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Ex-Vivo Treatment of Tumor Tissue Slices as a Predictive Preclinical Method to Evaluate Targeted Therapies for Patients with Renal Carcinoma
Clear cell renal cell carcinoma (ccRCC) is the third type of urologic cancer. At time of diagnosis, 30% of cases are metastatic with no effect of chemotherapy or radiotherapy. Current targeted therapies lead to a high rate of relapse and resistance after a short-term response. Thus, a major hurdle i...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7016787/ https://www.ncbi.nlm.nih.gov/pubmed/31963500 http://dx.doi.org/10.3390/cancers12010232 |
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author | Roelants, Caroline Pillet, Catherine Franquet, Quentin Sarrazin, Clément Peilleron, Nicolas Giacosa, Sofia Guyon, Laurent Fontanell, Amina Fiard, Gaëlle Long, Jean-Alexandre Descotes, Jean-Luc Cochet, Claude Filhol, Odile |
author_facet | Roelants, Caroline Pillet, Catherine Franquet, Quentin Sarrazin, Clément Peilleron, Nicolas Giacosa, Sofia Guyon, Laurent Fontanell, Amina Fiard, Gaëlle Long, Jean-Alexandre Descotes, Jean-Luc Cochet, Claude Filhol, Odile |
author_sort | Roelants, Caroline |
collection | PubMed |
description | Clear cell renal cell carcinoma (ccRCC) is the third type of urologic cancer. At time of diagnosis, 30% of cases are metastatic with no effect of chemotherapy or radiotherapy. Current targeted therapies lead to a high rate of relapse and resistance after a short-term response. Thus, a major hurdle in the development and use of new treatments for ccRCC is the lack of good pre-clinical models that can accurately predict the efficacy of new drugs and allow the stratification of patients into the correct treatment regime. Here, we describe different 3D cultures models of ccRCC, emphasizing the feasibility and the advantage of ex-vivo treatment of fresh, surgically resected human tumor slice cultures of ccRCC as a robust preclinical model for identifying patient response to specific therapeutics. Moreover, this model based on precision-cut tissue slices enables histopathology measurements as tumor architecture is retained, including the spatial relationship between the tumor and tumor-infiltrating lymphocytes and the stromal components. Our data suggest that acute treatment of tumor tissue slices could represent a benchmark of further exploration as a companion diagnostic tool in ccRCC treatment and a model to develop new therapeutic drugs. |
format | Online Article Text |
id | pubmed-7016787 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70167872020-02-28 Ex-Vivo Treatment of Tumor Tissue Slices as a Predictive Preclinical Method to Evaluate Targeted Therapies for Patients with Renal Carcinoma Roelants, Caroline Pillet, Catherine Franquet, Quentin Sarrazin, Clément Peilleron, Nicolas Giacosa, Sofia Guyon, Laurent Fontanell, Amina Fiard, Gaëlle Long, Jean-Alexandre Descotes, Jean-Luc Cochet, Claude Filhol, Odile Cancers (Basel) Article Clear cell renal cell carcinoma (ccRCC) is the third type of urologic cancer. At time of diagnosis, 30% of cases are metastatic with no effect of chemotherapy or radiotherapy. Current targeted therapies lead to a high rate of relapse and resistance after a short-term response. Thus, a major hurdle in the development and use of new treatments for ccRCC is the lack of good pre-clinical models that can accurately predict the efficacy of new drugs and allow the stratification of patients into the correct treatment regime. Here, we describe different 3D cultures models of ccRCC, emphasizing the feasibility and the advantage of ex-vivo treatment of fresh, surgically resected human tumor slice cultures of ccRCC as a robust preclinical model for identifying patient response to specific therapeutics. Moreover, this model based on precision-cut tissue slices enables histopathology measurements as tumor architecture is retained, including the spatial relationship between the tumor and tumor-infiltrating lymphocytes and the stromal components. Our data suggest that acute treatment of tumor tissue slices could represent a benchmark of further exploration as a companion diagnostic tool in ccRCC treatment and a model to develop new therapeutic drugs. MDPI 2020-01-17 /pmc/articles/PMC7016787/ /pubmed/31963500 http://dx.doi.org/10.3390/cancers12010232 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Roelants, Caroline Pillet, Catherine Franquet, Quentin Sarrazin, Clément Peilleron, Nicolas Giacosa, Sofia Guyon, Laurent Fontanell, Amina Fiard, Gaëlle Long, Jean-Alexandre Descotes, Jean-Luc Cochet, Claude Filhol, Odile Ex-Vivo Treatment of Tumor Tissue Slices as a Predictive Preclinical Method to Evaluate Targeted Therapies for Patients with Renal Carcinoma |
title | Ex-Vivo Treatment of Tumor Tissue Slices as a Predictive Preclinical Method to Evaluate Targeted Therapies for Patients with Renal Carcinoma |
title_full | Ex-Vivo Treatment of Tumor Tissue Slices as a Predictive Preclinical Method to Evaluate Targeted Therapies for Patients with Renal Carcinoma |
title_fullStr | Ex-Vivo Treatment of Tumor Tissue Slices as a Predictive Preclinical Method to Evaluate Targeted Therapies for Patients with Renal Carcinoma |
title_full_unstemmed | Ex-Vivo Treatment of Tumor Tissue Slices as a Predictive Preclinical Method to Evaluate Targeted Therapies for Patients with Renal Carcinoma |
title_short | Ex-Vivo Treatment of Tumor Tissue Slices as a Predictive Preclinical Method to Evaluate Targeted Therapies for Patients with Renal Carcinoma |
title_sort | ex-vivo treatment of tumor tissue slices as a predictive preclinical method to evaluate targeted therapies for patients with renal carcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7016787/ https://www.ncbi.nlm.nih.gov/pubmed/31963500 http://dx.doi.org/10.3390/cancers12010232 |
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