The transformation of a health care system: An ecology perspective

Beginning in the early 1980s, the health care system experienced momentous realignments. Fundamental changes in structures of traditional health care organizations, shifts in authority and relationships of professionals and institutions, and the increasing influence of managed care contributed to a relatively stable industry entering into a state of turbulence. The dynamics of these changes are recurring themes in the health services literature. The purpose of this dissertation was to examine the content of this literature over a defined time period and within the perspective of a theory of organizational change. ^ Using a theoretical framework based upon the organizational theory known as Organizational Ecology, secondary data from the period between 1983 and 1994 was reviewed. Analysis of the literature identified through a defined search methodology was focused upon determining the manner in which the literature characterized changes that were described. Using a model constructed from fundamentals of Organizational Ecology with which to structure an assessment of content, literature was summarized for the manner and extent of change in specific organizational forms and for the changes in emphasis by the environmental dynamics directing changes in the population of organizations. Although it was not the intent of the analysis to substantiate causal relationships between environmental resources selected as the determinants of organizational change and the observed changes in organizational forms, the structured review of content of the literature established a strong basis for inferring such a relationship. ^ The results of the integrative review of the literature and the power of the appraisal achieved through the theoretical framework constructed for the analysis indicate that there is considerable value in such an approach. An historical perspective on changes which have transformed the health care system developed within a defined organizational theory provide a unique insight into these changes and indicate the need for further development of such an analytical model. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.