Finding perturbed signaling pathways in TCGA ovarian cancers: graph models and algorithms
Abstract: Cancers are caused by somatic mutations that lead to hallmark changes in cellular signaling systems. Large-scale efforts have been devoted to identify somatic and germ-line mutations from a large number of tumor samples, including the Cancer Genome Atlas (TCGA) project and the international network of cancer genomic projects. It is not uncommon that cancer cells accumulate a large number of mutations during development; some are cancer-causing (driver mutations) while others have no relation to cancers (passenger mutations). A major thrust in cancer genomic research is to identify driver mutations and reconstruct perturbed signaling pathways that underlie the hallmark behaviors of cancers. This body of information will shed light on the disease mechanisms of cancers, reveal novel drug targets, and more importantly guide patient treatment based on personal genetic information.
In this talk, first, our new graph models of revealing perturbed signaling pathways by integrating genomic mutation data with functional genomic data will be introduced. The main idea underlying our approach is to use differential expression gene modules as the readouts of signaling pathway perturbations, which enable us to reconstruct a signaling pathway by finding the mutations that are strongly associated with a gene expression module. We developed a framework that unifies ontology-guided knowledge mining and graph-based data mining to achieve the goal.
As biological systems are complex, good models are usually very involved and need much computation, especially if they are NP-hard. NP-hard problems in general are so hard such that current algorithms cannot solve them in practical time even using supercomputers. Thus algorithms can become bottle-necks in the research of Bioinformatics. In the talk, we will introduce how we take advantage of the fact that the solution sizes in the computational problems from our models are small to design very efficient parameterized algorithm. Hence, we can finish the computation in reasonable time (in minutes or hours) by using a normal PC.
In summary, we will present our new graph models and algorithms to find perturbed signaling pathways in TCGA ovarian cancers in the talk. Our methods are able to discover perturbation of many well-known cancer signaling pathways, and we conjecture that some of our results may help to discover novel pathways in cancers.