Most cancer genomes contain a large number of mutations. Most of them are passenger mutations without direct effects on tumor signaling. A few of them are driver mutations that change the function of proteins in tumor cells and allow them to proliferate at a faster rate than normal tissue. Thus, the better we understand the processes that shape mutations in cancer genomes, the more precisely we can tailor therapies to a patient’s individual genome. However, passenger mutations cannot be clearly distinguished from driver mutations based on an individual tumor genome. Hence, genomic sequencing data from thousands of tumor patients have been generated, such as TCGA, for a detailed characterization of the landscape of driver mutations. While the most common driver events are well understood, large datasets and advanced computational tools are required to detect rare driver events. In this talk, I will give an overview of a tool for interpreting driver mutations I have worked on. I will explain the necessity of understanding passenger mutations and mutation distribution patterns, in general, to arrive at a clearer understanding of driver mutations.