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.

Quantitative Characterization of Macrophage, Lymphocyte, and Neutrophil Subtypes Within the Foreign Body Granuloma of Human Mesh Explants by 5-Marker Multiplex Fluorescence Microscopy

“…However, it is not clear how many cells of the FBG show these surface markers and if there is some overlapping. As multiplex staining can provide this valuable information, we examined the macrophage pattern, lymphocyte pattern, and neutrophil pattern on 12 explanted mesh samples with 5 markers each and analyzed and quantified their coexpression profiles using the scanning system TissueFAXS PLUS with the StrataQuest Analysis Software from TissueGnostics, Vienna, Austria ( 4 , 5 ).…”

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

“…These data can be categorized into different genomic, proteomic, and radiomic subcategories. Artificial intelligence, including machine learning (ML) and deep learning (DL) methods, have been implemented for various applications including biomarker discovery, and digital pathology (review [ 187 , 188 , 189 , 190 , 191 ]). In the case of cancer biomarkers, DL approaches have been utilized to detect ctDNA markers in cancer cases [ 192 , 193 ].…”

Digital Pathology in Latin America

“…Primary diagnosis in digital pathology to making the nal reported pathology diagnosis when reviewing WSI, without rst looking at the glass slide 3 . Scanners can perform whole slide imaging in different imaging modes such as bright eld, wide eld uorescence, confocal, structured illumination, multiplexing, and/or multispectral 42 ; bright eld scanning emulates standard bright eld microscopy and is the most common and cost-effective approach 18 .…”