Planning and implementation efficacy as a determinant of health risk communication outcomes: Case studies of the U.S. Public Health Service (PHS)

This study was conducted under the auspices of the Subcommittee on Risk Communication and Education of the Committee to Coordinate Environmental Health and Related Programs (CCEHRP) to determine how Public Health Service (PHS) agencies are communicating information about health risk, what factors contributed to effective communication efforts, and what specific principles, strategies, and practices best promote more effective health risk communication outcomes.^ Member agencies of the Subcommittee submitted examples of health risk communication activities or decisions they perceived to be effective and some examples of cases they thought had not been as effective as desired. Of the 10 case studies received, 7 were submitted as examples of effective health risk communication, and 3, as examples of less effective communication.^ Information contained in the 10 case studies describing the respective agencies' health risk communication strategies and practices was compared with EPA's Seven Cardinal Rules of Risk Communication, since similar rules were not found in any PHS agency. EPA's rules are: (1) Accept and involve the public as a legitimate partner. (2) Plan carefully and evaluate your efforts. (3) Listen to the public's specific concerns. (4) Be honest, frank, and open. (5) Coordinate and collaborate with other credible sources. (6) Meet the needs of the media. (7) Speak clearly and with compassion.^ On the basis of case studies analysis, the Subcommittee, in their attempts to design and implement effective health risk communication campaigns, identified a number of areas for improvement among the agencies. First, PHS agencies should consider developing a focus specific to health risk communication (i.e., office or specialty resource). Second, create a set of generally accepted practices and guidelines for effective implementation and evaluation of PHS health risk communication activities and products. Third, organize interagency initiatives aimed at increasing awareness and visibility of health risk communication issues and trends within and between PHS agencies.^ PHS agencies identified some specific implementation strategies the CCEHRP might consider pursuing to address the major recommendations. Implementation strategies common to PHS agencies emerged in the following five areas: (1) program development, (2) building partnerships, (3) developing training, (4) expanding information technologies, and (5) conducting research and evaluation. ^

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.