Precision oncology demands accurate portrayal of a disease at all molecular levels. However, current large-scale studies of omics are often isolated by data types. I have been developing computational tools to conduct integrative analyses of omics data, identifying unique molecular etiology in each tumor. Particularly, this dissertation presents the following contributions to the computational omics of cancer: (1) uncovering the predisposition landscape in 33 cancers and how germline genome collaborates with somatic alterations in oncogenesis; (2) pioneering methods to combine genomic and proteomic data to identify treatment opportunities; and (3) revealing selective phosphorylation of kinase-substrate pairs. These findings advance our understanding of tumor biology on a systematic scale and inform clinical practice of cancer diagnosis and treatment design
