Relevant bibliographies by topics / Cancer genomics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cancer genomics'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Cancer genomics"

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Siracusano, Salvatore, Riccardo Rizzetto, and Antonio Benito Porcaro. "Bladder cancer genomics." Urologia Journal 87, no. 2 (January 16, 2020): 49–56. http://dx.doi.org/10.1177/0391560319899011.

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Until recently, the treatment of bladder cancer, for several years, was limited to surgery and to immunotherapy or chemotherapy. Currently, the extensive analysis of molecular alterations has led to novel treatment approaches. The advent of polymerase chain reaction and genomic hybridization techniques has allowed to investigate alterations involved in bladder cancer at DNA level. By this way, bladder cancers can be classified as papillary or non-papillary based on genetic alterations with activation or mutations in FGFR3 papillary tumors and with inactivation or mutations involving TP53 and RB1 in non-papillary tumors. Recently, the patterns of gene expression allow to differentiate basal and luminal subtypes as reported in breast cancer. In particular, basal cancers are composed of squamous and sarcomatoid pathological findings, while luminal cancers are composed of papillary finding features and genetic mutations (FGFR3). In particular, specific investigative studies demonstrated that luminal cancers are associated with secondary muscle invasive cancer while basal tumors are related to advanced disease since they are often metastatic at diagnosis. Moreover, from therapeutic point of view, different researchers showed that mutations of DNA are related to the sensitivity of bladder cancer while performing cisplatin chemotherapy. In this prospective, the bladder cancer molecular subtyping classification might allow identifying the set of patients who can safely avoid neoadjuvant chemotherapy likely because of the low response to systemic chemotherapy (chemoresistant tumors). In this context, the Cancer Genome Atlas (TCGA) project has improved the knowledge of the molecular targets of invasive urothelial cancers allowing the researchers to propose hypothesis suggesting that agents targeting the genomic alterations may be an effective strategy in managing these cancers, which occur in about 68% of muscle invasive cancers. A future goal will be to combine treatment strategies of invasive bladder cancers according to their genetic mutational load defined by molecular pathology.
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NORRILD, BODIL, PER GULDBERG, and ELISABETH RALFKIAER. "Cancer genomics." APMIS 115, no. 10 (October 2007): 1037–38. http://dx.doi.org/10.1111/j.1600-0463.2007.apm_intro.xml.x.

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Lewin, Jeremy, and Lillian L. Siu. "Cancer genomics." Current Opinion in Oncology 27, no. 3 (May 2015): 250–57. http://dx.doi.org/10.1097/cco.0000000000000185.

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Weber, Barbara L. "Cancer genomics." Cancer Cell 1, no. 1 (February 2002): 37–47. http://dx.doi.org/10.1016/s1535-6108(02)00026-0.

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Mardis, Elaine. "Cancer Genomics." F1000Research 4 (October 28, 2015): 1162. http://dx.doi.org/10.12688/f1000research.6645.1.

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Modern cancer genomics has emerged from the combination of the Human Genome Reference, massively parallel sequencing, and the comparison of tumor to normal DNA sequences, revealing novel insights into the cancer genome and its amazing diversity. Recent developments in applying our knowledge of cancer genomics have focused on the utility of these data for clinical applications. The emergent results of this translation into the clinical setting already are changing the clinical care and monitoring of cancer patients.
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Nik-Zainal, S. "Abstract MS1-2: Genomics of DNA repair defects in breast cancer." Cancer Research 82, no. 4_Supplement (February 15, 2022): MS1–2—MS1–2. http://dx.doi.org/10.1158/1538-7445.sabcs21-ms1-2.

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Abstract While driver mutations in cancer genomes were the focus of cancer research for a long time, passenger mutational signatures - the imprints of DNA damage and DNA repair processes that have been operative during tumorigenesis - are also biologically informative. In this lecture, I provide an update of what has been uncovered in breast cancers in relation to genomic imprints of DNA repair defects and showcase how we have developed computational applications that we hope to translate toward clinical utility. Citation Format: S Nik-Zainal. Genomics of DNA repair defects in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr MS1-2.
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Riesco-Eizaguirre, Garcilaso, and Pilar Santisteban. "ENDOCRINE TUMOURS: Advances in the molecular pathogenesis of thyroid cancer: lessons from the cancer genome." European Journal of Endocrinology 175, no. 5 (November 2016): R203—R217. http://dx.doi.org/10.1530/eje-16-0202.

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Thyroid cancer is the most common endocrine malignancy giving rise to one of the most indolent solid cancers, but also one of the most lethal. In recent years, systematic studies of the cancer genome, most importantly those derived from The Cancer Genome Altas (TCGA), have catalogued aberrations in the DNA, chromatin, and RNA of the genomes of thousands of tumors relative to matched normal cellular genomes and have analyzed their epigenetic and protein consequences. Cancer genomics is therefore providing new information on cancer development and behavior, as well as new insights into genetic alterations and molecular pathways. From this genomic perspective, we will review the main advances concerning some essential aspects of the molecular pathogenesis of thyroid cancer such as mutational mechanisms, new cancer genes implicated in tumor initiation and progression, the role of non-coding RNA, and the advent of new susceptibility genes in thyroid cancer predisposition. This look across these genomic and cellular alterations results in the reshaping of the multistep development of thyroid tumors and offers new tools and opportunities for further research and clinical development of novel treatment strategies.
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Surrey, Lea F., Minjie Luo, Fengqi Chang, and Marilyn M. Li. "The Genomic Era of Clinical Oncology: Integrated Genomic Analysis for Precision Cancer Care." Cytogenetic and Genome Research 150, no. 3-4 (2016): 162–75. http://dx.doi.org/10.1159/000454655.

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Genomic alterations are important biological markers for cancer diagnosis and prognosis, disease classification, risk stratification, and treatment selection. Chromosomal microarray analysis (CMA) and next-generation sequencing (NGS) technologies are superb new tools for evaluating cancer genomes. These state-of-the-art technologies offer high-throughput, highly accurate, targeted and whole-genome evaluation of genomic alterations in tumor tissues. The application of CMA and NGS technologies in cancer research has generated a wealth of useful information about the landscape of genomic alterations in cancer and their implications in cancer care. As the knowledge base in cancer genomics and genome biology grows, the focus of research is now shifting toward the clinical applications of these technologies to improve patient care. Although not yet standard of care in cancer, there is an increasing interest among the cancer genomics communities in applying these new technologies to cancer diagnosis in the Clinical Laboratory Improvement Amendments (CLIA)-certified laboratories. Many clinical laboratories have already started adopting these technologies for cancer genomic analysis. We anticipate that CMA and NGS will soon become the major diagnostic means for cancer genomic analysis to meet the increasing demands of precision cancer care.
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Caulfield, Mark. "6 Translating genomics for clinical benefit." Postgraduate Medical Journal 95, no. 1130 (November 21, 2019): 686.3–686. http://dx.doi.org/10.1136/postgradmedj-2019-fpm.6.

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The UK 100,000 Genomes Project has focussed on transforming genomic medicine in the National Health Service using whole genome sequencing in rare disease, cancer and infection. Genomics England partnering with the NHS established 13 Genomic Medicine Centres, the NHS whole genome sequencing centre and the Genomics England Clinical Interpretation Partnership (3337 researchers from 24 countries). We sequenced the 100,000th genome on the 5th December 2019 and completed an initial analysis for participants in July 2019. Alongside these genomes we have assembled a longitudinal life course dataset for research and diagnosis including 2.6 billion clinical data points for the 3000 plus researchers to work on to drive up the value of the genomes for direct healthcare. In parallel we have partnered the NHS to establish one of the world’s most advanced Genomic Medicine Service where we re-evaluated 300,000 genomic tests and upgraded 25% of tests to newer technologies with an annual review. The Department of Health have announced the ambition to undertake 5 million genome analyses over the next 5 years focused on new areas tractable to health gain.
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Parikh, Ankur R. "Lung Cancer Genomics." Acta Medica Academica 48, no. 1 (June 26, 2019): 78. http://dx.doi.org/10.5644/ama2006-124.244.

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<p>The landscape of lung cancer treatment is rapidly evolving with the use of genomic testing which helps identify specific mutations or resistance mutations for these heterogenous tumors. Advanced lung cancer has a very poor prognosis but identifying other treatment options based on genomic profiling of the tumor can lead to improved outcomes. Evidence of benefit for genomic testing in lung cancer has now resulted in this test becoming part of national guidelines. There are challenges with genomic testing which need to be understood as well as understanding how to apply test results. These results can help identify treatment options or may serve as predictors to respond to specific therapies.</p><p><strong>Conclusion.</strong> In the current era of precision medicine, it is imperative clinicians be familiar with genomic testing and be able to offer it to their cancer patients, specifically those with advanced lung cancer.</p>
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Dissertations / Theses on the topic "Cancer genomics"

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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.

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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.
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Matteuzzi, Tommaso <1990&gt. "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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Books on the topic "Cancer genomics"

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Pfeffer, Ulrich, ed. Cancer Genomics. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5842-1.

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Fisher, Paul B., ed. Cancer Genomics and Proteomics. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-335-6.

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Wajapeyee, Narendra, ed. Cancer Genomics and Proteomics. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0992-6.

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Govindan, Ramaswamy, and Siddhartha Devarakonda, eds. Cancer Genomics for the Clinician. New York, NY: Springer Publishing Company, 2019. http://dx.doi.org/10.1891/9780826168689.

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B, Fisher Paul, ed. Cancer genomics and proteomics: Methods and protocols. Totowa, N.J: Humana Press, 2007.

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El Bairi, Khalid, ed. Illuminating Colorectal Cancer Genomics by Next-Generation Sequencing. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53821-7.

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Thomas-Tikhonenko, Andrei. Cancer genome and tumor microenvironment. New York: Springer, 2010.

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Agnes, Masny, Jenkins Jean F, Calzone Kathleen A, and Oncology Nursing Society, eds. Genetics and genomics in oncology nursing practice. Pittsburgh, Penn: Oncology Nursing Society, 2010.

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Calzone, Kathleen A. Genetics and genomics in oncology nursing practice. Pittsburgh, Penn: Oncology Nursing Society, 2010.

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Federico, Innocenti, ed. Genomics and pharmacogenomics in anticancer drug development and clinical response. Totowa, NJ: Humana, 2008.

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Book chapters on the topic "Cancer genomics"

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Pollack, Jonathan R. "Cancer Genomics." In The Molecular Basis of Human Cancer, 43–63. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-59745-458-2_3.

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Koul, Bhupendra. "Cancer Genomics." In Herbs for Cancer Treatment, 1–52. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9147-8_1.

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Carlberg, Carsten, and Eunike Velleuer. "Cancer Genomics." In Cancer Biology: How Science Works, 55–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75699-4_5.

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Corbo, Vincenzo, Andrea Mafficini, Eliana Amato, and Aldo Scarpa. "Pancreatic Cancer Genomics." In Cancer Genomics, 219–53. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5842-1_8.

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Scaltriti, Maurizio. "Breast Cancer Genomics." In Breast Cancer, 149–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48848-6_15.

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Kumar, Birendra. "Breast Cancer Genomics." In Omics Approaches in Breast Cancer, 53–103. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-0843-3_4.

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Bhatia, Avnish Kumar. "Genomics of Cancer." In Cancer Diagnostics and Therapeutics, 429–41. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4752-9_18.

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Craddock, Kenneth J., Shirley Tam, Chang-Qi Zhu, and Ming-Sound Tsao. "Genomic Pathology of Lung Cancer." In Cancer Genomics, 1–46. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5842-1_1.

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Mezzanzanica, Delia, Loris De Cecco, Marina Bagnoli, Patrizia Pinciroli, Marco A. Pierotti, and Silvana Canevari. "Genomic Landscape of Ovarian Cancer." In Cancer Genomics, 295–348. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5842-1_10.

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López-García, M. Angeles, Begoña Vieites, M. Angeles Castilla, Laura Romero-Pérez, Juan Díaz-Martín, Michele Biscuola, and José Palacios. "Genetics of Endometrial Carcinoma." In Cancer Genomics, 349–90. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5842-1_11.

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Conference papers on the topic "Cancer genomics"

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Haussler, David. "Cancer genomics." In the 17th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020414.

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Zhu, J., JZ Sanborn, T. Wang, F. Hsu, S. Benz, C. Szeto, L. Esserman, and D. Haussler. "UCSC cancer genomics browser." In CTRC-AACR San Antonio Breast Cancer Symposium: 2008 Abstracts. American Association for Cancer Research, 2009. http://dx.doi.org/10.1158/0008-5472.sabcs-2022.

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Cline, Melissa, Olena Morozova, Teresa Swatloski, Brian Craft, Mary Goldman, David Haussler, and Jingchun Zhu. "Abstract A33: Exploring pediatric cancer genomics with the UCSC Cancer Genomics Browser." In Abstracts: AACR Special Conference: Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; November 3-6, 2013; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.pedcan-a33.

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Ciccarelli, Francesca. "Computational approaches in cancer genomics." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3071178.3106410.

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Gadgeel, Shirish M. "Abstract IA15: Lung cancer genomics." In Abstracts: Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; September 25-28, 2016; Fort Lauderdale, FL. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7755.disp16-ia15.

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Haussler, David. "Cancer genomics and the TCGA project." In AACR International Conference: Molecular Diagnostics in Cancer Therapeutic Development– Sep 27-30, 2010; Denver, CO. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/diag-10-ed3b-1.

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Griffith, Malachi, Obi Griffith, Nicholas Spies, Benjamin Ainscough, Zachary Skidmore, Lee Trani, Avinash Ramu, Killanin Krysiak, and Elaine R. Mardis. "Abstract IA02: Cancer genomics: Translational challenges." In Abstracts: AACR Special Conference: Translation of the Cancer Genome; February 7-9, 2015; San Francisco, CA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.transcagen-ia02.

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Cai, Hongchen, Chuan Li, Su Kit Chew, Maryam Yousefi, Giorgia Foggetti, Wen-Yang Lin, Zoë N. Rogers, et al. "Abstract IA26: Multiplexed functional cancer genomics." In Abstracts: AACR Special Conference on the Evolving Landscape of Cancer Modeling; March 2-5, 2020; San Diego, CA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.camodels2020-ia26.

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Zeng, Jie, Jie Chen, Xiaoping Wang, Jianguo Hou, and Stephen Wong. "Nanoscale Biomarkers for Cancer Genomics and Protemics." In 2006 IEEE/NLM Life Science Systems and Applications Workshop. IEEE, 2006. http://dx.doi.org/10.1109/lssa.2006.250422.

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Zhu, Jingchun, Brian Craft, Teresa Swatloski, Kyle Ellrott, Mary Goldman, Christopher Wilks, Singer Ma, Christopher Szeto, Eric Collisson, and David Haussler. "Abstract 5087: UCSC Cancer Genomics Browser 2.0." In Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL. American Association for Cancer Research, 2012. http://dx.doi.org/10.1158/1538-7445.am2012-5087.

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Reports on the topic "Cancer genomics"

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Klein, Eric, and Yaw Nyame. The genetics and genomics of prostate cancer. BJUI Knowledge, July 2019. http://dx.doi.org/10.18591/bjuik.0152.

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Gelman, Irwin H. Advanced Cancer Genomics Institute: Genetic Signatures and Therapeutic Targets in Cancer Progression. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada595738.

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Bodemann, Brian. Harnessing Functional Genomics to Reverse Chemoresistance in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2007. http://dx.doi.org/10.21236/ada484470.

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Thompson, Erik W. Functional Genomics for Epithelial-Mesenchymal Transition in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada542255.

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Bodemann, Brian. Harnessing Functional Genomics to Reverse Chemoresistance in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2008. http://dx.doi.org/10.21236/ada512882.

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Thompson, Erik. Functional Genomics for Epithelial-Mesenchymal Transition in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada554588.

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Brown, Martin, and James A. Brown. A Novel Yeast Genomics Method for Identifying New Breast Cancer Susceptibility. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada435292.

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Brown, J. M., and James A. Brown. A Novel Yeast Genomics Method for Identifying New Breast Cancer Susceptibility Genes. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada471490.

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Bennett, Craig B. A Functional Genomics Approach to Identify Novel Breast Cancer Gene Targets in Yeast. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada437737.

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Bennett, Craig B. A Functional Genomics Approach to Idenitfy Novel Breast Cancer Gene Targets in Yeast. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada459201.

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