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

基本信息

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
项目类别：
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

项目摘要

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）：功能性基因组策略已被广泛实施，以定义癌症的独特分子亚型，以预测表型特性，例如转移性潜力和对治疗化合物的敏感性。但是，基因和蛋白质表达水平的变化可能是环境性的，因此在疾病的发展中没有作用。侵略性癌症的标志之一是它逃脱细胞环境并扩散到新组织的能力，该过程部分由细胞外蛋白酶的活性介导。蛋白酶活性受亚细胞定位，内源性蛋白酶抑制剂的存在以及不活跃的前体形式的必要转换来严格调节。因此，在这种情况下，仅知道蛋白酶表达水平是不够的。我们建议，细胞外蛋白酶活性的全球特征可能是癌症分子分层的强大功能工具。 CRAIK实验室已经开发了一种新型的基于质谱的筛选技术，可以通过使用一个小型的，理性设计的肽底物来鉴定单独和复杂生物混合物中蛋白酶的全球底物特异性和动力学效率。该技术通过质谱法（MSP-MS）称为多重底物分析，标志着蛋白酶分析的显着突破，允许在给定样品中对所有蛋白酶的无偏见和同时检测。在此提案中，CRAIK实验室将与Sali实验室合作，开发和测试根据蛋白酶特异性对癌症样本进行分类的计算模型，以构建亚型特异性成像的蛋白酶诱使诊断。使用MSP-MS测定法确定来自日益复杂的乳腺癌和前列腺癌样品的细胞外蛋白酶活性的全球谱。同时，机器学习算法将用于开发基于特异性的分类模型，这些模型将与已知的肿瘤侵略性指标相关。代表确定的主要分类组的肽序列的亚纤维序列将有助于设计可蛋白酶的可分离成像探针，这些成像探针将在实验中测试以提高亚型的选择性。探针裂解序列将通过迭代序列进行改进，以提高选择性，通过将切割速率掺入建模策略和针对目标蛋白酶的3D结构的肽对接。在项目的下一阶段，开发的新试剂类别将用于针对临床相关性的进一步优化。我们预计，我们为癌症功能分析生成量身定制诊断的策略将推动疾病的鉴定和监测帮助癌症生物标志物发现。