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.

Irritable bowel syndrome (IBS) in U.S. adult travelers': The importance of travel to the development of IBS

Background. Irritable bowel syndrome is a gastrointestinal disorder that is potentially linked to international travel at an undetermined frequency.^ Methods. A self-administered questionnaire was distributed through mail to five hundred and ninety-one patients that were twice diagnosed with irritable bowel syndrome at Kelsey Seybold Clinic in Houston, TX. Responses to survey questions were used to assess patient travel history, IBS symptomology, and disease classification.^ Results. Of the five hundred and ninety-one patients that were mailed a questionnaire, two hundred and twenty one patients returned questionnaires and two hundred and one met inclusion criteria. Of the participants reporting international travel within six months of developing their chronic intestinal disorder, 60% were classified as having PI-IBS, while 25% had IBS, 10% had PI-UFBD, and 5% had UFBD. A majority of the subjects who traveled six months before onset of their functional bowel disease had a post-infectious form of IBS and reported a start and worsening of symptoms with an acute bout of diarrhea. It was common for those traveling six months before travel and labeled PI-IBS to have enteric symptoms that led to lifestyle adjustments. ^ Conclusion. International travel had a significant effect on the classification of IBS among patients which relates to the differences in IBS symptoms and perhaps pathogenesis among travelers versus non-travelers. ^

Pathologic Markers of Prognosis in Ampullary Carcinoma

Ampullary cancer is a rare gastrointestinal malignancy that can be curable with surgical resection of localized disease. The benefit of adjuvant therapy, however, remains unknown in these patients partly because of difficulty in stratifying which patients are at high risk for recurrence. To better identify those patients who may benefit from adjuvant therapy, I conducted a retrospective analysis the pathology reports from 176 patients with surgically resected ampullary cancer who had not received any neoadjuvant therapy, the systemic therapy given, and the patient outcomes. A tissue microarray (TMA) of 95 surgically resected ampullary specimens was also constructed to examine whether there is a correlation between classical immunohistochemical profiles for intestinal and pancreaticobiliary tumors and their histologic classification. In this study, I confirmed the prognostic value of advanced T-stage, nodal metastases, and lymphovascular invasion. Patients whose tumors had “high risk” features had a significantly worse overall survival (p=.002). Furthermore, my research highlighted the importance of histology and its impact on survival, with pancreaticobiliary-like features being a negative prognostic factor (p=0.001). Importantly, patients whose tumors have pancreaticobiliary histology appear to benefit from adjuvant therapy, further implicating histology as an important pathologic marker (p=0.053). In addition, the TMA confirmed a correlation between classical immunohistochemical profiles for intestinal and pancreaticobiliary tumors and histologic classification. My research findings suggest that histology subtypes, T-stage, nodal metastases, and lymphovascular invasion should all be taken into consideration when determining which patients with ampullary cancer may benefit from further adjuvant therapy.