<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>

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.

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.

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.

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.

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.

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

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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

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