Image analysis of prostate cancer tissue biomarkers

University dissertation from Division of Urological Cancers

Abstract: Popular Abstract in English Prostate cancer is one of the most common cancers in the world and the second most common in men. The western world has the highest incidence rates. The causes of prostate cancer are not yet clear, however a number of risk factors have been identified such as familial history, ethnicity, diet and genetic events. Prostate cancer affects primarily elderly men with the majority of the cases happening above 65 years of age. If caught at an early stage, prostate cancer is curable by removal of the whole prostate whereas advanced or recurrent disease is lethal and only palliative methods are available for patients. Nowadays the tools to diagnose the disease include PSA blood test and a rectal examination conducted by a pathologist to detect suspicious lumps. PSA is a protein produced by the prostate; when its amount goes up beyond a certain level, it may indicate cancer or other pathological conditions that are not life threatening. The only way to be sure that a patient harbours a tumour in the prostate, is to perform a biopsy (generally from multiple areas at once) and analyse it using a microscope. The problem with blood PSA test is that it unfortunately detects many false positives. This can expose the patient to unnecessary treatment and side effects. The biopsy is used not only to diagnose, but also to assess the potential aggressiveness of the disease by looking at the architecture of the tumour lesions and assigning the so-called “Gleason grade”. The Gleason grade is a prognostic tool, meaning that it is able to predict, to a certain extent, the development of the disease and the response to treatments. In order to improve both diagnosis and prognosis, we need more reliable markers. A class of such markers is represented by proteins present in the prostatic tissue. Traditionally the way to look at them is by using a normal light microscope, however, this technique is slow and prone to errors and inconsistencies. In this thesis we investigated the role of ERG, TATI, PSA and AR proteins in prostate cancer by using novel methodologies based on Time Resolved Fluorescence Imaging, digital imaging and automated image analysis. In paper I we analysed the expression of ERG and TATI in prostate cancer from 4177 patients with a localized disease. We observed that the two proteins were mutually exclusive, as cancer cells that expressed one, did not express the other. This finding is very important because confirms the heterogeneity of prostate cancer 66 and identifies different families of cancer cells. As a result, the research could focus on targeted therapies and personalized treatments. In paper II, III and IV we introduced the use of image analysis to study tissue biomarkers. In paper II and III we develop a system for automatic analysis of PSA and AR in tissue sections employing mathematical algorithms for alignment of images, recognition of specific areas of interest within the tissue, and quantification of the markers in those areas. To quantify the markers, we used a novel fluorescence technique that has several advantages over other existing methods. Moreover the use of computerized image analysis allows for consistent and reproducible assessment of tissue sections. Our methods allowed us to observe some interesting expression patterns of the proteins in different clusters of tumour cells and in normal tissue. This kind of differential expression would need to be analysed further to uncover some aspects of the disease. Finally in paper IV we developed an algorithm for automated Gleason grading, which is a system that resembles the pathologist analysis. The system was able to recognize with high accuracy the different Gleason grades and it represents a promising supporting tool for aiding pathologists’ work and possibly increasing the accuracy of prognosis.