Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients
Menée à partir d'échantillons urinaires collectés auprès de 199 patients atteints d'un adénocarcinome du pancréas et 180 témoins, cette étude évalue la performance d'un système de score, basé sur trois biomarqueurs urinaires (LYVE1, REG1B et TFF1), pour identifier les patients susceptibles de présenter un cancer du pancréas
Background : An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK.
Methods : Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity.
Results : None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model.
Conclusion : PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.
British Journal of Cancer , article en libre accès, 2019