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Next-Generation Digital Histopathology of the Tumor Microenvironment

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 differ...

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Autores principales: Mungenast, Felicitas, Fernando, Achala, Nica, Robert, Boghiu, Bogdan, Lungu, Bianca, Batra, Jyotsna, Ecker, Rupert C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068063/
https://www.ncbi.nlm.nih.gov/pubmed/33917241
http://dx.doi.org/10.3390/genes12040538
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author Mungenast, Felicitas
Fernando, Achala
Nica, Robert
Boghiu, Bogdan
Lungu, Bianca
Batra, Jyotsna
Ecker, Rupert C.
author_facet Mungenast, Felicitas
Fernando, Achala
Nica, Robert
Boghiu, Bogdan
Lungu, Bianca
Batra, Jyotsna
Ecker, Rupert C.
author_sort Mungenast, Felicitas
collection PubMed
description 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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spelling pubmed-80680632021-04-25 Next-Generation Digital Histopathology of the Tumor Microenvironment Mungenast, Felicitas Fernando, Achala Nica, Robert Boghiu, Bogdan Lungu, Bianca Batra, Jyotsna Ecker, Rupert C. Genes (Basel) Review 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. MDPI 2021-04-07 /pmc/articles/PMC8068063/ /pubmed/33917241 http://dx.doi.org/10.3390/genes12040538 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Mungenast, Felicitas
Fernando, Achala
Nica, Robert
Boghiu, Bogdan
Lungu, Bianca
Batra, Jyotsna
Ecker, Rupert C.
Next-Generation Digital Histopathology of the Tumor Microenvironment
title Next-Generation Digital Histopathology of the Tumor Microenvironment
title_full Next-Generation Digital Histopathology of the Tumor Microenvironment
title_fullStr Next-Generation Digital Histopathology of the Tumor Microenvironment
title_full_unstemmed Next-Generation Digital Histopathology of the Tumor Microenvironment
title_short Next-Generation Digital Histopathology of the Tumor Microenvironment
title_sort next-generation digital histopathology of the tumor microenvironment
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068063/
https://www.ncbi.nlm.nih.gov/pubmed/33917241
http://dx.doi.org/10.3390/genes12040538
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