O câncer de mama é o carcinoma que mais acomete mulheres no Brasil. Os tratamentos disponíveis são recomendados a partir da análise de fatores de prognóstico como a classificação pelo sistema TNM, tipo histológico, status de receptores hormonais e marcadores de proliferação tumoral. No entanto, a classificação dos tumores de mama é muito variável e o poder prognóstico dos marcadores tumorais atuais ainda é limitado, levando muitas pacientes à terapia adjuvante desnecessária. Portanto, novos métodos de prognóstico mais sensíveis são necessários para melhorar a tomada de decisão na clínica oncológica de pacientes com câncer de mama. Do ponto de vista de ciência básica, as modificações transcricionais associadas à oncogênese e progressão do câncer de mama ainda são pouco conhecidas. Além da alteração na expressão de genes codificadores para proteínas, evidências recentes sugerem que RNAs não-codificadores (ncRNAs) podem ter um papel importante na transformação maligna. Este projeto teve como principais objetivos: i) investigar a expressão de ncRNAs intrônicos em amostras de adenocarcinoma de mama e ii) identificar assinaturas de expressão gênica associadas a características anatomo-patológicas e clínicas de tumores de mama com potencial aplicação clínica. Para isso, foram comparados os perfis de expressão gênica de 58 amostras de tecido tumoral de mama, com seguimento clínico conhecido, utilizando uma plataforma de microarranjos de cDNA, enriquecida em ncRNAs provenientes de regiões intrônicas de genes humanos conhecidos. 9 Durante o projeto foram testadas diferentes metodologias para análise da expressão gênica utilizando microarranjos de cDNA com uma ou duas cores. O desenho experimental das hibridizações incluiu a co-hibridização de cada microarranjo com alvos fluorescentes representando o transcritoma da amostra de tumor juntamente com um oligonucleotídeo referência complementar a uma região presente em todas as sondas de cDNA (RefOligo). Este desenho experimental permitiu a avaliação de duas abordagens de análise da expressão gênica: a primeira baseada nas intensidades diretas de cada transcrito (One-Color) e a segunda baseada em razões de expressão onde a intensidade de cada transcrito foi normalizada pelo oligonucleotídeo referência (RefOligo). A utilização direta das intensidades se mostrou mais reprodutível e sensível para a detecção de assinaturas de expressão correlacionadas com características das amostras de mama, e essa abordagem foi escolhida para as análises subseqüentes. Os dados provenientes dos experimentos de microarranjos revelaram níveis de expressão ubíqüos dos transcritos intrônicos nas amostras analisadas, extendendo para o câncer de mama a relevância do estudo desta classe de ncRNAs. Além disso, foi identificada uma assinatura contendo 95 transcritos, correlacionada com o status de expressão do receptor de estrogênio (REr), dos quais cerca de 15% correspondem a ncRNAs. Utilizando apenas amostras com seguimento clínico superior a 4 anos, foi identificada uma assinatura com 113 transcritos, dos quais cerca de 30% são ncRNAs intrônicos, capaz de distinguir com 100% de acurácia pacientes que desenvolveram metástase daqueles que permaneceram livres da doença. Além de contribuir com novos candidatos a marcadores de prognóstico no câncer de mama, este estudo aponta para a participação de ncRNAs intrônicos em complexas redes transcricionais, possivelmente modulando a expressão de genes codificadores para proteínas. A caracterização detalhada da função de ncRNAs com expressão correlacionada a características fenotípicas e clínicas dos tumores de mama deverá fornecer novas informações sobre as bases moleculares da tumorigênese e progressão desta neoplasia. / Breast carcinoma is the most frequently occurring cancer amongst women in Brazil. The treatments available for breast cancer are prescribed based on the results of prognostic factors, such as the TNM classification system, histological type, hormonal receptor status and tumoral markers for cell proliferation. Nevertheless, breast cancer classification can be variable and inconsistent, and the prognosis power of tumoral markers is still limited, resulting in many patients unnecessarily undergoing adjuvant therapy. Therefore, there is an urge for new prognosis methods that are more sensitive, as well as accurate, in order to improve treatment decisions for breast cancer patients. From a basic science perspective, transcriptional modifications associated with oncogenesis and breast cancer progression are still poorly understood. Beyond alterations of the expression of protein-coding genes, recent evidences suggest that non-coding RNAs (ncRNAs) might have an important role in malignant transformation. The main goals of this project are: i) to investigate the expression of intronic ncRNAs in breast cancer tissue and ii) to identify gene expression signatures correlated to anatomo-pathological and clinical characteristics of human breast tumors, with a potential clinical aplication. To achieve this, gene expression profiles of 58 breast tumor samples with clinical follow-up were compared using a microarray platform enriched in non-coding RNAs (ncRNAs) derived from intronic regions of known human genes. During this project different gene expression methodologies were tested for the analysis of one- or two-color cDNA microarrays. The experimental design included the co-hybridization of the microarrays with fluorescent targets representing the tumor sample transcriptome with a reference oligonucleotide that is complementary to a 12 common region present in all cDNA probes (RefOligo). This experimental design permited the evaluation of two gene expression analysis approaches: the first based on direct intensities of each transcript (One-Color) and the second based in expression ratios where the intensity of each transcript is normalized by the reference oligonucleotide (RefOligo). One-Color methodology has shown to provide a more reproducible and sensitive gene expression signatures correlated to the breast samples characteristics and, therefore, this approach was chosen for subsequent analysis. The data provided by the microarray experiments revealed that ubiquitous expression of intronic ncRNAs was observed, confirming the relevance of investigating the role of this class of ncRNAs in breast cancer. Furthermore, a gene expression signature comprising 95 transcripts and correlated to the estrogen receptor status of breast tumor samples was identified, from which approximately 15% are ncRNAs. Using only samples from patients with known follow-up, a signature of 113 transcripts was identified, of which 30% are ncRNAs. This gene expression signature was able to distinguish with 100% accuracy patients that developed metastasis from those that remained disease-free up to 4 years after surgery. Besides the contribution of new molecular prognostic markers for breast cancer, the present study indicates that intronic ncRNAs might play a role in complex transcriptional networks, possibly regulating the expression of protein-coding genes. The detailed caracterization of the functional roles of ncRNAs, whose expression levels are correlated to fenotypical and clinical characteristics of breast tumors, is likely to provide new insigths on the molecular basis of tumorigenesis and progression of this neoplasia.

X-Chromosom-Inaktivierung (XCI) ist der Mechanismus, den Säuger zur Dosiskompensierung zwischen weiblichen und männlichen Zellen verwenden. XCI wird ausgelöst durch die monoallelische Hochregulation der langen nicht-kodierenden RNA Xist von einem der zwei X-Chromosomen in weiblichen Zellen. Die Xist RNA vermittelt dann das Ausschalten der Gene auf diesem X-Chromosom. Das wirft einige interessante Fragen auf: Wie zählen Zellen ihre X-Chromosomen und stellen sicher, dass genau eines aktiv bleibt? Wie entscheiden sie, welches X-Chromosom aktiv bleibt und welches ausgeschaltet wird? Und wie erinnern sie sich an diese Entscheidung und behalten sie stabil bei durch alle weiteren Zellteilungen? Mithilfe eines stochastischen Modells zeigen wir, dass diese XCI Regulation prinzipiell durch nur zwei Regulatoren erklärt werden kann: Ein global (in trans) agierender XCI Aktivator und ein lokal (in cis) agierender XCI Repressor. Dieses Netzwerk aus nur zwei Regulatoren kann die Xist Expressionsmuster in verschiedenen Säugerspezies reproduzieren, von der Maus bis zum Mensch. Es sagt außerdem voraus, dass Zellen in der Lage sind, biallelische zu monoallelischer Xist Expression zu korrigieren, eine Vorhersage, für die wir tatsächlich experimentelle Belege finden. Mit einem mechanistischen Modell zeigen wir, dass das cis-Gedächtnis über den Xist Expressionszustand durch Antisense-Transkription zustande kommen könnte. Auf dieser Hypothese aufbauend untersucht der zweite Teil der Arbeit das Potential von Antisense-Transkription, ein lokales Gedächtnis über den Expressionszustand eines Gens zu generieren, genauer. Diese Analyse sagt vorher, dass Antisense-Repression den Expressionszustand eines Lokus tatsächlich für einige Tage stabil erhalten kann. / X-chromosome inactivation (XCI) is the mechanism for dosage compensation between the sexes in mammals. It is initiated through monoallelic upregulation of the long non-coding RNA Xist from one X chromosome, which mediates almost complete transcriptional silencing of this X chromosome. XCI regulation raises intriguing and thus far unanswered questions: How do cells count their X chromosomes and ensure that exactly one stays active? How do they make a mutually exclusive choice for one inactive X chromosome, and how do they then stably maintain this choice throughout subsequent cell divisions? Using stochastic modeling, we show that XCI onset only requires two regulators: A trans-acting Xist activator that ensures female specificity and a cis-acting Xist repressor that allows stable maintenance of alternative Xist expression states. This two-regulator network can recapitulate Xist expression patterns across different species and makes a novel prediction that is validated experimentally: Cells are able to revert biallelic Xist expression to monoallelic expression. With a mechanistic stochastic model we show that Xist's antisense transcript Tsix might be the cis-acting Xist repressor, uncovering the molecular mechanism behind the stabilization of the alternative Xist expression states. Building upon Tsix' possible functional role in stabilizing alternative Xist expression states on the active and inactive X chromosome, the second part of this thesis investigates the potential of antisense transcription to maintain a transient transcriptional memory. We find that mutual repression between a pair of antisense genes can allow the locus to remember the transcription state it has acquired due to a past signal for several days.