Image analysis of prostate cancer tissue biomarkers

Prostate cancer is the second most common cancer in men. In order to improve diagnosis and prognosis, new
sensitive and specific biomarkers are needed. Tissue biomarkers carry expression and morphological information of
the tissue where they are expressed. However their use is still limited by technological problems, lack of standardized
procedures and inadequate interpretation.
In this work we investigated a group of tissue biomarkers as well as new technologies and computerized approaches
for consistent and reproducible analyses. We also tested an automated approach for performing Gleason grading.
In order to validate previous in silico studies, we investigated the expression of ERG (as a surrogate marker of
TMPRSS2:ERG gene fusion status) and TATI (encoded by SPINK1) proteins in a large TMA of localized prostate
cancer patients. We observed a mutually exclusive expression pattern, further supporting the idea of tailored
treatment for genotypically different cancers. In the second and third studies we introduce the use of image analysis
for an integrated approach that uses Time Resolved Fluorescence Imaging on PSA and AR, immunofluorescence
on cytokeratin as well as brightfield microscopy on H&E and p63/AMACR. The workflow includes the following
automated steps: multi-modality image registration, identification of regions of interest, recognition of benign versus
cancer areas and protein quantification. PSA seemed to decrease in cancer while AR increased in AMACR+ and
decreased in AMACR- cancer tissue compared to benign. Finally, we developed a system based on SIFT features
and BoW approach to automatically perform Gleason grading. The system was able to distinguish between grades
with very high accuracy.