Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data

<p>Abstract</p> <p>Background</p> <p>Molecular studies of breast cancer revealed biological heterogeneity of the disease and opened new perspectives for personalized therapy. While multiple gene expression-based systems have been developed, current clinical practice is...

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Main Authors: Laurinavicius Arvydas, Laurinaviciene Aida, Ostapenko Valerijus, Dasevicius Darius, Jarmalaite Sonata, Lazutka Juozas
Format: Article
Language:English
Published: BMC 2012-03-01
Series:Diagnostic Pathology
Subjects:
Online Access:http://www.diagnosticpathology.org/content/7/1/27
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spelling doaj-f52351c3a7624772a949dd2528bcba032020-11-25T00:16:10ZengBMCDiagnostic Pathology1746-15962012-03-01712710.1186/1746-1596-7-27Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis dataLaurinavicius ArvydasLaurinaviciene AidaOstapenko ValerijusDasevicius DariusJarmalaite SonataLazutka Juozas<p>Abstract</p> <p>Background</p> <p>Molecular studies of breast cancer revealed biological heterogeneity of the disease and opened new perspectives for personalized therapy. While multiple gene expression-based systems have been developed, current clinical practice is largely based upon conventional clinical and pathologic criteria. This gap may be filled by development of combined multi-IHC indices to characterize biological and clinical behaviour of the tumours. Digital image analysis (DA) with multivariate statistics of the data opens new opportunities in this field.</p> <p>Methods</p> <p>Tissue microarrays of 109 patients with breast ductal carcinoma were stained for a set of 10 IHC markers (ER, PR, HER2, Ki67, AR, BCL2, HIF-1α, SATB1, p53, and p16). Aperio imaging platform with the Genie, Nuclear and Membrane algorithms were used for the DA. Factor analysis of the DA data was performed in the whole group and hormone receptor (HR) positive subgroup of the patients (n = 85).</p> <p>Results</p> <p>Major factor potentially reflecting aggressive disease behaviour (i-Grade) was extracted, characterized by opposite loadings of ER/PR/AR/BCL2 and Ki67/HIF-1α. The i-Grade factor scores revealed bimodal distribution and were strongly associated with higher Nottingham histological grade (G) and more aggressive intrinsic subtypes. In HR-positive tumours, the aggressiveness of the tumour was best defined by positive Ki67 and negative ER loadings. High Ki67/ER factor scores were strongly associated with the higher G and Luminal B types, but also were detected in a set of G1 and Luminal A cases, potentially indicating high risk patients in these categories. Inverse relation between HER2 and PR expression was found in the HR-positive tumours pointing at differential information conveyed by the ER and PR expression. SATB1 along with HIF-1α reflected the second major factor of variation in our patients; in the HR-positive group they were inversely associated with the HR and BCL2 expression and represented the major factor of variation. Finally, we confirmed high expression levels of p16 in Triple-negative tumours.</p> <p>Conclusion</p> <p>Factor analysis of multiple IHC biomarkers measured by automated DA is an efficient exploratory tool clarifying complex interdependencies in the breast ductal carcinoma IHC profiles and informative value of single IHC markers. Integrated IHC indices may provide additional risk stratifications for the currently used grading systems and prove to be useful in clinical outcome studies.</p> <p>Virtual Slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/1512077125668949</url></p> http://www.diagnosticpathology.org/content/7/1/27ImmunohistochemistryDigital pathologyBreast cancerAndrogen receptorsEstrogen receptorsProgesteron receptorsHypoxia-inducible factor 1αSpecial AT-rich sequence-binding protein 1
collection DOAJ
language English
format Article
sources DOAJ
author Laurinavicius Arvydas
Laurinaviciene Aida
Ostapenko Valerijus
Dasevicius Darius
Jarmalaite Sonata
Lazutka Juozas
spellingShingle Laurinavicius Arvydas
Laurinaviciene Aida
Ostapenko Valerijus
Dasevicius Darius
Jarmalaite Sonata
Lazutka Juozas
Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data
Diagnostic Pathology
Immunohistochemistry
Digital pathology
Breast cancer
Androgen receptors
Estrogen receptors
Progesteron receptors
Hypoxia-inducible factor 1α
Special AT-rich sequence-binding protein 1
author_facet Laurinavicius Arvydas
Laurinaviciene Aida
Ostapenko Valerijus
Dasevicius Darius
Jarmalaite Sonata
Lazutka Juozas
author_sort Laurinavicius Arvydas
title Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data
title_short Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data
title_full Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data
title_fullStr Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data
title_full_unstemmed Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data
title_sort immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data
publisher BMC
series Diagnostic Pathology
issn 1746-1596
publishDate 2012-03-01
description <p>Abstract</p> <p>Background</p> <p>Molecular studies of breast cancer revealed biological heterogeneity of the disease and opened new perspectives for personalized therapy. While multiple gene expression-based systems have been developed, current clinical practice is largely based upon conventional clinical and pathologic criteria. This gap may be filled by development of combined multi-IHC indices to characterize biological and clinical behaviour of the tumours. Digital image analysis (DA) with multivariate statistics of the data opens new opportunities in this field.</p> <p>Methods</p> <p>Tissue microarrays of 109 patients with breast ductal carcinoma were stained for a set of 10 IHC markers (ER, PR, HER2, Ki67, AR, BCL2, HIF-1α, SATB1, p53, and p16). Aperio imaging platform with the Genie, Nuclear and Membrane algorithms were used for the DA. Factor analysis of the DA data was performed in the whole group and hormone receptor (HR) positive subgroup of the patients (n = 85).</p> <p>Results</p> <p>Major factor potentially reflecting aggressive disease behaviour (i-Grade) was extracted, characterized by opposite loadings of ER/PR/AR/BCL2 and Ki67/HIF-1α. The i-Grade factor scores revealed bimodal distribution and were strongly associated with higher Nottingham histological grade (G) and more aggressive intrinsic subtypes. In HR-positive tumours, the aggressiveness of the tumour was best defined by positive Ki67 and negative ER loadings. High Ki67/ER factor scores were strongly associated with the higher G and Luminal B types, but also were detected in a set of G1 and Luminal A cases, potentially indicating high risk patients in these categories. Inverse relation between HER2 and PR expression was found in the HR-positive tumours pointing at differential information conveyed by the ER and PR expression. SATB1 along with HIF-1α reflected the second major factor of variation in our patients; in the HR-positive group they were inversely associated with the HR and BCL2 expression and represented the major factor of variation. Finally, we confirmed high expression levels of p16 in Triple-negative tumours.</p> <p>Conclusion</p> <p>Factor analysis of multiple IHC biomarkers measured by automated DA is an efficient exploratory tool clarifying complex interdependencies in the breast ductal carcinoma IHC profiles and informative value of single IHC markers. Integrated IHC indices may provide additional risk stratifications for the currently used grading systems and prove to be useful in clinical outcome studies.</p> <p>Virtual Slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/1512077125668949</url></p>
topic Immunohistochemistry
Digital pathology
Breast cancer
Androgen receptors
Estrogen receptors
Progesteron receptors
Hypoxia-inducible factor 1α
Special AT-rich sequence-binding protein 1
url http://www.diagnosticpathology.org/content/7/1/27
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