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BACKGROUND: Prostate cancer (PCa) is the most common solid organ cancer in men and the fifth leading cause of cancer-related deaths globally. PSA helps identify men at risk but has low specificity and has resulted in unnecessary biopsies. The PROSTest, a novel machine learning-based 27-gene mRNA liquid biopsy assay, was developed to detect PCa. We evaluated its utility as a stratification tool in symptomatic men undergoing biopsy or surgery for PSA > 2 ng/mL. METHODS: Of 123 men assessed, 105 (85%) met eligibility criteria (age > 55 years, PSA > 2 ng/mL, symptomatic) and underwent image-guided biopsy or surgery. Blood samples for PROSTest were collected prebiopsy, and RNA-stabilized samples underwent RNA isolation and cDNA production. PCR results were analyzed using a machine learning algorithm, generating a 0-100 score with a cutoff of 50 for a binary (positive/negative) readout. The performance of PROSTest was against PSA using AUROC and evaluated for Gleason Grade (GG) 1 versus GG2-5 patients. RESULTS: Median age was 68 years (55-86 years); median PSA was 8.2 ng/mL (IQR: 7.2-92 ng/mL). PCa was diagnosed in 65 men (62%) (27 GG1; 38 GG2-5). PROSTest was positive in 63/65 (97%) of those with PCa and in 2/40 (5%) without PCa. Sensitivity was 97%, specificity 96%. PROSTest outperformed PSA (AUROC: 0.99 vs. 0.61, p < 0.0001). GG2-5 had significantly higher (p < 0.0001) PROSTest scores (92 ± 3). CONCLUSIONS: PROSTest demonstrated superior sensitivity and specificity compared to PSA for detecting prostate cancer across all Gleason grades. Additionally, it showed potential for distinguishing GG2-5 from GG1 + BPH, which could help guide clinical decision-making and reduce unnecessary biopsies. By leveraging a machine learning-based approach, PROSTest may offer a more accurate and less invasive diagnostic tool for prostate cancer stratification. However, larger prospective studies are needed to validate these findings and further define its clinical utility.
Kidd et al. (Thu,) studied this question.