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

项目摘要 2020年，美国已有60多万人死于癌症。对机制有更好的理解 潜在的癌症进展导致了早期发现策略和新的治疗方法的发展 过去几十年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，迫切需要确定新的癌症脆弱性。这 将需要探索癌症研究不足的方面，这需要开发新技术。 癌症的一个研究不足的方面是细胞外基质(ECM)。ECM是一个复杂的网络 提供结构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 进步。克服了基本不溶的ECM蛋白带来的技术挑战，我们之前设计了 一条针对ECM蛋白的蛋白质组管道，表明肿瘤ECM由以下组成 200种不同的蛋白质。我们进一步确定了预测患者预后的ECM信号和新的ECM 在癌症进展中发挥功能作用的蛋白质。因此，ECM代表着一个重要的储备库 潜在的预后生物标志物和治疗靶点。然而，ECM还有更多的秘密要披露。 例如，ECM蛋白以各种不同的形式存在，并被广泛的翻译后修饰，然而，我们 不知道肿瘤细胞外基质中存在哪些蛋白形式。细胞外基质的蛋白质结构和构筑 细胞外基质网络是介导功能的关键，然而，对细胞外基质蛋白折叠及其对细胞外基质功能的影响知之甚少。 蛋白质的功能。由于蛋白质组学依赖于通过蛋白质分解和蛋白质从蛋白质中产生多肽 通过数据库搜索进行身份识别，我们建议加强这些步骤将提供更完整的图景 对癌症细胞外基质的研究和显著推进癌症研究。在这里，我们建议使用In-silo建模来 确定实现ECM蛋白序列接近完全覆盖的最佳切割条件(目标1)。 标准的蛋白质组学方案依赖于蛋白质消化之前的蛋白质变性。然而，我们知道许多人 ECM功能由其体系结构管理。因此，我们建议进行本地ECM消化以获得 对单个蛋白质的结构以及ECM网络的二级和三级结构的洞察 (目标2)。为了促进ECM研究，我们先前开发了一个可搜索的数据库MatrisomeDB， 编制ECM蛋白质组数据集。在这里，我们建议增强MatrisomeDB的内容和功能 包括我们的新预测模型和一个新的工具来可视化ECM的3D模型上的序列覆盖 谷歌AlphaFold(AIM 3)预测的蛋白质。我们的技术，提供了比 传统的蛋白质组学方法，以未满足的技术需求为目标进行分析，覆盖面深，覆盖率高 敏感性，肿瘤细胞外基质的蛋白质组成。部署后，它将显著降低技术 这为其他研究人员研究ECM设置了障碍，这将对癌症研究产生革命性影响。

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.