Effect of Preanalytic Variables on an Automated PTEN Immunohistochemistry Assay for Prostate Cancer.

CONTEXT.­: Phosphatase and tensin homolog (PTEN) is a promising prognostic and potentially predictive biomarker in prostate cancer. OBJECTIVE.­: To assess the effects of preanalytic variables on an analytically validated and fully automated PTEN immunohistochemistry assay. DESIGN.­: PTEN immunohistochemistry was performed on Ventana immunostaining systems. In benign prostate tissues, immunostaining intensity across variable conditions was assessed by digital image analysis. In prostate tumor tissues, immunostaining was scored visually. RESULTS.­: Delay of fixation for 4 hours or longer at room temperature or 48 hours or longer at 4°C and duration of formalin fixation did not significantly alter immunostaining intensity. Intensity of staining was highest in 10% formalin compared with other fixatives. Tumor tissues with PTEN loss processed using protocols from 11 academic institutions were all evaluable and scored identically. PTEN immunostaining of needle biopsies where tissue blocks had been stored for less than 10 years was more frequently scored as nonevaluable compared with blocks that had been stored for 10 years or longer. This effect was less evident for radical prostatectomy specimens, where low rates of nonevaluable staining were seen for 23 years or more of storage. Storage of unstained slides for 5 years at room temperature prior to immunostaining resulted in equivalent scoring compared with freshly cut slides. Machine-to-machine variability assessed across 3 Ventana platforms and 2 institutions was negligible in 12 tumors, and platform-to-platform variability was also minor comparing Ventana and Leica instruments across 77 tumors (κ = 0.926). CONCLUSIONS.­: Automated PTEN immunostaining is robust to most preanalytic variables in the prostate and may be performed on prostate tumor tissues subjected to a wide range of preanalytic conditions. These data may help guide assay development if PTEN becomes a key predictive biomarker.