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.

<div>Classification is an important supervised learning technique with numerous applications. This dissertation addresses two research problems in this area. The first is multicategory classification methods for high dimensional data. To handle high dimension low sample size (HDLSS) data with uneven group sizes (i.e., imbalanced data), we develop a new classification method called angle-based multicategory distance-weighted support vector machine (MDWSVM). It is motivated from its binary counterpart and has the merits of both the support vector machine (SVM) and distance-weighted discrimination (DWD) methods while alleviating both the data piling issue of SVM and the imbalanced data issue of DWD. Theoretical results and numerical studies are used to demonstrate the advantages of our MDWSVM method over existing methods.</div><div><br></div><div>The second part of the dissertation is on the application of classification methods to precision medicine problems. Because one-stage precision medicine problems can be reformulated as weighted classification problems, the subtle differences between classification methods may lead to different application performances under this setting. Among the margin-based classification methods, we propose to use the distance weighted discrimination outcome weighted learning (DWD-OWL) method. We also extend the model to handle negative rewards for better generality and apply the angle-based idea to handle multiple treatments. The proofs of Fisher consistency for DWD-OWL in both the binary and multicategory cases are provided. Under mild conditions, the insensitivity of DWD-OWL for imbalanced setting is also demonstrated.</div>

Response-Adaptive (RA) designs are used to adaptively allocate patients in clinical trials. These methods have been generalized to include Covariate-Adjusted Response-Adaptive (CARA) designs, which adjust treatment assignments for a set of covariates while maintaining features of the RA designs. Challenges may arise in multi-center trials if differential treatment responses and/or effects among sites exist. We propose Site-Adjusted Response-Adaptive (SARA) approaches to account for inter-center variability in treatment response and/or effectiveness, including either a fixed site effect or both random site and treatment-by-site interaction effects to calculate conditional probabilities. These success probabilities are used to update assignment probabilities for allocating patients between treatment groups as subjects accrue. Both frequentist and Bayesian models are considered. Treatment differences could also be attributed to differences in social determinants of health (SDH) that often manifest, especially if unmeasured, as spatial heterogeneity amongst the patient population. In these cases, patient residential location can be used as a proxy for these difficult to measure SDH. We propose the Location-Adjusted Response-Adaptive (LARA) approach to account for location-based variability in both treatment response and/or effectiveness. A Bayesian low-rank kriging model will interpolate spatially-varying joint treatment random effects to calculate the conditional probabilities of success, utilizing patient outcomes, treatment assignments and residential information. We compare the proposed methods with several existing allocation strategies that ignore site for a variety of scenarios where treatment success probabilities vary.

Next generation sequencing is increasingly used as a diagnostic tool in the clinical setting. This is driven by the vast increase in molecular targeted therapy, which requires detailed information on what genetic variants are present in patient samples. In the hospital setting, most cancer diagnostics are based on Formalin Fixed Paraffin Embedded (FFPE) samples. The FFPE routine is very beneficial for logistical purposes and for some histopathological analyses, but creates problems for molecular diagnostics based on DNA. These problems derive from sample immersion informalin, which results in DNA fragmentation, interstrand DNA crosslinking and sequence artefacts due to hydrolytic deamination. Distinguishing such artefacts from true somatic variants can be challenging, thus affecting both research and clinical analyses. In order to identify FFPE-artefacts from true variants in next generation sequencing data from FFPE samples, I developed the novelprogram FUSAC (FFPE tissue UMI based Sequence Artefact Classifier) for the facility Clinical Genomics in Uppsala. FUSAC utilizes UniqueMolecular Identifiers (UMI's) to identify and group sequencing reads based on their molecule of origin. By using UMI's to collapse duplicate paired reads into consensus reads, FFPE-artefacts are classified through comparative analysis of the positive and negative strand sequences. My findings indicate that FUSAC can succesfully classify UMI-tagged next generation sequencing reads with FFPE-artefacts, from sequencing reads with true variants. FUSAC thus presents a novel approach in bioinformatic pipelines for studying FFPE-artefacts.

abstract: Proteins are essential for most biological processes that constitute life. The function of a protein is encoded within its 3D folded structure, which is determined by its sequence of amino acids. A variation of a single nucleotide in the DNA during transcription (nSNV) can alter the amino acid sequence (i.e., a mutation in the protein sequence), which can adversely impact protein function and sometimes cause disease. These mutations are the most prevalent form of variations in humans, and each individual genome harbors tens of thousands of nSNVs that can be benign (neutral) or lead to disease. The primary way to assess the impact of nSNVs on function is through evolutionary approaches based on positional amino acid conservation. These approaches are largely inadequate in the regime where positions evolve at a fast rate. We developed a method called dynamic flexibility index (DFI) that measures site-specific conformational dynamics of a protein, which is paramount in exploring mechanisms of the impact of nSNVs on function. In this thesis, we demonstrate that DFI can distinguish the disease-associated and neutral nSNVs, particularly for fast evolving positions where evolutionary approaches lack predictive power. We also describe an additional dynamics-based metric, dynamic coupling index (DCI), which measures the dynamic allosteric residue coupling of distal sites on the protein with the functionally critical (i.e., active) sites. Through DCI, we analyzed 200 disease mutations of a specific enzyme called GCase, and a proteome-wide analysis of 75 human enzymes containing 323 neutral and 362 disease mutations. In both cases we observed that sites with high dynamic allosteric residue coupling with the functional sites (i.e., DARC spots) have an increased susceptibility to harboring disease nSNVs. Overall, our comprehensive proteome-wide analysis suggests that incorporating these novel position-specific conformational dynamics based metrics into genomics can complement current approaches to increase the accuracy of diagnosing disease nSNVs. Furthermore, they provide mechanistic insights about disease development. Lastly, we introduce a new, purely sequence-based model that can estimate the dynamics profile of a protein by only utilizing coevolution information, eliminating the requirement of the 3D structure for determining dynamics. / Dissertation/Thesis / Doctoral Dissertation Physics 2016

Background: Given the complex and progressive nature of Alzheimer's disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. Methods: Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer's Disease Assessment Scale-cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups' distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. Results: The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. Conclusions: Our results, 'learned' from clinical data, indicate the existence of at least three subgroups of Alzheimer's patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.

Precision medicine provides targeted treatment for an individual patient based on disease mechanisms, promoting health care. Matched transcriptomes derived from a single subject enable uncovering patient-specific dynamic changes associated with disease status. Yet, conventional statistical methodologies remain largely unavailable for single-subject transcriptome analysis due to the "single-observation" challenge. We hypothesize that, with statistical learning approaches and large-scale inferences, one can learn useful information from single-subject transcriptome data by identifying differentially expressed genes (DEG) / pathways (DEP) between two transcriptomes of an individual. This dissertation is an ensemble of my research work in single-subject transcriptome analytics, including three projects with varying focuses. The first project describes a two-step approach to identify DEPs by employing a parametric Gaussian mixture model followed by Fisher's exact tests. The second project relaxes the parametric assumption and develops a nonparametric algorithm based on k-means, which is more flexible and robust. The third project proposes a novel variance stabilizing framework to transform raw gene counts before identifying DEGs, and the transformation strategically by-passes the challenge of variance estimation in single-subject transcriptome analysis. In this dissertation, I present the main statistical methods and computational algorithms for all the three projects, as well as their real-data applications to personalized treatments.

Our increasing understanding of disease mechanisms coupled with technological advances has facilitated the generation of pharmaceutical proteins, which are able to address yet unmet medical needs. Diseases that were fatal in the past can now be treated with novel biological medications improving and prolonging life for many patients. Pharmaceutical protein production is, however, a complex undertaking, which is by no means problem-free. The demand for more complex proteins and the realization of the importance of post-translational modifications have led to an increasing use of mammalian cells for protein expression. Despite improvements in design and production, the costs required for the development of pharmaceutical proteins still are far greater than those for conventional, small molecule drugs. To render such treatments affordable for healthcare suppliers and assist in the implementation of precision medicine, further progress is needed. In five papers this thesis describes strategies and methods that can help to advance the development and manufacturing of pharmaceutical proteins. Two platforms for antibody engineering have been developed and evaluated, one of which allows for efficient screening of antibody libraries whilst the second enables the straightforward generation of bispecific antibodies. Moreover, a method for epitope mapping has been devised and applied to map the therapeutic antibody eculizumab’s epitope on its target protein. In a second step it was shown how this epitope information can be used to stratify patients and, thus, contribute to the realization of precision medicine. The fourth project focuses on the cell line development process during pharmaceutical protein production. A platform is described combining split-GFP and fluorescence-activated droplet sorting, which allows for the efficient selection of highly secreting cells from a heterogeneous cell pool. In an accompanying study, the split-GFP probe was improved to enable shorter assay times and increased sensitivity, desirable characteristics for high-throughput screening of cell pools. In summary, this thesis provides tools to improve design, development and production of future pharmaceutical proteins and as a result, it makes a contribution to the goal of implementing precision medicine through the generation of more cost-effective biopharmaceuticals for well-characterized patient groups. / <p>QC 20170828</p>

Multiple Myeloma (MM) is a treatable, yet incurable, malignancy of bone marrowplasma cells. This cancer affects many patients and many succumb to relapse of tumor burden despite a large number of available chemotherapeutic agents developed for therapy. This is because MM tumors are heterogeneous and receive protection from therapeutic agents by the microenvironment and other mechanisms including homologous MM-MM aggregation. Therefore, therapy failure and frequent patient relapse is due to the evolution of drug resistance, not a lack of available drugs. To analyze and understand this problem, the evolution of drug resistance has been explored and presented herein. We seek to describe the methods through which MM cells become resistant to therapy, and how this resistance evolves throughout a patient’s treatment history. We achieve this in five steps. First we review the patient’s clinical history, including treatments and changes in tumor burden. Second, we trace the evolutionary tree of sub-clones within the tumor burden using standard of care fluorescence in situ hybridization (FISH). Thirdly, immunohistochemistry slides are stained and aligned to quantify the level of environmental protection received by surrounding cells and plasma in the bone marrow microenvironment (coined environment mediated drug resistance score [EMDR]). The fourth analysis type is produced through a novel 384-well plate ex vivo chemosensitivity assay to quantify sensitivity of primary MM cells to chemotherapeutic agents and extrapolate these findings to 90-day clinical response predictions. In addition to direct clinical application in the choice of best treatment, this tool was also used to study changes in sensitivity of patient tumors to other drugs, and it was observed that, upon relapse, in addition to developing resistance to the current line of therapy, tumors become cross-resistant to agents that they were never exposed to. Finally, MM-MM homologous aggregation is quantified to assess the level of drug resistance contributed by clustering of patient tumor cells, which causes upregulation of Bcl-2 expression and other resistance mechanisms1. The findings of such experimentation improve comprehension of the driving factors that contribute to drug resistance evolution on a personalized treatment basis. The aforementioned factors all contribute in varying degrees for unique patient cases, seven of which are presented in depth for this project. In summary: Environmental protection plays a critical initial role in drug resistance, which is followed by increase in tumor genetic heterogeneity as a result of mutations and drug-induced Darwinian selection. Eventually, environment-independent drug resistant subpopulations emerge, allowing the tumor to spread to unexplored areas of the bone marrow while maintaining inherited drug resistant phenotype2. It is our hope that these findings will help in shifting perspective regarding optimal management of MM by finding new therapeutic procedures that address all aspects of drug resistance to minimize chance of relapse and improve quality of life for patients.

The Argus Commission identified three major federal priorities related to health care, including the precision medicine initiative, the Cancer Moonshot and the opioid abuse epidemic. Current activities at the federal level were summarized and an analysis of activities within the profession, and academic pharmacy specifically, was prepared. The implications for pharmacy education, research and practice are compelling in all three areas. Recommendations, suggestions and two policy statements aim to optimize the attention to these priorities by the academy. Further, aligning the AACP Strategic Engagement agenda with the opportunities and threats acknowledged in the analysis is essential.