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1
Pérez, Llamas Christian 1976. "Computational approaches for integrative cancer genomics." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/328729.
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Given the complexity and heterogeneity of cancer, the
development of new high-throughput wide-genome technologies
has open new possibilities for its study. Several projects around the
globe are exploiting these technologies for generating
unprecedented amount of data for cancer genomes. Its analysis,
integration and exploration are still a key challenge in the field. In
this dissertation, we first present Gitools, a tool for accessing
databases in biology, analysing high-throughput data, and
visualising multi-dimensional results with interactive heatmaps.
Then, we show IntOGen, the methodology employed for collection
and organization of the data, the methods used for its analysis, and
how the results and analysis were made available to other
researchers. Finally, we compare several methods for impact
prediction of non-synonymous mutations, showing that new tools
specifically designed for cancer outperform those traditionally used
for general diseases, and also the need for using other sources of
information for better prediction of cancer mutations.Davant de la complexitat i heterogeneitat del cancer, el desenvolupament de noves tecnologies per l'estudi de genomes, ha obert noves posibilitats. Diversos projectes al voltant del mon les fan servir per generar quantitats de dades de genomes de cancer mai vistes abans. En aquest treball, primer presentem Gitools, una eina que permet obtenir dades de bases de dades en biologia, anal itzar dades genomiques, i visual itzar els resul tats multidimensionals mitjançant mapes de calor interactius. Després mostrem IntOGen, les metodologies per obtenir i organitzar les dades, els metodes per el seu analisi, i com es van possar a disposició d'altres investigadors. Finalment, comparem diversos metods de predicció de l'impacte de les mutacions no sinonimes, que ens mostra com nou metods desenvolupats per cancer funcionen millor que els utilitzats tradicionalment per enfermetats generals, aixis com la necesitat de recorrer a altres fonts d'informació per tenir millor prediccions per mutacions de cancer.
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Chen, Maxine M. "Genetics and Genomics of Endometrial Cancer." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:27201719.
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Endometrial cancer (EC) is the most common gynecological cancer among women in the developed world and is hypothesized to arise from excess estrogen exposure from established risk factors like estrogen-only hormone therapy and obesity. EC is divided into the common “estrogen-dependent” endometrioid subtype and the rare “estrogen-independent” non- endometrioid subtype. However, this broad categorization of EC is not sufficient based on evidence for EC heterogeneity. Furthermore, family history and hereditary syndromes also increase risk, suggesting a genetic component. This dissertation examines the genetic and genomic architecture of EC to provide insight into its etiology and heterogeneity.
In Chapter 1, a four-study EC meta-analysis of 4,907 cases and 11,645 controls in women of European ancestry is presented. Four loci reached genome-wide significance. One novel susceptibility locus at 6p22.3 was identified and two previously discovered loci at 6q22.31 and 13q22.1 were confirmed. Genes near the 6p22.3 locus are implicated in malignancy and poor prognosis in many cancers, highlighting the potential importance of this region to general cancer susceptibility.
In Chapter 2, we conduct an exome-wide association study of EC. Using a new, commercially-developed exome array comprising ~260,000 putative functional exonic variants, we genotyped a multiethnic population of 3,067 women (1,169 EC cases and 1,898 controls) from the Epidemiology of Endometrial Cancer Consortium to test whether rare variants in coding regions are associated with endometrial cancer risk. No variants reached global significance in this study. Larger studies are needed to detect associations between rare exonic variants and EC.
In Chapter 3, we combined targeted next-generation sequencing from archival EC tissue with clinical, immunohistochemical, and epidemiologic data for a comprehensive characterization of EC in 37 women from the Nurses’ Health Study. Mutations most frequently occurred in TP53, PTEN, and PIK3CA. TP53 mutations were seen in the majority of tumors that were p53 abnormal. Low grade correlated with frequency of PTEN and PIK3CA mutation. The archival EC tissue had mutation profiles consistent with previous studies, supporting use of targeted sequencing panels on archival tissue for mutation detection. Our comprehensive annotation of EC tumors demonstrates the utility of integrating many data types to reveal differences between tumors.
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Schroeder, Michael Philipp 1986. "Analysis and visualization of multidimensional cancer genomics data." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/301436.
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Cancer is a complex disease caused by somatic alterations of the genome and epigenome in tumor cells. Increased investments and cheaper access to various technologies have built momentum for the generation of cancer genomics data. The availability of such large datasets offers many new possibilities to gain insight into cancer molecular properties. Within this scope I present two methods that exploit the broad availability of cancer genomic data: OncodriveROLE, an approach to classify mutational cancer driver genes into activating and loss of function mode of actions and MutEx, a statistical measure to assess the trend of the somatic alterations in a set of genes to be mutually exclusive across tumor samples. Nevertheless, the unprecedented dimension of the available data raises new complications for its accessibility and exploration which we try to solve with new visualization solutions: i) Gitools interactive heatmaps with prepared large scale cancer genomics datasets ready to be explored, ii) jHeatmap, an interactive heatmap browser for the web capable of displaying multidimensional cancer genomics data and designed for its inclusion into web portals, and iii) SVGMap, a web server to project data onto customized SVG figures useful for mapping experimental measurements onto the model.El cancer és una malaltia complexa causada per alteracions somàtiques del genoma i epigenoma de les cèl•lules tumorals. Un augment d’inversions i l'accés a tecnologies de baix cost ha provocat un increment important en la generació de dades genòmiques de càncer. La disponibilitat d’aquestes dades ofereix noves possibilitats per entendre millor les propietats moleculars del càncer. En aquest àmbit, presento dos mètodes que aprofiten aquesta gran disponibilitat de dades genòmiques de càncer: OncodriveROLE, un procediment per a classificar gens “drivers” del càncer segons si el seu mode d’acció ésl'activació o la pèrdua de funció del producte gènic; i MutEx, un estadístic per a mesurar la tendència de les mutacions somàtiques a l’exclusió mútua. Tanmateix, la manca de precedents d’aquesta gran dimensió de dades fa sorgir nous problemes en quant a la seva accessibilitat i exploració, els quals intentem solventar amb noves eines de visualització: i) Heatmaps interactius de Gitools amb dades genòmiques de càncer a gran escala, a punt per ser explorades, ii) jHeatmap, un heatmap interactiu per la web capaç de mostrar dades genòmiques de cancer multidimensionals i dissenyat per la seva inclusió a portals web; i iii) SVGMap, un servidor web per traslladar dades en figures SVG customitzades, útil per a la transl•lació de mesures experimentals en un model visual.
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Janvid, Vincent. "Building a genomic variant based prediction model for lung cancer toxicity." Thesis, KTH, Tillämpad fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297411.
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Since the completion of the the Human genome project in 2003, the evident complexity of our genome and its regulation has only grown. The idea that having sequenced the human genome would solve this mystery was quickly discarded. With the decreasing costs of DNA sequencing, a plethora of new methods have evolved to further understand the role of non-coding regions of our genome, which makes up 98% its length. Genetic variations in these regions are therefore abundant in the human population, but their e ects are hard to characterize. Many non-coding variants have been linked to complex diseases such as cancer predisposition. This thesis aims to investigate the potential e ects of non-coding variants on drug toxicity, that is, how severe the adverse e ects of a drug are to the treated patients. More specifically it will study the effects of two cancer drugs, Gemcitabine and Carboplatin, on a set of 96 patients with lung cancer. To do this we use spatial data acquired by the promoter-targeting method HiCap as well as expression data obtained from blood cell lines. Using the variants obtained through whole genome sequencing of the patients, a supervised learning approach was attempted to predict the final toxicity experienced by the patients. The large number of variants present among the comparably few patients resulted in poor accuracy. The conclusion was drawn that the resolution of HiCap is too low compared to the density of variants in the non-coding regions. Additional data, such as transcription factor Chip-Seq data, and transcription factor motifs are needed to locate potentially contributing variants within the interactions.Sedan den första sekvenseringen av det mänskliga genomet 2003 har vår bild av vårt genom och hur det regleras bara blivit mer komplex. Iden om att ha tillgång till ett helt genom skulle losa detta mysterium förkastades snabbt. Med de sjunkande kostnaderna for sekvensering har ett brett utbud av nya metoder utvecklats for att bättre förstå de icke-kodande regionernas roll i v art genom. Då dessa regioner utgör98% av vårt DNA ar innehåller de stor variation bland det mänskliga släktet, men att förutsaga deras effekt är mycket svårt. Många icke-kodande variationer har kopplats till komplexa sjukdomar så som ökad risk för cancer.Denna uppsats syftar till att undersoka de potentiella effekterna av icke-kodande varianter på hur allvarliga biverkningar en patient får av en cancerbehandling. Närmare undersöks två mediciners, Gemcitabins och Carboplatins effekt på 96 lungcancerpatienter. För detta används spatial data samt genuttrycksdata från blodcellinjer.Med utgångspunkt från genetiska varianter bland patienternas sekvenserade genom testades övervakad inlärning för att förutsäga graden av biverkningar hos patienterna. Den stora mängden varianter som bärs av de förhållandevis få patienterna resulterade i låg träffsäkerhet hos prediktorn. Slutsatsen drogs att upplösningen av HiCap är för låg i jämförelse med den höga densiteten av varianter i icke-kodanderegioner. Mer data, så som Chip-Seq data från transkriptionsfaktorer samt deras specifika bindningsekvenser behövs för att lokalisera varianter inom en interaktion, som potentiellt skulle kunna påverka biverkningarna.
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Day, Elizabeth Kate. "Single molecule genomics applied to the genome of colorectal cancer." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610227.
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Yen, Jennifer Lee. "Investigating the zebrafish system for modelling cancer genomics and biology." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648122.
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Ng, Kiu Yan Charlotte. "Tumour evolution in ovarian cancer using high-throughput genomics technologies." Thesis, University of Cambridge, 2012. https://www.repository.cam.ac.uk/handle/1810/265590.
Full textAbstract:
High-grade serous ovarian carcinoma (HGSOC) is characterised by genomic instability, ubiquitous TP53 loss, widespread disease at diagnosis and the frequent emergence of platinum resistance. This thesis explores the use of high-throughput genomics technologies to understand if resistance could be explained by the model of tumour evolution. We performed SNP array analysis of a cell line model system of platinum resistance consisting of matched cell lines from three cases of HGSOC established before and after clinical resistance developed, the OVOl clinical study consisting of six matched pairs of tumours before and after three cycles of chemotherapy, and the OV03/0V04 study consisting of 18 cases sampled at multiple timepoints and from multiple metastatic sites. The results showed evidence of metastatic site dependent divergence. Moreover, mutually exclusive loss of heterozygosity patterns between presentation and relapse genomes, including all the cases in the cell line system and one of two OV03 cases for which relapse material was available, suggest that the relapse arises from a minor subclone of the presentation disease, while in the remaining case, the subclone with an NFJ homozygous deletion was enriched in the relapsed disease. I then asked which mutational process drives evolution. Using next-generation sequencing (NGS), I compared the structural variants between and within cases in the model system and in 6 cases of the OV03 cohort. From the genomic signatures in the cell lines, I demonstrated that a case with homologous recombination (HR) deficiency acquired numerous translocations and small deletions (median size of 13.4kb) , whereas another showed a novel tandem duplicator phenotype (median size of tandem duplications was 350kb). Mutator phenotypes in both cases arose early in progression and persisted, but the tumour with HR deficiency showed evidence of re-stabilising its ,"genome and lost platinum sensitivity after a revertant BRCA2 mutation restored its HR function. A subset of tumours from the Cancer Genome Atlas (TCGA) dataset suggested that these two phenotypes were mutually exclusive. Amongst the six OV03 cases, preliminary analysis suggests that one case showed an amplifier phenotype and three cases showed evidence of parallel evolution. Taken together, early onset of mutator phenotypes and parallel evolution may provide a mechanism by which resistance evolves. Further work should aim to identify the processes involved in tumour evolution in 'purified' populations such as cancer stem cells.
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Cook, David. "Defining Epithelial-Mesenchymal Plasticity in Cancer Using Single-Cell Genomics." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42502.
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Epithelial-mesenchymal plasticity (EMP) describes the interconversion of cells between epithelial and mesenchymal phenotypes. During the epithelial-mesenchymal transition (EMT), epithelial cells lose defining characteristics, such as stable cell-cell junctions, and gain the ability to migrate and invade through extracellular matrices. This plasticity contributes to tumour progression, promoting therapy resistance and immune cell evasion. Despite its importance, defining molecular features of this plasticity have largely remained elusive due to the limited scale of most studies. Here, I present my studies applying comparative single-cell genomics to map transcriptional changes associated with the EMT in diverse experimental conditions and EMP in tumours, I identify regulatory features associated with these dynamics, and explore opportunities to pharmacologically restrict them. This work provides critical steps towards building quantitative models of EMP, which will inform effective strategies to restrict these dynamics in cancer and improve patient prognosis.
9
Matteuzzi, Tommaso <1990>. "Statistical and network dynamics approaches to cancer genomics data analytics." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9821/1/TommasoMatteuzziPhD.pdf.
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In this thesis we focus on some statistical and physical methods which attempts to tackle the problem of cancer genetic heterogeneity and its relationship to higher level biological properties.
The interactome allows to gain a system level view of mutational patters, providing a framework to understand how mutations act together to give rise to the cancer phenotype. Since different reconstruction of the interactome exist, in the first chapter of this thesis, we compare them from a topological perspective by analyzing their properties and we then study their overall resilience under node perturbation.
Cancer stems from the impairment of one or more biological functions due to mutations of genes taking part in them. The observation that different patterns of mutations lead to different responses to treatments highlights the importance of stratifying patients based on their genetics and cytogenetic alterations.
To this end, in the second chapter, we focus on hierarchical non parametric bayesian methods. Latent topic models allow to model hidden structures in the data and fit well with the hypothesis that cancer mutations impact specific gene groups in different proportions. In the second part of the chapter, we study a cohort of 2043 patients affected by Myelodysplastic Syndromes.
From a more general perspective, the view of cancer as an evolutionary process, frequently implies the assumption of a direct and univocal genotype-phenotype relationship. However, as for cell differentiation, such genetic deterministic view is not always satisfactory. In the third chapter, we focus on the hypothesis of cancer as an abnormal attractor in the epigenetic landscape of the cell. We study the connection between the empirical distribution of cell in the gene expression state space with network laplacian-based manifold reconstruction techniques and their application for inferring the epigenetic landscape from data.
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Rancoita, P. M. V. "Stochastic methods in cancer research. Applications to genomics and angiogenesis." Doctoral thesis, Università degli Studi di Milano, 2010. http://hdl.handle.net/2434/152007.
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In recent years, interactions between mathematicians and biomedical researchers have increased due to both the complexity of the biological/medical issues and the development of new technologies, producing “large” data rich of information. Biomathematics is applied in many areas, such as epidemiology, clinical trial design, neuroscience, disease modeling, genomics, proteomics, etc.
Cancer is a multistep process where the accumulation of genomic lesions alters cell biology. The latter is under control of several pathways and, thus, cancer can origin via different mechanisms affecting different pathways. However, usually, more than one of these mechanisms needs to be damaged before a cell becomes cancerous. Due to the general complexity of this disease and the different type of tumors, the efforts of cancer research cover several research areas such as, for example, immunology, genetics, cell biology, angiogenesis. As a consequence, many biostatistical topics can be applied.
The thesis is divided into two parts. In the former, two Bayesian regression methods for the analysis of two types of cancer genomic data are proposed. In the latter, the properties of two estimators of the intensity of a stationary fibre process are studied, which can be applied for the characterization of angiogenic and vascular processes. (Pubblicata - vedi http://hdl.handle.net/2434/159517)
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Rathnagiriswaran, Shruti. "Identifying genomic signatures for predicting breast cancer outcomes." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5906.
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Thesis (M.S.)--West Virginia University, 2008.Title from document title page. Document formatted into pages; contains viii, 85 p. : col. ill. Includes abstract. Includes bibliographical references (p. 81-85).
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Revi, Bhindu. "Novel approach to cancer therapeutics using comparative cancer biology." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28996.
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Developing personalized cancer therapies based on cancer genomics methodologies forms the basis for future cancer therapeutics. A genomics platform was developed based on canine cancer to produce a proof-of-concept for personalized genomics led therapeutic choices but also developing personalized therapeutics for canine cancer patients themselves. The platform identified the genetic state of a canine cancer patient within two drugable pathways; p53 and HSP90/IRF1. The former gene was wild-type p53 thus directing the use of p53 activating molecules. The latter mutations in both HSP90 and IRF1 suggested an investigation into HSP90 and interferon signalling molecules as drug leads. Drugs that target both of these pathways were subsequently used to measure drug effects in cell line models but also to identify novel biomarkers of drug responses. My study focused on the effect of the HSP90-inhibitor Ganetespib had on its client proteins, particularly IRF-1. Briefly my results indicated the following:(i) Ganetespib downregulated IRF-1 protein levels in A375 cell lines and this attenuation was not mediated by either MDM2 or CHIP (E3 ligase). IFNγ- induced IRF-1 was also observed to be downregulated when Ganetespib was used in combination therapy.(ii) Insitu proximity ligation assay showed induced HSC70 upregulation upon HSP90 inhibition by Ganetespib and HSC70/MDM2 complexes were seen to be stabilized compared to the usage of MDM2/p53 inhibitor-nutlin. I hypothesize that MDM2/HSC70 complex might chaperon IRF-1 into lysosome for degradation via chaperon mediated autophagy pathway. (iii) My results also indicate that Ganetespib can downregulate IFN γ- induced PDL-1 expression in melanoma cell lines. Pre-sensitizing the cells with Ganetespib prior to the addition of IFNγ could attenuate PDL-1 to basal levels. (iv) My results also showed that the downregulation of PDL-1 by Ganetespib is an IRF-1 dependent mechanism. Therefore, my results suggest that HSP90 represents an important emerging target for cancer therapy because its inactivation results in the simultaneous blockade of multiple signalling pathways and can also sensitize tumor cells to other anticancer agents. Targeting HSP90 could also help to disrupt PD1/PDL- 1 interaction and activate immune system to recognise tumor cells. I conclude that HSP90 and IRF-1 play a critical role in types II interferon pathways and these findings establish a novel basis for the design of future Ganetespib-based combinatorial approaches to improve patient outcomes in this disease. These approaches finally demonstrate that cancer genomics can stratify choice of cancer drugs used on patients but also provide evidence that cancer patient samples can be used for the specific stratification of cancer drug choice based on cancer genomics data.
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García, Alonso Luz María. "Functional profiling of human genomic data using the protein interactome." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/55848.
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[EN] Our understanding of the biological mechanisms for most common human diseases is far from complete. Even with well established genetic landscapes, our capacity to make accurate phenotypical predictions or determine personalised disease risk using genetics alone is not possible for most diseases due to our lack of understanding of the mechanisms by which genetic alterations cause disease. Several suggestions have been proposed to explain this manifested lack of direct relation between genotype and phenotype, including interactions with other molecules, pleiotropy and environmental perturbations. Due to their essential role in carrying cellular functions, proteins and its interactions seem crucial to translate genomic data to phenotypic states. In this thesis I present three different and independent approaches to integrate human genomic data with prior knowledge in terms of protein-protein interactions (PPIs). The overall objective is, by making use of the interactome structure, to propose functional hypotheses that help to interpret the genetic variability observed in different human phenotypes. First I developed a methodology to extract the network component associated to any gene list ranked by any experimental parameter, as the one coming from case-control genome-wide associations studies. Second I performed a systematic analysis of human variants in the context of the protein interactome. There I study how the interactome structure can help us to explain the amount of apparently deleterious variation observed in actual populations and, therefore, give insight in its role in shaping the patterns of variability. Results are compared against somatic mutation found in Leukemia patients. Finally, I structurally resolved the protein interactome and used it to study how somatic mutations found in primary tumours distribute across the interacting interfaces and identify those with a potential role in driving oncogenesis. Although each chapter covers a different question, all of them demonstrate the potential of the interactome in helping to interpret genomic variation observed under diverse research scenarios.[ES] Nuestro conocimiento acerca de los mecanismos biológicos causantes de la mayoría de enfermedades humanas comunes es aun pobre. Incluso con mapas genéticos de alta resolución, nuestra capacidad para hacer predicciones fenotípicas certeras o determinar el riesgo de una persona a padecer una enfermedad utilizando solamente marcadores genéticos es muy baja. Entre las principales causas de esta aparente falta de relación directa entre genotipo y fenotipo están las interacciones moleculares, los fenómenos de pleiotropía y la influencia de los factores externos. Debido al papel esencial que ejercen en llevar a cabo las funciones celulares, las proteínas y sus interacciones han adquirido una atención especial en la traducción de los datos genotípicos a estados fenotípicos. En esta tesis se presentan tres estrategias diferentes para la integración de datos genómicos humanos con la red de interacciones proteicas (interactoma). El objetivo común de todas ellas es, haciendo uso de la estructura del interactoma, proponer hipótesis funcionales que ayuden a interpretar los patrones de variabilidad observados en diferentes estados fenotípicos humanos. Primero, se propone una metodología para extraer el componente del interactoma asociado a los genes relevantes en una lista ranqueada por cualquier parámetro experimental, como el estadístico derivado de los estudios de asociación genómicos. Es segundo lugar se describe un análisis sistemático de las variantes genéticas observadas en humanos sanos en el contexto del interactoma. En él se estudia cómo la estructura del interactoma puede ayudar en explicar la aparentemente elevada cantidad de variantes deletéreas observadas en los últimos estudios poblacionales de secuenciación de genomas. Los resultados son comparados con las mutaciones somáticas observadas en pacientes de Leucemia. Finalmente, se presenta un estudio de las mutaciones somáticas observadas en tumores primarios utilizando una versión del interactoma que incluye la estructura tridimensional de las proteínas. Aunque cada estudio presentado en la tesis pretende resolver preguntas diferentes, todos ellos demuestran el potencial del interactoma de proteínas en ayudar a interpretar la variación genómica humana observada en un contexto tanto evolutivo como de enfermedad.
[CAT] El nostre coneixement sobre els mecanismes biològics causants de la majoria de malalties humanes comuns es encara pobre. Tot i que en l'actualitat tenim mapes genètics d'alta resolució, la nostra capacitat per a fer prediccions fenotípiques certeres utilitzant únicament marcadors genètics es encara molt baixa degut a que no entenem les bases moleculars a traves de les quals les alteracions genètiques condicionen un fenotip de malaltia. Entre les principals causes d'aquesta aparent falta de relació directa entre genotip i fenotip estan la complexitat introduïda per les interacciones moleculars, els fenòmens de peleiotropia i la influencia dels factors externs. Degut al paper clau en dur a terme la majoria de funcions cel·lulars, les proteïnes i les seues interaccions han adquirit una especial atenció en la traducció de les dades genotípiques en estats fenotípics. Aquesta tesi presenta tres estartègies diferents per a la integració de dades genòmiques humanes amb la xarxa d'interaccions proteiques (interactoma). L'objectiu comú es, fent ús de l'estructura del interactoma, proposar hipòtesis funcionals que ajuden a interpretar els patrons de variabilitat genètica observats en diferents estats fenotípics. En primer lloc, es proposa una metodologia per a extraure el component de l'interactoma associat als gens rellevants en una llista ranquejada per qualsevol paràmetre experimental, com l'estadístic derivat d'estudis d'assocaició de genoma. En segon lloc, es descriu un anàlisi sistemàtic de les variants genètiques observades en humans sans en el context del interactoma. Ací s'analitza com l'estructura del interactoma pot ajudar a explicar l'aparent elevada quantitat de variants deletèries observades en els últims estudis poblacionals de sequenciació de genomes. Els resultats son comparats amb les mutacions somàtiques observades en pacients de Leucèmia. Finalment, es presenta un estudi de les mutacions somàtiques observades en tumors primaris de més de 20 tipus utilitzant una versió del interactoma més resolutiva, que inclou l'estructura tridimensional de les proteïnes. Encara que cada estudi presentat en la tesi planteja resoldre qüestions diferents, tots ells demostren el potencial del interactoma de proteïnes en ajudar a interpretar la variació genòmica humana observada en un context tant poblacional com de malaltia.
García Alonso, LM. (2015). Functional profiling of human genomic data using the protein interactome [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/55848
TESIS
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Demarsh, Peter Alexander. "Public Health Genomics: Exploiting SNP-NSAID interactions to prevent colorectal cancer." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20005.
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Colorectal cancer (CRC) is a common disease with a high mortality rate. Non-steroidal anti-inflammatory drugs (NSAIDs) such as aspirin and ibuprofen have shown considerable promise as preventive agents for CRC. However, due to concerns over the balance between benefits and harms NSAIDs are not recommended as a means of preventing CRC in average risk groups. Investigation into genetic modifiers of NSAIDs' chemopreventive action may help to identify those for whom such drugs are beneficial or ineffective. This thesis explored genetic mediation of the effectiveness of NSAID prophylaxis for CRC. A review of basic CRC biology and methods for investigating interactions, a systematic review of the literature to identify candidate interactions, and a secondary analysis of a GWA case-control study were performed. Candidate SNPs were screened for potential interactions, and several possible interactions were identified. The joint effects were similar for aspirin and ibuprofen, but not acetaminophen, implying true biological effects. Potential interactions were investigated further using a stepwise model building procedure. This resulted in a model containing three SNPs, aspirin and/or Ibuprofen use, their interactions, sex and age. This model was better able to discriminate cases and controls, demonstrated better calibration, and had greater information content (by AIC) than models without the interaction terms. Finally, recommendations for further research were given.
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Chari, Rajagopal. "Development and application of an integrative genomics approach to lung cancer." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/26001.
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Lung cancer has the highest mortality rate amongst all diagnosed malignancies with adenocarcinoma (AC) being the most commonly diagnosed subtype of this disease in North America. The dismal survival statistics of lung cancer patients are largely due to the detection of the disease at an advanced stage and to a lesser extent, the limited efficacy of current front line treatments.
Genomic approaches, namely gene expression analysis, have provided tremendous insight into
lung cancer. While many gene expression changes have been identified, most changes are likely reactive to changes which have a primary role in cancer development. Moreover, one feature which can discern primary from reactive changes is the presence of concordant DNA level alteration.
Many well known genes involved in cancer such as TP53 and CDKN2A have been shown to be
affected by multiple mechanisms of alteration such as somatic mutation in or loss of DNA sequence. For a given gene, one tumor may be affected by one mechanism while another tumor may be affected by a different mechanism. Although this level of multi-dimensional analysis has been performed for specific genes, such analysis has not been done at the genome-wide level.
This thesis highlights the development and application of a multi-dimensional genetic and
epigenetic approach to identify frequently aberrant genes and pathways in lung AC. I present,
first, the design and implementation of the system for integrative genomic multi-dimensional analysis of cancer genomes, epigenomes and transcriptomes (SIGMA²). Next, analyzing a multi-dimensional dataset generated from ten lung AC specimens with non-malignant controls, I identified novel genes and pathways that would have been missed if a non-integrative approach were used. Finally, examining genes involved with EGFR signaling, I identified a gene, signal receptor protein alpha (SIRPA), which had not been previously shown to be associated with lung cancer.
Taken together, these findings demonstrate the power of a multi-dimensional approach to identify important genes and pathways in lung cancer. Moreover, identifying key genes using a multi-dimensional approach on a small sample set suggests the need of large datasets may be
circumvented by using a more comprehensive approach on a smaller set of samples.
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Roberts, Nicola Diane. "Patterns of somatic genome rearrangement in human cancer." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275454.
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Cancer development is driven by somatic genome alterations, ranging from single point mutations to larger structural variants (SV) affecting kilobases to megabases of one or more chromosomes. Studies of somatic rearrangement have previously been limited by a paucity of whole genome sequencing data, and a lack of methods for comprehensive structural classification and downstream analysis. The ICGC project on the Pan-Cancer Analysis of Whole Genomes provides an unprecedented opportunity to analyse somatic SVs at base-pair resolution in more than 2500 samples from 30 common cancer types. In this thesis, I build on a recently developed SV classification pipeline to present a census of rearrangement across the pan-cancer cohort, including chromoplexy, replicative two-jumps, and templated insertions connecting as many as eight distant loci. By identifying the precise structure of individual breakpoint junctions and separating out complex clusters, the classification scheme empowers detailed exploration of all simple SV properties and signatures. After illustrating the various SV classes and their frequency across cancer types and samples, Chapter 2 focuses on structural properties including event size and breakpoint homology. Then, in Chapter 3, I consider the SV distribution across the genome, and show patterns of association with various genome properties. Upon examination of rearrangement hotspot loci, I describe tissue-specific fragile site deletion patterns, and a variety of SV profiles around known cancer genes, including recurrent templated insertion cycles affecting TERT and RB1. Turning to co-occurring alteration patterns, Chapter 4 introduces the Hierarchical Dirichlet Process as a non-parametric Bayesian model of mutational signatures. After developing methods for consensus signature extraction, I detour to the domain of single nucleotide variants to test the HDP method on real and simulated data, and to illustrate its utility for simultaneous signature discovery and matching. Finally, I return to the PCAWG SV dataset, and extract SV signatures delineated by structural class, size, and replication timing. In Chapter 5, I move on to the complex SV clusters (largely set aside throughout Chapters 2—4) , and develop an improved breakpoint clustering method to subdivide the complex rearrangement landscape. I propose a raft of summary metrics for groups of five or more breakpoint junctions, and explore their utility for preliminary classification of chromothripsis and other complex phenomena. This comprehensive study of somatic genome rearrangement provides detailed insight into SV patterns and properties across event classes, genome regions, samples, and cancer types. To extrapolate from the progress made in this thesis, Chapter 6 suggests future strategies for addressing unanswered questions about complex SV mechanisms, annotation of functional consequences, and selection analysis to discover novel drivers of the cancer phenotype.
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Sanyal, Sudip. "The genomic and metabolomic profiling of pancreas cancer." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/the-genomic-and-metabolomic-profiling-of-pancreas-cancer(461c068d-8eb6-4f60-962c-4a30723101fd).html.
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Despite the considerable expansion of knowledge in the development of pancreatic cancer, there has been little progress made in facilitating an early diagnosis of this disease and predicting an accurate response to treatment. We aim to translate this knowledge to clinical practice by using a prospective database of precursor cystic lesions in pancreas cancer, assessing the use of over-expressed genes in pancreatic juice as a surrogate marker of these pancreas cancer and finally, downstream of these changes at the genetic level, use metabolomic techniques to look for biomarkers in pancreas cancer in serum. In the first study, we investigate the natural history of pancreatic cystic neoplasms, specifically IPMNs, using a prospectively collected database to examine the profiles and outcomes of main duct IPMN, branch duct IPMN and cystic lesions measuring less than 3 cm in size. A total of 99 patients with suspected pancreatic cystic tumours were enrolled over 3 years. Median follow-up was 24 months (range 0 – 124). Cystic tumours comprised of 13 MD-IPMN, 40 BD-IPMN, 11 MCN and 8 adenocarcinomas among others. The complete cohort showed an overall risk of adenocarcinoma of 8%. Main duct IPMN showed a cumulative risk of 46% with evidence of progression of disease in a further 23%. The associated mortality in MD-IPMN was related to the underlying adenocarcinoma and was 38% in our group. The incidence of adenocarcinoma in branch duct IPMN was 11% with disease progression seen 13.8%. Evidence of extra-pancreatic malignancies was seen in 37.7% of patients with IPMN. In the second study, we explore the feasibility of gene expression profiling from RNA isolated from matched pancreatic juice and tumour tissue in patients with pancreatic cancer and pancreatic cystic tumours. RNA was isolated and Poly(A) PCR was used to globally amplify the RNA. RT-PCR was used to measure expression levels of 18 genes common to both pancreas cancer and pancreatic cystic tumours. Spearman’s rank correlation test was used to examine the relationship of gene expression between pancreatic juice and tissue. One gene out of eighteen, MSLN (p<0.008), showed significant correlation in the expression levels between paired pancreatic juice and tissue samples in pancreas cancer. In the cystic tumour group, only one gene MMP-7 (p<0.01), showed a significant correlation between paired juice and tissue samples. When the whole cohort was analysed for the false discovery rate, these genes did not exhibit statistically significant correlation between the samples. RNA analysis of pancreatic juice is feasible using the Poly(A) cDNA technique and correlation of gene expression is shown to exist, albeit with low sensitivity, indicating its potential use in clinical practice with small tissue and juice samples. In the final study, we performed a literature review on the use of metabolomics in pancreas cancer. We performed metabolic profiling of serum samples from selected cancer patients and noncancerous controls using UPHLC-MS to generate and compare the metabolic profiles in serum samples from a cohort of patients with pancreas cancer, ampullary cancer and endocrine cancer. Thirty nine serum samples (including 19 pancreatic cancers, 9 ampullary cancers and 5 endocrine cancers) and 21 matched HUSERMET controls were analysed using Ultra high performance liquid chromatography mass spectrometry (UHPLC-MS) in both positive and negative ESI modes. The output was generated as a data matrix of mass spectral features with related accurate m/z and retention time pairs. The data was then signal corrected and individual peaks were normalised and the resultant spectra were compared against a metabolite reference library and analysed using univariate and multivariate statistical tests. We found a disparity in the metabolite peaks between the cases and controls on PCA that did not permit the accurate interpretation of the data in the case study set compared to the control set. No obvious reason other than metabolite degradation during storage could account for this difference. PC-DFA analysis of metabolite peaks between pancreas cancer, ampullary cancer and endocrine cancer showed significant difference between endocrine cancers and the other two groups. Significant ESI positive metabolites included those involved in lipid pathways and metabolites involved in glucose metabolism. There is encouraging scope for studies using prospective controls to identify and develop metabolic biomarkers in pancreas cancer.
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Newman, Scott. "The structure and evolution of breast cancer genomes." Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/239397.
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Chromosome changes in the haematological malignancies, lymphomas and sarcomas are known to be important events in the evolution of these tumours as they can, for example, form fusion oncogenes or disrupt tumour suppressor genes. The recently described recurrent fusion genes in prostate and lung cancer proved to be iconic examples as they indicated that important gene fusions are found in the common epithelial cancers also. Breast cancers often display extensive structural and numerical chromosome aberration and have among the most complex karyotyes of all cancers. Genome rearrangements are potentially an important source of mutation in breast cancer but little is known about how they might contribute to this disease. My first aim was to carry out a structural survey of breast cancer cell line genomes in order to find genes that were disrupted by chromosome aberrations in 'typical' breast cancers. I investigated three breast cancer cell lines, HCC1187, VP229 and VP267 using data from array painting, SNP6 array CGH, molecular cytogenetics and massively parallel paired end sequencing. I then used these structural genomic maps to predict fusion transcripts and demonstrated expression of five fusion transcripts in HCC1187, three in VP229 and four inVP267. Even though chromosome aberrations disrupt and fuse many genes in individual breast cancers, a major unknown is the relative importance and timing of genome rearrangements compared to sequence-level mutation. For example, chromosome instability might arise early and be essential to tumour suppressor loss and fusion gene formation or be a late event contributing little to cancer development. To address this question, I considered the evolution of these highly rearranged breast cancer karyotypes. The VP229 and VP267 cell lines were derived from the same patient before and after therapy-resistant relapse, so any chromosome aberration found in both cell lines was probably found in the common in vivo ancestor of the two cell lines. A large majority of structural variants detected by massively parallel paired end sequencing, including three fusion transcripts, were found in both cell lines, and therefore, in the common ancestor. This probably means that the bulk of genome rearrangement pre-dated the relapse. For HCC1187, I classified most of its mutations as earlier or later according to whether they occurred before or after a landmark event in the evolution of the genome-endoreduplication (duplication of its entire genome). Genome rearrangements and sequence-level mutations were fairly evenly divided between earlier and later, implying that genetic instability was relatively constant throughout the evolution of the tumour. Surprisingly, the great majority of inactivating mutations and expressed gene fusions happened earlier. The non-random timing of these events suggests many were selected.
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Adams, David James. "The Genetic and Therapeutic Landscape of Cancer." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29490.
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The analysis of cancer genomes and the germline sequence of cancer patients has fundamentally changed our understanding of tumorigenesis and the ways in which we treat the disease. My work has focused on understanding the genetics of skin cancer and the functional analysis of cancer genes using approaches such as genome editing. My thesis describes my contributions to these areas and includes the chapters: Understanding the Genetics of Skin and other Cancers, The Functional Genomics of Cancer and Defining the Function of Mammalian Genes at Scale.
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Sharpnack, Michael F. Sharpnack. "Integrative Genomics Methods for Personalized Treatment of Non-Small-Cell LungCancer." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523890139956055.
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Pyl, Paul-Theodor [Verfasser], and Paul [Akademischer Betreuer] Bertone. "Method development for comparative cancer genomics / Paul-Theodor Pyl ; Betreuer: Paul Bertone." Heidelberg : Universitätsbibliothek Heidelberg, 2014. http://d-nb.info/1180300521/34.
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Zhao, Sihai. "Survival Analysis with High-Dimensional c\Covariates, with Applications to Cancer Genomics." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10245.
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Recent technological advances have given cancer researchers the ability to gather vast amounts of genetic and genomic data from individual patients. These offer tantalizing possibilities for, for example, basic cancer biology, tailored therapies, and personalized risk predictions. At the same time, they have also introduced many analytical difficulties that cannot be properly addressed with current statistical procedures, because the number of genomic covariates in these datasets is often larger than the sample size. In this dissertation we study methods for addressing this so-called high-dimensional issue when genomic data are used to analyze time-to-event outcomes, so common to clinical cancer studies. In Chapter 1, we propose a regularization method for sparse estimation for estimating equations. Our method can be used even when the number of covariates exceeds the number of samples, and can be implemented using well-studied algorithms from the non-linear constrained optimization literature. Furthermore, for certain estimating equations and certain regularizers, including the lasso and group lasso, we prove a finite-sample probability bound on the accuracy of our estimator. However, it is well-known that these types of regularization methods can achieve better performance if a quick and simple procedure is first used to reduce the number of covariates. In Chapter 2, we propose and theoretically justify a principled method for reducing dimensionality in the analysis of censored data by selecting only the important covariates. Our procedure involves a tuning parameter that has a simple interpretation as the desired false positive rate of this selection. Similar types of model-based screening methods have also been proposed, but only for a few specific models. Model-free screening methods have also recently been studied, but can have lower power to detect important covariates. In Chapter 3 we propose a screening procedure that can be used with any model that can be fit using estimating equations, and provide unified results on its finite-sample screening performance. We thus generalize many recently proposed model-based and model-free screening procedures. We also propose an iterative version of our method and show that it is closely related to a recently studied boosting method for estimating equations.
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O'Shea, Margaret Rose. "Preparing the health system for new Lynch Syndrome colorectal and endometrial cancer genetic testing pathways." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25841.
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Background: ‘Mainstreaming’ is a proposed strategy to integrate genetic testing into oncology services, enhancing the identification of hereditary cancer and overcoming the barriers of genetics referral and clinician awareness.
Aims: To identify health system interventions and implementation factors for mainstreaming genetic testing in oncology; to draft a mainstreaming oncology genomics model.
Methods: A systematic review, qualitative interviews and a quantitative survey were conducted using the Consolidated Framework for Implementation Research. The qualitative implementation data was used to inform the quantitative survey design. Theory-informed implementation data was collectively mapped to the Genomic Medicine Integrative Research framework.
Results: The systematic review identified a lack of theory guided health system intervention design and evaluation for mainstreaming programs. The qualitative phase had 22 participants from 12 health organisations. The quantitative survey had 198 responses: 26% and 66% from genetic and oncology health professionals, respectively. The relative advantage and clinical utility of mainstreaming to improve genetic test access and to streamline care were identified. Optimization of current process was recognized for results delivery and follow up. The barriers identified focused on funding, infrastructure and resources, and the need for process and role delineation. Recommended interventions included improved communication and collaboration between specialties, embedded mainstream genetic counsellors, electronic medical record genetic test ordering, with centralized results tracking systems and development of dedicated resources.
Conclusions: The implementation factors identified informed the development of a preliminary mainstreaming oncology genomics model for Lynch syndrome. Implementation and evaluation of this model is required to inform future mainstreaming initiatives and can translate to other disease contexts.
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Dawany, Noor Tozeren Aydin. "Large-scale integration of microarray data : investigating the pathologies of cancer and infectious diseases /." Philadelphia, Pa. : Drexel University, 2010. http://hdl.handle.net/1860/3251.
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Robbe, Pauline. "Addressing challenges of molecular precision diagnostics for cancer patients in the genomics era." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:519799f5-8d73-4132-9288-87a5b0636a96.
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Genomic technologies have proven successful in research-led studies and their potential for characterising prognostic and predictive biomarkers in cancer patients has been recognised. However, such techniques and findings are yet to be applied in routine diagnostic environments. Indeed, many challenges remain before being able to establish a comprehensive genomics classification for all cancer patients, not least pre-analytical difficulties and variants interpretation. One of the most important pre-analytical challenges is the use of formalin-fixed paraffin-embedded (FFPE) tissues which are far more widely available in diagnostics compared with alternative fresh-frozen (FF) tissue samples. The key issue is that FFPE treatment of the tissue greatly impacts on the DNA quality and downstream analysis. This is important because genomic studies have discovered a significant number of mutations in multiple cancer driver genes. The presence of these mutations informs on patients' prognoses or responses to treatment and guides clinical management. With an increasing number of targeted therapies available it is critical to stratify patients using genomics technologies. However, current clinically available tests fail to identify known driver mutations in the majority of patients. The overall objective of the work presented in this thesis was to address current challenges and limitations of FFPE testing by improving knowledge in genomic technology and by providing a means to offer genomic-guided precision medicine to a greater number of cancer patients. The specific aims were (i) to explore the feasibility of using FFPE tissue from patients with solid tumour for clinical grade whole genome sequencing (WGS) and (ii) to use WGS and targeted sequencing data to search for novel genomic biomarkers of aggressive disease in CLL patients. In Chapter 3, a prospective WGS study of cancer patients using 52 matching FF and FFPE samples was performed. This highlighted 71% agreement in somatic single nucleotide variants and revealed sub-optimal copy number alteration (CNA) detection from FFPE samples (44% median correlation with FF) due to non-uniform coverage. In Chapter 4, after pre-analytical investigation and experimental optimisation, CNA detection was improved significantly using lower reverse crosslinking temperature in FFPE DNA extraction (80°C or 65°C depending the methods) or by controlling formalin fixation. The results showed that WGS data derived from FFPE tissue derived DNA is capable of detecting 96% of clinically actionable variants (including 97% of CNAs) and therefore the approach is considered suitable for implementation in the routine clinical diagnostic environment. Furthermore, the study of several clinical trial CLL cohorts presented in this thesis highlighted multiple novel genomic biomarkers of aggressive disease in CLL patients. In Chapter 5 relapse/refractory patients (R/R) were clustered according to the driver mutations identified using targeted sequencing; a multiple-hit profile defined as recurrent combinations of ≥ 2 mutations of TP53, SF3B1 and ATM was identified. This multiple-hit profile, found in 19% of patients was associated with a median progression-free survival of 12 months compared with 22.5 months in the remaining patients (p=0.003). Other potential biomarkers were identified by exploring the genome of 320 CLL patients, such as mutational signatures. Collectively, the work performed and the data generated provide new insights to overcome current challenges linked to the establishment of genomics technologies in the clinic in order to offer precision medicine to cancer patients.
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McFarland, Christopher Dennis. "The role of deleterious passengers in cancer." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070047.
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The development of cancer from a population of precancerous cells is a rapid evolutionary process. During progression, cells evolve several new traits for survive and proliferation via a few key `driver' mutations. However, these few driver alterations reside in a cancer genome alongside tens of thousands of additional `passenger' mutations. Passengers are widely believed to have no role in cancer, yet many passengers fall within functional genomic elements that may have potentially deleterious effects on the cancer cells. Here we investigate the potential of moderately deleterious passengers to accumulate and alter neoplastic progression.
Evolutionary simulations suggest that moderately-deleterious passengers accumulate during progression and largely evade natural selection. Accumulation is possible because of cancer's unique evolutionary constraints: an initially small population size, an elevated mutation rate, and a need to acquire several driver mutations within a short evolutionary timeframe. Cancer dynamics can be theoretically understood as a tug-of-war between rare, strongly-beneficial drives and frequent mildly-deleterious passengers. In this formalism, passengers present a barrier to cancer progression describable by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers collapse. In essence, cancer progression can be subverted by its own unique evolutionary constraints.
The collective burden of passengers explain several oncological phenomena that are difficult to explain otherwise. Genomics data confirms that many passengers are likely damaging and have largely evaded negative selection, while age-incidence curves and the distribution of mutation totals suggests that drivers and passengers exhibit competing effects. These data also provide estimates of the strength of drivers and passengers.
Finally, we use our model to explore cancer treatments. We identify two broad regimes of adaptive evolutionary dynamics and use these regimes to understand outcomes from various treatment strategies. Our theory explains previously paradoxical treatment outcomes and suggest that passengers could serve as a biomarker of response to mutagenic therapies. Deleterious passengers are targetable by either (i) increasing the mutation rate or (ii) exacerbating their deleterious effects. Our results suggest a unique framework for understanding cancer progression as a balance between driver and passenger mutations.
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GRIONI, ANDREA. "Application of modern data science to genomics and clinical research." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/279991.
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Dopo che il progetto sul genoma umano è stato completato nell'aprile del 2003, il flusso
continuo di nuovi database e dati di sequenziamento ha iniziato a trasformare il campo della
genomica in scienza basata sui dati. La bioinformatica analizza i dati sperimentali grezzi con
l'obiettivo di ottenere informazioni che descrivono le condizioni biologiche misurate, fornendo
così un potente strumento per studiare specifici meccanismi molecolari e genetici. Questa
conoscenza deve essere combinata con la genomica per decifrare le interrelazioni tra geni,
elementi regolatori, vie metaboliche e interazioni proteiche. L'apprendimento profondo,
conosciuto come Deep Learning, e’ una sottodisciplina dell'apprendimento automatico, è stato
recentemente applicato al campo della genomica, portando a risultati notevoli. I due obiettivi
principali di questo lavoro sono: lo sviluppo e le applicazioni di strumenti bioinformatici che
consentano lo studio delle basi genetiche della leucemia linfoblastica acuta e l'uso di tecniche
di apprendimento profondo per l'identificazione di piccoli elementi di RNA non codificanti del
genoma umano. Questa tesi fornisce al lettore una panoramica completa della recente
evoluzione della genomica come campo interdisciplinare di ricerca strettamente connesso con
l'informatica e l'analisi dei dati.After the completion of the human genome project in April 2003, the continuous flow of sequencing data and the development of new databases began to transform the field of genomics into data-driven science. Bioinformatics analyses raw experimental data with the aim to obtain information describing biological processes, thus providing a powerful tool to investigate specific molecular and genetic mechanisms. This domain knowledge in combination with genomics allows to decipher the interrelationships between genes, regulatory elements, metabolic pathways, and protein interactions. Deep learning, a subdiscipline of machine learning, has been recently applied to the field of genomics, leading to remarkable results. The two main objectives of this study were: the development and application of bioinformatic tools for the study of the genetic basis of acute lymphoblastic leukaemia, and the usage of deep learning techniques for the identification of small non-coding RNA elements in the human genome. This dissertation provides a comprehensive overview of the recent evolution of genomics as an interdisciplinary field of research strongly associated with computer science and data analysis.
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Coe, Bradley P. "The role of specific genomic alterations in small cell lung cancer aggressiveness." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2283.
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Small Cell Lung Cancer (SCLC) is a very aggressive neuroendocrine tumour of the lung, which demonstrates a 5 year survival of only 10% for extensive stage disease (20-30% for limited stage), with only modest improvement over the last few decades. Identification of new molecular diagnostic and therapeutic targets is thus imperative. Previous efforts in identifying molecular changes in SCLC by gene expression profiling using microarrays have facilitated disease classification but yielded very limited information on SCLC biology. Previous DNA studies have been successful in identifying several loci important to SCLC. However the low resolution of conventional chromosomal Comparative Genomic Hybridization (CGH) has limited the findings to large chromosomal regions with only a few specific candidate genes discovered to date. Thus, to further understand the biological behaviour of SCLC, better methods for studying the genomic alterations in SCLC are necessary.
This thesis highlights the development of array CGH technology for the high resolution dissection of aneuploidy in cancer genomes and the application of this new technology to the study of SCLC. I present the development of the first whole genome CGH array which offered unprecedented resolution in the profiling of cancer genomes allowing fine mapping of genes in a single experiment. Through application of DNA based analysis in conjunction with integrated expression analysis and comparison of SCLC to less aggressive non-small cell lung tumours I have identified novel patterns of pathway disruption specific to SCLC. This included alteration to Wnt pathway members and striking patterns of cell cycle activation through predominantly downstream disruption of signalling pathways including direct activation of the E2F transcription factors, which are normally repressed by the Rb gene.
Analysis of targets of the E2F/Rb pathway identified EZH2 as being specifically hyper-activated in SCLC, compared to NSCLC. EZH2 is a polycomb group gene involved in the control of many cellular functions including targeted DNA methylation and escape from senescence in hematopoietic stem cells.
Taken together these results suggest that in SCLC, downstream disruption may replace multiple upstream alterations leading to activation independent of a specific mitogenic pathway, and that EZH2 represents a potentially important therapeutic target.
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Cohen, Andrea. "Characterization of Altered Enhancer Usage Across the Human Colorectal Cancer Epigenome." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1491332948235594.
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Gerrard, Diana Lea. "Characterization Of Epigenetic Plasticity And Chromatin Dynamics In Cancer Cell Models." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1060.
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Cancer progression is driven by cumulative changes that promote and maintain the malignant phenotype. Epigenetic alterations are central to malignant transformation and to the development of therapy resistance. Changes in DNA methylation, histone acetylation and methylation, noncoding RNA expression and higher-order chromatin structures are epigenetic features of cancer, which are independent of changes in the DNA sequence. Despite the knowledge that these epigenetic alterations disrupt essential pathways that protect cells from uncontrolled growth, how these modifications collectively coordinate cancer gene expression programs remains poorly understood. In this dissertation, I utilize molecular and informatic approaches to define and characterize the genome-wide epigenetic patterns of two important human cancer cell models. I further explore the dynamic alterations of chromatin structure and its interplay with gene regulation in response to therapeutic agents.
In the first part of this dissertation, pancreatic ductal adenocarcinoma (PDAC) cell models were used to characterize genome-wide patterns of chromatin structure. The effects of histone acetyltransferase (HAT) inhibitors on chromatin structure patterns were investigated to understand how these potential therapeutics influence the epigenome and gene regulation. Accordingly, HAT inhibitors globally target histone modifications and also impacted specific gene pathways and regulatory domains such as super-enhancers. Overall, the results from this study uncover potential roles for specific epigenomic domains in PDAC cells and demonstrate epigenomic plasticity to HAT inhibitors.
In the second part of this dissertation, I investigate the dynamic changes of chromatin structure in response to estrogen signaling over a time-course using Estrogen Receptor (ER) positive breast cancer cell models. Accordingly, I generated genome-wide chromatin contact maps, ER, CTCF and regulatory histone modification profiles and compared and integrated these profiles to determine the temporal patterns of regulatory chromatin compartments. The results reveal that the majority of alterations occur in regions that correspond to active chromatin states, and that dynamic chromatin is linked to genes associated with specific cancer growth and metabolic signaling pathways. To distinguish ER-regulated processes in tamoxifen-sensitive and in tamoxifen-resistant (TAMR) cell models, we determined the corresponding chromatin and gene expression profiles using ER-positive TAMR cancer cell derivatives. Comparison of the patterns revealed characteristic features of estrogen responsiveness and show a global reprogramming of chromatin structure in breast cancer cells with acquired tamoxifen resistance.
Taken together, this dissertation reveals novel insight into dynamic epigenomic alterations that occur with extrinsic stimuli and provides insight into mechanisms underlying the therapeutic responses in cancer cells.
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Smith, Michael Louis. "Low-level artefacts affecting microarrays and next-generation sequencing in a cancer genomics environment." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648252.
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Mooney, Marie R. "Precision Medicine Approaches to Integrating Genomics with Cancer Therapy| Applications in Glioblastoma and Lymphoma." Thesis, Van Andel Research Institute, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10275288.
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The word "cancer" rarely stands alone, usually prefaced with its anatomical location: lung cancer, prostate cancer, brain cancer. With the advancement of high-throughput omics approaches, specific oncogenic events are reorganizing the landscape of cancer classification, at once creating commonalities between cancers arising in diverse anatomical locations and dividing organ-centric classifications of cancer into a multitude of subtypes. The term "precision medicine" postulates that these new, data-driven groupings based on molecular characterization are the key to making rational therapeutic choices.
The majority of this dissertation addresses the disconnect between extensive molecular characterization and poor cancer therapy outcomes for patients with glioblastoma multiforme (GBM). Despite clear evidence that hyperproduction of the ligand for PDGFR (platelet-derived growth factor receptor α) is sufficient to generate GBM of the proneural subtype, anti-PDGFRα therapeutics have proven disappointing in clinical trials. Cell adaptation contributes to therapeutic escape. In GBM, proneural tumor cells adopt transcriptional profiles of the mesenchymal subtype. The interconversion between the proneural and mesenchymal transcriptional classes within a tumor population presents both a challenge and an opportunity for therapeutic approaches. The proneural subtype has a proliferation phenotype and presents druggable targets such as PDGFRα. The mesenchymal subtype presents an invasive phenotype, but the targets are more challenging to drug. The typical screening for combination therapies that synergize to induce cell death is not as advantageous here, where the disease management is expected to include cytostatic drugs that act on two different aspects of the phenotype: proneurally mediated proliferation and mesenchymally mediated invasion. This work examines the applicability of a combination approach against a proneural target, PDGFRα, and mesenchymal targets in the STAT3 (signal transducer and activator of transcription 3) pathway, in the context of a proneural model of GBM.
The work is concluded with collection of work applying precision medicine in other disease contexts, most notably canine lymphoma.
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Feldhahn, Magdalena [Verfasser], and Oliver [Akademischer Betreuer] Kohlbacher. "Computational Methods for Personalized Cancer Therapy Based on Genomics Data / Magdalena Feldhahn ; Betreuer: Oliver Kohlbacher." Tübingen : Universitätsbibliothek Tübingen, 2013. http://d-nb.info/1162844434/34.
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Nguyen, Bastien. "Impact of reproductive history and pregnancy on breast cancer biology." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/278388.
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It is estimated that four in five women will give birth while one in eight women will be diagnosed with breast cancer at some point in her lifetime. It is also known that pregnancy at a young age is associated with a marked decrease in the risk of breast cancer and that this protection is different according to breast cancer subtypes. This thesis explores the impact of reproductive history on breast cancer biology and provides the molecular characterization of breast cancer diagnosed during pregnancy. The last part investigates the effect of RANKL inhibition on the biology of breast cancer in young women. In the first study, we investigated the impact of parity and age at first pregnancy on the clinicopathological features, the genomic and transcriptomic landscape, and the immune microenvironment of 313 breast cancers. For the first time, we highlighted a link between reproductive history and the genomic landscape of subsequent breast cancer. We demonstrated that, independently of clinicopathological features, age at first birth is associated with specific genomic alterations that could explain the differences in risk reduction associated with pregnancy according to breast cancer subtypes. This study represents a first step toward the recognition that reproductive factors matter in order to fully understand breast cancer biology and advocates that reproductive history should be routinely collected in future studies addressing the biology of breast cancer but also of other female cancers. The second study is focused on the molecular characterization of breast cancer diagnosed during pregnancy (BCP). We conducted a comparative analysis of a unique cohort of BCP patients and non-pregnant control patients by integrating gene expression, copy number alterations, and whole-genome sequencing data. We showed that BCP has unique molecular characteristics including an enrichment of non-silent mutations, a higher frequency of mutations in mucin gene family and an enrichment of mismatch repair deficiency mutational signature. This provides important insights into the biology of BCP and suggests that these features may be implicated in promoting tumor progression during pregnancy. In addition, it provides an unprecedented resource for further understanding of the biology of breast cancer in young women and how pregnancy could modulate tumor biology. In a previous study, the laboratory had reported up-regulation of RANKL in young and pregnant breast cancer patients. Therefore, in the last chapter, we investigated the biological effect of denosumab, a RANKL inhibitor, in a preoperative study including 27 young primary breast cancer patients. We demonstrated evidence that denosumab induces modulation of the tumor immune microenvironment with an increased level of tumor-infiltrating lymphocytes. This effect was likely due to upregulation of inflammatory cytokines and depletion of immunosuppressive regulatory T cells within the tumor microenvironment. These findings suggest a role for denosumab in reshaping the tumor immune microenvironment of breast cancer and that its use in combination could improve immunotherapy efficacy.Doctorat en Sciences biomédicales et pharmaceutiques (Médecine)
info:eu-repo/semantics/nonPublished
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Toh, Alan Kie Leong. "Functional roles of EMP-associated targets in breast cancer models." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/207818/1/Alan%20Kie%20Leong_Toh_Thesis.pdf.
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Epithelial mesenchymal plasticity in cancer generally refers to the ability of a cancer cell to transform into a different cell form, which facilitates the metastatic spread of a cancer. This thesis explores the roles of four cancer-associated genes that affect the transition of the cell state during cancer metastasis, and includes extensive research on two of the four gene targets, namely TRIM28 and TGFBI. The effects of these genes in breast cancer systems indicated great potential for improving therapeutic responses towards cancer drugs, which would alleviate the suffering of breast cancer patients.
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González, Rosado Santiago. "Identification and characterization of non-coding genomic variations associated to cancer diseases." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/397789.
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The genetic and molecular bases of most of the human diseases have become one of the main goals of the human biology in the last decades. To be able to unveil the genetic variations and the affected cellular processes associated with a specific disease is crucial in order to generate accurate diagnosis and further therapies. The Next Generation Sequencing (NGS) revolution, with the associated reduction in time and costs of sequencing, has allowed the scientist to access large number of human genomes to their biomedical studies. The study of genetic disorders, cancer in particular, has benefit from NGS identifying genetic variations associated with a given disorder. All these new results, some of them in regions with unknown function, have generated a double challenge in the scientific community. Firstly, detect as much as possible all the different variants associated with a disease, in some complex diseases several. Secondly, to understand the functional impact those modifications are causing in the cell.
Regarding the first challenge, this thesis contributes in the identification of genetic modifications throw the development of a bioinformatics tool named SMUFIN (Moncunill et al. 2014). SMUFIN can detect somatic variants related with tumour development and progression in a quickly and effective way. Not limited to the software development, several tumours has been analysed and their somatic variants characterized. These tumours include mantel cell lymphoma, paediatric medulloblastoma and chronic lymphocytic leukaemia (Moncunill et al. 2014; Puente et al. 2015).
In the evaluation of the functional impact, the thesis also includes a method, RELA, to determine when these annotated variants play a regulatory role as enhancers or promoters (Gonzalez et al. 2012). Combined with other available data and a spread methodology to unveil regulatory regions evaluation of variants affecting regulatory regions have been performed in chronic lymphocytic leukaemia (details included in the thesis discussion).
To sum up, this thesis cover with methodology and provide bioinformatics tools to perform a complete genomic analysis of genetic variants in biomedicine studies. It includes from the identification of variants for each of the patients to the evaluation of their functional impact in the disease development and progression. This kind of approach is currently common in the research laboratories and it will be part of the healthcare system in a close future to diagnose and classify patients.El estudio de las bases genéticas y moleculares de las patologías humanas ha constituido el centro de atención de gran parte de la investigación en biología durante las últimas décadas con el fin último de comprender los procesos celulares alterados en cada caso y la posibilidad de generar protocolos de diagnosis y terapias específicas. Con la llegada de la denominada Next Generation Sequencing (NGS) y su consiguiente reducción en tiempo y costes ha permitido el acceso a la secuenciación de numeroso genomas humanos en el entorno biomédico. El estudio de enfermedades genéticas, y del cáncer en particular, se ha visto enormemente favorecido al poder incorporar un importante número de genomas de pacientes a sus estudios y así poder identificar directamente las mutaciones asociadas a cada patología. A su vez, esta revolución junto con la capacidad de detectar modificaciones genéticas en regiones cuya función todavía se desconoce, ha generado un doble desafío en la comunidad científica: por un lado el análisis de variantes genéticas asociadas a cada tipo de enfermedad y, por el otro, el entender el impacto funcional que dichas modificaciones provocan en la célula. Esta tesis contribuye a solucionar estas limitaciones a través del desarrollo de una aplicación, SMUFIN (Moncunill et al. 2014), que permite de forma rápida y eficaz la identificación de variaciones somáticas asociadas al desarrollo o progresión de tumores. También se describen los resultados obtenidos relativos a la identificación y caracterización de las reorganizaciones cromosómicas en cáncer, así como los resultados obtenido en cuanto a sus mecanismos e impacto funcional (Puente et al. 2015). Además, como parte de la anotación genómica para la interpretación funcional de las variaciones detectadas, esta tesis incluye los resultados del desarrollo de estrategias y metodologías para la detección de regiones reguladoras en genomas de eucariotas (Gonzalez et al. 2012). En resumen esta tesis intenta cubrir y dotar de herramientas bionformáticas para completar los pasos necesarios para el análisis de genomas en biomedicina, desde que un grupo de pacientes son secuenciados hasta que sus diferentes variantes son identificadas y su impacto funcional determinado. Este tipo de análisis, que ahora esta ocurriendo en el campo de la investigación, pronto será una realidad y una rutina en el sistema sanitario.
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Arif, Km Taufiqul. "Functional association of Micrornas with molecular subtypes of breast cancer." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213110/1/Km%20Taufiqul_Arif_Thesis.pdf.
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This research study investigated the association of microRNA related single nucleotide polymorphisms (miRSNPs) with breast cancer susceptibility in Australian Caucasian women. The thesis then progressed with developing an in silico methodology for miRNA-target identification followed by the validation of miRNA-target relationships regarding the distinctive molecular subtypes of human breast cancers.
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Maughan, Nicola Joanne. "Integrating pathology with genomics to gain new insights into the biology and behaviour of colorectal cancer." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439550.
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Zacharioudakis, Emmanouil. "Unbiased discovery of druggable genomic targets by Click-sequencing (Click-seq)." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS234.
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Durant ma thèse, mes études ont été axées sur le développement de nouvelles sondes et des protocoles chimiques pour l'élucidation des mécanismes d'action et de résistance pour les trois médicaments anticancéreux (cisplatine, le vorinostat, topotécan). L'idée de sondes chimiques qui peuvent être étiquetés post-traitement in cellulo avec des fluorophores par exemple Alexa Fluor 488 ou affinité des fragments par exemple biotine, nous a fasciné pour effectuer petite visualisation de molécule par microscopie confocale, ou pour développer des protocoles tirer vers le bas pour les petites cibles moléculaires d'isolement. Beaucoup d'attention a été accordée à catalysée par du cuivre 1, 3 dipolaire cycloaddition Huisgen, connu sous le nom ''click'' la chimie au cours de la dernière décennie, car il est l'une des premières réactions qui peuvent être considérés comme des bio-orthogonale. Toutes les sondes portent soit un alcyne ou groupement azoture stratégiquement positionnés qui leur permet de participer à la réaction de clic avec des fluorophores ou biotine alcyne ou azoture fonctionnalisés que countrparts, et dans le même temps de conserver le profil pharmacologique du médicament parental. Un certain nombre de défis synthétiques ont été overcomed pour la préparation d'une sonde chimique de base de cisplatine. Les efforts visant à synthétiser des analogues alcynes fonctionnalisés de cisplatine ont été infructueuses, la sonde chimique à base de cisplatine était donc fonctionnalisés azoture. La mise au point d'un protocole d'imagerie pour la visualisation de lésions de l'ADN platiné nous a permis de cribler une banque limitée de petites molécules en même temps que la sonde de cisplatine, afin d'identifier des médicaments qui peuvent moduler le cisplatine ciblage ayant des lésions de l'ADN platiné comme une lecture. Il est frappant, nous avons constaté que le vorinostat a eu un effet dramatique sur motif de coloration de la sonde de cisplatine. D'autres études ont démontré que le prétraitement des cellules avec vorinostat augmente charge de platine sur certains loci génomiques. En outre, une évaluation biologique plus approfondie a montré que la combinaison de vorinostat avec des médicaments de platine atténue les dommages à l'ADN synthèse voie de tolérance de translésionnelles (TLS), ainsi, ce qui conduit à la mort cellulaire. Enfin, je l'ai mis au point un protocole tirer vers le bas pour l'isolement de l'ADN qui est lié à la cisplatine en étiquetant la sonde en platine avec un fragment de biotine. Cartographie des interactions ADN-platine en utilisant le séquençage à haut débit profonde est en cours d'exécution. Vorinostat sonde chimique à base a été préparé dans une voie de synthèse en quatre étapes. Bien que le vorinostat est un médicament connu pour inhiber des histone désacétylases (HDAC), petite visualisation de molécule a indiqué d'abord un profil de coloration cytoplasmique. Les futures études dans notre laboratoire se concentrera sur le développement d'un protocole déroulant, afin de caractériser complètement le vorinostat interactome. Quoique, trois sondes chimiques à base de topotécan différents ont été préparés de la personne a été visualisé les succès par microscopie confocale. L'échec a été attribué à l'échec de la réaction "click" pour marquer la sonde avec le fluorophore. Le topotécan est une petite molécule qui agit comme un inhibiteur d'interface, formant ainsi un complexe ternaire avec l'ADN et la topoisomérase 1. L'encombrement stérique généré par le complexe ternaire autour de la sonde, dicte l'inaccessibilité de l'alcyne de la sonde de "click" réactifsDuring my PhD, my studies have been focused on the development of novel chemical probes and protocols for the elucidation of the mechanisms of action and resistance for three anticancer drugs (cisplatin, vorinostat, topotecan). The idea of chemical probes that can be tagged post treatment in cellulo with fluorophores e.g. ALEXA FLUOR 488 or affinity moieties e.g. biotin, has fascinated us to perform small molecule visualization via confocal microscopy, or to develop pull down protocols for isolation small molecule targets. Much attention has been given to copper catalyzed 1, 3 dipolar Huisgen cycloaddition, known as ‘’click’’ chemistry over the past decade, since it is one of the first reactions that can be considered as bio-orthogonal. All the probes bear either an alkyne or azide moiety strategically positioned that allows them to participate in the click reaction with alkyne or azide functionalized fluorophores or biotin as countrparts, and at the same time to retain the pharmacological profile of the parental drug.A number of synthetic challenges have been overcomed for the preparation of a cisplatin based chemical probe. Efforts to synthesize alkyne functionalized analogues of cisplatin were unsuccessful, thus cisplatin based chemical probe was azide functionalized. The development of an imaging protocol for the visualization of platinated DNA lesions has enabled us to screen a limited library of small molecules along with the cisplatin probe, in order to identify drugs that can modulate cisplatin targeting having platinated DNA lesions as a readout. Strikingly, we found that vorinostat had a dramatic effect on cisplatin probe staining pattern. Further studies have demonstrated that pre-treatment of cells with vorinostat increases platinum loading on certain genomic loci. Furthermore, a more in depth biological evaluation has demonstrated that the combination of vorinostat with platinum drugs attenuates the DNA damage tolerance pathway translesion synthesis (TLS), thus, leading to cell death. Finally, I have developed a pull down protocol for the isolation of DNA that is bound to cisplatin by tagging the platinum probe with a biotin moiety. Mapping of DNA-platinum interactions using deep high throughput sequencing is currently running.Vorinostat based chemical probe has been prepared in a four step synthetic route. Although, vorinostat is a drug known to inhibit histone deacetylases (HDACs), small molecule visualization has indicated primarily a cytoplasmic staining pattern. Future studies in our laboratory will focus on the development of a pull down protocol, in order to fully characterize vorinostat interactome.Albeit, three different topotecan based chemical probes have been prepared none of the them has been successfully visualized by confocal microscopy. The failure has been attributed to the failure of the “click” reaction to tag the probe with the fluorophore. Topotecan is a small molecule that acts as an interfacial inhibitor, thus forming a ternary complex with DNA and topoisomerase 1. The steric hindrance generated by the ternary complex around the probe, dictates the inaccessibility of the alkyne moiety of the probe to “click” reagents
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Baca, Sylvan Charles. "The landscape of somatic mutations in primary prostate adenocarcinoma." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10824.
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Prostate cancer is the second leading cause of cancer deaths among men. Targeted analyses of DNA from prostate cancers have identified recurrent somatic alterations that promote tumor growth and survival. Only recently, however, has the comprehensive analysis of cancer genomes become possible due to rapid advances in DNA sequencing technology.
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Foroughi, pour Ali. "Linear Approximations for Second Order High Dimensional Model Representation of the Log Likelihood Ratio." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555419601408423.
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Smith, Richard LeRoy. "Cis-regulatory Sequence and Co-regulatory Transcription Factor Functions in ERα-Mediated Transcriptional Repression." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/2261.
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Estrogens exert numerous actions throughout the human body, targeting healthy tissue while also enhancing the proliferative capacity of breast cancers. Estrogen signaling is mediated by the estrogen receptor (ER), which binds DNA and ultimately affects the expression of adjacent genes. Current understanding of ER-mediated transcriptional regulation is mostly limited to genes whose transcript levels increase following estrogen exposure, though recent studies demonstrate that direct down-regulation of estrogen-responsive genes is also a significant feature of ER action. We hypothesized that differences in cis-regulatory DNA was a factor in determining target gene expression and performed computational and experimental studies to test this hypothesis. From our in silico analyses, we show that the binding motifs for certain transcription factors are enriched in cis-regulatory sequences adjacent to repressed target genes compared to induced target genes, including the motif for RUNX1. In silico analyses were tested experimentally using dual luciferase reporter assays, which indicate that several ER binding sites are estrogen responsive. Mutagenesis of transcription factor motifs (for ER and RUNX1) reduced the response of reporter gene. Further experiments demonstrated that co-recruitment of ER and RUNX1 is necessary for repression of gene expression at some target genes. These findings highlight a novel interaction between ER and RUNX1 and their role in transcriptional repression in breast cancer.
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Howe, Eleanor Arden. "MicroRNA expression and activity in high-grade serous ovarian cancer." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:9d17590c-550b-4ae9-ac8d-15387cf70e5f.
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miRNAs are critical modulators in the development and progression of cancer. Emerging evidence suggests that they are drivers of ovarian cancer. A better understanding of the molecular underpinnings of the development, progression and chemoresistance of the disease is critical for the development of new, more effective therapies. Here we explore the expression patterns of miRNAs as they relate to gene expression, as they differ across molecular subtypes of the disease. We examine the correlation structure of miRNA expression with mRNA expression in two distinct genomic datasets and report on patterns in correlation structure in several subsets of the data. We find that the datasets show consistency in their correlation structure, and in the specific miRNA-mRNA pairs that are either highly positively or negatively correlated. The data include a larger number of strong positive and strong negative correlations than would be expected by chance, indicating that biological relationships between the types of data are detectable in these datasets. We further find an enrichment for positively-correlated miRNA-mRNA pairs in which the miRNA is encoded in close proximity to the mRNA. The correlation of miRNA and mRNA is apparently unaffected by miRNA and mRNA expression level; similarly the two molecular subtypes do not contain differences in their correlation. We find that the recently described poorer prognosis, or angiogenic, subtype has a generally lower miRNA activity than the second, non-angiogenic, subtype. The subtypes are characterized by a consistent pattern of differential miRNA expression. We also report on a switch-like relationship between the expression levels of certain miRNAs and the genes that are anticorrelated with them. We propose these miRNAs drive many of the differences in the subtypes both directly, by RISC-mediated repression of target messages and indirectly, by repressing transcription factors that regulate expression in the cell. We build models of patient survival and time-to-relapse based on these miRNA expression data and inferred miRNA activity scores, using several types of univariate and variable selection models. We find essentially no survival-predictive information provided by the RE score data. While the direct miRNA expression measurements may contain some predictive power, we find that a larger dataset and the segretation of that dataset into distinct molecular phenotypes is likely to be necessary to produce a useful model of survival in ovarian cancer.
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Akhavanfard, Sara. "NEXT-GENERATION SEQUENCING APPROACHES TO CHARACTERIZE GENOMIC PREDISPOSITION OF SOLID TUMORS IN CHILDREN, ADOLESCENTS, AND YOUNG ADULTS (C-AYA)." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1575571085728229.
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Marwaha, Shruti. "A Genomics and Mathematical Modeling Approach for the Study of Helicobacter Pylori associated Gastritis and Gastric Cancer." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308645.
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Davis, Keira C. "Characterization of Zic2 as an Oncoprotein in Prostate Cancer." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 2017. http://digitalcommons.auctr.edu/cauetds/71.
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The field of prostate cancer research is in need of biological markers that predict which cancers do not need treatment, those that can be treated successfully with a localized treatment and more specific cases in which patients are likely to have an aggressive form of cancer that will require more aggressive surgical and chemotherapeutic treatments. ZIC2 is one of five members of a family of proteins that play critical roles in neural crest and mesoderm growth in normal embryonic brain development and in the adult cerebellum of vertebrates. Found throughout the animal kingdom, ZIC1-5 genes encode five distinct ZIC proteins containing five highly conserved C2H2-type zinc finger motifs whose structural integrity is important in carrying out its function as a transcription factor. We hypothesize that ZIC2 has functional significance at the molecular and cellular levels in the initiation of prostate adenocarcinoma (PRAD) and the progression to metastatic and/or castration resistant prostate cancer (CRPC). Bioinformatic predictions suggest that the function of ZIC2 is regulated by post-translational modifications, such as phosphorylation, ubiquitination and sumoylation. This proposal further outlines the research hypothesis for investigating the role of ZIC2 in prostate cancer progression and the effects of the post-translational modification, ubiquitination, on the loss or gain of function of ZIC2.
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Green, Clara Emily. "Understanding shared pathogenesis between chronic obstructive pulmonary disease (COPD) and lung cancer by means of cell specific genomics." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8230/.
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Introduction: COPD (Chronic Obstructive Pulmonary Disease) and lung cancer are related conditions associated with inflammation. Relatively little focus has been given to the endothelium, through which inflammatory cells transmigrate to reach the lung. We sought to determine if coding and non-coding alterations in pulmonary endothelium exist in COPD and lung cancer. Methods: Patients with and without COPD undergoing thoracic surgery were recruited. Pulmonary Endothelial Cells were isolated from lung and tumour and extracted RNA (ribonucleic acid) used for miRNA (micro-RNA) and mRNA (messenger RNA) microarrays. Ingenuity pathway analysis (IPA) was also carried out. Results: 2071 genes and 43 miRNAs were significantly upregulated in COPD. 4 targets were validated by quantitative polymerase chain reaction, of which miR-181b-3p was chosen for functional validation. Another target, miR-429, was also increased in lung tumour. Several cancer-related pathways such as transforming growth factor- β were altered in the IPA. There was significantly reduced tube formation and endothelial sprouting in Human umbilical vein endothelial cells transfected with miR-181b-3p, consistent with an effect on angiogenesis. Conclusions: Upregulation of miR-181b-3p reduces tube formation and sprouting by endothelial cells. This might be significant in the development of emphysema as lung vasculature is important in the structural maintenance of alveoli.
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Tanaka, Sunao. "In silico analysis-based identification of the target residue of integrin α6 for metastasis inhibition of basal-like breast cancer." Kyoto University, 2020. http://hdl.handle.net/2433/253190.
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Binatti, Andrea. "The genomic landscape of solid and hematologic malignancies characterized by new bioinformatics tools." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424919.
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Whole Exome Sequencing (WES) has high power to discover variants in cancer cells, allowing the identification of molecular features underlying diseases development and progression, with important outcomes for cancer diagnosis/prognostication as well as for development and selection of molecularly targeted therapies in personalized medicine. WES projects pose as well different challenges due to biological factors, such as tumour heterogeneity, altered ploidy, low tumor purity, and technical artifacts, that make not obvious the identification of relevant variants.
IWhale, an easy-to-use and customizable pipeline based on Docker and SCons, was developed to analyze cancer WES data, to detect and annotate somatic mutations by a combination of four different callers and integration of information deriving from different databases. Moreover, a systems genetics approach and custom data structures were built up to construct pathway-derived meta-networks of mutated genes depicting their direct interactions and functional relations, to ultimately identify key functions and pathways recurrently hit in cancer cells.
In collaboration with different groups, increasingly refined and customized versions of the pipeline were applied in three WES studies regarding Large granular lymphocyte leukemia (LGL-L), pediatric follicular lymphomas (PTNFL and PFLT) and High-Risk Neuroblastoma (HR-NB).
LGL-L is a rare chronic leukemia with persistent clonal increase of cytotoxic T cells or natural killer (NK) cells often associated to JAK/STAT pathway activation. By analysis of WES data in 19 patients, including cases without STAT mutations (STAT- patients), novel somatic mutations in recurrently mutated genes were identified. 16 selected variants, including those in the tumor suppressor gene FAT4 and in the epigenetic regulator KMT2D, were validated. The new Q706L and S715F STAT5B variants has been also functionally characterized. With pathway-derived network analysis, functional modules composed by several STAT-interacting or STAT-functional connected genes mutated in STAT-negative patients were discovered. Additional modules with putative pathogenic relevance in LGL-L and mutated in the absence of STAT mutations were identified.
In PTNFL, recently recognized as a defined clinicopathological entity, WES analysis of the largest cohort collected so far uncovered mutations in the few genes, TNFRSF14, IRF8 and MAP2K1 previously associated to PTNFL, identifying as well novel mutations and genes. Eleven validated variants prioritized as possible drivers hit the recurrently mutated ARHGEF1, MAP2K1 and TNFRSF14 genes, as well as ATG7, GNA13, RSF1, UBAP2, and ZNF608. G-protein coupled receptor signaling and chromatin modifying enzyme alterations was linked for the first time to PTNFL and PFLT according to obtained findings.
NB, a solid cancer arising from primitive neural crest cells and accounting for 9% of pediatric tumors, is characterized by high clinical heterogeneity and low mutation recurrence even in known driver (MYCN, ALK, ATRX). To clarify the biological basis of disease aggressiveness, WES was used to examine the genomic landscape of HR-NB patients at metastatic stage with short survival (SS) and long survival (LS). A few genes, including SMARCA4, SMO, ZNF44 and CHD2, were recurrently mutated only in the SS group and HotNet2 analysis revealed that in the two patient groups, mutations occurred in different pathways. Notably mutations of SS patients clustered into a six significantly mutated subnetworks, involved into MAPK pathway associated with the organization of the extracellular matrix, to cell motility through PTK2 signaling, to matrix metalloproteinase activity, to centrosome maturation and chromosome remodeling, to metabolism of nucleotides and lipoproteins, and to transport of small molecules. Since FDA-approved compounds targeting the deregulated pathways are available these findings may help to improve the treatment of HR-NB patients with most aggressive disease.Il sequenziamento dell’esoma (WES) rileva efficacemente varianti in cellule tumorali, identificando le caratteristiche molecolari coinvolte nella patogenesi e nella progressione della malattia, con importanti risvolti per la diagnosi e per lo sviluppo e la scelta di terapie personalizzate. L’analisi di dati WES di tumori presenta tuttavia varie complicazioni dovute all’eterogeneità tumorale, ad alterazioni della ploidia, a contaminazioni dei campioni o ad artefatti tecnici. La pipeline iWhale, basata su Docker e SCons, è stata sviluppata per analizzare dati WES di tumori con l’obiettivo di rilevare ed annotare mutazioni somatiche tramite l’uso di quattro diversi software (MuTect, MuTect2, Strelka2 e VarScan2) e l’integrazione di informazioni provenienti da vari database. Inoltre, ho collaborato allo sviluppo di un metodo per la costruzione di meta-reti di geni mutati che sono annotati in database di pathway e ho costruito una struttura di dati customizzata per rilevare statisticamente pathway ricorrentemente mutati in cellule tumorali. In collaborazione con diversi gruppi di ricerca, ho utilizzato ed adattato di volta in volta versioni progressivamente più rifinite della mia pipeline in studi riguardanti la leucemia linfocitica granulare a grandi cellule T (LGL-L), due tipi di linfomi follicolari pediatrici (PTNFL e PFLT) e Neuroblastoma ad alto rischio (HR-NB). LGL-L è una leucemia cronica rara caratterizzata da una persistente crescita clonale di cellule citotossiche T o natural killer (NK) dovuta all’attivazione del pathway JAK/STAT. Mediante analisi WES sono state identificate nuove mutazioni somatiche in geni ricorrentemente mutati in 19 pazienti con LGL-L, comprendenti casi senza mutazioni nei geni STAT. Sono state selezionate per validazione con sequenziamento Sanger 16 varianti in diversi geni, tra le quali l’oncosoppressore FAT4 e il regolatore epigenetico KMT2D. Nuove varianti Q706L e S715F in STAT5B sono state anche caratterizzate funzionalmente. Grazie ad analisi di reti derivate da pathway, sono state identificate delle componenti funzionali composte da geni mutati, funzionalmente o direttamente interagenti con i geni STAT, in pazienti STAT negativi. Altre componenti funzionali con una possibile rilevanza nella patogenesi di LGL-L in assenza di mutazioni nei geni STAT sono emerse dalle analisi. Una coorte di pazienti affetti da linfomi follicolari pediatrici è stata analizzata tramite WES. Sono state confermate mutazioni presenti in TNFRSF14, IRF8 e MAP2K1, geni precedentemente associati a PTNFL, e sono stati caratterizzati nuove mutazioni e geni con possibile coinvolgimento nello sviluppo di PTNFL. Undici varianti presenti in ARHGEF1, MAP2K1, TNFRSF14, ATG7, GNA13, RSF1, UBAP2 e ZNF608 sono state validate e selezionate come possibili eventi driver in PTNFL e PFLT. I nostri risultati hanno per la prima volta permesso di associare il pathway GPCR ed enzimi modificatori della cromatina ai linfomi follicolari pediatrici. NB è un tumore solido che origina dalle cellule della cresta neurale primitiva ed è caratterizzato da un’alta eterogeneità clinica e da pochi geni ricorrentemente mutati (MYCN, ALK, ATRX). Per investigare sulle basi biologiche coinvolte nell’aggressività di NB, è stato effettuato WES di pazienti affetti da HR-NB con metastasi e divisi in base alla sopravvivenza (pazienti SS e LS, rispettivamente con sopravvivenza inferiore o uguale e superiore a 5 anni). Solo i geni SMARCA4, SMO, ZNF44 e CHD2 sono stati trovati mutati ricorrentemente in modo specifico in pazienti SS. HotNet2 ha rivelato che le mutazioni rilevate nei due gruppi ricadevano in pathway diversi. Le mutazioni dei pazienti SS si sono raggruppate in sei sotto-reti significativamente mutate, coinvolte nell’organizzazione della matrice extracellulare tramite MAPK pathway, nella motilità cellulare tramite PTK2, nell’attività delle metalloproteinasi della matrice, nella maturazione del centrosoma e nel rimodellamento dei cromosomi. Grazie all’esistenza di farmaci già approvati dalla FDA che hanno come bersaglio alcune delle proteine mutate o delle pathway identificate, i risultati ottenuti possono facilitare lo sviluppo di terapie mirate ai pazienti con le forme più aggressive di HR-NB.
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Liao, Rachel Grace. "Functional Studies of Candidate Oncogenes in Non-Small Cell Lung Cancer." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11173.
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Cancer is a set of complex genetic diseases driven by diverse genomic alterations. The genomic study of cancer has enabled the discovery of novel, targetable events in almost all cancer types and in turn, has led to the development of new, targeted cancer therapies benefiting patients; however, the recent explosion of genomic datasets has also resulted in huge lists of new oncogenic factors of unknown biological relevance, and uncertainty over how best to use the data appropriately to influence patient care. Some of the most pressing questions surround the use of statistical methods to identify actionable genomic alterations in cancer and the identification of driving oncogenes in the context of the genomic evolution of cancer cells, undergone before, during, and after prolonged treatment regimens.