Quantitative Immunohistochemical Analysis Reveals Association between Sodium Iodide Symporter and Estrogen Receptor Expression in Breast Cancer

Background

Human sodium iodide symporter (hNIS) gene over-expression is under active consideration worldwide as an alternative target molecule for breast cancer (BC) diagnosis and targeted radio-iodine treatment. However, the field demands better stratified analysis of endogenous hNIS expression across major BC subtypes. Therefore, we have analyzed subtype-specific variation of hNIS overexpression in breast tumor tissue samples by immunohistochemistry (IHC) and also report the development of a homogeneous, quantitative analysis method of digital IHC images.

Methods

hNIS expression was analyzed from 108 BC tissue samples by IHC. Sub-cellular localization of hNIS protein was analyzed by dual immunofluorescence (IF) staining method using hNIS and HER2 antibodies. An ImageJ based two-step digital analysis method was developed and applied for the bias-free analysis of the images.

Results

Staining of the tumor samples show 70% cases are hNIS positive indicating high incidence of hNIS positive cases in BC. More importantly, a subtype specific analysis done for the first time shows that hNIS expression is overly dominated in estrogen receptor (ER) positive cases than the receptor negative cases. Further, 56% of the ER+ve, PgR+ve, HER2-ve and 36% of ER+ve, PgR+ve, HER2+ve cases show highest intensity staining equivalent to the thyroid tissue. A significant positive correlation is also observed between hNIS and estrogen receptor expression (p = 0.0033, CI = 95%) suggesting hNIS mediated targeted radio-iodine therapy procedures may benefit both ER+ve, PgR+ve, HER2–ve as well as HER2+ve cases. Further, in a few cases, hNIS and HER2 protein localization is demonstrated by overlapping membrane co-expression. ImageJ based image analysis method shows over 70% match with manual pathological scoring method.

Conclusion

The study indicates a positive link between hNIS and ER expression in BC. The quantitative IHC image analysis method reported here will further help in patient stratification and potentially benefit global clinical assessment where hNIS mediated targeted ¹³¹I radio-ablative therapy is aimed.     

Semiautomated computer-assisted image analysis to quantify 3,3'-diaminobenzidine tetrahydrochloride-immunostained small tissues

This work aimed to develop a technique to measure stained areas in images from sample tissue sections, namely when the structure of interest does not fill the entire image field of the microscope. We propose a semiautomated computer-assisted image analysis (SACAIA) method in which brightfield color images of 3,3'-diaminobenzidene tetrahydrochloride (DAB)-stained antigens are converted to their blue component and boundaries are delineated to extract the object of interest. The number of pixels of a defined color (elicited by DAB) is counted and used to measure the stained area relative to the total area of the tissue under study. The percentages of area stained with adenosine A(1) receptor were 40.76+/-2.08 and 42.44+/-2.26% for manual analysis and SACAIA, respectively (P=0.582). A strong linear correlation of A(1) receptor quantification was found (r=0.98, P<0.001, and 95% CI=0.97 to 0.99 for manual method; r=0.99, P<0.001, and 95% CI=0.98 to 0.99 for SACAIA method). The extent to which misclassification affected staining quantification was evaluated by Bland-Altman analysis, indicating that this method can be applied accurately to quantify the immunohistochemical staining area (occupied by a specific antigen) in small sample tissues that do not fill the entire image field of the microscope.     

Analytical validation of quantitative immunohistochemical assays of tumor infiltrating lymphocyte biomarkers

Tumor infiltrating lymphocytes (TIL), especially T-cells, have both prognostic and therapeutic applications. The presence of CD8+ effector T-cells and the ratio of CD8+ cells to FOXP3+ regulatory T-cells have been used as biomarkers of disease prognosis to predict response to various immunotherapies. Blocking the interaction between inhibitory receptors on T-cells and their ligands with therapeutic antibodies including atezolizumab, nivolumab, pembrolizumab and tremelimumab increases the immune response against cancer cells and has shown significant improvement in clinical benefits and survival in several different tumor types. The improved clinical outcome is presumed to be associated with a higher tumor infiltration; therefore, it is thought that more accurate methods for measuring the amount of TIL could assist prognosis and predict treatment response. We have developed and validated quantitative immunohistochemistry (IHC) assays for CD3, CD8 and FOXP3 for immunophenotyping T-lymphocytes in tumor tissue. Various types of formalin fixed, paraffin embedded (FFPE) tumor tissues were immunolabeled with anti-CD3, anti-CD8 and anti-FOXP3 antibodies using an IHC autostainer. The tumor area of stained tissues, including the invasive margin of the tumor, was scored by a pathologist (visual scoring) and by computer-based quantitative image analysis. Two image analysis scores were obtained for the staining of each biomarker: the percent positive cells in the tumor area and positive cells/mm² tumor area. Comparison of visual vs. image analysis scoring methods using regression analysis showed high correlation and indicated that quantitative image analysis can be used to score the number of positive cells in IHC stained slides. To demonstrate that the IHC assays produce consistent results in normal daily testing, we evaluated the specificity, sensitivity and reproducibility of the IHC assays using both visual and image analysis scoring methods. We found that CD3, CD8 and FOXP3 IHC assays met the fit-for-purpose analytical acceptance validation criteria and that they can be used to support clinical studies.     

The Prognostic Value of HPV Status and p16 Expression in Patients with Carcinoma of the Anal Canal

Background

In anal cancer studies, the detection frequency of high-risk HPV (human papillomavirus) is variable, depending on the method used. There are limited data reporting results of different HPV detection techniques in the same clinical series, and very few correlating results with clinical outcome.

Objectives

To evaluate tumor expression of p16/HPV16 using three different methods, and to determine their association with clinical outcome in patients with anal canal squamous cell carcinomas (SCC).

Design

This retrospective study included patients with anal canal SCC treated with definitive radiotherapy or chemoradiotherapy at a single institution between 1992 and 2005. Formalin-fixed paraffin–embedded tumor samples from 53 of the 89 (60%) patient pre-treatment biopsies were adequate for tissue microarray construction. HPV status was determined using: p16 expression by conventional immunohistochemistry (IHC) and quantitative IHC (AQUA), HPV genotype analysis by chromogenic in situ hybridization (CISH) and HPV linear array sub-typing. Expression status was correlated with clinical outcome.

Results

80% (28/35) of patient tumors had high p16 expression using conventional IHC. HPV16 CISH was positive in 81% (34/42) of tumors, and 78% (28/36) of tumors were HPV subtype 16. HPV16 CISH correlated with p16 evaluated by conventional IHC (correlation coefficient 0.46; p = 0.01) and by p16 AQUA score (correlation coefficient 0.49; p = 0.001). A subset of cases (15%) had very high p16 quantitative IHC scores (>244) and were associated with a higher incidence of local or distant recurrence (p = 0.04).

Conclusions

The vast majority (80%) of anal canal SCC in our series were positive for HPV16/p16, regardless of the testing method used. The exploratory analysis of automated quantitative IHC scoring was the only technique to define a subset of patients with a worse prognosis by p16 expression status on univariate analysis. Further exploration of the molecular mechanisms of treatment resistance in association with very high p16 expression is warranted.     

Image Analysis Algorithms for Immunohistochemical Assessment of Cell Death Events and Fibrosis in Tissue Sections

Cell death is of broad physiological and pathological importance, making quantification of biochemical events associated with cell demise a high priority for experimental pathology. Fibrosis is a common consequence of tissue injury involving necrotic cell death. Using tissue specimens from experimental mouse models of traumatic brain injury, cardiac fibrosis, and cancer, as well as human tumor specimens assembled in tissue microarray (TMA) format, we undertook computer-assisted quantification of specific immunohistochemical and histological parameters that characterize processes associated with cell death. In this study, we demonstrated the utility of image analysis algorithms for color deconvolution, colocalization, and nuclear morphometry to characterize cell death events in tissue specimens: (a) subjected to immunostaining for detecting cleaved caspase-3, cleaved poly(ADP-ribose)-polymerase, cleaved lamin-A, phosphorylated histone H2AX, and Bcl-2; (b) analyzed by terminal deoxyribonucleotidyl transferase–mediated dUTP nick end labeling assay to detect DNA fragmentation; and (c) evaluated with Masson''s trichrome staining. We developed novel algorithm-based scoring methods and validated them using TMAs as a high-throughput format. The proposed computer-assisted scoring methods for digital images by brightfield microscopy permit linear quantification of immunohistochemical and histochemical stainings. Examples are provided of digital image analysis performed in automated or semiautomated fashion for successful quantification of molecular events associated with cell death in tissue sections. (J Histochem Cytochem 57:649–663, 2009)     

A Further Investigation of Combined Mismatch Repair and BRAFV600E Mutation Specific Immunohistochemistry as a Predictor of Overall Survival in Colorectal Carcinoma

Mutation specific immunohistochemistry (IHC) is a promising new technique to detect the presence of the BRAFV600E mutation in colorectal carcinoma (CRC). When performed in conjunction with mismatch repair (MMR) IHC, BRAFV600E IHC can help to further triage genetic testing for Lynch Syndrome. In a cohort of 1426 patients undergoing surgery from 2004 to 2009 we recently demonstrated that the combination of MMR and BRAFV600E IHC holds promise as a prognostic marker in CRC, particularly because of its ability to identify the poor prognosis MMR proficient (MMRp) BRAFV600E mutant subgroup. We attempted to validate combined MMR and BRAFV600E IHC as a prognostic indicator in a separate cohort comprising consecutive CRC patients undergoing surgery from 1998 to 2003. IHC was performed on a tissue microarray containing tissue from 1109 patients with CRC. The 5 year survivals stratified by staining patterns were: MMRd/BRAFwt 64%, MMRd/BRAFV600E 64%, MMRp/BRAFwt 60% and MMRp/BRAFV600E 53%. Using the poor prognosis MMRp/BRAFV600E phenotype as baseline, univariate Cox regression modelling demonstrated the following hazard ratios for death: MMRd/BRAFwt HR = 0.71 (95%CI = 0.40–1.27), p = 0.31; MMRd/BRAFV600E HR = 0.74 (95%CI = 0.51–1.07), p = 0.11 and MMRp/BRAFwt HR = 0.79 (95%CI = 0.60–1.04), p = 0.09. Although the findings did not reach statistical significance, this study supports the potential role of combined MMR and BRAF IHC as prognostic markers in CRC.     

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

Background

Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.

Methods and Results

In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin—3,3’-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.

Validation

To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.

Context

Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.     

Quantification of vascular density using a semiautomated technique for immunostained specimens

OBJECTIVE: To develop a semiautomated, quantitative techniquefor the assessment of vascular density in immunohistochemically stained tissue sections using diaminobenzidine tetrahydrochloride (DAB) and hematoxylin as chromagens. STUDY DESIGN: A semiautomated thresholding technique was developed to quantitate vascular density in tissue sections stained with anti-CD31 (1 degrees antibody). The immunohistochemically stained specimens were digitally imaged using a 24-bit color camera. The blue component of the RGB image was segmented using a variable high-pass filter. After thresholding, the segmented areas (CD31 positive) were quantified and vascular density determined. The validity of the method was verified by calculating the precision of the technique using the coefficient of repeatability and by quantifying its agreement with manual analysis according to the Bland-Altman approach. RESULTS: Vascular endothelial cells were specifically selected using anti-CD31 as the primary antibody and the appropriate horseradish peroxidase-conjugated secondary antibody. Utilizing the semiautomated thresholding technique, the separation of DAB-stained tissuefrom non-DAB-stained tissue was achieved. The method developed possesses a low coefficient of repeatability (0.49%), agrees well with manual assessment (mean difference = -0.29 +/- 0.92%), is highly automated and is user friendly. CONCLUSION: A novel semiautomated technique for the quantification of vascular density was developed. This technique provides a method for reproducible measurement of immunostaining procedures (immunohistochemistry, immunocytochemistry and in situ hybridization) utilizing immunoperoxidase techniques with DAB as a chromagen.     

Including total EGFR staining in scoring improves EGFR mutations detection by mutation-specific antibodies and EGFR TKIs response prediction

Epidermal growth factor receptor (EGFR) is a novel target for therapy in subsets of non-small cell lung cancer, especially adenocarcinoma. Tumors with EGFR mutations showed good response to EGFR tyrosine kinase inhibitors (TKIs). We aimed to identify the discriminating capacity of immunohistochemical (IHC) scoring to detect L858R and E746-A750 deletion mutation in lung adenocarcinoma patients and predict EGFR TKIs response. Patients with surgically resected lung adenocarcinoma were enrolled. EGFR mutation status was genotyped by PCR and direct sequencing. Mutation-specific antibodies for L858R and E746-A750 deletion were used for IHC staining. Receiver operating characteristic (ROC) curves were used to determine the capacity of IHC, including intensity and/or quickscore (Q score), in differentiating L858R and E746-A750 deletion. We enrolled 143 patients during September 2000 to May 2009. Logistic-regression-model-based scoring containing both L858R Q score and total EGFR expression Q score was able to obtain a maximal area under the curve (AUC: 0.891) to differentiate the patients with L858R. Predictive model based on IHC Q score of E746-A750 deletion and IHC intensity of total EGFR expression reached an AUC of 0.969. The predictive model of L858R had a significantly higher AUC than L858R intensity only (p = 0.036). Of the six patients harboring complex EGFR mutations with classical mutation patterns, five had positive IHC staining. For EGFR TKI treated cancer recurrence patients, those with positive mutation-specific antibody IHC staining had better EGFR TKI response (p = 0.008) and longer progression-free survival (p = 0.012) than those without. In conclusion, total EGFR expression should be included in the IHC interpretation of L858R. After adjusting for total EGFR expression, the scoring method decreased the false positive rate and increased diagnostic power. According to the scoring method, the IHC method is useful to predict the clinical outcome and refine personalized therapy.     

Fully Automated Fluorescent in situ Hybridization (FISH) Staining and Digital Analysis of HER2 in Breast Cancer: A Validation Study

HER2 assessment is routinely used to select patients with invasive breast cancer that might benefit from HER2-targeted therapy. The aim of this study was to validate a fully automated in situ hybridization (ISH) procedure that combines the automated Leica HER2 fluorescent ISH system for Bond with supervised automated analysis with the Visia imaging D-Sight digital imaging platform. HER2 assessment was performed on 328 formalin-fixed/paraffin-embedded invasive breast cancer tumors on tissue microarrays (TMA) and 100 (50 selected IHC 2+ and 50 random IHC scores) full-sized slides of resections/biopsies obtained for diagnostic purposes previously. For digital analysis slides were pre-screened at 20x and 100x magnification for all fluorescent signals and supervised-automated scoring was performed on at least two pictures (in total at least 20 nuclei were counted) with the D-Sight HER2 FISH analysis module by two observers independently. Results were compared to data obtained previously with the manual Abbott FISH test. The overall agreement with Abbott FISH data among TMA samples and 50 selected IHC 2+ cases was 98.8% (κ = 0.94) and 93.8% (κ = 0.88), respectively. The results of 50 additionally tested unselected IHC cases were concordant with previously obtained IHC and/or FISH data. The combination of the Leica FISH system with the D-Sight digital imaging platform is a feasible method for HER2 assessment in routine clinical practice for patients with invasive breast cancer.     

Automated selection of DAB-labeled tissue for immunohistochemical quantification.

The increased use of immunohistochemistry (IHC) in both clinical and basic research settings has led to the development of techniques for acquiring quantitative information from immunostains. Staining correlates with absolute protein levels and has been investigated as a clinical tool for patient diagnosis and prognosis. For these reasons, automated imaging methods have been developed in an attempt to standardize IHC analysis. We propose a novel imaging technique in which brightfield images of diaminobenzidene (DAB)-labeled antigens are converted to normalized blue images, allowing automated identification of positively stained tissue. A statistical analysis compared our method with seven previously published imaging techniques by measuring each one's agreement with manual analysis by two observers. Eighteen DAB-stained images showing a range of protein levels were used. Accuracy was assessed by calculating the percentage of pixels misclassified using each technique compared with a manual standard. Bland-Altman analysis was then used to show the extent to which misclassification affected staining quantification. Many of the techniques were inconsistent in classifying DAB staining due to background interference, but our method was statistically the most accurate and consistent across all staining levels.     

Clinicopathological and immunohistochemical study of 29 cases of solid-pseudopapillary neoplasms of the pancreas in patients under 20 years of age along with detailed review of literature

Background

Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of uniquely colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel.

Methods

Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and (3) ensemble methods that employ both ColorAE and U-Net, collectively referred to as ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor).

Results

We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect six different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net in ensemble methods outperform ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME).

Summary

We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also utilized the ColorAE:U-Net ensemble method to analyze 3 mIHC WSIs with nearest neighbor spatial analysis. We demonstrate a proof of concept that these methods can be employed to quantitatively describe the spatial distribution of immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.

     

Proliferation and apoptosis measurements by color image analysis based on differential absorption.

Cell proliferation and apoptosis indices are important indicators for the prognosis and treatment of a variety of cancers. A method is described using differential absorption color image analysis to measure proliferation and apoptosis in tumor sections using BrdU (5' bromodeoxyuridine) incorporation and immunohistochemistry and terminal deoxytransferase nick end-labeling (TUNEL). Nuclei were labeled with streptavidin-peroxidase-diaminobenzidine (DAB) secondary detection. The differential absorption method uses a computer-controlled microscope equipped with a tunable filter and digital camera to take advantage of the spectral differences of stained objects of interest. Images collected at defined wavelengths are divided and scaled to form ratio images in which the hematoxylin- or DAB-stained nuclei have intensity ranges far above those of surrounding structures. Using brightness thresholding followed by selection based on nuclear size and shape parameters, binary images were formed of the BrdU/apoptotic-positive tumor and all the tumor nuclei for subsequent counting and calculations of proliferation and apoptotic indices.     

Analysis of stained objects in histological sections by spectral imaging and differential absorption.

We describe a new light microscopic imaging system and method to perform high through put color image analysis on histological tissue sections. The system features a computer-controlled, random-access liquid crystal tunable filter and high-resolution digital camera on a conventional brightfield microscope. For any combination of stains, the method determines the spectral transmittance of each stain on the slide and selects two or more wavelengths at which the differential absorption between stain and counterstain is greatest and the exposure time is reasonably short. Flatfield corrected digital images at these wavelengths are acquired and divided to produce a gray scale ratio image. The ratio image is calculated such that the stained features of interest are highlighted above a uniform background and the counterstained features are highlighted below background. Image threshold procedures using either visual inspection or a threshold value determined by the image mean intensity and standard deviation are used to segment the stained features of interest for subsequent morphometry. Results are presented for peroxidase-AEC-labeled tumor tissue and trichrome-stained biomaterial implant tissues. In principle, the method should work for any combination of colored stains. (J Histochem Cytochem 47:1307-1313, 1999)     

Synergistic Tissue Counterstaining and Image Segmentation Techniques for Accurate, Quantitative Immunohistochemistry

Quantitative analysis of digitized IHC-stained tissue sections is increasingly used in research studies and clinical practice. Accurate quantification of IHC staining, however, is often complicated by conventional tissue counterstains caused by the color convolution of the IHC chromogen and the counterstain. To overcome this issue, we implemented a new counterstain, Acid Blue 129, which provides homogeneous tissue background staining. Furthermore, we combined this counterstaining technique with a simple, robust, fully automated image segmentation algorithm, which takes advantage of the high degree of color separation between the 3-amino-9-ethyl-carbazole (AEC) chromogen and the Acid Blue 129 counterstain. Rigorous validation of the automated technique against manual segmentation data, using Ki-67 IHC sections from rat C6 glioma and β-amyloid IHC sections from transgenic mice with amyloid precursor protein (APP) mutations, has shown the automated method to produce highly accurate results compared with ground truth estimates based on the manually segmented images. The synergistic combination of the novel tissue counterstaining and image segmentation techniques described in this study will allow for accurate, reproducible, and efficient quantitative IHC studies for a wide range of antibodies and tissues. (J Histochem Cytochem 56:873–880, 2008)     

Sub-diffraction Limit Localization of Proteins in Volumetric Space Using Bayesian Restoration of Fluorescence Images from Ultrathin Specimens

Photon diffraction limits the resolution of conventional light microscopy at the lateral focal plane to 0.61λ/NA (λ = wavelength of light, NA = numerical aperture of the objective) and at the axial plane to 1.4nλ/NA² (n = refractive index of the imaging medium, 1.51 for oil immersion), which with visible wavelengths and a 1.4NA oil immersion objective is ∼220 nm and ∼600 nm in the lateral plane and axial plane respectively. This volumetric resolution is too large for the proper localization of protein clustering in subcellular structures. Here we combine the newly developed proteomic imaging technique, Array Tomography (AT), with its native 50–100 nm axial resolution achieved by physical sectioning of resin embedded tissue, and a 2D maximum likelihood deconvolution method, based on Bayes'' rule, which significantly improves the resolution of protein puncta in the lateral plane to allow accurate and fast computational segmentation and analysis of labeled proteins. The physical sectioning of AT allows tissue specimens to be imaged at the physical optimum of modern high NA plan-apochormatic objectives. This translates to images that have little out of focus light, minimal aberrations and wave-front distortions. Thus, AT is able to provide images with truly invariant point spread functions (PSF), a property critical for accurate deconvolution. We show that AT with deconvolution increases the volumetric analytical fidelity of protein localization by significantly improving the modulation of high spatial frequencies up to and potentially beyond the spatial frequency cut-off of the objective. Moreover, we are able to achieve this improvement with no noticeable introduction of noise or artifacts and arrive at object segmentation and localization accuracies on par with image volumes captured using commercial implementations of super-resolution microscopes.     

Neuroprotective Efficacy of Methylene Blue in Ischemic Stroke: An MRI Study

Methylene blue (MB) has unique energy-enhancing and antioxidant properties and is FDA-approved drug to treat methemoglobinemia and cyanide poisoning. This study evaluated the efficacy of MB to treat ischemic stroke in rats using longitudinal MRI and behavioral measures. Rats were subjected to 60-minute middle-cerebral-artery occlusion. In a randomized double-blinded design, vehicle or MB was administered after reperfusion. The initial lesion volumes at 30 minutes post-ischemia were not significantly different between the two groups (P = 0.92). The final infarct volumes two days after stroke increased in the vehicle group but decreased in the MB group, yielding a 30% difference in infarct volume (P = 0.03). Tracking tissue fate on a pixel-by-pixel basis showed that MB salvaged more initial core pixels compared to controls (22±3% versus 11±3%, P = 0.03), and more mismatch pixels compared to controls (83±3% versus 61±8%, P = 0.02). This study demonstrates MB treatment minimizes ischemic brain injury and improves functional outcomes.     

Prognostic Value of Polycomb Proteins EZH2, BMI1 and SUZ12 and Histone Modification H3K27me3 in Colorectal Cancer

Numerous changes in epigenetic mechanisms have been described in various types of tumors. In search for new biomarkers, we investigated the expression of Polycomb-group (PcG) proteins EZH2, BMI1 and SUZ12 and associated histone modification H3K27me3 in colorectal cancer. Nuclear expression of PcG proteins and histone modification H3K27me3 were immunohistochemically (IHC) stained on a tissue microarray (TMA), including 247 tumor tissues and 47 normal tissues, and scored using the semi-automated Ariol system. Tumor tissues showed higher expression of EZH2 (p = 0.05) and H3K27me3 (p<0.001) as compared to their normal counterparts. Combined marker trend analyses indicated that an increase in the number of markers showing high expression was associated with better prognosis. High expression of all four markers in the combined marker analyses was correlated with the best patient survival and the longest recurrence-free survival, with overall survival (p = 0.01, HR 0.42(0.21–0.84)), disease-free survival (p = 0.007, HR 0.23(0.08–0.67) and local recurrence-free survival (p = 0.02, HR 0.30(0.11–0.84)). In conclusion, we found that expression of PcG proteins and H3K27me3 showed prognostic value in our study cohort. Better stratification of patients was obtained by combining the expression data of the investigated biomarkers as compared to the individual markers, underlining the importance of investigating multiple markers simultaneously.     

Reduced Expression of Uroplakin 1A Is Associated with the Poor Prognosis of Gastric Adenocarcinoma Patients

Background

The aim of this study was to investigate the expression and prognostic significance of Uroplakin1A (UPK1A) in gastric adenocarcinoma patients. Functional studies were also analyzed in vitro.

Methodology/Principal Findings

Real-time quantitative PCR (RT-qPCR), western blotting, and immunohistochemical (IHC) staining methods were used to analyze the expression of UPK1A in primary gastric adenocarcinoma tissue samples. Compared with matched adjacent non-tumor, the expression of UPK1A in fresh surgical specimens was reduced, which was confirmed by RT-qPCR (P<0.01) and western blotting analysis (P<0.01). The paraffin specimens from a consecutive series of 445 gastric adenocarcinoma patients who underwent surgery between 2003 and 2006 were analyzed by IHC staining. The relationship between UPK1A expression, clinicopathological factors, and survival were evaluated. IHC staining analysis revealed that the reduced expression of UPK1A was observed in 224 cases (50.3%). Additionally, the correlation analysis of clinicopathological factors demonstrated that reduced expression of UPK1A was significantly associated with histological grade (P = 0.022), node metastasis (P<0.001) and tumor node metastasis (TNM) stage (P = 0.008) (7^th edition of the International Union Against Cancer (UICC)). Furthermore, Kaplan-Meier survival analysis revealed that the reduced expression of UPK1A was significantly associated with poor prognosis (P = 0.043). Cox hazards model analysis indicated that UPK1A expression was an independent risk factor at the 0.1 level (P = 0.094). The function of UPK1A in cell cycle, migration, and invasion was investigated by overexpressing UPK1A in the MKN45 gastric cancer cell line. The elevated expression of UPK1A cells induced G1 phase arrest and significantly inhibited migration and invasion.

Conclusions/Significance

The reduced expression of UPK1A might play a role in the progression of gastric cancer. Thus, UPK1A could be a potential favorable biomarker associated with gastric cancer prognosis.