ICAM-1-decorated extracellular vesicles loaded with miR-146a and <i>Glut1</i> drive immunomodulation and hinder tumor progression in a murine model of breast cancer

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Tissue nanotransfection (TNT)-driven extracellular vesicles mediate immunomodulation and hinder tumor progression in a mouse model of breast cancer.

Abstract

Tumor-associated immune cells play a crucial role in cancer progression. Myeloid-derived suppressor cells (MDSCs), for example, are immature innate immune cells that infiltrate the tumor to exert immunosuppressive activity and protect cancer cells from the host's immune system and/or cancer-specific immunotherapies. While tumor-associated immune cells have emerged as a promising therapeutic target, efforts to counter immunosuppression within the tumor niche have been hampered by the lack of approaches that selectively target the immune cell compartment of the tumor, to effectively eliminate “tumor-protecting” immune cells and/or drive an “anti-tumor” phenotype. Here we report on a novel nanotechnology-based approach to target tumor-associated immune cells and promote “anti-tumor” responses in a murine model of breast cancer. Engineered extracellular vesicles (EVs) decorated with ICAM-1 ligands and loaded with miR-146a and Glut1, were biosynthesized ( in vitro or in vivo) and administered to tumor-bearing mice once a week for up to 5 weeks. The impact of this treatment modality on the immune cell compartment and tumor progression was evaluated via RT-qPCR, flow cytometry, and histology. Our results indicate that weekly administration of the engineered EVs ( i.e., ICAM-1-decorated and loaded with miR-146a and Glut1) hampered tumor progression compared to ICAM-1-decorated EVs with no cargo. Flow cytometry analyses of the tumors indicated a shift in the phenotype of the immune cell population toward a more pro-inflammatory state, which appeared to have facilitated the infiltration of tumor-targeting T cells, and was associated with a reduction in tumor size and decreased metastatic burden. Altogether, our results indicate that ICAM-1-decorated EVs could be a powerful platform nanotechnology for the deployment of immune cell-targeting therapies to solid tumors.

Related collections

Most cited references 59

Record: found
Abstract: found
Article: found

Is Open Access

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

0 comments Cited 6501 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Causal analysis approaches in Ingenuity Pathway Analysis

John Osborne, Andreas Kramer, Jeff Green … (2015)

Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.

0 comments Cited 1630 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy.

Padmanee Sharma, Siwen Hu-Lieskovan, Jennifer A. Wargo … (2017)

Cancer immunotherapy can induce long lasting responses in patients with metastatic cancers of a wide range of histologies. Broadening the clinical applicability of these treatments requires an improved understanding of the mechanisms limiting cancer immunotherapy. The interactions between the immune system and cancer cells are continuous, dynamic, and evolving from the initial establishment of a cancer cell to the development of metastatic disease, which is dependent on immune evasion. As the molecular mechanisms of resistance to immunotherapy are elucidated, actionable strategies to prevent or treat them may be derived to improve clinical outcomes for patients.