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.

Effect of insurance status on stage of breast cancer diagnosis

Objective: The purpose of this study is to compare the stages of breast cancer presented between the insured and uninsured patients diagnosed at The Rose, an active non-profit breast healthcare organization to determine if uninsured patients present with more advanced stage breast cancer as compared to their insured counterparts. ^ Study Design: Retrospective cross-sectional study. ^ Methods: The study included 1,265 patients who received breast healthcare services and were diagnosed with breast cancer at The Rose between FY 2007 and FY 2012. 738 of the patients in the study were presumably uninsured since their breast healthcare services were sponsored through various funding sources and they were navigated into treatment through The Rose patient navigation program. We compared breast cancer stages for women who had insurance with those who did not have insurance. The effects of age and race/ethnicity along with the insurance status on the stage of reast cancer diagnosis were also analyzed. We calculated the odds ratio using the contingency tables; and estimated odds ratios (ORs) and 95% confidence intervals (CIs) using ordinal logistic regression by applying multiple imputation method for missing tumor stage data. ^ Results: The ordered logistic regression analysis with ordered tumor stage as dependent variable and uninsured as independent variable gave us an odds ratio of 1.73 (OR=1.73; p-value<0.05; 95% CI: 1.36 - 2.12). ^ Conclusions: Insurance status is a strong predictor of stage of breast cancer diagnosed among women seen at The Rose. Uninsured women seen at The Rose are almost twice as likely to present at a advanced stage of breast cancer as opposed to their insured counterparts.^

Characterization of anti -sulfatide autoantibodies in demyelinating diseases

Multiple sclerosis (MS) is the most common autoimmune disease of the central nerve system and Guillain Barré Syndrome (GBS) is an inflammatory neuropathy involving the peripheral nerves. Anti-myelin immunoglobins may play a role in the demyelination processes of the both diseases. Sulfatide is an abundant glycolipid on myelin and is a candidate target antigen for disease related autoantibodies. The objective of this study was to characterize anti-sulfatide antibodies and compare antibodies from GBS and MS patients with fetal antibodies. Our hypothesis is that some B cells producing disease-associated autoantibodies are derived from or related to B cells of the fetal repertoire. Here we report that reactivity of plasma IgM against sulfatide was elevated in twelve MS patients compared with twelve normal subjects. This result implies that anti-sulfatide antibodies are disease-related. A total of sixteen human B lymphocyte clones producing anti-sulfatide autoantibodies were isolated from MS patients, GBS patients and a human fetus. Seven of the clones were from three MS patients, four of the clones were from three GBS patients and five were from the spleen of a twenty-week human fetus. Sequences have been obtained for the heavy and light chain variable regions (VDJ and VJ regions) of all of the anti-sulfatide immunoglobulins. Seven of the sixteen antibodies used VH3 for the variable region gene of the heavy chain consistent with the rate of VH3 usage in randomly selected B cells. Somatic mutations were significantly more frequent in the patient antibodies than in the fetus and somatic mutations in CDR's (Complementarity Determining Region) were significantly more frequent than in framework regions. No significant difference was found between patients and fetus in length of VH CDRIII. However, it is reported that antibodies from randomly selected normal adult B cells have longer CDRIII lengths than those of the fetus (Sanz I, 1991 Journal of Immunology Sep 1;147(5):1720-9). Our results are consistent with derivation of the precursors of B cells producing these autoantibodies from B cells related to those of the fetal repertoire. These findings are consistent with a model in which quiescent B cells from clones produced early in development undergo proliferation in dysregulated disease states, accumulating somatic mutations and increasing in reactivity toward self-antigens. ^ Epitope mapping and molecular modeling were done to elucidate the relationships between antibody structure and binding characteristics. The autoantibodies were tested for binding activity to three different antigens: sulfatide, galactoceramide and ceramide. Molecular modeling suggests that antibodies with positive charge surrounded by or adjacent to hydrophobic groups in the binding pocket bind to the head of sulfatide via the sulfate group through electrostatic interactions. However, the antibodies with hydrophobic groups separated from positive charges appear to bind to the hydrophobic tail of sulfatide. This observation was supported by a study of the effect of NaCl concentration on antigen binding. The result suggested that electrostatic interactions played a major role in sulfate group binding and that hydrophobic interactions were of greater importance for binding to the ceramide group. Our three-dimensional structure data indicated that epitope specificity of these antibodies is more predictable at the level of tertiary than primary structure and suggested positive selection based on structure occurred in the. formation of those autoantibodies. ^