Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data - PubMed (original) (raw)

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

Arvydas Laurinavicius et al. Diagn Pathol. 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.

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Figures

Figure 1

Figure 1

The examples of immunohistochemistry and digital analysis output images. Immunohistochemistry and corresponding digital analysis outputs of SATB1 (a and b), HIF-1α (c and d), and BCL2 (e and f). The Nuclear algorithm (b and d) marks the positive cells with color mask according staining intensity (0 - blue, 1+ - yellow, 2+ - orange, 3+ - red). The Membrane algorithm (f) marks the positive cells with complete membranous staining with red outline.

Figure 2

Figure 2

Rotated factor pattern in the patients with breast ductal carcinoma: loadings of the factors 1 and 2 plotted. The loadings of the factor 1 (i-Grade) and factor 2 (SATB1/HIF-1α) plotted.

Figure 3

Figure 3

The distribution of the factor 1 and 2 scores in the patients with breast ductal carcinoma. a) scatter plot of the factor 1 and 2 scores; b) histogram of the factor 1 (i-Grade) scores; c) histogram of the factor 2 (SATB1/HIF-1α) scores.

Figure 4

Figure 4

Association between the i-Grade and the histological grade (G). The bar chart represents the distribution of i-Grade-Low (grey) and i-Grade-High (orange) tumours against the histological grade (G1, 2, and 3)

Figure 5

Figure 5

Representation of the factor score profiles of the intrinsic subtypes. The multiple line chart outlines the factor score profiles of the intrinsic subtypes. The lines connect the mean values of the factor 1-5 scores obtained by one-way ANOVA for each intrinsic subtype as explanatory variable.

Figure 6

Figure 6

Rotated factor pattern in the patients with hormone receptor positive breast ductal carcinoma: loadings of the factors 1 and 2 plotted. The loadings of the factor 1 (SATB1/HIF-1α - AR/ER/BCL2) and factor 2 (HER2-PR) plotted.

Figure 7

Figure 7

Rotated factor pattern in the patients with hormone receptor positive breast ductal carcinoma: loadings of the factors 1 and 3 plotted. The loadings of the factor 1 (SATB1/HIF-1α - AR/ER/BCL2) and factor 2 (Ki67-ER) plotted.

Figure 8

Figure 8

Association between the Ki67-ER and the histological grade (G). The bar chart represents the distribution of Ki67-ER-Low (grey) and Ki67-ER-High (orange) tumours against the histological grade (G1, 2, and 3).

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