INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Systems biology of breast cancer

Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach

The Potential Roles of Long Noncoding RNAs (lncRNA) in Glioblastoma Development

Long noncoding RNA (lncRNA) may contribute to the initiation and progression of tumor. In this study, we first systematically compared lncRNA and mRNA expression between glioblastoma and paired normal brain tissues using microarray data. We found 27 lncRNA and 82 mRNA significantly upregulated in glioblastoma, as well as 198 lncRNA and 285 mRNA significantly downregulated in glioblastoma. We identified 138 coexpressed lncRNA–mRNA pairs from these differentially expressed lncRNA and genes. Subsequent pathway analysis of the lncRNA-paired genes indicated that EphrinB–EPHB, p75-mediated signaling, TNFα/NF-κB, and ErbB2/ErbB3 signaling pathways might be altered in glioblastoma. Specifically, lncRNA RAMP2-AS1 had significant decrease of expression in glioblastoma tissues and showed coexpressional relationship with NOTCH3, an important tumor promoter in many neoplastic diseases. Our follow up experiment indicated that (i) an overexpression of RAMP2-AS1 reduced glioblastoma cell proliferation in vitro and also reduced glioblastoma xenograft tumors in vivo; (ii) NOTCH3 and RAMP2-AS1 coexpression rescued the inhibitory action of RAMP2-AS1 in glioblastoma cells; and (iii) RNA pull-down assay revealed a direct interaction of RAMP2-AS1 with DHC10, which may consequently inhibit, as we hypothesize, the expression of NOTCH3 and its downstream signaling molecule HES1 in glioblastoma. Taken together, our data revealed that lncRNA expression profile in glioblastoma tissue was significantly altered; and RAMP2-AS1 might play a tumor suppressive role in glioblastoma through an indirect inhibition of NOTCH3. Our results provided some insights into understanding the key roles of lncRNA–mRNA coregulation in human glioblastoma and the mechanisms responsible for glioblastoma progression and pathogenesis. Mol Cancer Ther; 15(12); 2977–86. ©2016 AACR

Predicting breast cancer risk, recurrence and survivability

This thesis focuses on predicting breast cancer at early stages by using machine learning algorithms based on biological datasets. The accuracy of those algorithms has been improved to enable the physicians to enhance the success of treatment, thus saving lives and avoiding several further medical tests

NOVEL APPLICATIONS OF MACHINE LEARNING IN BIOINFORMATICS

Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice junction classification, gene regulatory network reconstruction, and lesion detection in mammograms. A critical step in defining gene structures and mRNA transcript variants is to accurately identify splice junctions. In the first work, we built the first deep learning-based splice junction classifier, DeepSplice. It outperforms the state-of-the-art classification tools in terms of both classification accuracy and computational efficiency. To uncover transcription factors governing metabolic reprogramming in non-small-cell lung cancer patients, we developed TFmeta, a machine learning approach to reconstruct relationships between transcription factors and their target genes in the second work. Our approach achieves the best performance on benchmark data sets. In the third work, we designed deep learning-based architectures to perform lesion detection in both 2D and 3D whole mammogram images

Diseño de sistemas neurocomputacionales en el ámbito de la Biomedicina

El área de la biomedicina es un área extensa en el que las entidades públicas de cada país han invertido y continúan invirtiendo en investigación una gran cantidad de financiación a través de proyectos nacionales, europeos e internacionales. Los avances científicos y tecnológicos registrados en los últimos quince años han permitido profundizar en las bases genéticas y moleculares de enfermedades como el cáncer, y analizar la variabilidad de respuesta de pacientes individuales a diferentes tratamientos oncológicos, estableciendo las bases de lo que hoy se conoce como medicina personalizada. Ésta puede definirse como el diseño y aplicación de estrategias de prevención, diagnóstico y tratamiento adaptadas a un escenario que integra la información del perfil genético, clínico, histopatológico e inmuhistoquímico de cada paciente y patología. Dada la incidencia de la enfermedad de cáncer en la sociedad, y a pesar de que la investigación se ha centrado tradicionalmente en el aspecto de diagnóstico, es relativamente reciente el interés de los investigadores por el estudio del pronóstico de la enfermedad, aspecto integrado en la tendencia creciente de los sistemas nacionales de salud pública hacia un modelo de medicina personalizada y predictiva. El pronóstico puede ser definido como conocimiento previo de un evento antes de su posible aparición, y puede enfocarse a la susceptibilidad, supervivencia y recidiva de la enfermedad. En la literatura, existen trabajos que utilizan modelos neurocomputacionales para la predicción de casuísticas muy particulares como, por ejemplo, la recidiva en cáncer de mama operable, basándose en factores pronóstico de naturaleza clínico-histopatológica. En ellos se demuestra que estos modelos superan en rendimiento a las herramientas estadísticas tradicionalmente utilizadas en análisis de supervivencia por el personal clínico experto. Sin embargo, estos modelos pierden eficacia cuando procesan información de tumores atípicos o subtipos morfológicamente indistinguibles, para los que los factores clínicos e histopatológicos no proporcionan suficiente información discriminatoria. El motivo es la heterogeneidad del cáncer como enfermedad, para la que no existe una causa individual caracterizada, y cuya evolución se ha demostrado que está determinada por factores no sólo clínicos sino también genéticos. Por ello, la integración de los datos clínico-histopatológicos y proteómico-genómica proporcionan una mayor precisión en la predicción en comparación con aquellos modelos que utilizan sólo un tipo de datos, permitiendo llevar a la práctica clínica diaria una medicina personalizada. En este sentido, los datos de perfiles de expresión provenientes de experimentos con plataformas de microarrays de ADN, los datos de microarrays de miRNA, o más recientemente secuenciadores de última generación como RNA-Seq, proporcionan el nivel de detalle y complejidad necesarios para clasificar tumores atípicos estableciendo diferentes pronósticos para pacientes dentro de un mismo grupo protocolizado. El análisis de datos de esta naturaleza representa un verdadero reto para clínicos, biólogos y el resto de la comunidad científica en general dado el gran volumen de información producido por estas plataformas. Por lo general, las muestras resultantes de los experimentos en estas plataformas vienen representadas por un número muy elevado de genes, del orden de miles de ellos. La identificación de los genes más significativos que incorporen suficiente información discriminatoria y que permita el diseño de modelos predictivos sería prácticamente imposible de llevar a cabo sin ayuda de la informática. Es aquí donde surge la Bioinformática, término que hace referencia a cómo se aplica la ciencia de la información en el área de la biomedicina. El objetivo global que se intenta alcanzar en esta tesis consiste, por tanto, en llevar a la práctica clínica diaria una medicina personalizada. Para ello, se utilizarán datos de perfiles de expresión de alguna de las plataformas de microarrays más relevantes con objeto de desarrollar modelos predictivos que permitan obtener una mejora en la capacidad de generalización de los sistemas pronóstico actuales en el ámbito clínico. Del objetivo global de la tesis pueden derivarse tres objetivos parciales: el primero buscará (i) pre-procesar cualquier conjunto de datos en general y, datos de carácter biomédico en particular, para un posterior análisis; el segundo buscará (ii) analizar las principales deficiencias existentes en los sistemas de información actuales de un servicio de oncología para así desarrollar un sistema de información oncológico que cubra todas sus necesidades; y el tercero buscará (iii) desarrollar nuevos modelos predictivos basados en perfiles de expresión obtenidos a partir de alguna plataforma de secuenciación, haciendo hincapié en la capacidad predictiva de estos modelos, la robustez y la relevancia biológica de las firmas genéticas encontradas. Finalmente, se puede concluir que los resultados obtenidos en esta tesis doctoral permitirían ofrecer, en un futuro cercano, una medicina personalizada en la práctica clínica diaria. Los modelos predictivos basados en datos de perfiles de expresión que se han desarrollado en la tesis podrían integrarse en el propio sistema de información oncológico implantado en el Hospital Universitario Virgen de la Victoria (HUVV) de Málaga, fruto de parte del trabajo realizado en esta tesis. Además, se podría incorporar la información proteómico-genómica de cada paciente para poder aprovechar al máximo las ventajas añadidas mencionadas a lo largo de esta tesis. Por otro lado, gracias a todo el trabajo realizado en esta tesis, el doctorando ha podido profundizar y adquirir una extensa formación investigadora en un área tan amplia como es la Bioinformática

Building trajectories through clinical data to model disease progression

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modeling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This thesis describes the application of intelligent data analysis techniques for extracting information from time series generated by different diseases. The aim of this thesis is to identify intermediate stages in a disease process and sub-categories of the disease exhibiting subtly different symptoms. It explores the use of a bootstrap technique that fits trajectories through the data generating “pseudo time-series”. It addresses issues including: how clinical variables interact as a disease progresses along the trajectories in the data; and how to automatically identify different disease states along these trajectories, as well as the transitions between them. The thesis documents how reliable time-series models can be created from large amounts of historical cross-sectional data and a novel relabling/latent variable approach has enabled the exploration of the temporal nature of disease progression. The proposed algorithms are tested extensively on simulated data and on three real clinical datasets. Finally, a study is carried out to explore whether we can “calibrate” pseudo time-series models with real longitudinal data in order to improve them. Plausible directions for future research are discussed at the end of the thesis