A New Approach for Identifcation of Cancer-related Pathways using Protein Networks and Genomic Data

Cancer cells have anomalous development and proliferation due to disturbances in their control systems. Te study of the behavior of cellular control system requires high-throughput dynamical data. Unfortunately, this type of data is not largely available. Tis fact motivates the main issue of this article: how to use static omics data and available biological knowledge to get new information about the elements of the control system in cancer cells. Two important measures to access the state of the cellular control system are the gene expression profle and the signaling pathways. Tis article uses a combination of these two static omics data to gain insights on the states of a cancer cell. To extract information from this kind of data, a statistical computational model was formalized and implemented. In order to exemplify the application of some aspects of the developed conceptual framework, we verifed the hypothesis that different types of cancer cells have different disturbed signaling pathways. To this end, we developed a method that recovers small protein networks, called motifs, which are differentially represented in some subtypes of breast cancer. Tese differentially represented motifs are enriched with specifc gene ontologies as well as with new putative cancer genes.