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In order to provide an overview of protein expression patterns, all images of immunohistochemically stained tissue were
manually annotated by a board certified pathologist or by specially educated personel (followed by verification of a pathologist).
The pathologists are experienced in interpretation of tissue morphology under the microscope have used a specially designed
software to view and annotate the histological images. Annotation of each different normal and cancer tissue was performed
using a simplified scheme for classification of immunohistochemical outcome. Each tissue was examined for representativity
and immunoreactivity. The different tissue specific cell types included in each normal tissue type were annotated. For each
cancer, tumor cells and stroma were annotated. Basic annotation parameters included an evaluation of i) staining intensity
(negative, weak, moderate or strong), ii) fraction of stained cells (rare, <25%, 25-75% or >75%) and iii)
subcellular localization (nuclear and/or cytoplasmic/membranous). The manual annotation also provides a summarizing text comment for each antibody.
The terminology and ontology used is compliant with standards used in pathology and medical science. SNOMED classification
has been used for assignment of topography and morphology.
SNOMED classification also underlies the given original diagnosis from which normal as well as cancer samples were collected from.
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In order to provide an overview of protein expression patterns, all images of immunohistochemically stained cell lines were annotated using an automated recognition software for image analysis. The image analysis software, TMAx (Beecher Instruments, Sun Prairie, WI, USA), built on a object-oriented image analysis engine from Definiens, utilizes rule-based operations and multiple iterative segmentation processes together with fuzzy logic to identify cells and immunohistochemical stain deposits.
Output parameters from the software always displayed in conjunction with the annotated images are:
- number of objects defined as cells in the image
- staining intensity (negative, weak, moderate and strong)
- fraction (%) of positive cells
In addition, two overlay images with additional numerical information are presented to facilitate interpretation. The information displayed includes:
- object based view representing fraction (%) of immunostained cells. The color code for each cell represents a range of immunoreactivity, blue (negative/very weak), yellow (weak/moderate), orange (moderate/strong) and red (strong) cells. This classification is based on areas of different intensities within each object (cell). This differs slightly from the subjective classification provided by manual annotation of cells in normal and cancer tissue.
- area based view representing immunostained areas (%) within cells. The color code represents a range of immunoreactivity, yellow (weak/moderate), green (moderate/strong) and red (strong). Negative/very weak areas are transparent. The intensity score is generated from this area based analysis.
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In order to provide an interpretation of the staining patterns on a subcellular level, all images of immunofluorescently stained cell lines were manually annotated using a web-based annotation form.
For each image pair (i.e. one antibody in one cell line) the intensity and subcellular localization of the staining is described. The staining intensity was classified as negative, weak, moderate or strong based on the laser power and detector gain settings used for image acquisition in combination with the visual appearance of the image.
The description of the subcellular localization is further combined with parameters describing the staining characteristics (i.e. smooth, granular, speckled, fibrous, dotty or clusters).
A validation score of the observed staining is assigned for each cell line and is classified as either Supportive, Uncertain or Not supportive based on the concordance with available SwissProt/Uniprot data for subcellular localization. The respective categories were further divided into subcategories to account for differences in the observed localizations.
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