Searching for Collaborating Cancer Genes
Cancers are caused by an accumulation of multiple independent mutations that collectively deregulate cellular pathways, e.g. such as those regulating cell division and cell-death. Multiple independent mutations within one tumor hints towards a cooperation between the mutated genes. In this study we focus on the detection of statistically significant co-mutations, by analyzing a collection of publicly available retroviral insertional mutagenesis datasets. We propose a two-dimensional Gaussian Kernel Convolution method (2DGKC), a computational technique that identifies the cooperating mutations in insertional mutagenesis data. We define the Common Co-occurrence of Insertions (CCI), signifying the co- mutations that are statistically significant across all different screens in the RTCGD. Significance estimates are made on multiple scales, and the results visualized in a scale space, thereby providing valuable extra information on the putative cooperation.