PROTEOME SIGNATURES AND TARGET VALIDATION IN LYMPHOMAS

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，我们需要为每个肿瘤定制治疗的能力。药物基因组学的目标是利用微阵列为组织产生基因表达特征，然后与预后或治疗反应相关。然而，响应的复杂性和多样性很大程度上可能是由于蛋白质组的差异。到目前为止，探索蛋白质组多样性并将其与治疗效果联系起来一直是困难的。我们的总体目标是开发表面蛋白质组特征(SP)，并直接在原发肿瘤组织上进行功能蛋白质组靶向验证分析。直接在患者组织样本上评估不同化疗组合的疗效是非常有必要的。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。作为我们之前由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个表面蛋白质组分。在这项工作中，我们还开发了高通量的免疫细胞化学(ICC)方法，使用单链抗体和细胞凋亡检测，每次检测使用少量细胞(&lt；500)。总而言之，这些进展使我们能够利用少量的原发肿瘤组织产生SP并测量化疗后的细胞凋亡。细胞将被单独和联合使用一组药物进行测试，并分析细胞的凋亡情况。因此，对治疗的不同反应将与SPS相关。我们将在R21期使用来自三个小鼠模型的胸腺淋巴瘤小鼠细胞株来开发和测试这些检测方法：转基因MyrAkt和两个基因缺失，PTEN-/+或P53-/-。这是一个理想的模型系统，因为原发肿瘤是由单个致癌基因突变在基因上定义的。我们将为来自这些小鼠的胸腺淋巴瘤细胞系建立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。我们还将解决SPS和药物反应之间的因果联系，并将测试作为生物标记候选的单链抗体的功能作用。在R33阶段，我们将在三种小鼠模型中测试预测的治疗胸腺淋巴瘤的最佳药物方案，检查肿瘤负荷和存活率。我们还将把10个单链抗体定性为潜在的生物标记物或药物发现的靶标。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。

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.