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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| 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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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0178362</identifier><identifier>PMID: 28552967</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adenocarcinoma ; Adhesion ; Adhesive strength ; Aged ; Algorithms ; Analysis ; Antigens ; Apoptosis ; Automation ; Benign ; Bioengineering ; Biology and Life Sciences ; Biopsy ; Brain tumors ; Breast cancer ; Cancer therapies ; Carbonic anhydrase ; Cell adhesion molecules ; Cell cycle ; Cohort Studies ; Color ; Computation ; Computational neuroscience ; Computer and Information Sciences ; Computer applications ; Computer simulation ; Development and progression ; Diagnosis ; Differentiation ; Engineering and Technology ; Epidemiology ; Evaluation ; Gene expression ; Genetic aspects ; Glands ; Health risks ; High resolution ; Histology ; Humans ; Hypoxia ; Image processing ; Immune system ; Immunohistochemistry ; Incidence ; Ki-67 Antigen - metabolism ; Lymph nodes ; Lymphoma ; Magnetic resonance imaging ; Male ; Mathematical analysis ; Mathematical models ; Medicine and Health Sciences ; Membrane proteins ; Metabolism ; Metastases ; Microscopy ; Middle Aged ; Mortality ; Neoplasm Proteins - metabolism ; Neuroimaging ; Non-Hodgkin's lymphoma ; Ovarian cancer ; Ovarian carcinoma ; Pancreatic cancer ; Pharmacology ; Physiological aspects ; Platelet Endothelial Cell Adhesion Molecule-1 - metabolism ; Platelets ; Programming languages ; Prostate cancer ; Prostatic Neoplasms - metabolism ; Prostatic Neoplasms - pathology ; Protein expression ; Proteins ; Quality ; Quantitative analysis ; Radiation ; Research and Analysis Methods ; Resonance ; Risk factors ; Simulation ; Statistical analysis ; Stem cells ; Stimulation ; Studies ; Surgery ; Technology application ; Tissue Array Analysis</subject><ispartof>PloS one, 2017-05, Vol.12 (5), p.e0178362-e0178362</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Tennill et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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.</description><subject>Adenocarcinoma</subject><subject>Adhesion</subject><subject>Adhesive strength</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Antigens</subject><subject>Apoptosis</subject><subject>Automation</subject><subject>Benign</subject><subject>Bioengineering</subject><subject>Biology and Life Sciences</subject><subject>Biopsy</subject><subject>Brain tumors</subject><subject>Breast cancer</subject><subject>Cancer therapies</subject><subject>Carbonic anhydrase</subject><subject>Cell adhesion molecules</subject><subject>Cell cycle</subject><subject>Cohort Studies</subject><subject>Color</subject><subject>Computation</subject><subject>Computational neuroscience</subject><subject>Computer and Information 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proteins</subject><subject>Metabolism</subject><subject>Metastases</subject><subject>Microscopy</subject><subject>Middle Aged</subject><subject>Mortality</subject><subject>Neoplasm Proteins - metabolism</subject><subject>Neuroimaging</subject><subject>Non-Hodgkin's lymphoma</subject><subject>Ovarian cancer</subject><subject>Ovarian carcinoma</subject><subject>Pancreatic cancer</subject><subject>Pharmacology</subject><subject>Physiological aspects</subject><subject>Platelet Endothelial Cell Adhesion Molecule-1 - metabolism</subject><subject>Platelets</subject><subject>Programming languages</subject><subject>Prostate cancer</subject><subject>Prostatic Neoplasms - metabolism</subject><subject>Prostatic Neoplasms - pathology</subject><subject>Protein expression</subject><subject>Proteins</subject><subject>Quality</subject><subject>Quantitative analysis</subject><subject>Radiation</subject><subject>Research and Analysis Methods</subject><subject>Resonance</subject><subject>Risk factors</subject><subject>Simulation</subject><subject>Statistical analysis</subject><subject>Stem cells</subject><subject>Stimulation</subject><subject>Studies</subject><subject>Surgery</subject><subject>Technology application</subject><subject>Tissue Array 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Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tennill, Thomas A</au><au>Gross, Mitchell E</au><au>Frieboes, Hermann B</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated analysis of co-localized protein expression in histologic sections of prostate cancer</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-05-26</date><risdate>2017</risdate><volume>12</volume><issue>5</issue><spage>e0178362</spage><epage>e0178362</epage><pages>e0178362-e0178362</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28552967</pmid><doi>10.1371/journal.pone.0178362</doi><tpages>e0178362</tpages><orcidid>https://orcid.org/0000-0001-5959-4286</orcidid><oa>free_for_read</oa></addata></record> |
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| issn | 1932-6203 1932-6203 |
| language | eng |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Adenocarcinoma Adhesion Adhesive strength Aged Algorithms Analysis Antigens Apoptosis Automation Benign Bioengineering Biology and Life Sciences Biopsy Brain tumors Breast cancer Cancer therapies Carbonic anhydrase Cell adhesion molecules Cell cycle Cohort Studies Color Computation Computational neuroscience Computer and Information Sciences Computer applications Computer simulation Development and progression Diagnosis Differentiation Engineering and Technology Epidemiology Evaluation Gene expression Genetic aspects Glands Health risks High resolution Histology Humans Hypoxia Image processing Immune system Immunohistochemistry Incidence Ki-67 Antigen - metabolism Lymph nodes Lymphoma Magnetic resonance imaging Male Mathematical analysis Mathematical models Medicine and Health Sciences Membrane proteins Metabolism Metastases Microscopy Middle Aged Mortality Neoplasm Proteins - metabolism Neuroimaging Non-Hodgkin's lymphoma Ovarian cancer Ovarian carcinoma Pancreatic cancer Pharmacology Physiological aspects Platelet Endothelial Cell Adhesion Molecule-1 - metabolism Platelets Programming languages Prostate cancer Prostatic Neoplasms - metabolism Prostatic Neoplasms - pathology Protein expression Proteins Quality Quantitative analysis Radiation Research and Analysis Methods Resonance Risk factors Simulation Statistical analysis Stem cells Stimulation Studies Surgery Technology application Tissue Array Analysis |
| title | Automated analysis of co-localized protein expression in histologic sections of prostate cancer |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T01%3A40%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Automated%20analysis%20of%20co-localized%20protein%20expression%20in%20histologic%20sections%20of%20prostate%20cancer&rft.jtitle=PloS%20one&rft.au=Tennill,%20Thomas%20A&rft.date=2017-05-26&rft.volume=12&rft.issue=5&rft.spage=e0178362&rft.epage=e0178362&rft.pages=e0178362-e0178362&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0178362&rft_dat=%3Cgale_plos_%3EA492947256%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1902658255&rft_id=info:pmid/28552967&rft_galeid=A492947256&rft_doaj_id=oai_doaj_org_article_587bbefa4757443e836eca8523de5e1e&rfr_iscdi=true |