Recent advances in the comprehensive cataloging of genomic landscapes in cancer have led to the widespread use of genomic and transcriptomic analyses to classify tumors and predict a targeted course of therapy. However, the clinical performance of targeted therapies based solely on genomic analysis is disappointing, with only a subset of patients responding and minimal gains in median overall survival. This is not surprising, as drugs generally act on proteins, not genes, and the downstream effects of genetic mutations are often manifested by a cascade of changes in protein interactions within pathways. Integration of proteomic and genomic data is key to elucidating the complex biology determining tumor responses and resistance to cancer therapeutic agents, particularly by improving the ability to identify and characterize alterations in cancer-relevant pathways triggered by genomic alterations. In particular, the analysis of post-translational modifications such as phosphorylation provides pathway insights not obtainable from genomic analysis alone. By correlating proteogenomic and phosphoproteomic measurements from clinical trial patients with their accompanying clinical outcome data we are able to address clinical questions related to predicting drug response, toxicity, and resistance. The ultimate goal is of these studies is to address why a patient predicted to respond to a therapy by genomic data did not, and whether proteomics alone or in combination with genomics is a better predictor of response than genomics alone.