Abstract
BackgroundFor analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately.MethodsWe describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies.ResultsAmong all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possible with results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5±111.3 μm2.ConclusionsROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissue samples. The method competes well with existing commercial software kits. In difference to them, it is fully automated, externally repeatable, independent on training data and completely documented. Comparison with gene expression data indicates that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.
Highlights
For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes
Based on gene expression profiling (GEP) results, two biologically and clinically distinct molecular subtypes of DLBCL were identified, namely activated B-cell-like subtype (ABC) and germinal center B-cell-like subtype (GCB) [7, 8], the latter being associated with a favorable prognosis
We describe a novel ROF filter based segmentation approach, which allows for fully automated macrophage counting in whole tissue sections, and avoids the above mentioned difficulties, at least in part
Summary
For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. To the neoplastic B-cells, cancerous tissue contains high numbers of various subsets of T-cells, macrophages, mast cells and stromal cells [1, 2]. The composition of this tumor microenvironment has attracted considerable interest since it turned out to affect the clinical outcome. Besides of overall histological inspection, it has been largely investigated by molecular procedures as gene expression profiling (GEP) [3, 4] as well as by morphometric image analysis [5, 6]. A signature associated with increased overall survival included components of the extracellular matrix and genes that are characteristically expressed in cells from the monocytic lineage
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