Early prediction of disease progression in men with very low-risk (VLR) prostate cancer who selected active surveillance (AS) rather than immediate treatment could reduce morbidity associated with overtreatment.
METHODS:
We evaluated the association of six biomarkers [Periostin, (-5, -7) proPSA, CACNA1D, HER2/neu, EZH2, and Ki-67] with different Gleason scores and biochemical recurrence (BCR) on prostate cancer TMAs of 80 radical prostatectomy (RP) cases. Multiplex tissue immunoblotting (MTI) was used to assess these biomarkers in cancer and adjacent benign areas of 5 μm sections. Multivariate logistic regression (MLR) was applied to model our results.
RESULTS:
In the RP cases, CACNA1D, HER2/neu, and Periostin expression were significantly correlated with aggressive phenotype in cancer areas. An MLR model in the cancer area yielded a ROC-AUC = 0.98, whereas in cancer-adjacent benign areas, yielded a ROC-AUC = 0.94. CACNA1D and HER2/neu expression combined with Gleason score in a MLR model yielded a ROC-AUC = 0.79 for BCR prediction. In the small biopsies from an AS cohort of 61 VLR cases, an MLR model for prediction of progressors at diagnosis retained (-5, -7) proPSA and CACNA1D, yielding a ROC-AUC of 0.78, which was improved to 0.82 after adding tPSA into the model.
CONCLUSIONS:
The molecular profile of biomarkers is capable of accurately predicting aggressive prostate cancer on retrospective RP cases and identifying potential aggressive prostate cancer requiring immediate treatment on the AS diagnostic biopsy but limited in BCR prediction.
IMPACT:Comprehensive profiling of biomarkers using MTI predicts prostate cancer aggressive phenotype in RP and AS biopsies.
©2015 American Association for Cancer Research.
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Authors: Zhu G1, Liu Z1, Epstein JI2, Davis C1, Christudass CS1, Carter HB1, Landis P1, Zhang H2, Chung JY3, Hewitt SM3, Miller MC4, Veltri RW5