Enhanced mass-spectrometry-based approaches for in-depth profiling of the cancer extracellular matrix

增强型基于质谱的方法，用于深入分析癌症细胞外基质

• 批准号: 10493806
• 负责人: Yu Gao
• 金额: $ 21.11万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10493806
• 关键词: Acute; Adopted; Advanced Malignant Neoplasm; Amino Acid Sequence; Architecture; Area; Benchmarking; Biochemical; Categories; Cell physiology; Cessation of life; Communities; Complex; Computer software; DNA Sequence Alteration; Data; Data Set; Databases; Development; Digestion; Disease; Early Diagnosis; Enzymes; Extracellular Matrix; Extracellular Matrix Proteins; Extracellular Structure; Fibroblasts; Future; Generations; Genetic study; Growth Factor; Individual; Knowledge; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Mediating; Metabolic; Methods; Modality; Modeling; Neoplasm Metastasis; Normal tissue morphology; Outcome; Pathway interactions; Patient-Focused Outcomes; Peptide Hydrolases; Peptide Mapping; Peptides; Performance; Play; Post-Translational Protein Processing; Preparation; Prognostic Marker; Property; Protein Denaturation; Protein Isoforms; Proteins; Proteolysis; Proteomics; Protocols documentation; Publishing; Reagent; Research; Research Personnel; Resistance development; Resources; Role; Sampling; Signal Transduction; Structural Protein; Structure; Techniques; Technology; Time; Tissues; Tumor Tissue; Tumor stage; United States; Vial device; Visualization; Work; anticancer research; base; cancer proteomics; cell motility; chemical property; cost; crosslink; experimental study; in silico; innovation; insight; instrumentation; knowledge base; matrigel; medical specialties; neoplastic cell; new technology; novel; novel therapeutics; predictive modeling; predictive signature; prevent; protein complex; protein data bank; protein folding; protein function; protein structure; searchable database; targeted treatment; therapeutic target; three dimensional structure; three-dimensional modeling; tool; translational potential; tumor; tumor microenvironment; tumor progression

Project Summary Cancer has claimed over 600,000 lives in 2020 in the United States. A better understanding of the mechanisms underlying cancer progression has led to the development of early detection strategies and novel treatment modalities that have contributed to the decrease in cancer-related deaths observed for the past few decades. Yet, cancer remains a deadly disease. There is thus an acute need to identify new cancer vulnerabilities. This will require exploring understudied aspects of cancers, which requires the development of novel technologies. One understudied aspect of cancer is the extracellular matrix (ECM). The ECM is a complex meshwork of proteins providing architectural support and biochemical signals critical for cellular functions required for tumor progression. Overcoming technical challenges posed by largely insoluble ECM proteins, we previously devised a proteomic pipeline specifically geared towards ECM proteins and showed that the tumor ECM is composed of 200+ distinct proteins. We further identified ECM signatures predictive of patient outcome and novel ECM proteins playing functional roles in cancer progression. The ECM thus represents an important reservoir of potential prognostic biomarkers and therapeutic targets. However, the ECM has many more secrets to reveal. For example, ECM proteins exist in various isoforms and are extensively post-translationally modified, yet, we do not know which proteoforms are present in the tumor ECM. ECM protein structure and the architecture of the ECM meshwork is key to mediate function, yet, very little is known about ECM protein folding and its impact on protein functions. Since proteomics relies on the generation of peptides from protein via proteolysis and protein identification via database search, we propose that enhancing these steps will provide a more complete picture of the cancer ECM and significantly advance cancer research. Here, we propose to use in-silico modeling to define the optimal cleavage conditions to achieve near-complete coverage of ECM protein sequences (Aim 1). Standard proteomic protocols rely on protein denaturation prior to protein digestion. Yet, we know that many ECM functions are governed by its architecture. We thus propose to perform native ECM digestion to gain insights into the structure of individual proteins, and the secondary and tertiary structures of the ECM meshwork (Aim 2). To facilitate ECM research, we have previously developed a searchable database, MatrisomeDB, compiling ECM proteomic dataset. Here, we propose to enhance the content and functionalities of MatrisomeDB to include our new prediction model and a new tool to the visualize sequence coverage on 3D models of ECM proteins predicted by Google’s AlphaFold (Aim 3). Our technology, offering substantial improvements over conventional proteomic approaches, targets the unmet technical need to profile, with deep coverage and high sensitivity, the protein composition of the tumor ECM. When deployed it will significantly lower the technical barrier for other researchers to study the ECM, which will have a transformative impact on cancer research.

项目摘要 癌症在2020年在美国夺走了60万多人的生命。更好地理解机制 潜在的癌症进展导致了早期检测策略和新治疗的发展 在过去几十年中观察到的与癌症相关的死亡减少的模式。 然而，癌症仍然是一种致命的疾病。因此，急需确定新的癌症漏洞。这 将需要探索癌症的知识方面，这需要开发新技术。 癌症的一个知识是细胞外基质（ECM）。 ECM是一个复杂的网眼 提供建筑支持和生化信号的蛋白质对肿瘤所需的细胞功能至关重要 进展。我们以前设计了很大程度上不溶性ECM蛋白带来的技术挑战 专门针对ECM蛋白的蛋白质组学管道，表明肿瘤ECM由 200多个不同的蛋白质。我们进一步确定了ECM标志可预测患者结果和新型ECM 蛋白质在癌症进展中起功能作用。因此，ECM代表了重要的水库 潜在的预后生物标志物和治疗靶标。但是，ECM还有更多的秘密要揭示。 例如，ECM蛋白质存在于各种同工型中，并且经过广泛的翻译后修饰，但是，我们 不知道肿瘤ECM中存在哪些蛋白质类型。 ECM蛋白质结构和结构 ECM网格工作是调解功能的关键，但是，关于ECM蛋白质折叠及其对影响的影响很少 蛋白质功能。由于蛋白质的功能依赖于通过蛋白质解析从蛋白质和蛋白质中从蛋白质中产生辣椒。 通过数据库搜索识别，我们建议增强这些步骤将提供更完整的图片 癌症ECM并显着提高癌症研究。在这里，我们建议将内部建模用于 定义最佳切割条件，以实现ECM蛋白序列的几乎完整覆盖率（AIM 1）。 标准蛋白质组学方案依赖于蛋白质消化之前的蛋白质变性。但是，我们知道很多 ECM功能受其体系结构的约束。因此，我们建议执行本地ECM消化以获得 对单个蛋白质的结构以及ECM网状Works的二级和三级结构的见解 （目标2）。为了促进ECM研究，我们以前已经开发了可搜索的数据库MatrisomedB， 编译ECM蛋白质组学数据集。在这里，我们建议增强母质b的内容和功能 包括我们的新预测模型和一个新工具，可在ECM的3D模型上可视化序列覆盖率 Google的Alphafold预测的蛋白质（AIM 3）。我们的技术，提供了实质性的改进 常规的蛋白质组学方法，针对未满足的技术需求，具有深层覆盖范围和高度 敏感性，肿瘤ECM的蛋白质组成。当部署时，它将大大降低技术 其他研究人员研究ECM的障碍，这将对癌症研究产生变革性的影响。