Polygenic risk scores (PRS) and the prostate specific antigen (PSA) test have been shown to be successful tools for predicting prostate cancer (PCa) incidence. In this study, we assessed the potential of combining PRS and PSA as biomarkers for PCa aggressiveness and subsequent mortality from patients with low to intermediate risk PCa from the TAPG-TURP cohort. Targeted sequencing of 140 PCa-related genes was performed using 162 prostate samples from PCa patients with a Gleason score of 6 or 7, 80 of whom died from the disease. An additional 305 genome samples from healthy participants of the 1000 Genomes Project phase 3 were selected as controls. Two novel PRSs were developed using 21 single nucleotide polymorphisms (SNPs) selected from those differentiated between alive (n = 82) and dead (n = 80) PCa patients. The first PRS was used in decision tree-based models, such as random forest (rf) able to accurately distinguish cancer from healthy samples (sensitivity = 100%, specificity = 100%, AUC = 1). The second PRS was used together with Gleason score and PSA in an artificial neural network model able to determine the aggressiveness of PCa by predicting PCa mortality with intermediate to high accuracy (sensitivity = 90%, specificity = 68.8%, AUC = 0.718). Further work must be done using our two machine learning classifiers to validate them further and apply them in the clinic, bypassing the necessity of invasive and more expensive approaches. Their application will potentially guide the clinical decision-making process and reduce costs of the clinical management of PCa patients.
Santiago et al. (Tue,) studied this question.