Genome-wide DNA methylation detection by MethylCap-seq and Infinium HumanMethylation450 BeadChips: an independent large-scale comparison.

Two cost-efficient genome-scale methodologies to assess DNA-methylation are MethylCap-seq and Illumina's Infinium HumanMethylation450 BeadChips (HM450). Objective information regarding the best-suited methodology for a specific research question is scant. Therefore, we performed a large-scale evaluation on a set of 70 brain tissue samples, i.e. 65 glioblastoma and 5 non-tumoral tissues. As MethylCap-seq coverages were limited, we focused on the inherent capacity of the methodology to detect methylated loci rather than a quantitative analysis. MethylCap-seq and HM450 data were dichotomized and performances were compared using a gold standard free Bayesian modelling procedure. While conditional specificity was adequate for both approaches, conditional sensitivity was systematically higher for HM450. In addition, genome-wide characteristics were compared, revealing that HM450 probes identified substantially fewer regions compared to MethylCap-seq. Although results indicated that the latter method can detect more potentially relevant DNA-methylation, this did not translate into the discovery of more differentially methylated loci between tumours and controls compared to HM450. Our results therefore indicate that both methodologies are complementary, with a higher sensitivity for HM450 and a far larger genome-wide coverage for MethylCap-seq, but also that a more comprehensive character does not automatically imply more significant results in biomarker studies

Doctor of Philosophy

dissertationPost-transcriptional RNA modifications provide new structural and functional features to modified RNA molecules. Extensive research in the past has resulted in isolation of over 100 distinct nucleotide modifications from different organisms and in different RNA species. These modified nucleotides are distributed within the entire transcriptome comprising the cellular epitranscriptome. The ultimate goal of the research in the field is to address what the specific functions of specific modifications are, and also the impact of each on cellular physiology. However, the first question to be addressed is how these > 100 modified nucleotides are distributed within the transcriptome. RNA modification profiling using conventional techniques has provided a great body of knowledge about the distribution of many modifications in RNAs. However, these findings remained limited mostly to tRNAs and rRNAs, the two most abundant and also highly modified RNA species in different organisms. This is partly because of the lower sensitivity of applied classical technologies. Here in this dissertation, in Chapter 2, we are reporting an optimized new RNA bisulfite protocol suitable for high-throughput RNA cytosine methylation profiling. We present the results of application of this technique for 5-methyl-cytosine (m5C) profiling in mouse embryonic fibroblasts (MEFs) RNAs, isolated from wt and dnmt2-/- mice to explore the target specificity of DNA methyltransferase 2 (DNMT2) enzyme. In Chapter 3, we present a substantially novel technique: Aza-IP, for enrichment and identification of the direct targets of RNA cytosine methyltransferases (m5C-RMTs) as well as iv determination of the exact modified bases in the same experiment. We provide the results of the Aza-IP technique for two human m5C-RMTs; DNMT2 and NSUN2, representing their known and novel RNA targets/modified bases. In Chapter 4 we discuss how similar technologies to both of the RNA bisulfite sequencing and Aza-IP techniques as well as other methodologies can be applied and extended for transcriptome-wide profiling of RNA modifications other than m5C. In Chapter 5 we present the future directions of the work focused on cataloguing the direct targets of all human m5C-RMTs in human cultured cells in mouse and fish model systems, to elucidate the functions of cytosine methylation in RNA molecules

Differential analysis of biological networks

In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest

The ever-evolving concept of the gene: The use of RNA/Protein experimental techniques to understand genome functions

The completion of the human genome sequence together with advances in sequencing technologies have shifted the paradigm of the genome, as composed of discrete and hereditable coding entities, and have shown the abundance of functional noncoding DNA. This part of the genome, previously dismissed as "junk" DNA, increases proportionally with organismal complexity and contributes to gene regulation beyond the boundaries of known protein-coding genes. Different classes of functionally relevant nonprotein-coding RNAs are transcribed from noncoding DNA sequences. Among them are the long noncoding RNAs (lncRNAs), which are thought to participate in the basal regulation of protein-coding genes at both transcriptional and post-transcriptional levels. Although knowledge of this field is still limited, the ability of lncRNAs to localize in different cellular compartments, to fold into specific secondary structures and to interact with different molecules (RNA or proteins) endows them with multiple regulatory mechanisms. It is becoming evident that lncRNAs may play a crucial role in most biological processes such as the control of development, differentiation and cell growth. This review places the evolution of the concept of the gene in its historical context, from Darwin's hypothetical mechanism of heredity to the post-genomic era. We discuss how the original idea of protein-coding genes as unique determinants of phenotypic traits has been reconsidered in light of the existence of noncoding RNAs. We summarize the technological developments which have been made in the genome-wide identification and study of lncRNAs and emphasize the methodologies that have aided our understanding of the complexity of lncRNA-protein interactions in recent years

A survey of best practices for RNA-seq data analysis.

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.This is the final published version. It first appeared at http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery.

Since the publication of the Society for Immunotherapy of Cancer\u27s (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients

A functional data analytic approach for region level differential DNA methylation detection

DNA methylation is an epigenetic modification that can alter gene expression without a DNA sequence change. The role of DNA methylation in biological processes and human health is important to understand, with many studies identifying associations between specific methylation patterns and diseases such as cancer. In mammals, DNA methylation almost always occurs when a methyl group attaches to a cytosine followed by a guanine (i.e. CpG dinucleotides) on the DNA sequence. Many statistical methods have been developed to test for a difference in DNA methylation levels between groups (e.g. healthy vs disease) at individual cytosines. Site level testing is often followed by a post hoc aggregation procedure that explores regional differences. Although analyzing CpGs individually provides useful information, there are both biological and statistical reasons to test entire genomic regions for differential methylation. The individual loci may be noisy but the overall regions tend to be informative. Also, the biological function of regions is better studied and are more correlated to gene expression, so the interpretation of results will be more meaningful for region-level tests. This study focuses on developing two techniques, functional principal component analysis (FPCA) and smoothed functional principal component analysis (SFPCA), to identify differentially methylated regions (DMRs) that will enable discovery of epigenomic structural variations in NGS data. Using real and simulated data, the performance of these novel approaches are compared with an alternative method (M3D) for region level testing --Abstract, page iv

Exploring the neuroblastoma DNA methylome: from biology to biomarker

Neuroblastoma (NB), a childhood tumor arising from immature sympathetic nervous system cells, is a heterogeneous disease with prognosis ranging from excellent long-term survival to high-risk with fatal outcome. In order to determine the most appropriate treatment modality, patients are stratified into risk groups at the time of diagnosis, based on combinations of clinical and biological parameters, namely age of the patient, tumor stage, histology, grade of differentiation, MYCN oncogene amplification, chromosome 11q aberration and DNA ploidy. However, use of this risk classification system has shown that accurate assessment of NB prognosis remains difficult and that additional prognostic markers are warranted. Therefore, we aimed to identify prognostic tumor DNA methylation biomarkers for NB. To find new biomarkers, we profiled the primary tumor DNA methylome using methyl-CpG-binding domain (MBD) sequencing, i.e. massively parallel sequencing of methylation-enriched DNA fractions, captured using the high affinity of MBD to bind methylated cytosines. As proof of principle, we applied this technology to 8 NB cell lines, and in combination with mRNA expression studies, this led to a first selection of 43 candidate biomarkers. Next, methylation-specific PCR (MSP) assays were designed, to allow candidate-specific methylation analysis in a primary tumor cohort of 89 samples. As such, we identified new prognostic DNA methylation biomarkers, and delineated the technological aspects and data analysis pipeline to set up a more extended biomarker study. In this follow-up study, the DNA methylome of 102 primary tumors, selected for risk classification and survival, was characterized by MBD sequencing. Differential methylation analyses between the prognostic patient groups put forward 78 top-ranking biomarker candidates, which were subsequently tested on two independent cohorts of 132 and 177 samples, adopting the high-throughput MSP pipeline of our pilot study. Multiple individual MSP assays were prognostically validated and through the implementation of a newly developed statistical framework, a robust 58-marker methylation signature predicting overall and event-free survival was established. This study represents the largest DNA methylation (biomarker) study in NB so far. The MBD sequencing data were shared with the research community through the format of a data descriptor. As such, these data are fully available to others, ensuring its reusability for other research purposes. To illustrate how these data can be applied to gain new insights into the NB pathology, we characterized the DNA methylome of stage 4S NB, a special type of NB found in infants with widespread metastases at diagnosis that paradoxically is associated with an excellent outcome due to its remarkable capacity to undergo spontaneous regression. More specifically, we compared promoter methylation levels between stage 4S, stage 1/2 (localized disease with favorable prognosis) and stage 4 (metastatic disease with dismal prognosis) tumors, and showed that specific chromosomal locations are enriched in stage 4S differentially methylated promoters and that specific subtelomeric promoters are hypermethylated in stage 4S. Furthermore, genes involved in important oncogenic pathways, in neural crest development and differentiation, and in epigenetic processes are differentially methylated and expressed in stage 4S. In conclusion, by exploring the DNA methylome of NB, we have not only demonstrated that DNA methylation patterns are intimately related to NB biology, but also found additional clinically relevant prognostic biomarkers