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.
Highlights
Cancer is a crucial global health challenge
To achieve high-content phenotyping, optionally in combination with applying genetic markers for well-defined DNA loci as well as total RNA or specific mRNA measurements, the importance of multiplexing staining techniques continues to increase in research and clinics, especially for the purpose of determining the complex immune and tumor microenvironment status in patients suffering from cancer, graft versus host disease, and other pathological conditions related to immune responses [25]
We show several application fields that contribute to next-generation digital pathology, including the analysis of RNA in situ hybridization (RNA In Situ Hybridization (ISH)), conventional and/or multiplexed immunophenotyping, and blood vessel detection in the tumor microenvironment
Summary
Cancer is a crucial global health challenge. The incidence of new cancer cases is predicted to increase by around 70% over the coming two decades [1]. In the past few years, in-depth profiling of cancer cells/tissues has determined the cancer genome, the transcriptome, and the proteome as powerful sources of diagnostic, prognostic, and predictive markers/biomarkers [8,9] In this regard, spatially mapped cellular gene expression has appeared as a critical method to understand the localization and complicated multicellular interactions of DNA, RNA, and proteins within cells located in the tumor as well as in the TME [10,11]. To evaluate the reliability of these next-generation digital pathology platforms in clinics in terms of prognostication and patient management, Nagpal et al conducted a comprehensive study using prostatectomy specimens They established a deep convolutional neural network addressing Gleason scoring, which was trained by pathologists on 912 hematoxylin and eosin (HE) stained tissue slides. We are going one step further by addressing the concepts of next-generation digital pathology using imaging-based tissue cytometry, in combination with multiplexing and RNA ISH technologies, as an emerging and central method within precision diagnostics, and discussing various applications
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