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.

Instrumental variable method for the causal relationship between soluble receptor for advanced glycation end-products (sRAGE) and risk of pancreatic cancer

This thesis project is motivated by the potential problem of using observational data to draw inferences about a causal relationship in observational epidemiology research when controlled randomization is not applicable. Instrumental variable (IV) method is one of the statistical tools to overcome this problem. Mendelian randomization study uses genetic variants as IVs in genetic association study. In this thesis, the IV method, as well as standard logistic and linear regression models, is used to investigate the causal association between risk of pancreatic cancer and the circulating levels of soluble receptor for advanced glycation end-products (sRAGE). Higher levels of serum sRAGE were found to be associated with a lower risk of pancreatic cancer in a previous observational study (255 cases and 485 controls). However, such a novel association may be biased by unknown confounding factors. In a case-control study, we aimed to use the IV approach to confirm or refute this observation in a subset of study subjects for whom the genotyping data were available (178 cases and 177 controls). Two-stage IV method using generalized method of moments-structural mean models (GMM-SMM) was conducted and the relative risk (RR) was calculated. In the first stage analysis, we found that the single nucleotide polymorphism (SNP) rs2070600 of the receptor for advanced glycation end-products (AGER) gene meets all three general assumptions for a genetic IV in examining the causal association between sRAGE and risk of pancreatic cancer. The variant allele of SNP rs2070600 of the AGER gene was associated with lower levels of sRAGE, and it was neither associated with risk of pancreatic cancer, nor with the confounding factors. It was a potential strong IV (F statistic = 29.2). However, in the second stage analysis, the GMM-SMM model failed to converge due to non- concaveness probably because of the small sample size. Therefore, the IV analysis could not support the causality of the association between serum sRAGE levels and risk of pancreatic cancer. Nevertheless, these analyses suggest that rs2070600 was a potentially good genetic IV for testing the causality between the risk of pancreatic cancer and sRAGE levels. A larger sample size is required to conduct a credible IV analysis.^

Risk factors for surgical site infections among patients undergoing major colon surgery in the United States 2007-2009

Background: Surgical site infections (SSIs) after abdominal surgeries account for approximately 26% of all reported SSIs. The Center for Disease Control and Prevention (CDC) defines 3 types of SSIs: superficial incisional, deep incisional, and organ/space. Preventing SSIs has become a national focus. This dissertation assesses several associations with the individual types of SSI in patients that have undergone colon surgery. ^ Methods: Data for this dissertation was obtained from the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP); major colon surgeries were identified in the database that occurred between the time period of 2007 and 2009. NSQIP data includes more than 50 preoperative and 30 intraoperative factors; 40 collected postoperative occurrences are based on a follow-up period of 30 days from surgery. Initially, four individual logistic regressions were modeled to compare the associations between risk factors and each of the SSI groups: superficial, deep, organ/space and a composite of any single SSI. A second analysis used polytomous regression to assess simultaneously the associations between risk factors and the different types of SSIs, as well as, formally test the different effect estimates of 13 common risk factors for SSIs. The final analysis explored the association between venous thromboembolism (VTEs) and the different types of SSIs and risk factors. ^ Results: A total of 59,365 colon surgeries were included in the study. Overall, 13% of colon cases developed a single type of SSI; 8% of these were superficial SSIs, 1.4% was deep SSIs, and 3.8% were organ/space SSIs. The first article identifies the unique set of risk factors associated with each of the 4 SSI models. Distinct risk factors for superficial SSIs included factors, such as alcohol, chronic obstructive pulmonary disease, dyspnea and diabetes. Organ/space SSIs were uniquely associated with disseminated cancer, preoperative dialysis, preoperative radiation treatment, bleeding disorder and prior surgery. Risk factors that were significant in all models had different effect estimates. The second article assesses 13 common SSI risk factors simultaneously across the 3 different types of SSIs using polytomous regression. Then each risk factor was formally tested for the effect heterogeneity exhibited. If the test was significant the final model would allow for the effect estimations for that risk factor to vary across each type of SSI; if the test was not significant, the effect estimate would remain constant across the types of SSIs using the aggregate SSI value. The third article explored the relationship of venous thromboembolism (VTE) and the individual types of SSIs and risk factors. The overall incidence of VTEs after the 59,365 colon cases was 2.4%. All 3 types of SSIs and several risk factors were independently associated with the development of VTEs. ^ Conclusions: Risk factors associated with each type of SSI were different in patients that have undergone colon surgery. Each model had a unique cluster of risk factors. Several risk factors, including increased BMI, duration of surgery, wound class, and laparoscopic approach, were significant across all 4 models but no statistical inferences can be made about their different effect estimates. These results suggest that aggregating SSIs may misattribute and hide true associations with risk factors. Using polytomous regression to assess multiple risk factors with the multiple types of SSI, this study was able to identify several risk factors that had significant effect heterogeneity across the 3 types of SSI challenging the use of aggregate SSI outcomes. The third article recognizes the strong association between VTEs and the 3 types of SSIs. Clinicians understand the difference between superficial, deep and organ/space SSIs. Our results indicate that they should be considered individually in future studies.^