Late-stage drug attrition rates in oncology remain higher than in other therapeutic areas. To reduce attrition, it is critical to identify appropriate drug targets and pre-clinical models. The integrative analysis framework was designed to systematically compute associations among driver mutations, fusions and copy alterations to define the driver aberration landscape of common cancers, then correlate the drivers to clinical metadata. Our driver selection methodology was developed through an interrogation of genomic aberrations in a training set of gold standard oncogenes such as EGFR, ALK and PIK3CA, and tumor suppressors such as TP53 and PTEN. The resulting platform was used to rank genes through an assessment of driver genomic aberrations, associations with patient survival, and potential clinical actionability. In this session, Dr. Khazanov will highlight proof of concept identification of a potentially clinically relevant candidate driver gene using a novel systematic integrative analysis of multi-dimensional cancer genomic data.