Cancer-specific glycosylation of CD13 impacts its detection and activity in preclinical cancer tissues
View/
Main article (3.928Mb)
Download
Publication date
2023-10
Keyword
CancerTherapeutics
Anti-cancer drugs
CD13
Glycosylation
Cancer-specific CD13 glycoforms
Selective targeting
Rights
© 2023 The Authors. Published by Cell Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer-Reviewed
YesOpen Access status
openAccessAccepted for publication
2023-10-12Metadata
Show full item recordAbstract
Harnessing the differences between cancer and non-cancer tissues presents new opportunities for selective targeting by anti-cancer drugs. CD13, a heavily glycosylated protein, is one example with significant unmetclinical potential in cancer drug discovery. Despite its high expression and activity in cancers, CD13 is also expressed in many normal tissues. Here, we report differential tissue glycosylation of CD13 across tissues and demonstrate for the first time that the nature and pattern of glycosylation of CD13 in preclinical cancer tissues are distinct compared to normal tissues. We identify cancer-specific O-glycosylation of CD13, which selectively blocks its detection in cancer models but not in normal tissues. In addition, the metabolism activity of cancer-expressed CD13 was observed to be critically dependent on its unique glycosylation. Thus, our data demonstrate the existence of discrete cancer-specific CD13 glycoforms and propose cancer-specific CD13 glycoforms as a clinically useful target for effective cancer-targeted therapy.Version
Published versionCitation
Mprah Barnieh F, Galuska SP, Loadman P et al (2023) Cancer-specific glycosylation of CD13 impacts its detection and activity in preclinical cancer tissues. iScience. 26: 108219Link to Version of Record
https://doi.org/10.1016/j.isci.2023.108219Type
ArticleScopus Count
Collections
Related items
Showing items related by title, author, creator and subject.
-
Novel Ran-RCC1 inhibitory peptide-loaded nanoparticles have anti-cancer efficacy in vitro and in vivoThe delivery of anticancer agents to their subcellular sites of action is a significant challenge for effective cancer therapy. Peptides, which are integral to several oncogenic pathways, have significant potential to be utilised as cancer therapeutics due to their selectivity, high potency and lack of normal cell toxicity. Novel Ras protein-Regulator of chromosome condensation 1 (Ran-RCC1) inhibitory peptides designed to interact with Ran, a novel therapeutic target in breast cancer, were delivered by entrapment into polyethylene glycol-poly (lactic-co-glycolic acid) PEG-PLGA polymeric nanoparticles (NPs). A modified double emulsion solvent evaporation technique was used to optimise the physicochemical properties of these peptide-loaded biodegradable NPs. The anti-cancer activity of peptide-loaded NPs was studied in vitro using Ran-expressing metastatic breast (MDA-MB-231) and lung cancer (A549) cell lines, and in vivo using Solid Ehrlich Carcinoma-bearing mice. The anti-metastatic activity of peptide-loaded NPs was investigated using migration, invasion and colony formation assays in vitro. A PEG-PLGA-nanoparticle encapsulating N-terminal peptide showed a pronounced antitumor and anti-metastatic action in lung and breast cancer cells in vitro and caused a significant reduction of tumor volume and associated tumor growth inhibition of breast cancer model in vivo. These findings suggest that the novel inhibitory peptides encapsulated into PEGylated PLGA NPs are delivered effectively to interact and deactivate Ran. This novel Ran-targeting peptide construct shows significant potential for therapy of breast cancer and other cancers mediated by Ran overexpression.
-
A cell level automated approach for quantifying antibody staining in immunohistochemistry images. A structural approach for quantifying antibody staining in colonic cancer spheroid images by integrating image processing and machine learning towards the implementation of computer aided scoring of cancer markers.Immunohistological (IHC) stained images occupy a fundamental role in the pathologist¿s diagnosis and monitoring of cancer development. The manual process of monitoring such images is a subjective, time consuming process that typically relies on the visual ability and experience level of the pathologist. A novel and comprehensive system for the automated quantification of antibody inside stained cell nuclei in immunohistochemistry images is proposed and demonstrated in this research. The system is based on a cellular level approach, where each nucleus is individually analyzed to observe the effects of protein antibodies inside the nuclei. The system provides three main quantitative descriptions of stained nuclei. The first quantitative measurement automatically generates the total number of cell nuclei in an image. The second measure classifies the positive and negative stained nuclei based on the nuclei colour, morphological and textural features. Such features are extracted directly from each nucleus to provide discriminative characteristics of different stained nuclei. The output generated from the first and second quantitative measures are used collectively to calculate the percentage of positive nuclei (PS). The third measure proposes a novel automated method for determining the staining intensity level of positive nuclei or what is known as the intensity score (IS). The minor intensity features are observed and used to classify low, intermediate and high stained positive nuclei. Statistical methods were applied throughout the research to validate the system results against the ground truth pathology data. Experimental results demonstrate the effectiveness of the proposed approach and provide high accuracy when compared to the ground truth pathology data.
-
Systematic associations between germ-line mutations and human cancersThe revolution in Big Data has opened the gate for new research challenges in biomedical science. The aim of this study was to investigate whether germ-line gene mutations are a significant factor in 29 major primary human cancers. Using data obtained from multiple biological databases, we identified 424 genes from 8879 cancer mutation records. By integrating these gene mutation records a human cancer map was constructed from which several key results were obtained. These include the observations that missense/nonsense and regulatory mutations might play central role in connecting cancers/genes, and tend to be distributed in all chromosomes. This suggests that, of all mutation classes missense/nonsense and regulatory mutation classes are over-expressed in human genome and so are likely to have a significant impact on human cancer aetiology and pathomechanism. This offers new insights into how the distribution and interconnections of gene mutations influence the development of cancers.
