Funtional Proteomics and Response to Preoperative Theraphy in Breast Cancer

描述（由申请人提供）：在过去的10年里，乳腺癌的预后有了显著的改善。然而，尽管采用目前的方法，仍有大量患者复发，并且由于我们无法区分哪些患者是低风险或高风险疾病，许多患者接受了不必要的治疗。乳腺癌行为在一定程度上受4个重要蛋白信号通路（PI3K/PTEN/AKT、JAK/STAT、tgf - β /Smad和MAPK/ERK1/2）的调控。它们与激素受体双向相互作用，并在多个层面进一步相互作用，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全面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PI3K/PTEN/AKT、JAK/STAT、tgf - β /Smad和MAPK/ERK1/2四个信号转导级联的功能蛋白质组学表达/激活特征来对乳腺癌进行分类；(2)确定基于功能蛋白质组学的分类是否可以预测术前化疗的反应；(3)将基于功能蛋白质组学的分类与基因表达特征相关联。我们将对90例冷冻乳腺癌术前样本进行逆相蛋白微阵列（RPPA）检测，并分析4个信号转导级联。这些数据将被用作训练集，以确定与术前化疗反应相关的乳腺癌信号谱。我们将获得50个样本的验证集，并应用蛋白质组学谱来确定其预测治疗反应的特异性和敏感性。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。我们迫切需要一种方法来预测哪些乳腺癌会对治疗产生反应。由于蛋白质是细胞功能的直接执行者，而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，因此该技术可以更准确地反映肿瘤生物学。全面评估癌变和细胞存活的分子机制显然涉及基因组和蛋白质组的评估。本提案中的研究将有助于更好地理解乳腺癌行为和对治疗反应性的广泛差异背后的机制，并有可能在常规治疗不足的患者中确定其他治疗靶点。

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.