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.

Public Health Relevance

Kidney cancer is the 6th most common cancer in the US. This disease, which often has no symptoms, is frequently diagnosed at a late stage when the prospects for cure are dismal. This project will set the stage to ultimately lead to a simple, office-based urine test for detection of kidney cancer, which can be utilized in the primary care clinic and which will lead to many lives being saved through early detection of this deadly cancer.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Cancer Biomarkers Study Section (CBSS)
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Mckee, Tawnya C
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University of California Davis
Internal Medicine/Medicine
Schools of Medicine
United States
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Weiss, Robert H (2018) Metabolomics and Metabolic Reprogramming in Kidney Cancer. Semin Nephrol 38:175-182
Abu Aboud, Omran; Habib, Samy L; Trott, Josephine et al. (2017) Glutamine Addiction in Kidney Cancer Suppresses Oxidative Stress and Can Be Exploited for Real-Time Imaging. Cancer Res 77:6746-6758
Hwang, Vicki J; Zhou, Xia; Chen, Xiaonan et al. (2017) Anticystogenic activity of a small molecule PAK4 inhibitor may be a novel treatment for autosomal dominant polycystic kidney disease. Kidney Int 92:922-933
Abu Aboud, Omran; Chen, Ching-Hsien; Senapedis, William et al. (2016) Dual and Specific Inhibition of NAMPT and PAK4 By KPT-9274 Decreases Kidney Cancer Growth. Mol Cancer Ther 15:2119-29
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Kim, Jeffrey; Stewart, Benjamin; Weiss, Robert H (2016) Extraction and Quantification of Tryptophan and Kynurenine from Cultured Cells and Media Using a High Performance Liquid Chromatography (HPLC) System Equipped with an Ultra-Sensitive Diode Array Detector. Bio Protoc 6:
Abu Aboud, Omran; Donohoe, Dallas; Bultman, Scott et al. (2015) PPAR? inhibition modulates multiple reprogrammed metabolic pathways in kidney cancer and attenuates tumor growth. Am J Physiol Cell Physiol 308:C890-8
Wecksler, Aaron T; Hwang, Sung Hee; Liu, Jun-Yan et al. (2015) Biological evaluation of a novel sorafenib analogue, t-CUPM. Cancer Chemother Pharmacol 75:161-71
Wettersten, Hiromi I; Hakimi, A Ari; Morin, Dexter et al. (2015) Grade-Dependent Metabolic Reprogramming in Kidney Cancer Revealed by Combined Proteomics and Metabolomics Analysis. Cancer Res 75:2541-52
Hwang, Vicki J; Kim, Jeffrey; Rand, Amy et al. (2015) The cpk model of recessive PKD shows glutamine dependence associated with the production of the oncometabolite 2-hydroxyglutarate. Am J Physiol Renal Physiol 309:F492-8

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