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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
31

Identification of T cell receptors targeting a neoantigen derived from recurrently mutated FGFR3 / FGFR3由来の共通ネオアンチゲンを標的としたT細胞受容体の同定

Tate, Tomohiro 23 May 2023 (has links)
京都大学 / 新制・課程博士 / 博士(医学) / 甲第24803号 / 医博第4995号 / 新制||医||1067(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 金子 新, 教授 伊藤 能永, 教授 上野 英樹 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM
32

Health improvement framework for actionable treatment planning using a surrogate Bayesian model / 階層ベイズモデルを利用した実行可能な健康改善プランを提案するAI技術の開発

Nakamura, Kazuki 23 March 2023 (has links)
京都大学 / 新制・課程博士 / 博士(人間健康科学) / 甲第24539号 / 人健博第110号 / 新制||人健||8(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 木下 彩栄, 教授 中尾 恵, 教授 中山 健夫 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM
33

Stratification of autism spectrum conditions by deep encodings

Landi, Isotta 13 February 2020 (has links)
This work aims at developing a novel machine learning method to investigate heterogeneity in neurodevelopmental disorders, with a focus on autism spectrum conditions (ASCs). In ASCs, heterogeneity is shown at several levels of analysis, e.g., genetic, behavioral, throughout developmental trajectories, which hinders the development of effective treatments and the identification of biological pathways involved in gene-cognition-behavior links. ASC diagnosis comes from behavioral observations, which determine the cohort composition of studies in every scientific field (e.g., psychology, neuroscience, genetics). Thus, uncovering behavioral subtypes can provide stratified ASC cohorts that are more representative of the true population. Ideally, behavioral stratification can (1) help to revise and shorten the diagnostic process highlighting the characteristics that best identify heterogeneity; (2) help to develop personalized treatments based on their effectiveness for subgroups of subjects; (3) investigate how the longitudinal course of the condition might differ (e.g., divergent/convergent developmental trajectories); (4) contribute to the identification of genetic variants that may be overlooked in case-control studies; and (5) identify possible disrupted neuronal activity in the brain (e.g., excitatory/inhibitory mechanisms). The characterization of the temporal aspects of heterogeneous manifestations based on their multi-dimensional features is thus the key to identify the etiology of such disorders and establish personalized treatments. Features include trajectories described by a multi-modal combination of electronic health records (EHRs), cognitive functioning and adaptive behavior indicators. This thesis contributes in particular to a data-driven discovery of clinical and behavioral trajectories of individuals with complex disorders and ASCs. Machine learning techniques, such as deep learning and word embedding, that proved successful for e.g., natural language processing and image classification, are gaining ground in healthcare research for precision medicine. Here, we leverage these methods to investigate the feasibility of learning data-driven pathways that have been difficult to identify in the clinical practice to help disentangle the complexity of conditions whose etiology is still unknown. In Chapter 1, we present a new computational method, based on deep learning, to stratify patients with complex disorders; we demonstrate the method on multiple myeloma, Alzheimer’s disease, and Parkinson’s disease, among others. We use clinical records from a heterogeneous patient cohort (i.e., multiple disease dataset) of 1.6M temporally-ordered EHR sequences from the Mount Sinai health system’s data warehouse to learn unsupervised patient representations. These representations are then leveraged to identify subgroups within complex condition cohorts via hierarchical clustering. We investigate the enrichment of terms that code for comorbidities, medications, laboratory tests and procedures, to clinically validate our results. A data analysis protocol is developed in Chapter 2 that produces behavioral embeddings from observational measurements to represent subjects with ASCs in a latent space able to capture multiple levels of assessment (i.e., multiple tests) and the temporal pattern of behavioral-cognitive profiles. The computational framework includes clustering algorithms and state-of-the-art word and text representation methods originally developed for natural language processing. The aim is to detect subgroups within ASC cohorts towards the identification of possible subtypes based on behavioral, cognitive, and functioning aspects. The protocol is applied to ASC behavioral data of 204 children and adolescents referred to the Laboratory of Observation Diagnosis and Education (ODFLab) at the University of Trento. In Chapter 3 we develop a case study for ASCs. From the learned representations of Chapter 1, we select 1,439 individuals with ASCs and investigate whether such representations generalize well to any disorder. Specifically, we identify three subgroups within individuals with ASCs that are further clinically validated to detect clinical profiles based on different term enrichment that can inform comorbidities, therapeutic treatments, medication side effects, and screening policies. This work has been developed in partnership with ODFLab (University of Trento) and the Predictive Models for Biomedicine and Environment unit at FBK. The study reported in Chapter 1 has been conducted at the Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai (NY).
34

Modeling Startegies for Computational Systems Biology

Simoni, Giulia 20 March 2020 (has links)
Mathematical models and their associated computer simulations are nowadays widely used in several research fields, such as natural sciences, engineering, as well as social sciences. In the context of systems biology, they provide a rigorous way to investigate how complex regulatory pathways are connected and how the disruption of these processes may contribute to the develop- ment of a disease, ultimately investigating the suitability of specific molecules as novel therapeutic targets. In the last decade, the launching of the precision medicine initiative has motivated the necessity to define innovative computational techniques that could be used for customizing therapies. In this context, the combination of mathematical models and computer strategies is an essential tool for biologists, which can analyze complex system pathways, as well as for the pharmaceutical industry, which is involved in promoting programs for drug discovery. In this dissertation, we explore different modeling techniques that are used for the simulation and the analysis of complex biological systems. We analyze the state of the art for simulation algorithms both in the stochastic and in the deterministic frameworks. The same dichotomy has been studied in the context of sensitivity analysis, identifying the main pros and cons of the two approaches. Moreover, we studied the quantitative system pharmacology (QSP) modeling approach that elucidates the mechanism of action of a drug on the biological processes underlying a disease. Specifically, we present the definition, calibration and validation of a QSP model describing Gaucher disease type 1 (GD1), one of the most common lysosome storage rare disorders. All of these techniques are finally combined to define a novel computational pipeline for patient stratification. Our approach uses modeling techniques, such as model simulations, sensitivity analysis and QSP modeling, in combination with experimental data to identify the key mechanisms responsible for the stratification. The pipeline has been applied to three test cases in different biological contexts: a whole-body model of dyslipidemia, the QSP model of GD1 and a QSP model of cardiac electrophysiology. In these test cases, the pipeline proved to be accurate and robust, allowing the interpretation of the mechanistic differences underlying the phenotype classification.
35

Integrative Analysis of Genomic Aberrations in Cancer and Xenograft Models

January 2015 (has links)
abstract: No two cancers are alike. Cancer is a dynamic and heterogeneous disease, such heterogeneity arise among patients with the same cancer type, among cancer cells within the same individual’s tumor and even among cells within the same sub-clone over time. The recent application of next-generation sequencing and precision medicine techniques is the driving force to uncover the complexity of cancer and the best clinical practice. The core concept of precision medicine is to move away from crowd-based, best-for-most treatment and take individual variability into account when optimizing the prevention and treatment strategies. Next-generation sequencing is the method to sift through the entire 3 billion letters of each patient’s DNA genetic code in a massively parallel fashion. The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2015
36

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes

Li, Qike, Schissler, A. Grant, Gardeux, Vincent, Achour, Ikbel, Kenost, Colleen, Berghout, Joanne, Li, Haiquan, Zhang, Hao Helen, Lussier, Yves A. 24 May 2017 (has links)
Background: Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. Results: We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. Conclusion: The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.
37

Molecular Profiling in Pediatric Oncology – the MOSCATO-01 Experience // Characterization of SMARCB1-Altered Soft Tissue Sarcomas in Response to Pharmacological HDAC Inhibition / Profilage moléculaire en oncologie pédiatrique – expérience de l'essai MOSCATO-01 // Caractérisation des sarcomes des tissus mous présentant des altérations de SMARCB1 traités par des inhibiteurs des HDAC

Harttrampf, Anne Catherine 10 December 2018 (has links)
1ère partie: Bien que les patients pédiatriques présentent généralement des taux de survie élevés, environ 20% d’entre eux ne peuvent être guéris avec des approches thérapeutiques standards, principalement ceux souffrant de sarcomes métastatiques, neuroblastomes, tumeurs cérébrales et tumeurs rares. Afin d’établir de nouvelles modalités thérapeutiques en identifiant des facteurs moléculaires qui puissent être ciblés par des approches pharmacologiques, les essais cliniques MOSCATO-01 (Molecular Screening for Cancer Treatment Optimization, NCT01566019) et MAPPYACTS (NCT02613962) ont été respectivement initiés en 2011 et en 2016. Une caractérisation moléculaire systématique des biopsies tumorales de patients avec des tumeurs réfractaires ou en rechute a été réalisée afin de pouvoir proposer des traitements ciblés. Chez 75 patients pédiatriques inclus dans MOSCATO-01 avec des cancers solides comprenant des tumeurs cérébrales, nous avons mis en oeuvre des approches d’hybridation génomique comparée (CGH), de séquençage d’un panel de mutations présélectionnées et de séquençage des exomes et transcrits. Des altérations génomiques pouvant être ciblées ont été identifiées dans 60% des cas, incluant des variations du nombre de copies (42%), des mutations (33%), et des transcrits de fusion (2%). Ces altérations affectent des voies de signalisation oncogéniques majeures, incluant des récepteurs tyrosine kinases et leurs cibles. Des mutations germinales ont été identifiées chez 10% des patients. Quatorze patients ont reçu 17 traitements ciblés; une réponse objective ou une maladie sans progression a été observée chez 5 patients. Nos résultats montrent qu’une approche de médecine de précision peut être envisagée et que des altérations ciblables sont présentes dans les cancers pédiatriques. Les obstacles à franchir concernent l’identification et la sélection des cibles, et la mise en pratique clinique des approches thérapeutiques.2ème partie: Les sarcomes épithélioïdes, des tumeurs très rares, affectent tous les groupes d’âge, dont les adolescents, et présentent à la fois des caractères épithéliaux et mésenchymateux. Comme les tumeurs rhabdoïdes affectant les jeunes enfants, les sarcomes épithélioïdes sont caractérisés par des altérations de SMARCB1, un membre central du complexe de remodelage de la chromatine SWI/SNF. Le phénotype agressif et l’implication fréquente des RTKs dans ces deux tumeurs ainsi que leur similarité (épi-)génétique nous ont conduit à approfondir les relations fonctionnelles entre la régulation des RTKs, la signalisation cellulaire, et les effets de la modulation épigénétique. Des lignées cellulaires de chacun des types de tumeurs ont été étudiées pour déterminer la réponse à l’inhibition pharmacologique des RTKs et des HDAC, ainsi que mTOR et EZH2. La sensibilité la plus grande in vitro a été observée avec le Panobinostat, un inhibiteur de HDAC. Le Panobinostat induit la mort cellulaire et inverse partiellement la transition épithéliale-mésenchymateuse, qui pourrait être corrélée à une régulation différentielle des RTKs EGFR et FGFR2. Comme souvent observé dans le traitement des tumeurs solides par les seuls inhibiteurs de HDAC, le Panobinostat est associé à une inhibition modérée de la croissance tumorale in vivo dans un modèle de sarcome épithélioïde. La combinaison de l’inhibition des HDAC et de l’EGFR augmente la sensibilité vis-à-vis de cette dernière à la fois dans les sarcomes épithélioïdes et les tumeurs rhabdoïdes et représente une stratégie prometteuse pour le traitement des tumeurs solides. / Part 1: Although pediatric cancer patients have high survival rates, 20% cannot be cured with standard therapeutic regimens, predominantly those with metastatic sarcomas, neuroblastomas, malignant brain tumors and rare tumor types. To provide a new rationale for treatment definition by identifying new molecular targets that can be pharmacologically addressed, the Molecular Screening for Cancer Treatment Optimization (MOSCATO-01/NCT01566019) and the MAPPYACTS (NCT02613962) trials are running at Gustave Roussy since 2011 and 2016, respectively. Patients with relapsed or refractory malignancies are undergoing biopsy or surgical intervention at treatment failure for molecular characterization that allows the suggestion of a targeted treatment. In 75 pediatric patients with solid malignancies including brain tumors included in MOSCATO-01, we developed further the initial CGHarray and targeted gene sequencing panel, to whole-exome and RNA sequencing which is currently employed in the international follow-up trial MAPPYACTS. Actionable genomic alterations were identified in 60%, representing a copy number change in 42%, a mutation in 33% and a fusion transcript in 2%. Pathway allocation showed that these targets mainly affected prominent oncogenic signaling pathways including receptor tyrosine kinases and associated downstream signaling. Germline alterations were identified in 10% of patients. Fourteen patients received 17 targeted treatment approaches; objective response or prolonged stable disease was seen in five patients. Our results showed that this approach is safe and feasible in minors and that actionable alterations are present. Significant challenges were encountered in pipeline workflows, target definition, interpretation and selection, and clinical implementation.Part 2: Epithelioid sarcoma is an exceedingly rare soft tissue sarcoma occuring in all age groups, including adolescents and displays both epithelial and mesenchymal features. As rhabdoid tumors, a deleterious entity affecting very young children, epithelioid sarcoma is characterized by alterations affecting core SWI/SNF chromatin remodeling complex member SMARCB1. The aggressive phenotype and frequent involvement of receptor tyrosine kinases in both epithelioid sarcoma and rhabdoid tumor as well as their (epi-)genetic parallels led us to take deeper insight into the relation between tyrosine kinase regulation, signaling and effects of epigenetic modulation. Cell lines of both tumor types were studied in order to determine response to pharmacological inhibition by receptor tyrosine kinase and HDAC inhibitors as well as by agents inhibiting mTOR and EZH2. Both tumor types displayed highest in-vitro sensitivity towards pan-HDAC inhibitor panobinostat. Panobinostat sufficiently induced cell death and partially reversed epithelial-to-mesenchymal transition which could be related to differential regulation of receptor tyrosine kinases EGFR and FGFR2. As often observed in treatment of solid tumors with single agent HDAC inhibitors, panobinostat led to slight tumor growth inhibition in an in-vivo epithelioid sarcoma model. The combination of HDAC and EGFR inhibition increased sensitivity towards the latter in both epithelioid sarcoma and rhabdoid tumor and might be a promising strategy to sufficiently translate HDAC inhibitors into clinics for solid tumors.
38

Organizational Antecedents to the Implementation of Precision Medicine: Overcoming Resistance to Change

Sammut, Stephen, 0000-0003-2350-4159 January 2020 (has links)
Precision medicine (PM) is “the treatment and prevention of disease that takes into account individual variability in genes, environment, and lifestyle for each person” (NIH, 2015). PM was poised to transform clinical practice in 2003 when the Human Genome Project reached completion but resistance to implementation at virtually all health care providers provides the basis for novel study on the diffusion of innovation as well as operational strategy. Existing studies on resistance to PM explore the role of reimbursement, economics, regulatory affairs, and public policies. Investigation into the antecedent conditions for implementation at the physician and organizational levels has been overlooked, a gap this study fills. The research captures the reasons for resistance at the physician and organizational levels and identifies operational strategies for successful implementation at three health care institutions with fully integrated PM programs. The research produced 42 findings with managerial implications and six testable propositions for future research. The dynamics of resistance to PM has revealed key implications for theories of organizational change. These include the observation that the formulation processes of clinical standards of practice in PM are not predicted by prevailing organizational theory; that conventional theories of resistance to change do not fully anticipate the effects of Kuhnian level historic paradigm shifts; and, that communities of practice play a critical role in transformational clinical change. Further, the research demonstrated that PM implementation is characterizable through reproducible organizational and cultural actions; that positive clinical outcomes are measurable and persuasive; and that the needs of stakeholders can be reconciled by aligning physician standards of practice with patient expectations and organizational needs. / Business Administration/Strategic Management
39

“It's Not Only About Them:“ Female Family Members' Understanding of Indeterminate Negative BRCA1/2 Test Results

Gibbons, Deborah Kay 01 December 2018 (has links)
Genetic test results have important implications for close family members. Indeterminate negative results are the most common outcome of BRCA1/2 mutation testing. Little is known about family members' understanding of indeterminate negative BRCA1/2 test results. The purpose of this qualitative descriptive study was to investigate how daughters and sisters received and understood genetic test results as shared by their mothers or sisters. Participants included 81 women aged 40-74 with mothers or sisters previously diagnosed with breast cancer and who received indeterminate negative BRCA1/2 test results. Participants had never been diagnosed with breast cancer nor received their own genetic testing or counseling. This IRB approved study utilized semi-structured interviews administered via telephone. The research team developed descriptive codes, and NVIVO software was used during qualitative analysis. Participants reported low amounts of information shared with them. Most women described test results as negative and incorrectly interpreted the test to mean there was no genetic component to the pattern of cancer in their families. Only 7 of 81 women accurately described test results consistent with the meaning of an indeterminate negative result — meaning a genetic cause for cancer in their family could still exist. Our findings demonstrate that indeterminate negative genetic test results are not well understood by family members. Lack of understanding may lead to an inability to effectively communicate results to primary care providers and missed opportunities for prevention, screening and further genetic testing. We recommend providing family members letters they can share with their own primary care providers whenever genetic testing is performed.
40

Functional characterization of INTS11 loss-of-function in zebrafish

Herold, Aveeva 10 1900 (has links)
Le gène INTS11 est une sous-unité catalytique du complexe Integrator qui joue un rôle central dans le traitement de divers ARN naissants. Récemment, des patients présentant des mutations de perte de fonction dans le gène INTS11 ont été signalés comme ayant des problèmes neurodéveloppementaux graves, des problèmes ataxiques et des retards de développement globaux. À ce jour, aucune mutation dans INTS11 n'a été liée à des maladies humaines, et aucune preuve ne soutient leur rôle dans des problèmes neurodéveloppementaux. Par conséquent, nous avons développé un modèle INTS11 knock-out (KO) F0 CRISPRant chez le poisson-zèbre pour caractériser fonctionnellement les mutations de perte de fonction de ce gène in vivo. Nos larves INTS11-KO présentent une accumulation accrue de snARN mal traités, confirmant la perturbation de la fonction du gène. De plus, les larves INTS11-KO meurent prématurément à 14 jours et présentent un phénotype comportemental aberrant, similaire à d'autres modèles génétiques de poisson-zèbre des troubles neurodéveloppementaux. Aussi, les larves INTS11-KO présentent une réduction de la taille du cerveau avec une réduction du contenu neuronal. Enfin, nos résultats d'immunomarquage ont révélé une réduction de la taille du cervelet chez les larves INTS11-KO. Dans l'ensemble, ces données soutiennent le rôle d'INTS11 dans le développement cérébral et sont cohérentes avec les retards neurodéveloppementaux décrits chez les patients présentant des mutations délétères dans ce gène. Notre étude montre comment des organismes modèles simples tels que le poisson-zèbre peuvent aider à caractériser l'étiologie génétique des troubles génétiques. Les résultats de nos recherches pourraient contribuer à des diagnostics plus précis et ouvrir la voie à la découverte de mécanismes pathogènes clés qui pourraient être exploités pour le développement de traitements pour les patients présentant des mutations dans INTS11. / The INTS11 gene is a catalytic subunit of the Integrator complex that plays a central role in processing various nascent RNAs. Recently, patients with loss-of-function mutations in the INTS11 gene have been reported to have severe neurodevelopmental issues, ataxic problems, and global developmental delays. To date, mutations in INTS11 have not been linked to human diseases, and no evidence supports their role in neurodevelopmental problems. Therefore, we developed an ints11 F0 CRISPRant knock-out (KO) model in zebrafish to functionally characterize loss-of-function mutations in this gene in vivo. Our ints11-KO larvae exhibited an increased accumulation of unprocessed snRNAs, confirming the disruption of the ints11 function. Moreover, ints11-KO larvae die prematurely by 14 days of age and display an aberrant behavioural phenotype, similar to other zebrafish genetic models of neurodevelopmental disorders. Furthermore, ints11-KO larvae show reduced brain size with reduced neuronal content. Finally, immunostaining results revealed a reduction in cerebellum size in our ints11-KO. Altogether, these data support the role of INTS11 in brain development and are consistent with the neurodevelopment delays described in patients with deleterious mutations in this gene. Our study shows how simple model organisms like zebrafish can help characterize the genetic etiology of genetic disorders. The results from our research could aid in more accurate diagnoses and open the path to unveiling key pathogenic mechanisms that could be leveraged for the development of treatment for patients with mutations in INTS11.

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