Impact of tissue sampling on accuracy of Ki67 immunohistochemistry evaluation in breast cancer.

BACKGROUND: Gene expression studies have identified molecular subtypes of breast cancer with implications to chemotherapy recommendations. For distinction of these types, a combination of immunohistochemistry (IHC) markers, including proliferative activity of tumor cells, estimated by Ki67 labeling index is used. Clinical studies are frequently based on IHC performed on tissue microarrays (TMA) with variable tissue sampling. This raises the need for evidence-based sampling criteria for individual IHC biomarker studies. We present a novel tissue sampling simulation model and demonstrate its application on Ki67 assessment in breast cancer tissue taking intratumoral heterogeneity into account. METHODS: Whole slide images (WSI) of 297 breast cancer sections, immunohistochemically stained for Ki67, were subjected to digital image analysis (DIA). Percentage of tumor cells stained for Ki67 was computed for hexagonal tiles super-imposed on the WSI. From this, intratumoral Ki67 heterogeneity indicators (Haralick's entropy values) were extracted and used to dichotomize the tumors into homogeneous and heterogeneous subsets. Simulations with random selection of hexagons, equivalent to 0.75 mm circular diameter TMA cores, were performed. The tissue sampling requirements were investigated in relation to tumor heterogeneity using linear regression and extended error analysis. RESULTS: The sampling requirements were dependent on the heterogeneity of the biomarker expression. To achieve a coefficient error of 10 %, 5-6 cores were needed for homogeneous cases, 11-12 cores for heterogeneous cases; in mixed tumor population 8 TMA cores were required. Similarly, to achieve the same accuracy, approximately 4,000 nuclei must be counted when the intratumor heterogeneity is mixed/unknown. Tumors of low proliferative activity would require larger sampling (10-12 TMA cores, or 6,250 nuclei) to achieve the same error measurement results as for highly proliferative tumors. CONCLUSIONS: Our data show that optimal tissue sampling for IHC biomarker evaluation is dependent on the heterogeneity of the tissue under study and needs to be determined on a per use basis. We propose a method that can be applied to determine the sampling strategy for specific biomarkers, tissues and study targets. In addition, our findings highlight the benefit of high-capacity computer-based IHC measurement techniques to improve accuracy of the testing.

Comprehensive Immunohistochemistry: Digital, Analytical and Integrated.

Immunohistochemistry (IHC) is widely used in contemporary pathology as a diagnostic and, increasingly, as a prognostic and predictive tool. The main value of the method today comes from a sensitive and specific detection of a protein of interest in the context of tissue architecture and cell populations. One of the major limitations of conventional IHC is related to the fact that the results are usually obtained by visual qualitative or semiquantitative evaluation. While this is sufficient for diagnostic purposes, measurement of prognostic and predictive biomarkers requires better accuracy and reproducibility. Also, objective evaluation of the spatial heterogeneity of biomarker expression as well as the development of combined/integrated biomarkers are in great demand. On the other end of the scale, the rapid development of tissue proteomics accounting for 2D spatial aspects has led to a disruptive concept of next-generation IHC, promising high multiplexing and broad dynamic range quantitative/spatial data on tissue protein expression. This 'evolutionary gap' between conventional and next-generation IHC can be filled by comprehensive IHC based on digital technologies (empowered by quantification and spatial and multiparametric analytics) and integrated into the pathology workflow and information systems. In this paper, we share our perspectives on a comprehensive IHC road map as a multistep development process.

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.

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.

Quantification of myocardial fibrosis by digital image analysis and interactive stereology.

BACKGROUND: Cardiac fibrosis disrupts the normal myocardial structure and has a direct impact on heart function and survival. Despite already available digital methods, the pathologist's visual score is still widely considered as ground truth and used as a primary method in histomorphometric evaluations. The aim of this study was to compare the accuracy of digital image analysis tools and the pathologist's visual scoring for evaluating fibrosis in human myocardial biopsies, based on reference data obtained by point counting performed on the same images. METHODS: Endomyocardial biopsy material from 38 patients diagnosed with inflammatory dilated cardiomyopathy was used. The extent of total cardiac fibrosis was assessed by image analysis on Masson's trichrome-stained tissue specimens using automated Colocalization and Genie software, by Stereology grid count and manually by Pathologist's visual score. RESULTS: A total of 116 slides were analyzed. The mean results obtained by the Colocalization software (13.72 ± 12.24%) were closest to the reference value of stereology (RVS), while the Genie software and Pathologist score gave a slight underestimation. RVS values correlated strongly with values obtained using the Colocalization and Genie (r>0.9, p<0.001) software as well as the pathologist visual score. Differences in fibrosis quantification by Colocalization and RVS were statistically insignificant. However, significant bias was found in the results obtained by using Genie versus RVS and pathologist score versus RVS with mean difference values of: -1.61% and 2.24%. Bland-Altman plots showed a bidirectional bias dependent on the magnitude of the measurement: Colocalization software overestimated the area fraction of fibrosis in the lower end, and underestimated in the higher end of the RVS values. Meanwhile, Genie software as well as the pathologist score showed more uniform results throughout the values, with a slight underestimation in the mid-range for both. CONCLUSION: Both applied digital image analysis methods revealed almost perfect correlation with the criterion standard obtained by stereology grid count and, in terms of accuracy, outperformed the pathologist's visual score. Genie algorithm proved to be the method of choice with the only drawback of a slight underestimation bias, which is considered acceptable for both clinical and research evaluations. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/9857909611227193.

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

INTRODUCTION: Immunohistochemical 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. METHODS: Tissue 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. RESULTS: ANOVA 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 particularfor 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. CONCLUSIONS: Our 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.

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

BACKGROUND: Digital 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. METHODS: Consecutive 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. RESULTS: Slide-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. CONCLUSIONS: Our 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.

BACKGROUND: Digital 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. METHODS: Digital 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. RESULTS: The 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. CONCLUSIONS: Digital 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.

Building of a composite virtual slide from contiguous tissue samples.

BACKGROUND: Currently available microscope slide scanners produce whole slide images at various resolutions from histological sections. Nevertheless, acquisition area and so visualization of large tissue samples are limited by the standardized size of glass slides, used daily in pathology departments. The proposed solution has been developed to build composite virtual slides from images of large tumor fragments. MATERIALS AND METHODS: Images of HES or immunostained histological sections of carefully labeled fragments from a representative slice of breast carcinoma were acquired with a digital slide scanner at a magnification of 20×. The tiling program involves three steps: the straightening of tissue fragment images using polynomial interpolation method, and the building and assembling of strips of contiguous tissue sample whole slide images in × and y directions. The final image is saved in a pyramidal BigTiff file format. The program has been tested on several tumor slices. A correlation quality control has been done on five images artificially cut. RESULTS: Sixty tumor slices from twenty surgical specimens, cut into two to twenty six pieces, were reconstructed. A median of 98.71% is obtained by computing the correlation coefficients between native and reconstructed images for quality control. CONCLUSIONS: The proposed method is efficient and able to adapt itself to daily work conditions of classical pathology laboratories.

Diagnostic reproducibility of tumour budding in colorectal cancer: a multicentre, multinational study using virtual microscopy.

AIMS: Despite the established prognostic relevance of tumour budding in colorectal cancer, the reproducibility of the methods reported for its assessment has not yet been determined, limiting its use and reporting in routine pathology practice. METHODS AND RESULTS: A morphometric system within telepathology was devised to evaluate the reproducibility of the various methods published for the assessment of tumour budding in colorectal cancer. Five methods were selected to evaluate the diagnostic reproducibility among 10 investigators, using haematoxylin and eosin (H&E) and AE1-3 cytokeratin-immunostained, whole-slide digital scans from 50 pT1-pT4 colorectal cancers. The overall interobserver agreement was fair for all methods, and increased to moderate for pT1 cancers. The intraobserver agreement was also fair for all methods and moderate for pT1 cancers. Agreement was dependent on the participants' experience with tumour budding reporting and performance time. Cytokeratin immunohistochemistry detected a higher percentage of tumour budding-positive cases with all methods compared to H&E-stained slides, but did not influence agreement levels. CONCLUSION: An overall fair level of diagnostic agreement for tumour budding in colorectal cancer was demonstrated, which was significantly higher in early cancer and among experienced gastrointestinal pathologists. Cytokeratin immunostaining facilitated detection of budding cancer cells, but did not result in improved interobserver agreement.

Digital image analysis in pathology: benefits and obligation.

Pathology has recently entered the era of personalized medicine. This brings new expectations for the accuracy and precision of tissue-based diagnosis, in particular, when quantification of histologic features and biomarker expression is required. While for many years traditional pathologic diagnosis has been regarded as ground truth, this concept is no longer sufficient in contemporary tissue-based biomarker research and clinical use. Another major change in pathology is brought by the advancement of virtual microscopy technology enabling digitization of microscopy slides and presenting new opportunities for digital image analysis. Computerized vision provides an immediate benefit of increased capacity (automation) and precision (reproducibility), but not necessarily the accuracy of the analysis. To achieve the benefit of accuracy, pathologists will have to assume an obligation of validation and quality assurance of the image analysis algorithms. Reference values are needed to measure and control the accuracy. Although pathologists' consensus values are commonly used to validate these tools, we argue that the ground truth can be best achieved by stereology methods, estimating the same variable as an algorithm is intended to do. Proper adoption of the new technology will require a new quantitative mentality in pathology. In order to see a complete and sharp picture of a disease, pathologists will need to learn to use both their analogue and digital eyes.

Standardization of whole slide image morphologic assessment with definition of a new application: Digital slide dynamic morphometry.

BACKGROUND: In histopathology, the quantitative assessment of various morphologic features is based on methods originally conceived on specific areas observed through the microscope used. Failure to reproduce the same reference field of view using a different microscope will change the score assessed. Visualization of a digital slide on a screen through a dedicated viewer allows selection of the magnification. However, the field of view is rectangular, unlike the circular field of optical microscopy. In addition, the size of the selected area is not evident, and must be calculated. MATERIALS AND METHODS: A digital slide morphometric system was conceived to reproduce the various methods published for assessing tumor budding in colorectal cancer. Eighteen international experts in colorectal cancer were invited to participate in a web-based study by assessing tumor budding with five different methods in 100 digital slides. RESULTS: The specific areas to be tested by each method were marked by colored circles. The areas were grouped in a target-like pattern and then saved as an .xml file. When a digital slide was opened, the .xml file was imported in order to perform the measurements. Since the morphometric tool is composed of layers that can be freely moved on top of the digital slide, the technique was named digital slide dynamic morphometry. Twelve investigators completed the task, the majority of them performing the multiple evaluations of each of the cases in less than 12 minutes. CONCLUSIONS: Digital slide dynamic morphometry has various potential applications and might be a useful tool for the assessment of histologic parameters originally conceived for optical microscopy that need to be quantified.

Nuclear morphology for the detection of alterations in bronchial cells from lung cancer: an attempt to improve sensitivity and specificity.

OBJECTIVE: To identify which morphologic or densitometric parameters are modified in cell nuclei from bronchopulmonary cancer based on 18 parameters involving shape, intensity, chromatin, texture, and DNA content and develop a bronchopulmonary cancer screening method relying on analysis of sputum sample cell nuclei. STUDY DESIGN: A total of 25 sputum samples from controls and 22 bronchial aspiration samples from patients presenting with bronchopulmonary cancer who were professionally exposed to cancer were used. After Feulgen staining, 18 morphologic and DNA content parameters were measured on cell nuclei, via image cytom- etry. A method was developed for analyzing distribution quantiles, compared with simply interpreting mean values, to characterize morphologic modifications in cell nuclei. RESULTS: Distribution analysis of parameters enabled us to distinguish 13 of 18 parameters that demonstrated significant differences between controls and cancer cases. These parameters, used alone, enabled us to distinguish two population types, with both sensitivity and specificity > 70%. Three parameters offered 100% sensitivity and specificity. When mean values offered high sensitivity and specificity, comparable or higher sensitivity and specificity values were observed for at least one of the corresponding quantiles. CONCLUSION: Analysis of modification in morphologic parameters via distribution analysis proved promising for screening bronchopulmonary cancer from sputum.

Towards a computer aided diagnosis system dedicated to virtual microscopy based on stereology sampling and diffusion maps.

An original strategy is presented, combining stereological sampling methods based on test grids and data reduction methods based on diffusion maps, in order to build a knowledge image database with no bias introduced by a subjective choice of exploration areas. The practical application of the exposed methodology concerns virtual slides of breast tumors.

Stromal compartment as a survival prognostic factor in advanced ovarian carcinoma.

OBJECTIVE: We investigated the prognostic significance of stromal compartment on the overall survival of patients with advanced epithelial ovarian cancer. METHODS: We evaluated retrospectively the stroma proportion of the tumor surgical specimens of 194 patients with stages III and IV disease, using histochemical staining and fully automatic virtual slide processing. The prognostic significance of stroma proportion and clinical parameters were evaluated using log-rank test and Cox regression. RESULTS: Stroma proportion was found to be an independent prognostic factor by both univariate (P = 0.016) and multivariate analyses (hazards ratio = 1.45, P = 0.011). CONCLUSION: The present data indicate that a high stroma proportion is related to a poor prognosis of stage III and IV ovarian carcinomas.

Prognostic significance of tumour vascularisation on survival of patients with advanced ovarian carcinoma.

OBJECTIVE: The prognostic significance of microvessel density in ovarian cancer is still a matter of debate. Classically, the degree of vascularisation is assessed in areas of high vascular density (hot spots), considered as regions of increased probability of metastasis. Since ovarian tumours have a particular progression and dissemination behaviour, vascularisation outside hot spots may also contribute to their evolution. METHODS: In the present study, the degree of tumour vascularisation was estimated both in whole histogical sections and in hot spots, in 235 patients with ovarian carcinoma, using fully automatic image analysis methods. Six parameters were estimated: mean microvessel density (MVD) and mean microvessel surface fraction (MSP) on the whole section, mean and maximum values of MVD and MSP inside hot spots (MVDHS1, MSPHS1 and MVDHS2, MSPHS2). Relationships between vascular parameters and clinicopathologic features were analysed. RESULTS: In stage III-IV patients multivariate analysis showed that stage IV disease (hazards ratio (HR)=1.72, p=0.001), post-surgical residual disease 1cm (HR=2.86, p<0.001), upper MVD tercile (HR=1.45, p<0.022) and medial MVDHS1 tercile (HR=1.36, p=0.060) retained an independent prognostic value upon overall survival. CONCLUSION: Our results suggest that quantification of blood vessels, both on the whole histological section and in hot spots might be helpful in evaluating prognosis in advanced ovarian carcinomas.

[A potential contribution of virtual slides and automatic image processing to the estimation of the vascularization of cranial sutures]. / Apport potentiel des lames virtuelles et de l'analyse d'images automatique dans l'estimation de la vascularisation des sutures crâniennes.

AIM: Determining age at the time of death is a difficult problem in forensic practice. The study of the vascularization of cranial sutures is an original approach, which may mark the process of synostosis associated with aging. Counting sections of blood vessels on a histological section of cranial suture, raises however a number of practical problems related to the quality of the preparation and to the method of quantification. The purpose of this study is to illustrate the potential contribution of an automatic analysis of virtual slides to overcome these problems. MATERIALS AND METHODS: The proposed method of analysis is illustrated from three samples of frontosphenoidal suture whose vessels were immunostained after decalcification of bone tissue. A high resolution image (x 20 objective and microscopic resolution of 0.5 microm) of each microscopic preparation was acquired through a microscopic scanning device. The automatic image analysis protocol takes advantage of a processing of virtual slides at two resolutions. RESULTS: The chosen strategy ensures the identification of the specific area of interest, the enumeration of blood vessel sections on the whole preparation and the visual control of the detected structures. CONCLUSION: The quantitative estimate of the vascularization of a large structure, such as a cranial suture, can benefit from scanning and fully automatic processing of virtual slides. The automatic analysis requires however an optimal preparation of tissues; decalcification and immunohistochemical staining must be done with great care.

Usefulness of automatic quantification of immunochemical staining on whole tumor sections for correlation with oncological small animal PET studies: an example with cell proliferation, glucose transporter 1 and FDG.

AIM: To highlight the use of automatic quantification of immunochemical staining on digitized images of whole tumor sections in preclinical positron emission tomography (PET) studies. MATERIALS AND METHODS: Xenografted human testicular tumors (36) were imaged with 2-deoxy-2[F-18]fluoro-D: -glucose (FDG) small animal PET (SA-PET). Tumor cell proliferation and glucose transportation were assessed with cyclin A and Glut-1 immunostaining. Tumor slides were digitized and processed with PixCyt software enabling whole slide quantification, then compared with junior and senior pathologist manual scoring. Manual and automatic quantification results were correlated to FDG uptake. RESULTS: For cyclin A, inter- and intra-observer agreement for manual scoring was 0.52 and 0.72 and concordance between senior pathologist and automatic quantification was 0.84. Correlations between Tumor/Background ratio and tumor cell proliferation assessed by automatic quantification, junior and senior pathologists were 0.75, 0.55, and 0.61, respectively. Correlation between Tumor/Background ratio and Glut-1 assessed by automatic quantification was 0.74. CONCLUSION: Automatic quantification of immunostaining is a valuable tool to overcome inter- and intra-observer variability for correlation of cell proliferation or other markers with tumor tracer uptake.

Antiangiogenic and anti-invasive effects of sunitinib on experimental human glioblastoma.

Angiogenesis inhibitors appear to be promising therapies for highly vascularized tumors such as glioblastoma multiforme (GBM). Sunitinib is an oral multitargeted tyrosine kinase inhibitor with both antiangiogenic and antitumor activities due to selective inhibition of various receptor tyrosine kinases, including those important for angiogenesis (vascular endothelial growth factor receptors and platelet-derived growth factor receptors). Here we evaluated the antitumor activities of sunitinib on orthotopic models of GBM in vitro and in vivo. Sunitinib potently inhibited angiogenesis that was stimulated by implantation of U87MG and GL15 cells into organotypic brain slices at concentrations as low as 10 nM. At high dose (10 microM), sunitinib induced direct antiproliferative and proapoptotic effects on GL15 cells and decreased invasion of these cells implanted into brain slices by 49% (p < 0.001). Treatment was associated with decreases in Src (35%) and focal adhesion kinase (44%) phosphorylation. However, anti-invasive activity was not observed in vivo at the highest dose level utilized (80 mg/kg per day). Survival experiments involving athymic mice bearing intracerebral U87MG GBM demonstrated that oral administration of 80 mg/kg sunitinib (five days on, two days off) improved median survival by 36% (p < 0.0001). Sunitinib treatment caused a 74% reduction in microvessel density (p < 0.05), an increase in tumor necrosis, and a decrease in number of GBM cells positive for MIB antibody. Sunitinib exhibited potent antiangiogenic activity that was associated with a meaningful prolongation of survival of mice bearing intracerebral GBM. These data support the potential utility of sunitinib in the treatment of GBM.