Precision medicine for prostate cancer-improved outcome prediction for low-intermediate risk disease using a six-gene copy number alteration classifier

Abstract

A multiplex 6-gene copy number classifier was used to distinguish between low- or intermediate-risk prostate cancer patients. The study analysed a cohort of 448 patients and previously published datasets from radical prostatectomies. The classifier performs better than conventional stratification methods, is low cost, and can be performed easily in clinical laboratories.

Fig. 1. Development of prostate cancer six-gene classifier for accurate prediction of recurrence in patients with low-intermediate disease.

Schematic depiction of gene copy number selection for classifier using Random Forest survival and Cox proportional hazard modelling. Genes are shown in blue = loss and red = gain. The cohort analysis involved double sampling using needle cores from differing Gleason score regions (A and B) that can address variation in tumour sampling due to technical issues or genetic heterogeneity. Analysis of CNAs for all six genes from both needle core samples was used to determine the risk of recurrence. The classifier’s performance was compared to the CAPRA-S risk score and four public domain outcome cohorts.