Correlation between the number of interstitial neurons of the white matter and number of neurons within cortical layers: Histological analyses in postnatal macaque.

We have examined the number and distribution of NeuN-immunoreactive cortical white matter interstitial cells (WMICs) and compared them to the neurons in layers 1-6 across the overlying cortex in coronal sections from postnatal macaques. The data have been gathered from over 300 selected regions at gyral crowns, at sulci, and at linear regions of the cortex where we also determined cortical layer thicknesses: standard thicknesses and tangential thicknesses. Cortical thicknesses and cell numbers showed variability according to gyral, linear, or sulcal regions. In spite of these variations, our standardized cell numbers in layers 1 to 6b and interstitial cells underlying layer 6b-white matter boundary have shown a consistent correlation between the number of WMICs and the number of layer 5 and 6a cortical neurons on all cortical regions studied: for each WMIC, there are on the order of five cortical neurons in layer 5 and approximately three cortical neurons in layer 6a, irrespective of the origins of the selected cortical area or whether they are from gyral, linear, or sulcal regions. We propose that the number of interstitial neurons in the postnatal macaque cortex is correlated to the density of neurons within layers 5 and 6a and, from a clinical perspective, the change in density or distribution of interstitial neurons in schizophrenia or epilepsy may in fact be linked to the number of layers 5 and 6a neurons.

Improved Preservation of Mouse Intestinal Tissue Using a Formalin/Acetic Acid Fixative and Quantitative Histological Analysis Using QuPath.

The architecture and morphology of the intestinal tissue from mice or other small animals are difficult to preserve for histological and molecular analysis due to the fragile nature of this tissue. The intestinal mucosa consists of villi and crypts lined with epithelial cells. In between the epithelial folds extends the lamina propria, a loose connective tissue that contains blood and lymph vessels, fibroblasts, and immune cells. Underneath the mucosa are two layers of contractile smooth muscle and nerves. The tissue experiences significant changes during fixation, which can impair the reliability of histologic analysis. Poor-quality histologic sections are not suitable for quantitative image-based tissue analysis. This article offers a new fixative composed of neutral buffered formalin (NBF) and acetic acid, called FA. This fixative significantly improved the histology of mouse intestinal tissue compared to traditional NBF and enabled precise, reproducible histologic molecular analyses using QuPath software. Algorithmic training of QuPath allows for automated segmentation of intestinal compartments, which can be further interrogated for cellular composition and disease-related changes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Improved preservation of mouse intestinal tissue using a formalin/acetic acid fixative Support Protocol: Quantitative tissue analysis using QuPath.

Identification and Quantification of Radiotherapy-related Protein Expression in Cancer Tissues Using the Qupath Software and Prediction of Treatment Response.

BACKGROUND/AIM: Automated measurement of immunostained samples can enable more convenient and objective prediction of treatment outcome from radiotherapy. We aimed to validate the performance of the QuPath image analysis software in immune cell markers detection by comparing QuPath cell counting results with those of physician manual cell counting. PATIENTS AND METHODS: CD8- and FoxP3-stained cervical, CD8-stained oropharyngeal, and Ku70-stained prostate cancer tumor sections were analyzed in 104 cervical, 92 oropharyngeal, and 58 prostate cancer patients undergoing radiotherapy at our Institution. RESULTS: QuPath and manual counts were highly correlated. When divided into two groups using ROC curves, the agreement between QuPath and manual counts was 89.4% for CD8 and 88.5% for FoxP3 in cervical cancer, 87.0% for CD8 in oropharyngeal cancer and 80.7% for Ku70 in prostate cancer. In cervical cancer, the high CD8 group based on QuPath counts had a better prognosis and the low CD8 group had a significantly worse prognosis [p=0.0003; 5-year overall survival (OS), 65.9% vs. 34.7%]. QuPath counts were more predictive than manual counts. Similar results were observed for FoxP3 in cervical cancer (p=0.002; 5-year OS, 62.1% vs. 33.6%) and CD8 in oropharyngeal cancer (p=0.013; 5-year OS, 80.2% vs. 47.2%). In prostate cancer, high Ku70 group had worse and low group significantly better outcome [p=0.007; 10-year progression-free survival (PFS), 56.0% vs. 93.8%]. CONCLUSION: QuPath showed a strong correlation with manual counting, confirming its utility and accuracy and potential applicability in clinical practice.

An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers.

PURPOSE: The prognostic and predictive significance of pathologist-read tumor infiltrating lymphocytes (TILs) in head and neck cancers have been demonstrated through multiple studies over the years. TILs have not been broadly adopted clinically, perhaps due to substantial inter-observer variability. In this study, we developed a machine-based algorithm for TIL evaluation in head and neck cancers and validated its prognostic value in independent cohorts. EXPERIMENTAL DESIGN: A network classifier called NN3-17 was trained to identify and calculate tumor cells, lymphocytes, fibroblasts and "other" cells on hematoxylin-eosin stained sections using the QuPath software. These measurements were used to construct three predefined TIL variables. A retrospective collection of 154 head and neck squamous cell cancer cases was used as the discovery set to identify optimal association of TIL variables and survival. Two independent cohorts of 234 cases were used for validation. RESULTS: We found that electronic TIL variables were associated with favorable prognosis in both the HPV-positive and -negative cases. After adjusting for clinicopathologic factors, Cox regression analysis demonstrated that electronic total TILs% (p = 0.025) in the HPV-positive and electronic stromal TILs% (p < 0.001) in the HPV-negative population were independent markers of disease specific outcomes (disease free survival). CONCLUSIONS: Neural network TIL variables demonstrated independent prognostic value in validation cohorts of HPV-positive and HPV-negative head and neck cancers. These objective variables can be calculated by an open-source software and could be considered for testing in a prospective setting to assess potential clinical implications.

Expression of cell surface zinc transporter LIV1 in triple negative breast cancer is an indicator of poor prognosis and therapy failure.

Triple negative breast cancers (TNBC) are an aggressive molecular subtype of breast carcinoma (BC) identified by the lack of receptor expression for estrogen, progesterone, & human epidermal growth factor receptor-2. Lack of tangible drug targets warrants further research in TNBC. LIV1, is a zinc (Zn) transporter known to be overexpressed in few cancer types including BCs. Recently, in the United States of America, FDA approved the use of a new drug targeting LIV1, antibody drug conjugate SGN-LIV1A for treatment of TNBC patients. Though LIV1 also has a role in modulating immune cells by its differential transport of Zn, a correlation between the tumor cell expression of LIV1 and immune cell infiltrations were scantily reported. Further adequate baseline data on LIV1 expression in other populations have not been documented. Our objective was to screen a large Indian cohort of TNBC patient samples for LIV1, categorize the immune cell infiltration using CD4/CD8 expression and correlate the findings with therapy outcomes. Further, we also investigated for LIV1 expression in matched samples of primary & secondary tumors; pre & postchemotherapy in TNBC patients. Results showed an elevated expression of LIV1 in TNBC samples as compared to adjacent normal, the mean Q scores being 183.06 ± 6.39 and 120.78 ± 7.37 (p < 0.0001), respectively. Similarly, LIV1 levels were elevated in secondary tumors than primary & in patient samples postchemotherapy as compared to naïve. In the TNBC cohort, using automated method, cell morphology parameters were computed and analysis showed LIV1 levels were elevated in grade 3 TNBC samples presenting with altered cell morphology parameters namely cell size, cell perimeter, & nucleus size. Thus indicating LIV1 expressing TNBC samples portrayed an aggressive phenotype. Finally, TNBC patients with 3+ staining intensity showed poor survival (4.44 year) as compared to patients with 2+ LIV1 expression (5.47 year), emphasizing that LIV1 expression is a poor prognostic factor in TNBC. In conclusion, the study reports elevated expression of LIV1 in a large Indian TNBC cohort; high expression is a poor prognostic factor and correlated with aggressive disease and indicating the need for LIV1 targeted therapies.

Quantitative Evaluation by Digital Pathology of Immunohistochemical Expression of CK7, CK19, and EpCAM in Advanced Stages of NASH.

(1) Background: Nonalcoholic Steatohepatitis/Nonalcoholic Fatty Liver Disease (NASH/NAFLD) is the most recurrent chronic liver disease. NASH could present with a cholestatic (C) or hepatic (H) pattern of damage. Recently, we observed that increased Epithelial Cell Adhesion Molecule (EpCAM) expression was the main immunohistochemical feature to distinguish C from H pattern in NASH. (2) Methods: In the present study, we used digital pathology to compare the quantitative results of digital image analysis by QuPath software (Q-results), with the semi-quantitative results of observer assessment (S-results) for cytokeratin 7 and 19, (CK7, CK19) as well as EpCAM expression. Patients were classified into H or C group on the basis of the ratio between alanine transaminase (ALT) and alkaline phosphatase (ALP) values, using the "R-ratio formula". (3) Results: Q- and S-results showed a significant correlation for all markers (p < 0.05). Q-EpCAM expression was significantly higher in the C group than in the H group (p < 0.05). Importantly ALP, an indicator of hepatobiliary disorder, was the only biochemical parameter significantly correlated with Q-EpCAM. Instead, Q-CK7, but not Q-CK19, correlated only with γGlutamyl-Transferase (γGT). Of note, Stage 4 fibrosis correlated with Q-EpCAM, Q-CK19, and ALP but not with γGT or ALT. Conclusions: Image analysis confirms the relation between cholestatic-like pattern, associated with a worse prognosis, with increased ALP values, EpCAM positive biliary metaplasia, and advanced fibrosis. These preliminary data could be useful for the implementation of AI algorithms for the assessment of cholestatic NASH.

Manual and Semi-Automated Measurement and Calculation of Osteosarcoma Treatment Effect Using Whole Slide Image and Qupath.

INTRODUCTION: In osteosarcoma, the most significant indicator of prognosis is the histologic changes related to tumor response to preoperative chemotherapy, such as necrosis. We have developed a method to measure the osteosarcoma treatment effect using whole slide image (WSI) with an open-source digital image analytical software Qupath. MATERIALS AND METHODS: In Qupath, each osteosarcoma case was treated as a project. All H&E slides from the entire representative slice of osteosarcoma were scanned into WSIs and imported into a project in Qupath. The regions of tumor and tumor necrosis were annotated, and their areas were measured in Qupath. In order to measure the osteosarcoma treatment effect, we needed to calculate the percentage of total necrosis area over total tumor area. We developed a tool that can automatically extract all values of tumor and necrosis areas from a Qupath project into an Excel file, sum these values for necrosis and whole tumor respectively, and calculate necrosis/tumor percentage. CONCLUSION: Our method that combines WSI with Qupath can provide an objective measurement to facilitate pathologist's assessment of osteosarcoma response to treatment. The proposed approach can also be used for other types of tumors that have clinical need for post-treatment response assessment.

Evaluation of immune infiltrate according to the HER2 status in colorectal cancer.

BACKGROUND AND AIMS: In colorectal cancer (CRC), HER2 targeting is a promising treatment and immune infiltrate is an important area of research and strategy. Data regarding HER2 status and immune infiltrate are lacking. The aim of this study was to compare the immune infiltrate between HER2 amplified and non-amplified categories in proficient MisMatchRepair (pMMR)/microsatellite stable (MSS) CRC. METHODS: HER2 immunohistochemistry (IHC) and fluorescence in situ hybridization were performed in a retrospective series of 654 CRC. Lymphocyte infiltrate was analysed by anti-CD3, CD8 and CD4 IHC and evaluated digitally using QuPath software. RESULTS: Among the 654 CRC, we first observed a decreased CD3+ and CD8+ infiltrate between HER2 amplified (all IHC 3+ except one 2+) and non-amplified HER2 2+ IHC CRC (p = 0.059 and 0.072 respectively). A supplementary analysis of 258 pMMR/MSS CRC from the previous cohort, displaying all the IHC scores (0, 1+, 2+, 3+), showed a lower CD3+ infiltrate between HER2 amplified versus HER2 0 (p = 0.002), 1+ (p = 0.088) and non-amplified 2+ (p = 0.081) IHC cases. CONCLUSIONS: Our original findings suggest that in pMMR/MSS CRC, the immune infiltrate is reduced in HER2 amplified versus other HER2 categories. These data might be useful for future strategies combining anti-HER2 treatments and immune checkpoint inhibitors and need to be confirmed in larger CRC cohorts.

Development of a deep learning-based model to diagnose mixed-type gastric cancer accurately.

OBJECTIVE: The accurate diagnosis of mixed-type gastric cancer from pathology images presents a formidable challenge for pathologists, given its intricate features and resemblance to other subtypes of gastric cancer. Artificial Intelligence has the potential to overcome this hurdle. This study aimed to leverage deep machine learning techniques to establish a precise and efficient diagnostic approach for this cancer type which can also predict the metastatic risk using two software, U-Net and QuPath, which have not been trialled in gastric cancers. METHODS: A U-Net neural network was trained to recognise, and segment differentiated components from 186 pathology images of mixed-type gastric cancer. Undifferentiated components in the same images were annotated using the open-source pathology imaging software QuPath. The outcomes from U-Net and QuPath were used to calculate the ratios of differentiation/undifferentiated components which were correlated to lymph node metastasis. RESULTS: The models established by U-Net recognised ∼91% of the regions of interest, with precision, recall, and F1 values of 90.2%, 90.9% and 94.6%, respectively, indicating a high level of accuracy and reliability. Furthermore, the receiver operating characteristic curve analysis showed an area under the cure of 91%, indicating good performance. A bell-curve correlation between the differentiated/undifferentiated ratio and lymphatic metastasis was found (highest risk between 0.683 and 1.03), which is paradigm-shifting. CONCLUSION: U-Net and QuPath exhibit promising accuracy in the identification of differentiated and undifferentiated components in mixed-type gastric cancer, as well as paradigm-shifting prediction of metastasis. These findings bring us one step closer to their potential clinical application.

QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray.

Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accurately identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) from a tissue microarray containing normal colon and IBD-CRC. The tissue microarray (n = 162 cores) was immunostained for MLH1, digitalised, and imported into QuPath. A small sample (n = 14) was used to train QuPath to detect positive versus no MLH1 and tissue histology (normal epithelium, tumour, immune infiltrates, stroma). This algorithm was applied to the tissue microarray and correctly identified tissue histology and MLH1 expression in the majority of valid cases (73/99, 73.74%), incorrectly identified MLH1 status in one case (1.01%), and flagged 25/99 (25.25%) cases for manual review. Qualitative review found five reasons for flagged cores: small quantity of tissue, diverse/atypical morphology, excessive inflammatory/immune infiltrations, normal mucosa, or weak/patchy immunostaining. Of classified cores (n = 74), QuPath was 100% (95% CI 80.49, 100) sensitive and 98.25% (95% CI 90.61, 99.96) specific for identifying MLH1-deficient IBD-CRC; κ = 0.963 (95% CI 0.890, 1.036) (p < 0.001). This process could be efficiently automated in diagnostic laboratories to examine all colonic tissue and tumours for MLH1 expression.

Automated Analysis of PD1 and PDL1 Expression in Lymph Nodes and the Microenvironment of Transmissible Tumors in Tasmanian Devils.

The wild Tasmanian devil (Sarcophilus harrisii) population has suffered a devastating decline due to two clonal transmissible cancers. The first devil facial tumor 1 (DFT1) was observed in 1996, followed by a second genetically distinct transmissible tumor, the devil facial tumor 2 (DFT2), in 2014. DFT1/2 frequently metastasize, with lymph nodes being common metastatic sites. MHC-I downregulation by DFT1 cells is a primary means of evading allograft immunity aimed at polymorphic MHC-I proteins. DFT2 cells constitutively express MHC-I, and MHC-I is upregulated on DFT1/2 cells by interferon gamma, suggesting other immune evasion mechanisms may contribute to overcoming allograft and anti-tumor immunity. Human clinical trials have demonstrated PD1/PDL1 blockade effectively treats patients showing increased expression of PD1 in tumor draining lymph nodes, and PDL1 on peritumoral immune cells and tumor cells. The effects of DFT1/2 on systemic immunity remain largely uncharacterized. This study applied the open-access software QuPath to develop a semiautomated pipeline for whole slide analysis of stained tissue sections to quantify PD1/PDL1 expression in devil lymph nodes. The QuPath protocol provided strong correlations to manual counting. PD-1 expression was approximately 10-fold higher than PD-L1 expression in lymph nodes and was primarily expressed in germinal centers, whereas PD-L1 expression was more widely distributed throughout the lymph nodes. The density of PD1 positive cells was increased in lymph nodes containing DFT2 metastases, compared to DFT1. This suggests PD1/PDL1 exploitation may contribute to the poorly immunogenic nature of transmissible tumors in some devils and could be targeted in therapeutic or prophylactic treatments.Abbreviations: PD1: programmed cell death protein 1; PDL1: programmed death ligand 1; DFT1: devil facial tumor 1; DFT2: devil facial tumor 2; DFTD: devil facial tumor disease; MCC: Matthew's correlation coefficient; DAB: diaminobenzidine; ROI: region of interest.

Measurement of Aß Amyloid Plaques and Tau Protein in Postmortem Human Alzheimer's Disease Brain by Autoradiography Using [18F]Flotaza, [125I]IBETA, [124/125I]IPPI and Immunohistochemistry Analysis Using QuPath.

High-resolution scans of immunohistochemical (IHC) stains of Alzheimer's disease (AD) brain slices and radioligand autoradiography both provide information about the distribution of Aß plaques and Tau, the two common proteinopathies in AD. Accurate assessment of the amount and regional location of Aß plaques and Tau is essential to understand the progression of AD pathology. Our goal was to develop a quantitative method for the analysis of IHC-autoradiography images. Postmortem anterior cingulate (AC) and corpus callosum (CC) from AD and control (CN) subjects were IHC stained with anti-Aß for Aß plaques and autoradiography with [18F]flotaza and [125I]IBETA for Aß plaques. For Tau, [124I]IPPI, a new radiotracer, was synthesized and evaluated in the AD brain. For Tau imaging, brain slices were IHC stained with anti-Tau and autoradiography using [125I]IPPI and [124I]IPPI. Annotations for Aß plaques and Tau using QuPath for training and pixel classifiers were generated to measure the percent of the area of Aß plaques and Tau in each slice. The binding of [124I]IPPI was observed in all AD brains with an AC/CC ratio > 10. Selectivity to Tau was shown by blocking [124I]IPPI with MK-6240. Percent positivity for Aß plaques was 4-15%, and for Tau, it was 1.3 to 35%. All IHC Aß plaque-positive subjects showed [18F]flotaza and [125I]IBETA binding with a positive linear correlation (r2 > 0.45). Tau-positive subjects showed [124/125I]IPPI binding with a stronger positive linear correlation (r2 > 0.80). This quantitative IHC-autoradiography approach provides an accurate measurement of Aß plaques and Tau within and across subjects.

Quantitative Analysis of Inflammatory Uterine Lesions of Pregnant Gilts with Digital Image Analysis Following Experimental PRRSV-1 Infection.

Reproductive disorders caused by porcine reproductive and respiratory syndrome virus-1 are not yet fully characterized. We report QuPath-based digital image analysis to count inflammatory cells in 141 routinely, and 35 CD163 immunohistochemically stained endometrial slides of vaccinated or unvaccinated pregnant gilts inoculated with a high or low virulent PRRSV-1 strain. To illustrate the superior statistical feasibility of the numerical data determined by digital cell counting, we defined the association between the number of these cells and endometrial, placental, and fetal features. There was strong concordance between the two manual scorers. Distributions of total cell counts and endometrial and placental qPCR results differed significantly between examiner1's endometritis grades. Total counts' distribution differed significantly between groups, except for the two unvaccinated. Higher vasculitis scores were associated with higher endometritis scores, and higher total cell counts were expected with high vasculitis/endometritis scores. Cell number thresholds of endometritis grades were determined. A significant correlation between fetal weights and total counts was shown in unvaccinated groups, and a significant positive correlation was found between these counts and endometrial qPCR results. We revealed significant negative correlations between CD163+ counts and qPCR results of the unvaccinated group infected with the highly virulent strain. Digital image analysis was efficiently applied to assess endometrial inflammation objectively.

Predicting IHC staining classes of NF1 using features in the hematoxylin channel.

Immunohistochemistry (IHC) highlights specific cell types in tissues and traditionally involves antibody staining together with a hematoxylin counterstain. The intensity and pattern of hematoxylin staining differs between cell types and reveals morphological characteristics of cells. Here, we propose that features in the hematoxylin stain can be used to predict IHC labels, such as Neurofibromin (encoded by the gene NF1). The dataset consists of 7.2 million cells from benign and kidney cancer cores in a tissue microarray. Morphology and hematoxylin (H&M) features defined within QuPath are subjected to a clustering analysis in CytoMap. H&M features are also used to train 4 different XGBoost models to predict high, low, and negative NF1 stain classes in benign renal tubules, clear cell (ccRCC), papillary (PRCC), and chromophobe (ChRCC) renal carcinoma. The prediction accuracies of NF1 staining classes in benign, ccRCC, ChRCC, and PRCC range between 70% and 90% with areas under the precision recall curve PRAUCNF1-high = 0.82+0.12, PRAUCNF1-low = 0.62+0.25, and PRAUCNF1-negative = 0.83+0.16. The most important feature for predicting the NF1 class involves the minimum cellular hematoxylin staining intensity. Together, these results demonstrate the feasibility to predict NF1 expression solely from features in hematoxylin staining using open source software. Since the hematoxylin features can be obtained from regular H&E and IHC slides, the proposed workflow has broad applicability.

Quantitative Analysis of Gene Expression in RNAscope-processed Brain Tissue.

Molecular characterization of different cell types in rodent brains is a widely used and important approach in neuroscience. Fluorescent detection of transcripts using RNAscope (ACDBio) has quickly became a standard in situ hybridization (ISH) approach. Its sensitivity and specificity allow for the simultaneous detection of between three and forty-eight low abundance mRNAs in single cells (i.e., multiplexing or hiplexing), and, in contrast to other ISH techniques, it is performed in a shorter amount of time. Manual quantification of transcripts is a laborious and time-consuming task even for small portions of a larger tissue section. Herein, we present a protocol for creating high-quality images for quantification of RNAscope-labeled neurons in the rat brain. This protocol uses custom-made scripts within the open-source software QuPath to create an automated workflow for the careful optimization and validation of cell detection parameters. Moreover, we describe a method to derive mRNA signal thresholds using negative controls. This protocol and automated workflow may help scientists to reliably and reproducibly prepare and analyze rodent brain tissue for cell type characterization using RNAscope. Graphical abstract.

Characterization of the immune infiltrate in mouse tissue by multiplex immunofluorescence.

Multiplexed immunofluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides allow the identification of multiple cell phenotypes while retaining spatial and morphological context. Multiplex immunofluorescence protocols have already been developed and validated for mouse tissues. Immunophenotyping analysis reliably depicts the immune landscape of cancer tissues that has been demonstrated to influence cancer development and progression as well as to have an impact on therapy responsiveness and resistance. Here, we describe a method for multiplexed fluorescence image analysis, enabling analysis of mouse cancer morphology and cell phenotypes in FFPE sections.

Proposal of an automated tumor-stromal ratio assessment algorithm and a nomogram for prognosis in early-stage invasive breast cancer.

BACKGROUND: The tumor-stromal ratio (TSR) has been verified to be a prognostic factor in many solid tumors. In most studies, it was manually assessed on routinely stained H&E slides. This study aimed to assess the TSR using image analysis algorithms developed by the Qupath software, and integrate the TSR into a nomogram for prediction of the survival in invasive breast cancer (BC) patients. METHODS: A modified TSR assessment algorithm based on the recognition of tumor and stroma tissues was developed using the Qupath software. The TSR of 234 invasive BC specimens in H&E-stained tissue microarrays (TMAs) were assessed with the algorithm and categorized as stroma-low or stroma-high. The consistency of TSR estimation between Qupath prediction and pathologist annotation was analyzed. Univariable and multivariable analyses were applied to select potential risk factors and a nomogram for predicting survival in invasive BC patients was constructed and validated. An extra TMA containing 110 specimens was obtained to validate the conclusion as an independent cohort. RESULTS: In the discovery cohort, stroma-low and stroma-high were identified in 43.6% and 56.4% cases, respectively. Good concordance was observed between the pathologist annotated and Qupath predicted TSR. The Kaplan-Meier curve showed that stroma-high patients were associated with worse 5-DFS compared to stroma-low patients (p = 0.007). Multivariable analysis identified age, T stage, N status, histological grade, ER status, HER-2 gene, and TSR as potential risk predictors, which were included in the nomogram. The nomogram was well calibrated and showed a favorable predictive value for the recurrence of BC. Kaplan-Meier curves showed that the nomogram had a better risk stratification capability than the TNM staging system. In the external validation of the nomogram, the results were further validated. CONCLUSIONS: Based on H&E-stained TMAs, this study successfully developed image analysis algorithms for TSR assessment and constructed a nomogram for predicting survival in invasive BC.

Sigma-1 receptor agonist, (+)-pentazocine, is neuroprotective in a Brown Norway rat microbead model of glaucoma.

PURPOSE: Glaucoma is a worldwide leading cause of irreversible blindness. Standard treatments lower intraocular pressure (IOP). Novel treatments to prevent optic nerve (ON) degeneration are needed. Here, we investigate the hypothesis that sigma-1 receptor (S1R) agonist (+)-pentazocine (PTZ) is neuroprotective in a Brown Norway (BN) rat, microbead model of glaucoma. METHODS: BN rats (9-11 weeks, male and female) were treated by intraperitoneal injection, 3 times per week with (+)-PTZ (2 mg/kg) or vehicle (VEH) alone. Treatment started 1 week prior to intraocular injection of polystyrene microbeads to elevate IOP. IOP was measured 2-3 times per week. Five weeks post microbead injection, rats were euthanized. ONs were removed, then fixed and processed for 63x oil, light microscope imaging of toluidine blue stained ON cross sections. To facilitate comparison of ON morphology from VEH and (+)-PTZ treated rats with similar ocular hypertensive insults, rats were assigned to low (IOP ≤15.8 mmHg), moderate (15.8 < IOP <28.0 mmHg), and high (IOP ≥28.0 mmHg) groups based on average IOP in the microbead injected eye. Axon numbers, axon density, axonal and glial areas, axon loss, and axon size distributions of naïve, bead, and contralateral ONs were assessed using QuPath program for automated image analysis. RESULTS: (+)-PTZ treatment of BN rats protected ONs from damage caused by moderate IOP elevation. Treatment with (+)-PTZ significantly reduced axon loss and glial areas, and increased axon density and axonal areas compared to ONs from VEH treated rats with moderate IOP. (+)-PTZ-mediated neuroprotection was independent of IOP lowering effects. At average IOP ≥28.0 mmHg, (+)-PTZ treatment did not provide measurable neuroprotection. ONs from contralateral eyes exhibited subtle, complex changes in response to conditions in the bead eyes. CONCLUSIONS: S1R agonist (+)-PTZ shows promise as a neuroprotective treatment for glaucoma. Future studies to understand the complex molecular mechanisms by which (+)-PTZ provides this neuroprotection are needed.

Elevated Monoamine Oxidase-A in Anterior Cingulate of Post-Mortem Human Parkinson's Disease: A Potential Surrogate Biomarker for Lewy Bodies?

Lewy bodies (LB) play a neuropathological role in Parkinson's disease (PD). Our goal was to evaluate LB using anti-ubiquitin immunohistochemistry (UIHC) and find correlations with monoamine oxidase-A (MAO-A) using imaging agent, [18F]FAZIN3. Human post-mortem anterior cingulate (AC) and corpus callosum (CC) from control subjects (CN), n = 6; age 81-90 LB = 0 and PD, n = 6, age 77-89, LB = III-IV were sectioned (10 µm slices). Brain slices were immunostained with anti-ubiquitin for LB (UIHC) and analyzed using QuPath for percent anti-ubiquitin per unit area (µm2). Adjacent brain slices were incubated with [18F]FAZIN3 and cortical layers I-III, IV-VI and CC (white matter) regions were quantified for the binding of [18F]FAZIN3. UIHC was correlated with [18F]FAZIN3 binding. All PD brains were positively UIHC stained and confirmed presence of LB. Outer cortical layers (I-III) of PD AC had 21% UIHC while inner layers (IV-VI) had >75% UIHC. In the CN brains LB were absent (<1% UIHC). Increased [18F]FAZIN3 binding to MAO-A in AC was observed in all PD subjects. [18F]FAZIN3 ratio in PD was AC/CC = 3.57 while in CN subjects it was AC/CC = 2.24. Increases in UIHC µm2 correlated with [18F]FAZIN3 binding to MAO-A in DLU/mm2. Increased [18F]FAZIN3 binding to MAO-A in PD is a potential novel "hot spot" PET imaging approach.

Immunohistochemistry scoring of breast tumor tissue microarrays: A comparison study across three software applications.

Digital pathology can efficiently assess immunohistochemistry (IHC) data on tissue microarrays (TMAs). Yet, it remains important to evaluate the comparability of the data acquired by different software applications and validate it against pathologist manual interpretation. In this study, we compared the IHC quantification of 5 clinical breast cancer biomarkers-estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and cytokeratin 5/6 (CK5/6)-across 3 software applications (Definiens Tissue Studio, inForm, and QuPath) and benchmarked the results to pathologist manual scores. IHC expression for each marker was evaluated across 4 TMAs consisting of 935 breast tumor tissue cores from 367 women within the Nurses' Health Studies; each women contributing three 0.6-mm cores. The correlation and agreement between manual and software-derived results were primarily assessed using Spearman's ρ, percentage agreement, and area under the curve (AUC). At the TMA core-level, the correlations between manual and software-derived scores were the highest for HER2 (ρ ranging from 0.75 to 0.79), followed by ER (0.69-0.71), PR (0.67-0.72), CK5/6 (0.43-0.47), and EGFR (0.38-0.45). At the case-level, there were good correlations between manual and software-derived scores for all 5 markers (ρ ranging from 0.43 to 0.82), where QuPath had the highest correlations. Software-derived scores were highly comparable to each other (ρ ranging from 0.80 to 0.99). The average percentage agreements between manual and software-derived scores were excellent for ER (90.8%-94.5%) and PR (78.2%-85.2%), moderate for HER2 (65.4%-77.0%), highly variable for EGFR (48.2%-82.8%), and poor for CK5/6 (22.4%-45.0%). All AUCs across markers and software applications were ≥0.83. The 3 software applications were highly comparable to each other and to manual scores in quantifying these 5 markers. QuPath consistently produced the best performance, indicating this open-source software is an excellent alternative for future use.