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 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

Bimodality of intratumor Ki67 expression is an independent prognostic factor of overall survival in patients with invasive breast carcinoma

Proliferative activity, assessed by Ki67 immunohistochemistry (IHC), is an established prognostic and predictive biomarker of breast cancer (BC). However, it remains under-utilized due to lack of standardized robust measurement methodologies and significant intratumor heterogeneity of expression. A recently proposed methodology for IHC biomarker assessment in whole slide images (WSI), based on systematic subsampling of tissue information extracted by digital image analysis (DIA) into hexagonal tiling arrays, enables computation of a comprehensive set of Ki67 indicators, including intratumor variability. In this study, the tiling methodology was applied to assess Ki67 expression in WSI of 152 surgically removed Ki67-stained (on full-face sections) BC specimens and to test which, if any, Ki67 indicators can predict overall survival (OS). Visual Ki67 IHC estimates and conventional clinico-pathologic parameters were also included in the study. Analysis revealed linearly independent intrinsic factors of the Ki67 IHC variance: proliferation (level of expression), disordered texture (entropy), tumor size and Nottingham Prognostic Index, bimodality, and correlation. All visual and DIA-generated indicators of the level of Ki67 expression provided significant cutoff values as single predictors of OS. However, only bimodality indicators (Ashman’s D, in particular) were independent predictors of OS in the context of hormone receptor and HER2 status. From this, we conclude that spatial heterogeneity of proliferative tumor activity, measured by DIA of Ki67 IHC expression and analyzed by the hexagonal tiling approach, can serve as an independent prognostic indicator of OS in BC patients that outperforms the prognostic power of the level of proliferative activity

Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays

<p>Abstract</p> <p>Introduction</p> <p>The human epidermal growth factor receptor 2 (HER2) is an established biomarker for management of patients with breast cancer. While conventional testing of HER2 protein expression is based on semi-quantitative visual scoring of the immunohistochemistry (IHC) result, efforts to reduce inter-observer variation and to produce continuous estimates of the IHC data are potentiated by digital image analysis technologies.</p> <p>Methods</p> <p>HER2 IHC was performed on the tissue microarrays (TMAs) of 195 patients with an early ductal carcinoma of the breast. Digital images of the IHC slides were obtained by Aperio ScanScope GL Slide Scanner. Membrane connectivity algorithm (HER2-CONNECT™, Visiopharm) was used for digital image analysis (DA). A pathologist evaluated the images on the screen twice (visual evaluations: VE1 and VE2). HER2 fluorescence <it>in situ </it>hybridization (FISH) was performed on the corresponding sections of the TMAs. The agreement between the IHC HER2 scores, obtained by VE1, VE2, and DA was tested for individual TMA spots and patient's maximum TMA spot values (VE1max, VE2max, DAmax). The latter were compared with the FISH data. Correlation of the continuous variable of the membrane connectivity estimate with the FISH data was tested.</p> <p>Results</p> <p>The pathologist intra-observer agreement (VE1 and VE2) on HER2 IHC score was almost perfect: kappa 0.91 (by spot) and 0.88 (by patient). The agreement between visual evaluation and digital image analysis was almost perfect at the spot level (kappa 0.86 and 0.87, with VE1 and VE2 respectively) and at the patient level (kappa 0.80 and 0.86, with VE1max and VE2max, respectively). The DA was more accurate than VE in detection of FISH-positive patients by recruiting 3 or 2 additional FISH-positive patients to the IHC score 2+ category from the IHC 0/1+ category by VE1max or VE2max, respectively. The DA continuous variable of the membrane connectivity correlated with the FISH data (HER2 and CEP17 copy numbers, and HER2/CEP17 ratio).</p> <p>Conclusion</p> <p>HER2 IHC digital image analysis based on membrane connectivity estimate was in almost perfect agreement with the visual evaluation of the pathologist and more accurate in detection of HER2 FISH-positive patients. Most immediate benefit of integrating the DA algorithm into the routine pathology HER2 testing may be obtained by alerting/reassuring pathologists of potentially misinterpreted IHC 0/1+ versus 2+ cases.</p

Automated Image Analysis Of Her2 Fish Enables New Definitions Of Genetic Heterogeneity In Breast Cancer Tissue

INTRODUCTION / BACKGROUND: Therapy decisions for breast cancer rely on human epidermal growth factor receptor 2 gene (HER2) amplification testing. The HER2 status of tumors is primarily established by immunohistochemistry; borderline (2+) cases undergo further testing by fluorescence in situ hybridization (FISH). Current guidelines state that HER2 amplification is determined from manual counts of HER2 and CEP17 signals in 40 nuclei per case with an additional 20 nuclei in equivocal cases. The definitions are based on expert opinion rather than on objective statistical inference; they are complex, involving different quantities and cut-offs all at once (the signal counts and their ratio, proportion of cells amplified) and hard to follow. Automated image analysis (IA) extracts data from hundreds of nuclei and can aid HER2 testing in borderline cases. AIMS: We therefore explored if objective, statistically-derived indicators of HER2 heterogeneity can be obtained from automated HER2 FISH IA data. METHODS: 50 cases of female invasive ductal breast carcinoma with HER2 2+ immunohistochemistry status, evaluated by the standard manual FISH methodology, were subjected to IA. The IA, developed using StrataQuest (TissueGnostics,Austria), segmented and counted individual nuclei and HER2 and CEP17 signals. A range of 192 to 789 nuclei per tumor were evaluated by the IA. All segmented nuclei and FISH signals were inspected manually for quality assurance and accuracy estimates. Bimodality indicator (Ashman’s D) was computed for HER2 signal and HER2/CEP17 ratio in individual cells and included in factor analysis along with the data from manual and automated HER2 FISH analyses. RESULTS: No significant bias was found between the automated and manually corrected HER2 FISH nuclei or signals, obtained by IA. However, the manual HER2, CEP17 counts and HER2/CEP17 ratio were significantly underestimated by the automated procedure due to differences in cell selection in the techniques. By formal criteria (Ashman’s D&gt;2), 5 cases were classified as bimodal by HER2/CEP17 ratio and 23 cases by HER2 counts. Of those, 3 and 18 cases, respectively, were not amplified according to the cutoff of HER2/CEP17&lt;2 by manual procedure. Factor analysis of the data set extracted 3 intrinsic factors of variation, representing amplification, “polysomy”, and bimodality. Importantly, the factor scores could be seen as “purified” indicators independent of well-known interactions between the absolute counts of HER2 and CEP17 signals per cell and their ratios. Therefore, the tumor cases may be independently characterized by the three vectors. Remarkably, the distribution of the tumors by the bimodality factor scores revealed a distinct peak of “highly bimodal” cases, suggestive of the possibility of robust stratification of the patients according to the bimodality indicators. We conclude that analysis of continuous HER2 FISH data obtained by IA enables new strategies for evidence-based stratification of heterogeneous breast tumors. In particular, indicators of bimodality of cell distribution according to their HER2 FISH signals may be useful in detection of heterogeneous cell populations, along with the currently used criteria based on cell proportions at a certain amplification cutoff. While clinical validity remains to be tested, we suggest that detection of bimodal distribution of cells can serve as robust, evidence-based stratification and decision support tool, highlighting potentially heterogeneous tumors