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Polish Journal of Pathology
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1/2020
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Original paper

Image analysis discloses differences in nuclear parameters between ERG+ and ERG– prostatic carcinomas

Krzysztof Okoń
1
,
Grzegorz Dyduch
1
,
Magdalena B. Białas
1
,
Katarzyna Milian-Ciesielska
1
,
Joanna Szpor
1
,
Iga Leszczyńska
1
,
Katarzyna Tyrak
2
,
Tomasz Szopiński
3
,
Piotr Chłosta
4

1.
Department of Pathomorphology, Jagiellonian University Medical College, Krakow, Poland
2.
2nd Department of Internal Medicine after Professor Andrzej Szczeklik, Jagiellonian University Medical College, Krakow, Poland
3.
Mazovia Hospital, Warsaw, Poland
4.
Department of Urology, Jagiellonian University Medical College, Krakow, Poland
Pol J Pathol 2020; 71 (1): 20-29
Online publish date: 2020/05/20
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Introduction

Prostatic carcinoma (PC) is one of the most frequent tumors worldwide and in the Western world it is indeed the most common cancer in males and an important cause of death [1]. The most frequent genetic event in PC is a translocation involving ETS family genes, most often ERG; this results in ERG protein product overexpression [2], a feature present in about half of patients in Europe, including whose from our material [3]. The biologic and prognostic significance of ERG expression in PC remains unclear and is a subject of intense analysis.
The aim of the study was to collect a set of nuclear parameters, including both geometric and texture features, and to analyze them in relation to ERG expression. As it has been shown before that the PCs of different Gleason grade show differences in nuclear parameters [4, 5, 6, 7], we decided to compare lower- and higher-grade tumors. The preliminary version of the results was presented at 26th European Congress of Pathology [8].

Material and methods

The study material consisted of prostatectomy specimens from the files of Pathology Department. Immunohistochemistry for ERG was performed on tissue microarrays, as previously reported [3]. The cases were reevaluated and reclassified according to the current criteria [9, 10, 11]. From the obtained dataset four groups were established as a combination of the following features: lower-grade (Gleason pattern 3) or higher-grade (Gleason pattern 4) and ERG– or ERG+ (Fig. 1). The Table I shows the details of grading, however for making the analysis more evident, only Gleason pattern in the TMA core was used for analysis.
The images of hematoxylin-eosin stained tissue microarrays were taken on a Zeiss Axioscope microscope equipped with a 100× oil immersion lens using a Nikon D5100 digital camera. Pictures (Fig. 2) were transferred to a personal computer, converted from Nikon raw image format into TIF format and processed using color deconvolution algorithm. The resulting files were used for the segmentation of nuclei. The properly segmented nuclei were being selected by the operator until fifty nuclei were available for each case (Fig. 3). The images of the nuclei were then processed by a program which measured the geometric and textural features listed in Table II.
Definitions of the form factors used:
SF =
4πS
L2
Rf =
Lh
Lv
Rc =
2√S/π
L/π
compactnes =
Dmin
Dmax
Where: L – nuclear perimeter, S – nuclear area, Lh – horizontal diameter, Lv – vertical diameter, Dmin – minimum diameter, Dmax – maximum diameter.
The image processing was performed with ImageJ 1.47V (National Institutes of Health), AnalySIS 3.2 (Soft Imaging Systems GmbH) software, color deconvolution macro (G. Landini, http://www.mecourse.com/landinig/) as well as macros developed by one of the authors (KO). Student's t test was used for comparison between groups. Interactions between factors was assessed using ANOVA. Significance level was set to 0.05. The data was analyzed using Statistica 12 (StatSoft, Tulsa CA, USA) and R package (R Foundation for Statistical Computing) [12].

Results

The group under study consisted of 39 cases; the mean age of the patients was 62.58 (range 50 to 75 years, SD 5.89). The age of the patients did not show significant relationship with any of the analyzed variables. Eleven cases were pT2 (29.2%), 26 (66.7%) were pT3 and 2 (5.1%) were pT4. Details of grading are shown in Table I. For the study, areas with Gleason patter 3 or 4, either ERG+ or ERG–were selected. Among the four established study groups the following numbers were obtained: 9 cases of Gleason pattern 3, ERG– cancers; 11 cases of Gleason pattern 3, ERG+ cancers; 9 cases of Gleason pattern 4, ERG– cancers and 10 cases of Gleason pattern 4, ERG+ cancers. For each case at least fifty nuclei were measured. The results are shown in Table III. Individual case profiles are shown on Fig. 4, and the profiles averaged over the study group on Fig. 5. As might have been expected, the nuclei of Gleason pattern 4 cases were significantly larger and slightly more irregular than those of pattern 3 cases; there were also differences in the majority of textural features (Table IV). When analyzing the nuclei of ERG+ and ERG– cases it could be seen that in ERG+ cases they were significantly larger, yet they showed no difference in form factor values. However, there were differences in their textural parameters (Table V). Table VI shows comparison of unifactorial and multifactorial with interactions models.

Discussion

For some time now, it is known and accepted that a subset of PCs develop through a translocation involving ETS family genes and TMPRSS2 gene [2, 13]. Under normal conditions, ETS family genes are expressed mainly by endothelial cells [14] and in translocation-related PC their genes comes under control of the androgen receptor. Androgen gene may be upregulated by the previous NKX3.1 gene loss. This results in a significant expression of the transcription factors of the ETS family by the prostatic epithelial cells. Such carcinogenic mechanism is quite unusual for a carcinoma and similar to the phenomena which cause some mesenchymal or hematopoietic cancers [2, 13, 15]. Importantly, the expression of the ETS family transcription factors, ERG may be tested by immunohistochemistry and the results are highly correlated with the TMPRSS2-ETS translocation [13]. This offers an easy and cheap method for classifying PC genotype. Although a number of studies were published on the subject, it is still unclear whether translocation-associated prostate cancers are different in morphology or behavior [15]. In our opinion, some differences exist in terms of both morphology and stage as well as interaction with the tumor microenvironment [3, 16, 17, 18].
KDM1A, CHD1, and androgen receptor were identified as forming a complex responsible for targeted DNA breaks, which lead to TMPRSS2-ETS translocation [19]. TMPRSS2-ETS translocation may influence chromatin structure and stability by an upregulation of PIM1 kinase and a deregulation of Poly(ADP-Ribose) Polymerase [20, 21]. Another enzyme important for chromatin structure, which has been shown to participate in the generation of translocations in PC, is topoisomerase II beta. It is required for an expression of androgen-receptor regulated gene as well as it was shown to mediate double strand breaks of DNA in PC and in prostatic intraepithelial neoplasia (PIN) in an androgen receptor-mediated mechanism [22]. TMPRSS2-ETS translocation is the only one of the recurrent translocation – deletion events peculiar for PC. Another frequent alteration in PC is the 5q21 deletion. It has been shown that it causes the loss of CHD1, which protein product – chromodomain helicase DNA binding protein 1, participates in chromatin remodeling. Interestingly, this alteration is mutually exclusive with respect to TMPRSS2-ETS translocation because the CHD1 protein product is required for the occurrence of this translocation [23, 24]. Altered expression of proteins acting on chromatin structure and the chromatin destabilization could result in a change of its structure visible at the microscopic level, similar to the one that may be noticed in other organs [24].
Image analysis is a powerful tool in histopathology. It may allow for a detection of differences between groups of cases which may be not evident visually. In the PC pathology the visual nuclear grading may fail [25] while image analysis may show significant results [4, 6, 7]. Currently this method is used for research purpose, although an implementation of the computer-aided diagnosis systems has been proposed [26]. Application of image analysis to histopathology is often difficult because of a large size and a complexity of the image, variability in staining as well as difficulty in segmentation [26]. Color deconvolution is a relatively new tool in image analysis that has already gained a wide acceptance in quantitative pathology, as it allows an effective thresholding of histologic image [27, 28]. For the best of our knowledge, no publications concerning relationship between nuclear morphometry and ERG status in PC are available, although several studies on the application of image analysis in PC were published. Most of them concentrate on automatic cancer diagnosis or computer-aided grading.
Loeffler et al. [27] aimed to obtain a classification of PC on the same rules, but more objective than the standard Gleason method. Using two relatively simple parameters, they were able to classify the tumors into Gleason pattern 3 and Gleason pattern 4/5 with high accuracy. Venkataraman et al. [7] compared the features of Feulgen stained nuclei in Gleason pattern 4 PC. Although the aim of that study is very different from ours, they employed a similar analytical approach, using a large set of geometric and textural features on manually segmented nuclei. They found significant differences between tumors that were either Gleason 7 = 3 + 4 or 7 = 4 + 3. The later nuclei tended to be larger, more irregular and have coarser chromatin. That adds an argument to the separation of these categories, as seen in the new ISUP grading system [9]. Alexandratou et al. [29] used gray level correlation matrix method to emulate grading of PC by Gleason method. They achieved over 85% accuracy of the classification. In contrast to the present study, the analysis was applied to the overall image of the tumor without an extraction of the structures such as nuclei, cytoplasm or extracellular compartment. Veltri et al. group published a number of very interesting papers on quantitative methods in prostate cancer pathology [30, 31, 32, 33, 34, 35]. They used a Feulgen-stained tissue microarray and measured a large set of morphologic and textural features, similarly as in our study. These features were combining into 'quantitative nuclear grade'. They analyzed nuclear features of PC with different Gleason grades and compared them to normally appearing nuclei adjacent to the cancer [30]. Although some differences were seen between benign and malignant nuclei, as well as between PC with different grades, the features under study showed large overlap between the groups. Similar methodology was used to identify the cases with biochemical recurrence [36] with an accuracy exceeding this of combined stage and Gleason score, and also to predict survival in patients with biochemical recurrence [34]. These results were obtained using older staging and grading systems, and it would be interesting to see the influence of recent modifications of TNM and Gleason systems. Farjam et al. [37] tested the image analysis for diagnosis of PC achieved the accuracy exceeding 95%. In the segmented image they measured geometric features of the glands, Bektas et al. [38] compared the basic nuclear parameters in PC with different Gleason score. As could be expected, they showed an increase in nuclear size and irregularity with progression of the tumor grade.
Isharwal et al. [39] analyzed several morphometric features of the PC nuclei, including geometric and textural parameters, for determining the differences between organ-confined and advanced cancers. They found the ploidy status to be by far the most important difference. In multiparametric models, inclusion of ploidy status improved the model performance by 1.5% in relation to more traditional dataset. Waliszewski et al. proposed the use of fractal geometry to classify PCs as an alternative to Gleason score [40, 41, 42]. One of the interesting results was the difference between 3+4 and 4+3 cancer, a difference which is seen in other studies and emphasized by the new ISUP grading system [42, 43]. Huang et al. [44] also used fractal geometry for emulation PC classification by Gleason method. They achieved overall accuracy reaching 94.6%. It is also of important to note, that the previous classification system contained many poorly defined elements, extremely difficult to assess even by highly trained humans. Gertych et al. [28] analyzed PCs by machine learning approach. They used a set of descriptors to obtain classification of the image into stromal and epithelial compartments and then epithelial elements into benign and cancerous. The features used were related to gray level and the texture. Tabesh et al. [45, 46] used image analysis system of automatic diagnosis of PC as well as assessment of Gleason score. The accuracy of cancer diagnosis exceeded 95%, while accuracy of the classification into low and high grades exceeded 81%. They used features extracted from the color histograms, fractal dimensions and wavelets analysis combined with different classifiers, including Gaussian and KNN. Weyn et al. [47] analyzed chromatin structure in a set of normal and neoplastic precursor lesion from the colon, esophagus and prostate. A large set of features was normalized and grouped to form nuclear signatures used to compare different groups of cases. Prostate cases consisted of entirely normal glands, normal gland adjacent to carcinoma and PIN. They noticed significant differences between normal, low grade PIN and high grade PIN, but did not study PC cases.
DNA ploidy was analyzed by several authors. For example Lorenzato et al. [48] analyzed DNA ploidy Gleason 3 + 3 PC on core biopsy material. They found that clinically organ-confined cancers tended to be diploid significantly more frequently than the advanced ones. This difference was more significant for tumors with low PSA level. In our material, the nuclei of Gleason 3 + 3 cases were slightly larger, but significant differences were seen in few textural features only (data not shown).

Conclusions

We have shown that the ERG+ and ERG– differ in their nuclear features. We hypothesize that this may be due to differences in their molecular pathogenesis, but this has to be clarified by further studies.
We thank Krzysztof Skomski for help in the preparation of microphotographs.
The study was supported by Jagiellonian University grant K/ZDS/006384.
The authors declare no conflict of interest.

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Copyright: © 2020 Polish Association of Pathologists and the Polish Branch of the International Academy of Pathology This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
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