<p>Gliomagenesis is driven by a complex network of genetic alterations and while the glioma genome has been a focus of investigation for many years; critical gaps in our knowledge of this disease remain. The identification of novel molecular biomarkers remains a focus of the greater cancer community as a method to improve the consistency and accuracy of pathological diagnosis. In addition, novel molecular biomarkers are drastically needed for the identification of targets that may ultimately result in novel therapeutics aimed at improving glioma treatment. Through the identification of new biomarkers, laboratories will focus future studies on the molecular mechanisms that underlie glioma development. Here, we report a series of genomic analyses identifying novel molecular biomarkers in multiple histopathological subtypes of glioma and refine the classification of malignant gliomas. We have completed a large scale analysis of the WHO grade II-III astrocytoma exome and report frequent mutations in the chromatin modifier, alpha thalassemia mental retardation x-linked (<italic>ATRX<italic>), isocitrate dehydrogenase 1 and 2 (<italic>IDH1<italic> and <italic>IDH2<italic>), and mutations in tumor protein 53 (<italic>TP53<italic>) as the most frequent genetic mutations in low grade astrocytomas. Furthermore, by analyzing the status of recurrently mutated genes in 363 brain tumors, we establish that highly recurrent gene mutational signatures are an effective tool in stratifying homogeneous patient populations into distinct groups with varying outcomes, thereby capable of predicting prognosis. Next, we have established mutations in the promoter of telomerase reverse transcriptase (<italic>TERT<italic>) as a frequent genetic event in gliomas and in tissues with low rates of self renewal. We identify <italic>TERT<italic> promoter mutations as the most frequently mutated gene in primary glioblastoma. Additionally, we show that <italic>TERT<italic> promoter mutations in combination with <italic>IDH1<italic> and <italic>IDH2<italic> mutations are able to delineate distinct clinical tumor cohorts and are capable of predicting median overall survival more effectively than standard histopathological diagnosis alone. Taken together, these data advance our understanding of the genetic alterations that underlie the transformation of glial cells into neoplasms and we provide novel genetic biomarkers and multi &ndash; gene mutational signatures that can be utilized to refine the classification of malignant gliomas and provide opportunity for improved diagnosis.</p> / Dissertation

Cancer is the second most common cause of death in the United States. It is a complex disease with environmental, genetic, and lifestyle factors influencing the likelihood of getting cancer and the development of any resulting tumor. Understanding the genetics of cancer is integral to developing novel patient-specific treatments. However, due to complexity, hundreds to thousands of tumors are required for sufficient power to identify the network of relationships among these genes. Animal models of cancer are commonly used to reduce cost and to control experimental variables allowing for more specific hypothesis testing. The Sleeping Beauty transposon mutagenesis system can be used to model cancer in mice. While the Sleeping Beauty mutagenesis system is an important tool in understanding cancer, it has specific computational needs. Experiments need to be analyzed in a fast, unbiased, and efficient manner. A computational method must also accurately model the system allowing for validation and interpretation. Here I present an updated Integration Analysis System and use this system to validate the assumptions present in forward genetic screens of cancer using the Sleeping Beauty. This system allows for rapid identification of cancer genes, but does not directly aid in understanding the relationship between the genes. Given the complexity of cancer, understanding the relationship between cancer genes is very difficult. I have created a connectedness network utilizing the STRING database to better derive an understanding of cancer genes. STRING is a database of known and predicted protein-protein interactions. The connectedness between pairs of genes is calculated using a network reliability metric. This database allows for increased power to detect known pathways when compared to STRING alone. Combining this connectivity network with the set of cancer genes identified by the Integration Analysis System is a strategy for rapid and efficient interpretation of the genetic results.

In my thesis, I focused on integrative analysis of high-throughput oncogenomic data. This was done in two parts: In the first part, I describe IntOGen, an integrative data mining tool for the study of cancer. This system collates, annotates, pre-processes and analyzes large-scale data for transcriptomic, copy number aberration and mutational profiling of a large number of tumors in multiple cancer types. All oncogenomic data is annotated with ICD-O terms. We perform analysis at different levels of complexity: at the level of genes, at the level of modules, at the level of studies and finally combination of studies. The results are publicly available in a web service. I also present the Biomart interface of IntOGen for bulk download of data. In the final part, I propose a methodology based on sample-level enrichment analysis to identify patient subgroups from high-throughput profiling of tumors. I also apply this approach to a specific biological problem and characterize properties of worse prognosis tumor in multiple cancer types. This methodology can be used in the translational version of IntOGen.

Recent advances in high throughput technology have opened the door to systematic studies of epigenetic mechanisms. One of the key components in the regulation of the cell cycle and differentiation is the retinoblastoma protein (pRB), a component of the RB/E2F tumor suppressor pathway that is frequently deregulated in cancer. The RBP2/KDM5A histone demethylase was shown to interact with pRB and regulate pRB function during differentiation. However, how precisely differentiation is coupled with halted cell cycle progression and whether an epigenetic mechanism is involved remain unknown. In the present study, I analyzed gene expression levels of human histone methyltransferases (HMT) and demethylases (HDM), as well as their targets in human cancers; and focused on RB/KDM5A connection in control of cell cycle and differentiation. In particular, I used Drosophila as a model to describe a novel mechanism where the RB/E2F pathway interacts with the Hippo tumor suppressor pathway to synergistically control cell cycle exit upon differentiation. Studying the role of miR-11, I found that the inhibition of dE2F1-induced cell death is its highly specialized function. Furthermore, I studied the induction of differentiation and apoptosis as the consequences of KDM5A deletion in cells derived from Rb knockout mice. I concluded that during differentiation, KDM5A plays a critical role at the enhancers of cell type-specific genes and at the promoters of E2F targets; in cooperation with other repressor complexes, it silences cell cycle genes. I found that KDM5A binds to transcription start sites of the majority of genes with H3K4 methylation. These are highly expressed genes, involved in certain biological processes, and occupied by KDM5A in an isoform-specific manner. KDM5A plays a unique and non-redundant role in histone demethylation and its promoter binding pattern highly overlaps with the opposing enzyme, MLL1. Finally, I found that HMT and HDM enzymes exhibit a distinct co-expression pattern in different cancer types, and this determines the level of expression of their target genes. / Los avances recientes en las tecnologías de alto flujo han abierto el camino a los estudios sistemáticos de los mecanismos epigenéticos. La proteína retinoblastoma (pRB), uno de los elementos de la ruta de supresión de tumores RB/E2F que se encuentra desregulado con frecuencia en el cáncer, es uno de los componentes esenciales de la regulación del ciclo celular y la diferenciación. Sin embargo, aún no se conoce de qué manera precisa la diferenciación se acopla a la detención del avance del ciclo celular y si hay algún mecanismo epigenético vinculado a este proceso. En este estudio, he analizado los niveles de expresión de histona metiltransferasas (HMT) y desmetilasas humanas (HDM), así como sus dianas en cánceres humanos, y me he centrado en la conexión de RB/KDM5A en el control del ciclo celular y la diferenciación. Específicamente, utilicé Drosophila como modelo para describir un mecanismo nuevo mediante el cual RB/E2F interactúa con la ruta Hippo de supresión de tumores para controlar de manera sinérgica la detención del ciclo celular relacionada con la diferenciación. Mediante la investigación del papel de miR-11, determiné que su función altamente especializada es la inhibición de la muerte celular inducida por dE2F1. Además, estudié la inducción de la diferenciación y la apoptosis como consecuencia de la pérdida de KDMA5 en células obtenidas a partir de ratones sin Rb. Extraje como conclusión que, durante la diferenciación, KDMA5 desempeña un papel esencial sobre los estimuladores de los genes específicos de los tipos celulares, así como en los promotores de las dianas de E2F; en cooperación con otros complejos represores silencia a los genes del ciclo celular. Investigué el mecanismo de reclutamiento de KDM5A y encontré que se une al sitio de inicio de la transcripción de la mayoría de los genes que poseen metilación en H3K4. Estos genes tienen elevados niveles de expresión, están involucrados en determinados procesos biológicos y están ocupados por diferentes isoformas de KDM5A. KDM5A desempeña un papel único y no redundante en la desmetilación de las histonas y que en gran medida se solapa con la enzima con la función opuesta, MLL1. Para terminar, encontré que las enzimas HMT y HDM muestran patrones de co-expresión distintos en diferentes tipos de cáncer, y que este hecho determina los niveles de expresión de sus genes diana.

Náplní této práce je teoretický úvod a následné praktické zpracování tématu Molekulární signatura jako optimální multi-objektivní funkce s aplikací v predikci v onkogenomice. Úvodní kapitoly jsou zaměřeny na téma rakovina, zejména pak rakovina prsu a její podtyp triple negativní rakovinu prsu. Následuje literární přehled z oblasti optimalizačních metod, zejména se zaměřením na metaheuristické metody a problematiku strojového učení. Část se odkazuje na onkogenomiku a principy microarray a také na statistiku a s důrazem na výpočet p-hodnoty a bimodálního indexu. Praktická část je pak zaměřena na konkrétní průběh výzkumu a nalezené závěry, vedoucí k dalším krokům výzkumu. Implementace vybraných metod byla provedena v programech Matlab a R, s využitím dalších programovacích jazyků a to konkrétně programů Java a Python.