We develop computational and machine learning methods to understand cancer in its tissue context. By integrating proteomics, genomics, spatial omics, tissue imaging and clinical data, we map how tumor cells and their microenvironment are organized and how that organization shapes disease progression and response to therapy. Our work centers on gynecologic cancers and small cell lung cancer, and the role of retrotransposition in cancer, with the aim of translating molecular insight into precision medicine.