Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma

Accurate assessment of neuroblastoma outcome prediction remains challenging. Therefore, this study aims at establishing novel prognostic tumor DNA methylation biomarkers. In total, 396 low- and high-risk primary tumors were analyzed, of which 87 were profiled using methyl-CpG-binding domain (MBD) sequencing for differential methylation analysis between prognostic patient groups. Subsequently, methylation-specific PCR (MSP) assays were developed for 78 top-ranking differentially methylated regions and tested on two independent cohorts of 132 and 177 samples, respectively. Further, a new statistical framework was used to identify a robust set of MSP assays of which the methylation score (i.e. the percentage of methylated assays) allows accurate outcome prediction. Survival analyses were performed on the individual target level, as well as on the combined multimarker signature. As a result of the differential DNA methylation assessment by MBD sequencing, 58 of the 78 MSP assays were designed in regions previously unexplored in neuroblastoma, and 36 are located in non-promoter or non-coding regions. In total, 5 individual MSP assays (located in CCDC177, NXPH1, lnc-MRPL3-2, lnc-TREX1-1 and one on a region from chromosome 8 with no further annotation) predict event-free survival and 4 additional assays (located in SPRED3, TNFAIP2, NPM2 and CYYR1) also predict overall survival. Furthermore, a robust 58-marker methylation signature predicting overall and event-free survival was established. In conclusion, this study encompasses the largest DNA methylation biomarker study in neuroblastoma so far. We identified and independently validated several novel prognostic biomarkers, as well as a prognostic 58-marker methylation signature

Computational approaches for high-throughput single-cell data analysis

International audienceDuring the past decade, the number of novel technologies to interrogate biological systems at the single-cell level has skyrocketed. Numerous approaches for measuring the proteome, genome, transcriptome and epigenome at the single-cell level have been pioneered, using a variety of technologies. All these methods have one thing in common: they generate large and high-dimensional datasets that require advanced computational modelling tools to highlight and interpret interesting patterns in these data, potentially leading to novel biological insights and hypotheses. In this work, we provide an overview of the computational approaches used to interpret various types of single-cell data in an automated and unbiased way

Local topological data analysis to uncover the global structure of data approaching graph-structured topologies

Gene expression data of differentiating cells, galaxies distributed in space, and earthquake locations, all share a common property: they lie close to a graph-structured topology in their respective spaces [1, 4, 9, 10, 20], referred to as one-dimensional stratified spaces in mathematics. Often, the uncovering of such topologies offers great insight into these data sets. However, methods for dimensionality reduction are clearly inappropriate for this purpose, and also methods from the relatively new field of Topological Data Analysis (TDA) are inappropriate, due to noise sensitivity, computational complexity, or other limitations. In this paper we introduce a new method, termed Local TDA (LTDA), which resolves the issues of pre-existing methods by unveiling (global) graph-structured topologies in data by means of robust and computationally cheap local analyses. Our method rests on a simple graph-theoretic result that enables one to identify isolated, end-, edge- and multifurcation points in the topology underlying the data. It then uses this information to piece together a graph that is homeomorphic to the unknown one-dimensional stratified space underlying the point cloud data. We evaluate our method on a number of artificial and real-life data sets, demonstrating its superior effectiveness, robustness against noise, and scalability. Code related to this paper is available at: https://bitbucket.org/ghentdatascience/gltda-public