DIAGNOSTIC TEST FOR METASTATIC KIDNEY CANCER

摘要 此第一阶段SBIR的目标是解决缺乏可测量和靶向代谢物的问题 肾透明细胞癌(CcRCC)组织中可预测疾病转移的生物标志物。一个 据估计，2017年有69,990名美国人被诊断患有肾癌，约14,400人死于肾癌 CcRCC的结果，主要是由于癌症的转移。最终，我们试图确定那些患有 更具侵袭性的肾癌，可能会从更积极的治疗中受益。患有惰性疾病的患者 CcRCC可能选择侵袭性较小的治疗，而转移风险较高的可能选择辅助治疗 可能包括药物治疗的治疗。 目前市场上还没有预测手术后疾病进展的诊断测试。我们的 方法学将利用由以下公司开发的新的液相色谱(LC)和质谱(MS)方法 我们的合作者，以及我们开发的几种新方法，这些方法将大大提高数量 准确性和健壮性。这将解决困扰可靠开发的一系列问题 用于诊断肾癌进展和转移的生物标记物小组。 到目前为止，代谢组学研究的一个主要问题是癌症组织的异质性 经常被忽视。因此，我们方法的一个重要部分包括归一化、校准和 在发现模式和目标模式下对代谢物进行定量。具体而言，我们的方法允许 分析小组织活检中的代谢物，并将允许对完全相同的组织病理学进行分析 组织。而归一化和量化通常是在数据采集之后处理的，即后处理。 ，我们增加了采集前的归一化，这将确保注入等量的样本 对于女士来说，我们使用化学标记和样品归一化将最大限度地减少离子抑制的影响， 信号饱和、柱污染、老化和仪器性能漂移。从本质上讲，日常生活 而且，经常影响MS的实验室之间的可变性将被最小化。 在这项提案中，我们将利用东弗吉尼亚医学院生物信息库探测队列，该队列 存储300多个肾脏肿瘤和相关的临床数据，包括治疗、生命状态、成像数据和 初次肿瘤切除后的纵向随访。生物库附带的数据库定期 更新，使我们能够识别那些“进步的”人和那些被称为“不进步的人”。为了这个 I期概念验证研究，26名发生转移的患者的组织将被分析和 与未发生转移的174例患者(n=200)相比。所有肿瘤组织-原发和 转移性--将与边缘清晰的“正常”肿瘤邻近组织配对。此外，更大的数字 的患者将使用现有的组织病理学和分期数据作为替代 临床结果。这项研究的一个附带好处是我们的ccRCC银行预期将增加第二阶段研究。

ABSTRACT The goal of this Phase I SBIR is to address the absence of measureable and targeted metabolite biomarkers in clear cell renal cell carcinoma (ccRCC) tissues that can predict disease metastasis. An estimated 69,990 Americans were diagnosed with kidney cancer in 2017, and approximately 14,400 died as a result of ccRCC, primarily due to metastasis of the cancer. Ultimately, we seek to identify those patients with more aggressive renal cancers that might benefit from more aggressive treatment. Patients with indolent ccRCC may choose less aggressive treatment while those with a greater risk of metastasis may elect adjuvant therapy that may include drug treatments. There are no diagnostic tests that predict post-surgical disease progression on the market. Our methodology will utilize novel liquid chromatography (LC) and mass spectrometry (MS) methods developed by our collaborators, along with several new methods we developed that will greatly increase quantitative accuracy and robustness. This will address a series of issues that have plagued the development of a reliable biomarker panel for the diagnosis of renal cancer progression and metastasis. A major problem with metabolomic studies to date is that the heterogeneity of cancer tissues is frequently overlooked. Thus, an important part of our approach includes normalization, calibration, and quantitation of metabolites in both discovery and targeted modes. Specifically, our method allows for the analysis of metabolites in small biopsies and will permit histopathology to be performed on exactly the same tissue. Whereas normalization and quantitation are usually addressed after data has been acquired, i.e. post- acquisition, we add pre-acquisition normalization, which will ensure that equal amounts of sample are injected for MS. Our use of chemical labeling and sample normalization will minimize the effects of ion suppression, signal saturation, column contamination, aging, and instrument performance drift. Essentially, the day-to-day and lab-to-lab variability, which frequently affect MS, will be minimized. In this proposal, we will utilize the Eastern Virginia Medical School Biorepository PROBE cohort, which houses over 300 renal tumors and associated clinical data, including treatment, vital status, imaging data, and longitudinal follow-up after primary tumor resection. The database accompanying the Biorepository is regularly updated, allowing us to identify those who “progressed” vs. those who are termed “non-progressors”. For this Phase I proof-of-concept study, tissues from 26 patients that developed metastasis will be analyzed and compared to 174 patients that did not develop metastasis (n=200 patients). All tumor tissues – primary and metastatic – will be paired with “normal” tumor-adjacent tissue from clear margins. In addition, larger numbers of patients in the cohort will be analyzed using available histopathological and staging data as surrogates for clinical outcome. A side benefit to this study is the prospective addition to our ccRCC bank for Phase II studies.