PROJECT SUMMARY Discovering and understanding novel pathological features that correlate with proteogenomics is crucial in improving and streamlining cancer prognosis and treatment. Currently, biomedical research efforts to predict cancer outcomes rely on sequencing approaches not readily accessible in a clinical setting. Instead, clinicians frequently rely on histopathology images to visually assess for aberrant changes to tissue morphology. Subsequently, a tool to infer clinical and molecular signatures directly from histopathology images would harness the power of omics research with the feasibility of image-based diagnosis. Histopathology imaging data is still vastly underutilized in the quest to better understand tumor biology, largely because of inadequate tools for analysis and data integration. To identify pan-cancer hallmarks and conserved pathways of tumorigenesis, I therefore propose to develop multi-resolution deep convolutional neural network (CNN) models across 10 different cancer types and predict clinical annotations, histology outcomes, and critical mutations based on tumor histopathology images (Aim 1). Through our lab’s collaboration with the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), we have access to multi-omics, clinical, and histopathologic data obtained from 1,602 patients. In addition, to understand the biological mechanisms driving morphology changes, I propose to develop computational methods that integrate transcriptomic and proteomic expression datasets with imaging to facilitate pathway-level knowledge discovery (Aim 2). Our proposal is the first to correlate expression perturbations with morphology patterns and identify enriched canonical pathways directly from histopathology images. Importantly, this proposal aims to connect scientific efforts of biomedical research with the diagnostic tools of clinicians to expand diagnostic power and improve clinical practice.