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

项目摘要 2020年，癌症在美国夺去了60多万人的生命。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有助于减少癌症相关死亡的方式。 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 需要探索癌症研究不足的方面，这需要开发新技术。 癌症的一个未充分研究的方面是细胞外基质（ECM）。ECM是一个复杂的网络， 为肿瘤所需的细胞功能提供结构支持和生化信号的蛋白质 进展克服了很大程度上不溶性ECM蛋白所带来的技术挑战，我们以前设计了 一个专门针对ECM蛋白的蛋白质组学管道，并显示肿瘤ECM由以下组成： 200+不同的蛋白质。我们进一步鉴定了预测患者结果的ECM特征和新型ECM， 在癌症进展中发挥功能作用的蛋白质。因此，ECM代表了一个重要的储存库， 潜在的预后生物标志物和治疗靶点。然而，ECM还有更多的秘密要揭示。 例如，ECM蛋白存在于各种异构体中，并且在胚胎发育后被广泛修饰，然而，我们 不知道哪些蛋白质存在于肿瘤ECM中。ECM蛋白结构和细胞外基质的结构 ECM网络是介导功能的关键，然而，关于ECM蛋白折叠及其对细胞生长的影响知之甚少。 蛋白质功能由于蛋白质组学依赖于通过蛋白质水解从蛋白质生成肽， 通过数据库搜索识别，我们建议加强这些步骤将提供更完整的图片 癌症细胞外基质的研究，并显著推进癌症研究。在这里，我们建议使用计算机模拟建模， 定义最佳切割条件，以实现ECM蛋白序列的接近完全覆盖（目标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。然而，我们知道， ECM功能由其架构管理。因此，我们建议进行天然ECM消化以获得 深入了解单个蛋白质的结构，以及ECM网络的二级和三级结构 (Aim 2）。为了促进ECM研究，我们以前开发了一个可搜索的数据库MatrisomeDB， 编译ECM蛋白质组数据集。在这里，我们建议增强MatrisomeDB的内容和功能 包括我们新的预测模型和一个新的工具，以可视化ECM 3D模型上的序列覆盖 谷歌AlphaFold预测的蛋白质（Aim 3）。我们的技术，提供了实质性的改进， 传统的蛋白质组学方法，目标是未满足的技术需求，以深覆盖和高 敏感性，肿瘤ECM的蛋白质组成。部署后，它将大大降低技术 这是其他研究人员研究ECM的障碍，这将对癌症研究产生变革性影响。

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.