CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer
Menée à partir d'échantillons urinaires prélevés sur 823 patients présentant un taux sérique du PSA inférieur à 15ng/ml, cette étude évalue la sensibilité et la spécificité d'un algorithme, basé sur la présence de 19 peptides, pour distinguer un cancer de la prostate non significatif (score de Gleason inférieur à 7) d'un cancer de la prostate significatif
Background : Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management.
Methods : Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (nSig = 98; nnon-Sig = 445) and a validation set of 280 patients (nSig = 48, nnon-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm.
Results : Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set.
Conclusions : This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.
British Journal of Cancer , résumé, 2019