Wilms’ tumor is the most common type of childhood kidney cancer. To improve risk stratification and identify novel therapeutic targets for patients with Wilms’ tumor, we used high-resolution mass spectrometry proteomics to identify urine tumor markers associated with Wilms’ tumor relapse. We determined the urine proteomes at diagnosis of 49 patients with Wilms’ tumor, non–Wilms’ tumor renal tumors, and age-matched controls, leading to the quantitation of 6520 urine proteins. Supervised analysis revealed specific urine markers of renal rhabdoid tumors, kidney clear cell sarcomas, renal cell carcinomas as well as those detected in patients with cured and relapsed Wilms’ tumor. In particular, urine prohibitin was significantly elevated at diagnosis in patients with relapsed as compared with cured Wilms’ tumor. In a validation cohort of 139 patients, a specific urine prohibitin ELISA demonstrated that prohibitin concentrations greater than 998 ng/mL at diagnosis were significantly associated with ultimate Wilms’ tumor relapse. Immunohistochemical analysis revealed that prohibitin was highly expressed in primary Wilms’ tumor specimens and associated with disease stage. Using functional genetic experiments, we found that prohibitin was required for the growth and survival of Wilms’ tumor cells. Overexpression of prohibitin was sufficient to block intrinsic mitochondrial apoptosis and to cause resistance to diverse chemotherapy drugs, at least in part by dysregulating factors that control apoptotic cytochrome c release from mitochondrial cristae. Thus, urine prohibitin may improve therapy stratification, noninvasive monitoring of treatment response, and early disease detection. In addition, therapeutic targeting of chemotherapy resistance induced by prohibitin dysregulation may offer improved therapies for patients with Wilms’ and other relapsed or refractory tumors.
Michael V. Ortiz, Saima Ahmed, Melissa Burns, Anton G. Henssen, Travis J. Hollmann, Ian MacArthur, Shehana Gunasekera, Lyvia Gaewsky, Gary Bradwin, Jeremy Ryan, Anthony Letai, Ying He, Arlene Naranjo, Yueh-Yun Chi, Michael LaQuaglia, Todd Heaton, Paolo Cifani, Jeffrey S. Dome, Samantha Gadd, Elizabeth Perlman, Elizabeth Mullen, Hanno Steen, Alex Kentsis
Usage data is cumulative from June 2022 through June 2023.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.