Predicting actionable cancer vulnerabilities enabled by mutant-directed protein-protein interactions

癌症是全球第二大死因，每年造成1000多万人死亡。在

Cancer is the second leading cause of death worldwide, causing more than 10 million deaths every year. In response, tremendous efforts have been made over the past decades to understand the molecular mechanisms of tumorigenesis and inform new therapeutic strategies in cancer. Unraveling the cancer genome and proteome landscapes revealed that genomic alterations, such as missense mutations, promote tumorigenesis by rewiring networks of protein-protein interactions (PPI). However, the understanding of how mutant-directed neomorph PPIs (neoPPI) lead to the acquisition of cancer hallmarks and the discovery of neoPPI-enabled cancer vulnerabilities remain major challenges. We propose to address this challenge by developing novel computational methods termed Averon Notebook to discover actionable vulnerabilities enabled by rewired oncogenic networks. To achieve this goal, we will leverage our expertise in both cancer bioinformatics and experimental cancer biology demonstrated in numerous publications and long-time participation in the Cancer Target Discovery and Development (CTD^2) Network of the National Cancer Institute. Over the past decade, we have established comprehensive bioinformatics workflows and novel analytical tools to collect, process, integrate, and analyze different types of cancer-related data. To integrate cancer genomics data with protein- protein interaction networks and clinical compounds, we have developed the OncoPPi Portal, which has already enabled the discovery of multiple new molecular mechanisms of tumorigenesis. In this project, we will capitalize on our expertise in computational science and cancer biology to develop i) a new algorithm to determine the neoPPI-regulated biological programs, and ii) methods to determine actionable vulnerabilities in neoPPI- regulated pathways. Ultimately, this project will provide the first computational environment specially designed to rapidly identify actionable targets and pathways enabled by mutant-directed protein-protein interactions to inform target discovery in cancer.