Abstract: There is described herein a method of predicting disease progression risk in a subject with prostate cancer, the method comprising: a) providing a sample containing RNA and DNA material from tumour cells; b) determining or measuring values for substantially all of patient features listed for PRONTO-e or PRONTO-m in Table 6, and some or all reference or control features set forth in Table 6; c) comparing said patient features to the reference or control features; and d) computing a prediction score using a classifier that takes said patient feature values as input, the classifier having been previously trained on samples from a population of early prostate cancer patients.

Abstract: A multi-gene model is employed in methods for accurately classifying benign and malignant prostate cancer and reliably identifying prostate cancer in samples, with false positive and negative rates below 7%. A single gene model is employed in methods for detecting aggressive prostate cancer, prostate cancer patients at risk of developing biochemical recurrence, and prostate cancer patients suitable for treatment with an additional and/or alternative therapy. The methods may be implemented with next generation sequencing (NGS) or methylation-specific PCR (MSP). The MSP may use a mastermix specifically designed for use with bisulfite converted DNA in singleplex and multiplex assays.