Next-Generation Digital Histopathology of the Tumor Microenvironment

Abstract

Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology-which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.

Keywords: RNA ISH; cancer; multiplexing; next-generation digital histopathology; tissue cytometry; tumor immune microenvironment; tumor microenvironment.

Figure 1

A representative example of high-dimensional automated tissue cytometry shown on a colon sample stained for seven markers. (a) Original multicolor immunofluorescence image data set acquired by a multispectral imaging technology. Nuclei stained by 4′,6-diamidino-2-phenylindole (DAPI) in blue; immune markers/immune checkpoint markers CD4 in green/PD-L1 in yellow/PD1 in red/CD68 in pink/CD8 in orange; pan-cytokeratin marker in turquoise. As this raw data image contains overlapping emission signals from the fluorochromes, the colors appear mixed. (b) Image with clearly separated fluorescent signals obtained by a mathematical procedure referred to as spectral unmixing. (c) Nuclei detection, highlighted by the green contour mask shown in overlay to the original image. (d) Metastructure detection of epithelial cells, highlighted in orange overlay. (e) Proximity measurements in relation to detected metastructures with various distance zones highlighted by different colors. (f) Analysis of spatial connections among and between single cells of a specific cellular phenotype highlighted by a green mask and white connecting lines. The images were provided by and analyzed using TissueGnostics’ image cytometry solution StrataQuest.

Figure 2

A representative example of automated analysis of fluorescence in situ hybridization (FISH) and RNA in situ hybridization (ISH) stained cells using a next-generation digital pathology platform. (a) FISH staining (blue, nuclei stained for 4′,6-diamidino-2-phenylindole (DAPI); red and yellow dots, FISH probes); on the left the original image is shown, in the middle the corresponding analyzed image including cell and dot detection mask, and on the right the analyzed data visualized in a scattergram. (b) RNAscope staining (blue, nuclei stained for hematoxylin; brown, RNAscope staining); on the left the original image is shown, in the middle the original image overlaid with the detected dot mask, and on the right the original image overlaid with the nuclei mask, the cellular mask, and the identified dot mask. Both images were provided by and analyzed using TissueGnostics’ image cytometry solution StrataQuest.

Figure 3

Analysis of the tumor immune microenvironment using next-generation digital pathology. A representative example of the automated detection of CD8+ immune cells within the tumor microenvironment of ovarian cancer by Developer XD (Definiens, Munich, Germany). Figure adapted from Desbois et al., 2020 [153].