Image analysis of prostate cancer tissue biomarkers
(2015) In Lund University Faculty of Medicine Doctoral Dissertation Series 2015:65.- 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) - 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5367942
- author
- Lippolis, Giuseppe LU
- supervisor
- opponent
-
- MD, PhD Lundin, Johan, Finnish Institute of Molecular Medicine FIMM, University of Helsinki, Finland
- organization
- publishing date
- 2015
- 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 13:00:00
- 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
- 2016-04-01 14:33:21
- date last changed
- 2019-05-22 05:38:45
@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&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}}, keywords = {{prostate cancer; image analysis; Time Resolved Fluorescence; automated Gleason; PSA; AR; fusion gene; TMAs}}, language = {{eng}}, 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}}, url = {{https://lup.lub.lu.se/search/files/4035837/5367952.pdf}}, volume = {{2015:65}}, year = {{2015}}, }