Funtional Proteomics and Response to Preoperative Theraphy in Breast Cancer

描述(由申请人提供)：在过去的10年中，乳腺癌的预后有了显著的改善。然而，尽管采取了现有的治疗方法，但仍有相当数量的患者复发，一些患者接受了不必要的治疗，因为我们无法区分哪些患者患有低风险疾病或高风险疾病。乳腺癌的行为在一定程度上受4条重要的蛋白质信号通路(PI3K/PTEN/AKT、JAK/STAT、转化生长因子-β/Smad和MAPK/ERK1/2)调控。它们与激素受体双向相互作用，并进一步在多个水平上相互作用，形成一个复杂的信号网络。我们的假设是，对乳腺癌信号事件的综合分析将确定可能对特定治疗方法有反应的患者。我们的具体目标是：(1)通过表征四个信号转导通路：PI3K/PTEN/AKT、JAK/STAT、TGF-β/Smad和MAPK/ERK1/2的功能蛋白质组表达/激活特征来对乳腺癌进行分类，(2)确定基于功能蛋白质组的分类是否可以预测术前化疗的疗效，以及(3)将基于功能蛋白质组的分类与基因表达特征相关联。我们将对90例乳腺癌冰冻标本进行逆相蛋白芯片(RPPA)分析，并对4个信号转导通路进行分析。这些数据将被用作训练集，以定义与术前化疗反应相关的乳腺癌信号特征。我们将获得50个样本的验证集，并应用蛋白质组学分析来确定其在预测治疗反应方面的特异性和敏感性。我们还将把功能蛋白质组学数据的预测价值与基因表达的预测价值联系起来。迫切需要一种方法来预测哪些乳腺癌对治疗有反应。由于蛋白质是细胞功能的直接执行者，而且RPPA可以同时评估总蛋白水平、蛋白磷酸化、细胞周期、细胞凋亡和新生血管，该技术可能更准确地反映肿瘤生物学。对癌症发生和细胞存活的分子机制的全面评估显然涉及到基因组和蛋白质组的评估。这项提案中的研究将有助于更好地理解乳腺癌行为和治疗反应性差异巨大的机制，并有可能确定常规治疗不足以治疗的患者的其他治疗靶点。

DESCRIPTION (provided by applicant): Breast cancer outcome has improved significantly over the last 10 years. However, a significant number of patients relapse despite current approaches, and a number of patients receive un-needed therapy as we are unable to distinguish which patients have low risk or high risk disease. Breast cancer behavior is regulated, in part, by 4 important protein signaling pathways (PI3K/PTEN/AKT, JAK/STAT, TGF-beta/Smad and MAPK/ERK1/2). They interact bidirectionally with hormone receptors and further interact at multiple levels creating a complex signaling network. Our hypothesis is that a comprehensive analysis of signaling events in breast cancer will identify patients likely to respond to particular therapeutic approaches. Our specific aims are: (1) To classify breast cancer by characterizing the functional proteomic expression/activation signature of four signal transduction cascades: PI3K/PTEN/AKT, JAK/STAT, TGF-beta/Smad and MAPK/ERK1/2, (2) to determine whether the functional proteomics based classification will predict response to preoperative chemotherapy, and (3) to correlate the functional proteomics based classification with gene expression signatures. We will perform reverse phase protein microarray (RPPA) in 90 frozen preoperative breast cancer samples and analyze the 4 signal transduction cascades. The data will be used as a training set to define a breast cancer signaling profile associated with response to preoperative chemotherapy. We will obtain a validation set of 50 samples and apply the proteomic profile to determine its specificity and sensitivity in predicting response to therapy. We will also correlate the predictive value of the functional proteomics data with that of gene expression. There is a crucial need for approaches which will predict which breast cancers will respond to therapy. As proteins are the direct executors of cell function, and since RPPA can concurrently assess total protein levels, protein phosphorylation, cell cycle, apoptosis and neovascularization, this technology may reflect tumor biology more acurately. A comprehensive assessment of the molecular mechanisms of carcinogenesis and cell survival obviously involves the evaluation of both the genome and proteome. The research in this proposal will lead to a better understanding of the mechanisms underlying the wide variation in breast cancer behavior and responsiveness to therapy and has potential to identify other therapeutic targets in patients for whom conventional treatment is inadequate.