PROTEOME SIGNATURES AND TARGET VALIDATION IN LYMPHOMAS

淋巴瘤的蛋白质组特征和靶标验证

• 批准号: 7622961
• 负责人: Daniel G. Jay
• 金额: $ 65.86万
• 依托单位: TUFTS UNIVERSITY BOSTON
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7622961
• 关键词: Address; Antibodies; Antigens; Apoptosis; Biological Assay; Biological Markers; Biological Models; Cell Count; Cell Line; Cell Survival; Cells; Classification; Combination Chemotherapy; Complement; Custom; Development; Disease; Doxorubicin; Drug Combinations; End Point; Funding; Gene Expression Profile; Genetic; Goals; In Vitro; Libraries; Link; Lymphoma; Malignant Neoplasms; Mass Spectrum Analysis; Measures; Methods; Modeling; Mouse Cell Line; Mus; Mutation; Oncogenic; PTEN gene; Patients; Phage Display; Pharmaceutical Preparations; Pharmacogenomics; Phase; Positioning Attribute; Predictive Value; Predictive Value of Tests; Primary Neoplasm; Proteins; Proteome; Proteomics; Research; Role; Route; Sampling; Surface; Surface Antigens; TP53 gene; Technology; Testing; Therapeutic; Thymic Lymphoma; Thymic Tissue; Tissue Sample; Tissues; Transgenic Organisms; Treatment Efficacy; Treatment Protocols; Tumor Burden; Tumor Tissue; Uncertainty; Validation; Work; base; chemotherapy; drug discovery; drug efficacy; drug sensitivity; falls; immunocytochemistry; in vivo; mouse model; neoplastic cell; novel; outcome forecast; response; therapeutic target; therapy design; tumor

DESCRIPTION (provided by applicant): To address tumor diversity and their varied responses to chemotherapy, we require the ability to custom design treatment for each tumor. The goal of pharmacogenomics is to generate gene expression signatures for tissue using microarrays that are then correlated with prognosis or response to therapeutics. However, much of the complexity and diversity of response may be due to proteomic differences. Thus far, probing for proteomic diversity and linking this to therapeutic efficacy has been difficult. Our overall goals are to develop surface proteome signatures (SPS) and perform functional proteomic target validation analysis directly on primary tumor tissue. There is a great need to assess the efficacy of different chemotherapy combinations directly on patient tissue samples. A major difficulty is this respect is our inability to assess the small amounts of primary tumor tissue available from patient-derived samples. As part of our previous IMAT-funded research, we developed a library of single chain (scFv) phage display antibodies that recognize approximately 500 components of the surface proteome. In this work we also developed high-throughput methods for immunocytochemistry (ICC) using scFvs and apoptosis assays that use small number of cells (< 500) per assay. Together, these developments allow us to generate SPS and measure apoptosis after chemotherapy treatment using small amounts of primary tumor tissue. Cells will be tested with a battery of drugs alone and in combination and analyzed for apoptosis. Thus, a differential response to therapeutics will be correlated with a SPS. We will develop and test these assays in the R21 phase using thymic lymphoma mouse cell lines derived from three mouse models: transgenic MyrAkt and two genetic deletions, PTEN-/+ or p53-/-. This is an ideal model system, as the primary tumors are genetically defined by single oncogenic mutations. We will establish SPS for cell lines derived from thymic lymphoma lines from these mice. We will test for the efficacy of different drug regimens to obtain the optimum combination for inducing apoptosis of the cells. We will then test these drug combinations in primary tumor tissue from MyrAkt mice. We will also address the causal link between SPS and drug response and will test the functional role of scFvs that are biomarker candidates. In the R33 phase we will test the predicted optimal drug regimen on thymic lymphomas in the three mouse models, examining tumor load and survival. We will also characterize ten of the scFvs as potential biomarkers or targets for drug discovery. Our studies complement pharmacogenomics and provide a novel route to pharmacoproteomics.

描述（由申请人提供）：为了解决肿瘤的多样性及其对化疗的不同反应，我们需要为每种肿瘤定制设计治疗方案的能力。药物基因组学的目标是使用微阵列生成组织的基因表达特征，然后与预后或治疗反应相关。然而，许多复杂性和多样性的反应可能是由于蛋白质组的差异。到目前为止，探测蛋白质组多样性并将其与治疗效果联系起来一直很困难。我们的总体目标是开发表面蛋白质组特征（SPS），并直接对原发肿瘤组织进行功能蛋白质组靶标验证分析。有很大的需要评估不同的化疗组合的疗效直接对病人的组织样本。这方面的一个主要困难是我们无法评估从患者来源的样本中获得的少量原发肿瘤组织。作为我们之前imat资助的研究的一部分，我们开发了一个单链（scFv）噬菌体展示抗体库，可以识别大约500种表面蛋白质组的成分。在这项工作中，我们还开发了使用scFvs和凋亡检测的高通量免疫细胞化学（ICC）方法，每次检测使用少量细胞（< 500）。总之，这些进展使我们能够在使用少量原发肿瘤组织进行化疗后产生SPS并测量细胞凋亡。细胞将单独或联合使用一系列药物进行测试，并分析细胞凋亡情况。因此，对治疗的不同反应将与SPS相关。我们将在R21期使用来自三种小鼠模型的胸腺淋巴瘤小鼠细胞系开发和测试这些检测：转基因MyrAkt和两种基因缺失，PTEN-/+或p53-/-。这是一个理想的模型系统，因为原发肿瘤是由单一的致癌突变基因定义的。我们将建立来自这些小鼠胸腺淋巴瘤细胞系的SPS。我们将测试不同药物方案的疗效，以获得诱导细胞凋亡的最佳组合。然后，我们将在MyrAkt小鼠的原发肿瘤组织中测试这些药物组合。我们还将讨论SPS和药物反应之间的因果关系，并将测试作为生物标志物候选的scFvs的功能作用。在R33期，我们将在三种小鼠模型中测试预测的胸腺淋巴瘤最佳药物方案，检查肿瘤负荷和生存。我们还将描述10个scFvs作为潜在的生物标志物或药物发现的靶点。我们的研究补充了药物基因组学，并为药物蛋白质组学提供了一条新的途径。