Atherosclerosis, the pathophysiology of many cardiovascular diseases (CVD), is a chronic inflammatory process caused by the sustained accumulation of cholesterol, followed by endothelial dysfunction, and the resulting vascular inflammation. The established treatment for atherosclerosis, to date, involves the use of statins. These medications are hydroxymethylglutaryl coenzyme A reductase (HMG-CoA) inhibitors and lower the levels of by inhibiting HMG-CoA, a rate limiting step in the biosynthesis of cholesterol. Statin therapy varies in effectiveness based on dosage and individual differences, making effective treatment of patients challenging. More recently, extracellular vesicles (EVs) have emerged as a promising field in cardiovascular research. Once thought of as “platelet dust,” EVs are now recognized for their potential as therapeutic targets and tools. In this review, a comprehensive characterization of EVs is provided to explain how EVs are involved in normal physiological function and pathological processes of atherosclerosis. Evidence supports a model where EVs participate in the initiation and progression of atherosclerosis and may also be used as a delivery tool in disease therapy. Currently, cell-derived EVs can be therapeutic agents in animal models, an effective tool in gene therapy, or a drug delivery vehicle. Future experiments enhancing the therapeutic potential of EVs promise to deepen our understanding of EV-based therapy for atherosclerosis precision medicine.

Cancer therapy has historically been determined by a tumor’s tissue of origin. Now, thanks to advances in genomics technology, scientists are looking further into one’s cancer; into the very genome that drives the tumor growth. The growth of genomics in cancer research has been astronomical. In a little over ten years since the completion of the Human Genome Project, genomic profiling technologies have developed into an incredibly powerful, relatively cheap, and immensely underutilized tool for oncologists. In the midst of the advances in cancer profiling, there has been reluctance from oncologists to incorporate genomic profiling into their treatment decisions. Saddled by outdated clinical trial designs, and cancer drug regulation programs, the true measure of the clinical utility of genomic profiling has yet to be seen. Cancer scientists will continue to profile cancers at a pace well beyond the limits of the field of oncology. Without coordinated efforts to update the oncology healthcare system, compendia of data will continue to be generated with limited ability to translate the information into personalized medicines. There are significant barriers to overcome before genomic data can universally be incorporated into the daily practice of cancer medicine. In the meantime, resources are available for physicians to help begin the process of integrating a more personalized approach to cancer therapy. Third-party bioinformatics companies are in the best position to be the agents of this change. As cancer research continues to adopt a genomic approach, it is paramount that, for the sake of millions of cancer patients, the healthcare system adapts in a way to best utilize this new information.

Poster exhibited at GPSC Student Showcase, February 24th, 2016, University of Arizona. / Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P<0.05, n¼80 invasive car- cinoma; TCGA RNA-sequences). Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpret- ability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome).

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in Canada each year. Currently, most therapeutic approaches target rapidly dividing cancer cells by inhibition of normal cellular processes, however these therapies are not selective for cancer cells and unwanted side effects occur. Accordingly, novel cancer-targeted therapeutic strategies and drug targets are urgently needed to diminish the morbidity and mortality rates associated with CRC. Synthetic lethality is a new therapeutic approach that is designed to better target and kill cancer cells by exploiting a cancer-associated mutation (i.e. RAD54B-deficiency) thereby minimizing adverse side effects. We hypothesize that RAD54B-deficient CRC cells will be selectively killed via a synthetic lethal (SL) interaction with PARP1. We have identified and validated a novel drug target, PARP1, within CRC cells harboring RAD54B-deficiencies through a SL paradigm. This study represents the first steps necessary to identify and develop precision medicine based therapeutic strategies to combat CRC. / October 2016

Most medical treatments have been developed aiming at the best-on-average efficacy for large populations, resulting in treatments successful for some patients but not for others. It necessitates the need for precision medicine that tailors medical treatment to individual patients. Omics data holds comprehensive genetic information on individual variability at the molecular level and hence the potential to be translated into personalized therapy. However, the attempts to transform omics data-driven insights into clinically actionable models for individual patients have been limited. Meanwhile, advances in deep learning, one of the most promising branches of artificial intelligence, have produced unprecedented performance in various fields. Although several deep learning-based methods have been proposed to predict individual phenotypes, they have not established the state of the practice, due to instability of selected or learned features derived from extremely high dimensional data with low sample sizes, which often results in overfitted models with high variance. To overcome the limitation of omics data, recent advances in deep learning models, including representation learning models, generative models, and interpretable models, can be considered. The goal of the proposed work is to develop deep learning models that can overcome the limitation of omics data to enhance the prediction of personalized medical decisions. To achieve this, three key challenges should be addressed: 1) effectively reducing dimensions of omics data, 2) systematically augmenting omics data, and 3) improving the interpretability of omics data. / Doctor of Philosophy / Most medical treatments have been developed aiming at the best-on-average efficacy for large populations, resulting in treatments successful for some patients but not for others. It necessitates the need for precision medicine that tailors medical treatment to individual patients. Biological data such as DNA sequences and snapshots of genetic activities hold comprehensive information on individual variability and hence the potential to accelerate personalized therapy. However, the attempts to transform data-driven insights into clinical models for individual patients have been limited. Meanwhile, advances in deep learning, one of the most promising branches of artificial intelligence, have produced unprecedented performance in various fields. Although several deep learning-based methods have been proposed to predict individual treatment or outcome, they have not established the state of the practice, due to the complexity of biological data and limited availability, which often result in overfitted models that may work on training data but not on test data or unseen data. To overcome the limitation of biological data, recent advances in deep learning models, including representation learning models, generative models, and interpretable models, can be considered. The goal of the proposed work is to develop deep learning models that can overcome the limitation of omics data to enhance the prediction of personalized medical decisions. To achieve this, three key challenges should be addressed: 1) effectively reducing the complexity of biological data, 2) generating realistic biological data, and 3) improving the interpretability of biological data.

In this dissertation, we aimed to bring together a team of clinical experts, translational researchers, biostaticians and bioinformaticians to develop and implement innovative scientific methodologies in precision medicine applied to High Grade Serous Ovarian Cancer (HGS OvCa). We used a variety of translational and computational methods in order to generate impactful outcomes. These pipelines produced statistically robust results, with particular emphasis on drawing clinical and biological correlations. The results presented here contribute to the body of evidence necessary to substantiate these findings in a clinical setting. Bioassays, PDX models and ancillary specimen evaluation of previous clinical trials will help to validate our candidate biomarkers. Enhanced understanding of the molecular pathology of disease grounded in acquisition of genomic knowledge will facilitate the development of targeted treatment in cancer. Because clinical trials must be developed with correct metrics, patient selection and drug efficacy should incorporate adaptive designs. / Doctorat en Sciences médicales (Santé Publique) / info:eu-repo/semantics/nonPublished

Le cancer est la cause la plus fréquente de décès dans le monde, avec 8,2 millions de décès par an. Des études génomiques à grande échelle ont montré que chaque tumeur est caractérisée par un profil génomique unique, conduisant au développement de la médecine de précision, où le traitement est adapté aux altérations génomiques de la tumeur du patient. Dans le cancer du sein précoce HR+/HER2-, les caractéristiques clinicopathologiques des patientes, bien qu’elles aient une valeur pronostique claire, ne sont pas suffisantes pour expliquer entièrement le risque de rechute à distance. L'objectif principal de ce projet de thèse était de déterminer les altérations génomiques impliquées dans la rechute à distance, en plus des paramètres cliniques des patientes, en utilisant des méthodes statistiques et informatiques. Ce projet a été réalisé à partir de données cliniques et génomiques (nombre de copies et mutations) issues des études PACS04 et METABRIC.Dans la première partie de mon projet de thèse, j’ai tout d’abord évalué la valeur pronostique du nombre de copies de gènes prédéfinis (FGFR1, Fibroblast Growth Factor Receptor 1 ; CCND1, Cyclin D1 ; ZNF217, Zinc Finger protein 217 ; ERBB2 ou HER2, Humain Epidermal Growth Factor) ainsi qu’un panel de mutations de gènes « driver ». Les résultats de l’étude PACS04 ont montrés que l’amplification de FGFR1 augmente le risque de rechute à distance alors que les mutations de MAP3K1 diminuent le risque de rechute. Ensuite, un score génomique fondé sur FGFR1 et MAP3K1 a été créé et a permis de déceler trois niveaux de risques de rechute à distance : risque faible (patientes ayant une mutation du gène MAP3K1), risque modéré (patientes n’ayant pas d’altération du nombre de copies de FGFR1 et n’ayant pas de mutation de MAP3K1) et risque élevé (patientes ayant une amplification de FGFR1 et n’ayant pas de mutation de MAP3K1). Enfin, ce score génomique a été validé sur une base de données publique, METABRIC. Dans la seconde partie de mon projet de thèse, de nouveaux biomarqueurs génomiques pronostiques de la survie ont pu être identifiés grâce aux méthodes pénalisées de type LASSO, prenant en compte la structure en bloc des données.Mots-clés : Altération du nombre de copies, mutations, cancer du sein, biomarqueurs, méthode de sélection de variables, réduction de dimension, modèle de Cox / Cancer is the most frequent cause of death in the world, with 8.2 million death / year. Large-scale genome studies have shown that each cancer is characterized by a unique genomic profile. This has led to the development of precision medicine, which aims at targeting treatment using tumor genomic alterations that are patient-specific. In hormone-receptor positive/human epidermal growth factor receptor-2 negative early breast cancer, clinicopathologic characteristics are not sufficient to fully explain the risk of distant relapse, despite their well-established prognostic value. The main objective of this thesis project was to use statistical and computational methods to assess to what extent genomic alterations are involved in distant breast cancer relapse in addition to classic prognostic clinicopathologic parameters. This project used clinical and genomic data (i.e., copy numbers and driver gene mutations) from the PACS04 and METABRIC trial.In the first part of my thesis project, I first evaluated prognostic value of copy numbers of predefined genes including FGFR1, Fibroblast Growth Factor Receptor 1; CCND1, Cyclin D1; ZNF217, Zinc Finger Protein 217; ERBB2 or HER2, Human Epidermal Growth Factor, as well as a panel of driver gene mutations. Results from the PACS04 trial showed that FGFR1 amplification increases the risk of distant relapse, whereas mutations of MAP3K1 decrease the risk of relapse. Second, a genomic score based on FGFR1 and MAP3K1, allowed to identify three levels of risk of distant relapse: low risk (patients with a MAP3K1 mutation), moderate risk (patients without FGFR1 copy number aberration and without MAP3K1 mutation) and high risk (patients with FGFR1 amplification and without MAP3K1 mutation). Finally, this genomic score was validated in METABRIC, a publicly available database. In the second part of my thesis project, new prognostic genomic biomarkers of survival were identified using penalized methods of LASSO type, taking into account the block structure of the data.Keywords: Copy number aberrations (CNA), mutations, breast cancer (BC), biomarkers, variable selection methods, dimension reduction, cox regression

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Purpose: Knowledge of breast cancer genetics is critical for those at increased risk whomust make decisions about breast cancer screening options. The purpose of this study was toexplore cognitive and emotional variables that might influence knowledge of breast cancergenetics.Methods: This descriptive, exploratory study analyzed theory-based relationships amongvariables related to knowledge of breast cancer genetics in cancer families. Participants includedfirst-degree relatives of women with breast cancer who had received genetic counseling andtesting; participants themselves did not have breast cancer and had not received geneticcounseling or testing. Data were collected by telephone interview and survey. Variables analyzedinclude numeracy, health literacy, cancer-related distress, age, education, and the reportedamount of information shared by the participants family members about genetic counseling.Results: The multiple regression model explained 13.9% of variance in knowledge of breastcancer genetics (p = 0.03). Best fit of the multiple regression model included all variables excepteducation. Reported amount of information shared was the only independently significantpredictor variable (p = 0.01).Conclusion: Participants who reported higher levels of information shared by a familymember about genetic counseling also demonstrated increased knowledge about breast cancergenetics.