Raimundas Meskauskas

A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue

IntroductionImmunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles. We aimed to develop a methodology to ensure and improve accuracy of the digital image analysis (DIA) approach.MethodsTissue microarrays (one 1-mm spot per patient, n = 164) from invasive ductal breast carcinoma were stained for Ki67 and scanned. Criterion standard (Ki67-Count) was obtained by counting positive and negative tumour cell profiles using a stereology grid overlaid on a spot image. DIA was performed with Aperio Genie/Nuclear algorithms. A bias was estimated by ANOVA, correlation and regression analyses. Calibration steps of the DIA by adjusting the algorithm settings were performed: first, by subjective DIA quality assessment (DIA-1), and second, to compensate the bias established (DIA-2). Visual estimate (Ki67-VE) on the same images was performed by five pathologists independently.ResultsANOVA revealed significant underestimation bias (P < 0.05) for DIA-0, DIA-1 and two pathologists’ VE, while DIA-2, VE-median and three other VEs were within the same range. Regression analyses revealed best accuracy for the DIA-2 (R-square = 0.90) exceeding that of VE-median, individual VEs and other DIA settings. Bidirectional bias for the DIA-2 with overestimation at low, and underestimation at high ends of the scale was detected. Measurement error correction by inverse regression was applied to improve DIA-2-based prediction of the Ki67-Count, in particular for the clinically relevant interval of Ki67-Count < 40%. Potential clinical impact of the prediction was tested by dichotomising the cases at the cut-off values of 10, 15, and 20%. Misclassification rate of 5-7% was achieved, compared to that of 11-18% for the VE-median-based prediction.ConclusionsOur experiments provide methodology to achieve accurate Ki67-LI estimation by DIA, based on proper validation, calibration, and measurement error correction procedures, guided by quantified bias from reference values obtained by stereology grid count. This basic validation step is an important prerequisite for high-throughput automated DIA applications to investigate tissue heterogeneity and clinical utility aspects of Ki67 and other immunohistochemistry (IHC) biomarkers.

A methodology for comprehensive breast cancer Ki67 labeling index with intra-tumor heterogeneity appraisal based on hexagonal tiling of digital image analysis data.

Digital image analysis (DIA) enables higher accuracy, reproducibility, and capacity to enumerate cell populations by immunohistochemistry; however, the most unique benefits may be obtained by evaluating the spatial distribution and intra-tissue variance of markers. The proliferative activity of breast cancer tissue, estimated by the Ki67 labeling index (Ki67 LI), is a prognostic and predictive biomarker requiring robust measurement methodologies. We performed DIA on whole-slide images (WSI) of 302 surgically removed Ki67-stained breast cancer specimens; the tumour classifier algorithm was used to automatically detect tumour tissue but was not trained to distinguish between invasive and non-invasive carcinoma cells. The WSI DIA-generated data were subsampled by hexagonal tiling (HexT). Distribution and texture parameters were compared to conventional WSI DIA and pathology report data. Factor analysis of the data set, including total numbers of tumor cells, the Ki67 LI and Ki67 distribution, and texture indicators, extracted 4 factors, identified as entropy, proliferation, bimodality, and cellularity. The factor scores were further utilized in cluster analysis, outlining subcategories of heterogeneous tumors with predominant entropy, bimodality, or both at different levels of proliferative activity. The methodology also allowed the visualization of Ki67 LI heterogeneity in tumors and the automated detection and quantitative evaluation of Ki67 hotspots, based on the upper quintile of the HexT data, conceptualized as the “Pareto hotspot”. We conclude that systematic subsampling of DIA-generated data into HexT enables comprehensive Ki67 LI analysis that reflects aspects of intra-tumor heterogeneity and may serve as a methodology to improve digital immunohistochemistry in general.

Primary cardiac lymphoma: two cases and a review of literature.

BackgroundPrimary cardiac lymphoma is one of the rarest tumours of the heart. It belongs to the extra-nodal non-Hodgkin’s lymphomas. The most common type of this tumour is diffuse large B cell lymphoma. Usually, right atrium and right ventricle are involved. This tumour is fatal unless diagnosed and treated in time. In this article two female patients who were diagnosed with primary cardiac lymphoma and treated at our clinic are described. The first patient went to remission after the treatment, while the second patient died. The goals of this article are to show the difficulties of diagnosing and treating this disease, the role of cardiac surgery in its treatment and to raise awareness of this disease.Case reportsIn this article two female patients who were diagnosed with primary cardiac lymphoma and treated at our clinic are described. The first patient went to remission after the treatment, while the second patient died.ConclusionsPrimary cardiac lymphoma is a very rare disease. At the moment the most effective treatment is chemotherapy. Palliative surgery may be necessary to correct hemodynamics when venous blood flow to the lungs is disturbed.

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.

Ki67/SATB1 ratio is an independent prognostic factor of overall survival in patients with early hormone receptor-positive invasive ductal breast carcinoma

Biological diversity of breast cancer presents challenges for personalized therapy and necessitates multiparametric approaches to understand and manage the disease. Multiple protein biomarkers tested by immunohistochemistry (IHC), followed by digital image analysis and multivariate statistics of the data, have been shown to be effective in exploring latent profiles of tumor tissue immunophenotype. In this study, based on tissue microarrays of 107 patients with hormone receptor (HR) positive invasive ductal breast carcinoma, we investigated the prognostic value of the integrated immunophenotype to predict overall survival (OS) of the patients. A set of 10 IHC markers (ER, PR, HER2, Ki67, AR, BCL2, HIF-1α, SATB1, p53, and p16) was used. The main factor of the variance was characterized by opposite loadings of ER/PR/AR/BCL2 and Ki67/HIF-1α; it was associated with histological grade but did not predict OS. The second factor was driven by SATB1 expression along with moderate positive HIF-1α and weak negative Ki67 loadings. Importantly, this factor did not correlate with any clinicopathologic parameters, but was an independent predictor of better OS. Ki67 and SATB1 did not reach statistical significance as single predictors; however, high Ki67/SATB1 ratio was an independent predictor of worse OS. In addition, our data indicate potential double prognostic meaning of HIF-1α expression in breast cancer and necessitate focused studies, taking into account the immunophenotype interactions and tissue heterogeneity aspects.

Digital immunohistochemistry: new horizons and practical solutions in breast cancer pathology.

Digital image analysis (DA) brings new opportunities to enhance breast cancer pathology testing by providing tools to read tissue microscopy-based data in a more precise, accurate, and high-throughput manner. The applications vary from very practical solutions assisting in quantification of biomarker expression to more complex efforts to analyse multi-dimensional data. We present our DA experiments to enhance HER2 immunohistochemistry (IHC) and FISH testing and to explore multiple IHC biomarker variation profiles in breast cancer.

Digital immunohistochemistry platform for the staining variation monitoring based on integration of image and statistical analyses with laboratory information system

BackgroundDigital immunohistochemistry (IHC) is one of the most promising applications brought by new generation image analysis (IA). While conventional IHC staining quality is monitored by semi-quantitative visual evaluation of tissue controls, IA may require more sensitive measurement. We designed an automated system to digitally monitor IHC multi-tissue controls, based on SQL-level integration of laboratory information system with image and statistical analysis tools.MethodsConsecutive sections of TMA containing 10 cores of breast cancer tissue were used as tissue controls in routine Ki67 IHC testing. Ventana slide label barcode ID was sent to the LIS to register the serial section sequence. The slides were stained and scanned (Aperio ScanScope XT), IA was performed by the Aperio/Leica Colocalization and Genie Classifier/Nuclear algorithms. SQL-based integration ensured automated statistical analysis of the IA data by the SAS Enterprise Guide project. Factor analysis and plot visualizations were performed to explore slide-to-slide variation of the Ki67 IHC staining results in the control tissue.ResultsSlide-to-slide intra-core IHC staining analysis revealed rather significant variation of the variables reflecting the sample size, while Brown and Blue Intensity were relatively stable. To further investigate this variation, the IA results from the 10 cores were aggregated to minimize tissue-related variance. Factor analysis revealed association between the variables reflecting the sample size detected by IA and Blue Intensity. Since the main feature to be extracted from the tissue controls was staining intensity, we further explored the variation of the intensity variables in the individual cores. MeanBrownBlue Intensity ((Brown+Blue)/2) and DiffBrownBlue Intensity (Brown-Blue) were introduced to better contrast the absolute intensity and the colour balance variation in each core; relevant factor scores were extracted. Finally, tissue-related factors of IHC staining variance were explored in the individual tissue cores.ConclusionsOur solution enabled to monitor staining of IHC multi-tissue controls by the means of IA, followed by automated statistical analysis, integrated into the laboratory workflow. We found that, even in consecutive serial tissue sections, tissue-related factors affected the IHC IA results; meanwhile, less intense blue counterstain was associated with less amount of tissue, detected by the IA tools.

Digital immunohistochemistry wizard: image analysis-assisted stereology tool to produce reference data set for calibration and quality control.

BackgroundDigital image analysis (DIA) enables better reproducibility of immunohistochemistry (IHC) studies. Nevertheless, accuracy of the DIA methods needs to be ensured, demanding production of reference data sets. We have reported on methodology to calibrate DIA for Ki67 IHC in breast cancer tissue based on reference data obtained by stereology grid count. To produce the reference data more efficiently, we propose digital IHC wizard generating initial cell marks to be verified by experts.MethodsDigital images of proliferation marker Ki67 IHC from 158 patients (one tissue microarray spot per patient) with an invasive ductal carcinoma of the breast were used. Manual data (mD) were obtained by marking Ki67-positive and negative tumour cells, using a stereological method for 2D object enumeration. DIA was used as an initial step in stereology grid count to generate the digital data (dD) marks by Aperio Genie and Nuclear algorithms. The dD were collected into XML files from the DIA markup images and overlaid on the original spots along with the stereology grid. The expert correction of the dD marks resulted in corrected data (cD). The percentages of Ki67 positive tumour cells per spot in the mD, dD, and cD sets were compared by single linear regression analysis. Efficiency of cD production was estimated based on manual editing effort.ResultsThe percentage of Ki67-positive tumor cells was in very good agreement in the mD, dD, and cD sets: regression of cD from dD (R2=0.92) reflects the impact of the expert editing the dD as well as accuracy of the DIA used; regression of the cD from the mD (R2=0.94) represents the consistency of the DIA-assisted ground truth (cD) with the manual procedure. Nevertheless, the accuracy of detection of individual tumour cells was much lower: in average, 18 and 219 marks per spot were edited due to the Genie and Nuclear algorithm errors, respectively. The DIA-assisted cD production in our experiment saved approximately 2/3 of manual marking.ConclusionsDigital IHC wizard enabled DIA-assisted stereology to produce reference data in a consistent and efficient way. It can provide quality control measure for appraising accuracy of the DIA steps.

Abstract B116: A methodology to ensure and improve accuracy of Ki67 digital immunohistochemistry analysis in breast cancer tissue

Background: Immunohistochemical Ki67 evaluation reflects proliferative activity and is one of most important prognostic/predictive markers of breast cancer. However, standardized and efficient methodologies to accurately and reproducibly measure the Ki67 expression are still to come. Besides tissue processing, sampling, intra-tumour variability, and many other aspects to be considered, key element of the methodology remains accurate enumeration of Ki67-labelling index (LI). We aimed to develop a methodology to estimate and improve accuracy of automated image analysis (IA) approach. Methods: Tissue microarrays (1 mm diameter spot per patient, n=164) from invasive ductal breast carcinoma, stained for Ki67 and digitized by Aperio XT scanner, were used for the study. Reference values (RV) were obtained by counting the LI using stereological frame overlaid on a spot image. To test the degree of inter-observer variation in establishing the RV, the frame counts were performed by 3 observers independently in a subset (n=30) of the TMA images. IA was performed with Aperio Genie/Nuclear algorithms enabling automated selection of tumour tissue. Accuracy of the IA compared to the RV was estimated based on ANOVA, correlation and regression analyses performed with SAS 9.3. Agreement between individual measurements was also estimated based on 95% confidence intervals calculated from the RV according to stereology rules. Several iterations of the IA with adjusted algorithm settings were performed to improve the accuracy. Highly automated calibration cycles were enabled by developing software to integrate processes of the image and statistical analyses. Visual evaluation for the LI on the same images was performed by 3 pathologists (P1, P2, P3). Results: Inter-observer variation between 3 independent frame counts (n=30) was negligible by ANOVA (respectively, mean RV were 28.5, 28.6 and 29.9%) with correlation coefficients 0.97 and above. RV correlated strongly with IA (r=0.95) and P1, P2, P3 (r=0.86, r=0.90, r=0.92, respectively), p Conclusion: Our experiments provide sound and efficient methodology to achieve accurate immunohistochemical Ki67 enumeration by IA, enabled by proper validation and calibration of the measurement against RV obtained by stereological frame counts. Citation Format: Arvydas Laurinavicius, Benoit Plancoulaine, Aida Laurinaviciene, Paulette Herlin, Raimundas Meskauskas, Indra Baltrusaityte, Justinas Besusparis, Nicolas Elie, Philippe Belhomme, Yasir Iqbal, Catherine Bor-Angelier. A methodology to ensure and improve accuracy of Ki67 digital immunohistochemistry analysis in breast cancer tissue. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr B116.

Rare types of breast carcinoma

Abstract Breast cancer (BC) is a heterogeneous disease that encompasses several distinct entities with remarkably different characteristics. Histological type is one of important BC characteristics. Histological type is associated with differences in epidemiology, diagnostic issues, clinical course, prognosis. When we talk about BC, ductal and lobular carcinoma is usually what we have in mind. However, the other types that comprise less than 10% of BC are also very important. The rarity of many of these neoplasms does not allow large or randomized studies to define the optimal treatment. Many of the descriptions are from case reports and small series. The aim of this retrospective study was to analyze the data on rare breast cancers, to describe their main characteristics, and to calculate survival rates. We believe that the experience of our institution will contribute to the available data about these rare breast cancers and help in better understanding of this subgroup.