Single-Cell Protein Activity Profiling in Breast Cancer Cells and Tissues

乳腺癌细胞和组织中的单细胞蛋白质活性分析

• 批准号: 10640998
• 负责人: Raymond E Moellering
• 金额: $ 38.19万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-08 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640998
• 关键词: Address; Advanced Development; Atlas of Cancer Mortality in the United States; Benchmarking; Biological; Biological Markers; Biopsy; Breast; Breast Cancer Cell; Breast Cancer Patient; Cancer cell line; Cell Line; Cells; Chemicals; Clinic; Clinical; Complex; Data; Development; Diagnosis; Disease; Early Intervention; Elements; Enzymes; Family; Future; Goals; Grant; Human; Image; In Situ; Individual; Knowledge; Ligation; Malignant Neoplasms; Mammary Neoplasms; Maps; Measurement; Measures; Messenger RNA; Methods; Microfluidics; Molecular; Molecular Probes; Molecular Profiling; Neoplasm Circulating Cells; Neoplasm Metastasis; Operative Surgical Procedures; Organism; Outcome; Patient Care; Patients; Phosphotransferases; Positioning Attribute; Primary Neoplasm; Protein Family; Proteins; Proteome; Proteomics; Publishing; Punch Biopsy; Reagent; Reporting; Research; Resolution; Sampling; Services; Severity of illness; Signal Transduction; Specimen; Technology; Testing; Therapeutic; Tissue Sample; Tissues; Tumor Tissue; Validation; Visualization; anticancer research; cancer cell; chemoproteomics; clinical application; design; detection platform; enzyme activity; human disease; improved; innovation; malignant breast neoplasm; molecular phenotype; novel; protein biomarkers; single cell proteins; technology platform; technology validation; translational applications; treatment response; tumor

Project Summary Advances in molecular profiling technologies have enabled the identification of ever-increasing numbers of disease biomarkers, each raising the potential for more precise, and possibly even patient-specific, diagnoses. Current methods of quantifying protein biomarkers, however, are slow and laborious, often require large quantities of biopsied tissues, and ultimately fail to provide a complete, intact quantification of functional protein activity. To address these limitations, we have developed an innovative activity dependent proximity ligation platform (ADPL), with the ultimate goal of obtaining multiplexed, ultrasensitive quantification of protein activity directly in complex biological samples. In the context of cancer specimens, we hypothesize that defining a molecular profile based on protein activity – the end result of biologic signaling – can more precisely and accurately inform diagnoses and treatment regimes, allowing early intervention and improving clinical outcomes. This proposal outlines the advanced development and validation of two reliable, high-resolution ADPL-based platforms, with the power to alternately visualize and quantify dozens of active enzymes simultaneously from tumor tissue, cells and biofluids. Our current knowledge and expertise in the use of this integrated platform will be leveraged in the service of the following aims: 1) development of novel chemoproteomic probes for multiplexed, family-wide activity mapping of cancer-relevant enzymes, including kinases; 2) advanced development of on-tissue Exchange-ADPL and on-chip single-cell ADPL (scADPL) platforms for simultaneous, high resolution visualization and quantification of a panel of active enzymes in situ; and 3) applying these technologies to breast cancer tissues and cells to create accurate and precise activity signatures and identify potential correlations between these signatures and clinical outcomes. To accomplish these aims we will develop new chemical proteomic molecular probes, validate a panel of ADPL reagents for cancer-associated enzyme biomarkers, and perform rigorous, metric-driven benchmarking analyses in cell lines and tumor tissues. Completion of this project will validate ADPL platforms for use in diverse cancers, provide the first single-cell activity profiles of breast tumors, and provide novel correlations between breast cancer tumor activity signatures, disease severity and response to therapy.

项目总结