Maria Frantzi1, Jochen Metzger2, Rosamonde E Banks3, Holger Husi4, Julie Klein2, Mohammed Dakna2, William Mullen4, Jonathan J Cartledge5, Joost P Schanstra6, Korbinian Brand7, Markus A Kuczyk8, Harald Mischak9, Antonia Vlahou10, Dan Theodorescu11, Axel S Merseburger8. 1. Mosaiques diagnostics GmbH, Hannover, Germany; Biomedical Research Foundation, Academy of Athens, Biotechnology Division, Athens, Greece. Electronic address: frantzi@mosaiques-diagnostics.com. 2. Mosaiques diagnostics GmbH, Hannover, Germany. 3. St James's University Hospital, Cancer Research UK Clinical Centre, Clinical and Biomedical Proteomics Group, Leeds, United Kingdom. 4. University of Glasgow, Institute of Cardiovascular and Medical Sciences, Glasgow, United Kingdom. 5. St James's University Hospital, Department of Urology, Leeds, United Kingdom. 6. Inserm, U858/I2MR, Department of Renal and Cardiac Remodeling, Team #5, 1 Avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France; Université Toulouse III Paul Sabatier, Institut de Médecine Moléculaire de Rangueil, Toulouse F-31000, France. 7. Hannover Medical School, Institute of Clinical Chemistry, Hannover, Germany. 8. Hannover Medical School, Department of Urology and Urological Oncology, Hannover, Germany. 9. Mosaiques diagnostics GmbH, Hannover, Germany; University of Glasgow, Institute of Cardiovascular and Medical Sciences, Glasgow, United Kingdom. 10. Biomedical Research Foundation, Academy of Athens, Biotechnology Division, Athens, Greece. 11. University of Colorado, Department of Surgery and Pharmacology, Aurora, CO, USA; University of Colorado Comprehensive Cancer Center, Aurora, CO, USA.
Abstract
INTRODUCTION: Renal cell carcinoma (RCC) is often accompanied by non-specific symptoms. The increase of incidentally discovered small renal masses also presents a diagnostic dilemma. This study investigates whether RCC-specific peptides with diagnostic potential can be detected in urine and whether a combination of such peptides could form a urinary screening tool. MATERIALS AND METHODS: For the discovery of RCC-specific biomarkers, we have employed CE-MS to analyze urine samples from patients with RCC (N=40) compared to non-diseased controls (N=68). RESULTS AND DISCUSSION: 86 peptides were found to be specifically associated to RCC, of which sequence could be obtained for 40. A classifier based on these peptides was evaluated in an independent set of 76 samples, resulting in 80% sensitivity and 87% specificity. The specificity of the marker panel was further validated in a historical dataset of 1077 samples including age-matched controls (N=218), patients with related cancer types and renal diseases (N=859). In silico protease prediction based on the cleavage sites of differentially excreted peptides, suggested modified activity of certain proteases including cathepsins, ADAMTS and kallikreins some of which were previously found to be associated to RCC. CONCLUSIONS: RCC can be detected with high accuracy based on specific urinary peptides. BIOLOGICAL SIGNIFICANCE: Clear cell renal cell carcinoma (RCC) has the highest incidence among the renal malignancies, often presenting non-specific or no symptoms at all. Moreover, with no diagnostic marker being available so far, almost 30% of the patients are diagnosed with metastatic disease and 30-40% of the patients initially diagnosed with localized tumor relapse. These facts introduce the clinical need of early diagnosis. This study is focused on the investigation of a marker model based on urinary peptides, as a tool for the detection of RCC in selected patients at risk. Upon evaluation of the marker model in an independent blinded set of 76 samples, 80% sensitivity and 87% specificity were reported. An additional dataset of 1077 samples was subsequently employed for further evaluation of the specificity of the classifier.
INTRODUCTION: Renal cell carcinoma (RCC) is often accompanied by non-specific symptoms. The increase of incidentally discovered small renal masses also presents a diagnostic dilemma. This study investigates whether RCC-specific peptides with diagnostic potential can be detected in urine and whether a combination of such peptides could form a urinary screening tool. MATERIALS AND METHODS: For the discovery of RCC-specific biomarkers, we have employed CE-MS to analyze urine samples from patients with RCC (N=40) compared to non-diseased controls (N=68). RESULTS AND DISCUSSION: 86 peptides were found to be specifically associated to RCC, of which sequence could be obtained for 40. A classifier based on these peptides was evaluated in an independent set of 76 samples, resulting in 80% sensitivity and 87% specificity. The specificity of the marker panel was further validated in a historical dataset of 1077 samples including age-matched controls (N=218), patients with related cancer types and renal diseases (N=859). In silico protease prediction based on the cleavage sites of differentially excreted peptides, suggested modified activity of certain proteases including cathepsins, ADAMTS and kallikreins some of which were previously found to be associated to RCC. CONCLUSIONS: RCC can be detected with high accuracy based on specific urinary peptides. BIOLOGICAL SIGNIFICANCE: Clear cell renal cell carcinoma (RCC) has the highest incidence among the renal malignancies, often presenting non-specific or no symptoms at all. Moreover, with no diagnostic marker being available so far, almost 30% of the patients are diagnosed with metastatic disease and 30-40% of the patients initially diagnosed with localized tumor relapse. These facts introduce the clinical need of early diagnosis. This study is focused on the investigation of a marker model based on urinary peptides, as a tool for the detection of RCC in selected patients at risk. Upon evaluation of the marker model in an independent blinded set of 76 samples, 80% sensitivity and 87% specificity were reported. An additional dataset of 1077 samples was subsequently employed for further evaluation of the specificity of the classifier.
Authors: Clizia Chinello; Marta Cazzaniga; Gabriele De Sio; Andrew James Smith; Erica Gianazza; Angelica Grasso; Francesco Rocco; Stefano Signorini; Marco Grasso; Silvano Bosari; Italo Zoppis; Mohammed Dakna; Yuri E M van der Burgt; Giancarlo Mauri; Fulvio Magni Journal: PLoS One Date: 2014-09-09 Impact factor: 3.240