Identifying key somatic alterations in cancer is a critical step in understanding the mechanisms that ultimately determine a patient's treatment outcome. High-throughput data are providing a comprehensive view of these molecular changes for individual samples, and new technologies allow for the simultaneous genome-wide assay of genome copy number variations, gene expression, DNA methylation, and epigenetics of patient tumor samples and established cancer cell lines. Analyses of current datasets find that genetic alterations between tumors can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. This work presents a novel method called PARADIGM for inferring tumor-specific genetic pathway activities incorporating curated gene-pathway interactions. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of itself and its upstream and downstream products, allowing the incorporation of many types of -omic data as evidence. The method predicts the degree to which a given pathway's activities (e.g. internal gene states, interactions, or high-level outputs) are altered in the tumor sample using probabilistic inference. Compared to a competing pathway activity inference approaches, PARADIGM identifies altered activities in cancer-related pathways with fewer false-positives, as shown in glioblastoma multiform (GBM), ovarian (OV) and breast cancer datasets. PARADIGM also identified consistent pathway-level activities for subsets of the GBM and ovarian serous cystadenocarcinoma patients that are overlooked when genes are considered in isolation. Furthermore, grouping GBM and OV patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. Further analysis was done using an integrated pathway termed the SuperPathway that gives a more consistent global view of biology, illustrated through the analysis of ovarian, breast, and cross-cancer analysis. Finally, PARADIGM was used to simulated knockdowns in a pathway model as an approach to understand drug effects and provide a rational approach towards combination therapies. These findings suggest that therapeutics might be personalized and chosen to hit target genes at critical points in the commonly perturbed cancer pathway(s) of specific patients using this model.
|Commitee:||Pourmand, Nader, Stuart, Joshua|
|School:||University of California, Santa Cruz|
|Department:||Biomolecular Engineering and Bioinformatics|
|School Location:||United States -- California|
|Source:||DAI-B 74/01(E), Dissertation Abstracts International|
|Keywords:||Bayes net, Cancer pathways, Factor graphs, Glioblastoma multiforme|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be