KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer - a targeted molecular approach

Background: There remains a need to identify and validate biomarkers for predicting prostate cancer (CaP) outcomes using robust and routinely available pathology techniques to identify men at most risk of premature death due to prostate cancer. Previous immunohistochemical studies suggest the proliferation marker Ki67 might be a predictor of survival, independently of PSA and Gleason score. We performed a validation study of Ki67 as a marker of survival and disease progression and compared its performance against another candidate biomarker, DLX2, selected using artificial neural network analysis. Methods: A tissue microarray (TMA) was constructed from transurethral resected prostatectomy histology samples (n=192). Artificial neural network analysis was used to identify candidate markers conferring increased risk of death and metastasis in a public cDNA array. Immunohistochemical analysis of the TMA was carried out and univariate and multivariate tests performed to explore the association of tumour protein levels of Ki67 and DLX2 with time to death and metastasis. Results: Univariate analysis demonstrated Ki67 as predictive of CaP-specific survival (DSS; P=0.022), and both Ki67 (P=0.025) and DLX2 (P=0.001) as predictive of future metastases. Multivariate analysis demonstrated Ki67 as independent of PSA, Gleason score and D’Amico risk category for DSS (HR=2.436, P=0.029) and both Ki67 (HR=3.296, P=0.023) and DLX2 (HR=3.051, P=0.003) as independent for future metastases. Conclusions: High Ki67 expression is only present in 6.8% of CaP patients and is predictive of reduced survival and increased risk of metastasis, independent of PSA, Gleason score and D’Amico risk category. DLX2 is a novel marker of increased metastasis risk found in 73% patients and 8.2% showed co-expression with a high Ki67 score. Two cancer cell proliferation markers, Ki67 and DLX2, may be able to inform clinical decision-making when identifying patients for active surveillance.