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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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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. |
format | Online Article Text |
id | pubmed-5446169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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