Funtional Proteomics and Response to Preoperative Theraphy in Breast Cancer

功能蛋白质组学和乳腺癌术前治疗的反应

• 批准号: 7075588
• 负责人: Ana M. Gonzalez-Angulo
• 金额: $ 14.63万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-27 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7075588
• 关键词: breast neoplasms; chemotherapy; clinical research; neoplasm /cancer; proteins; proteomics

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.

描述（由申请人提供）：乳腺癌的结局在过去10年中有了显著改善。然而，尽管采用了当前的方法，仍有大量患者复发，并且许多患者接受了不需要的治疗，因为我们无法区分哪些患者患有低风险或高风险疾病。乳腺癌的行为在一定程度上受4种重要的蛋白质信号通路（PI 3 K/PTEN/AKT、JAK/STAT、TGF-β/Smad和MAPK/ERK 1/2）的调节。它们与激素受体双向相互作用，并在多个水平上进一步相互作用，形成复杂的信号网络。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产生反应的患者。我们的具体目标是：（1）通过表征四个信号转导级联的功能性蛋白质组学表达/激活特征来对乳腺癌进行分类：PI 3 K/PTEN/AKT、JAK/STAT、TGF-β/Smad和MAPK/ERK 1/2，（2）确定基于功能性蛋白质组学的分类是否将预测对术前化疗的响应，以及（3）将基于功能性蛋白质组学的分类与基因表达特征相关联。我们将在90个冷冻的术前乳腺癌样本中进行反相蛋白质微阵列（RPPA），并分析4个信号转导级联。这些数据将用作训练集，以定义与术前化疗反应相关的乳腺癌信号传导谱。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灵敏度。我们还将功能蛋白质组学数据的预测价值与基因表达的预测价值相关联。目前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，RPPA可以同时评估总蛋白水平、蛋白磷酸化、细胞周期、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。对癌发生和细胞存活的分子机制的全面评估显然涉及对基因组和蛋白质组的评估。该提案中的研究将有助于更好地了解乳腺癌行为和治疗反应性广泛变化的机制，并有可能在常规治疗不足的患者中确定其他治疗靶点。