Cargando…
Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model
BACKGROUND: Ovarian cancer (OC) is typically diagnosed late, associated with high rates of metastasis and the onset of ascites during late stage disease. Understanding the tumor microenvironment and how it impacts the efficacy of current treatments, including immunotherapies, needs effective in vivo...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506632/ https://www.ncbi.nlm.nih.gov/pubmed/34709744 http://dx.doi.org/10.1002/ctm2.551 |
_version_ | 1784581733349326848 |
---|---|
author | Pisano, Simone Lenna, Stefania Healey, Gareth D. Izardi, Fereshteh Meeks, Lucille Jimenez, Yajaira S. Velazquez, Oscar S Gonzalez, Deyarina Conlan, Robert Steven Corradetti, Bruna |
author_facet | Pisano, Simone Lenna, Stefania Healey, Gareth D. Izardi, Fereshteh Meeks, Lucille Jimenez, Yajaira S. Velazquez, Oscar S Gonzalez, Deyarina Conlan, Robert Steven Corradetti, Bruna |
author_sort | Pisano, Simone |
collection | PubMed |
description | BACKGROUND: Ovarian cancer (OC) is typically diagnosed late, associated with high rates of metastasis and the onset of ascites during late stage disease. Understanding the tumor microenvironment and how it impacts the efficacy of current treatments, including immunotherapies, needs effective in vivo models that are fully characterized. In particular, understanding the role of immune cells within the tumor and ascitic fluid could provide important insights into why OC fails to respond to immunotherapies. In this work, we comprehensively described the immune cell infiltrates in tumor nodules and the ascitic fluid within an optimized preclinical model of advanced ovarian cancer. METHODS: Green Fluorescent Protein (GFP)‐ID8 OC cells were injected intraperitoneally into C57BL/6 mice and the development of advanced stage OC monitored. Nine weeks after tumor injection, mice were sacrificed and tumor nodules analyzed to identify specific immune infiltrates by immunohistochemistry. Ascites, developed in tumor bearing mice over a 10‐week period, was characterized by mass cytometry (CyTOF) to qualitatively and quantitatively assess the distribution of the immune cell subsets, and their relationship to ascites from ovarian cancer patients. RESULTS: Tumor nodules in the peritoneal cavity proved to be enriched in T cells, antigen presenting cells and macrophages, demonstrating an active immune environment and cell‐mediated immunity. Assessment of the immune landscape in the ascites showed the predominance of CD8(+), CD4(+), B(–), and memory T cells, among others, and the coexistance of different immune cell types within the same tumor microenvironment. CONCLUSIONS: We performed, for the first time, a multiparametric analysis of the ascitic fluid and specifically identify immune cell populations in the peritoneal cavity of mice with advanced OC. Data obtained highlights the impact of CytOF as a diagnostic tool for this malignancy, with the opportunity to concomitantly identify novel targets, and define personalized therapeutic options. |
format | Online Article Text |
id | pubmed-8506632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85066322021-10-18 Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model Pisano, Simone Lenna, Stefania Healey, Gareth D. Izardi, Fereshteh Meeks, Lucille Jimenez, Yajaira S. Velazquez, Oscar S Gonzalez, Deyarina Conlan, Robert Steven Corradetti, Bruna Clin Transl Med Research Articles BACKGROUND: Ovarian cancer (OC) is typically diagnosed late, associated with high rates of metastasis and the onset of ascites during late stage disease. Understanding the tumor microenvironment and how it impacts the efficacy of current treatments, including immunotherapies, needs effective in vivo models that are fully characterized. In particular, understanding the role of immune cells within the tumor and ascitic fluid could provide important insights into why OC fails to respond to immunotherapies. In this work, we comprehensively described the immune cell infiltrates in tumor nodules and the ascitic fluid within an optimized preclinical model of advanced ovarian cancer. METHODS: Green Fluorescent Protein (GFP)‐ID8 OC cells were injected intraperitoneally into C57BL/6 mice and the development of advanced stage OC monitored. Nine weeks after tumor injection, mice were sacrificed and tumor nodules analyzed to identify specific immune infiltrates by immunohistochemistry. Ascites, developed in tumor bearing mice over a 10‐week period, was characterized by mass cytometry (CyTOF) to qualitatively and quantitatively assess the distribution of the immune cell subsets, and their relationship to ascites from ovarian cancer patients. RESULTS: Tumor nodules in the peritoneal cavity proved to be enriched in T cells, antigen presenting cells and macrophages, demonstrating an active immune environment and cell‐mediated immunity. Assessment of the immune landscape in the ascites showed the predominance of CD8(+), CD4(+), B(–), and memory T cells, among others, and the coexistance of different immune cell types within the same tumor microenvironment. CONCLUSIONS: We performed, for the first time, a multiparametric analysis of the ascitic fluid and specifically identify immune cell populations in the peritoneal cavity of mice with advanced OC. Data obtained highlights the impact of CytOF as a diagnostic tool for this malignancy, with the opportunity to concomitantly identify novel targets, and define personalized therapeutic options. John Wiley and Sons Inc. 2021-10-12 /pmc/articles/PMC8506632/ /pubmed/34709744 http://dx.doi.org/10.1002/ctm2.551 Text en © 2021 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Pisano, Simone Lenna, Stefania Healey, Gareth D. Izardi, Fereshteh Meeks, Lucille Jimenez, Yajaira S. Velazquez, Oscar S Gonzalez, Deyarina Conlan, Robert Steven Corradetti, Bruna Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model |
title | Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model |
title_full | Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model |
title_fullStr | Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model |
title_full_unstemmed | Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model |
title_short | Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model |
title_sort | assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506632/ https://www.ncbi.nlm.nih.gov/pubmed/34709744 http://dx.doi.org/10.1002/ctm2.551 |
work_keys_str_mv | AT pisanosimone assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT lennastefania assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT healeygarethd assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT izardifereshteh assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT meekslucille assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT jimenezyajairas assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT velazquezoscars assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT gonzalezdeyarina assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT conlanrobertsteven assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel AT corradettibruna assessmentoftheimmunelandscapesofadvancedovariancancerinanoptimizedinvivomodel |