abstract: ABSTRACT Whole genome sequencing (WGS) and whole exome sequencing (WES) are two comprehensive genomic tests which use next-generation sequencing technology to sequence most of the 3.2 billion base pairs in a human genome (WGS) or many of the estimated 22,000 protein-coding genes in the genome (WES). The promises offered from WGS/WES are: to identify suspected yet unidentified genetic diseases, to characterize the genomic mutations in a tumor to identify targeted therapeutic agents and, to predict future diseases with the hope of promoting disease prevention strategies and/or offering early treatment. Promises notwithstanding, sequencing a human genome presents several interrelated challenges: how to adequately analyze, interpret, store, reanalyze and apply an unprecedented amount of genomic data (with uncertain clinical utility) to patient care? In addition, genomic data has the potential to become integral for improving the medical care of an individual and their family, years after a genome is sequenced. Current informed consent protocols do not adequately address the unique challenges and complexities inherent to the process of WGS/WES. This dissertation constructs a novel informed consent process for individuals considering WGS/WES, capable of fulfilling both legal and ethical requirements of medical consent while addressing the intricacies of WGS/WES, ultimately resulting in a more effective consenting experience. To better understand components of an effective consenting experience, the first part of this dissertation traces the historical origin of the informed consent process to identify the motivations, rationales and institutional commitments that sustain our current consenting protocols for genetic testing. After understanding the underlying commitments that shape our current informed consent protocols, I discuss the effectiveness of the informed consent process from an ethical and legal standpoint. I illustrate how WGS/WES introduces new complexities to the informed consent process and assess whether informed consent protocols proposed for WGS/WES address these complexities. The last section of this dissertation describes a novel informed consent process for WGS/WES, constructed from the original ethical intent of informed consent, analysis of existing informed consent protocols, and my own observations as a genetic counselor for what constitutes an effective consenting experience. / Dissertation/Thesis / Ph.D. Biology 2013

Charles University in Prague Faculty of Pharmacy in Hradec Králové Department of Pharmacology & Toxicology Student: Martina Kobrlová Supervisor: prof. PharmDr. Petr Pávek, Ph.D. Title of diploma thesis: Pharmacogenetics in rheumatoid arthritis Based on scientific progress in the research of human genome and the discovery of polymorphisms, which are involved in the interindividual differences in human population, there is also a growing interest in pharmacogenetics. It is a field combining pharmacology and genetics with the aim of identifying specific features that could explain the different responses of patients to treatment by clinically used drugs. Applying this knowledge could contribute to a simpler choice of medication for a particular patient and it could reduce the risk of side effects or poor response. In this diploma thesis I dealt with the latest scientific knowledge on pharmacogenetics in rheumatology, in particular the rheumatoid arthritis. From available studies, reviews, and meta-analyzes that have been published, I summarized current data on the relationship between polymorphisms and disease modifying drugs (DMARDs) used for the treatment of this disease. The largest amount of data was found on the most commonly used methotrexate. Further, the work examines the leflunomide and other...

Background: Hypertension, a leading risk factor for cardiovascular disease, exhibited in 17.7% of the Canadian population, and attributed to 13% of world mortality, is influenced by both the environment and genetics. Salt sensitivity is described at higher rates in the hypertensive population. The NEDD4-like (NEDD4L) protein is important in sodium reabsorption and has been implicated in essential hypertension and salt sensitivity. Objectives: Two variations (rs4149601/rs2288774) found in NEDD4L have been associated with salt sensitivity and hypertension; a third (rs576416) is in linkage disequilibrium with rs4149601. The purpose of this study is to assess the relationship between the NEDD4L rs4149601, rs2288774, and rs576416 single nucleotide polymorphisms with sodium and age on blood pressure (BP). Methods: Canadian hypertensive patients were recruited through the University of Ottawa Heart Institute, with genotyped data from Leuven, Belgium, and the DNA of subjects from Warsaw, Poland also included in the study. Eligible subjects were studied off anti-hypertensive medications. Daytime BP was measured using 24hr ambulatory BP monitoring in 662 Caucasian hypertensives (BP ≥130/85 mmHg). 24hr urine Na+ was collected. DNA from Canada and Poland was genotyped on the GeneTitan Affymetrix Axiom platform and through TaqMan MGB probe-based RT-PCR, while the Belgium samples were analyzed on Illumina 1M-duo arrays. Simple and multivariate linear regression modelling with SAS 9.4.0 was used for genotypic comparisons affecting BP, combined with age and corrected urine sodium. Results: The three hypertensive populations were significantly different (P<0.05) across all demographic and clinical measures, even when stratified by sex. The Polish and female hypertensives from Canada and Belgium were removed from the analysis for lacking the general populations’ trend of increasing BP with age. Multiple linear regressive modelling found a significant association (Pmodel=0.0034) of rs4149601 GA (P=0.0129) and GG (P=0.0082), with age and urine sodium, on SBP in the Belgium male hypertensives (n=273). No significant models analyzing the association of rs576416 and rs2288774 with BP in the Belgium population were found. In the Canadian hypertensive population (n=120) no association on the discrete analyses of the rs4149601, rs576416, and rs2288774 genotypes were found; however the combination of the GG rs4149601 and AA rs576416 (β=0.021, P=0.03) and the GG rs4149601 and CC rs2288774 (β=0.020, P=0.04) genotypes showed significant associations with BP in borderline significant models (P=0.055 and P=0.094 respectively), when analyzed with urine sodium levels and age. Conclusions: A significant influence of the rs4149601 G-allele, with urine sodium and age, was found to be associated with an increase in BP in the Belgium males. Multiple linear modelling describing borderline significant findings in the interaction of rs4149601 with rs576416, and rs4149601 with rs2288774 in Canadian male hypertensives suggests of the possible synergism between polymorphisms and development of salt sensitive hypertension. Future research could evaluate the role of NEDD4L on the sex differences in early-onset salt-sensitive hypertension.

Complex diseases such as cancer, cardiovascular diseases and diabetes are often diagnosed too late, which significantly impairs treatment options and, in turn, lowers patient’s survival rate drastically and increases the costs significantly. Moreover, the growth of medical data is faster than the ability of healthcare systems to utilize them. Almost 80% of medical data are unstructured, but they are clinically relevant. On the other hand, technological advancements have made it possible to create different  igital health solutions where healthcare and ICT meet. Also, some individuals have already started to measure their body function parameters, track their health status, research their symptoms and even intervene in treatment options which means a great deal of data is being produced and also indicates that patient-driven health care models are transforming how health care functions. These models include quantified self-tracking, consumer-personalized-medicine and health social networks. This research aims to present an open innovation digital health platform which creates value  y using the overlaps between healthcare, information technology and artificial intelligence. This platform could potentially be utilized for early detection of complex diseases by leveraging Big Data technology which could improve awareness by recognizing pooled symptoms of a specific disease. This would enable individuals to effortlessly and quantitatively track and become aware of changes in their health, and through a dialog with a doctor, achieve diagnosis at a significantly earlier stage. This thesis focuses on a case study of the platform for detecting breast cancer at a  ignificantly earlier stage. A qualitative research method is implemented through reviewing the literature, determining the knowledge gap, evaluating the need, performing market research, developing a conceptual prototype and presenting the open innovation platform. Finally, the value creation, applications and challenges of such platform are investigated, analysed and discussed based on the collected data from interviews and surveys. This study combines an explanatory and an analytical research approach, as it aims not only to describe the case, but also to explain the value creation for different stakeholders in the value chain. The findings indicate that there is an urgent need for early diagnosis of complex diseases such as breast cancer) and also handling direct and indirect consequences of late diagnosis. A significant outcome of this research is the conceptual prototype which was developed based on the general proposed concept through a customer development process. According to the conducted surveys, 95% of the cancer patients and 84% of the healthy individuals are willing to use the proposed platform. The results indicate that it can create significant values for patients, doctors, academic institutions, hospitals and even healthy individuals.

La médecine personnalisée représente une grande attente et un grand espoir dans la lutte contre le cancer. Cette approche vise à adapter les traitements aux caractéristiques personnelles du patient, principalement des biomarqueurs génétiques. Dans notre premier travail, nous avons analysé l'ensemble des médicaments approuvés par la FDA avec un biomarqueur pharmacogénétique dans leur label et montré (1) que l'oncologie représentait un tiers des médicaments avec un biomarqueur dans leur notice et (2) qu'une part importante des médicaments en oncologie mentionnaient le biomarqueur pour requérir un test avant la prescription du médicament contrairement aux autres domaines thérapeutiques. Notre deuxième travail a analysé les essais cliniques soumis à la FDA en vue de la mise sur le marché des thérapies ciblées pour lesquelles il existait une indication restreinte aux patients présentant un certain biomarqueur. Nous concluons que dans deux tiers des cas, l'utilisation du biomarqueur pour sélectionner les patients à traiter était basée sur les résultats des essais cliniques restreints aux patients biomarqueur-positifs et, qu'ainsi, il n'existait aucune donnée clinique permettant de conclure à une différence d'effet traitement selon les valeurs du biomarqueur. Pour notre troisième travail, nous avons réalisé une cartographie de l'ensemble des essais enregistrés sur le registre américain des essais cliniques pour les médicaments anti-cancéreux avec la mention d'un biomarqueur dans leur label. Nous avons mis en évidence des variations très importantes entre les médicaments quant au recours à des essais enrichis et au fait de tester un médicament dans plusieurs indications ou avec plusieurs biomarqueurs prédictifs. Dans notre quatrième travail, nous avons étudié la question du bénéfice apporté par les médicaments anti-cancéreux dans un contexte d'augmentation très importante des prix et grâce à la publication récente de deux échelles par les sociétés européenne et américaine d'oncologie (ESMO et ASCO). Nous avons analysé le bénéfice de tous les médicaments anti-cancéreux approuvés entre 2000 et 2015 pour le traitement d'une tumeur solide. Nous avons montré (1) la faible valeur des récents médicaments anti-cancéreux, (2) l'absence de relation entre le prix et la valeur de ces médicaments et (3) l'absence de différence de bénéfice entre médicaments de médecine personnalisée et médicaments classiques. En conclusion, la présence de biomarqueurs prédictifs dans le label des médicaments---souvent citée comme critère de succès de la médecine personnalisée---est pour l'instant restreinte en grande partie à l'oncologie. Le niveau de preuve pour l'utilité clinique est souvent inconnu car les études sont restreintes à un sous-groupe de patients positifs pour le biomarqueur dès les phases initiales du développement du médicament. Enfin, seul un tiers des médicaments anti-cancéreux approuvés par la FDA entre 2000 et 2015 ont un bénéfice cliniquement pertinent, sans différence de bénéfice clinique entre les médicaments avec et sans biomarqueur et sans relation entre le prix et le bénéfice de ces médicaments. / Personalized medicine represents great expectations and hopes in oncology. This approach aims to adapt treatments to the personal characteristics of the patient, mainly genetic biomarkers. In our first work, we analyzed all the FDA-approved drugs with a pharmacogenetic biomarker in their label and showed (1) that oncology represented one-third of the drugs with a biomarker in their label and (2) a significant portion of oncology drugs mentioned the biomarker to require a biomarker test, contrary to other therapeutic areas. Our second work analyzed the clinical trials submitted to the FDA for the approval of targeted therapies for which there was a indication restricted to biomarker-positive patients. We conclude that in two-thirds of the cases, the use of the biomarker to select the patients to be treated was based on the results of the clinical trials restricted to the biomarker-positive patients. Thus, in these cases, there was no clinical evidence to conclude to a treatment-by-biomarker interaction. For our third work, we mapped all the trials recorded on the US ClinicalTrials.gov registry for anti-cancer drugs with a biomarker labeling. We found very important variations between drugs in the use of enriched trials and in testing of the drug in several indications or with several predictive biomarkers. In our last work, we examined the benefit of anti-cancer drugs in a context of very significant price increases and the recent publication of two scales by the European and American oncology societies (ESMO and ASCO). We analyzed the benefit of all anti-cancer drugs approved between 2000 and 2015 for the treatment of a solid tumor. We have shown (1) the low value of recent anti-cancer drugs, (2) the lack of relationship between the price and the value of these drugs, and (3) the lack of difference of benefice between personalized and “classical” medicines. In conclusion, the presence of predictive biomarkers in the label of drugs --- often cited as a criterion of success of personalized medicine --- is, at least for now, being restricted in large part to oncology. The level of evidence for clinical utility is often unknown because studies are restricted to the subgroup of biomarker-positive patients from the initial stages of the drug development. Finally, only one third of the anti-cancer drugs approved by the FDA between 2000 and 2015 have meaningful clinical benefit and there is no difference in clinical benefit between drugs with and without biomarkers and no relation between the price and the benefit of anti-cancer drugs.

The ultimate goal of personalized or precision medicine is to identify the best treatment for each patient. An N-of-1 trial is a multiple-period crossover trial performed within a single individual, which focuses on individual outcome instead of population or group mean responses. As in a conventional crossover trial, it is critical to understand carryover effects of the treatment in an N-of-1 trial, especially in situations where there are no washout periods between treatment periods and high volume of measurements are made during the study. Existing statistical methods for analyzing N-of-1 trials include nonparametric tests, mixed effect models and autoregressive models. These methods may fail to simultaneously handle measurements autocorrelation and adjust for potential carryover effects. Distributed lag model is a regression model that uses lagged predictors to model the lag structure of exposure effects. In the dissertation, we first introduce a novel Bayesian distributed lag model that facilitates the estimation of carryover effects for single N-of-1 trial, while accounting for temporal correlations using an autoregressive model. In the second part, we extend the single N-of-1 trial model to multiple N-of-1 trials scenarios. In the third part, we again focus on single N-of-1 trials. But instead of modeling comparison with one treatment and one placebo (or active control), multiple treatments and one placebo (or active control) is considered. In the first part, we propose a Bayesian distributed lag model with autocorrelated errors (BDLM-AR) that integrate prior knowledge on the shape of distributed lag coefficients and explicitly model the magnitude and duration of carryover effect. Theoretically, we show the connection between the proposed prior structure in BDLM-AR and frequentist regularization approaches. Simulation studies were conducted to compare the performance of our proposed BDLM-AR model with other methods and the proposed model is shown to have better performance in estimating total treatment effect, carryover effect and the whole treatment effect coefficient curve under most of the simulation scenarios. Data from two patients in the light therapy study was utilized to illustrate our method. In the second part, we extend the single N-of-1 trial model to multiple N-of-1 trials model and focus on estimating population level treatment effect and carryover effect. A Bayesian hierarchical distributed lag model (BHDLM-AR) is proposed to model the nested structure of multiple N-of-1 trials within the same study. The Bayesian hierarchical structure also improve estimates for individual level parameters by borrowing strength from the N-of-1 trials of others. We show through simulation studies that BHDLM-AR model has best average performance in terms of estimating both population level and individual level parameters. The light therapy study is revisited and we applied the proposed model to all patients’ data. In the third part, we extend BDLM-AR model to multiple treatments and one placebo (or active control) scenario. We designed prior precision matrix on each treatment. We demonstrated the application of the proposed method using a hypertension study, where multiple guideline recommended medications were involved in each single N-of-1 trial.

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.

The control of type-I error is a focal point for clinical trials. On the other hand, it is also critical to be able to detect a truly efficacious treatment in a clinical trial. With recent success in supervised learning (classification and regression problems), artificial intelligence (AI) and machine learning (ML) can play a vital role in identifying efficacious new treatments. However, the high performance of the AI methods, particularly the deep learning neural networks, requires a much larger dataset than those we commonly see in clinical trials. It is desirable to develop a new ML method that performs well with a small sample size (ranges from 20 to 200) and has advantages as compared with the classic statistical models and some of the most relevant ML methods. In this dissertation, we propose a Similarity-Principle-Based Machine Learning (SBML) method based on the similarity principle assuming that identical or similar subjects should behave in a similar manner. SBML method introduces the attribute-scaling factors at the training stage so that the relative importance of different attributes can be objectively determined in the similarity measures. In addition, the gradient method is used in learning / training in order to update the attribute-scaling factors. The method is novel as far as we know. We first evaluate SBML for continuous outcomes, especially when the sample size is small, and investigate the effects of various tuning parameters on the performance of SBML. Simulations show that SBML achieves better predictions in terms of mean squared errors or misclassification error rates for various situations under consideration than conventional statistical methods, such as full linear models, optimal or ridge regressions and mixed effect models, as well as ML methods including kernel and decision tree methods. We also extend and show how SBML can be flexibly applied to binary outcomes. Through numerical and simulation studies, we confirm that SBML performs well compared to classical statistical methods, even when the sample size is small and in the presence of unmeasured predictors and/or noise variables. Although SBML performs well with small sample sizes, it may not be computationally efficient for large sample sizes. Therefore, we propose Recursive SBML (RSBML), which can save computing time, with some tradeoffs for accuracy. In this sense, RSBML can also be viewed as a combination of unsupervised learning (dimension reduction) and supervised learning (prediction). Recursive learning resembles the natural human way of learning. It is an efficient way of learning from complicated large data. Based on the simulation results, RSBML performs much faster than SBML with reasonable accuracy for large sample sizes.

L’avènement du séquençage haut débit a mis en lumière l’hétérogénéité des cancers du sein qui peuvent être groupés en fonction d’altérations moléculaires spécifiques qui sont pour certaines à la base de thérapies ciblées dans le cadre de la médecine personnalisée. Néanmoins de nombreuses complications viennent compromettre le succès thérapeutique de ces approches. En effet, l’une des thérapies ciblées les plus efficaces développées récemment, les inhibiteurs de CDK4/6, sont prescrits chez tous les patients HR+/HER aux stades avancés de la maladie alors même qu’aucun biomarqueur n’a pour l’heure été identifié. Ainsi les données pharmacodynamiques et les marqueurs pronostics font cruellement défaut pour ces patients. Afin d’identifier de tels marqueurs, nous avons conduit une étude clinique « fenêtre d’opportunité » incluant 100 patients à un stade précoce de la maladie. L’analyse en IHC et les études de profilage expression génomique des tumeurs a permis de montrer qu’une courte exposition au palbociclib, un inhibiteur de CDK4/6 induisait un arrêt du cycle cellulaire révélé par une diminution de phospho-Rb et Ki67. Cette corrélation entre diminution du phospho-Rb et la diminution de la prolifération suggère d’ailleurs son utilisation comme biomarqueur de la réponse au palbociclib. Une analyse sur puce à cDNA a permis d’identifier un panel de gènes régulateurs de la prolifération (MKI67, TOP2A, BIRC5) et de la machinerie du cycle cellulaire (PLK1, FOXM1) modulé par le palbociclib. Bien que nous n’ayons pu identifier de marqueurs de résistance au palbociclib en condition basale, nous avons observé des niveaux élevés de CCNE chez les patients traités résistants au palbociclib. Cette donnée a été confirmée chez les patients aux stades avancés de la maladie dans le cadre d’une étude menée en collaboration avec un groupe britannique. D’autres données obtenues en collaboration avec une équipe de l’université Vanderbilt, ont par ailleurs permis de suggérer une contribution des inhibiteurs de CDK4/6 à la réversion de la résistances aux hormonothérapie en inhibant l’expression les gènes cibles du facteur de transcription E2F4. Pour finir, les activités biologiques et cliniques des différents inhibiteurs de CDK4/6 disponibles n’étant pas exactement identiques, un second essai clinique « fenêtre d’opportunité » nous a permis de mettre en évidence un profil de toxicité distinct de l’abemaciclib et de montrer que, contrairement au palbociclib, l’abemaciclib montre une efficacité lorsqu’il est utilisé seul. Une des explications possible de ces différentes activités serait un spectre d’action de l’abemaciclib ciblant plus efficacement la CDK9, même si l’impact clinique associé n’a pas été examiné en détail et qu’une comparaison rigoureuse de l’activité de ces deux inhibiteurs de CDK n’a pas encore été réalisée. / New sequencing methods have revealed that breast cancer is heterogeneous and characterized by different subgroups harboring specific molecular alterations for which targeted therapies have been developed with the hope of implementing personalized medicine. However, this approach has been proven far too simplistic. Indeed, one of the latest and more efficient targeted therapies to be developed in breast cancer are the CDK4/6 inhibitors, approved for all HR+/HER2- advanced breast cancers. So far, and despite the significant number of patients treated with these drugs, no biomarkers of efficacy have been identified and no clear information about pharmacodynamics have been presented. In order to determine biomarkers of efficacy and pharmacodynamics of palbociclib, the first approved CDK4/6 inhibitor, we conducted a window of opportunity clinical trial in 100 early breast cancer patients. IHC and GE analyses identified that a short period of palbociclib treatment was able to induce cell cycle arrest as determined by decreased phospho-Rb expression and this was accompanied by a profound decrease in proliferation as determined by lnKi67<1 after treatment, with a correlation between changes in proliferation and changes in phospho-Rb, suggesting that early decrease in phospho-Rb could be linked to sensitivity to this drug. Microarray analyses identified that palbociclib modulates genes involved in proliferation (such as MKI67, TOP2A, BIRC5) and cell cycle (such as PLK1, FOXM1). Despite we were not able to identify baseline biomarkers of resistance to this treatment, we observed that levels of CCNE remained high in palbociclib-resistant patients. This finding was further validated in collaboration with an-UK research group who had conducted biomarker research in the advanced setting. Moreover, our data helped also to determine in a different collaboration with Vanderbilt University, that CDK/6 inhibitors might contribute to reverse endocrine resistance generated by activation of genes linked to the E2F4 transcription factor. Finally, as preclinical and clinical data suggest some diversity between different CDK4/6 inhibitors, we decided to conduct a second window of opportunity trial with a second CDK4/6 inhibitor, abemaciclib, who has shown different toxicity profile and, unlike palbociclib, significant efficacy as single-agent. One suggested explanation could be due to a higher impact on CDK9, although its clinical impact has not been determined and no comparison between these two drugs has been performed.

No description available.