New Robust and Reproducible Stereological IHC Ki67 Breast Cancer Proliferative Assessment to Replace Traditional Biased Labeling Index. Issue 10 (November 2017)

• Record Type: Journal Article
• Authors: Bigras, Gilbert; Dong, Wei-Feng; Canil, Sarah; Hugh, Judith; Berendt, Richard; Wood, George; Yang, Hua
• Is Part Of: Applied immunohistochemistry & molecular morphology. Volume 25:Issue 10(2017)
• Publication Date: 2017-11
• Subjects: MIB-1 -- stereology -- reproducibility -- breast cancer -- image analysis; Diagnostic immunohistochemistry -- Periodicals; Immunohistochemistry -- Periodicals; Cells -- Morphology -- Periodicals; Molecular diagnosis -- Periodicals; 616.079
• DOI: 10.1097/PAI.0000000000000371
• Languages: English
• ISSNs: 1541-2016

Abstract

Abstract : There is a pressing need for an objective decision tool to guide therapy for breast cancer patients that are estrogen receptor positive and HER2/neu negative. This subset of patients contains a mixture of luminal A and B tumors with good and bad outcomes, respectively. The 2 main current tools are on the basis of immunohistochemistry (IHC) or gene expression, both of which rely on the expression of distinct molecular groups that reflect hormone receptors, HER2/neu status, and most importantly, proliferation. Despite the success of a proprietary molecular test, definitive superiority of any method has not yet been demonstrated. Ki67 IHC scoring assessments have been shown to be poorly reproducible, whereas molecular testing is costly with a longer turnaround time. This work proposes an objective Ki67 index using image analysis that addresses the existing methodological issues of Ki67 quantitation using IHC on paraffin-embedded tissue. Intrinsic bias related to numerical assessment performed on IHC is discussed as well as the sampling issue related to the "peel effect" of tiny objects within a thin section. A new nonbiased stereological parameter (VV ) based on the Cavalieri method is suggested for use on a double-stained Ki67/cytokeratin IHC slide. The assessment is performed with open-source ImageJ software with interobserver concordance between 3 pathologists being high at 93.5%. Furthermore, VV was found to be a superior method to predict an outcome in a small subset of breast cancer patients when compared with other image analysis methods being used to determine the Ki67 labeling index. Calibration methodology is also discussed to further this IHC approach.