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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.
21

INTEGRATIVE OMICS REVEALS INSIGHTS INTO HUMAN LIVER DEVELOPMENT, DISEASE ETIOLOGY, AND PRECISION MEDICINE

Zhipeng Liu (8126406) 20 December 2019 (has links)
<div><div><div><p>Transcriptomic regulation of human liver is a tightly controlled and highly dynamic process. Genetic and environmental exposures to this process play pivotal roles in the development of multiple liver disorders. Despite accumulating knowledge have gained through large-scale genomics studies in the developed adult livers, the contributing factors to the interindividual variability in the pediatric livers remain largely uninvestigated. In the first two chapters of the present study, we addressed this question through an integrative analysis of both genetic variations and transcriptome-wide RNA expression profiles in a pediatric human liver cohort with different developmental stages ranging from embryonic to adulthood. Our systematic analysis revealed a transcriptome-wide transition from stem-cell-like to liver-specific profiles during the course of human liver development. Moreover, for the first time, we observed different genetic control of hepatic gene expression in different developmental stages. Motivated by the critical roles of genetics variations and development in regulating hepatic gene expression, we constructed robust predictive models to impute the virtual liver gene expression using easily available genotype and demographic information. Our model is promising in improving both PK/PD modeling and disease diagnosis for pediatric patients. In the last two chapters of the study, we analyzed the genomics data in a more liver disease- related context. Specifically, in the third chapter, we identified Macrophage migration inhibitory factor (MIF) and its related pathways as potential targets underlying human liver fibrosis through an integrative omics analysis. In the last chapter, utilizing the largest-to-date publicly available GWAS summary data, we dissected the causal relationships among three important and clinically related metabolic diseases: non-alcoholic fatty liver disease (NAFLD), type 2 diabetes (T2D), and obesity. Our analysis suggested new subtypes and provided insights into the precision treatment or prevention for the three complex diseases. Taken together, through integrative analysis of multiple levels of genomics information, we improved the current understanding of human liver development, the pathogenesis of liver disorders, and provided implications to precision medicine.</p></div></div></div>
22

Approaches to targeted therapy in multiple myeloma

Bhardwaj, Abhinav 19 February 2021 (has links)
Multiple Myeloma (MM) is the second most common hematological malignancy, and although patient outcomes have significantly improved since the introduction of autologous stem cell transplantation (ASCT) and novel pharmacological agents such as immunomodulators (IMID), proteasome inhibitors (PI), and monoclonal antibodies (mAb), the disease remains incurable. The pathological complexity of MM results from accumulating mutations in clonal populations of malignant B-cells, which are cytogenetically heterogenous and selectively sensitive to different therapeutic agents. Drug regimens therefore include a diverse combination of therapeutics designed to target specific pathways to inhibit cell proliferation. Recent advances in genomic analytics and novel pharmacological agents potentially allow for more targeted treatments which improve patient outcomes and frequency of remission with minimal adverse effects. Only recently have studies began to correlate an increased understanding of the many subtypes of MM with optimal treatment regimens, and practices such as “Direct to Drug” screening can give clinicians a look at a patient’s likely response to a combination of drugs. By incorporating emerging pharmaceutical agents into studies based on patient characteristics, the management of MM is making incremental strides towards a more targeted treatment paradigm.
23

Analyse génomique en médecine de précision : Optimisations et outils de visualisation / Genomic Analysis within Precision Medicine : Optimizations and visualization tools

Commo, Frederic 24 November 2015 (has links)
Un nouveau paradigme tente de s’imposer en oncologie ; identifier les anomalies moléculaires dans la tumeur d’un patient, et proposer une thérapie ciblée, en relation avec ces altérations moléculaires. Nous discutons ici des altérations moléculaires considérées pour une orientation thérapeutique, ainsi que de leurs méthodes d’identification : parmi les altérations recherchées, les anomalies de nombre de copies tiennent une place importante, et nous nous concentrons plus précisément sur leur identification par hybridation génomique comparative (aCGH). Nous montrons, d’abord à partir de lignées cellulaires caractérisées, que l’analyse du nombre de copies par aCGH n’est pas triviale et qu’en particulier le choix de la centralisation peut être déterminant ; différentes stratégies de centralisation peuvent conduire à des profils génomiques différents, certains aboutissant à des interprétations erronées. Nous montrons ensuite, à partir de cohortes de patients, qu’une conséquence majeure est de retenir ou non certaines altérations actionnables dans la prise de décision thérapeutique. Ce travail nous a conduit à développer un workflow complet dédié à l’analyse aCGH, capable de prendre en charge les sources de données les plus courantes. Ce workflow intègre les solutions discutées, assure une entière traçabilité des analyses, et apporte une aide à l’interprétation des profils grâce à des solutions interactives de visualisation. Ce workflow, dénommé rCH, a été implémenté sous forme d’un package R, et déposé sur le site Bioconductor. Les solutions de visualisation interactives sont disponibles en ligne. Le code de l’application est disponible pour une installation sur un serveur institutionnel. / In oncology, a new paradigm tries to impose itself ; analyzing patient’s tumors, and identifying molecular alterations matching with targeted therapies to guide a personalized therapeutic orientation. Here, We discuss the molecular alterations possibly relevant for a therapeutic orientation, as well as the methods used for their identification : among the alterations of interest, copy number variations are widely used, and we more specifically focus on comparative genomic hybridization (aCGH). We show, using well characterized cell lines, that identification of CNV is not trivial. In particular, the choice for centralizing profiles can be critical, and different strategies for adjusting profiles on a theoretical 2n baseline can lead to erroneous interpretations. Next, we show, using tumor samples, that a major consequence is to include, or miss, targetable alterations within the decision procedure. This work lead us to develop a comprehensive workflow, dedicated to aCGH analysis. This workflow supports the major aCGH platforms, ensure a full traceability of the entire process and provides interactive visualization tools to assist the interpretation. This workflow, called rCGH, has been implemented as a R package, and is available on Bioconductor. The interactive visualization tools are available on line, and are ready to be installed on any institutional server.
24

Investigating possible approval paths of individualized neoantigen based therapeutic vaccines from a regulatory perspective

Eng, Layla January 2020 (has links)
Introduction: Mortality in cancer has declined during recent years due to more efficient cancer treatments. Some of the novel immunotherapies against cancer under development has a risk of lacking regulatory guidance for the approval, such as neoantigen-based therapeutic vaccines. Aim: The purpose of the degree project is to evaluate the potential process of approval of neoantigen based vaccines from a regulatory perspective. Methods: The method used to gather data in the study is through qualitative interviews, regulatory documents, guidelines and data from current trials. Results: The results indicate that during the preclinical phase, neoantigen vaccines can receive warrants of a case-by-case approach leading to a more optimized development pathway. During clinical trials, the trial design could consist of master protocols, singlearm trials, comparative trials and adaptive trials where the control group may use the standard of care or historical controls. The indication in the trials should be cancer types with a high tumor mutational burden, low mortality rate, and high medical need. The biomarkers should evaluate immune and tumor response. Endpoints in early trials should evaluate safety and efficacy, and the tumor and immune response in pivotal trials. Neoantigen vaccines can use several incentives during the development and can be approved through conventional or conditional approval. Finally, the study suggests that it is yet too early for the agencies to have established any guidelines. However, advices from regulatory agencies can guide developers towards regulatory approval. Conclusion: This study shows that different study designs and various incentives can be used for the regulatory approval of individualized neoantigen based vaccines. Further guidelines need to be developed to enhance future development.
25

Biomarker-And Pathway-Informed Polygenic Risk Scores for Alzheimer's Disease and Related Disorders

Chasioti, Danai 05 1900 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / Determining an individual’s genetic susceptibility in complex diseases like Alzheimer’s disease (AD) is challenging as multiple variants each contribute a small portion of the overall risk. Polygenic Risk Scores (PRS) are a mathematical construct or composite that aggregates the small effects of multiple variants into a single score. Potential applications of PRS include risk stratification, biomarker discovery and increased prognostic accuracy. A systematic review demonstrated that methodological refinement of PRS is an active research area, mostly focused on large case-control genome-wide association studies (GWAS). In AD, where there is considerable phenotypic and genetic heterogeneity, we hypothesized that PRS based on endophenotypes, and pathway-relevant genetic information would be particularly informative. In the first study, data from the NIA Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to develop endophenotype-based PRS based on amyloid (A), tau (T), neurodegeneration (N) and cerebrovascular (V) biomarkers, as well as an overall/combined endophenotype-PRS. Results indicated that combined phenotype-PRS predicted neurodegeneration biomarkers and overall AD risk. By contrast, amyloid and tau-PRSs were strongly linked to the corresponding biomarkers. Finally, extrinsic significance of the PRS approach was demonstrated by application of AD biological pathway-informed PRS to prediction of cognitive changes among older women with breast cancer (BC). Results from PRS analysis of the multicenter Thinking and Living with Cancer (TLC) study indicated that older BC patients with high AD genetic susceptibility within the immune-response and endocytosis pathways have worse cognition following chemotherapy±hormonal therapy rather than hormonal-only therapy. In conclusion, PRSs based on biomarker- or pathway- specific genetic information may provide mechanistic insights beyond disease susceptibility, supporting development of precision medicine with potential application to AD and other age-associated cognitive disorders.
26

New Microfluidic Technologies for Studying Histone Modifications and Long Non-Coding RNA Bindings

Hsieh, Yuan-Pang 01 June 2020 (has links)
Previous studies have shown that genes can be switched on or off by age, environmental factors, diseases, and lifestyles. The open or compact structures of chromatin is a crucial factor that affects gene expression. Epigenetics refers to hereditary mechanisms that change gene expression and regulations without changing DNA sequences. Epigenetic modifications, such as DNA methylation, histone modification, and non-coding RNA interaction, play critical roles in cell differentiation and disease processes. The conventional approach requires the use of a few million or more cells as starting material. However, such quantity is not available when samples from patients and small lab animals are examined. Microfluidic technology offers advantages to utilize low-input starting material and for high-throughput. In this thesis, I developed novel microfluidic technologies to study epigenomic regulations, including 1) profiling epigenomic changes associated with LPS-induced murine monocytes for immunotherapy, 2) examining cell-type-specific epigenomic changes associated with BRCA1 mutation in breast tissues for breast cancer treatment, and 3) developing a novel microfluidic oscillatory hybridized ChIRP-seq assay to profile genome-wide lncRNA binding for numerous human diseases. We used 20,000 and 50,000 primary cells to study histone modifications in inflammation and breast cancer of BRCA1 mutation, respectively. In the project of whole-genome lncRNA bindings, our microfluidic ChIRP-seq assay, for the first time, allowed us to probe native lncRNA bindings in mouse tissue samples successfully. The technology is a promising approach for scientists to study lncRNA bindings in primary patients. Our works pave the way for low-input and high-throughput epigenomic profiling for precision medicine development. / Doctor of Philosophy / Traditionally, physicians treat patients with a one-size-fits-all approach, in which disease prevention and treatment are designed for the average person. The one-size-fits-all approach fits many patients, but does not work on some. Precision medicine is launched to improve the low efficiency and diminish side effects, and all of these drawbacks are happening in the traditional approaches. The genomic, transcriptomic, and epigenomic data from patients is a valuable resource for developing precision medicine. Conventional approaches in profiling functional epigenomic regulation use tens to hundreds of millions cells per assay, that is why applications in clinical samples are restricted for several decades. Due to the small volume manipulated in microfluidic devices, microfluidic technology exhibits high efficiency in easy operation, reducing the required number of cells, and improving the sensitivity of assays. In order to examine functional epigenomic regulations, we developed novel microfluidic technologies for applications with the small number of cells. We used 20,000 cells from mice to study the epigenomic changes in monocytes. We also used 50,000 cells from patients and mice to study epigenomic changes associated with BRCA1 mutation in different cell types. We developed a novel microfluidic technology for studying lncRNA bindings. We used 100,000-500,000 cells from cell lines and primary tissues to test several lncRNAs. Traditional approaches require 20-100 million cells per assay, and these cells are infected by virus for over-producing specific lncRNA. However, our technology just needs 100,000 cells (non-over-producing state) to study lncRNA bindings. To the best of our knowledge, this is the first allowed us to study native lncRNA bindings in mouse samples successfully. Our efforts in developing microfluidic technologies and studying epigenomic regulations pave the way for precision medicine development.
27

An interoperable electronic medical record-based platform for personalized predictive analytics

Abedtash, Hamed 31 May 2017 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / Precision medicine refers to the delivering of customized treatment to patients based on their individual characteristics, and aims to reduce adverse events, improve diagnostic methods, and enhance the efficacy of therapies. Among efforts to achieve the goals of precision medicine, researchers have used observational data for developing predictive modeling to best predict health outcomes according to patients’ variables. Although numerous predictive models have been reported in the literature, not all models present high prediction power, and as the result, not all models may reach clinical settings to help healthcare professionals make clinical decisions at the point-of-care. The lack of generalizability stems from the fact that no comprehensive medical data repository exists that has the information of all patients in the target population. Even if the patients’ records were available from other sources, the datasets may need further processing prior to data analysis due to differences in the structure of databases and the coding systems used to record concepts. This project intends to fill the gap by introducing an interoperable solution that receives patient electronic health records via Health Level Seven (HL7) messaging standard from other data sources, transforms the records to observational medical outcomes partnership (OMOP) common data model (CDM) for population health research, and applies predictive models on patient data to make predictions about health outcomes. This project comprises of three studies. The first study introduces CCD-TOOMOP parser, and evaluates OMOP CDM to accommodate patient data transferred by HL7 consolidated continuity of care documents (CCDs). The second study explores how to adopt predictive model markup language (PMML) for standardizing dissemination of OMOP-based predictive models. Finally, the third study introduces Personalized Health Risk Scoring Tool (PHRST), a pilot, interoperable OMOP-based model scoring tool that processes the embedded models and generates risk scores in a real-time manner. The final product addresses objectives of precision medicine, and has the potentials to not only be employed at the point-of-care to deliver individualized treatment to patients, but also can contribute to health outcome research by easing collecting clinical outcomes across diverse medical centers independent of system specifications.
28

Integrative Analysis for Identifying Multi-Layer Modules in Precision Medicine

Yazdanparast, Aida 12 1900 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / Precision medicine aims to employ information from all modalities to develop a comprehensive view of disease progression and administer therapies tailored to the individual patient. A set of genomic features (gene CNVs, mutations, mRNA expressions, and protein abundances) is associated with each patient and it is hard to explain the phenotypic similarities such as gene essentiality or variability in drug response in a single genomic level. Thus, to extract biological principles it is critical to seek mutual information from multi-dimensional datasets. To address these concerns, we first conduct an integrated mRNA/protein analysis in both breast cancer cell lines and tumors, and most interestingly in the breast cancer subtypes. We identified cell lines that provide optimum heterogeneity models for studying the underlying biological processes of tumors. Our systematic observation across multi-omics data identifies distinct subgroups of cancer cells and patients. Based on this identified signal transduction between mRNA and RPPA, we developed a biclustering model to characterize key genetic alterations that are shared in both cancer cell lines and patients. We integrated two types of omics data including copy number variations, transcriptome, and proteome. Bi-EB adopts a data-driven statistics strategy by using Expected-Maximum (EM) algorithm to extract the foreground bicluster pattern from its background noise data in an iterative search. Using Bi-EB algorithm we selected translational gene sets that are characterized by highly correlated molecular profiles among RNA and proteins. To further investigate cell line and tissue in breast cancer we explore the relationship vii between genomic features and the phenotypic factors. Using in vitro/in vivo drug screening data, we adopt partial least square regression method and develop a multi-modular approach to predict anticancer therapy benefits for ER-negative breast cancer patients. The identified joint multi-dimensional modules here provide us new insights into the molecular mechanisms of drugs and cancer treatment. / 2021-12-28
29

Microfluidic tools for molecular analysis and engineering

Murphy, Travis Wilson 01 July 2019 (has links)
The shift of medical technology from a doctor's application of individualized medicine toward precision medicine has been accelerated by the advent of Next Generation Sequencing. Individualized medicine is where a doctor tries to understand the intricacies of a patient's medical state, where precision medicine uses a wealth of data to understand the individuality of a patient on a biological level to determine treatment course. Next Generation Sequencing allows for the collection of genome wide analyses such as genomic, transcriptomic, and epigenomic sequencing, which provides the backbone of the data driven precision medicine. In order to obtain and use this data, it needs to be produced from minimal amounts of patient tissue, such as the amount from a needle biopsy. In order to perform so many different assays it is paramount that we develop high sensitivity methodologies, such that we can gain an understanding of the patient's physiology without causing much discomfort in gathering large amounts of sample. In pursuit of making more tests, data, and assays available for use in precision medicine, we have developed 3 different microfluidic technologies, which automate and simplify the assays needed for the data collection at a high sensitivity, as well as a versatile platform for therapeutic production. First, we developed a epigenomic assay for chromatin immunoprecipitation, which gives us information on histone modifications across the genome. These histone modifications heavily impact gene expression and how the chromatin is organized, as well promoting or inhibiting transcription of genes. Our technology allowed us to perform multiple parallel assays from as few as 50 cells quickly and reliably using our fluidized bed technology. Next, we developed a library preparation system, which reduces the cost of library preparation by 20x and reduces operator pipetting by 100x. Our system uses a droplet based reactor to quickly and reliably prepare sequencing libraries using the lowest amount of DNA to date, 10 pg. Finally, we designed a therapeutics-on-a-chip platform which is capable of producing clinically relevant proteins on demand from temperature stable components. Using our system, we are capable of producing a number of different therapeutics on demand quickly without rearrangement of the system. / Doctor of Philosophy / Technical advances in the healthcare industry have made a range of new data available to physicians and patients. Home use DNA testing kits have made it possible to examine one’s predisposition to certain genetic diseases. Using these advanced methods, we are able to gain insights into a patient’s disease state where we were previously unable. Unfortunately, some of these new analyses currently require large amounts of patient sample, which make the examinations largely impractical to perform. In order to overcome the sample requirements, which make these analyses impractical, we develop microscale reactor systems capable of reducing the amount of material required for these new analyses. Here I demonstrate our developed technologies to automate 3 different processes aimed at enabling the study of protein-DNA interactions and produce therapeutics at the point of care. First, we developed an analytical system to study protein-DNA interactions (which are important to understanding patient responses to treatment), that allow for parallel analyses which can be done with sample from less than one needle biopsy, where existing methods would require dozens or more (50 vs 10,000,000 cells.) Next, we developed automated system for preparing DNA sequencing libraries using as little as 10 pg DNA (~2 cells of DNA). The device run multiple reactions simultaneously while reducing batch to batch variation and operator hands-on time. Finally, we developed a v Therapeutics-On-a-Chip platform that produces clinically relevant therapeutic proteins in clinically relevant dosages using a cell-free approach, while saving the trouble and cost associated with protein storage and transportation.
30

Prognostic and Predictive Computational Pathology-Based Companion Diagnostics for Genitourinary Cancers

Leo, Patrick J. 25 January 2022 (has links)
No description available.

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