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Mungenast F, Fernando A, Nica R, Boghiu B, Lungu B, Batra J, Ecker RC. Next-Generation Digital Histopathology of the Tumor Microenvironment. Genes (Basel) 2021; 12:538. [PMID: 33917241 PMCID: PMC8068063 DOI: 10.3390/genes12040538] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 12/11/2022] Open
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.
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Affiliation(s)
- Felicitas Mungenast
- Institute of Pathophysiology and Allergy Research, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, 1090 Vienna, Austria
- TissueGnostics GmbH, 1020 Vienna, Austria;
| | - Achala Fernando
- Translational Research Institute, 37 Kent Street, Woolloongabba, QLD 4102, Australia; (A.F.); (J.B.)
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4059, Australia
| | | | - Bogdan Boghiu
- TissueGnostics SRL, 700028 Iasi, Romania; (B.B.); (B.L.)
| | - Bianca Lungu
- TissueGnostics SRL, 700028 Iasi, Romania; (B.B.); (B.L.)
| | - Jyotsna Batra
- Translational Research Institute, 37 Kent Street, Woolloongabba, QLD 4102, Australia; (A.F.); (J.B.)
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4059, Australia
| | - Rupert C. Ecker
- TissueGnostics GmbH, 1020 Vienna, Austria;
- Translational Research Institute, 37 Kent Street, Woolloongabba, QLD 4102, Australia; (A.F.); (J.B.)
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4059, Australia
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