Extracellular Proteolysis as a Molecular Stratification Tool for Cancer

细胞外蛋白水解作为癌症的分子分层工具

• 批准号: 8829207
• 负责人: Charles Scott Craik
• 金额: $ 16.83万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8829207
• 关键词: Algorithms; Animal Model; Biological; Biological Assay; Biological Markers; Blood; Breast Cancer Model; Breast Cancer cell line; Cancer cell line; Cell Line; Cell physiology; Cell secretion; Classification; Clinical; Complex; Computer Simulation; Coupling; Decision Making; Detection; Development; Diagnosis; Diagnostic; Disease; Docking; Drug resistance; Functional Imaging; Gene Expression; Genomic approach; Goals; Health; Homology Modeling; Image; Kinetics; Laboratories; Libraries; Life; Machine Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Mass Spectrum Analysis; Measurement; Mediating; Methods; Modeling; Molecular; Monitor; Neoplasm Circulating Cells; Patients; Peptide Hydrolases; Peptides; Phase; Play; Positioning Attribute; Primary Neoplasm; Process; Property; Protease Inhibitor; Proteins; Proteolysis; Proteomics; Reagent; Role; Sampling; Screening for cancer; Specificity; Stratification; Structure-Activity Relationship; Substrate Specificity; Techniques; Technology; Testing; Therapeutic; Tissues; Tumor Markers; Weight; base; cellular imaging; clinically relevant; design; extracellular; functional genomics; high throughput screening; imaging probe; improved; malignant breast neoplasm; malignant phenotype; new technology; novel; personalized diagnostics; pre-clinical; preference; prostate cancer cell line; protein aminoacid sequence; protein expression; screening; three dimensional structure; tool; tumor

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.

描述（由申请人提供）：功能基因组策略已被广泛实施来定义癌症的独特分子亚型，以预测表型特性，例如转移潜力和对治疗化合物的敏感性。然而，基因和蛋白质表达水平的变化可能是间接的，因此在疾病的发展中不发挥功能性作用。侵袭性癌症的标志之一是其能够逃离细胞环境并扩散到新组织，这一过程部分是由细胞外蛋白酶的活性介导的。蛋白酶活性受到亚细胞定位、内源蛋白酶抑制剂的存在以及无活性前体形式的必要转化的严格调节。因此，在这些情况下，仅了解蛋白酶表达水平是不够的。我们认为细胞外蛋白酶活性的整体概况可能会成为癌症分子分层的强大功能工具。 Craik 实验室开发了一种基于质谱的新型筛选技术，通过采用合理设计的小型、多样化的肽底物库，可以识别单独的蛋白酶和复杂生物混合物中的蛋白酶的整体底物特异性和动力学效率。该技术被称为质谱多重底物分析 (MSP-MS)，通过允许公正且同时检测给定样品中的所有蛋白酶活性，标志着蛋白酶分析领域的重大突破。在这项提案中，Craik 实验室将与 Sali 实验室合作开发和测试计算模型，根据蛋白酶特异性对癌症样本进行分类，目标是为亚型特异性成像建立蛋白酶激活的诊断方法。将使用 MSP-MS 测定来确定日益复杂的乳腺癌和前列腺癌样本的细胞外蛋白酶活性的整体概况。与此同时，机器学习算法将用于开发基于特异性的分类模型，该模型将与已知的肿瘤侵袭性指标相关联。代表所识别的主要分类组的肽序列子文库将有助于设计可通过实验测试亚型选择性的蛋白酶激活成像探针。通过将裂解率纳入建模策略以及针对目标蛋白酶 3D 结构的肽对接，探针裂解序列将得到迭代细化，以提高选择性。开发的新型试剂将在项目下一阶段应用于临床相关性并进一步优化。我们预计，我们为癌症功能分析生成定制诊断的策略将促进疾病的识别和监测以及 帮助发现癌症生物标志物。