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Image analysis of prostate cancer tissue biomarkers

Lippolis, Giuseppe LU (2015) In Lund University, Faculty of Medicine Doctoral Dissertation Series 2015:65.
Abstract (Swedish)
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... (More)
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. (Less)
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • MD, PhD Lundin, Johan, Finnish Institute of Molecular Medicine FIMM, University of Helsinki, Finland
organization
publishing date
type
Thesis
publication status
published
subject
keywords
prostate cancer, image analysis, Time Resolved Fluorescence, automated Gleason, PSA, AR, fusion gene, TMAs
categories
Higher Education
in
Lund University, Faculty of Medicine Doctoral Dissertation Series
volume
2015:65
pages
85 pages
publisher
Division of Urological Cancers
defense location
Lecture Hall, Pathology building, Jan Waldenströms gata 59, Malmö
defense date
2015-05-28 13:00
ISSN
1652-8220
ISBN
978-91-7619-144-6
language
English
LU publication?
yes
id
8f197d16-e6a5-4953-b8f1-e885a1337e17 (old id 5367942)
date added to LUP
2015-05-08 12:19:50
date last changed
2016-09-19 08:44:50
@phdthesis{8f197d16-e6a5-4953-b8f1-e885a1337e17,
  abstract     = {Prostate cancer is the second most common cancer in men. In order to improve diagnosis and prognosis, new<br/><br>
sensitive and specific biomarkers are needed. Tissue biomarkers carry expression and morphological information of<br/><br>
the tissue where they are expressed. However their use is still limited by technological problems, lack of standardized<br/><br>
procedures and inadequate interpretation.<br/><br>
In this work we investigated a group of tissue biomarkers as well as new technologies and computerized approaches<br/><br>
for consistent and reproducible analyses. We also tested an automated approach for performing Gleason grading.<br/><br>
In order to validate previous in silico studies, we investigated the expression of ERG (as a surrogate marker of<br/><br>
TMPRSS2:ERG gene fusion status) and TATI (encoded by SPINK1) proteins in a large TMA of localized prostate<br/><br>
cancer patients. We observed a mutually exclusive expression pattern, further supporting the idea of tailored<br/><br>
treatment for genotypically different cancers. In the second and third studies we introduce the use of image analysis<br/><br>
for an integrated approach that uses Time Resolved Fluorescence Imaging on PSA and AR, immunofluorescence<br/><br>
on cytokeratin as well as brightfield microscopy on H&amp;E and p63/AMACR. The workflow includes the following<br/><br>
automated steps: multi-modality image registration, identification of regions of interest, recognition of benign versus<br/><br>
cancer areas and protein quantification. PSA seemed to decrease in cancer while AR increased in AMACR+ and<br/><br>
decreased in AMACR- cancer tissue compared to benign. Finally, we developed a system based on SIFT features<br/><br>
and BoW approach to automatically perform Gleason grading. The system was able to distinguish between grades<br/><br>
with very high accuracy.},
  author       = {Lippolis, Giuseppe},
  isbn         = {978-91-7619-144-6},
  issn         = {1652-8220},
  keyword      = {prostate cancer,image analysis,Time Resolved Fluorescence,automated Gleason,PSA,AR,fusion gene,TMAs},
  language     = {eng},
  pages        = {85},
  publisher    = {Division of Urological Cancers},
  school       = {Lund University},
  series       = {Lund University, Faculty of Medicine Doctoral Dissertation Series},
  title        = {Image analysis of prostate cancer tissue biomarkers},
  volume       = {2015:65},
  year         = {2015},
}