In the prostate, normal glands, glandular hyperplasia, and adenocarcinoma are often accompanied by inflammation. Infiltrating inflammatory cells may affect glandular cells and either promote their proliferation or, alternatively act in tumor suppression. To better understand the function of the inflammatory cells in tumor development, type and number of inflammatory cells and their proximity to glandular structures have to be analyzed in situ and correlated with disease state. To replace the time-consuming, error-prone human evaluation of stained tissue sections, we aimed at developing an automated method capable of detecting inflammatory and prostate glandular cells and of measuring the distance between these structures.

Methods: Formaldehyde-fixed, paraffin-embedded prostate cancer biopsies (n=53) were stained with DAB-conjugated anti-CD3-antibody and hematoxylin (H). Large tissue areas were digitized with TissueFAXS 4.0 using a 20x objective and the virtual stitched images were used to develop a StrataQuest 5.0 analysis profile. Manual annotations were used for validation.

Results: Color unmixing generated the virtual channels for H and DAB optical densities. Cells were then identified using nuclear segmentation on the H channel. For CD3+ cell counts, each cell was quantified for its DAB signal in a peri-nuclear mask, for which cutoff was set interactively. The epithelial cell (EC) nuclei were selected by gating for nuclear compactness and area. EC density was computed using Parzen windows and thresholded for obtaining the epithelial mask (EM). Whole tissue area excluding lumen was computed based on color. Stroma was then extracted as remaining area. In-silico Distance Transformation (DT) from EM was calculated as a monochrome channel aligned with the original virtual slide. Thus, by using the average of the DT inside each nuclear mask, the distance of each cell to the closest EM was computed. Three peri-glandular proximity areas (0-25um, 25-50um and 50-75um) were used for CD3+ population distribution assessment. Validation of the method revealed an F-score of 0.87 for EM detection and a p value of <0.001 for automated versus manual CD3+counting. The percentage of EM versus total area ranged from 18% to 50%. Between 54% to 89% of all CD3+ cells were found in the closest proximity of EM (in the 0-25um mask). A positive correlation between EM area and T-lymphocyte density in the closest vicinity mask was observed (p < 0.001).

Conclusion: The described method enables reliable automated detection of prostate tissue compartments (EC, stroma), CD3+ T-cells counting as well as measurement of their distance from the glandular epithelial cells. Such automated measurements can lead to reliable and rapid measurements of cell location distribution in normal versus tumor prostate tissues, opening new routes in diagnosis and prognosis.

Citation Format: Radu Rogojanu, Bogdan Boghiu, Georg Schaefer, Theresia Thalhammer, Georg Steiner, Rupert Ecker, Bettina Schlick, Thomas Szekeres, Isabella Ellinger. Towards automated analysis of prostate inflammatory state: Assessing density and distance of infiltrating T-cells (CD3+) to glandular structures in prostate biopsies by a new software. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1662. doi:10.1158/1538-7445.AM2014-1662