Reverse Sensitivity Analysis for Identifying Predictive Proteomics Signatures of Cancer

A major aim of cancer systems biology is to build models that can predict the impact of these genetic disruptions to guide therapeutic interventions. A prominent driver of cancer cell growth is signaling pathway deregulation from mutations in key regulatory nodes and loss/gain in gene copy number (CNV). Recent work by our group discovered that the abundances of most signaling pathway proteins are highly conserved with signaling being controlled by only a few, low abundance key nodes. The activity of these nodes appears to be regulated by maintaining low abundance together with feedback phosphorylation. However, some nodes, such as Grb2 and Shc, appear preferentially amplified in many cancers. We hypothesize that CNV and genetic mutations dysregulate signaling pathways in cancer by shifting control from tightly regulated nodes to poorly regulated ones. Unfortunately, current mathematical modeling approaches do not adequately capture the impact of CNV on signaling pathway topology and feedback. We propose to address this critical gap by implementing a new approach for identifying the functional topology of signaling networks. This method, termed Reverse Sensitivity Analysis, uses targeted CRISPR libraries to modulate the abundance of pathway components together with flow cytometry and highly sensitive and quantitative targeted proteomics and phosphoproteomics to measure the subsequent impact. These data will be used with modeling approaches to generate models that should recapitulate the impact of CNV on cancer cell signaling behavior and suggest pathway nodes that can be targeted for therapeutic interventions. We will initially use reverse sensitivity analysis to identify key differences in the regulation of signaling pathways between normal and cancer cells with alterations in the ERK and AKT pathways. This work will develop and validate a general platform that can identify proteomics signatures of altered signaling pathways in cancer, build predictive models of these altered pathways, and explore how these alterations contribute to mechanisms of drug resistance.