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Automated analysis of co-localized protein expression in histologic sections of prostate cancer

An automated approach based on routinely-processed, whole-slide immunohistochemistry (IHC) was implemented to study co-localized protein expression in tissue samples. Expression of two markers was chosen to represent stromal (CD31) and epithelial (Ki-67) compartments in prostate cancer. IHC was perf...

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Detalles Bibliográficos
Autores principales: Tennill, Thomas A., Gross, Mitchell E., Frieboes, Hermann B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5446169/
https://www.ncbi.nlm.nih.gov/pubmed/28552967
http://dx.doi.org/10.1371/journal.pone.0178362
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author Tennill, Thomas A.
Gross, Mitchell E.
Frieboes, Hermann B.
author_facet Tennill, Thomas A.
Gross, Mitchell E.
Frieboes, Hermann B.
author_sort Tennill, Thomas A.
collection PubMed
description An automated approach based on routinely-processed, whole-slide immunohistochemistry (IHC) was implemented to study co-localized protein expression in tissue samples. Expression of two markers was chosen to represent stromal (CD31) and epithelial (Ki-67) compartments in prostate cancer. IHC was performed on whole-slide sections representing low-, intermediate-, and high-grade disease from 15 patients. The automated workflow was developed using a training set of regions-of-interest in sequential tissue sections. Protein expression was studied on digital representations of IHC images across entire slides representing formalin-fixed paraffin embedded blocks. Using the training-set, the known association between Ki-67 and Gleason grade was confirmed. CD31 expression was more heterogeneous across samples and remained invariant with grade in this cohort. Interestingly, the Ki-67/CD31 ratio was significantly increased in high (Gleason ≥ 8) versus low/intermediate (Gleason ≤7) samples when assessed in the training-set and the whole-tissue block images. Further, the feasibility of the automated approach to process Tissue Microarray (TMA) samples in high throughput was evaluated. This work establishes an initial framework for automated analysis of co-localized protein expression and distribution in high-resolution digital microscopy images based on standard IHC techniques. Applied to a larger sample population, the approach may help to elucidate the biologic basis for the Gleason grade, which is the strongest, single factor distinguishing clinically aggressive from indolent prostate cancer.
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spelling pubmed-54461692017-06-12 Automated analysis of co-localized protein expression in histologic sections of prostate cancer Tennill, Thomas A. Gross, Mitchell E. Frieboes, Hermann B. PLoS One Research Article An automated approach based on routinely-processed, whole-slide immunohistochemistry (IHC) was implemented to study co-localized protein expression in tissue samples. Expression of two markers was chosen to represent stromal (CD31) and epithelial (Ki-67) compartments in prostate cancer. IHC was performed on whole-slide sections representing low-, intermediate-, and high-grade disease from 15 patients. The automated workflow was developed using a training set of regions-of-interest in sequential tissue sections. Protein expression was studied on digital representations of IHC images across entire slides representing formalin-fixed paraffin embedded blocks. Using the training-set, the known association between Ki-67 and Gleason grade was confirmed. CD31 expression was more heterogeneous across samples and remained invariant with grade in this cohort. Interestingly, the Ki-67/CD31 ratio was significantly increased in high (Gleason ≥ 8) versus low/intermediate (Gleason ≤7) samples when assessed in the training-set and the whole-tissue block images. Further, the feasibility of the automated approach to process Tissue Microarray (TMA) samples in high throughput was evaluated. This work establishes an initial framework for automated analysis of co-localized protein expression and distribution in high-resolution digital microscopy images based on standard IHC techniques. Applied to a larger sample population, the approach may help to elucidate the biologic basis for the Gleason grade, which is the strongest, single factor distinguishing clinically aggressive from indolent prostate cancer. Public Library of Science 2017-05-26 /pmc/articles/PMC5446169/ /pubmed/28552967 http://dx.doi.org/10.1371/journal.pone.0178362 Text en © 2017 Tennill et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tennill, Thomas A.
Gross, Mitchell E.
Frieboes, Hermann B.
Automated analysis of co-localized protein expression in histologic sections of prostate cancer
title Automated analysis of co-localized protein expression in histologic sections of prostate cancer
title_full Automated analysis of co-localized protein expression in histologic sections of prostate cancer
title_fullStr Automated analysis of co-localized protein expression in histologic sections of prostate cancer
title_full_unstemmed Automated analysis of co-localized protein expression in histologic sections of prostate cancer
title_short Automated analysis of co-localized protein expression in histologic sections of prostate cancer
title_sort automated analysis of co-localized protein expression in histologic sections of prostate cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5446169/
https://www.ncbi.nlm.nih.gov/pubmed/28552967
http://dx.doi.org/10.1371/journal.pone.0178362
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