Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Structural and Functional Characterization of the MBD2-NuRD Co-Repressor Complex

The MBD2-NuRD co-repressor complex is an epigenetic regulator of the developmental silencing of embryonic and fetal β-type globin genes in adult erythroid cells as well as aberrant methylation-dependent silencing of tumor suppressor genes in neoplastic diseases. Biochemical characterization of the MBD2-NuRD complex in chicken erythroid cells identified RbAp46/48, HDAC1/2, MTA1/2/3, p66α/β, Mi2α/β and MBD2 to comprise this multi-protein complex. In the work presented in Chapter 2, we have pursued biophysical and molecular studies to describe a previously uncharacterized domain of human MBD2 (MBD2IDR). Biophysical analyses show that MBD2IDR is an intrinsically disordered region (IDR). Despite this inherent disorder, MBD2IDR increases the overall binding affinity of MBD2 for methylated DNA. MBD2IDR also recruits the histone deacetylase core components (RbAp48, HDAC2 and MTA2) of NuRD through a critical area of contact requiring two contiguous amino acid residues, Arg286 and Leu287. Mutation of these critical residues abrogates interaction of MBD2 with the histone deacetylase core and impairs the ability of MBD2 to repress the methylated tumor suppressor gene Prostasin in MDA-MB-435 breast cancer cells. These findings expand our knowledge of the multi-dimensional interactions of the MBD2-NuRD complex that govern its function. In Chapter 3, we have discussed a novel mechanism for MBD2-mediated silencing of the fetal γ-globin gene. Through microarray expression analyses in adult erythroid cells of MBD2-/- mice, we identified ZBTB32 and miR-210 as downstream targets of MBD2. Over-expression of ZBTB32 and miR-210 in adult erythroid cells causes increased expression of the silenced fetal γ-globin gene. Thus, our results indicate that MBD2 may regulate γ-globin gene expression indirectly though ZBTB32 and miR-210 in adult erythroid cells

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

INTEGRATED GENOMIC MARKERS FOR CHEMOTHERAPEUTICS

Ph.DDOCTOR OF PHILOSOPH

Systems medicine in colorectal cancer: from a mathematical model toward a new type of clinical trial

Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro-RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1-S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical-trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314\u2013336. doi: 10.1002/wsbm.1342. For further resources related to this article, please visit the WIREs website

Identifying the molecular components that matter: a statistical modelling approach to linking functional genomics data to cell physiology

Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured in single experiments, have contributed to reshape biological investigations. One of the most important issues in the analysis of the generated large datasets is the selection of relatively small sub-sets of variables that are predictive of the physiological state of a cell or tissue. In this thesis, a truly multivariate variable selection framework using diverse functional genomics data has been developed, characterized, and tested. This framework has also been used to prove that it is possible to predict the physiological state of the tumour from the molecular state of adjacent normal cells. This allows us to identify novel genes involved in cell to cell communication. Then, using a network inference technique networks representing cell-cell communication in prostate cancer have been inferred. The analysis of these networks has revealed interesting properties that suggests a crucial role of directional signals in controlling the interplay between normal and tumour cell to cell communication. Experimental verification performed in our laboratory has provided evidence that one of the identified genes could be a novel tumour suppressor gene. In conclusion, the findings and methods reported in this thesis have contributed to further understanding of cell to cell interaction and multivariate variable selection not only by applying and extending previous work, but also by proposing novel approaches that can be applied to any functional genomics data

Molecular subclassification of medulloblastoma and its utility for disease prognostication

Medulloblastoma is the most common malignant brain tumour of childhood. Transcriptomic classification of the disease has indicated the existence of discrete molecular subgroups of medulloblastoma, although the precise number, nature and clinical significance of these subgroups remains unclear. Two groups, characterised by activation of the WNT and SHH signalling pathways, are common to all published studies. An assay for the rapid diagnosis of medulloblastoma subgroups was therefore designed, using transcriptomic gene signatures of pathway activation for the WNT and SHH signalling pathways. The successful validation of these gene signatures in vitro and in silico enabled a meta-analysis of 173 new and published cases to be performed, which defined the molecular and clinico-pathological correlates of the disease subgroups more precisely. WNT subgroup cases were associated with CTNNB1 mutation, chromosome 6 loss and classic histology and were diagnosed > 5 years of age. SHH cases predominated in infants and showed an age-dependent relationship to desmoplastic / nodular histology. WNT / SHH independent tumours showed all histologies, peaked at 3 to 6 years and were associated with chromosome 17p loss. A novel DNA methylation array-based approach was next applied to disease subclassification. Using consensus clustering, based on non-negative matrix factorisation, four methylomic subgroups were identified in a training cohort (n = 100), which were robustly validated in a test cohort (n = 130). The subgroups were characterised by significant relationships to specific clinico-pathological and molecular markers. Two subgroups were characterised by activation of the WNT and SHH signalling pathways and showed equivalent clinico-pathological and molecular characteristics to the previously defined transcriptomic subgroups. For the WNT / SHH independent subgroups, group I was associated with a loss of chromosome 17p, whereas group II was enriched for large cell / anaplastic (LCA) histology. The WNT subgroup was associated with a favourable prognosis, while no survival differences were apparent between the remaining subgroups (SHH, group I, group II). Specific methylation biomarkers were identified for the discrimination of all subgroups. Assays of DNA methylation status were robust in derivatives of FFPE tissues, enabling testing in routinely-collected clinical material. Finally, the prognostic potential of methylomic biomarkers was investigated in a large clinical trials-based cohort (n = 191), with particular focus on the non-WNT subgroups (n = 163), where subgroup membership was not prognostic. Using the Cox Boost algorithm, which adds high dimensional data to mandatory clinical covariates to form cross-validated prognostic Cox survival models, the methylation status of MXI1 and IL8 were each identified as independent prognostic markers. These were incorporated into a novel risk stratification scheme, based on the cumulative assessment of disease risk using clinical (metastatic disease; poor prognosis), pathological (LCA pathology, poor prognosis) and methylomic variables (WNT subgroup, favourable prognosis; MXI1 and IL8 status). Importantly, this scheme assigns 46% of cases to a low risk group of patients (>90% survival) who could potentially be treated less intensively, with the aim of reducing therapy-associated late effects. This model out-performed the current clinical and other state-of-the-art medulloblastoma risk classification schemes. These data provide clear precedent for the utility of DNA methylation biomarkers for disease subclassification and prognostication in medulloblastoma, and their clinical application in diagnostic tumour biopsies.EThOS - Electronic Theses Online ServiceKatie TrustGBUnited Kingdo