Metabolomic discovery and validation of urinary biomarkers for kidney cancer
Metabolomic discovery and validation of urinary biomarkers for kidney cancer
批准号:
8433239
负责人:
ROBERT H. WEISS
金额:
$29.14万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-03-23 至 2015-01-31
关键词:
African AmericanAreaBiological AssayBiological MarkersCancer PatientCause of DeathCell LineCellsChemicalsClinicCollaborationsCollectionConventional (Clear Cell) Renal Cell CarcinomaDataDetectionDevelopmentDiagnosisDiagnosticDiagnostic testsDiscriminationDiseaseEarly DiagnosisEnrollmentFutureGenomicsGleanGrantHealthHematuriaImaging TechniquesIncidenceIndividualInformation NetworksInstitutionKidneyKidney DiseasesLaboratoriesLeadMalignant NeoplasmsMetabolicMetabolic PathwayMethodsMicroscopicModelingMolecularMonitorNon-MalignantOncogenicOncologistPathogenesisPathologistPathway AnalysisPathway interactionsPatient MonitoringPatientsPatternPrimary Health CareProteinsProteomicsPublicationsRenal Cell CarcinomaRenal carcinomaSamplingScienceScientistSignal PathwaySmokerSolidStagingSymptomsTechnologyTestingTimeTissuesTrainingTreatment ProtocolsUrineValidationWorkWritingbasechemotherapyclinical materialcomputerized toolsdesignhigh riskimprovedin vivometabolomicsnovelresearch studyresponsesoundstatisticstooltumorigenesisurinaryurologic
中文摘要
描述(由申请人提供):肾癌(也称为肾细胞癌[RCC])在美国被诊断为36,000例患者,每年导致11-13,000人死亡;不幸的是(由于未知的原因),这种疾病的发病率在所有人群中都在增加。三分之一的病例(其中许多是无症状的)在诊断时已转移,目前没有可用的生物液诊断测试或诊断后的适当治疗。鉴于肾脏与尿液的关系,肾细胞癌非常适合于尿液标志物的鉴定。在本次修订的提案中,我们将利用代谢组学的新科学来发现尿液代谢物的模式,这些代谢物可作为RCC高危患者的生物标志物。我们将使用RCC组织和细胞系,通过途径和网络分析来确认在这种疾病中哪些代谢途径出错,从而支持我们的生物标志物发现。最后,我们将在RCC和非RCC患者的新样本中测试我们的生物标志物,包括非恶性肾脏疾病的对照患者和非肾癌患者。对于这次修订,我们通过添加所有要求的初步数据来改进提案。我们还提交了两篇与RCC代谢组学和蛋白质组学相关的出版物。我们的提议是不同寻常的,因为我们聚集了一个独特的合作者核心:一个细胞生物学家,同时也是临床医生-科学家肾病学家(韦斯博士),一个蛋白质组学和基因组学专家(佩鲁德博士),四位代谢组学专家(费恩博士、哈莫克博士、米歇尔莫尔博士和格兰特博士),两位生物统计学家(格兰特博士)。Kim和Rocke),两位肿瘤病理学家(dr。Grizzle和Borowsky),以及两位泌尿肿瘤学家(dr。De Vere White和Evans)利用代谢组学来解决难以诊断的癌症的诊断和治疗问题,这种癌症的发病率正在增加,目前的治疗方案令人沮丧。我们很高兴审稿人同意我们提案中的实验是合理的。这些实验的成功完成将导致诊断方面的重大进展,并最终为该疾病选择最佳治疗方案。我们将是第一个在泌尿系统恶性肿瘤中使用这项技术的人,也是第一个在任何癌症中利用这项技术的人。此外,我们的工作可以作为使用代谢组学收集各种癌症的致癌途径和网络数据的模型。
英文摘要
DESCRIPTION (provided by applicant): Kidney cancer (also known as renal cell carcinoma [RCC]) is diagnosed in 36,000 patients and is the cause of death of 11-13,000 individuals yearly in the US; unfortunately (and for unknown reasons), the incidence of this disease is increasing in all groups. One-third of cases, many of whom are asymptomatic, are metastatic at diagnosis and there is currently no available biofluid diagnostic test or adequate treatment once diagnosed. Given the relationship of the kidney to the urine, RCC is ideally suited for identification of urinary markers. In this revised proposal, we will exploit the new science of metabolomics to discover a pattern of urinary metabolites which serve as biomarkers for RCC in patients who are at high risk for this disease. We will support our biomarkers discovery by using pathway and network analysis to confirm which metabolic pathways go awry in this disease, using RCC tissues and cell lines. Finally, we will test our biomarkers in new samples from both RCC non-RCC patients, including as controls patients with non-malignant renal disease as well as patients who have non-renal cancers. For this revision, we have improved the proposal by adding all of the requested preliminary data. We have also submitted two publications related to RCC metabolomics as well as proteomics. Our proposal is extraordinary in that we have assembled a unique cadre of collaborators: a cell biologist who is also a clinician- scientist nephrologist (Dr. Weiss), a proteomics and genomics expert (Dr. Perroud), four metabolomics experts (Drs Fiehn, Hammock, Michelmore, and Grant), two biostatisticians (Drs. Kim and Rocke), two oncologic pathologists (Drs. Grizzle and Borowsky), and two urologic oncologists (Drs. De Vere White and Evans) to utilize metabolomics to tackle the problem of diagnosis and treatment of a cancer which is difficult to diagnose, whose incidence is increasing, and for which current treatment options are dismal. We are pleased that the reviewers agreed that the experiments in our proposal were sound. Successful completion of these experiments will result in a major advance in diagnosis as well as, ultimately, the selection of optimal treatment regimens for this disease. Ours will be the first described use of this technology in urologic malignancy, and one of the first to exploit this technology in any cancer. Furthermore, our work can serve as a model for using metabolomics to glean oncogenic pathway and network data from a variety of cancers.
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