Quantitative comparison and reproducibility of pathologist scoring and digital image analysis of estrogen receptor β2 immunohistochemistry in prostate cancer

Reproducibility of c-Met Immunohistochemical Scoring (Clone SP44) for Non-Small Cell Lung Cancer Using Conventional Light Microscopy and Whole Slide Imaging

Emerging therapies for non-small cell lung cancer targeting c-Met overexpression have recently demonstrated promising results. However, the evaluation of c-Met expression can be challenging. We aimed to study the inter and intraobserver reproducibility of c-Met expression evaluation. One hundred ten cases with non-small cell lung cancer (40 biopsies and 70 surgical specimens) were retrospectively selected in a single laboratory (LPCE) and evaluated for c-Met expression. Six pathologists (4 seniors and 2 juniors) evaluated the H-score and made a 3-tier classification of c-Met expression for all cases, using conventional light microscopy (CLM) and whole slide imaging (WSI). The interobserver reproducibility with CLM gave global Cohen Kappa coefficients (ƙ) ranging from 0.581 (95% CI: 0.364-0.771) to 0.763 (95% CI: 0.58-0.92) using the c-Met 3-tier classification and H-score, respectively. ƙ was higher for senior pathologists and biopsy samples. The interobserver reproducibility with WSI gave a global ƙ ranging from 0.543 (95% CI: 0.33-0.724) to 0.905 (95% CI: 0.618-1) using the c-Met H-score and 2-tier classification (≥25% 3+), respectively. ƙ for intraobserver reproducibility between CLM and WSI ranged from 0.713 to 0.898 for the c-Met H-score and from 0.600 to 0.779 for the c-Met 3-tier classification. We demonstrated a moderate to excellent interobserver agreement for c-Met expression with a substantial to excellent intraobserver agreement between CLM and WSI, thereby supporting the development of digital pathology. However, some factors (scoring method, type of tissue samples, and expertise level) affect reproducibility. Our findings highlight the importance of establishing a consensus definition and providing further training, particularly for inexperienced pathologists, for c-Met immunohistochemistry assessment in clinical practice.

Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images

Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification.

Comparison of glass and digital slides for cervical cytopathology screening and interpretation

Cervical cancer is the second most common form of cancer and a leading cause of premature death among women aged 15 to 44 worldwide. In Brazil, there is a high prevalence of infection by the human papillomavirus - HPV. Digital pathology optimizes time and space for reading cervicovaginal cytology slides. We evaluated the feasibility of using whole slide images (WSI) for the routine interpretation of cytology exams. A total of 99 cases of vaginal cytology were selected from a reference laboratory in Northeastern Brazil. Three cytotechnicians participated in the study. Cellular atypia was the one that most presented concordance values. Two observers almost perfectly agreed (k = 0.86 and k = 0.84, respectively) on the negative diagnoses. The performance of the evaluators for NILM (negative for intraepithelial lesion and malignancy) showed high reproducibility and sensitivity in the digital slides, mainly between evaluators A and C. In contrast, the microbiology group showed disagreement between the diagnoses by digital slides and the standard- gold. The concordance between the digital diagnoses and the gold standard for ASCUS was 89%. In the inflammatory category, Spearman's test showed similar results between raters A, B, and C (rs = 0.47, rs = 0.41, and rs = 0.47, respectively). This study reports the diagnostic validation using digital slides in view of the need to optimize the cytology visualization process. Our experience shows good diagnostic agreement between digital and optical microscopy in several analyzed categories, but mainly in relation to cellular atypia and inflammatory processes.

Phytoestrogens and Health Effects

Phytoestrogens are literally estrogenic substances of plant origin. Although these substances are useful for plants in many aspects, their estrogenic properties are essentially relevant to their predators. As such, phytoestrogens can be considered to be substances potentially dedicated to plant-predator interaction. Therefore, it is not surprising to note that the word phytoestrogen comes from the early discovery of estrogenic effects in grazing animals and humans. Here, several compounds whose activities have been discovered at nutritional concentrations in animals and humans are examined. The substances analyzed belong to several chemical families, i.e., the flavanones, the coumestans, the resorcylic acid lactones, the isoflavones, and the enterolignans. Following their definition and the evocation of their role in plants, their metabolic transformations and bioavailabilities are discussed. A point is then made regarding their health effects, which can either be beneficial or adverse depending on the subject studied, the sex, the age, and the physiological status. Toxicological information is given based on official data. The effects are first presented in humans. Animal models are evoked when no data are available in humans. The effects are presented with a constant reference to doses and plausible exposure.

Digital quantitative tissue image analysis of hypoxia in resected pancreatic ductal adenocarcinomas

Background

Tumor hypoxia is theorized to contribute to the aggressive biology of pancreatic ductal adenocarcinoma (PDAC). We previously reported that hypoxia correlated with rapid tumor growth and metastasis in patient-derived xenografts. Anticipating a prognostic relevance of hypoxia in patient tumors, we developed protocols for automated semi-quantitative image analysis to provide an objective, observer-independent measure of hypoxia. We further validated this method which can reproducibly estimate pimonidazole-detectable hypoxia in a high-through put manner.

Methods

We studied the performance of three automated image analysis platforms in scoring pimonidazole-detectable hypoxia in resected PDAC (n = 10) in a cohort of patients enrolled in PIMO-PANC. Multiple stained tumor sections were analyzed on three independent image-analysis platforms, Aperio Genie (AG), Definiens Tissue Studio (TS), and Definiens Developer (DD), which comprised of a customized rule set.

Results

The output from Aperio Genie (AG) had good concordance with manual scoring, but the workflow was resource-intensive and not suited for high-throughput analysis. TS analysis had high levels of variability related to misclassification of cells class, while the customized rule set of DD had a high level of reliability with an intraclass coefficient of more than 85%.

Discussion

This work demonstrates the feasibility of developing a robust, high-performance pipeline for an automated, quantitative scoring of pimonidazole-detectable hypoxia in patient tumors.

Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry

Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen’s kappa, and overall agreement was poor within every experimental group using Fleiss’ kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring.

Placental Expression of the Forelimb Patterning Transcription Factor MEIS2 in Trisomy 15

BackgroundObservations of first trimester human trisomy 15 (T15) embryos have identified meromelic changes in the upper limbs. These changes are similar to those observed in animal studies investigating the effects of overexpression of Meis2, a signaling transcription factor expressed during forelimb development. Although it would be exceedingly difficult to assess MEIS2 expression in the human embryonic arm, MEIS2 has been reported as consistently expressed in first trimester placental villus stroma. Methods: This study addresses whether gene dosage effect might underlie meromelia in T15 by comparing MEIS2 expression in placentas from T15 and euploid spontaneous abortions employing manual and automated assessment of MEIS2 immunohistochemical scoring. Results: Average MEIS2 expression is increased in T15 first trimester placental tissue compared to euploid controls but that the difference is marginal. Manual and automated scoring showed moderately strong correlation. Conclusion: Extrapolation of these results suggests MEIS2 overexpression may not be required for meromelia in T15.

Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

Best Practices for Technical Reproducibility Assessment of Multiplex Immunofluorescence

Multiplex immunofluorescence (mIF) tyramide signal amplification is a new and useful tool for the study of cancer that combines the staining of multiple markers in a single slide. Several technical requirements are important to performing high-quality staining and analysis and to obtaining high internal and external reproducibility of the results. This review manuscript aimed to describe the mIF panel workflow and discuss the challenges and solutions for ensuring that mIF panels have the highest reproducibility possible. Although this platform has shown high flexibility in cancer studies, it presents several challenges in pre-analytic, analytic, and post-analytic evaluation, as well as with external comparisons. Adequate antibody selection, antibody optimization and validation, panel design, staining optimization and validation, analysis strategies, and correct data generation are important for reproducibility and to minimize or identify possible issues during the mIF staining process that sometimes are not completely under our control, such as the tissue fixation process, storage, and cutting procedures.

Immune Responses Vary in Preinvasive Colorectal Lesions by Tumor Location and Histology

Immune responses vary in colorectal cancers, which strongly influence prognosis. However, little is known about the variance in immune response within preinvasive lesions. The study aims to investigate how the immune contexture differs by clinicopathologic features (location, histology, dysplasia) associated with progression and recurrence in early carcinogenesis. We performed a cross-sectional study using preinvasive lesions from the surgical pathology laboratory at the Medical University of South Carolina. We stained the tissues with immunofluorescence antibodies, then scanned and analyzed expression using automated image analysis software. We stained CD117 as a marker of mast cells, CD4/RORC to indicate Th17 cells, MICA/B as a marker of NK-cell ligands, and also used antibodies directed against cytokines IL6, IL17A, and IFNγ. We used negative binomial regression analysis to compare analyte density counts by location, histology, degree of dysplasia adjusted for age, sex, race, and batch. All immune markers studied (except IL17a) had significantly higher density counts in the proximal colon than distal colon and rectum. Increases in villous histology were associated with significant decreases in immune responses for IL6, IL17a, NK ligand, and mast cells. No differences were observed in lesions with low- and high-grade dysplasia, except in mast cells. The lesions of the proximal colon were rich in immune infiltrate, paralleling the responses observed in normal mucosa and invasive disease. The diminishing immune response with increasing villous histology suggests an immunologically suppressive tumor environment. Our findings highlight the heterogeneity of the immune responses in preinvasive lesions, which may have implications for prevention strategies. PREVENTION RELEVANCE: Our study is focused on immune infiltrate expression in preinvasive colorectal lesions; our results suggest important differences by clinicopathologic features that have implications for immune prevention research.

Using Delaunay triangulation to sample whole-specimen color from digital images

Color variation is one of the most obvious examples of variation in nature, but biologically meaningful quantification and interpretation of variation in color and complex patterns are challenging. Many current methods for assessing variation in color patterns classify color patterns using categorical measures and provide aggregate measures that ignore spatial pattern, or both, losing potentially important aspects of color pattern.Here, we present Colormesh, a novel method for analyzing complex color patterns that offers unique capabilities. Our approach is based on unsupervised color quantification combined with geometric morphometrics to identify regions of putative spatial homology across samples, from histology sections to whole organisms. Colormesh quantifies color at individual sampling points across the whole sample.We demonstrate the utility of Colormesh using digital images of Trinidadian guppies (Poecilia reticulata), for which the evolution of color has been frequently studied. Guppies have repeatedly evolved in response to ecological differences between up- and downstream locations in Trinidadian rivers, resulting in extensive parallel evolution of many phenotypes. Previous studies have, for example, compared the area and quantity of discrete color (e.g., area of orange, number of black spots) between these up- and downstream locations neglecting spatial placement of these areas. Using the Colormesh pipeline, we show that patterns of whole-animal color variation do not match expectations suggested by previous work.Colormesh can be deployed to address a much wider range of questions about color pattern variation than previous approaches. Colormesh is thus especially suited for analyses that seek to identify the biologically important aspects of color pattern when there are multiple competing hypotheses or even no a priori hypotheses at all.

High Tissue TLR5 Expression Predicts Better Outcomes in Colorectal Cancer Patients

BACKGROUND

Colorectal cancer (CRC), the third most common cancer globally, caused 881,000 cancer deaths in 2018. Toll-like receptors (TLRs), the primary sensors of pathogen-associated molecular patterns and damage-associated molecular patterns, activate innate and adaptive immune systems and participate in the development of an inflammatory tumor microenvironment. We aimed to explore the prognostic value of TLR3, TLR5, TLR7, and TLR9 tissue expressions in CRC patients.

METHODS

Using immunohistochemistry, we analyzed tissue microarray samples from 825 CRC patients who underwent surgery between 1982 and 2002 at the Department of Surgery, Helsinki University Hospital, Finland. After analyzing a pilot series of 205 tissue samples, we included only TLR5 and TLR7 in the remainder of the patient series. We evaluated the associations between TLR5 and TLR7 tissue expressions, clinicopathologic variables, and survival. Using the Kaplan-Meier method, we generated survival curves, determining significance using the log-rank test. Univariate and multivariate survival analyses relied on the Cox proportional hazards model.

RESULTS

The 5-year disease-specific survival was 55.9% among TLR5-negative (95% confidence interval [CI] 50.6-61.2%) and 61.9% (95% CI 56.6-67.2%; p = 0.011, log-rank test) among TLR5-positive patients. In the Cox multivariate survival analysis adjusted for age, sex, stage, location, and grade, positive TLR5 immunoexpression (hazard ratio [HR] 0.74; 95% CI 0.59-0.92; p = 0.007) served as an independent positive prognostic factor. TLR7 immunoexpression exhibited no prognostic value in the survival analysis across the entire cohort (HR 0.97; 95% CI 0.78-1.20; p = 0.754) nor in subgroup analyses.

CONCLUSIONS

We show for the first time that a high TLR5 tumor tissue expression associates with a better prognosis in CRC patients.

Application of deep learning to understand resilience to Alzheimer's disease pathology

People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be considered to be “resilient” to the effects of ADNC. We have previously reported lower neocortical levels of hyperphosphorylated tau (pTau) and less limbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC) in the resilient participants compared to those with dementia and similar ADNC as determined by current NIA‐AA recommendations using traditional semi‐quantitative assessments of amyloid β and pathological tau burden. To better understand differences between AD‐dementia and resilient participants, we developed and applied a deep learning approach to analyze the neuropathology of 14 brain donors from the Adult Changes in Thought study, including seven stringently defined resilient participants and seven age‐matched AD‐dementia controls. We created two novel, fully automated deep learning algorithms to quantify the level of phosphorylated TDP‐43 (pTDP‐43) and pTau in whole slide imaging. The models performed better than traditional techniques for quantifying pTDP‐43 and pTau. The second model was able to segment lesions staining for pTau into neurofibrillary tangles (NFTs) and tau neurites (neuronal processes positive for pTau). Both groups had similar quantities of pTau localizing to neurites, but the pTau burden associated with NFTs in the resilient group was significantly lower compared to the group with dementia. These results validate use of deep learning approaches to quantify clinically relevant microscopic characteristics from neuropathology workups. These results also suggest that the burden of NFTs is more strongly associated with cognitive impairment than the more diffuse neuritic tau commonly seen with tangle pathology and suggest that additional factors may underlie resilience mechanisms defined by traditional means.

Pathomics in urology

PURPOSE OF REVIEW

Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summarize its most relevant applications in urology.

RECENT FINDINGS

There is a steady rise in the number of studies employing Pathomics, and especially deep learning, in urology. In prostate cancer, several algorithms have been developed for the automatic differentiation between benign and malignant lesions and to differentiate Gleason scores. Furthermore, several applications have been developed for the automatic cancer cell detection in urine and for tumor assessment in renal cancer. Despite the explosion in research, Pathomics is not fully ready yet for widespread clinical application.

SUMMARY

In prostate cancer and other urologic pathologies, Pathomics is avidly being researched with commercial applications on the close horizon. Pathomics is set to improve the accuracy, speed, reliability, cost-effectiveness and generalizability of pathology, especially in uro-oncology.

The role of COX expression in the prognostication of overall survival of canine and feline cancer: A systematic review

Cyclooxygenase (COX) isoforms-1 and -2 have been extensively investigated in cancer. Although COX-2 is the isoform most studied and has been described in several malignancies associated with histologic criteria of malignancy and worse prognosis, COX-1 has also been linked to some forms of cancer. With the present review our aim was to summarize the current state of knowledge and clarify if and in which type of tumours COX-1 and/or COX-2 expression have real prognostic implications. We searched PubMed database for prognostic studies using predefined inclusion criteria in order to ascertain the prognostic value of COX-1 and COX-2 in malignant neoplasia in dogs and cats. Eighteen studies were analysed. COX-2 was shown to be a negative prognostic factor in canine and feline mammary tumours, canine mast cell tumour, canine melanoma, canine osteosarcoma and canine renal cell carcinoma. COX-1 showed a negative prognostic value in feline oral squamous cell carcinoma (SCC). We found high heterogeneity among studies regarding COX immunohistochemical evaluation methodology even in the same type of neoplasia pointing out the need for its standardization at least by tumour type. The available data support the use of COX-2 as a prognostic factor in canine (mammary carcinoma, mast cell tumour, melanoma, osteosarcoma and renal carcinoma) and feline (mammary carcinoma) cancers. For COX-1, its use is advised in feline oral SCC.

Screening of Potential Biomarkers for Gastric Cancer with Diagnostic Value Using Label-free Global Proteome Analysis

Gastric cancer (GC) is known as a top malignant type of tumors worldwide. Despite the recent decrease in mortality rates, the prognosis remains poor. Therefore, it is necessary to find novel biomarkers with early diagnostic value for GC. In this study, we present a large-scale proteomic analysis of 30 GC tissues and 30 matched healthy tissues using label-free global proteome profiling. Our results identified 537 differentially expressed proteins, including 280 upregulated and 257 downregulated proteins. The ingenuity pathway analysis (IPA) results indicated that the sirtuin signaling pathway was the most activated pathway in GC tissues whereas oxidative phosphorylation was the most inhibited. Moreover, the most activated molecular function was cellular movement, including tissue invasion by tumor cell lines. Based on IPA results, 15 hub proteins were screened. Using the receiver operating characteristic curve, most of hub proteins showed a high diagnostic power in distinguishing between tumors and healthy controls. A four-protein (ATP5B-ATP5O-NDUFB4-NDUFB8) diagnostic signature was built using a random forest model. The area under the curve (AUC) values of this model were 0.996 and 0.886 for the training and testing sets, respectively, suggesting that the four-protein signature has a high diagnostic power. This signature was further tested with independent datasets using plasma enzyme-linked immune sorbent assays, resulting in an AUC value of 0.778 for distinguishing GC tissues from healthy controls, and using immunohistochemical tissue microarray analysis, resulting in an AUC value of 0.805. In conclusion, this study identifies potential biomarkers and improves our understanding of the pathogenesis, providing novel therapeutic targets for GC.

Automated quantification of vomeronasal glomeruli number, size, and color composition after immunofluorescent staining

Glomeruli are neuropil-rich regions of the main or accessory olfactory bulbs (AOB) where the axons of olfactory or vomeronasal neurons and dendrites of mitral/tufted cells form synaptic connections. In the main olfactory system, olfactory sensory neurons (OSNs) expressing the same receptor innervate 1 or 2 glomeruli. However, in the accessory olfactory system, vomeronasal sensory neurons (VSNs) expressing the same receptor can innervate up to 30 different glomeruli in the AOB. Genetic mutation disrupting genes with a role in defining the identity/diversity of olfactory and vomeronasal neurons can alter the number and size of glomeruli. Interestingly, 2 cell surface molecules, Kirrel2 and Kirrel3, have been indicated as playing a critical role in the organization of axons into glomeruli in the AOB. Being able to quantify differences in glomeruli features, such as number, size, or immunoreactivity for specific markers, is an important experimental approach to validate the role of specific genes in controlling neuronal connectivity and circuit formation in either control or mutant animals. Since the manual recognition and quantification of glomeruli on digital images is a challenging and time-consuming task, we generated a program in Python able to identify glomeruli in digital images and quantify their properties, such as size, number, and pixel intensity. Validation of our program indicates that our script is a fast and suitable tool for high-throughput quantification of glomerular features of mouse lines with different genetic makeup.

Changes in Cellular Regulatory Factors before and after Decompression of Odontogenic Keratocysts

Decompression followed by enucleation, which is one of the treatments used for odontogenic keratocysts (OKCs), is frequently used in OKC lesions of large sizes. This method offers the advantage of minimizing the possibility of sensory impairment without creating a wide-range bone defect; moreover, the recurrence rate can be significantly lower than following simple enucleation. This study aimed to assess the changes in histology and expression of proliferation markers in OKCs before and after decompression treatment. A total of 38 OKC tissue samples from 19 patients who had undergone decompression therapy were examined morphologically and immunohistochemically to observe changes in proliferative activity before and after decompression. The markers used for immunohistochemistry (IHC) staining were Bcl-2, epidermal growth factor receptor (EGFR), Ki-67, P53, PCNA, and SMO. The immunohistochemistry positivity of the 6 markers was scored by using software ImageJ, version 1.49, by quantifying the intensity and internal density of IHC-stained epithelium. The values of Bcl-2, Ki-67, P53, proliferating cell nuclear antigen (PCNA), and SMO in OKCs before and after decompression showed no significant change. No correlation between clinical shrinkage and morphologic changes or expression of proliferation and growth markers could be found. There was no statistical evidence that decompression treatment reduces potentially aggressive behavior of OKC within the epithelial cyst lining itself. This might indicate that decompression does not change the biological behavior of the epithelial cyst lining or the recurrence rate.

The cutoff for estrogen and progesterone receptor expression in endometrial cancer revisited: a European Network for Individualized Treatment of Endometrial Cancer collaboration study

There is no consensus on the cutoff for positivity of estrogen receptor (ER) and progesterone receptor (PR) in endometrial cancer (EC). Therefore, we determined the cutoff value for ER and PR expression with the strongest prognostic impact on the outcome. Immunohistochemical expression of ER and PR was scored as a percentage of positive EC cell nuclei. Cutoff values were related to disease-specific survival (DSS) and disease-free survival (DFS) using sensitivity, specificity, and multivariable regression analysis. The results were validated in an independent cohort. The study cohort (n = 527) included 82% of grade 1-2 and 18% of grade 3 EC. Specificity for DSS and DFS was highest for the cutoff values of 1-30%. Sensitivity was highest for the cutoff values of 80-90%. ER and PR expression were independent markers for DSS at cutoff values of 10% and 80%. Consequently, three subgroups with distinct clinical outcomes were identified: 0-10% of ER/PR expression with, unfavorable outcome (5-year DSS = 75.9-83.3%); 20-80% of ER/PR expression with, intermediate outcome (5-year DSS = 93.0-93.9%); and 90-100% of ER/PR expression with, favorable outcome (5-year DSS = 97.8-100%). The association between ER/PR subgroups and outcomes was confirmed in the validation cohort (n = 265). We propose classification of ER and PR expression based on a high-risk (0-10%), intermediate-risk (20-80%), and low-risk (90-100%) group.

The future of pathology is digital

Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases' referral information, as needed for example, by the pathologist, and this support will be increased in the future even more. In radiology, established standards define information models, data transmission mechanisms, and workflows. Other disciplines, such as pathology, cardiology, and radiation therapy, now define further demands in addition to these established standards. Pathology may have the highest technical demands on the systems, with very complex workflows, and the digitization of slides generating enormous amounts of data up to Gigabytes per biopsy. This requires enormous amounts of data to be generated per biopsy, up to the gigabyte range. Digital pathology allows a change from classical histopathological diagnosis with microscopes and glass slides to virtual microscopy on the computer, with multiple tools using artificial intelligence and machine learning to support pathologists in their future work.

Digital Microscopy, Image Analysis, and Virtual Slide Repository

Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

Estrogen Receptor, Progesterone Receptor, and HER-2 Expression in Recurrent Pleomorphic Adenoma

Pleomorphic adenoma (PA) is the most common salivary gland neoplasm and, although mostly benign, recurrences, being called recurrent pleomorphic adenoma (RPA) and malignant transformation to carcinoma ex pleomorphic adenoma (CXPA), do occur. Recently, attention has been focused on molecular targeted cancer therapy in various tumors, including salivary gland tumors. The aim of this study was to investigate the role of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER-2) in PA, RPA, and CXPA. In total, 20 cases of PA, 18 of RPA, and 7 cases of CXPA were immunohistochemically studied for ER, PR, and HER-2. For evaluation of ER and PR, only nuclear expression and greater than 10% positive cells were regarded as cutoff criteria. HER-2 was evaluated semiquantitatively and graded from 0 to 3+. HER-2 amplification was assessed by chromogenic in situ hybridization (CISH). Tumors were negative for ER, PR, and HER-2 in all cases of PA and RPA. A case of CXPA showed moderate and complete membranous staining, and 6 cases were negative. HER-2 amplification was not observed in any case. In conclusion, the lack of ER, PR, and HER-2 expression in PA, RPA, and CXPA suggests that these proteins are not involved in progression, recurrence, or malignant transformation of PA.

MCM2, MCM4, and MCM6 in Breast Cancer: Clinical Utility in Diagnosis and Prognosis

Breast cancer is a heterogeneous disease comprising the estrogen receptor (ER)-positive luminal subtype which is subdivided into luminal A and luminal B and ER-negative breast cancer which includes the triple-negative subtype. This study has four aims: 1) to examine whether Minichromosome Maintenance (MCM)2, MCM4, and MCM6 can be used as markers to differentiate between luminal A and luminal B subtypes; 2) to study whether MCM2, MCM4, and MCM6 are highly expressed in triple-negative breast cancer, as there is an urgent need to search for surrogate markers in this aggressive subtype, for drug development purposes; 3) to compare the prognostic values of these markers in predicting relapse-free survival; and 4) to compare the three approaches used for scoring the protein expression of these markers by immunohistochemistry (IHC). MCM2, MCM4, MCM6, and MKI67 mRNA expression was first studied using in silico analysis of available breast cancer datasets. We next used IHC to evaluate their protein expression on tissue microarrays using three scoring methods. MCM2, MCM4, and MCM6 can help in distinction between luminal A and luminal B whose therapeutic management and clinical outcomes are different. MCM2, MCM4, MCM6, and Ki-67 are highly expressed in breast cancer of high histological grades that comprise clinically aggressive tumors such as luminal B, HER2-positive, and triple-negative subtypes. Low transcript expression of these markers is associated with increased probability of relapse-free survival. A positive relationship exists among the three scoring methods of each of the four markers. An independent validation cohort is needed to confirm their clinical utility.

Utilisation of the STEAP protein family in a diagnostic setting may provide a more comprehensive prognosis of prostate cancer

Prostate cancer is the second most common cancer diagnosed in men worldwide; however, few patients are affected by clinically significant disease within their lifetime. Unfortunately, the means to discriminate between patients with indolent disease and those who progress to aggressive prostate cancer is currently unavailable, resulting in over-treatment of patients. We therefore aimed to determine biomarkers of prostate cancer that can be used in the clinic to aid the diagnosis and prognosis. Immunohistochemistry analysis was carried out on prostate cancer specimens with a range of Gleason scores. Samples were stained and analysed for intensity of the Seven Transmembrane Epithelial Antigen of the Prostate (STEAP)-1, -2, -3, -4 and the Divalent Metal Transporter 1 (DMT1) proteins to determine suitable biomarkers for classification of patients likely to develop aggressive prostate cancer. Additionally, these proteins were also analysed to determine whether any would be able to predict future relapse using Kaplan Meier analysis. Data generated demonstrated that the protein expression levels of STEAP2 correlated significantly with Gleason score; furthermore, STEAP4 was a significant predictor of relapse. This data indicates that STEAP2 could be potential prognostic candidate for use in combination with the current prostate cancer detection methods and the presence of STEAP4 could be an indicator of possible relapse.

Resistance and resilience to Alzheimer's disease pathology are associated with reduced cortical pTau and absence of limbic-predominant age-related TDP-43 encephalopathy in a community-based cohort

Alzheimer's disease neuropathologic change (ADNC) is defined by progressive accumulation of β-amyloid plaques and hyperphosphorylated tau (pTau) neurofibrillary tangles across diverse regions of brain. Non-demented individuals who reach advanced age without significant ADNC are considered to be resistant to AD, while those burdened with ADNC are considered to be resilient. Understanding mechanisms underlying ADNC resistance and resilience may provide important clues to treating and/or preventing AD associated dementia. ADNC criteria for resistance and resilience are not well-defined, so we developed stringent pathologic cutoffs for non-demented subjects to eliminate cases of borderline pathology. We identified 14 resistant (85+ years old, non-demented, Braak stage ≤ III, CERAD absent) and 7 resilient (non-demented, Braak stage VI, CERAD frequent) individuals out of 684 autopsies from the Adult Changes in Thought study, a long-standing community-based cohort. We matched each resistant or resilient subject to a subject with dementia and severe ADNC (Braak stage VI, CERAD frequent) by age, sex, year of death, and post-mortem interval. We expanded the neuropathologic evaluation to include quantitative approaches to assess neuropathology and found that resilient participants had lower neocortical pTau burden despite fulfilling criteria for Braak stage VI. Moreover, limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) was robustly associated with clinical dementia and was more prevalent in cases with high pTau burden, supporting the notion that resilience to ADNC may depend, in part, on resistance to pTDP-43 pathology. To probe for interactions between tau and TDP-43, we developed a C. elegans model of combined human (h) Tau and TDP-43 proteotoxicity, which exhibited a severe degenerative phenotype most compatible with a synergistic, rather than simply additive, interaction between hTau and hTDP-43 neurodegeneration. Pathways that underlie this synergy may present novel therapeutic targets for the prevention and treatment of AD.

Prostate cancer aggressiveness and age: Impact of p53, BCL-2 and microvessel density

Older men are more likely to have advanced prostate cancer at time of their diagnosis, but whether prostate tumors are inherently (biologically) more aggressive with advancing age is uncertain. To address this gap in knowledge, we analyzed data from veterans (n=971) diagnosed with prostate cancer during 1991–1995. Factors included age, detection of prostate cancer by screening, prostate-specific antigen (PSA) level, anatomic stage, and Gleason score. Information on molecular markers obtained from immunohistochemical staining of prostate tissue, included B cell lymphoma-2 (bcl-2), p53, and microvessel density (MVD), each having a previously documented association with disease progression and increased risk of prostate cancer death. We first examined the bivariate association of demographic, clinical, and molecular factors with age, and found evidence that race, screening status, Gleason score, PSA, bcl-2, p53, and MVD varied across categories of age in this study population. After further characterizing the association between age and Gleason score, we used logistic regression to examine the association between age and molecular markers—accounting for race, screening status, PSA, and Gleason score. Comparing men older than 80 years to those younger than 70 years, adjusted ORs and 95% CIs were 1.89 (0.73 to 4.92), 1.91 (1.05 to 3.46), and 2.00 (1.06 to 3.78), for positive bcl-2, p53, and MVD markers, respectively; no statistically significant associations were found for men 70–79 years old, compared with men younger than 70 years. These novel findings suggest that very elderly men often present with biologically aggressive prostate cancer; the results also have potential implications for therapeutic decision-making.

Comparison of evaluation techniques, including digital image analysis, for MYC protein expression by immunohistochemical stain in aggressive B-cell lymphomas

Incorporation of an MYC immunohistochemical stain in the workup of large B-cell lymphomas has become common in hematopathology practice. Evaluation of this stain can be difficult because of staining heterogeneity and can have interobserver variability, particularly when performed on the entire tumor sections. We identified 87 cases of aggressive B-cell lymphoma (34 core needle and 53 excisional biopsies) and compared the following methods of MYC immunohistochemical staining evaluation: the original pathologist's interpretation, a systematic retrospective method of evaluation by manual analysis, and a retrospective method of evaluation by digital image analysis (using scanned slides analyzed via the Aperio Nuclear algorithm). Overall, concordance among these methods was around 80% with κ statistics showing good agreement. However, nearly one-third of our cases had a percent MYC positivity in the 30% to 50% range, and for these cases, concordance among the various methods was marginal/poor. This suggests limited utility as a prognostic or predictive marker using 40% as a cutoff value. In our series, core biopsy specimens were poor predictors of MYC gene rearrangement, and there was no association between MYC immunohistochemical stain and MYC gene gain/amplification. Our retrospective digital image analysis showed strong correlation in MYC percent positivity with our retrospective manual review (correlation coefficient of 0.90) and similar concordance to pathologist interpretation as among pathologists, suggesting that digital image analysis is a viable alternative to manual determination of MYC percent positivity. Digital image analysis provides further opportunities for more sophisticated and standardized scoring systems, which may be helpful in future prognostic/predictive studies.

Principles and approaches for reproducible scoring of tissue stains in research

Evaluation of tissues is a common and important aspect of translational research studies. Labeling techniques such as immunohistochemistry can stain cells/tissues to enhance identification of specific cell types, cellular activation states, and protein expression. While qualitative evaluation of labeled tissues has merit, use of semiquantitative and quantitative scoring approaches can greatly enhance the rigor of the tissue data. Adhering to key principles for reproducible scoring can enhance the quality and reproducibility of the tissue data so as to maximize its biological relevance and scientific impact.

Impact of Phosphoproteomics in the Era of Precision Medicine for Prostate Cancer

Prostate cancer is the most common malignancy in men in the United States. While androgen deprivation therapy results in tumor responses initially, there is relapse and progression to metastatic castration-resistant prostate cancer. Currently, all prostate cancer patients receive essentially the same treatment, and there is a need for clinically applicable technologies to provide predictive biomarkers toward personalized therapies. Genomic analyses of tumors are used for clinical applications, but with a paucity of obvious driver mutations in metastatic castration-resistant prostate cancer, other applications, such as phosphoproteomics, may complement this approach. Immunohistochemistry and reverse phase protein arrays are limited by the availability of reliable antibodies and evaluates a preselected number of targets. Mass spectrometry-based phosphoproteomics has been used to profile tumors consisting of thousands of phosphopeptides from individual patients after surgical resection or at autopsy. However, this approach is time consuming, and while a large number of candidate phosphopeptides are obtained for evaluation, limitations are reduced reproducibility, sensitivity, and precision. Targeted mass spectrometry can help eliminate these limitations and is more cost effective and less time consuming making it a practical platform for future clinical testing. In this review, we discuss the use of phosphoproteomics in prostate cancer and other clinical cancer tissues for target identification, hypothesis testing, and possible patient stratification. We highlight the majority of studies that have used phosphoproteomics in prostate cancer tissues and cell lines and propose ways forward to apply this approach in basic and clinical research. Overall, the implementation of phosphoproteomics via targeted mass spectrometry has tremendous potential to aid in the development of more rational, personalized therapies that will result in increased survival and quality of life enhancement in patients suffering from metastatic castration-resistant prostate cancer.

Quantitative Analysis of a Multiplexed Immunofluorescence Panel in T-Cell Lymphoma

Immunohistochemistry (IHC) provides clinically useful information on protein expression in cancer cells. However, quantification of colocalizing signals using conventional IHC and visual scores is challenging. Here we describe the application of quantitative immunofluorescence in angioimmunoblastic T-cell lymphoma (AITL), a peripheral T-cell lymphoma characterized by cellular heterogeneity that impedes IHC interpretation and quantification. A multiplexed immunofluorescence (IF) panel comprising T- and B-lymphocyte markers along with T-follicular helper (T_FH) markers was validated for appropriate cellular localization in sections of benign tonsillar tissue and tested in two samples of AITL, using a Vectra microscope for spectral imaging and InForm software for analysis. We measured the percentage positivity of the T_FH markers, BCL6 and PD1, in AITL CD4-positive cells to be approximately 26% and 45%, with 12% coexpressing both markers. The pattern is similar to CD4 cells within the germinal center of normal tonsils and clearly distinct from extragerminal CD4 cells. This study demonstrates the feasibility of automated and quantitative imaging of a multiplexed panel of cellular markers in formalin-fixed, paraffin-embedded tissue sections of a cellularly heterogenous lymphoma. Multiplexed IF allows the simultaneous scoring of markers in malignant and immune cell populations and could potentially increase accuracy for establishment of diagnostic thresholds.

Automated analysis of co-localized protein expression in histologic sections of prostate cancer

An automated approach based on routinely-processed, whole-slide immunohistochemistry (IHC) was implemented to study co-localized protein expression in tissue samples. Expression of two markers was chosen to represent stromal (CD31) and epithelial (Ki-67) compartments in prostate cancer. IHC was performed on whole-slide sections representing low-, intermediate-, and high-grade disease from 15 patients. The automated workflow was developed using a training set of regions-of-interest in sequential tissue sections. Protein expression was studied on digital representations of IHC images across entire slides representing formalin-fixed paraffin embedded blocks. Using the training-set, the known association between Ki-67 and Gleason grade was confirmed. CD31 expression was more heterogeneous across samples and remained invariant with grade in this cohort. Interestingly, the Ki-67/CD31 ratio was significantly increased in high (Gleason ≥ 8) versus low/intermediate (Gleason ≤7) samples when assessed in the training-set and the whole-tissue block images. Further, the feasibility of the automated approach to process Tissue Microarray (TMA) samples in high throughput was evaluated. This work establishes an initial framework for automated analysis of co-localized protein expression and distribution in high-resolution digital microscopy images based on standard IHC techniques. Applied to a larger sample population, the approach may help to elucidate the biologic basis for the Gleason grade, which is the strongest, single factor distinguishing clinically aggressive from indolent prostate cancer.

A high throughput approach for analysis of cell nuclear deformability at single cell level

Various physiological and pathological processes, such as cell differentiation, migration, attachment, and metastasis are highly dependent on nuclear elasticity. Nuclear morphology directly reflects the elasticity of the nucleus. We propose that quantification of changes in nuclear morphology on surfaces with defined topography will enable us to assess nuclear elasticity and deformability. Here, we used soft lithography techniques to produce 3 dimensional (3-D) cell culture substrates decorated with micron sized pillar structures of variable aspect ratios and dimensions to induce changes in cellular and nuclear morphology. We developed a high content image analysis algorithm to quantify changes in nuclear morphology at the single-cell level in response to physical cues from the 3-D culture substrate. We present that nuclear stiffness can be used as a physical parameter to evaluate cancer cells based on their lineage and in comparison to non-cancerous cells originating from the same tissue type. This methodology can be exploited for systematic study of mechanical characteristics of large cell populations complementing conventional tools such as atomic force microscopy and nanoindentation.