The Cancer Genome Project initiatives such as The Cancer Genome Atlas (TCGA) have generated a daunting amount of genomic and next-gene sequencing data for tens of thousands of human tumors. This provided unprecedented opportunity to systematically analyze the cancer genome to develop novel therapeutics, and also calls for innovative computational technologies that can reveal viable cancer targets and driving genetic aberrations from these multidimensional datasets.
In the past a few years, we have developed multiple integrative computational technologies to discovery viable cancer targets from the genomic and next-generation sequencing datasets. Here we provide the detailed introduction and protocols for the bioinformatics analyses that have been published in our previous studies. These analyses have led to the discovery of recurrent ESR1-CCDC170 gene fusions in more aggressive Luminal B breast cancers (Nature Comm 2014), NFE2 rearrangements in lung cancer (Nature biotech 2009), KRAS gene fusions in a rare subset of metastatic breast cancers (Cancer Discovery 2011), and multiple tumor specific antigen targets (Cancer Research 2012).