Email updates

Keep up to date with the latest news and content from Diagnostic Pathology and BioMed Central.

Open Access Highly Accessed Research

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

Arvydas Laurinavicius12*, Aida Laurinaviciene13, Valerijus Ostapenko3, Darius Dasevicius12, Sonata Jarmalaite34 and Juozas Lazutka4

Author affiliations

1 National Center of Pathology, affiliate of Vilnius University Hospital Santariskiu Clinics, P.Baublio 5, LT-08406 Vilnius, Lithuania

2 Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania

3 Institute of Oncology, Vilnius University, Santariskiu 1, LT-08660 Vilnius, Lithuania

4 Faculty of Natural Sciences, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania

For all author emails, please log on.

Citation and License

Diagnostic Pathology 2012, 7:27  doi:10.1186/1746-1596-7-27

Published: 16 March 2012

Abstract

Background

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.

Methods

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).

Results

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.

Conclusion

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.

Virtual Slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1512077125668949 webcite

Keywords:
Immunohistochemistry; Digital pathology; Breast cancer; Androgen receptors; Estrogen receptors; Progesteron receptors; Hypoxia-inducible factor 1α; Special AT-rich sequence-binding protein 1