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