CTLA-4 Immunohistochemistry and Quantitative Image Analysis for Profiling of Human Cancers

Brown, Charles O.; Sekhavati, Farzad; Cárdenes, Rubén; Windmueller, Claudia; Dacosta, Karma; Rodriguez‐Canales, Jaime; Steele, Keith

doi:10.1369/0022155419882292

Cited by 14 publications

(15 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the available data on CTLA-4 expression is limited to human T cells. Accordingly, we found that immunolabeling CTLA-4 with the antibody clone CAL49 vs. T lymphocytes markers CD3, CD4, and CD8 showed that CTLA-4(+) T lymphocytes in tonsils were typically CD3(+) and CD4(+), but not CD8(+), as was earlier reported by Brown et al [26]. However, cells that were immunostained with antibodies to the CTLA-4 (UMAB249 clone) were predominantly detected in the extrafollicular regions of the tonsil epithelium-both in the reticular epithelium and in the integumentary stratified squamous non-keratinized epithelium-but not in T-lymphocytes.…”

Section: Discussionsupporting

confidence: 86%

Identification of CTLA-4-Positive Cells in the Human Tonsil

Tiemann

Atiakshin

Samoilova

et al. 2021

Cells

View full text Add to dashboard Cite

CTLA-4 (cytotoxic T-lymphocyte-associated protein 4) was originally defined as a T-lymphocyte antigen and was used as a target in cancer immunotherapy. Unfortunately, the existence of CTLA-4 in cells other than T-lymphocytes is often overlooked. The goal of the present study was to analyze the distribution pattern of CTLA-4 in the human tonsils using a panel of anti–CTLA-4 antibodies of different clones. We found that CTLA-4 was expressed in T-lymphocyte cells of various geneses, including hematopoietic cells and their derivatives (monocytes, macrophages, dendritic, plasma cells, mast cells, and neutrophils), as well as stromal cells of mesodermal (mesenchymal) origin and reticular epithelial cells of ectodermal origin. The expression of CTLA-4 in cells of different origins supports the proposition that CTLA-4 is not restricted to the lymphoid cell lineage and can provide broader effects of CTLA-4 on immune regulation.

show abstract

Section: Discussionsupporting

confidence: 86%

Identification of CTLA-4-Positive Cells in the Human Tonsil

Tiemann

Atiakshin

Samoilova

et al. 2021

Cells

View full text Add to dashboard Cite

show abstract

“…In recent years, the development of imaging analysis algorithms has mainly focused on positivity and intensity determination for cancer-associated biomarkers in IHC specimens. The few studies that were involved in the development of automatic TIL number measurements are shown in Table II (24)(25)(26)(27)(28)(29)(30)(31).…”

Section: Figure 6 the Significant Correlation Between The Manual Counting And Automatic Counting In Cd8 + Or Pd-1 + T Cell Measurements Omentioning

confidence: 99%

“…Brown et al reported that image analysis scoring based on a color deconvolution algorithm was highly comparable with pathologist scoring for CTLA-4 and CTLA-4/FoxP3 staining of regulatory T cells (24). Meanwhile, Yoo et al demonstrated that machine-learning-based image analysis such as QuPath can be useful for extracting quantitative information about the TMIT (tumor microenvironment immune types) using wholeslide histopathologic images (25).…”

Section: Figure 6 the Significant Correlation Between The Manual Counting And Automatic Counting In Cd8 + Or Pd-1 + T Cell Measurements Omentioning

confidence: 99%

Development of an Automatic Measurement Method for CD8 and PD-1 Positive T Cells Using Image Analysis Software

et al. 2021

View full text Add to dashboard Cite

Background/Aim: With the progress in cancer immunotherapy using immune checkpoint blockade (ICB) therapy, histological observations of tumor-infiltrating lymphocyte (TIL) status are needed to evaluate the antitumor effect of ICB using imaging analysis software. Materials and Methods: Formalin-fixed paraffin-embedded sections obtained from colorectal cancer and gastric cancer patients with more than 500 single nucleotide variants were stained with anti-CD8 and anti-PD-1 antibodies. Based on our own algorithm and imaging analysis software, an automatic TIL measurement method was established and compared to the manual counting methods. Results: In the CD8 + T cell number measurement, there was a good correlation (r=0.738 by Pearson test) between the manual and automated counting methods. However, in the PD-1 + T cell measurement, there was a large difference in TIL numbers in both groups. After adjustment of the parameter settings, the correlation between the manual and automated methods in the PD-1 + T cell measurements improved (r=0.668 by Pearson test). Conclusion: An imaging software-based automatic measurement could be a simple and useful tool for evaluating the therapeutic effect of cancer immunotherapies in terms of TIL status.Since the clinical applications of immune checkpoint blockade (ICB) therapy have had great success in various cancer patients, the number of clinical trials focused on novel therapies, including the combination of ICB with other targeting agents, have been increasing (1, 2). The greater the number of ICBbased clinical trials, the more difficult visual inspections by trained pathologists have become for the evaluation of immune effects by ICB, such as tumor-infiltrating lymphocyte (TIL) measurement and programmed death-ligand 1 (PD-L1) staining.In fact, many pathologists have intensively studied methods to enumerate TIL numbers accurately and efficiently and demonstrated that a well-functioning immune-scoring system could contribute to determine the TIL status of tumors (3-6). However, manual visual inspections are time-consuming and require manual labor; therefore, an efficient and user-friendly automatic immunohistochemistry (IHC) imaging analysis system is urgently needed. Specifically, since the development of whole slide scanners that can generate ultralarge 2D images in the field of digital pathology, open source bioimage analysis software has been developed, such as ImageJ, Fiji, Icy, and CellProfiler (7-10). However, in terms of high variability and limited reproducibility, it has been difficult for computational researchers to develop state-of-the-art imaging software 419 This article is freely accessible online.

show abstract

“…63 The challenges of segmenting nuclei using the deep learning module included prolonged processing times and difficulty in correctly determining nuclear borders. 2,3 To decrease up to 1 hr per digital image processing time required for deep learning segmentation, uniform random sampling of the WSDI was performed (Fig. 6B).…”

Section: Less Precise Than Stereologymentioning

confidence: 99%

“…Advances in reagents, methodologies, analytic platforms, and tools have resulted in a dramatic transformation of the research pathology laboratory. 1,2 The development of digital cameras, computer hardware and software, and microscopes that convert stained tissue sections on glass slides into high-resolution whole slide digital images (WSDIs) has been central to these advances. Access to WSDIs, which can be viewed over the Internet using virtual microscopy, has resulted in the growth of the subfield of digital pathology, a paradigm shift in the pathology laboratory.…”

Section: Introductionmentioning

confidence: 99%

The Use of Quantitative Digital Pathology to Measure Proteoglycan and Glycosaminoglycan Expression and Accumulation in Healthy and Diseased Tissues

Davis

Chang

Brune

et al. 2020

J Histochem Cytochem.

View full text Add to dashboard Cite

Advances in reagents, methodologies, analytic platforms, and tools have resulted in a dramatic transformation of the research pathology laboratory. These advances have increased our ability to efficiently generate substantial volumes of data on the expression and accumulation of mRNA, proteins, carbohydrates, signaling pathways, cells, and structures in healthy and diseased tissues that are objective, quantitative, reproducible, and suitable for statistical analysis. The goal of this review is to identify and present how to acquire the critical information required to measure changes in tissues. Included is a brief overview of two morphometric techniques, image analysis and stereology, and the use of artificial intelligence to classify cells and identify hidden patterns and relationships in digital images. In addition, we explore the importance of preanalytical factors in generating high-quality data. This review focuses on techniques we have used to measure proteoglycans, glycosaminoglycans, and immune cells in tissues using immunohistochemistry and in situ hybridization to demonstrate the various morphometric techniques. When performed correctly, quantitative digital pathology is a powerful tool that provides unbiased quantitative data that are difficult to obtain with other methods.

show abstract

CTLA-4 Immunohistochemistry and Quantitative Image Analysis for Profiling of Human Cancers

Cited by 14 publications

References 39 publications

Identification of CTLA-4-Positive Cells in the Human Tonsil

Identification of CTLA-4-Positive Cells in the Human Tonsil

Development of an Automatic Measurement Method for CD8 and PD-1 Positive T Cells Using Image Analysis Software

The Use of Quantitative Digital Pathology to Measure Proteoglycan and Glycosaminoglycan Expression and Accumulation in Healthy and Diseased Tissues

Contact Info

Product

Resources

About