Mass Spectrometry-Based Biomarkers to Detect Prostate Cancer: A Multicentric Study Based on Non-Invasive Urine Collection without Prior Digital Rectal Examination

Maria Frantzi*, Zoran Culig, Isabel Heidegger, Marika Mokou, Agnieszka Latosinska, Marie C. Roesch, Axel S. Merseburger, Manousos Makridakis, Antonia Vlahou, Ana Blanca-Pedregosa, Julia Carrasco-Valiente, Harald Mischak, Enrique Gomez-Gomez

*Corresponding author for this work
3 Citations (Scopus)

Abstract

(1) Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Wide application of prostate specific antigen test has historically led to over-treatment, starting from excessive biopsies. Risk calculators based on molecular and clinical variables can be of value to determine the risk of PCa and as such, reduce unnecessary and invasive biopsies. Urinary molecular studies have been mostly focusing on sampling after initial intervention (digital rectal examination and/or prostate massage). (2) Methods: Building on previous proteomics studies, in this manuscript, we aimed at developing a biomarker model for PCa detection based on urine sampling without prior intervention. Capillary electrophoresis coupled to mass spectrometry was applied to acquire proteomics profiles from 970 patients from two different clinical centers. (3) Results: A case-control comparison was performed in a training set of 413 patients and 181 significant peptides were subsequently combined by a support vector machine algorithm. Independent validation was initially performed in 272 negative for PCa and 138 biopsy-confirmed PCa, resulting in an AUC of 0.81, outperforming current standards, while a second validation phase included 147 PCa patients. (4) Conclusions: This multi-dimensional biomarker model holds promise to improve the current diagnosis of PCa, by guiding invasive biopsies.

Original languageEnglish
Article number1166
JournalCancers
Volume15
Issue number4
ISSN2072-6694
DOIs
Publication statusPublished - 11.02.2023

Research Areas and Centers

  • Research Area: Luebeck Integrated Oncology Network (LION)
  • Centers: University Cancer Center Schleswig-Holstein (UCCSH)

DFG Research Classification Scheme

  • 205-14 Haematology, Oncology
  • 205-23 Reproductive Medicine, Urology

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