Poster session 6: Prostate cancer| Volume 8, ISSUE 8, P686-687, September 2009

C87 Balance between apoptotic and proliferative tissue markers in prostate cancer needle biopsies correlates with stage and Gleason score

      Introduction and Objectives

      Tumor growth depends on balance between cellular growth (proliferation) and cellular death (apoptosis). Both processes are reflected in changes of tissue markers expression. Identifying a model which would take into account opposing nature of both processes and relate it to cancer stage and grade would be a useful adjunct for study of disease behavior.

      Material and Methods

      Retrospective pilot study on formalin fixed paraffin embedded needle biopsy tissue samples from prostate cancer patients was performed. Patient age was 59 – 86 years, median 72, Gleason score 6 – 9, median 7. Apoptotic markers studied were p53 and fragmented DNA (TUNEL), expressed as apoptotic index. Proliferative markers studied were Bcl-2, Ki-67, AgNOR. Immunohistological staining results of cancerous tissue were determined. Individual markers and models which considered opposing nature of apoptosis and proliferation were consecutively correlated to patient and disease characteristics. Parametric or nonparametric correlations were calculated according to variables distributions.


      Among individual markers, p53 staining inversely correlated with age of patients (p = 0.022) and Bcl-2 staining correlated with disease stage (r = 0.65, p = 0.004). Model which incorporated coded staining intensity of Bcl-2 and AgNOR on proliferative side and p53 on apoptotic side was significantly related to Gleason score (r = 0.57, p = 0.018) and disease stage (r = 0.54, p = 0.026).


      Individual histological markers, studied here, were previously related to prostate cancer with mixed results. We believe their incorporation into models which account for opposing roles of biological processes involved (apoptosis and proliferation), should provide better insight and finally better disease behavior prediction and control.