Detection of driver pathways using mutated gene network in cancer

Abstract

Distinguishing driver pathways has been extensively studied because they are critical for understanding the development and molecular mechanisms of cancers. Most existing methods for driver pathways are based on high coverage as well as high mutual exclusivity, with the underlying assumption that mutations are exclusive. However, in many cases, mutated driver genes in the same pathways are not strictly mutually exclusive. Based on this observation, we propose an index for quantifying mutual exclusivity between gene pairs. Then, we construct a mutated gene network for detecting driver pathways by integrating the proposed index and coverage. The detection of driver pathways on the mutated gene network consists of two steps: raw pathways are obtained using a CPM method, and the final driver pathways are selected using a strict testing strategy. We apply this method to glioblastoma and breast cancers and find that our method is more accurate than state-of-the-art methods in terms of enrichment of KEGG pathways. Furthermore, the detected driver pathways intersect with well-known pathways with moderate exclusivity, which cannot be discovered using the existing algorithms. In conclusion, the proposed method provides an effective way to investigate driver pathways in cancers.