a report that includes a detailed summary table, a Zipper plot and several statistics derived from this plot. Conclusion: Using the Zipper plot, we found evidence of transcription for a set of well-characterized lncRNAs and observed that fewer mono-exonic lncRNAs have CAGE peaks overlapping with their TSSs compared to multi-exonic lncRNAs. Using publicly available RNA-seq data, we found more than one hundred cases where junction reads connected protein-coding gene exons with a downstream mono-exonic lncRNA, revealing the need for a careful evaluation of lncRNA 5′-boundaries. Our method is implemented using the statistical programming language R and is freely available as a webtool

A Written Statement

I am concerned with objects that refer to a broader field than their own physical and cultural dimensions. Objects that refer to and utilize the space and activity surrounding them. An art of street corner signs and antennas. Also, I like to make objects that we can touch, carry, sit on, walk through, and walk with. An art to pick up, open up, and wind up. n art to walk within and carry down the street. A responsive art, an art of participation and collaboration. An art that calls to view our own activities. I made all the attempts to change the gallery into a store, because I didn't want the objects to be too precious; but to be more like what already exists around us, both spatially and culturally. The image of the store allows the people inside it to shop and compare. Pick up, walk through, laugh at, make fun of--this is all part of shopping at the big store. &nbsp;</p

Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions

The search for feature enrichment is a widely used method to characterize a set of genes. While several tools have been designed for nominal features such as Gene Ontology annotations or KEGG Pathways, very little has been proposed to tackle numerical features such as the chromosomal positions of genes. For instance, microarray studies typically generate gene lists that are differentially expressed in the sample subgroups under investigation, and when studying diseases caused by genome alterations, it is of great interest to delineate the chromosomal regions that are significantly enriched in these lists. In this article, we present a positional gene enrichment analysis method (PGE) for the identification of chromosomal regions that are significantly enriched in a given set of genes. The strength of our method relies on an original query optimization approach that allows to virtually consider all the possible chromosomal regions for enrichment, and on the multiple testing correction which discriminates truly enriched regions versus those that can occur by chance. We have developed a Web tool implementing this method applied to the human genome (http://www.esat.kuleuven.be/~bioiuser/pge). We validated PGE on published lists of differentially expressed genes. These analyses showed significant overrepresentation of known aberrant chromosomal regions

Network inference by integrating biclustering and feature selection

In order to develop better therapies to combat specific abnormalities present in the gene regulatory network (GRN) of cancer patients, it is crucial to gain a better understanding of regulatory networks in complex biological systems. An important class of methods in systems biology are network inference (NI) methods, which aim to reconstruct a GRN from high-throughput data (e.g. microarrays or next-generation sequencing). GENIE3 is a state-of-the-art method which employs feature selection to identify the best subset of regulators for each gene. While this method is amongst the best performing NI methods, it fails to take into account expected topological properties of a GRN: a GRN consists of modules, each of which consists of genes coregulated by a common set of regulators. We present BiGENIE, a method which takes the modular topology of a GRN into account. By firstly inferring modules – groups of genes coregulated by a common regulator – using several biclustering methods, the overall topology of the network is reconstructed. Subsequently, the regulator genes for each of the modules is inferred using GENIE3

The pitfalls and promise of liquid biopsies for diagnosing and treating solid tumors in children : a review

Cell-free DNA profiling using patient blood is emerging as a non-invasive complementary technique for cancer genomic characterization. Since these liquid biopsies will soon be integrated into clinical trial protocols for pediatric cancer treatment, clinicians should be informed about potential applications and advantages but also weaknesses and potential pitfalls. Small retrospective studies comparing genetic alterations detected in liquid biopsies with tumor biopsies for pediatric solid tumor types are encouraging. Molecular detection of tumor markers in cell-free DNA could be used for earlier therapy response monitoring and residual disease detection as well as enabling detection of pathognomonic and therapeutically relevant genomic alterations. Conclusion: Existing analyses of liquid biopsies from children with solid tumors increasingly suggest a potential relevance for molecular diagnostics, prognostic assessment, and therapeutic decision-making. Gaps remain in the types of tumors studied and value of detection methods applied. Here we review the current stand of liquid biopsy studies for pediatric solid tumors with a dedicated focus on cell-free DNA analysis. There is legitimate hope that integrating fully validated liquid biopsy-based innovations into the standard of care will advance patient monitoring and personalized treatment of children battling solid cancers

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

BACKGROUND: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. RESULTS: We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. CONCLUSIONS: The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences