Proteomic Predictors Of AASK Renal Disease Progression

描述(申请人提供)：临床治疗研究表明，有几种潜在的治疗方法，包括转化酶抑制剂、严格的糖尿病控制和血压控制，可以在几种疾病状态下减缓肾脏疾病的进展。然而，在像AASK这样的试验中，即使在那些接受最佳治疗的受试者中，肾脏疾病的进展率和终末期肾病的发病率仍然很高。目前的数据几乎没有提供明显的治疗靶点来改善这一不可避免的进展。最近主要针对卵巢癌和前列腺癌的研究表明，表面增强激光解吸/电离飞行时间质谱仪(SELDI-TOF)产生的血清蛋白质组模式可以高特异性和敏感性地识别癌症患者和正常受试者。由我们小组成员开发的数据逻辑分析(LAD)技术，当应用于相同的数据集时，显著提高了识别卵巢癌的敏感性和特异性。此外，这项技术能够识别临床数据集中的预测/预后模式。 这是一项针对AASK受试者子集的探索性研究，目的是确定我们是否能够开发一种使用蛋白质组数据来预测肾脏疾病进展的模型，以及我们是否能够从我们的模型中识别预测蛋白质峰。 我们假设，可以使用SELDI技术产生的血清蛋白质组模式可以预测AASK受试者肾脏疾病的进展或非进展，LAD可以识别这些模式。我们将使用AASK试验的存储样本来验证这一假设。至少，这将允许识别有进展风险的肾功能衰竭患者的亚群。最终分离和鉴定组成该模式的蛋白质可能为进行性肾脏疾病的治疗提供新的靶点。我们还假设LAD将检测AASK数据集中预测AASK结果的基线值模式；为此，我们将使用LAD重新检查AASK数据集。

DESCRIPTION (provided by applicant): Clinical treatment studies have demonstrated that there are several potential therapies including converting enzyme inhibitors, tight diabetes control and blood pressure control that can slow the progression of renal disease in several disease states. However in trials such as AASK the rate of progression of renal disease and incidence of ESRD remains substantial even in those subjects receiving optimal therapy. Current data provide little in the way of obvious therapeutic targets to ameliorate this inexorable progression. Recent studies predominately in ovarian and prostate cancer have demonstrated that serum proteomic patterns generated by Surface-enhanced Laser Desorption/Ionization time of flight (SELDI-tof) mass spectrometry can identify cancer patients versus normal subjects with high specificity and sensitivity. The Logical Analysis of Data (LAD) technique, developed by members of our group, has substantially improved the sensitivity and specificity for identifying ovarian cancer when applied to the same data sets. Further, this technique is able to identify predictive/prognostic patterns in clinical data sets. This is an exploratory study on a subset of AASK subjects to determine whether we will be able to develop a model that uses proteomic data to predict progression of renal disease and whether we will be able to identify predictive protein peaks from our model. We hypothesize that there are serum proteomic patterns that can be generated using the SELDI technique that will predict either progression or non-progression of renal disease in AASK subjects and that LAD can identify these patterns. We will use stored specimens from the AASK trial to test this hypothesis. At the minimum, this will allow identification of the subpopulation of patients with renal failure at risk for progression. Eventual isolation and identification of the proteins comprising the pattern may provide new targets for the therapy of progressive renal disease. We also hypothesize that LAD will detect patterns of baseline values in the AASK data set that will predict AASK outcomes; we will re-examine the AASK data set with LAD for this aim.