Extracellular Proteolysis as a Molecular Stratification Tool for Cancer

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DESCRIPTION (provided by applicant): Functional genomic strategies have been widely implemented to define unique molecular subtypes of cancer in order to predict phenotypic properties such as metastatic potential and sensitivity to therapeutic compounds. However, changes in the level of gene and protein expression can be circumstantial and therefore play no functional role in the development of the disease. One of the hallmarks of aggressive cancer is its ability to escape the cellular milieu and spread to new tissues, a process that is mediated in part by the activity of extracellular proteases. Protease activity is tightly regulated by subcelluar localization, the presence of endogenous protease inhibitors, and requisite conversion from inactive precursor forms. Therefore, in these circumstances, it is not enough to know protease expression levels alone. We propose that global profiles of extracellular protease activity may emerge as a powerful functional tool for the molecular stratification of cancer. The Craik laboratory has developed a novel mass spectrometry-based screening technology that can identify the global substrate specificity and kinetic efficiency of proteases alone and in complex biological mixtures by employing a small, diverse library of rationally designed peptide substrates. This technology, referred to as Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS), marks a significant breakthrough in protease profiling by allowing for the unbiased and simultaneous detection of all protease activities in a given sample. In this proposal, the Craik laboratory will partner with the Sali laboratory to develop and test computational models that classify cancer samples on the basis of protease specificity with the goal of building protease-activatable diagnostics for subtype-specific imaging. Global profiles of extracellular protease activity from increasingly complex breast and prostate cancer samples will be determined using the MSP-MS assay. In parallel, machine learning algorithms will be used to develop specificity-based classification models that will be correlated to known metrics for tumor aggressiveness. Sub-libraries of peptide sequences that represent the major classification groups identified will aid in designing protease-activatable imaging probes that will be tested experimentally for subtype selectivity. Probe cleavage sequences will be iteratively refined to improve selectivity through both incorporation of cleavage rates into the modeling strategy and peptide docking against the 3D structures of the target proteases. The new class of reagents developed will be applied to and further optimized against clinical correlations in the next phase of the project. We anticipate that our strategy for generating tailored diagnostics for the functional profiling of cancer will advance the identification and monitoring of disease as well as aid in cancer biomarker discovery.