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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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.
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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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