Recent advances in technology have empowered the cancer community to collect an almost unlimited amount of molecular data points from even small, routinely collected primary human tumor samples. It is still unclear, however, how to best extract biologically relevant information from such datasets for subsequent validation studies and biomarker development. The need for robust computational tools is particularly pressing in the systematic analysis of the cancer genome which is hampered by the lack of a statistical framework to distinguish between “driver” mutations and “passenger” mutations. In this chapter, we review a new bioinformatic method, called genomic identification of significant targets in cancer (GISTIC), designed for analyzing chromosomal aberrations in cancer under specific consideration for such random events. This chapter describes the original development of this method on primary glioma tumor samples, its subsequent application to other cancer types, and further modifications of algorithm.