PROTEIN PROTEIN INTERACTIONS AS BIOMARKERS

描述(申请人提供)：癌症被认为是细胞基因组改变的结果，导致异常信号蛋白导致细胞生长、存活和转移失控。这些变化改变了整个信号“回路”，导致异常生长和转移。信号复合体和信号网络的形成是蛋白质功能和信号传递的关键，这些信号复合体和网络协同作用产生生理信号。最先进的质谱学现在能够准确地绘制蛋白质-蛋白质相互作用(PPI)复合体和网络。这使得现在可以更好地理解癌症蛋白是如何驱动信号网络来转化细胞的。将网络理论应用于生物学可能有助于更好地了解癌症，提高对肿瘤进行分类的能力，并建议针对癌症“中枢”蛋白质的治疗方法或建议合理的组合方法(10-14)。尽管PPI数据集呈爆炸式增长，但大多数都局限于临床前空间，而且缺乏对人类癌症标本中PPI的询问。那么，如何让‘网络医学’成为现实呢？这些网络方法到肿瘤样本的转换受到许多障碍的阻碍，这些障碍排除了在人类癌症样本中识别和量化这些网络的能力。使用基于质谱学的蛋白质组学绘制网络图的一种解决方案是近端连接分析(PLA)。这项技术能够检测单个蛋白质事件，如蛋白质相互作用。该分析提供了有关事件位置的准确空间信息和量化事件的客观手段。由于建立生物标记物系统来测量癌症中基于蛋白质的生物标记物的工作还很少，本提案的目标是开发能够定量测量肺癌标本中由表皮生长因子受体(EGFR)激活所驱动的确定的蛋白质-蛋白质相互作用并将这些相互作用的表达与临床结果变量相关联的聚乳酸。我们将利用定义EGFR网络中相互作用的蛋白质的实验获得的质谱学数据，指导为分析开发选择蛋白质复合体。在目标1中，我们将建立和验证测量EGFR蛋白-蛋白质相互作用的近端连接试验。在目标2中，我们将在已知EGFR突变状态的细胞和肿瘤模型中表征EGFR蛋白的相互作用。在目标3中，我们将在原发肺癌异种移植模型和患者样本中表征EGFR蛋白-蛋白相互作用对EGFR酪氨酸激酶抑制剂的响应的变化。在目标4中，我们将确定在人类肺癌样本中，EGFR蛋白-蛋白相互作用是否与对EGFR酪氨酸激酶抑制剂的反应有关。

DESCRIPTION (provided by applicant): Cancer is recognized to be a result of changes in cellular genomes resulting in aberrant signaling proteins causing deregulated cell growth, survival, and metastasis. These changes rewire entire signaling 'circuits' resulting in aberrant growth and metastasis. Critical to protein function and signaling is the formation of signaling complexes and networks of signaling proteins that act in concert to produce a physiological signal. State of the art mass spectrometry is now able to accurately map protein-protein interaction (PPI) complexes and networks. This now allow a better understanding of how cancer proteins drive a signaling network to transform cells. The application of network theory to biology may enable a better understanding of cancer, improve ability to classify tumors, and suggest therapeutic approaches against cancer 'hub' proteins or suggest rational combination approaches (10-14). Despite the explosion of PPI datasets, most are limited in pre-clinical space and interrogations of PPI in human cancer specimens is lacking. Thus, how to make 'network medicine' a reality? Translation of these network approaches to tumor samples is hampered by a number of hurdles that preclude the ability to identify and quantify these networks in human cancer samples. One solution to mapping networks identified using mass spectrometry-based proteomics is proximal ligation assays (PLA). This technology is capable of detecting single protein events such as protein interactions. The assay provides exact spatial information on the location of the events and an objective means of quantifying the events. As little has been done to establish biomarker systems to measure protein-protein based biomarkers in cancer, the goal of this proposal is to develop PLA that can quantitatively measure defined protein-protein interactions driven by activation of the epidermal growth factor receptor (EGFR) in lung cancer specimens and relate expression of these interactions to clinical outcome variables. We will leverage experimentally derived mass spectrometry data defining interacting proteins within the EGFR network will guide selection of protein complexes for assay development. In Aim 1, we will establish and validate proximal ligation assays that measure EGFR protein-protein interactions. In Aim 2, we will characterize EGFR protein interactions in cell and tumor models with known EGFR mutation status. In Aim 3, we will characterize changes in EGFR protein- protein interactions in response to EGFR tyrosine kinase inhibitors in primary lung cancer xenograft models and patient samples. In Aim 4, we will determine if EGFR protein-protein interactions are associated with responses to EGFR tyrosine kinase inhibitors in human lung cancer samples.