Analysis of microarray and next generation sequencing data for classification and biomarker discovery in relation to complex diseases

PhDThis thesis presents an investigation into gene expression profiling, using microarray and next generation sequencing (NGS) datasets, in relation to multi-category diseases such as cancer. It has been established that if the sequence of a gene is mutated, it can result in the unscheduled production of protein, leading to cancer. However, identifying the molecular signature of different cancers amongst thousands of genes is complex. This thesis investigates tools that can aid the study of gene expression to infer useful information towards personalised medicine. For microarray data analysis, this study proposes two new techniques to increase the accuracy of cancer classification. In the first method, a novel optimisation algorithm, COA-GA, was developed by synchronising the Cuckoo Optimisation Algorithm and the Genetic Algorithm for data clustering in a shuffle setup, to choose the most informative genes for classification purposes. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) artificial neural networks are utilised for the classification step. Results suggest this method can significantly increase classification accuracy compared to other methods. An additional method involving a two-stage gene selection process was developed. In this method, a subset of the most informative genes are first selected by the Minimum Redundancy Maximum Relevance (MRMR) method. In the second stage, optimisation algorithms are used in a wrapper setup with SVM to minimise the selected genes whilst maximising the accuracy of classification. A comparative performance assessment suggests that the proposed algorithm significantly outperforms other methods at selecting fewer genes that are highly relevant to the cancer type, while maintaining a high classification accuracy. In the case of NGS, a state-of-the-art pipeline for the analysis of RNA-Seq data is investigated to discover differentially expressed genes and differential exon usages between normal and AIP positive Drosophila datasets, which are produced in house at Queen Mary, University of London. Functional genomic of differentially expressed genes were examined and found to be relevant to the case study under investigation. Finally, after normalising the RNA-Seq data, machine learning approaches similar to those in microarray was successfully implemented for these datasets

MSI-based mapping strategies in tumour-heterogeneity

Since the early 2000s, considerable innovations in MS technology and associated gene sequencing systems have enabled the "-omics" revolution. The data collected from multiple omics research can be combined to gain a better understanding of cancer's biological activity. Breast and ovarian cancer are among the most common cancers worldwide in women. Despite significant advances in diagnosis, treatment, and subtype identification, breast cancer remains the world's second leading cause of cancer-related deaths in women, with ovarian cancer ranking fifth. Tumour heterogeneity is a significant hurdle in cancer patient prognosis, response to therapy, and metastasis. As such, heterogeneity is one of the most significant and clinically relevant areas of cancer research nowadays. Metabolic reprogramming is a hallmark of malignancy that has been widely acknowledged in recent literature. Metabolic heterogeneity in tumours poses a challenge in developing therapies that exploit metabolic vulnerabilities. Consequently, it is crucial to approach tumour heterogeneity with an unlabeled yet spatially specific read-out of metabolic and genetic information. The advantage of DESI-MSI technology originates from its untargeted nature, which allows for the investigation of thousands of component distributions, at a micrometre scale, in a single experiment. Most notably, using a DESI-MSI clustering approach could potentially offer novel insights into metabolism, providing a method to characterise metabolically distinct sub-regions and subsequently delineate the underlying genetic drivers through genomic analyses. Hence, in this study, we aim to map the inter-and intra-tumour metabolic heterogeneity in breast and ovarian cancer by integrating multimodal MSI-based mapping strategies, comprising DESI and MALDI, with IMC (Imaging Mass Cytometry) analysis of the tumour section, using CyTOF, and high- throughput genetic characterisation of metabolically-distinct regions by transcriptomics. The multimodal analysis workflow was initially performed using sequential breast cancer Patient-Derived Xenografts (PDX) models and was expanded on primary tumour sections. Moreover, a newly developed DESI-MSI friendly, hydroxypropyl-methylcellulose and polyvinylpyrrolidone (HPMC/PVP) hydrogel-based embedding was successfully established to allow simultaneous preparation and analysis of numerous fresh frozen core-size biopsies in the same Tissue Microarray (TMA) block for the investigation of tumour heterogeneity. Additionally, a single section strategy was combined with DESI-MSI coupled to Laser Capture Microdissection (LCM) application to integrate gene expression analysis and Liquid Chromatography-Mass Spectrometry (LC-MS) on the same tissue segment. The developed single section methodology was then tested with multi-region collected ovarian tumours. DESI-MSI-guided spatial transcriptomics was performed for co-registration of different omics datasets on the same regions of interest (ROIs). This co-registration of various omics could unravel possible interactions between distinct metabolic profiles and specific genetic drivers that can lead to intra-tumour heterogeneity. Linking all these findings from MSI-based or guided various strategies allows for a transition from a qualitative approach to a conceptual understanding of the architecture of multiple molecular networks responsible for cellular metabolism in tumour heterogeneity.Open Acces

Identifying markers of cell identity from single-cell omics data

Einzelzell-Omics-Daten stehen derzeit im Fokus der Entwicklung computergestützter Methoden in der Molekularbiologie und Genetik. Einzelzellexperimenten lieferen dünnbesetzte, hochdimensionale Daten über zehntausende Gene oder hunderttausende regulatorische Regionen in zehntausenden Zellen. Diese Daten bieten den Forschenden die Möglichkeit, Gene und regulatorische Regionen zu identifizieren, welche die Bestimmung und Aufrechterhaltung der Zellidentität koordinieren. Die gängigste Strategie zur Identifizierung von Zellidentitätsmarkern besteht darin, die Zellen zu clustern und dann Merkmale zu finden, welche die Cluster unterscheiden, wobei davon ausgegangen wird, dass die Zellen innerhalb eines Clusters die gleiche Identität haben. Diese Annahme ist jedoch nicht immer zutreffend, insbesondere nicht für Entwicklungsdaten bei denen sich die Zellen in einem Kontinuum befinden und die Definition von Clustergrenzen biologisch gesehen potenziell willkürlich ist. Daher befasst sich diese Dissertation mit Clustering-unabhängigen Strategien zur Identifizierung von Markern aus Einzelzell-Omics-Daten. Der wichtigste Beitrag dieser Dissertation ist SEMITONES, eine auf linearer Regression basierende Methode zur Identifizierung von Markern. SEMITONES identifiziert (Gruppen von) Markern aus verschiedenen Arten von Einzelzell-Omics-Daten, identifiziert neue Marker und übertrifft bestehende Marker-Identifizierungsansätze. Außerdem ermöglicht die Identifizierung von regulatorischen Markerregionen durch SEMITONES neue Hypothesen über die Regulierung der Genexpression während dem Erwerb der Zellidentität. Schließlich beschreibt die Dissertation einen Ansatz zur Identifizierung neuer Markergene für sehr ähnliche, dennoch underschiedliche neurale Vorlauferzellen im zentralen Nervensystem von Drosphila melanogaster. Ingesamt zeigt die Dissertation, wie Cluster-unabhängige Ansätze zur Aufklärung bisher uncharakterisierter biologischer Phänome aus Einzelzell-Omics-Daten beitragen.Single-cell omics approaches are the current frontier of computational method development in molecular biology and genetics. A single single-cell experiment provides sparse, high-dimensional data on tens of thousands of genes or hundreds of thousands of regulatory regions (i.e. features) in tens of thousands of cells (i.e. samples). This data provides researchers with an unprecedented opportunity to identify those genes and regulatory regions that determine and coordinate cell identity acquisition and maintenance. The most common strategy for identifying cell identity markers consists of clustering the cells and then identifying differential features between these clusters, assuming that cells within a cluster share the same identity. This assumption is, however, not guaranteed to hold, particularly for developmental data where cells lie along a continuum and inferring cluster boundaries becomes non-trivial and potentially biologically arbitrary. In response, this thesis presents clustering-independent strategies for marker feature identification from single-cell omics data. The primary contribution of this thesis is a linear regression-based method for marker feature identification from single-cell omics data called SEMITONES. SEMITONES can identify markers or marker sets from diverse single-cell omics data types, identifies novel markers, outperforms existing marker identification approaches. The thesis also describes how the identification of marker regulatory regions by SEMITONES enables the generation of novel hypotheses regarding gene regulation during cell identity acquisition. Lastly, the thesis describes the clustering-independent identification of novel marker genes for highly similar yet distinct neural progenitor cells in the Drosophila melanogaster central nervous system. Altogether, the thesis demonstrates how clustering-independent approaches aid the elucidation of yet uncharacterised biological patterns from single cell-omics data

The application of genomic technologies to cancer and companion diagnostics.

This thesis describes work undertaken by the author between 1996 and 2014. Genomics is the study of the genome, although it is also often used as a catchall phrase and applied to the transcriptome (study of RNAs) and methylome (study of DNA methylation). As cancer is a disease of the genome the rapid advances in genomic technology, specifically microarrays and next generation sequencing, are creating a wave of change in our understanding of its molecular pathology. Molecular pathology and personalised medicine are being driven by discoveries in genomics, and genomics is being driven by the development of faster, better and cheaper genome sequencing. The next decade is likely to see significant changes in the way cancer is managed for individual cancer patients as next generation sequencing enters the clinic. In chapter 3 I discuss how ERBB2 amplification testing for breast cancer is currently dominated by immunohistochemistry (a single-gene test); and present the development, by the author, of a semi-quantitative PCR test for ERBB2 amplification. I also show that estimating ERBB2 amplification from microarray copy-number analysis of the genome is possible. In chapter 4 I present a review of microarray comparison studies, and outline the case for careful and considered comparison of technologies when selecting a platform for use in a research study. Similar, indeed more stringent, care needs to be applied when selecting a platform for use in a clinical test. In chapter 5 I present co-authored work on the development of amplicon and exome methods for the detection and quantitation of somatic mutations in circulating tumour DNA, and demonstrate the impact this can have in understanding tumour heterogeneity and evolution during treatment. I also demonstrate how next-generation sequencing technologies may allow multiple genetic abnormalities to be analysed in a single test, and in low cellularity tumours and/or heterogenous cancers. Keywords: Genome, exome, transcriptome, amplicon, next-generation sequencing, differential gene expression, RNA-seq, ChIP-seq, microarray, ERBB2, companion diagnostic

Pathophysiological mechanisms in Parkinson`s Disease and Dystonia – converging aetiologies

In this thesis I used a range of experimental approaches including genetics, enzyme activity measurements, histology and imaging to explore converging pathophysiological mechanisms of Parkinson`s disease and dystonia, two conditions with frequent clinical overlap. First, based on a combined retro- and prospective cohort of patients, using a combination of lysosomal enzyme activity measurements in peripheral blood and brain samples, as well as a target gene approach, I provide first evidence of reduced levels of enzyme activity in Glucocerebrosidase and the presence of GBA mutations, indicating lysosomal abnormality, in a relevant proportion of patients with dystonia of previously unknown origin. Second, based on a retrospective cohort of patients, I detail that a relevant proportion of genetically confirmed mitochondrial disease patients present with a movement disorder phenotype - predominantly dystonia and parkinsonism. Analysing volumetric MRI data, I describe a patterned cerebellar atrophy in these particular patients. This also includes the first cases of isolated dystonia due to mitochondrial disease, adding the latter as a potential aetiology for dystonia of unknown origin. Third, I used a combination of post-GWAS population genetic approaches and tissue-based experiments to explore in how far the strong association between advancing age and Parkinson ́s disease is mediated via telomere length. Although the initial finding of an association between genetically determined telomere length and PD risk did not replicate in independent cohorts, I provide evidence that telomere length in human putamen physiologically shortens with advancing age and 3 is regulated differently than in other brain regions. This is unique in the human brain, implying a particular age-related vulnerability of the striatum, part of the nigro-striatal network, crucially involved in PD pathophysiology. I conclude by discussing the above findings in light of the current literature, expand on their relevance and possible direction of future experiments

Mechanisms of germ cell formation during zebrafish embryogenesis

In metazoans, the germ fate is acquired during embryogenesis either via oocyte-inherited cytoplasmic aggregates or via chemical induction from the surrounding embryonic cells. Most of the model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Xenopus laevis and Danio rerio, rely on maternal determinants necessary to generate the germ line of the offspring. Although it has been largely established that germ determinants are required for the formation of germ cells, the specific molecular mechanisms driving the onset of the germ line are still unclear. Germ granules have been implicated in transcriptional inhibition contributing to skipping somatic differentiation. Also, epigenetic reprogramming of the embryonic germ line has been shown in several model organisms. However, little is known about the role of the germ plasm in transcription and epigenetics. Here, we show that the germ plasm and the epigenetic landscape of zebrafish primordial germ cells (PGCs) are tightly linked. The early germ line shows similar transcriptional timing, transcriptomic and chromatin profiles with the rest of the embryo and the germ fate is gradually acquired during the first day of development. A PGC-like chromatin profile is acquired while germ plasm re-localises within the cells and PGCs and somatic cells undergo significant epigenetic and transcriptional divergence. By performing time series of chromatin and transcript profiles in the PGCs, we could identify candidate PGC-specific cis-regulatory elements and transcripts. We detect both hypermethylation and chromatin compaction around putative developmental enhancers indicating that the germ fate is acquired avoiding lineage differentiation. Finally, to link epigenetic dynamics to germ plasm behaviour, we inhibited the translation of Tudor Domain 7 (Tdrd7), a germ-plasm-localised protein involved in structural organisation of the germ granules. The mutant embryos reprogram the PGC-specific chromatin state and resemble the somatic cells, suggesting that the germ plasm is primarily responsible for epigenetically preserving the pluripotent state of the PGCs