Computational solutions for addressing heterogeneity in DNA methylation data

DNA methylation, a reversible epigenetic modification, has been implicated with various bi- ological processes including gene regulation. Due to the multitude of datasets available, it is a premier candidate for computational tool development, especially for investigating hetero- geneity within and across samples. We differentiate between three levels of heterogeneity in DNA methylation data: between-group, between-sample, and within-sample heterogeneity. Here, we separately address these three levels and present new computational approaches to quantify and systematically investigate heterogeneity. Epigenome-wide association studies relate a DNA methylation aberration to a phenotype and therefore address between-group heterogeneity. To facilitate such studies, which necessar- ily include data processing, exploratory data analysis, and differential analysis of DNA methy- lation, we extended the R-package RnBeads. We implemented novel methods for calculating the epigenetic age of individuals, novel imputation methods, and differential variability analysis. A use-case of the new features is presented using samples from Ewing sarcoma patients. As an important driver of epigenetic differences between phenotypes, we systematically investigated associations between donor genotypes and DNA methylation states in methylation quantitative trait loci (methQTL). To that end, we developed a novel computational framework –MAGAR– for determining statistically significant associations between genetic and epigenetic variations. We applied the new pipeline to samples obtained from sorted blood cells and complex bowel tissues of healthy individuals and found that tissue-specific and common methQTLs have dis- tinct genomic locations and biological properties. To investigate cell-type-specific DNA methylation profiles, which are the main drivers of within-group heterogeneity, computational deconvolution methods can be used to dissect DNA methylation patterns into latent methylation components. Deconvolution methods require pro- files of high technical quality and the identified components need to be biologically interpreted. We developed a computational pipeline to perform deconvolution of complex DNA methyla- tion data, which implements crucial data processing steps and facilitates result interpretation. We applied the protocol to lung adenocarcinoma samples and found indications of tumor in- filtration by immune cells and associations of the detected components with patient survival. Within-sample heterogeneity (WSH), i.e., heterogeneous DNA methylation patterns at a ge- nomic locus within a biological sample, is often neglected in epigenomic studies. We present the first systematic benchmark of scores quantifying WSH genome-wide using simulated and experimental data. Additionally, we created two novel scores that quantify DNA methyla- tion heterogeneity at single CpG resolution with improved robustness toward technical biases. WSH scores describe different types of WSH in simulated data, quantify differential hetero- geneity, and serve as a reliable estimator of tumor purity. Due to the broad availability of DNA methylation data, the levels of heterogeneity in DNA methylation data can be comprehensively investigated. We contribute novel computational frameworks for analyzing DNA methylation data with respect to different levels of hetero- geneity. We envision that this toolbox will be indispensible for understanding the functional implications of DNA methylation patterns in health and disease.DNA Methylierung ist eine reversible, epigenetische Modifikation, die mit verschiedenen biologischen Prozessen wie beispielsweise der Genregulation in Verbindung steht. Eine Vielzahl von DNA Methylierungsdatensätzen bildet die perfekte Grundlage zur Entwicklung von Softwareanwendungen, insbesondere um Heterogenität innerhalb und zwischen Proben zu beschreiben. Wir unterscheiden drei Ebenen von Heterogenität in DNA Methylierungsdaten: zwischen Gruppen, zwischen Proben und innerhalb einer Probe. Hier betrachten wir die drei Ebenen von Heterogenität in DNA Methylierungsdaten unabhängig voneinander und präsentieren neue Ansätze um die Heterogenität zu beschreiben und zu quantifizieren. Epigenomweite Assoziationsstudien verknüpfen eine DNA Methylierungsveränderung mit einem Phänotypen und beschreiben Heterogenität zwischen Gruppen. Um solche Studien, welche Datenprozessierung, sowie exploratorische und differentielle Datenanalyse beinhalten, zu vereinfachen haben wir die R-basierte Softwareanwendung RnBeads erweitert. Die Erweiterungen beinhalten neue Methoden, um das epigenetische Alter vorherzusagen, neue Schätzungsmethoden für fehlende Datenpunkte und eine differentielle Variabilitätsanalyse. Die Analyse von Ewing-Sarkom Patientendaten wurde als Anwendungsbeispiel für die neu entwickelten Methoden gewählt. Wir untersuchten Assoziationen zwischen Genotypen und DNA Methylierung von einzelnen CpGs, um sogenannte methylation quantitative trait loci (methQTL) zu definieren. Diese stellen einen wichtiger Faktor dar, der epigenetische Unterschiede zwischen Gruppen induziert. Hierzu entwickelten wir ein neues Softwarepaket (MAGAR), um statistisch signifikante Assoziationen zwischen genetischer und epigenetischer Variation zu identifizieren. Wir wendeten diese Pipeline auf Blutzelltypen und komplexe Biopsien von gesunden Individuen an und konnten gemeinsame und gewebespezifische methQTLs in verschiedenen Bereichen des Genoms lokalisieren, die mit unterschiedlichen biologischen Eigenschaften verknüpft sind. Die Hauptursache für Heterogenität innerhalb einer Gruppe sind zelltypspezifische DNA Methylierungsmuster. Um diese genauer zu untersuchen kann Dekonvolutionssoftware die DNA Methylierungsmatrix in unabhängige Variationskomponenten zerlegen. Dekonvolutionsmethoden auf Basis von DNA Methylierung benötigen technisch hochwertige Profile und die identifizierten Komponenten müssen biologisch interpretiert werden. In dieser Arbeit entwickelten wir eine computerbasierte Pipeline zur Durchführung von Dekonvolutionsexperimenten, welche die Datenprozessierung und Interpretation der Resultate beinhaltet. Wir wendeten das entwickelte Protokoll auf Lungenadenokarzinome an und fanden Anzeichen für eine Tumorinfiltration durch Immunzellen, sowie Verbindungen zum Überleben der Patienten. Heterogenität innerhalb einer Probe (within-sample heterogeneity, WSH), d.h. heterogene Methylierungsmuster innerhalb einer Probe an einer genomischen Position, wird in epigenomischen Studien meist vernachlässigt. Wir präsentieren den ersten Vergleich verschiedener, genomweiter WSH Maße auf simulierten und experimentellen Daten. Zusätzlich entwickelten wir zwei neue Maße um WSH für einzelne CpGs zu berechnen, welche eine verbesserte Robustheit gegenüber technischen Faktoren aufweisen. WSH Maße beschreiben verschiedene Arten von WSH, quantifizieren differentielle Heterogenität und sagen Tumorreinheit vorher. Aufgrund der breiten Verfügbarkeit von DNA Methylierungsdaten können die Ebenen der Heterogenität ganzheitlich beschrieben werden. In dieser Arbeit präsentieren wir neue Softwarelösungen zur Analyse von DNA Methylierungsdaten in Bezug auf die verschiedenen Ebenen der Heterogenität. Wir sind davon überzeugt, dass die vorgestellten Softwarewerkzeuge unverzichtbar für das Verständnis von DNA Methylierung im kranken und gesunden Stadium sein werden

Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature

[ES] El futuro de la imagen médica está ligado a la inteligencia artificial. El análisis manual de imágenes médicas es hoy en día una tarea ardua, propensa a errores y a menudo inasequible para los humanos, que ha llamado la atención de la comunidad de Aprendizaje Automático (AA). La Imagen por Resonancia Magnética (IRM) nos proporciona una rica variedad de representaciones de la morfología y el comportamiento de lesiones inaccesibles sin una intervención invasiva arriesgada. Sin embargo, explotar la potente pero a menudo latente información contenida en la IRM es una tarea muy complicada, que requiere técnicas de análisis computacional inteligente. Los tumores del sistema nervioso central son una de las enfermedades más críticas estudiadas a través de IRM. Específicamente, el glioblastoma representa un gran desafío, ya que, hasta la fecha, continua siendo un cáncer letal que carece de una terapia satisfactoria. Del conjunto de características que hacen del glioblastoma un tumor tan agresivo, un aspecto particular que ha sido ampliamente estudiado es su heterogeneidad vascular. La fuerte proliferación vascular del glioblastoma, así como su robusta angiogénesis han sido consideradas responsables de la alta letalidad de esta neoplasia. Esta tesis se centra en la investigación y desarrollo del método Hemodynamic Tissue Signature (HTS): un método de AA no supervisado para describir la heterogeneidad vascular de los glioblastomas mediante el análisis de perfusión por IRM. El método HTS se basa en el concepto de hábitat, que se define como una subregión de la lesión con un perfil de IRM que describe un comportamiento fisiológico concreto. El método HTS delinea cuatro hábitats en el glioblastoma: el hábitat HAT, como la región más perfundida del tumor con captación de contraste; el hábitat LAT, como la región del tumor con un perfil angiogénico más bajo; el hábitat IPE, como la región adyacente al tumor con índices de perfusión elevados; y el hábitat VPE, como el edema restante de la lesión con el perfil de perfusión más bajo. La investigación y desarrollo de este método ha originado una serie de contribuciones enmarcadas en esta tesis. Primero, para verificar la fiabilidad de los métodos de AA no supervisados en la extracción de patrones de IRM, se realizó una comparativa para la tarea de segmentación de gliomas de grado alto. Segundo, se propuso un algoritmo de AA no supervisado dentro de la familia de los Spatially Varying Finite Mixture Models. El algoritmo propone una densidad a priori basada en un Markov Random Field combinado con la función probabilística Non-Local Means, para codificar la idea de que píxeles vecinos tienden a pertenecer al mismo objeto. Tercero, se presenta el método HTS para describir la heterogeneidad vascular del glioblastoma. El método se ha aplicado a casos reales en una cohorte local de un solo centro y en una cohorte internacional de más de 180 pacientes de 7 centros europeos. Se llevó a cabo una evaluación exhaustiva del método para medir el potencial pronóstico de los hábitats HTS. Finalmente, la tecnología desarrollada en la tesis se ha integrado en la plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofrece dos servicios: 1) segmentación de tejidos de glioblastoma, y 2) evaluación de la heterogeneidad vascular del tumor mediante el método HTS. Los resultados de esta tesis han sido publicados en diez contribuciones científicas, incluyendo revistas y conferencias de alto impacto en las áreas de Informática Médica, Estadística y Probabilidad, Radiología y Medicina Nuclear y Aprendizaje Automático. También se emitió una patente industrial registrada en España, Europa y EEUU. Finalmente, las ideas originales concebidas en esta tesis dieron lugar a la creación de ONCOANALYTICS CDX, una empresa enmarcada en el modelo de negocio de los companion diagnostics de compuestos farmacéuticos.[EN] The future of medical imaging is linked to Artificial Intelligence (AI). The manual analysis of medical images is nowadays an arduous, error-prone and often unaffordable task for humans, which has caught the attention of the Machine Learning (ML) community. Magnetic Resonance Imaging (MRI) provides us with a wide variety of rich representations of the morphology and behavior of lesions completely inaccessible without a risky invasive intervention. Nevertheless, harnessing the powerful but often latent information contained in MRI acquisitions is a very complicated task, which requires computational intelligent analysis techniques. Central nervous system tumors are one of the most critical diseases studied through MRI. Specifically, glioblastoma represents a major challenge, as it remains a lethal cancer that, to date, lacks a satisfactory therapy. Of the entire set of characteristics that make glioblastoma so aggressive, a particular aspect that has been widely studied is its vascular heterogeneity. The strong vascular proliferation of glioblastomas, as well as their robust angiogenesis and extensive microvasculature heterogeneity have been claimed responsible for the high lethality of the neoplasm. This thesis focuses on the research and development of the Hemodynamic Tissue Signature (HTS) method: an unsupervised ML approach to describe the vascular heterogeneity of glioblastomas by means of perfusion MRI analysis. The HTS builds on the concept of habitats. A habitat is defined as a sub-region of the lesion with a particular MRI profile describing a specific physiological behavior. The HTS method delineates four habitats within the glioblastoma: the HAT habitat, as the most perfused region of the enhancing tumor; the LAT habitat, as the region of the enhancing tumor with a lower angiogenic profile; the potentially IPE habitat, as the non-enhancing region adjacent to the tumor with elevated perfusion indexes; and the VPE habitat, as the remaining edema of the lesion with the lowest perfusion profile. The research and development of the HTS method has generated a number of contributions to this thesis. First, in order to verify that unsupervised learning methods are reliable to extract MRI patterns to describe the heterogeneity of a lesion, a comparison among several unsupervised learning methods was conducted for the task of high grade glioma segmentation. Second, a Bayesian unsupervised learning algorithm from the family of Spatially Varying Finite Mixture Models is proposed. The algorithm integrates a Markov Random Field prior density weighted by the probabilistic Non-Local Means function, to codify the idea that neighboring pixels tend to belong to the same semantic object. Third, the HTS method to describe the vascular heterogeneity of glioblastomas is presented. The HTS method has been applied to real cases, both in a local single-center cohort of patients, and in an international retrospective cohort of more than 180 patients from 7 European centers. A comprehensive evaluation of the method was conducted to measure the prognostic potential of the HTS habitats. Finally, the technology developed in this thesis has been integrated into an online open-access platform for its academic use. The ONCOhabitats platform is hosted at https://www.oncohabitats.upv.es, and provides two main services: 1) glioblastoma tissue segmentation, and 2) vascular heterogeneity assessment of glioblastomas by means of the HTS method. The results of this thesis have been published in ten scientific contributions, including top-ranked journals and conferences in the areas of Medical Informatics, Statistics and Probability, Radiology & Nuclear Medicine and Machine Learning. An industrial patent registered in Spain, Europe and EEUU was also issued. Finally, the original ideas conceived in this thesis led to the foundation of ONCOANALYTICS CDX, a company framed into the business model of companion diagnostics for pharmaceutical compounds.[CA] El futur de la imatge mèdica està lligat a la intel·ligència artificial. L'anàlisi manual d'imatges mèdiques és hui dia una tasca àrdua, propensa a errors i sovint inassequible per als humans, que ha cridat l'atenció de la comunitat d'Aprenentatge Automàtic (AA). La Imatge per Ressonància Magnètica (IRM) ens proporciona una àmplia varietat de representacions de la morfologia i el comportament de lesions inaccessibles sense una intervenció invasiva arriscada. Tanmateix, explotar la potent però sovint latent informació continguda a les adquisicions de IRM esdevé una tasca molt complicada, que requereix tècniques d'anàlisi computacional intel·ligent. Els tumors del sistema nerviós central són una de les malalties més crítiques estudiades a través de IRM. Específicament, el glioblastoma representa un gran repte, ja que, fins hui, continua siguent un càncer letal que manca d'una teràpia satisfactòria. Del conjunt de característiques que fan del glioblastoma un tumor tan agressiu, un aspecte particular que ha sigut àmpliament estudiat és la seua heterogeneïtat vascular. La forta proliferació vascular dels glioblastomes, així com la seua robusta angiogènesi han sigut considerades responsables de l'alta letalitat d'aquesta neoplàsia. Aquesta tesi es centra en la recerca i desenvolupament del mètode Hemodynamic Tissue Signature (HTS): un mètode d'AA no supervisat per descriure l'heterogeneïtat vascular dels glioblastomas mitjançant l'anàlisi de perfusió per IRM. El mètode HTS es basa en el concepte d'hàbitat, que es defineix com una subregió de la lesió amb un perfil particular d'IRM, que descriu un comportament fisiològic concret. El mètode HTS delinea quatre hàbitats dins del glioblastoma: l'hàbitat HAT, com la regió més perfosa del tumor amb captació de contrast; l'hàbitat LAT, com la regió del tumor amb un perfil angiogènic més baix; l'hàbitat IPE, com la regió adjacent al tumor amb índexs de perfusió elevats, i l'hàbitat VPE, com l'edema restant de la lesió amb el perfil de perfusió més baix. La recerca i desenvolupament del mètode HTS ha originat una sèrie de contribucions emmarcades a aquesta tesi. Primer, per verificar la fiabilitat dels mètodes d'AA no supervisats en l'extracció de patrons d'IRM, es va realitzar una comparativa en la tasca de segmentació de gliomes de grau alt. Segon, s'ha proposat un algorisme d'AA no supervisat dintre de la família dels Spatially Varying Finite Mixture Models. L'algorisme proposa un densitat a priori basada en un Markov Random Field combinat amb la funció probabilística Non-Local Means, per a codificar la idea que els píxels veïns tendeixen a pertànyer al mateix objecte semàntic. Tercer, es presenta el mètode HTS per descriure l'heterogeneïtat vascular dels glioblastomas. El mètode HTS s'ha aplicat a casos reals en una cohort local d'un sol centre i en una cohort internacional de més de 180 pacients de 7 centres europeus. Es va dur a terme una avaluació exhaustiva del mètode per mesurar el potencial pronòstic dels hàbitats HTS. Finalment, la tecnologia desenvolupada en aquesta tesi s'ha integrat en una plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofereix dos serveis: 1) segmentació dels teixits del glioblastoma, i 2) avaluació de l'heterogeneïtat vascular dels glioblastomes mitjançant el mètode HTS. Els resultats d'aquesta tesi han sigut publicats en deu contribucions científiques, incloent revistes i conferències de primer nivell a les àrees d'Informàtica Mèdica, Estadística i Probabilitat, Radiologia i Medicina Nuclear i Aprenentatge Automàtic. També es va emetre una patent industrial registrada a Espanya, Europa i els EEUU. Finalment, les idees originals concebudes en aquesta tesi van donar lloc a la creació d'ONCOANALYTICS CDX, una empresa emmarcada en el model de negoci dels companion diagnostics de compostos farmacèutics.En este sentido quiero agradecer a las diferentes instituciones y estructuras de financiación de investigación que han contribuido al desarrollo de esta tesis. En especial quiero agradecer a la Universitat Politècnica de València, donde he desarrollado toda mi carrera acadèmica y científica, así como al Ministerio de Ciencia e Innovación, al Ministerio de Economía y Competitividad, a la Comisión Europea, al EIT Health Programme y a la fundación Caixa ImpulseJuan Albarracín, J. (2020). Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149560TESI

Computational methods and analyses to dissect the pathogenesis of Frontotemporal Dementia

rontotemporal Dementia (FTD) is a devastating neurodegenerative disorder that typi cally manifests before the age of 65 and is characterized by progressive degeneration of frontal and temporal lobes as well as behavioural changes and problems with speech. Although great advancements in our understanding of FTD have been made during the last decades, there still does not exist a treatment that halts the progression of this dis ease. It is therefore necessary to further advance our understanding of the molecular mechanisms that cause FTD and that drive disease progression forward. In this thesis, I have contributed to the field of FTD research through the development of computational methods and the analysis of multi-omics datasets in order the develop new hypotheses for disease mechanisms in FTD. Studying complex tissues such as the human brain requires to carefully consider the contributions of diverse components of such systems. A major factor in transcriptomic experiments is cell type composition, as every cell has a unique transcriptional profile. In chapter 2, we have developed a deep learning-based cell type deconvolution algorithm that outperforms other methods and, importantly, also works well on post-mortem human brain tissue. The algorithm was rigorously tested and made available to the community as an open source, accessible python package and as web application. In chapter 3, we have analysed multi-omics datasets from post-mortem human brain tissue of FTD patients with mutations in the genes GRN, MAPT and C9orf72. Using an integrative data analysis approach, we could identify common and distinct affected path ways in these three genetic FTD subtypes. We leveraged the rich multi-omics datasets to identify new aspects of the disease, such as vulnerable neurons and increasing blood vessel percentages. In-depth analysis could highlight several regulator molecules, such as micro RNAs and transcription factors, that are likely to play important roles in FTD and therefore depict promising subjects for future studies. In chapter 4, we have performed co-expression module analysis of transcriptional data from seven different brain regions of patients with genetic subtypes of FTD. Using this comprehensive dataset, we have highlighted regions that are transcriptionally affected in different FTD subtypes and regions that seem not to suffer from the disease. We further more highlighted region- and disease-specific co-expression modules and pinpointed hub genes of potentially important function for these modules. Our analysis is the first that evaluates transcriptional deregulation at such diversity in FTD, and therefore provides ixAbstract valuable novel insights for the field of FTD

Understanding Disease and Disease Relationships Using Transcriptomic Data

As the volume of transcriptomic data continues to increase, so too does its potential to deepen our understanding of disease; for example, by revealing gene expression patterns shared between diseases. However, key questions remain around the strength of the transcriptomic signal of disease and the identification of meaningful commonalities between datasets, which are addressed in this thesis as follows. The first chapter, Concordance of Microarray Studies of Parkinson’s Disease, examines the agreement between differential expression signatures across 33 studies of Parkinson’s disease. Comparison of these studies, which cover a range of microarray platforms, tissues, and disease models, reveals a characteristic pattern of differential expression in the most highly-affected tissues in human patients. Using correlation and clustering analyses to measure the representativeness of different study designs to human disease, the work described acts as a guideline for the comparison of microarray studies in the following chapters. In the next chapter, Using Dysregulated Signalling Paths to Understand Disease, gene expression changes are linked on the human signalling network, enabling identification of network regions dysregulated in disease. Applying this method across a large dataset of 141 common and rare diseases identifies dysregulated processes shared between diverse conditions, which relate to known disease- and drug-sharing-relationships. The final chapter, Understanding and Predicting Disease Relationships Through Similarity Fusion, explores the integration of gene expression with other data types – in this case, ontological, phenotypic, literature co-occurrence, genetic, and drug data – to understand relationships between diseases. A similarity fusion approach is proposed to overcome the differences in data type properties between each space, resulting in the identification of novel disease relationships spanning multiple bioinformatic levels. The similarity of disease relationships between each data type is considered, revealing that relationships in differential expression space are distinct from those in other molecular and clinical spaces. In summary, the work described in this thesis sets out a framework for the comparative analysis of transcriptomic data in disease, including the integration of biological networks and other bioinformatic data types, in order to further our knowledge of diseases and the relationships between them.PhD funded by the Biotechnology and Biological Sciences Research Council Doctoral Training Partnershi

Clear Cell Renal Cell Carcinoma 2021–2022

Clear cell renal cell carcinoma is currently one of the most interesting areas of study in oncology. Despite the advances made in this field, this tumor continues to be a health problem of major concern in Western societies, seriously affecting public health services. Several characteristics of this tumor make it an exciting meeting point for translational collaboration between clinicians and basic researchers. Clear cell renal cell carcinoma is a paradigmatic example of inter- and intra-tumor heterogeneity from morphological, immunohistochemical, and molecular viewpoints. This tumor is also a good example to investigate the complexity of tumor/tumor and tumor/environment relationships from an ecological perspective. A deeper identification of the varied internal tumor self-organization through the specialization of cell clones and subclones as local invaders and metastasizers, on one hand, and the interactions of specific subsets of tumor cells with the local host microenvironment, on the other, will significantly enrich our knowledge of this neoplasm. Clear cell renal cell carcinoma is also a paradigmatic test bench for antiangiogenic and immune checkpoint blockage therapies. The refinement of these therapeutic tools administered alone or in combination is a hot issue in oncology, and several international trials are underway