Nucleolus organizer regions (AgNORs) in ductal mammary carcinoma. Comparison with classifications and prognosis

Association between Argyrophilic Proteins of Nucleolar Organizer Regions, Clinicomorphological Parameters, and Survival in Non-Small-Cell Lung Cancer

We studied argyrophilic proteins associated with nucleolar organizer regions (AgNOR) in non-small-cell cancer. We determined the area index (AI) and coefficient of variation (CV) of AgNOR. AI is associated with the key clinicomorphological parameters within the TNM system: T and N values, greatest tumor dimension up to 3 cm and more, disease stage, histogenesis, and tumor differentiation. CV is associated with T value, greatest tumor dimension up to 3 cm and more, histogenesis, and tumor differentiation. Survival of patients is longer in low AI or CV values versus high AI or CV values, longer in low AI and CV values (−AI/−CV type), shorter in high AI and CV values (+AI/+CV type), and intermediate in opposite AI and CV values (−AI/+CV and +AI/−CV types). Independent predictors in non-small-cell lung cancer include N value, greatest tumor dimension, histogenesis, and CV. Assessment of quantitative values and heterogeneity of AgNOR is important for differential diagnosis and prognosis of non-small-cell lung cancer.

Proliferation assessment in breast cancer: a double-staining technique for AgNOR quantification in MIB-1 positive cells especially adapted for image cytometry

There are two ways of measuring the cell proliferation. The first one consists of quantifying the number of cycling cells with the help of antibodies directed against cells either in G1, S, G2 or M phase. The second way is to assess the cell cycle duration by the quantification of AgNOR proteins. Measuring both the features on the same slide represents an attractive way to tackle the proliferating activity of a cell culture or a tumor. Here, we propose a MIB-1 and AgNOR double staining method especially adapted to image cytometry measurement using MIB-1 antibody coupled to FITC in order to avoid the thresholding problems encountered with such a multilabeling technique. We have applied this new method on a series of 39 breast cancer cases, with at least 4 years follow-up, in order to determine the prognosis significance of this measurement. MIB-1 alone is not linked to prognosis, while the global mean AgNOR area is significantly linked to prognosis in terms of development of visceral metastasis or death. However, the global mean AgNOR area is insufficient to determine the time limit of appearance of metastasis or relapse. Our results clearly demonstrate that a high mean AgNOR area within a cell population having a high MIB-1 index can discern tumors with a high metastatic potential. By multiplying AgNOR area by the percentage of MIB-1 positive cells we calculate the proliferative activity, P, which brings very important information concerning the time limit of relapse.

AgNOR staining and quantification

Nucleolar organiser regions (NORs) are defined as nucleolar components containing a set of argyrophilic proteins, which are selectively stained by silver methods. After silver-staining, the NORs can be easily identified as black dots exclusively localised throughout the nucleolar area, and are called "AgNORs". The NORs' argyrophilia is due to a group of nucleolar proteins, which have a high affinity for silver (AgNOR proteins). A number of studies carried out in different tumour types demonstrated that malignant cells frequently present a greater AgNOR protein amount than corresponding non-malignant cells. Moreover, in cancer tissues AgNOR protein expression was found to be strictly related to the cell duplication rate. Over the past 12 years, the "AgNOR method" has been applied in tumour pathology for both diagnostic and prognostic purposes. However, the lack of a standardised silver-staining protocol has led to much misinterpretation of actual structures evaluated in individual studies. Indeed, the absolute AgNOR scores reported by different authors for the same types of tumour are scarcely comparable and the results produced by these investigations sometimes seem to be conflicting. In order to achieve definitive standardisation of the AgNOR method and produce comparable data in all laboratories, the "International Committee on AgNOR Quantitation" was founded, and during the first Workshop "AgNORs in Oncology" held in Berlin in 1993 guidelines for AgNOR protein evaluation were first defined. The present paper discusses the main technical aspects of NOR silver-staining, and critically evaluates the methods commonly employed for AgNOR protein quantification in routine cyto-histopathology.

AgNORs in breast tumours

There is evidence that the quantitative distribution of AgNOR proteins is a proliferation-related parameter that can be used as a prognostic index in tumour pathology. In breast cancer, some authors found a significant prognostic correlation of AgNOR protein quantity, whereas other did not. However, in all the reports dealing with AgNOR area (as opposed to count) this parameter was always turned out to be an independent prognostic indicator. The present study tests the significance of AgNOR proteins in a large series of primary breast carcinomas, exploring the associations between the AgNOR protein amount, as evaluated by image cytometry, and the other well-established prognostic markers commonly considered for breast cancer, along with patients' survival. Our results demonstrated a highly significant association between AgNOR protein quantity and tumour prognosis. Moreover, when the AgNOR area values were entered in multivariate analysis together with the other predictive parameters commonly considered in breast carcinomas, they showed an independent prognostic value together with Ki67-labelling index (LI), N-status and tumour size. Considering node-negative and -positive cases separately, the AgNOR protein area and Ki67-LI both come out as a independent predictors only in the latter group: the short follow-up time of our series (36 months median) could be responsible for this discrepancy.

Standardized in situ AgNOR analysis in breast pathology: diagnostic and cell kinetic implications

The aim of the present study was to assess the diagnostic value of the recently standardized morphometric analysis of silver-stained nucleolar organizer region-associated proteins (AgNORs) [30] in a variety of 155 routinely processed benign and malignant breast lesions. 5 normal breast samples, 21 adenoses, 20 ductal hyperplasias, 10 atypical ductal hyperplasias, 20 in situ and 43 invasive ductal carcinomas, 10 in situ and 26 invasive lobular carcinomas were investigated. A statistically highly significant difference was found between normal/ordinary hyperplastic and neoplastic breast lesions with all 4 consensus AgNOR parameters (mean area, mean number, CV of area, CV of number) evaluated. AgNOR quantity was significantly related to histological grade of both in situ and invasive carcinomas. However, variable overlap was found between AgNOR values in different diagnostic groups. We conclude that standardized AgNOR analysis is a prerequisite for objective and reproductible AgNOR assessment in archival tissues. Despite its limited diagnostic utility for individual breast lesions, standardized AgNOR analysis bears a significant potential for characterizing cell kinetic and metabolical activity of breast lesions. This may give insight into the biological background of breast carcinogenesis, differentiation and tumor progression and may also underlie the independent prognostic value of AgNORs in breast cancer.

Canine neuroendocrine tumors of the pancreas: a study using image analysis techniques for the discrimination of metastatic versus nonmetastatic tumors

Canine pancreatic neuroendocrine tumors were studied using different image analysis techniques (nuclear image histometry, analysis of argyrophilic proteins of nucleolar organizer regions, determination of the mouse anti-Ki 67 antigen proliferation index, and DNA densitometry) to correlate their biological behavior with objective phenotypic markers. The methods were compared to determine the best method for distinguishing between metastatic and nonmetastatic tumors. Discrimination between the two types of tumor was possible using nuclear image histometry in combination with morphometric analysis of argyrophilic proteins of nucleolar organizer regions. In contrast, the mouse anti-Ki 67 antigen proliferation index, DNA measurement, and immunohistochemical parameters revealed no significant difference between the two types of tumors.

Demonstration of silver-stained nucleolar organizer region associated proteins (AgNORs) after wet autoclave pretreatment in breast carcinoma: correlation to tumor stage and long-term survival

Argyrophilic nucleolar organizer region associated proteins (AgNORs) are known to reflect cellular and nucleolar activity. Due to a novel staining procedure, which substantially improves visualisation of AgNORs on formalin-fixed and paraffin-embedded material, AgNORs can be reliably demonstrated as true substructures of the nucleoli. The aim of the present study was to apply a standardized morphometric AgNOR quantification on a large series of breast carcinomas with regard to its prognostic relevance. AgNOR quantity was evaluated on archival tumor tissues of 115 adenocarcinomas of the breast treated with the wet autoclave method prior to standardized silver-staining and morphometric analysis. AgNOR parameters were correlated to prognostic features (steroid hormonal receptor status, tumor type, tumor size, histological grading, pTNM, and UICC stage) carrying out both univariate and multivariate survival analyses. AgNOR number and area were proven to be statistically significantly related (Pearson correlation coefficient: 0.67, Bonferroni adjusted P = 0.0001). Almost all AgNOR parameters, in particular CV (coefficient of variation) of corrected area (delta-area) and CV of number, were statistically significantly correlated to estrogen and progesterone receptor status as well as histological grading of tumors. Increased AgNOR parameters were statistically significantly associated with early tumor relapse and cancer related death. Univariate and multivariate analysis by means of Cox regression revealed independent prognostic significance for CV of delta-area and number of AgNORs. Various AgNOR parameters (CV of number, CV of delta-area, CV of area, mean delta-area, and mean area of AgNORs per nucleus) determined on wet autoclave pre-treated formalin-fixed and paraffin-embedded breast cancer tissues are statistically highly significantly associated with the prognostic outcome, independently predicting tumor-free and overall survival.

Methods in quantitative image analysis

The main steps of image analysis are image capturing, image storage (compression), correcting imaging defects (e.g. non-uniform illumination, electronic-noise, glare effect), image enhancement, segmentation of objects in the image and image measurements. Digitisation is made by a camera. The most modern types include a frame-grabber, converting the analog-to-digital signal into digital (numerical) information. The numerical information consists of the grey values describing the brightness of every point within the image, named a pixel. The information is stored in bits. Eight bits are summarised in one byte. Therefore, grey values can have a value between 0 and 256 (2(8)). The human eye seems to be quite content with a display of 5-bit images (corresponding to 64 different grey values). In a digitised image, the pixel grey values can vary within regions that are uniform in the original scene: the image is noisy. The noise is mainly manifested in the background of the image. For an optimal discrimination between different objects or features in an image, uniformity of illumination in the whole image is required. These defects can be minimised by shading correction [subtraction of a background (white) image from the original image, pixel per pixel, or division of the original image by the background image]. The brightness of an image represented by its grey values can be analysed for every single pixel or for a group of pixels. The most frequently used pixel-based image descriptors are optical density, integrated optical density, the histogram of the grey values, mean grey value and entropy. The distribution of the grey values existing within an image is one of the most important characteristics of the image. However, the histogram gives no information about the texture of the image. The simplest way to improve the contrast of an image is to expand the brightness scale by spreading the histogram out to the full available range. Rules for transforming the grey value histogram of an existing image (input image) into a new grey value histogram (output image) are most quickly handled by a look-up table (LUT). The histogram of an image can be influenced by gain, offset and gamma of the camera. Gain defines the voltage range, offset defines the reference voltage and gamma the slope of the regression line between the light intensity and the voltage of the camera. A very important descriptor of neighbourhood relations in an image is the co-occurrence matrix. The distance between the pixels (original pixel and its neighbouring pixel) can influence the various parameters calculated from the co-occurrence matrix. The main goals of image enhancement are elimination of surface roughness in an image (smoothing), correction of defects (e.g. noise), extraction of edges, identification of points, strengthening texture elements and improving contrast. In enhancement, two types of operations can be distinguished: pixel-based (point operations) and neighbourhood-based (matrix operations). The most important pixel-based operations are linear stretching of grey values, application of pre-stored LUTs and histogram equalisation. The neighbourhood-based operations work with so-called filters. These are organising elements with an original or initial point in their centre. Filters can be used to accentuate or to suppress specific structures within the image. Filters can work either in the spatial or in the frequency domain. The method used for analysing alterations of grey value intensities in the frequency domain is the Hartley transform. Filter operations in the spatial domain can be based on averaging or ranking the grey values occurring in the organising element. The most important filters, which are usually applied, are the Gaussian filter and the Laplace filter (both averaging filters), and the median filter, the top hat filter and the range operator (all ranking filters). Segmentation of objects is traditionally based on threshold grey values. (AB