Use of two gene panels for prostate cancer diagnosis and patient risk stratification

Currently, no ideal prostate cancer (PCa) diagnostic or prognostic test is available due to the lack of biomarkers with high sensitivity and specificity. There is an unmet medical need to develop combinations of multiple biomarkers which may have higher accuracy in detection of PCa and stratification of aggressive and indolent cancer patients. The aim of this study was to test two biomarker gene panels in distinguishing PCa from benign prostate and high-risk, aggressive PCa from low-risk, indolent PCa, respectively. We identified a five-gene panel that can be used to distinguish PCa from benign prostate. The messenger RNA (mRNA) expression signature of the five genes was determined in 144 PCa and benign prostate specimens from prostatectomy. We showed that the five-gene panel distinguished PCa from benign prostate with sensitivity of 96.59 %, specificity of 92.86 %, and area under the curve (AUC) of 0.992 (p < 0.0001). The five-gene panel was further validated in a 137 specimen cohort and showed sensitivity of 84.62 %, specificity of 91.84 %, and AUC of 0.942 (p < 0.0001). To define subtypes of PCa for treatment guidance, we examined mRNA expression signature of an eight-gene panel in 87 PCa specimens from prostatectomy. The signature of the eight-gene panel was able to distinguish aggressive PCa (Gleason score >6) from indolent PCa (Gleason score ≤6) with sensitivity of 90.28 %, specificity of 80.00 %, and AUC of 0.967 (p < 0.0001). This panel was further validated in a 158 specimen cohort and showed significant difference between aggressive PCa and indolent PCa with sensitivity of 92.57 %, specificity of 70.00 %, and AUC of 0.962 (p < 0.0001). Our findings in assessing multiple biomarkers in combination may provide new tools to detect PCa and distinguish aggressive and indolent PCa for precision and personalized treatment. The two biomarker panels may be used in clinical settings for accurate PCa diagnosis and patient risk stratification for biomarker-guided treatment.